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Apache Druid 29.0.1 is a patch release that fixes some issues in the Druid 29.0.0 release.
targetDataSource
attribute to return a string containing the name of the datasource. This reverts the breaking change introduced in Druid 29.0.0 for INSERT and REPLACE MSQ queries #16004 #16031
flattenSpec
in the wrong location #15946
arrayIngestMode
to array
when you explicitly opt in to use arrays #15927
EXTERN
function #15969
targetDataSource
in EXPLAIN queriesDruid 29.0.1 includes a breaking change that restores the behavior for targetDataSource
to its 28.0.0 and earlier state, different from Druid 29.0.0 and only 29.0.0. In 29.0.0, targetDataSource
returns a JSON object that includes the datasource name. In all other versions, targetDataSource
returns a string containing the name of the datasource.
If you're upgrading from any version other than 29.0.0, there is no change in behavior.
If you are upgrading from 29.0.0, this is an incompatible change.
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Published by LakshSingla 8 months ago
Apache Druid 29.0.0 contains over 350 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 67 contributors.
See the complete set of changes for additional details, including bug fixes.
Review the upgrade notes before you upgrade to Druid 29.0.0.
If you are upgrading across multiple versions, see the Upgrade notes page, which lists upgrade notes for the most recent Druid versions.
This section contains important information about new and existing features.
Druid 29.0.0 adds experimental support for export statements to the MSQ task engine. This allows query tasks to write data to an external destination through the EXTERN
function.
Druid 29.0.0 adds experimental support for the SQL PIVOT and UNPIVOT operators.
The PIVOT operator carries out an aggregation and transforms rows into columns in the output. The following is the general syntax for the PIVOT operator:
PIVOT (aggregation_function(column_to_aggregate)
FOR column_with_values_to_pivot
IN (pivoted_column1 [, pivoted_column2 ...])
)
The UNPIVOT operator transforms existing column values into rows. The following is the general syntax for the UNPIVOT operator:
UNPIVOT (values_column
FOR names_column
IN (unpivoted_column1 [, unpivoted_column2 ... ])
)
Window functions (experimental) now support ranges where both endpoints are unbounded or are the current row. Ranges work in strict mode, which means that Druid will fail queries that aren't supported. You can turn off strict mode for ranges by setting the context parameter windowingStrictValidation
to false
.
The following example shows a window expression with RANGE frame specifications:
(ORDER BY c)
(ORDER BY c RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
(ORDER BY c RANGE BETWEEN CURRENT ROW AND UNBOUNDED PRECEDING)
Druid now supports arbitrary join conditions for INNER join. Any sub-conditions that can't be evaluated as part of the join are converted to a post-join filter. Improved join capabilities allow Druid to more effectively support applications like Tableau.
You no longer have to manually determine the task lock type for concurrent append and replace (experimental) with the taskLockType
task context. Instead, Druid can now determine it automatically for you. You can use the context parameter "useConcurrentLocks": true
for individual tasks and datasources or enable concurrent append and replace at a cluster level using druid.indexer.task.default.context
.
Druid now supports first and last aggregators for the double, float, and long types in native and MSQ ingestion spec and MSQ queries. Previously, they were only supported for native queries. For more information, see First and last aggregators.
Additionally, the following functions can now return numeric values:
You can use these functions as aggregators at ingestion time.
Added support for logging audit events and improved coverage of audited REST API endpoints.
To enable logging audit events, set config druid.audit.manager.type
to log
in both the Coordinator and Overlord or in common.runtime.properties
. When you set druid.audit.manager.type
to sql
, audit events are persisted to metadata store.
In both cases, Druid audits the following events:
Also fixed an issue with the basic auth integration test by not persisting logs to the database.
The MSQ task engine now allows empty ingest queries by default. Previously, ingest queries that produced no data would fail with the InsertCannotBeEmpty
MSQ fault.
For more information, see Empty ingest queries in the upgrade notes.
In the web console, you can use a toggle to control whether an ingestion fails if the ingestion query produces no data.
The MSQ task engine now supports Google Cloud Storage (GCS). You can use durable storage with GCS. See Durable storage configurations for more information.
Druid 29.0.0 adds the following extensions.
A new DDSketch extension is available as a community contribution. The DDSketch extension (druid-ddsketch
) provides support for approximate quantile queries using the DDSketch library.
A new histogram extension is available as a community contribution. The Spectator-based histogram extension (druid-spectator-histogram
) provides approximate histogram aggregators and percentile post-aggregators based on Spectator fixed-bucket histograms.
A new Delta Lake extension is available as a community contribution. The Delta Lake extension (druid-deltalake-extensions
) lets you use the Delta Lake input source to ingest data stored in a Delta Lake table into Apache Druid.
This section contains detailed release notes separated by areas.
Added support for array types for all the ingestion wizards.
When loading multi-value dimensions or arrays using Druid's Query console, note the value of the arrayIngestMode
parameter. Druid now configures the arrayIngestMode
parameter in the data loading flow, and its value can persist across the SQL tab, even if you execute unrelated Data Manipulation Language (DML) operations within the same tab.
The Load query detail archive now supports loading queries by selecting a JSON file directly or dragging the file into the dialog.
The lookup dialog in the web console now includes following optional fields. See JDBC lookup for more information.
Improved the web console Explore view as follows:
EXPLAIN PLAN
queries in the workbench and run them individually #15570
waitUntilSegmentLoad
would always be set to true
even if explicitly set to false
#15781
Added the option to return system fields when defining an input source. This allows for ingestion of metadata, such as an S3 object's URI.
When the requested granularity is a month or larger but a segment can't be allocated, Druid resorts to day partitioning.
Unless explicitly specified, Druid skips week-granularity segments for data partitioning because these segments don't align with the end of the month or more coarse-grained intervals.
Columns ingested with the auto
column indexer that contain only empty or null containing arrays are now stored as ARRAY<LONG>
instead of COMPLEX<json>
.
Druid now skips compaction for datasources with segments that have an interval start or end which coincides with Eternity interval end-points.
Improved kill tasks as follows:
maxUsedStatusLastUpdatedTime
. When set to a date time, the kill task considers segments in the specified interval marked as unused no later than this time. The default behavior is to kill all unused segments in the interval regardless of the time when segments where marked as unused.Improved segment allocation as follows:
evalDimension
method in the RowFunction
interface #15452
taskQueue
reaches maxSize
#15409
hasMultipleValues = UNKNOWN
#15300
IOException
obfuscated S3 exceptions #15238
[1000, 9999]
#15608
IncrementalIndex
and OnHeapIncrementalIndex
by removing some parameters #15448
OnheapIncrementalIndex
to no longer try to offer a thread-safe "add" method #15697
castToType
parameterAdded optional castToType
parameter to auto
column schema.
The EXTEND operator now supports the following array types: VARCHAR ARRAY
, BIGINT ARRAY
, FLOAT ARRAY
, and DOUBLE ARRAY
.
The following example shows an extern input with Druid native input types ARRAY<STRING>
, ARRAY<LONG>
and STRING
:
EXTEND (a VARCHAR ARRAY, b BIGINT ARRAY, c VARCHAR)
REPLACE
queryMSQ REPLACE
queries now generate tombstone segments honoring the segment granularity specified in the query rather than generating irregular tombstones. If a query generates more than 5000 tombstones, Druid returns an MSQ TooManyBucketsFault
error, similar to the behavior with data segments.
Improved consistency of JOIN behavior for queries using either the native or MSQ task engine to prune based on base (left-hand side) columns only.
You can now limit the pages size for results of SELECT queries run using the MSQ task engine. See rowsPerPage
in the SQL-based ingestion reference.
Changed Amazon Kinesis automatic reset behavior to only reset the checkpoints for partitions where sequence numbers are unavailable.
Added IPv6_MATCH SQL function for matching IPv6 addresses in a subnet:
IPV6_MATCH(address, subnet)
Added JSON_QUERY_ARRAY which is similar to JSON_QUERY except the return type is always ARRAY<COMPLEX<json>>
instead of COMPLEX<json>
. Essentially, this function allows extracting arrays of objects from nested data and performing operations such as UNNEST, ARRAY_LENGTH, ARRAY_SLICE, or any other available ARRAY operations.
aggregateMultipleValues
Improved the ANY_VALUE(expr)
function to support the boolean option aggregateMultipleValues
. The aggregateMultipleValues
option is enabled by default. When you run ANY_VALUE on an MVD, the function returns the stringified array. If aggregateMultipleValues
is set to false
, ANY_VALUE returns the first value instead.
arrayContainsElement
filterAdded native arrayContainsElement
filter to improve performance when using ARRAY_CONTAINS on array columns.
Also ARRAY_OVERLAP now uses the arrayContainsElement
filter when filtering ARRAY typed columns, so that it can use indexes like ARRAY_CONTAINS.
Improved nested JSON columns as follows:
ValueIndexes
and ArrayElementIndexes
for nested arrays.ValueIndexes
for nested long and double columns.timestamp_extract
functionThe timestamp_extract(expr, unit, [timezone])
Druid native query function now supports dynamic values.
Added support for using expressions to compute the JSON path argument for JSON_VALUE and JSON_QUERY functions dynamically. The JSON path argument doesn't have to be a constant anymore.
Enhanced filtering performance for lookups as follows:
sqlReverseLookupThreshold
SQL query context parameter. sqlReverseLookupThreshold
represents the maximum size of an IN filter that will be created as part of lookup reversal #15832
When query scheduler threads are less than server HTTP threads, total laning turns on.
This reserves some HTTP threads for non-query requests such as health checks.
The total laning previously would reject any query request that exceeds the lane capacity.
Now, excess requests will instead be queued with a timeout equal to MIN(Integer.MAX_VALUE, druid.server.http.maxQueryTimeout)
.
NullValueIndex
to be used by NullFilter
. This improvement should speed up is null
and is not null
filters on JSON columns #15687
ExpressionPostAggregator
to handle ARRAY types output by the grouping engine #15543
nvl
not clearing out stale null vectors for vector expression processing #15587
JSON_QUERY
#15643
ClassCastException
when comparing RangeValue
in CombineAndSimplifyBounds
#15778
numCorePartitions
to 0 for tombstonesTombstone segments now have 0 core partitions. This means they can be dropped or removed independently without affecting availability of other appended segments in the same co-partition space. Prior to this change, removing tombstones with 1 core partition that contained appended segments in the partition space could make the appended segments unavailable.
Added MarkEternityTombstonesAsUnused
to clean up non-overlapping eternity tombstones—tombstone segments that either start at -INF
or end at INF
and don't overlap with any overshadowed used segments in the datasource.
Also added a new metric segment/unneededEternityTombstone/count
to count the number of dropped non-overshadowed eternity tombstones per datasource.
Druid now skips compaction for datasources with segments that have their interval start or end coinciding with Eternity interval end-points.
The JSON parser unexpected token error now includes the context of the expected VALUE_STRING
token. This makes it easier to track mesh/proxy network error messages and to avoid unnecessary research into Druid server rest endpoint responses.
400
status code instead of 503
when a Coordinator was temporarily unavailable, such as during a rolling upgrade #15756
_acceptQueueSize
based on value of net.core.somaxconn
#15596
serviceName
for segment/count
metric to match the configured metric name within the StatsD emitter #15347
The computed hash values of passwords are now cached for the druid-basic-security
extension to boost authentication validator performance.
[N, 0, 0, ...]
, where N is the number of values in the sketch, and the length of the list is equal to the value assigned to numBins
#15381
batchDeleteFiles
method in Azure Storage #15730
InterruptedException
logging in ingestion task logs #15519
You can configure the pushgateway
strategy to delete metrics from Prometheus push gateway on task shutdown using the following Prometheus emitter configurations:
druid.emitter.prometheus.deletePushGatewayMetricsOnShutdown
: When set to true, peon tasks delete metrics from the Prometheus push gateway on task shutdown. Default value is false.druid.emitter.prometheus.waitForShutdownDelay
: Time in milliseconds to wait for peon tasks to delete metrics from pushgateway
on shutdown. Applicable only when druid.emitter.prometheus.deletePushGatewayMetricsOnShutdown
is set to true. Default value is none, meaning that there is no delay between peon task shutdown and metrics deletion from the push gateway.Improved the Iceberg extension as follows:
snapshotTime
to the iceberg input source spec that allows the user to ingest data files associated with the most recent snapshot. This helps the user ingest data based on older snapshots by specifying the associated snapshot time #15348
range
to filter on ranges of column values #15782
equals
filter for native queriesThe equality filter on mixed type auto
columns that contain arrays must now be filtered as their presenting type. This means that if any rows are arrays (for example, the segment metadata and information_schema
reports the type as some array type), then the native queries must also filter as if they are some array type.
This change impacts mixed type auto
columns that contain both scalars and arrays. It doesn't impact SQL, which already has this limitation due to how the type presents itself.
arrayIngestMode
for MSQ queriesDruid console now configures the arrayIngestMode
parameter in the data loading flow, and its value can persist across the SQL tab unless manually updated. Therefore, when loading multi-value dimensions or arrays in the Druid web console, note the value of the arrayIngestMode parameter, to prevent mixing MVDs and Arrays in the same column of a data source accidentally.
You no longer have to manually determine the task lock type for concurrent append and replace (experimental) with the taskLockType
task context. Instead, Druid can now determine it automatically for you. You can use the context parameter "useConcurrentLocks": true
for individual tasks and datasources or enable concurrent append and replace at a cluster level using druid.indexer.task.default.context
.
The MSQ task engine now allows empty ingest queries by default. For queries that don't generate any output rows, the MSQ task engine reports zero values for numTotalRows
and totalSizeInBytes
instead of null. Previously, ingest queries that produced no data would fail with the InsertCannotBeEmpty
MSQ fault.
To revert to the original behavior, set the MSQ query parameter failOnEmptyInsert
to true
.
When query scheduler threads are less than server HTTP threads, total laning turns on.
This reserves some HTTP threads for non-query requests such as health checks.
The total laning previously would reject any query request that exceeds the lane capacity.
Now, excess requests will instead be queued with a timeout equal to MIN(Integer.MAX_VALUE, druid.server.http.maxQueryTimeout)
.
Columns ingested with the auto column indexer that contain only empty or null arrays are now stored as ARRAY<LONG\>
instead of COMPLEX<json\>
.
When the requested granularity is a month or larger but a segment can't be allocated, Druid resorts to day partitioning.
Unless explicitly specified, Druid skips week-granularity segments for data partitioning because these segments don't align with the end of the month or more coarse-grained intervals.
auto
search strategyRemoved the auto
search strategy from the native search query. Setting searchStrategy
to auto
is now equivalent to useIndexes
.
InDimFilter
reverse-lookup optimizationThis improvement includes the following changes:
mayIncludeUnknown
parameter to DimFilter#optimize
.InDimFilter#optimizeLookup
to handle mayIncludeUnknown
and perform reverse lookups in a wider range of cases.unapply
method in LookupExtractor
protected and relocated callers to unapplyAll
.If your extensions provide a DimFilter
, you may need to rebuild them to ensure compatibility with this release.
The web console now logs request errors in end-to-end tests to help with debugging.
The following dependencies have been updated:
Added chronoshift
as a dependency #14990
Added gson
to pom.xml
#15488
Updated Confluent's dependencies to 6.2.12 #15441
Excluded jackson-jaxrs
from ranger-plugin-common
, which isn't required, to address CVEs #15481
Updated AWS SDK version to 1.12.638
#15814
Updated Avro to 1.11.3 #15419
Updated Ranger libraries to the newest available version #15363
Updated the iceberg core version to 1.4.1 #15348
Reduced dependency footprint for the iceberg extension #15280
Updated com.github.eirslett
version to 1.15.0 #15556
Updated multiple webpack dependencies:
webpack
to 5.89.0webpack-bundle-analyzer
to 4.10.1webpack-cli
to 5.1.4webpack-dev-server
to 4.15.1Updated pac4j-oidc
java security library version to 4.5.7 #15522
Updated io.kubernetes.client-java
version to 19.0.0 and docker-java-bom
to 3.3.4 #15449
Updated core Apache Kafka dependencies to 3.6.1 #15539
Updated and pruned multiple dependencies for the web console, including dropping Babel. As a result, Internet Explorer 11 is no longer supported with the web console #15487
Updated Apache Zookeeper to 3.8.3 from 3.5.10 #15477
Updated Gauva to 32.0.1 from 31.1 #15482
Updated multiple dependencies to address CVEs:
dropwizard-metrics
to 4.2.22 to address GHSA-mm8h-8587-p46h in com.rabbitmq:amqp-client
ant
to 1.10.14 to resolve GHSA-f62v-xpxf-3v68, GHSA-4p6w-m9wc-c9c9, GHSA-q5r4-cfpx-h6fh, and GHSA-5v34-g2px-j4fwcomomons-compress
to 1.24.0 to resolve GHSA-cgwf-w82q-5jrrjose4j
to 0.9.3 to resolve GHSA-7g24-qg88-p43q and GHSA-jgvc-jfgh-rjvvkotlin-stdlib
to 1.6.0 to resolve GHSA-cqj8-47ch-rvvq and CVE-2022-24329Updated Jackson to version 2.12.7.1 to address CVE-2022-42003 and CVE-2022-42004 which affects jackson-databind
#15461
Updated com.google.code.gson:gson
from 2.2.4 to 2.10.1 since 2.2.4 is affected by CVE-2022-25647 #15461
Updated Jedis to version 5.0.2 #15344
Updated commons-codec:commons-codec
from 1.13 to 1.16.0 #14819
Updated Nimbus version to 8.22.1
#15753
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Published by LakshSingla 10 months ago
Apache Druid 28.0.1 is a patch release that fixes some issues in the 28.0.0 release. See the complete set of changes for additional details.
RetrieveSegmentsToReplaceAction
which would not be available on the overlord at the time of rolling upgradeThanks to everyone who contributed to this release!
@cryptoe
@gianm
@kgyrtkirk
@LakshSingla
@vogievetsky
Published by LakshSingla 11 months ago
Apache Druid 28.0.0 contains over 420 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 57 contributors.
See the complete set of changes for additional details, including bug fixes.
Review the upgrade notes and incompatible changes before you upgrade to Druid 28.0.0.
In Druid 28.0.0, we have made substantial improvements to querying to make the system more ANSI SQL compatible. This includes changes in handling NULL and boolean values as well as boolean logic. At the same time, the Apache Calcite library has been upgraded to the latest version. While we have documented known query behavior changes, please read the upgrade notes section carefully. Test your application before rolling out to broad production scenarios while closely monitoring the query status.
Druid continues to make SQL query execution more consistent with how standard SQL behaves. However, there are feature flags available to restore the old behavior if needed.
Druid native filters now observe SQL three-valued logic (true
, false
, or unknown
) instead of Druid's classic two-state logic by default, when the following default settings apply:
druid.generic.useThreeValueLogicForNativeFilters = true
druid.expressions.useStrictBooleans = true
druid.generic.useDefaultValueForNull = false
druid.expressions.useStrictBooleans
is now enabled by default.
Druid now handles booleans strictly using 1
(true) or 0
(false).
Previously, true and false could be represented either as true
and false
as well as 1
and 0
, respectively.
In addition, Druid now returns a null value for Boolean comparisons like True && NULL
.
If you don't explicitly configure this property in runtime.properties
, clusters now use LONG types for any ingested boolean values and in the output of boolean functions for transformations and query time operations.
For more information, see SQL compatibility in the upgrade notes.
druid.generic.useDefaultValueForNull
is now disabled by default.
Druid now differentiates between empty records and null records.
Previously, Druid might treat empty records as empty or null.
For more information, see SQL compatibility in the upgrade notes.
Druid uses Apache Calcite for SQL planning and optimization. Starting in Druid 28.0.0, the Calcite version has been upgraded from 1.21 to 1.35. This upgrade brings in many bug fixes in SQL planning from Calcite.
As part of the Calcite upgrade, the behavior of type inference for dynamic parameters has changed. To avoid any type interference issues, explicitly CAST
all dynamic parameters as a specific data type in SQL queries. For example, use:
SELECT (1 * CAST (? as DOUBLE))/2 as tmp
Do not use:
SELECT (1 * ?)/2 as tmp
Query from deep storage is no longer an experimental feature. When you query from deep storage, more data is available for queries without having to scale your Historical services to accommodate more data. To benefit from the space saving that query from deep storage offers, configure your load rules to unload data from your Historical services.
Query from deep storage now supports multiple result formats.
Previously, the /druid/v2/sql/statements/
endpoint only supported results in the object
format. Now, results can be written in any format specified in the resultFormat
parameter.
For more information on result parameters supported by the Druid SQL API, see Responses.
Users with the STATE
permission can interact with status APIs for queries from deep storage. Previously, only the user who submitted the query could use those APIs. This enables the web console to monitor the running status of the queries. Users with the STATE
permission can access the query results.
The MSQ task engine can now include real time segments in query results. To do this, use the includeSegmentSource
context parameter and set it to REALTIME
.
You can now use the MSQ task engine to run UNION ALL queries with UnionDataSource
.
You can now ingest streaming data from multiple Kafka topics to a datasource using a single supervisor.
You configure the topics for the supervisor spec using a regex pattern as the value for topicPattern
in the IO config. If you add new topics to Kafka that match the regex, Druid automatically starts ingesting from those new topics.
If you enable multi-topic ingestion for a datasource, downgrading will cause the Supervisor to fail.
For more information, see Stop supervisors that ingest from multiple Kafka topics before downgrading.
The UNNEST function is no longer experimental.
Druid now supports UNNEST in SQL-based batch ingestion and query from deep storage, so you can flatten arrays easily. For more information, see UNNEST and Unnest arrays within a column.
You no longer need to include the context parameter enableUnnest: true
to use UNNEST.
The recommended syntax for SQL UNNEST has changed. We recommend using CROSS JOIN instead of commas for most queries to prevent issues with precedence. For example, use:
SELECT column_alias_name1 FROM datasource CROSS JOIN UNNEST(source_expression1) AS table_alias_name1(column_alias_name1) CROSS JOIN UNNEST(source_expression2) AS table_alias_name2(column_alias_name2), ...
Do not use:
SELECT column_alias_name FROM datasource, UNNEST(source_expression1) AS table_alias_name1(column_alias_name1), UNNEST(source_expression2) AS table_alias_name2(column_alias_name2), ...
You can use window functions in Apache Druid to produce values based upon the relationship of one row within a window of rows to the other rows within the same window. A window is a group of related rows within a result set. For example, rows with the same value for a specific dimension.
Enable window functions in your query with the enableWindowing: true
context parameter.
Druid 28.0.0 adds experimental support for concurrent append and replace.
This feature allows you to safely replace the existing data in an interval of a datasource while new data is being appended to that interval. One of the most common applications of this is appending new data to an interval while compaction of that interval is already in progress.
For more information, see Concurrent append and replace.
Segment locking will be deprecated and removed in favor of concurrent append and replace that is much simpler in design. With concurrent append and replace, Druid doesn't lock compaction jobs out because of active realtime ingestion.
Append batch ingestion jobs can now share locks. This allows you to run multiple append batch ingestion jobs against the same time internal. Replace batch ingestion jobs still require an exclusive lock. This means you can run multiple append batch ingestion jobs and one replace batch ingestion job for a given interval.
Streaming jobs reading from Kafka and Kinesis with APPEND
locks can now ingest concurrently with compaction running with REPLACE
locks. The segment granularity of the streaming job must be equal to or finer than that of the concurrent replace job.
This section contains detailed release notes separated by areas.
The web console supports the waitUntilSegmentsLoad
query context parameter.
The web console includes concurrent append and replace switches.
The following screenshot shows the concurrent append and replace switches in the classic batch ingestion wizard:
The following screenshot shows the concurrent append and replace switches in the compaction configuration UI:
The web console supports ingesting streaming data from multiple Kafka topics to a datasource using a single supervisor.
query_controller
tasks in the task view instead of the generic raw task details dialog. You can still access the raw task details from the ellipsis (...) menu #14930)
The json
column type is now equivalent to using auto
in JSON-based batch ingestion dimension specs. Upgrade your ingestion specs to json
to take advantage of the features and functionality of auto
, including the following:
json
type columns created with Druid 28.0.0 are not backwards compatible with Druid versions older than 26.0.0.
If you upgrade from one of these versions, you can continue to write nested columns in a backwards compatible format (version 4).
For more information, see Nested column format in the upgrade notes.
Ingestion reports now include a segmentLoadStatus
object that provides information related to the ingestion, such as duration and total segments.
SQL-based ingestion now supports storing ARRAY typed values in ARRAY typed columns as well as storing both VARCHAR and numeric typed arrays.
Previously, the MSQ task engine stored ARRAY typed values as multi-value dimensions instead of ARRAY typed columns.
The MSQ task engine now includes the arrayIngestMode
query context parameter, which controls how
ARRAY
types are stored in Druid segments.
Set the arrayIngestMode
query context parameter to array
to ingest ARRAY types.
In Druid 28.0.0, the default mode for arrayIngestMode
is mvd
for backwards compatibility, which only supports VARCHAR typed arrays and stores them as multi-value dimensions. This default is subject to change in future releases.
For information on how to migrate to the new behavior, see the Ingestion options for ARRAY typed columns in the upgrade notes.
For information on inserting, filtering, and grouping behavior for ARRAY
typed columns, see Array columns.
Row-based frames and, by extension, the MSQ task engine now support numeric array types. This means that all queries consuming or producing arrays work with the MSQ task engine. Numeric arrays can also be ingested using SQL-based ingestion with MSQ. For example, queries like SELECT [1, 2]
are valid now since they consume a numeric array instead of failing with an unsupported column type exception.
Added support for Microsoft Azure Blob Storage.
You can now use fault tolerance and durable storage with Microsoft Azure Blob Storage.
For more information, see Durable storage.
rowsPerPage
context parameter for the MSQ task engine.rowsPerPage
to limit the number of rows per page. For more information on context parameters for the MSQ task engine, see Context parameters #14994
ServiceClosedException
on postCounters
while the controller is offline #14707
Added a new API endpoint /druid/indexer/v1/supervisor/:supervisorId/resetOffsets
to reset specific partition offsets for a supervisor without resetting the entire set.
This endpoint clears only the specified offsets in Kafka or sequence numbers in Kinesis, prompting the supervisor to resume data reading.
PropertyNamingStrategies
from Jackson to fix Hadoop ingestion and make it compatible with newer Jackson #14671
TaskLocation
object for Kubernetes task scheduling to make debugging easier #14758
KubernetesTaskRunner
#14790
SqlStatementResource
and SqlTaskResource
to set request attribute #14878
DeterminePartitionsJob
#13840
usedClusterCapacity
to the GET
/totalWorkerCapacity
response. Use this API to get the total ingestion capacity on the overlord #14888
task/pending/time
metrics for Kubernetes-based ingestion #14698
k8s/peon/startup/time
metrics for Kubernetes-based ingestion #14771
handoffConditionTimeout
now defaults to 15 minutes—the default change won't affect existing supervisors #14539
objects.toString
as a result of transform expressions #15127
PodTemplateTaskAdapter
now accounts for queryable tasks #14789
taskDuration
#14396
deleteObjects
requests are now retried if the failure state allows retry #14776
The LOOKUP function now accepts an optional constant string as a third argument. This string is used to replace missing values in results. For example, the query LOOKUP(store, 'store_to_country', 'NA')
, returns NA
if the store_to_country
value is missing for a given store
.
The AVG aggregation function now returns a double
instead of a long
.
Improved EARLIEST and LATEST operators as follows:
__time
column reference explicit to Calcite. #15095
maxBytesPerValue
parameter.maxBytesPerValue
parameter, the aggregations default to 1024 bytes for the buffer. #14848
New SQL and native query functions allow you to evaluate whether two expressions are distinct or not distinct.
Expressions are distinct if they have different values or if one of them is NULL.
Expressions are not distinct if their values are the same or if both of them are NULL.
Because the functions treat NULLs as known values when used as a comparison operator, they always return true or false even if one or both expressions are NULL.
The following table shows the difference in behavior between the equals sign (=) and IS [NOT] DISTINCT FROM:
A | B | A=B | A IS NOT DISTINCT FROM B |
---|---|---|---|
0 | 0 | true | true |
0 | 1 | false | false |
0 | null | unknown | false |
null | null | unknown | true |
New SQL and native query functions allow you to evaluate whether a condition is true or false. These functions are different from x == true
and x != true
in that they never return null even when the variable is null.
SQL function | Native function |
---|---|
IS_TRUE |
istrue() |
IS_FALSE |
isfalse() |
IS_NOT_TRUE |
nottrue() |
IS_NOT_FALSE |
notfalse() |
The new SQL and native query function, decode_base64_utf8
decodes a Base64-encoded string and returns the UTF-8-encoded string. For example, decode_base64_utf8('aGVsbG8=')
.
You can now set the maxSubqueryBytes
guardrail to one of the following:
disabled
: Default setting. Druid doesn't apply the guardrail around the number of bytes a subquery can generate.
auto
: Druid calculates the amount of memory to use for the materialization of results as a portion of the fixed memory of the heap.
In the query context, Druid uses the following formula to determine the upper limit on the number of bytes a subquery can generate:
((total JVM space - memory occupied by lookups) * 0.5) / maximum queries that the system can handle concurrently
INTEGER: The number of bytes to use for materializing subquery results. Set a specific value if you understand the query patterns and want to optimize memory usage.
For example, set the maxSubqueryBytes
parameter to 300000000 (300 * 1000 * 1000
) for a 300 MB limit.
Set the maxSubqueryBytes
parameter to 314572800 (300 * 1024 * 1024
) for a 300 MiB limit.
Druid now stops loading and moving segments as soon as they are marked as unused. This prevents Historical processes from spending time on superfluous loads of segments that will be unloaded later. You can mark segments as unused by a drop rule, overshadowing, or by calling the Data management API.
The net.spy.memcached
client has been replaced with the AWS ElastiCache client. This change allows Druid to encrypt data in transit using TLS.
Configure it with the following properties:
Property | Description | Default |
---|---|---|
druid.cache.enableTls |
Enable TLS based connection for Memcached client. Boolean | false |
druid.cache.clientMode |
Client Mode. Static mode requires the user to specify individual cluster nodes. Dynamic mode uses AutoDiscovery feature of AWS Memcached. String. "static" or "dynamic" | static |
druid.cache.skipTlsHostnameVerification |
Skip TLS Hostname Verification. Boolean. | true |
The Druid segments table now has a column called used_flag_last_updated
(VARCHAR (255)). This column is a UTC date string corresponding to the last time that the used column was modified.
Note that this is an incompatible change to the table. For upgrade information, see Upgrade Druid segments table.
replicationThrottleLimit
used for smart segment loading has been increased from 2% to 5% of total number of used segments. The total number of replicas in the load queue at the start of a run plus the replicas assigned in a run is kept less than or equal to the throttle limit #14913
balancerComputeThreads
is now calculated based on the number of CPUs divided by 2. Previously, the value was 1
. Smart segment loading uses this computed value #14902
InvalidNullByteFault
errors. They now include the output column name instead of the query column name for ease of use #14780
DruidLeaderClient
doesn't find leader node #14775
IndexingServiceDuties
and MetadataStoreManagementDuties
anymore. These are meant to be core coordinator built-in flows and should not be affected by custom duties. Users can still define a CustomCoordinatorDuty
with a custom duty group and period #14891
balancerComputeThreads
and maxSegmentsToMove
automatically based on usage skew between the Historical processes in a tier #14584
druid.coordinator.compaction.skipLockedIntervals
because it should always be true
#14807
Improved alert message for segment assignments when an invalid tier is specified in a load rule or when no rule applies on a segment.
Added includeUnused
as an optional parameter to the Coordinator API.
You can send a GET
request to /druid/coordinator/v1/metadata/datasources/{dataSourceName}/segments/{segmentId}?includeUnused=true
to retrieve the metadata for a specific segment as stored in the metadata store.
The API also returns unused segments if the includeUnused
parameter is set.
killTaskSlotRatio
and maxKillTaskSlots
dynamic configuration properties to allow control of task resource usage spawned by the KillUnusedSegments
coordinator task #14769
druid.coordinator.kill.period
can now be greater than or equal to druid.coordinator.period.indexingPeriod
. Previously, it had to be greater than druid.coordinator.period.indexingPeriod
. Additionally, the leader Coordinator now keeps track of the last submitted kill
task for a datasource to avoid submitting duplicate kill
tasks #14831
druid.coordinator.kill.bufferPeriod
for a buffer period. This config defines the amount of time that a segment is unused before KillUnusedSegment
can kill it. Using the default PT24H
, if you mark a segment as unused at 2022-06-01T00:05:00.000Z
, then the segment cannot be killed until at or after 2022-06-02T00:05:00.000Z
#12599
kill
task:
kill
tasks by batch deleting multiple segments stored in S3 #14131
numSegmentsKilled
, numBatchesProcessed
, and numSegmentsMarkedAsUnused
#15023
IndexerSQLMetadataStorageCoordinator
now uses the JDBI PreparedBatch
instead of issuing single update statements inside a transaction to mitigate scaling challenges #14639
Metric | Description | Dimensions | Normal value |
---|---|---|---|
ingest/input/bytes |
Number of bytes read from input sources, after decompression but prior to parsing. This covers all data read, including data that does not end up being fully processed and ingested. For example, this includes data that ends up being rejected for being unparseable or filtered out. |
dataSource , taskId , taskType , groupId , tags
|
Depends on the amount of data read. |
Metric | Description | Dimensions | Normal value |
---|---|---|---|
mergeBuffer/pendingRequests |
Number of requests waiting to acquire a batch of buffers from the merge buffer pool. | This metric is exposed through the QueryCountStatsMonitor module for the Broker. |
Metric | Description | Dimensions | Normal value |
---|---|---|---|
zk/connected |
Indicator of connection status. 1 for connected, 0 for disconnected. Emitted once per monitor period. |
None | 1 |
zk/reconnect/time |
Amount of time, in milliseconds, that a server was disconnected from ZooKeeper before reconnecting. Emitted on reconnection. Not emitted if connection to ZooKeeper is permanently lost, because in this case, there is no reconnection. | None | Not present |
The new SubqueryCountStatsMonitor
emits metrics corresponding to the subqueries and their execution.
Metric | Description | Dimensions | Normal value |
---|---|---|---|
subquery/rowLimit/count |
Number of subqueries whose results are materialized as rows (Java objects on heap). | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
subquery/byteLimit/count |
Number of subqueries whose results are materialized as frames (Druid's internal byte representation of rows). | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
subquery/fallback/count |
Number of subqueries which cannot be materialized as frames | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
subquery/fallback/insufficientType/count |
Number of subqueries which cannot be materialized as frames due to insufficient type information in the row signature. | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
subquery/fallback/unknownReason/count |
Number of subqueries which cannot be materialized as frames due other reasons. | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
query/rowLimit/exceeded/count |
Number of queries whose inlined subquery results exceeded the given row limit | This metric is only available if the SubqueryCountStatsMonitor module is included. |
|
query/byteLimit/exceeded/count |
Number of queries whose inlined subquery results exceeded the given byte limit | This metric is only available if the SubqueryCountStatsMonitor module is included. |
Metric | Description | Dimensions | Normal value |
---|---|---|---|
killTask/availableSlot/count |
Number of available task slots that can be used for auto kill tasks in the auto kill run. This is the max number of task slots minus any currently running auto kill tasks. | Varies | |
killTask/maxSlot/count |
Maximum number of task slots available for auto kill tasks in the auto kill run. | Varies | |
kill/task/count |
Number of tasks issued in the auto kill run. | Varies | |
kill/pendingSegments/count |
Number of stale pending segments deleted from the metadata store. | dataSource |
Varies |
Metric | Description | Dimensions | Normal value |
---|---|---|---|
compact/segmentAnalyzer/fetchAndProcessMillis |
Time taken to fetch and process segments to infer the schema for the compaction task to run. |
dataSource , taskId , taskType , groupId ,tags
|
Varies. A high value indicates compaction tasks will speed up from explicitly setting the data schema. |
Added a new metric to figure out the usage of druid.processing.numThreads
on the Historicals/Indexers/Peons.
Metric | Description | Dimensions | Normal value |
---|---|---|---|
segment/scan/active |
Number of segments currently scanned. This metric also indicates how many threads from druid.processing.numThreads are currently being used. |
Close to druid.processing.numThreads
|
Added the following Kafka consumer metrics:
kafka/consumer/bytesConsumed
: Equivalent to the Kafka consumer metric bytes-consumed-total
. Only emitted for Kafka tasks.kafka/consumer/recordsConsumed
: Equivalent to the Kafka consumer metric records-consumed-total
. Only emitted for Kafka tasks.service/heartbeat
metric to statsd-reporter
#14564
service/heartbeat
metric to expose the leader
dimension #14593
Added ingest/tombstones/count
and ingest/segments/count
metrics in MSQ to report the number of tombstones and segments after Druid finishes publishing segments.
You can now provide compressed task payloads larger than 128 KB when you run MiddleManager-less ingestion jobs.
The Prometheus emitter now supports a new optional configuration parameter, druid.emitter.prometheus.extraLabels
.
This addition offers the flexibility to add arbitrary extra labels to Prometheus metrics, providing more granular control in managing and identifying data across multiple Druid clusters or other dimensions.
For more information, see Prometheus emitter extension.
We've moved Jupyter notebooks that guide you through query, ingestion, and data management with Apache Druid to the new Learn Druid repository.
The repository also contains a Docker Compose file to get you up and running with a learning lab.
Druid 28.0.0 adds a new column to the Druid metadata table that requires an update to the table.
If druid.metadata.storage.connector.createTables
is set to true
and the metadata store user has DDL privileges, the segments table gets automatically updated at startup to include the new used_flag_last_updated
column. No additional work is needed for the upgrade.
If either of those requirements are not met, pre-upgrade steps are required. You must make these updates before you upgrade to Druid 28.0.0, or the Coordinator and Overlord processes fail.
Although you can manually alter your table to add the new used_flag_last_updated
column, Druid also provides a CLI tool to do it.
In the example commands below:
lib
is the Druid lib directoryextensions
is the Druid extensions directorybase
corresponds to the value of druid.metadata.storage.tables.base
in the configuration, druid
by default.--connectURI
parameter corresponds to the value of druid.metadata.storage.connector.connectURI
.--user
parameter corresponds to the value of druid.metadata.storage.connector.user
.--password
parameter corresponds to the value of druid.metadata.storage.connector.password
.--action
parameter corresponds to the update action you are executing. In this case, it is add-last-used-to-segments
cd ${DRUID_ROOT}
java -classpath "lib/*" -Dlog4j.configurationFile=conf/druid/cluster/_common/log4j2.xml -Ddruid.extensions.directory="extensions" -Ddruid.extensions.loadList=[\"mysql-metadata-storage\"] -Ddruid.metadata.storage.type=mysql org.apache.druid.cli.Main tools metadata-update --connectURI="<mysql-uri>" --user USER --password PASSWORD --base druid --action add-used-flag-last-updated-to-segments
cd ${DRUID_ROOT}
java -classpath "lib/*" -Dlog4j.configurationFile=conf/druid/cluster/_common/log4j2.xml -Ddruid.extensions.directory="extensions" -Ddruid.extensions.loadList=[\"postgresql-metadata-storage\"] -Ddruid.metadata.storage.type=postgresql org.apache.druid.cli.Main tools metadata-update --connectURI="<postgresql-uri>" --user  USER --password PASSWORD --base druid --action add-used-flag-last-updated-to-segments
ALTER TABLE druid_segments
ADD used_flag_last_updated varchar(255);
The recommended syntax for SQL UNNEST has changed. We recommend using CROSS JOIN instead of commas for most queries to prevent issues with precedence. For example, use:
SELECT column_alias_name1 FROM datasource CROSS JOIN UNNEST(source_expression1) AS table_alias_name1(column_alias_name1) CROSS JOIN UNNEST(source_expression2) AS table_alias_name2(column_alias_name2), ...
Do not use:
SELECT column_alias_name FROM datasource, UNNEST(source_expression1) AS table_alias_name1(column_alias_name1), UNNEST(source_expression2) AS table_alias_name2(column_alias_name2), ...
The Apache Calcite version has been upgraded from 1.21 to 1.35. As part of the Calcite upgrade, the behavior of type inference for dynamic parameters has changed. To avoid any type interference issues, explicitly CAST
all dynamic parameters as a specific data type in SQL queries. For example, use:
SELECT (1 * CAST (? as DOUBLE))/2 as tmp
Do not use:
SELECT (1 * ?)/2 as tmp
json
type columns created with Druid 28.0.0 are not backwards compatible with Druid versions older than 26.0.0.
If you are upgrading from a version prior to Druid 26.0.0 and you use json
columns, upgrade to Druid 26.0.0 before you upgrade to Druid 28.0.0.
Additionally, to downgrade to a version older than Druid 26.0.0, any new segments created in Druid 28.0.0 should be re-ingested using Druid 26.0.0 or 27.0.0 prior to further downgrading.
When upgrading from a previous version, you can continue to write nested columns in a backwards compatible format (version 4).
In a classic batch ingestion job, include formatVersion
in the dimensions
list of the dimensionsSpec
property. For example:
"dimensionsSpec": {
"dimensions": [
"product",
"department",
{
"type": "json",
"name": "shipTo",
"formatVersion": 4
}
]
},
To set the default nested column version, set the desired format version in the common runtime properties. For example:
druid.indexing.formats.nestedColumnFormatVersion=4
Starting with Druid 28.0.0, the default way Druid treats nulls and booleans has changed.
For nulls, Druid now differentiates between an empty string and a record with no data as well as between an empty numerical record and 0
.
You can revert to the previous behavior by setting druid.generic.useDefaultValueForNull
to true
.
This property affects both storage and querying, and must be set on all Druid service types to be available at both ingestion time and query time. Reverting this setting to the old value restores the previous behavior without reingestion.
For booleans, Druid now strictly uses 1
(true) or 0
(false). Previously, true and false could be represented either as true
and false
as well as 1
and 0
, respectively. In addition, Druid now returns a null value for boolean comparisons like True && NULL
.
You can revert to the previous behavior by setting druid.expressions.useStrictBooleans
to false
.
This property affects both storage and querying, and must be set on all Druid service types to be available at both ingestion time and query time. Reverting this setting to the old value restores the previous behavior without reingestion.
The following table illustrates some example scenarios and the impact of the changes.
Query | Druid 27.0.0 and earlier | Druid 28.0.0 and later |
---|---|---|
Query empty string | Empty string ('' ) or null |
Empty string ('' ) |
Query null string | Null or empty | Null |
COUNT(*) | All rows, including nulls | All rows, including nulls |
COUNT(column) | All rows excluding empty strings | All rows including empty strings but excluding nulls |
Expression 100 && 11 | 11 | 1 |
Expression 100 || 11 | 100 | 1 |
Null FLOAT/DOUBLE column | 0.0 | Null |
Null LONG column | 0 | Null |
Null __time column |
0, meaning 1970-01-01 00:00:00 UTC | 1970-01-01 00:00:00 UTC |
Null MVD column | '' |
Null |
ARRAY | Null | Null |
COMPLEX | none | Null |
Before upgrading to Druid 28.0.0, update your queries to account for the changed behavior as described in the following sections.
If your queries use NULL in the filter condition to match both nulls and empty strings, you should add an explicit filter clause for empty strings. For example, update s IS NULL
to s IS NULL OR s = ''
.
COUNT(column)
now counts empty strings. If you want to continue excluding empty strings from the count, replace COUNT(column)
with COUNT(column) FILTER(WHERE column <> '')
.
GroupBy queries on columns containing null values can now have additional entries as nulls can co-exist with empty strings.
If you have added supervisors that ingest from multiple Kafka topics in Druid 28.0.0 or later, stop those supervisors before downgrading to a version prior to Druid 28.0.0 because the supervisors will fail in versions prior to Druid 28.0.0.
lenientAggregatorMerge
deprecatedlenientAggregatorMerge
property in segment metadata queries has been deprecated. It will be removed in future releases.
Use aggregatorMergeStrategy
instead. aggregatorMergeStrategy
also supports the latest
and earliest
strategies in addition to strict
and lenient
strategies from lenientAggregatorMerge
.
The paths for druid.processing.merge.pool.*
and druid.processing.merge.task.*
have been flattened to use druid.processing.merge.*
instead. The legacy paths for the configs are now deprecated and will be removed in a future release. Migrate your settings to use the new paths because the old paths will be ignored in the future.
Starting with Druid 28.0.0, the MSQ task engine can detect and ingest arrays as ARRAY typed columns when you set the query context parameter arrayIngestMode
to array
.
The arrayIngestMode
context parameter controls how ARRAY type values are stored in Druid segments.
When you set arrayIngestMode
to array
(recommended for SQL compliance), the MSQ task engine stores all ARRAY typed values in ARRAY typed columns and supports storing both VARCHAR and numeric typed arrays.
For backwards compatibility, arrayIngestMode
defaults to mvd
. When "arrayIngestMode":"mvd"
, Druid only supports VARCHAR typed arrays and stores them as multi-value string columns.
When you set arrayIngestMode
to none
, Druid throws an exception when trying to store any type of arrays.
For more information on how to ingest ARRAY
typed columns with SQL-based ingestion, see SQL data types and Array columns.
Support for Hadoop 2 has been removed.
Migrate to SQL-based ingestion or JSON-based batch ingestion if you are using Hadoop 2.x for ingestion today.
If migrating to Druid's built-in ingestion is not possible, you must upgrade your Hadoop infrastructure to 3.x+ before upgrading to Druid 28.0.0.
The GroupBy v1 engine has been removed. Use the GroupBy v2 engine instead, which has been the default GroupBy engine for several releases.
There should be no impact on your queries.
Additionally, AggregatorFactory.getRequiredColumns
has been deprecated and will be removed in a future release. If you have an extension that implements AggregatorFactory
, then this method should be removed from your implementation.
The decommissioningMaxPercentOfMaxSegmentsToMove
config has been removed.
The use case for this config is handled by smart segment loading now, which is enabled by default.
cachingCost
strategyThe cachingCost
strategy for segment loading has been removed.
Use cost
instead, which has the same benefits as cachingCost
.
If you have cachingCost
set, the system ignores this setting and automatically uses cost
.
InsertCannotOrderByDescending
The deprecated MSQ fault InsertCannotOrderByDescending
has been removed.
The backward compatibility code for the Handoff API in CoordinatorBasedSegmentHandoffNotifier
has been removed.
If you are upgrading from a Druid version older than 0.14.0, upgrade to a newer version of Druid before upgrading to Druid 28.0.0.
The following dependencies have had their versions bumped:
31.1-jre
. If you use an extension that has a transitive Guava dependency from Druid, it may be impacted #14767
RoaringBitmap
has been upgraded from 0.9.0 to 0.9.49 #15006
snappy-java
has been upgraded to 1.1.10.3 #14641
decode-uri-component
has been upgraded to 0.2.2 #13481
word-wrap
has been upgraded to 1.2.4 #14613
tough-cookie
has been upgraded to 4.1.3 #14557
qs
has been upgraded to 6.5.3 #13510
api-util
has been upgraded to 2.1.3 #14852
commons-cli
has been upgraded from 1.3.1 to 1.5.0 #14837
tukaani:xz
has been upgraded from 1.8 to 1.9 #14839
commons-compress
has been upgraded from 1.21 to 1.23.0 #14820
protobuf.version
has been upgraded from 3.21.7 to 3.24.0 #14823
dropwizard.metrics.version
has been upgraded from 4.0.0 to 4.2.19 #14824
assertj-core
has been upgraded from 3.19.0 to 3.24.2 #14815
maven-source-plugin
has been upgraded from 2.2.1 to 3.3.0 #14812
scala-library
has been upgraded from 2.13.9 to 2.13.11 #14826
oshi-core
has been upgraded from 6.4.2 to 6.4.4 #14814
maven-surefire-plugin
has been upgraded from 3.0.0-M7 to 3.1.2 #14813
apache-rat-plugin
has been upgraded from 0.12 to 0.15 #14817
jclouds.version
has been upgraded from 1.9.1 to 2.0.3 #14746
dropwizard.metrics:metrics-graphite
has been upgraded from 3.1.2 to 4.2.19 #14842
postgresql
has been upgraded from 42.4.1 to 42.6.0 #13959
org.mozilla:rhino
has been upgraded #14765
apache.curator.version
has been upgraded from 5.4.0 to 5.5.0 #14843
jackson-databind
has been upgraded to 2.12.7 #14770
icu4j
from 55.1 to 73.2 has been upgraded from 55.1 to 73.2 #14853
joda-time
has been upgraded from 2.12.4 to 2.12.5 #14855
tough-cookie
has been upgraded from 4.0.0 to 4.1.3 #14557
word-wrap
has been upgraded from 1.2.3 to 1.2.4 #14613
decode-uri-component
has been upgraded from 0.2.0 to 0.2.2 #13481
snappy-java
has been upgraded from 1.1.10.1 to 1.1.10.3 #14641
jetty
has been upgraded from 9.4.51.v20230217 to 9.4.53.v20231009 #15129
netty4
has been upgraded from 4.1.94.Final to 4.1.100.Final #15129
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Published by AmatyaAvadhanula about 1 year ago
Apache Druid 27.0.0 contains over 316 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 50 contributors.
See the complete set of changes for additional details, including bug fixes.
Review the upgrade notes and incompatible changes before you upgrade to Druid 27.0.0.
The Explore view is a simple, stateless, SQL backed, data exploration view to the web console. It lets users explore data in Druid with point-and-click interaction and visualizations (instead of writing SQL and looking at a table). This can provide faster time-to-value for a user new to Druid and can allow a Druid veteran to quickly chart some data that they care about.
The Explore view is accessible from the More (...) menu in the header:
Druid now supports querying segments that are stored only in deep storage. When you query from deep storage, you can query larger data available for queries without necessarily having to scale your Historical processes to accommodate more data. To take advantage of the potential storage savings, make sure you configure your load rules to not load all your segments onto Historical processes.
Note that at least one segment of a datasource must be loaded onto a Historical process so that the Broker can plan the query. It can be any segment though.
For more information, see the following:
Type-aware schema auto-discovery is now generally available. Druid can determine the schema for the data you ingest rather than you having to manually define the schema.
As part of the type-aware schema discovery improvements, array column types are now generally available. Druid can determine the column types for your schema and assign them to these array column types when you ingest data using type-aware schema auto-discovery with the auto
column type.
For more information about this feature, see the following:
The Coordinator is now much more stable and user-friendly. In the new smartSegmentLoading mode, it dynamically computes values for several configs which maximize performance.
The Coordinator can now prioritize load of more recent segments and segments that are completely unavailable over load of segments that already have some replicas loaded in the cluster. It can also re-evaluate decisions taken in previous runs and cancel operations that are not needed anymore. Moreoever, move operations started by segment balancing do not compete with the load of unavailable segments thus reducing the reaction time for changes in the cluster and speeding up segment assignment decisions.
Additionally, leadership changes have less impact now, and the Coordinator doesn't get stuck even if re-election happens while a Coordinator run is in progress.
Lastly, the cost
balancer strategy performs much better now and is capable of moving more segments in a single Coordinator run. These improvements were made by borrowing ideas from the cachingCost
strategy. We recommend using cost
instead of cachingCost
since cachingCost
is now deprecated.
For more information, see the following:
Druid now supports the following filters:
Note that Druid's SQL planner uses these new filters in place of their older counterparts by default whenever druid.generic.useDefaultValueForNull=false
or if sqlUseBoundAndSelectors
is set to false on the SQL query context.
You can use these filters for filtering equality and ranges on ARRAY columns instead of only strings with the previous selector and bound filters.
For more information, see Query filters.
Users can now add a guardrail to prevent subquery’s results from exceeding the set number of bytes by setting druid.server.http.maxSubqueryRows
in the Broker's config or maxSubqueryRows
in the query context. This guardrail is recommended over row-based limiting.
This feature is experimental for now and defaults back to row-based limiting in case it fails to get the accurate size of the results consumed by the query.
Added a new OSHI system monitor (OshiSysMonitor
) to replace SysMonitor
. The new monitor has a wider support for different machine architectures including ARM instances. We recommend switching to the new monitor. SysMonitor
is now deprecated and will be removed in future releases.
Druid now fully supports Java 17.
Support for Hadoop 2 is now deprecated. It will be removed in a future release.
For more information, see the upgrade notes.
Druid now fails query planning if a CLUSTERED BY column contains descending order.
Previously, queries would successfully plan if any CLUSTERED BY columns contained descending order.
The MSQ fault, InsertCannotOrderByDescending
, is deprecated. An INSERT or REPLACE query containing a CLUSTERED BY expression cannot be in descending order. Druid's segment generation code only supports ascending order. Instead of the fault, Druid now throws a query ValidationException
.
The default clusterStatisticsMergeMode
is now SEQUENTIAL
, which provide more accurate segment sizes.
index
and length
parameters #14480
max_allowed_packet
limit, the response now returns 400 Bad request
. This prevents an index_parallel
task from retrying the insertion of a bad sub-task indefinitely and causes it to fail immediately. #14271
In addition to the new query from deep storage feature, SELECT queries using the MSQ task engine have been improved.
You can now query lookup tables directly, such as SELECT * FROM lookup.xyz
, when using the MSQ task engine.
SELECT queries executed using MSQ generate only a subset of the results in the query reports.
To fetch the complete result set, run the query using the native engine.
Added a query context parameter MultiStageQueryContext
to determine whether the result of an MSQ SELECT query is limited.
Druid now supports a query-results
directory in durable storage to store query results after the task finishes. The auto cleaner does not remove this directory unless the task ID is not known to the Overlord.
The new function REGEXP_REPLACE
allows you to replace all instances of a pattern with a replacement string.
You can now use HLL_SKETCH_ESTIMATE
and THETA_SKETCH_ESTIMATE
as expressions. These estimates work on sketch columns and have the same behavior as postAggs
.
Updated EARLIEST_BY and LATEST_BY function signatures as follows:
EARLIEST(expr, timeColumn)
to EARLIEST_BY(expr, timeColumn)
LATEST(expr, timeColumn)
to LATEST_BY(expr, timeColumn)
Use the new INFORMATION_SCHEMA.ROUTINES
to programmatically get information about the functions that Druid SQL supports.
For more information, such as the available columns, see ROUTINES table.
You can now better control how Druid reacts to schema changes between segments. This can make querying more resilient when newer segments introduce different types, such as if a column previously contained LONG values and newer segments contain STRING.
Use the new Broker configuration, druid.sql.planner.metadataColumnTypeMergePolicy
to control how column types are computed for the SQL table schema when faced with differences between segments.
Set it to one of the following:
leastRestrictive
: the schema only updates once all segments are reindexed to the new type.latestInterval
: the SQL schema gets updated as soon as the first job with the new schema publishes segments in the latest time interval of the data.leastRestrictive
can have better query time behavior and eliminates some query time errors that can occur when using latestInterval
.
The EXPLAIN PLAN result includes a new column ATTRIBUTES
that describes the attributes of a query.
For more information, see SQL translation
Added a new monitor ServiceStatusMonitor
to monitor the service health of the Overlord and Coordinator.
The following metrics are now available for Brokers:
Metric | Description | Dimensions |
---|---|---|
segment/metadatacache/refresh/count |
Number of segments to refresh in broker segment metadata cache. Emitted once per refresh per datasource. | dataSource |
segment/metadatacache/refresh/time |
Time taken to refresh segments in broker segment metadata cache. Emitted once per refresh per datasource. | dataSource |
Metric | Description | Dimensions | Normal value |
---|---|---|---|
segment/loadQueue/assigned |
Number of segments assigned for load or drop to the load queue of a server. |
dataSource ,server
|
Varies |
segment/loadQueue/success |
Number of segment assignments that completed successfully. |
dataSource , server
|
Varies |
segment/loadQueue/cancelled |
Number of segment assignments that were canceled before completion. |
dataSource ,server
|
0 |
segment/loadQueue/failed |
Number of segment assignments that failed to complete. |
dataSource , server
|
0 |
Metric | Description | Normal value |
---|---|---|
task/status/queue/count |
Monitors the number of queued items | Varies |
task/status/updated/count |
Monitors the number of processed items | Varies |
groupId
to Overlord task metricsAdded groupId
to task metrics emitted by the Overlord. This is helpful for grouping metrics like task/run/time by a single task group, such as a single compaction task or a single MSQ query.
HttpServerInventoryView
TBD for name change
Metric | Description | Dimensions | Normal value |
---|---|---|---|
serverview/sync/healthy |
Sync status of the Coordinator/Broker with a segment-loading server such as a Historical or Peon. Emitted by the Coordinator and Brokers only when HTTP-based server view is enabled. This metric can be used in conjunction with serverview/sync/unstableTime to debug slow startup of the Coordinator. |
server , tier
|
1 for fully synced servers, 0 otherwise |
serverview/sync/unstableTime |
Time in milliseconds for which the Coordinator/Broker has been failing to sync with a segment-loading server. Emitted by the Coordinator and Brokers only when HTTP-based server view is enabled. |
server , tier
|
Not emitted for synced servers. |
The new property druid.indexer.queue.taskCompleteHandlerNumThreads
controls the number of threads used by the Overlord TaskQueue
to handle task completion updates received from the workers.
For the related metrics, see new metrics for task completion updates.
Druid now allows empty tiered replicants in load rules. Use this feature along with query from deep storage to increase the amount of data you can query without needing to scale your Historical processes.
HttpServerInventoryView
The initialization of HttpServerInventoryView
maintained by Brokers and Coordinator is now resilient to Historicals and Peons crashing. The crashed servers are marked as stopped and not waited upon during the initialization.
New metrics are available to monitor the sync status of HttpServerInventoryView
with different servers.
The console uses the new async
statements API for all sql-msq-task engine queries.
While this has relatively few impacts on the UX of the query view, you are invited to peek under the hood and check out the new network requests being sent as working examples of the new API.
You can now specify durableStorage
as the result destination for SELECT queries (when durable storage is configured):
![Choose to write the results for SELECT queries to durable storage]
After running a SELECT query that wrote its results to durableStorage
, download the full, unlimited result set directly from the Broker:
This release of Druid supports having datasources with segments that are not replicated on any Historicals. These datasources appear in the console like so:
There's now a dialog for managing your dynamic compaction config:
replication_factor
to the sys.segments
table. This returns the total number of replicants of the segment across all tiers. The column is set to -1 if the information is not available. #14403
The Kafka emitter extension has been improved. You can now publish events related to segments and their metadata to Kafka.
You can set the new properties such as in the following example:
druid.emitter.kafka.event.types=["metrics", "alerts", "segment_metadata"]
druid.emitter.kafka.segmentMetadata.topic=foo
You can now ingest data stored in Iceberg and query that data directly by querying from deep storage. Support for Iceberg is available through the new community extension.
For more information, see Iceberg extension.
The following dependencies have had their versions bumped:
Introduced a new unified exception, DruidException
, for surfacing errors. It is partially compatible with the old way of reporting error messages. Response codes remain the same, all fields that previously existed on the response will continue to exist and be populated, including errorMessage
. Some error messages have changed to be more consumable by humans and some cases have the message restructured. There should be no impact to the response codes.
org.apache.druid.common.exception.DruidException
is deprecated in favor of the more comprehensive org.apache.druid.error.DruidException
.
org.apache.druid.metadata.EntryExistsException
is deprecated and will be removed in a future release.
The maximum input bytes for each worker for SQL-based ingestion is now 512 MiB (previously 10 GiB).
When using the built-in FileConfigProvider
for Kafka, interpolations are now intercepted by the JsonConfigurator instead of being passed down to the Kafka provider. This breaks existing deployments.
For more information, see KIP-297.
Many of the important dependent libraries that Druid uses no longer support Hadoop 2. In order for Druid to stay current and have pathways to mitigate security vulnerabilities, the community has decided to deprecate support for Hadoop 2.x releases starting this release. Starting with Druid 28.x, Hadoop 3.x is the only supported Hadoop version.
Consider migrating to SQL-based ingestion or native ingestion if you are using Hadoop 2.x for ingestion today. If migrating to Druid ingestion is not possible, plan to upgrade your Hadoop infrastructure before upgrading to the next Druid release.
GroupBy queries using the v1 legacy engine has been deprecated. It will be removed in future releases. Use v2 instead. Note that v2 has been the default GroupBy engine.
For more information, see GroupBy queries.
Support for push-based real-time ingestion has been deprecated. It will be removed in future releases.
cachingCost
segment balancing strategy deprecatedThe cachingCost
strategy has been deprecated and will be removed in future releases. Use an alternate segment balancing strategy instead, such as cost
.
The following segment related configs are now deprecated and will be removed in future releases:
maxSegmentsInNodeLoadingQueue
maxSegmentsToMove
replicationThrottleLimit
useRoundRobinSegmentAssignment
replicantLifetime
maxNonPrimaryReplicantsToLoad
decommissioningMaxPercentOfMaxSegmentsToMove
Use smartSegmentLoading
mode instead, which calculates values for these variables automatically.
Additionally, the defaults for the following Coordinator dynamic configs have changed:
maxsegmentsInNodeLoadingQueue
: 500, previously 100maxSegmentsToMove
: 100, previously 5replicationThrottleLimit
: 500, previously 10These new defaults can improve performance for most use cases.
SysMonitor
support deprecatedSwitch to OshiSysMonitor
as SysMonitor
is now deprecated and will be removed in future releases.
druid.processing.columnCache.sizeBytes
has been removed since it provided limited utility after a number of internal changes. Leaving this config is harmless, but it does nothing.
The following Coordinator dynamic configs have been removed:
emitBalancingStats
: Stats for errors encountered while balancing will always be emitted. Other debugging stats will not be emitted but can be logged by setting the appropriate debugDimensions
.useBatchedSegmentSampler
and percentOfSegmentsToConsiderPerMove
: Batched segment sampling is now the standard and will always be on.Use the new smart segment loading mode instead.
Thanks to everyone who contributed to this release!
@317brian
@a2l007
@abhishek-chouhan
@abhishekagarwal87
@abhishekrb19
@adarshsanjeev
@AlexanderSaydakov
@amaechler
@AmatyaAvadhanula
@asdf2014
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@zachjsh
Published by clintropolis over 1 year ago
Apache Druid 26.0.0 contains over 390 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 65 contributors.
See the complete set of changes for additional details.
Review the upgrade notes and incompatible changes before you upgrade to Druid 26.0.0.
A new "auto" type column schema and indexer has been added to native ingestion as the next logical iteration of the nested column functionality. This automatic type column indexer that produces the most appropriate column for the given inputs, producing either STRING
, ARRAY<STRING>
, LONG
, ARRAY<LONG>
, DOUBLE
, ARRAY<DOUBLE>
, or COMPLEX<json>
columns, all sharing a common 'nested' format.
All columns produced by 'auto' have indexes to aid in fast filtering (unlike classic LONG
and DOUBLE
columns) and use cardinality based thresholds to attempt to only utilize these indexes when it is likely to actually speed up the query (unlike classic STRING columns).
COMPLEX<json>
columns produced by this 'auto' indexer store arrays of simple scalar types differently than their 'json' (v4) counterparts, storing them as ARRAY typed columns. This means that the JSON_VALUE
function can now extract entire arrays, for example JSON_VALUE(nested, '$.array' RETURNING BIGINT ARRAY)
. There is no change with how arrays of complex objects are stored at this time.
This improvement also adds a completely new functionality to Druid, ARRAY
typed columns, which unlike classic multi-value STRING
columns behave with ARRAY semantics. These columns can currently only be created via the 'auto' type indexer when all values are an arrays with the same type of elements.
An array data type is a data type that allows you to store multiple values in a single column of a database table. Arrays are typically used to store sets of related data that can be easily accessed and manipulated as a group.
This release adds support for storing arrays of primitive values such as ARRAY<STRING>
, ARRAY<LONG>
, and ARRAY<DOUBLE>
as specialized nested columns instead of breaking them into separate element columns.
These changes affect two additional new features available in 26.0: schema auto-discovery and unnest.
We’re adding schema-auto discovery with type inference to Druid. With this feature, the data type of each incoming field is detected when schema is available. For incoming data which may contain added, dropped, or changed fields, you can choose to reject the nonconforming data (“the database is always correct - rejecting bad data!”), or you can let schema auto-discovery alter the datasource to match the incoming data (“the data is always right - change the database!”).
Schema auto-discovery is recommend for new use-cases and ingestions. For existing use-cases be careful switching to schema auto-discovery because Druid will ingest array-like values (e.g. ["tag1", "tag2]
) as ARRAY<STRING>
type columns instead of multi-value (MV) strings, this could cause issues in downstream apps replying on MV behavior. Hold off switching until an official migration path is available.
To use this feature, set spec.dataSchema.dimensionsSpec.useSchemaDiscovery
to true
in your task or supervisor spec or, if using the data loader in the console, uncheck the Explicitly define schema
toggle on the Configure schema
step. Druid can infer the entire schema or some of it if you explicitly list dimensions in your dimensions list.
Schema auto-discovery is available for native batch and streaming ingestion.
Part of what’s cool about UNNEST is how it allows a wider range of operations that weren’t possible on Array data types. You can unnest arrays with either the UNNEST function (SQL) or the unnest
datasource (native).
Unnest converts nested arrays or tables into individual rows. The UNNEST function is particularly useful when working with complex data types that contain nested arrays, such as JSON.
For example, suppose you have a table called "orders" with a column called "items" that contains an array of products for each order. You can use unnest to extract the individual products ("each_item") like in the following SQL example:
SELECT order_id, each_item FROM orders, UNNEST(items) as unnested(each_item)
This produces a result set with one row for each item in each order, with columns for the order ID and the individual item
Note the comma after the left table/datasource (orders
in the example). It is required.
#13268 #13943 #13934 #13922 #13892 #13576 #13554 #13085
We can now perform shuffle joins by setting by setting the context parameter sqlJoinAlgorithm
to sortMerge
for the sort-merge algorithm or omitting it to perform broadcast joins (default).
Multi-stage queries can use a sort-merge join algorithm. With this algorithm, each pairwise join is planned into its own stage with two inputs. This approach is generally less performant but more scalable, than broadcast.
Set the context parameter sqlJoinAlgorithm
to sortMerge
to use this method.
Broadcast hash joins are similar to how native join queries are executed.
Switching to using frontcoding dictionary compression (experimental) can save up to 30% with little to no impact to query performance.
This release further improves the frontCoded
type of stringEncodingStrategy
on indexSpec
with a new segment format version, which typically has faster read speeds and reduced segment size. This improvement is backwards incompatible with Druid 25.0. Added a new formatVersion
option, which defaults to the the current version 0
. Set formatVersion
to 1
to start using the new version.
Additionally, overall storage size, particularly with using larger buckets, has been improved.
Added support for array-valued parameters for SQL queries using. You can now reuse the same SQL for every ingestion, only passing in a different set of input files as query parameters.
You can now use an EXTEND clause to provide a list of column definitions for your source data in standard SQL format.
The web console now defaults to using the EXTEND clause syntax for all queries auto-generated in the web console. This means that SQL-based ingestion statements generated by the web console in Druid 26 (such as from the SQL based data loader) will not work in earlier versions of Druid.
Added the ability for MSQ controller task to retry worker task in case of failures. To enable, pass faultTolerance:true
in the query context.
Connections to S3 for fault tolerance and durable shuffle storage are now more resilient. #13741
Improved S3 connector #13960
REPLACE
for SQL-based ingestion now generates tombstones instead of marking segments as unused.
If you downgrade Druid, you can only downgrade to a version that also supports tombstones.
The MSQ task engine now considers file size when determining splits. Previously, file size was ignored; all files were treated as equal weight when determining splits.
Also applies to native batch.
Druid now supports composable storage for intermediate data. This allows the data to be stored on multiple storage systems through local disk and durable storage. Behavior is enabled when the runtime config druid.indexer.task.tmpStorageBytesPerTask
is set and the query context parameter durableShuffleStorage
is set to true.
NOT_ENOUGH_MEMORY_FAULT
error, the error message now suggests a JVM Xmx
setting to provide. #13846
maxResultsSize
has been removed from the S3OutputConfig and a default chunkSize
of 100MiB is now present. This change primarily affects users who wish to use durable storage for MSQ jobs.You can now use multiple disks for indexing tasks. In the runtime properties for the MiddleManager/Indexer, use the following property to set the disks and directories:
druid.worker.baseTaskDirs=[\"PATH1\",\"PATH2\",...]
Updated the following fetch settings for the Kinesis indexing service:
fetchThreads
: Twice the number of processors available to the task.fetchDelayMillis
: 0 (no delay between fetches).recordsPerFetch
: 100 MB or an estimated 5% of available heap, whichever is smaller, divided by fetchThreads
.recordBufferSize
: 100 MB or an estimated 10% of available heap, whichever is smaller.maxRecordsPerPoll
: 100 for regular records, 1 for aggregated records.sampler
API responseThe response from /druid/indexer/v1/sampler
now includes the following:
logicalDimension
: list of the most restrictive typed dimension schemasphysicalDimension
: list of dimension schemas actually used to sample the datalogicalSegmentSchema
: full resulting segment schema for the set of rows sampledHadoop-based ingestion now supports multi-dimensional range partitioning. #13303
context
map to HadoopIngestionSpec
. You can set the context
map directly in HadoopIngestionSpec
using the command line (non-task) version or in the context
map for HadoopIndexTask
which is then automatically added to HadoopIngestionSpec
. #13624
Many of the querying improvements for Druid 26.0 are discussed in the highlights section. This section describes additional improvements to querying in Druid.
You can now do the following operations with Tuple sketches using post aggregators:
Added SQL functions for creating and operating on Tuple sketches.
Improve nested column performance by adding cardinality based thresholds for range and predicate indexes to choose to skip using bitmap indexes. #13977
Logs for query errors now include more information about the exception that occurred, such as error code and class.
SQL operators NVL and COALESCE with 2 arguments now plan to a native NVL expression, which supports the vector engine. Multi-argument COALESCE still plans into a case_searched, which is not vectorized.
Composite key joins are now faster.
QueryException
would throw away the causes making it hard to determine what failed in the SQL planner. #13609
The following metrics are now available for Brokers:
Metrics | Description | Normal value |
---|---|---|
init/serverview/time |
Time taken to initialize the broker server view. Useful to detect if brokers are taking too long to start. | Depends on the number of segments. |
init/metadatacache/time |
Time taken to initialize the broker segment metadata cache. Useful to detect if brokers are taking too long to start | Depends on the number of segments. |
The following metric is now available for Coordinators:
Metrics | Description | Normal value |
---|---|---|
init/serverview/time |
Time taken to initialize the coordinator server view. | Depends on the number of segments |
You can now add additional metadata to the ingestion metrics emitted from the Druid cluster. Users can pass a map of metadata in the ingestion spec context parameters. These get added to the ingestion metrics. You can then tag these metrics with other metadata besides the existing tags like taskId
. For more information, see General native ingestion metrics.
You can now override druid.monitoring.monitors
if you don't want to inherit monitors from the Overlord. Use the following property: druid.indexer.runner.peonMonitors
.
Round-robin segment assignment greatly speeds up Coordinator run times and is hugely beneficial to all clusters. Batch segment allocation works extremely well when you have multiple concurrent real-time tasks for a single supervisor.
The client change counter is now more efficient and resets in fewer situations.
You can now override the default ZooKeeper connection retry count. In situations where the underlying k8s node loses network connectivity or is no longer able to talk to ZooKeeper, configuring a fast fail can trigger pod restarts which can then reassign the pod to a healthy k8s node.
Reduced segment heap footprint.
The following property has been added to improve support for sidecars:
druid.indexer.runner.primaryContainerName=OVERLORD_CONTAINER_NAME
: Set this to the name of your Druid container, such as druid-overlord
. The default setting is the first container in thepodSpec
list.Use this property when Druid is not the first container, such as when you're using Istio and the istio-proxy
sidecar gets injected as the first container.
druid-kubernetes-overlord-extensions
can now be loaded in any Druid service. #13872
druid.monitoring.monitors
. If you don't want to inherit monitors from the Overlord, you can override the monitors with the following config: druid.indexer.runner.peonMonitors
.#14028
KubernetesTaskRunner
. #13986
Added API endpoint CoordinatorCompactionConfigsResource#getCompactionConfigHistory
to return the history of changes to automatic compaction configuration history. If the datasource does not exist or it has no compaction history, an empty list is returned
Added support for the HTTP Strict-Transport-Security
response header.
Druid does not include this header by default. You must enable it in runtime properties by setting druid.server.http.enableHSTS
to true
.
Expands the OIDC based auth in Druid by adding a JWT Authenticator that validates ID Tokens associated with a request. The existing pac4j authenticator works for authenticating web users while accessing the console, whereas this authenticator is for validating Druid API requests made by Direct clients. Services already supporting OIDC can attach their ID tokens to the Druid requests
under the Authorization request header.
Updated OpenID Connect extension configuration with scope information.
Applications use druid.auth.pac4j.oidc.scope
during authentication to authorize access to a user's details.
The streaming data loader in the console added support for the Kafka input format, which gives you access to the Kafka metadata fields like the key and the Kafka timestamp. This is used by default when you choose a Kafka stream as the data source.
Added a form with JSON fallback to the Overlord dynamic config dialog.
https://github.com/apache/druid/pull/13993
NULL
datatype. https://github.com/apache/druid/pull/13786
""
. https://github.com/apache/druid/pull/13786
Added a new tutorial to our collection of Jupyter Notebook-based Druid tutorials.
This interactive tutorial introduces you to the unique aspects of Druid SQL with the primary focus on the SELECT statement. For more information, see Learn the basics of Druid SQL.
Added a Python API for use in Jupyter notebooks.
This release includes several improvements to the docker-compose.yml
file that Druid tutorials reference:
docker-compose.yml
file.docker-compose.yml
file.docker-compose.yml
file.Druid 26.0.0 contains 80 bug fixes, the complete list is available here.
The following dependencies have had their versions bumped:
The full list is available here.
Optimized query performance by lowering the default maxRowsInMemory
for real-time ingestion, which might lower overall ingestion throughput #13939
The firehose/parser specification used by legacy Druid streaming formats is removed.
Firehose ingestion was deprecated in version 0.17, and support for this ingestion was removed in version 24.0.
The Druid system table (INFORMATION_SCHEMA
) now uses SQL types instead of Druid types for columns. This change makes the INFORMATION_SCHEMA
table behave more like standard SQL. You may need to update your queries in the following scenarios in order to avoid unexpected results if you depend either of the following:
frontCoded
segment format changeThe frontCoded
type of stringEncodingStrategy
on indexSpec
with a new segment format version, which typically has faster read speeds and reduced segment size. This improvement is backwards incompatible with Druid 25.0.
For more information, see the frontCoded
string encoding strategy highlight.
Null values input to and created by the Druid native expression processing engine no longer coerce null
to the type appropriate 'default' value if druid.generic.useDefaultValueForNull=true
. This should not impact existing behavior since this has been shifted onto the consumer and internally operators will still use default values in this mode. However, there may be subtle behavior changes around the handling of null
values. Direct callers can get default values by using the new valueOrDefault()
method of ExprEval
, instead of value()
.
druid-core
, extendedset
, and druid-hll
modules have been consolidated into druid-processing
to simplify dependencies. Any extensions referencing these should be updated to use druid-processing
instead. Existing extension binaries should continue to function normally when used with newer versions of Druid.
This change does not impact end users. It does impact anyone who develops extensions for Druid.
Thanks to everyone who contributed to this release!
@317brian
@a2l007
@abhagraw
@abhishekagarwal87
@abhishekrb19
@adarshsanjeev
@AdheipSingh
@amaechler
@AmatyaAvadhanula
@anshu-makkar
@ApoorvGuptaAi
@asdf2014
@benkrug
@capistrant
@churromorales
@clintropolis
@cryptoe
@dependabot[bot]
@dongjoon-hyun
@drudi-at-coffee
@ektravel
@EylonLevy
@findingrish
@frankgrimes97
@g1y
@georgew5656
@gianm
@hqx871
@imply-cheddar
@imply-elliott
@isandeep41
@jaegwonseo
@jasonk000
@jgoz
@jwitko
@kaijianding
@kfaraz
@LakshSingla
@maytasm
@nlippis
@p-
@paul-rogers
@pen4
@raboof
@rohangarg
@sairamdevarashetty
@sergioferragut
@somu-imply
@soullkk
@suneet-s
@SurajKadam7
@techdocsmith
@tejasparbat
@tejaswini-imply
@tijoparacka
@TSFenwick
@varachit
@vogievetsky
@vtlim
@winminsoe
@writer-jill
@xvrl
@yurmix
@zachjsh
@zemin-piao
Published by kfaraz almost 2 years ago
Apache Druid 25.0.0 contains over 300 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 51 contributors.
See the complete set of changes for additional details.
The multi-stage query (MSQ) task engine used for SQL-based ingestion is now production ready. Use it for any supported workloads. For more information, see the following pages:
The new start-druid
script greatly simplifies deploying any combination of Druid services on a single-server. It comes pre-packaged with the required configs and can be used to launch a fully functional Druid cluster simply by invoking ./start-druid
. For experienced Druids, it also gives complete control over the runtime properties and JVM arguments to have a cluster that exactly fits your needs.
The start-druid
script deprecates the existing profiles such as start-micro-quickstart
and start-nano-quickstart
. These profiles may be removed in future releases. For more information, see Single server deployment.
Added support for front coded string dictionaries for smaller string columns, leading to reduced segment sizes with only minor performance penalties for most Druid queries.
This can be enabled by setting IndexSpec.stringDictionaryEncoding
to {"type":"frontCoded", "bucketSize": 4}
, where bucketSize
is any power of 2 less than or equal to 128. Setting this property instructs indexing tasks to write segments using compressed dictionaries of the specified bucket size.
Any segment written using string dictionary compression is not readable by older versions of Druid.
For more information, see Front coding.
https://github.com/apache/druid/pull/12277
Druid can now use Kubernetes to launch and manage tasks, eliminating the need for middle managers.
To use this feature, enable the druid-kubernetes-overlord-extensions in the extensions load list for your Overlord process.
https://github.com/apache/druid/pull/13156
Druid now comes packaged as a dedicated binary for Hadoop-3 users, which contains Hadoop-3 compatible jars. If you do not use Hadoop-3 with your Druid cluster, you may continue using the classic binary.
MSQ task query engine is now enabled for Docker by default.
https://github.com/apache/druid/pull/13069
Multi-stage queries no longer show up in the Query history dialog. They are still available in the Recent query tasks panel.
When using the MSQ task engine to ingest data, the number of columns that can be passed in the CLUSTERED BY clause is now limited to 1500.
https://github.com/apache/druid/pull/13352
The MSQ task engine supports the front-coding of String dictionaries for better compression. This can be enabled for INSERT or REPLACE statements by setting indexSpec
to a valid json string in the query context.
https://github.com/apache/druid/pull/13275
Workers can now gather key statistics, used to generate partition boundaries, either sequentially or in parallel. Set clusterStatisticsMergeMode
to PARALLEL
, SEQUENTIAL
or AUTO
in the query context to use the corresponding sketch merging mode. For more information, see Sketch merging mode.
https://github.com/apache/druid/pull/13205
pendingTasks
and runningTasks
to the worker report. See Query task status information for related web console changes. https://github.com/apache/druid/pull/13263
Prevented JDBC timeouts on long queries by returning empty batches when a batch fetch takes too long. Uses an async model to run the result fetch concurrently with JDBC requests.
https://github.com/apache/druid/pull/13196
To accommodate large value sets arising from large IN filters or from joins pushed down as IN filters, Druid now uses a sorted merge algorithm for merging the set and dictionary for larger values.
https://github.com/apache/druid/pull/13133
Added the following configuration properties that refine the query context security model controlled by druid.auth.authorizeQueryContextParams
:
druid.auth.unsecuredContextKeys
: A JSON list of query context keys that do not require a security check.druid.auth.securedContextKeys
: A JSON list of query context keys that do require a security check.If both are set, unsecuredContextKeys
acts as exceptions to securedContextKeys
.
https://github.com/apache/druid/pull/13071
The HTTP response for a SQL query now correctly sets response headers, same as a native query.
https://github.com/apache/druid/pull/13052
The following metrics have been newly added. For more details, see the complete list of Druid metrics.
These metrics pertain to batched segment allocation.
Metric | Description | Dimensions |
---|---|---|
task/action/batch/runTime |
Milliseconds taken to execute a batch of task actions. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskActionType=segmentAllocate
|
task/action/batch/queueTime |
Milliseconds spent by a batch of task actions in queue. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskActionType=segmentAllocate
|
task/action/batch/size |
Number of task actions in a batch that was executed during the emission period. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskActionType=segmentAllocate
|
task/action/batch/attempts |
Number of execution attempts for a single batch of task actions. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskActionType=segmentAllocate
|
task/action/success/count |
Number of task actions that were executed successfully during the emission period. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskId , taskType , taskActionType=segmentAllocate
|
task/action/failed/count |
Number of task actions that failed during the emission period. Currently only being emitted for batched segmentAllocate actions
|
dataSource , taskId , taskType , taskActionType=segmentAllocate
|
Metric | Description | Dimensions |
---|---|---|
ingest/kafka/partitionLag |
Partition-wise lag between the offsets consumed by the Kafka indexing tasks and latest offsets in Kafka brokers. Minimum emission period for this metric is a minute. |
dataSource , stream , partition
|
ingest/kinesis/partitionLag/time |
Partition-wise lag time in milliseconds between the current message sequence number consumed by the Kinesis indexing tasks and latest sequence number in Kinesis. Minimum emission period for this metric is a minute. |
dataSource , stream , partition
|
ingest/pause/time |
Milliseconds spent by a task in a paused state without ingesting. |
dataSource , taskId , taskType
|
ingest/handoff/time |
Total time taken in milliseconds for handing off a given set of published segments. |
dataSource , taskId , taskType
|
https://github.com/apache/druid/pull/13238
https://github.com/apache/druid/pull/13331
https://github.com/apache/druid/pull/13313
taskActionType
which may take values such as segmentAllocate
, segmentTransactionalInsert
, etc. This dimension is reported for task/action/run/time
and the new batched segment allocation metrics. https://github.com/apache/druid/pull/13333
namespace/cache/heapSizeInBytes
for global cached lookups now accounts for the String
object overhead of 40 bytes. https://github.com/apache/druid/pull/13219
jvm/gc/cpu
has been fixed to report nanoseconds instead of milliseconds. https://github.com/apache/druid/pull/13383
Improved NestedDataColumnSerializer
to no longer explicitly write null values to the field writers for the missing values of every row. Instead, passing the row counter is moved to the field writers so that they can backfill null values in bulk.
https://github.com/apache/druid/pull/13101
Druid nested columns and the associated JSON transform functions now support Avro, ORC, and Parquet.
https://github.com/apache/druid/pull/13325
https://github.com/apache/druid/pull/13375
When data requires "flattening" during processing, the operator now takes in an array and then flattens the array into N (N=number of elements in the array) rows where each row has one of the values from the array.
https://github.com/apache/druid/pull/13085
You can now stop at arbitrary subfolders using glob syntax in the ioConfig.inputSource.filter
field for native batch ingestion from cloud storage, such as S3.
https://github.com/apache/druid/pull/13027
You can now enable asynchronous communication between the stream supervisor and indexing tasks by setting chatAsync
to true in the tuningConfig
. The async task client uses its own internal thread pool and thus ignrores the chatThreads
property.
https://github.com/apache/druid/pull/13354
You can now better control how Druid reads JSON data for streaming ingestion by setting the following fields in the input format specification:
assumedNewlineDelimited
to parse lines of JSON independently.useJsonNodeReader
to retain valid JSON events when parsing multi-line JSON events when a parsing exception occurs.The web console has been updated to include these options.
https://github.com/apache/druid/pull/13089
When a Kafka stream becomes inactive, the supervisor ingesting from it can be configured to stop creating new indexing tasks. The supervisor automatically resumes creation of new indexing tasks once the stream becomes active again. Set the property dataSchema.ioConfig.idleConfig.enabled
to true in the respective supervisor spec or set druid.supervisor.idleConfig.enabled
on the overlord to enable this behaviour. Please see the following for details:
https://github.com/apache/druid/pull/13144
You can now configure the Kafka Consumer's custom deserializer after its instantiation.
https://github.com/apache/druid/pull/13097
Kafka supervisor logs are now less noisy. The supervisors now log events at the DEBUG level instead of INFO.
https://github.com/apache/druid/pull/13392
Fixed a problem where Overlord leader election failed due to lock reacquisition issues. Druid now fails these tasks and clears all locks so that the Overlord leader election isn't blocked.
https://github.com/apache/druid/pull/13172
Added a new inline
type protoBytesDecoder
that allows a user to pass inline the contents of a Protobuf descriptor file, encoded as a Base64 string.
https://github.com/apache/druid/pull/13192
For streaming ingestion, notices that are the same as one already in queue won't be enqueued. This will help reduce notice queue size.
https://github.com/apache/druid/pull/13334
Fixed a problem where sampling from a stream input, such as Kafka or Kinesis, failed to respect the configured timeout when the stream had no records available. You can now set the maximum amount of time in which the entry iterator will return results.
https://github.com/apache/druid/pull/13296
Fixed a problem where streaming ingestion tasks continued to run until their duration elapsed after the Overlord leader had issued a pause to the tasks. Now, when the Overlord switch occurs right after it has issued a pause to the task, the task remains in a paused state even after the Overlord re-election.
https://github.com/apache/druid/pull/13223
Fixed an issue with Parquet list conversion, where lists of complex objects could unexpectedly be wrapped in an extra object, appearing as [{"element":<actual_list_element>},{"element":<another_one>}...]
instead of the direct list. This changes the behavior of the parquet reader for lists of structured objects to be consistent with other parquet logical list conversions. The data is now fetched directly, more closely matching its expected structure.
https://github.com/apache/druid/pull/13294
Introduced a tree
type to flattenSpec
. In the event that a simple hierarchical lookup is required, the tree
type allows for faster JSON parsing than jq
and path
parsing types.
https://github.com/apache/druid/pull/12177
Compaction behavior has changed to improve the amount of time it takes and disk space it takes:
granularitySpec
, dimensionsSpec
, and metricsSpec
, Druid skips fetching segments.For more information, see the documentation on Compaction and Automatic compaction.
https://github.com/apache/druid/pull/13280
You can now set the Supervisor to idle, which is useful in cases where freeing up slots so that autoscaling can be more effective.
To configure the idle behavior, use the following properties:
Property | Description | Default |
---|---|---|
druid.supervisor.idleConfig.enabled |
(Cluster wide) If true , supervisor can become idle if there is no data on input stream/topic for some time. |
false |
druid.supervisor.idleConfig.inactiveAfterMillis |
(Cluster wide) Supervisor is marked as idle if all existing data has been read from input topic and no new data has been published for inactiveAfterMillis milliseconds. |
600_000 |
inactiveAfterMillis |
(Individual Supervisor) Supervisor is marked as idle if all existing data has been read from input topic and no new data has been published for inactiveAfterMillis milliseconds. |
no (default == 600_000 ) |
https://github.com/apache/druid/pull/13311
Fixed issues with delayed supervisor termination during certain transient states.
https://github.com/apache/druid/pull/13072
The HttpPostEmitter
option now has a backoff. This means that there should be less noise in the logs and lower CPU usage if you use this option for logging.
https://github.com/apache/druid/pull/12102
The DumpSegment tool can now be used on nested columns with the --dump nested
option.
For more information, see dump-segment tool.
https://github.com/apache/druid/pull/13356
Segment allocation on the Overlord can take some time to finish, which can cause ingestion lag while a task waits for segments to be allocated. Performing segment allocation in batches can help improve performance.
There are two new properties that affect how Druid performs segment allocation:
Property | Description | Default |
---|---|---|
druid.indexer.tasklock.batchSegmentAllocation |
If set to true, Druid performs segment allocate actions in batches to improve throughput and reduce the average task/action/run/time . See batching segmentAllocate actions for details. |
false |
druid.indexer.tasklock.batchAllocationWaitTime |
Number of milliseconds after Druid adds the first segment allocate action to a batch, until it executes the batch. Allows the batch to add more requests and improve the average segment allocation run time. This configuration takes effect only if batchSegmentAllocation is enabled. |
500 |
In addition to these properties, there are new metrics to track batch segment allocation. For more information, see New metrics for segment allocation.
For more information, see the following:
https://github.com/apache/druid/pull/13369
https://github.com/apache/druid/pull/13503
The cachingCost
balancer strategy now behaves more similarly to cost strategy. When computing the cost of moving a segment to a server, the following calculations are performed:
https://github.com/apache/druid/pull/13321
You can now use a round-robin segment strategy to speed up initial segment assignments. Set useRoundRobinSegmentAssigment
to true
in the Coordinator dynamic config to enable this feature.
https://github.com/apache/druid/pull/13367
Batch sampling is now the default method for sampling segments during balancing as it performs significantly better than the alternative when there is a large number of used segments in the cluster.
As part of this change, the following have been deprecated and will be removed in future releases:
useBatchedSegmentSampler
percentOfSegmentsToConsiderPerMove
The unused coordinator property druid.coordinator.loadqueuepeon.repeatDelay
has been removed. Use only druid.coordinator.loadqueuepeon.http.repeatDelay
to configure repeat delay for the HTTP-based segment loading queue.
https://github.com/apache/druid/pull/13391
Improved the process of checking server inventory to prevent over-replication of segments during segment balancing.
https://github.com/apache/druid/pull/13114
Provided an option to override log4j configs setup at the service level directories so that it works with Druid-operator based deployments.
https://github.com/apache/druid/pull/13020
gcr.io/distroless/java11-debian11
image as base by default.bash-static
to the Docker image so that scripts that require bash can be executed.3.8.4-jdk-11-slim
to 3.8.6-jdk-11-slim
.amd64/busybox:1.30.0-glibc
to busybox:1.35.0-glibc
.https://github.com/apache/druid/pull/13059
Added JsonInclude
to various properties, to avoid population of default values in serialized JSON.
https://github.com/apache/druid/pull/13064
Improved direct memory check on startup by providing better support for Java 9+ in RuntimeInfo
, and clearer log messages where validation fails.
https://github.com/apache/druid/pull/13207
Improved the run time of the MarkAsUnusedOvershadowedSegments
duty by iterating over all overshadowed segments and marking segments as unused in batches.
https://github.com/apache/druid/pull/13287
You can now pick an interval to delete from a dropdown in the kill task dialog.
https://github.com/apache/druid/pull/13431
The old query view is removed. Use the new query view with tabs.
For more information, see Web console.
https://github.com/apache/druid/pull/13169
The web console now allows you to add to existing filters for a selected column.
https://github.com/apache/druid/pull/13169
Added support for Kafka-based lookups rendering and input in the web console.
https://github.com/apache/druid/pull/13098
The web console now exposes a textual indication about running and pending tasks when a query is stuck due to lack of task slots.
https://github.com/apache/druid/pull/13291
Optimized the compareTo
function in CompressedBigDecimal
.
https://github.com/apache/druid/pull/13086
Removed unnecessary generic type from CompressedBigDecimal, added support for number input types, added support for reading aggregator input types directly (uningested data), and fixed scaling bug in buffer aggregator.
https://github.com/apache/druid/pull/13048
Added POD_NAME
and POD_NAMESPACE
env variables to all Kubernetes Deployments and StatefulSets.
Helm deployment is now compatible with druid-kubernetes-extension
.
https://github.com/apache/druid/pull/13262
We released our first Jupyter Notebook-based tutorial to learn the basics of the Druid API. Download the notebook and follow along with the tutorial to learn how to get basic cluster information, ingest data, and query data.
For more information, see Jupyter Notebook tutorials.
https://github.com/apache/druid/pull/13342
https://github.com/apache/druid/pull/13345
Updated the Apache Kafka core dependency to version 3.3.1.
https://github.com/apache/druid/pull/13176
Updated dependencies for the Druid image for Docker, including JRE 11. Docker BuildKit cache is enabled to speed up building.
https://github.com/apache/druid/pull/13059
Consider the following changes and updates when upgrading from Druid 24.0.x to 25.0.0. If you're updating from an earlier version, see the release notes of the relevant intermediate versions.
The default segment discovery method now uses HTTP instead of ZooKeeper.
This update changes the defaults for the following properties:
Property | New default | Previous default |
---|---|---|
druid.serverview.type for segment management |
http | batch |
druid.coordinator.loadqueuepeon.type for segment management |
http | curator |
druid.indexer.runner.type for the Overlord |
httpRemote | local |
To use ZooKeeper instead of HTTP, change the values for the properties back to the previous defaults. ZooKeeper-based implementations for these properties are deprecated and will be removed in a subsequent release.
https://github.com/apache/druid/pull/13092
The aggregation functions for HLL and quantiles sketches returned sketches or numbers when they are finalized depending on where they were in the native query plan.
Druid no longer finalizes aggregators in the following two cases:
This change aligns the behavior of HLL and quantiles sketches with theta sketches.
To restore old behaviour, you can set sqlFinalizeOuterSketches=true
in the query context.
https://github.com/apache/druid/pull/13247
When you issue a kill task, Druid marks the underlying segments as unused only if explicitly specified. For more information, see the API reference
https://github.com/apache/druid/pull/13104
Apache Curator upgraded to the latest version, 5.3.0. This version drops support for ZooKeeper 3.4 but Druid has already officially dropped support in 0.22. In 5.3.0, Curator has removed support for Exhibitor so all related configurations and tests have been removed.
https://github.com/apache/druid/pull/12939
The behavior of the parquet reader for lists of structured objects has been changed to be consistent with other parquet logical list conversions. The data is now fetched directly, more closely matching its expected structure. See parquet list conversion for more details.
https://github.com/apache/druid/pull/13294
Thanks to everyone who contributed to this release!
@317brian
@599166320
@a2l007
@abhagraw
@abhishekagarwal87
@adarshsanjeev
@adelcast
@AlexanderSaydakov
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Published by kfaraz almost 2 years ago
Apache Druid 24.0.2 is a bug fix release that fixes some issues in the 24.0.1 release.
See the complete set of changes for additional details.
https://github.com/apache/druid/pull/13138 to fix dependency errors while launching a Hadoop task.
@kfaraz
@LakshSingla
Published by kfaraz almost 2 years ago
Apache Druid 24.0.1 is a bug fix release that fixes some issues in the 24.0 release.
See the complete set of changes for additional details.
https://github.com/apache/druid/pull/13214 to fix SQL planning when using the JSON_VALUE function.
https://github.com/apache/druid/pull/13297 to fix values that match a range filter on nested columns.
https://github.com/apache/druid/pull/13077 to fix detection of nested objects while generating an MSQ SQL in the web-console.
https://github.com/apache/druid/pull/13172 to correctly handle overlord leader election even when tasks cannot be reacquired.
https://github.com/apache/druid/pull/13259 to fix memory leaks from SQL statement objects.
https://github.com/apache/druid/pull/13273 to fix overlord API failures by de-duplicating task entries in memory.
https://github.com/apache/druid/pull/13049 to fix a race condition while processing query context.
https://github.com/apache/druid/pull/13151 to fix assertion error in SQL planning.
Thanks to everyone who contributed to this release!
@abhishekagarwal87
@AmatyaAvadhanula
@clintropolis
@gianm
@kfaraz
@LakshSingla
@paul-rogers
@vogievetsky
Published by abhishekagarwal87 about 2 years ago
Apache Druid 24.0.0 contains over 300 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 67 contributors. See the complete set of changes for additional details.
Starting with this release, we have dropped the leading 0
from the release version and promoted all other digits one place to the left. Druid is now at major version 24, a jump up from the prior 0.23.0 release. In terms of backward-compatibility or breaking changes, this release is not significantly different than other previous major releases such as 0.23.0 or 0.22.0. We are continuing with the same policy as we have used in prior releases: minimizing the number of changes that require special attention when upgrading, and calling out any that do exist in the release notes. For this release, please refer to the Upgrading to 24.0.0 section for a list of backward-incompatible changes in this release.
SQL-based ingestion for Apache Druid uses a distributed multi-stage query architecture, which includes a query engine called the multi-stage query task engine (MSQ task engine). The MSQ task engine extends Druid's query capabilities, so you can write queries that reference external data as well as perform ingestion with SQL INSERT and REPLACE. Essentially, you can perform SQL-based ingestion instead of using JSON ingestion specs that Druid's native ingestion uses. In addition to the easy-to-use syntax, the SQL interface lets you perform transformations that involve multiple shuffles of data.
SQL-based ingestion using the multi-stage query task engine is recommended for batch ingestion starting in Druid 24.0.0. Native batch and Hadoop-based ingestion continue to be supported as well. We recommend you review the known issues and test the feature in a staging environment before rolling out in production. Using the multi-stage query task engine with plain SELECT
statements (not INSERT ... SELECT
or REPLACE ... SELECT
) is experimental.
If you're upgrading from an earlier version of Druid or you're using Docker, you'll need to add the druid-multi-stage-query
extension to druid.extensions.loadlist
in your common.runtime.properties
file.
For more information, refer to the Overview documentation for SQL-based ingestion.
#12524
#12386
#12523
#12589
Druid now supports directly storing nested data structures in a newly added COMPLEX<json>
column type. COMPLEX<json>
columns store a copy of the structured data in JSON format as well as specialized internal columns and indexes for nested literal values—STRING
, LONG
, and DOUBLE
types. An optimized virtual column allows Druid to read and filter these values at speeds consistent with standard Druid LONG
, DOUBLE
, and STRING
columns.
Newly added Druid SQL, native JSON functions, and virtual column allow you to extract, transform, and create COMPLEX<json>
values in at query time. You can also use the JSON functions in INSERT
and REPLACE
statements in SQL-based ingestion, or in a transformSpec
in native ingestion as an alternative to using a flattenSpec
object to "flatten" nested data for ingestion.
See SQL JSON functions, native JSON functions, Nested columns, virtual columns, and the feature summary for more detail.
#12753
#12714
#12753
#12920
Java 11 is fully supported is no longer experimental. Java 17 support is improved.
#12839
Reworked column indexes to be extraordinarily flexible, which will eventually allow us to model a wide range of index types. Added machinery to build the filters that use the updated indexes, while also allowing for other column implementations to implement the built-in index types to provide adapters to make use indexing in the current set filters that Druid provides.
#12388
You can now use the Druid SQL operator TIME_IN_INTERVAL to filter query results based on time. Prefer TIME_IN_INTERVAL over the SQL BETWEEN operator to filter on time. For more information, see Date and time functions.
#12662
If a values
array contains null
, the "in" filter matches null values. This differs from the SQL IN filter, which does not match null values.
For more information, see Query filters and SQL data types.
#12863
Previously, a search query could only search on dimensions that existed in the data source. Search queries now support virtual columns as a parameter in the query.
#12720
Simple queries like select max(__time) from ds
now run as a timeBoundary
queries to take advantage of the time dimension sorting in a segment. You can set a feature flag to enable this feature.
#12472
#12491
The first/last string aggregator now only compares based on values. Previously, the first/last string aggregator’s values were compared based on the _time
column first and then on values.
If you have existing queries and want to continue using both the _time
column and values, update your queries to use ORDER BY MAX(timeCol).
#12773
Introduced and implemented new helper functions in JacksonUtils
to enable reuse of
SerializerProvider
objects.
Additionally, disabled backwards compatibility for map-based rows in the GroupByQueryToolChest
by default, which eliminates the need to copy the heavyweight ObjectMapper
. Introduced a configuration option to allow administrators to explicitly enable backwards compatibility.
#12468
Added a new IPAddress Java library dependency to handle IP addresses. The library includes IPv6 support. Additionally, migrated IPv4 functions to use the new library.
#11634
Optimized SQL operations and functions as follows:
isEmpty()
and equals()
on RangeSets (#12477)Previously, consumers that were registered and used for ingestion persisted until Kafka deleted them. They were only used to make sure that an entire topic was consumed. There are no longer consumer groups that linger.
#12842
You can now perform Kinesis ingestion even if there are empty shards. Previously, all shards had to have at least one record.
#12792
You can now ingest data from endpoints that are different from your default S3 endpoint and signing region.
For more information, see S3 config.
#11798
This release includes the following improvements for ingestion in general.
Added setNumProcessorsPerTask
to prevent various automatically-sized thread pools from becoming unreasonably large. It isn't ideal for each task to size its pools as if it is the only process on the entire machine. On large machines, this solves a common cause of OutOfMemoryError
due to "unable to create native thread".
#12592
The JDBC driver now follows the JDBC standard and uses two kinds of statements, Statement and PreparedStatement.
#12709
Druid now accepts the EIGHT_HOUR
granularity. You can segment incoming data to EIGHT_HOUR
buckets as well as group query results by eight hour granularity.
#12717
The previous Avro extension leaked objects from the parser. If these objects leaked into your ingestion, you had objects being stored as a string column with the value as the .toString(). This string column will remain after you upgrade but will return Map.toString()
instead of GenericRecord.toString
. If you relied on the previous behavior, you can use the Avro extension from an earlier release.
#12828
The sampler API has additional limits: maxBytesInMemory
and maxClientResponseBytes
. These options augment the existing options numRows
and timeoutMs
. maxBytesInMemory
can be used to control the memory usage on the Overlord while sampling. maxClientResponseBytes
can be used by clients to specify the maximum size of response they would prefer to handle.
#12947
The DruidSchema
and SegmentMetadataQuery
properties now preserve column order instead of ordering columns alphabetically. This means that query order better matches ingestion order.
#12754
You can improve performance by pushing JOINs partially or fully to the base table as a filter at runtime by setting the enableRewriteJoinToFilter
context parameter to true
for a query.
Druid now pushes down join filters in case the query computing join references any columns from the right side.
#12749
#12868
Added is_active
as shorthand for (is_published = 1 AND is_overshadowed = 0) OR is_realtime = 1)
. This represents "all the segments that should be queryable, whether or not they actually are right now".
#11550
useNativeQueryExplain
now defaults to trueThe useNativeQueryExplain
property now defaults to true
. This means that EXPLAIN PLAN FOR returns the explain plan as a JSON representation of equivalent native query(s) by default. For more information, see Broker Generated Query Configuration Supplementation.
#12936
Some queries that do not refer to any table, such as select 1
, are now always translated to a native Druid query with InlineDataSource
before execution. If translation is not possible, for queries such as SELECT (1, 2)
, then an error occurs. In earlier versions, this query would still run.
#12897
You can now set druid.coordinator.kill.durationToRetain
to a negative period to configure the Druid cluster to kill segments whose interval_end
is a date in the future. For example, PT-24H would allow segments to be killed if their interval_end date was 24 hours or less into the future at the time that the kill task is generated by the system.
A cluster operator can also disregard the druid.coordinator.kill.durationToRetain
entirely by setting a new configuration, druid.coordinator.kill.ignoreDurationToRetain=true
. This ignores interval_end
date when looking for segments to kill, and can instead kill any segment marked unused. This new configuration is turned off by default, and a cluster operator should fully understand and accept the risks before enabling it.
Reduced contention between the management thread and the reception of status updates from the cluster. This improves the stability of Overlord and all tasks in a cluster when there are large (1000+) task counts.
#12099
Updated Coordinator load rule logging to include current replication levels. Added missing segment ID and tier information from some of the log messages.
#12511
Addressed the significant memory overhead caused by the web-console indirectly calling the Overlord’s GET tasks API. This could cause unresponsiveness or Overlord failure when the ingestion tab was opened multiple times.
#12404
In order to optimize segment cost computation time by reducing time taken for interval creation, store segment interval instead of creating it each time from primitives and reduce memory overhead of storing intervals by interning them. The set of intervals for segments is low in cardinality.
#12670
Brokers now have a default of 25MB maximum queued per query. Previously, there was no default limit. Depending on your use case, you may need to increase the value, especially if you have large result sets or large amounts of intermediate data. To adjust the maximum memory available, use the druid.broker.http.maxQueuedBytes
property.
For more information, see Configuration reference.
Prepare to have your Web Console experience elevated! - @vogievetsky
You can use the new query view to execute multi-stage, task based, queries with the /druid/v2/sql/task and /druid/indexer/v1/task/* APIs as well as native and sql-native queries just like the old Query view. A key point of the sql-msq-task based queries is that they may run for a long time. This inspired / necessitated many UX changes including, but not limited to the following:
You can now have many queries stored and running at the same time, significantly improving the query view UX.
You can open several tabs, duplicate them, and copy them as text to paste into any console and reopen there.
Queries run with the multi-stage query task engine have detailed progress reports shown in the summary progress bar and the in detail execution table that provides summaries of the counters for every step.
Queries run with the multi-stage query task engine present user friendly warnings and errors should anything go wrong.
The new query view has components to visualize these with their full detail including a stack-trace.
Queries run with the multi-stage query task engine are tasks. This makes it possible to show queries that are executing currently and that have executed in the recent past.
For any query in the Recent query tasks panel you can view the execution details for it and you can also attach it as a new tab and continue iterating on the query. It is also possible to download the "query detail archive", a JSON file containing all the important details for a given query to use for troubleshooting.
Connect external data flow lets you use the sampler to sample your source data to, determine its schema and generate a fully formed SQL query that you can edit to fit your use case before you launch your ingestion job. This point-and-click flow will save you much typing.
The Preview button appears when you type in an INSERT or REPLACE SQL query. Click the button to remove the INSERT or REPLACE clause and execute your query as an "inline" query with a limi). This gives you a sense of the shape of your data after Druid applies all your transformations from your SQL query.
The query results table has been improved in style and function. It now shows you type icons for the column types and supports the ability to manipulate nested columns with ease.
The Web Console now has some UI affordances for notebook and CTE users. You can reference helper queries, collapsable elements that hold a query, from the main query just like they were defined with a WITH statement. When you are composing a complicated query, it is helpful to break it down into multiple queries to preview the parts individually.
More tools are available from the ... menu:
The data loader exists as a GUI wizard to help users craft a JSON ingestion spec using point and click and quick previews. The SQL data loader is the SQL-based ingestion analog of that.
Like the native based data loader, the SQL-based data loader stores all the state in the SQL query itself. You can opt to manipulate the query directly at any stage. See (#12919) for more information about how the data loader differs from the Connect external data workflow.
See (#12919) for more details and other improvements
Sysmonitor stats, like memory or swap, are no longer reported since Peons always run on the same host as MiddleManagerse. This means that duplicate stats will no longer be reported.
#12802
You can now include the host and service as labels for Prometheus by setting the following properties to true:
druid.emitter.prometheus.addHostAsLabel
druid.emitter.prometheus.addServiceAsLabel
#12769
(Experimental) You can now see the average number of rows in a segment and the distribution of segments in predefined buckets with the following metrics: segment/rowCount/avg
and segment/rowCount/range/count
.
Enable the metrics with the following property: org.apache.druid.server.metrics.SegmentStatsMonitor
#12730
sqlQuery/planningTimeMs
metricThere’s a new sqlQuery/planningTimeMs
metric for SQL queries that computes the time it takes to build a native query from a SQL query.
#12923
The StatsD metrics reporter extension now includes the following metrics:
Added a new monitor, WorkerTaskCountStatsMonitor
, that allows each middle manage worker to report metrics for successful / failed tasks, and task slot usage.
#12446
The JvmMonitor can now handle more generation and collector scenarios. The monitor is more robust and works properly for ZGC on both Java 11 and 15.
#12469
Garbage collection metrics now use MXBeans.
#12481
Introduced the metric task/pending/time
to measure how long a task stays in the pending queue.
#12492
Adds vectorized metric for scan, timeseries and groupby queries.
#12484
Druid now emits metrics so you can monitor and assess the use of different types of batch ingestion, in particular replace and tombstone creation.
#12488
#12840
queryType
The KafkaEmitter now properly emits the queryType
property for native queries.
#12915
You can now hide properties that are sensitive in the API response from /status/properties
, such as S3 access keys. Use the druid.server.hiddenProperties
property in common.runtime.properties
to specify the properties (case insensitive) you want to hide.
#12950
druid.request.logging.durationToRetain
property. Set the retention period to be longer than P1D
(#12559)Zstandard
compression library to CompressionStrategy
(#12408)inputSegmentSizeBytes
in Compaction configuration to 100,000,000,000,000 (~100TB)Druid 24.0 contains over 68 bug fixes. You can find the complete list here
To read external data using the multi-stage query task engine, you must have READ permissions for the EXTERNAL resource type. Users without the correct permission encounter a 403 error when trying to run SQL queries that include EXTERN.
The way you assign the permission depends on your authorizer. For example, with [basic security]((/docs/development/extensions-core/druid-basic-security.md) in Druid, add the EXTERNAL READ
permission by sending a POST
request to the roles API.
The example adds permissions for users with the admin
role using a basic authorizer named MyBasicMetadataAuthorizer
. The following permissions are granted:
curl --location --request POST 'http://localhost:8081/druid-ext/basic-security/authorization/db/MyBasicMetadataAuthorizer/roles/admin/permissions' \
--header 'Content-Type: application/json' \
--data-raw '[
{
"resource": {
"name": ".*",
"type": "DATASOURCE"
},
"action": "READ"
},
{
"resource": {
"name": ".*",
"type": "DATASOURCE"
},
"action": "WRITE"
},
{
"resource": {
"name": ".*",
"type": "CONFIG"
},
"action": "READ"
},
{
"resource": {
"name": ".*",
"type": "CONFIG"
},
"action": "WRITE"
},
{
"resource": {
"name": ".*",
"type": "STATE"
},
"action": "READ"
},
{
"resource": {
"name": ".*",
"type": "STATE"
},
"action": "WRITE"
},
{
"resource": {
"name": "EXTERNAL",
"type": "EXTERNAL"
},
"action": "READ"
}
]'
Druid automatically retains any segments marked as unused. Previously, Druid permanently deleted unused segments from metadata store and deep storage after their duration to retain passed. This behavior was reverted from 0.23.0
.
#12693
druid.processing.fifo
The default for druid.processing.fifo
is now true. This means that tasks of equal priority are treated in a FIFO manner. For most use cases, this change can improve performance on heavily loaded clusters.
#12571
In previous releases, Druid automatically closed the JDBC Statement when the ResultSet was closed. Druid closed the ResultSet on EOF. Druid closed the statement on any exception. This behavior is, however, non-standard.
In this release, Druid's JDBC driver follows the JDBC standards more closely:
The ResultSet closes automatically on EOF, but does not close the Statement or PreparedStatement. Your code must close these statements, perhaps by using a try-with-resources block.
The PreparedStatement can now be used multiple times with different parameters. (Previously this was not true since closing the ResultSet closed the PreparedStatement.)
If any call to a Statement or PreparedStatement raises an error, the client code must still explicitly close the statement. According to the JDBC standards, statements are not closed automatically on errors. This allows you to obtain information about a failed statement before closing it.
If you have code that depended on the old behavior, you may have to change your code to add the required close statement.
#12709
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Published by abhishekagarwal87 over 2 years ago
Apache Druid 0.23.0 contains over 450 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 81 contributors. See the complete set of changes for additional details.
You can now group on a multi-value dimension as an array. For a datasource named "test":
{"timestamp": "2011-01-12T00:00:00.000Z", "tags": ["t1","t2","t3"]} #row1
{"timestamp": "2011-01-13T00:00:00.000Z", "tags": ["t3","t4","t5"]} #row2
{"timestamp": "2011-01-14T00:00:00.000Z", "tags": ["t5","t6","t7"]} #row3
{"timestamp": "2011-01-14T00:00:00.000Z", "tags": []} #row4
The following query:
{
"queryType": "groupBy",
"dataSource": "test",
"intervals": [
"1970-01-01T00:00:00.000Z/3000-01-01T00:00:00.000Z"
],
"granularity": {
"type": "all"
},
"virtualColumns" : [ {
"type" : "expression",
"name" : "v0",
"expression" : "mv_to_array(\"tags\")",
"outputType" : "ARRAY<STRING>"
} ],
"dimensions": [
{
"type": "default",
"dimension": "v0",
"outputName": "tags"
"outputType":"ARRAY<STRING>"
}
],
"aggregations": [
{
"type": "count",
"name": "count"
}
]
}
Returns the following:
[
{
"timestamp": "1970-01-01T00:00:00.000Z",
"event": {
"count": 1,
"tags": "[]"
}
},
{
"timestamp": "1970-01-01T00:00:00.000Z",
"event": {
"count": 1,
"tags": "["t1","t2","t3"]"
}
},
{
"timestamp": "1970-01-01T00:00:00.000Z",
"event": {
"count": 1,
"tags": "[t3","t4","t5"]"
}
},
{
"timestamp": "1970-01-01T00:00:00.000Z",
"event": {
"count": 2,
"tags": "["t5","t6","t7"]"
}
}
]
(#12078)
(#12253)
You can pass time column in *first
/*last
aggregators by using LATEST_BY
/ EARLIEST_BY
SQL functions. This provides support for cases where the time is stored as a part of a column different than "__time". You can also specify another logical time column.
(#11949)
(#12145)
This release includes several improvements for querying:
SegmentMetadata
query (#11429)druid.query.groupBy.maxSelectorDictionarySize
when grouping on string or array-valued expressions that do not have pre-existing dictionaries.useNativeQueryExplain
to true in query context (#11908)We've introduced a Kafka input format so you can ingest header data in addition to the message contents. For example:
(#11630)
We have made following improvements in kinesis ingestion
skipIgnorableShards
to true
in kinesis ingestion tuning config to ignore such shards. (#12235)DescribeStream
to fetch the list of shards. This call is deprecated and slower. In this release, you can switch to a newer API listShards
by setting useListShards
to true
in kinesis ingestion tuning config. (#12161)Multi-dimension range partitioning allows users to partition their data on the ranges of any number of dimensions. It develops further on the concepts behind "single-dim" partitioning and is now arguably the most preferable secondary partitioning, both for query performance and storage efficiency.
(#11848)
(#11973)
In previous versions of Druid, if ingested data with dropExisting
flag to replace data, Druid would retain the existing data for a time chunk if there was no new data to replace it. Now, if you set dropExisting
to true
in your ioSpec
and ingest data for a time range that includes a time chunk with no data, Druid uses a tombstone to overshadow the existing data in the empty time chunk.
(#12137)
This release includes several improvements for native batch ingestion:
segmentAvailabilityWaitTimeMs
, the duration in milliseconds that a task waited for its segments to be handed off to Historical nodes, to IngestionStatsAndErrorsTaskReportData
(#11090)RowStats
in druid/indexer/v1/task/{task_id}/reports
API for multi-phase parallel indexing task (#12280)This release includes several improvements for ingestion in general:
IncrementalIndex<AggregatorType>
because it is no longer requiredJsonPath
functions in JsonPath
expressions during ingestion (#11722)IndexSpec
to the main "merge" method in IndexMerger
(#11940)Granularity.granularitiesFinerThan
now returns ALL if you pass in ALL (#12003)SchemaRegistryBasedAvroBytesDecoder
now throws a ParseException
instead of RE when it fails to retrieve a schema (#12080)includeAllDimensions
to dimensionsSpec
to put all explicit dimensions first in InputRow
and subsequently any other dimensions found in input data (#12276)This release includes several improvements for compaction:
Until version 0.22.1, if you issued an unsupported SQL query, Druid would throw very cryptic and unhelpful error messages. With this change, error messages include exactly the part of the SQL query that is not supported in Druid. For example, if you run a scan query that is ordered on a dimension other than the time column.
(#11911)
We've added a new API to cancel SQL queries, so you can now cancel SQL queries just like you can cancel native queries. You can use the API from the web console. In previous versions, cancellation from the console only closed the client connection while the SQL query kept running on Druid.
(#11643)
(#11738)
(#11710)
We have made changes to expressions that make expression evaluation more SQL compliant. This new behaviour is disabled by default. It can be enabled by setting druid.expressions.useStrictBooleans
to true
. We recommend enabling this behaviour since it is also more performant in some cases.
(#11184)
This release includes several additional improvements for SQL:
/
for JDBC connections to Druid (#11737)DruidRexExecutor
handles numeric arrays (#11968)druid.global.http.eagerInitialization
to false
in common runtime properties.Segment size
(in bytes) column to the Datasources view (#11797)vectorized
dimension by default. This can be helpful in understanding performance profile of queries.This release includes several additional improvements for metrics:
conversionFactor
in Prometheus emitter (12338)ingest/events/messageGap
metric (#12337)Cpu
and CpuSet
to java.util.metrics.cgroups
, ProcFsUtil
for procfs
info, and CgroupCpuMonitor
and CgroupCpuSetMonitor
(#11763)partitioningType
dimension to segment/added/bytes
metric to track usage of different partitioning schemes (#11902)BalanceSegments#balanceServers
now exits early when there is no balancing work to do (#11768)DimensionHandler
now allows you to define a DimensionSpec
appropriate for the type of dimension to handle (#11873)Today, any context params are allowed to users. This can cause 1) a bad UX if the context param is not matured yet or 2) even query failure or system fault in the worst case if a sensitive param is abused, ex) maxSubqueryRows. Druid now has an ability to limit context params per user role. That means, a query will fail if you have a context param set in the query that is not allowed to you.
The context parameter authorization can be enabled using Druid.auth.authorizeQueryContextParam
s. This is disabled by default to enable a smoother upgrade experience.
(#12396)
This release includes several additional improvements for security:
inSubQueryThreshold
in SQL query context. (#12357)time_shift
is now vectorized (#12254)Druid 0.23.0 contains over 68 bug fixes. You can find the complete list here
Consider the following changes and updates when upgrading from Druid 0.22.x to 0.23.0. If you're updating from an earlier version than 0.22.1, see the release notes of the relevant intermediate versions.
In 0.23.0
, Auto killing of segments is now enabled by default (#12187). The new defaults should kill all unused segments older than 90 days. If users do not want this behavior on an upgrade, they should explicitly disable the behavior. This is a risky change since depending on the interval, segments will be killed immediately after being marked unused. this behavior will be reverted or changed in the next druid release. Please see (#12693) for more details.
listShards
API access on the stream.3.0.0
(#11735)https://github.com/airlift/airline is no longer maintained and so druid has upgraded to https://github.com/rvesse/airline (Airline 2) to use an actively
maintained version, while minimizing breaking changes.
This is a backwards incompatible change, and custom extensions relying on the CliCommandCreator extension point will also need to be updated.
Earlier supervisor/task endpoint return 400 when a supervisor or a task is not found. This status code is not friendly and confusing for the 3rd system. And according to the definition of HTTP status code, 404 is right code for such case. So we have changed the status code from 400 to 404 to eliminate the ambigiuty. Any clients of these endpoints should change the response code handling accordingly.
Any SQL query that cannot be planned by Druid is not considered a bad request. For such queries, we now return 400. Developers using SQL API should change the response code handling if needed.
0.23.0
changes the the ResponseContext
and it's keys in a breaking way. The prior version of the response context suggested that keys be defined in an enum, then registered. This version suggests that keys be defined as objects, then registered. See the ResponseContext
class itself for the details.
(#11828)
SingleServerInventoryView
has been removed. (#11770)LocalInputSource
does not allow ingesting same file multiple times. (#11965)getType()
in PostAggregator
is deprecated in favour of getType(ColumnInspector)
(#11818)For a full list of open issues, please see Bug .
Thanks to everyone who contributed to this release!
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Published by jihoonson almost 3 years ago
Apache Druid 0.22.1 is a bug fix release that fixes some security issues. See the complete set of changes for additional details.
https://github.com/apache/druid/pull/12051 Update log4j to 2.15.0 to address CVE-2021-44228
https://github.com/apache/druid/pull/11787 JsonConfigurator no longer logs sensitive properties
https://github.com/apache/druid/pull/11786 Update axios to 0.21.4 to address CVE-2021-3749
https://github.com/apache/druid/pull/11844 Update netty4 to 4.1.68 to address CVE-2021-37136 and CVE-2021-37137
Thanks to everyone who contributed to this release!
@abhishekagarwal87
@andreacyc
@clintropolis
@gianm
@jihoonson
@kfaraz
@xvrl
Published by clintropolis about 3 years ago
Apache Druid 0.22.0 contains over 400 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 73 contributors. See the complete set of changes for additional details.
Druid now can support multiple DISTINCT
'exact' counts using the grouping aggregator typically used with grouping sets. Note that this only applies to exact counts - when druid.sql.planner.useApproximateCountDistinct
is false, and can be enabled by setting druid.sql.planner.useGroupingSetForExactDistinct
to true
.
https://github.com/apache/druid/pull/11014
The ARRAY_AGG
aggregation function has been added, to allow accumulating values or distinct values of a column into a single array result. This release also adds STRING_AGG
, which is similar to ARRAY_AGG
, except it joins the array values into a single string with a supplied 'delimiter' and it ignores null values. Both of these functions accept a maximum size parameter to control maximum result size, and will fail if this value is exceeded. See SQL documentation for additional details.
https://github.com/apache/druid/pull/11157
https://github.com/apache/druid/pull/11241
Several new SQL functions functions for performing 'bitwise' math (along with corresponding native expressions), including BITWISE_AND
, BITWISE_OR
, BITWISE_XOR
and so on. Additionally, aggregation functions BIT_AND
, BIT_OR
, and BIT_XOR
have been added to accumulate values in a column with the corresponding bitwise function. For complete details see SQL documentation.
https://github.com/apache/druid/pull/10605
https://github.com/apache/druid/pull/10823
https://github.com/apache/druid/pull/11280
Three new SQL and native expression number format functions have been added in Druid 0.22.0, HUMAN_READABLE_BINARY_BYTE_FORMAT
, HUMAN_READABLE_DECIMAL_BYTE_FORMAT
, and HUMAN_READABLE_DECIMAL_FORMAT
, which allow transforming results into a more friendly consumption format for query results. For more information see SQL documentation.
https://github.com/apache/druid/issues/10584
https://github.com/apache/druid/pull/10635
Druid 0.22.0 adds a new 'native' JSON query expression aggregator function, that lets you use Druid native expressions to perform "fold" (alternatively known as "reduce") operations to accumulate some value on any number of input columns. This adds significant flexibility to what can be done in a Druid aggregator, similar in a lot of ways to what was possible with the Javascript aggregator, but in a much safer, sandboxed manner.
Expressions now being able to perform a "fold" on input columns also really rounds out the abilities of native expressions in addition to the previously possible "map" (expression virtual columns), "filter" (expression filters) and post-transform (expression post-aggregators) functions.
Since this uses expressions, performance is not yet optimal, and it is not directly documented yet, but it is the underlying technology behind the SQL ARRAY_AGG
, STRING_AGG
, and bitwise aggregator functions also added in this release.
https://github.com/apache/druid/pull/11104
Druid 0.22 adds some new facilities to provide extension writers with enhanced control over how queries are routed between Druid routers and brokers. The first adds a new manual
broker selection strategy to the Druid router, which allows a query to manually specify which Druid brokers a query should be sent to based on a query context parameter brokerService
to any broker pool defined in druid.router.tierToBrokerMap
(this corresponds to the 'service name' of the broker set, druid.service
).
The second new feature allows the Druid router to parse and examine SQL queries so that broker selection strategies can also function for SQL queries. This can be enabled by setting druid.router.sql.enable
to true. This does not affect JDBC queries, which use a different mechanism to facilitate "sticky" connections to a single broker.
https://github.com/apache/druid/pull/11566
https://github.com/apache/druid/pull/11495
Druid now supports using Avatica Protobuf JDBC connections, such as for use with the Avatica Golang Driver, and has a separate endpoint from the JSON JDBC uri.
String url = "jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica-protobuf/;serialization=protobuf";
https://github.com/apache/druid/pull/10543
Query exceptions have been changed from WARN
level to ERROR
level to include additional information in the logs to help troubleshoot query failures. Additionally, a new query context flag, enableQueryDebugging
has been added that will include stack traces in these query error logs, to provide even more information without the need to enable logs at the DEBUG
level.
https://github.com/apache/druid/pull/11519
Druid 0.22.0 now offers experimental support for dynamic Kafka and Kinesis task scaling. The included strategies are driven by periodic measurement of stream lag (which is based on message count for Kafka, and difference of age between the message iterator and the oldest message for Kinesis), and will adjust the number of tasks based on the amount of 'lag' and several configuration parameters. See Kafka and Kinesis documentation for complete information.
https://github.com/apache/druid/pull/10524
https://github.com/apache/druid/pull/10985
Druid streaming ingestion now has support for Avro and Protobuf in the updated InputFormat
specification format, which replaces the deprecated firehose/parser specification used by legacy Druid streaming formats. Alongside this, comes support for obtaining schemas for these formats from Confluent Schema Registry. See data formats documentation for further information.
https://github.com/apache/druid/pull/11040
https://github.com/apache/druid/pull/11018
https://github.com/apache/druid/pull/10314
https://github.com/apache/druid/pull/10839
Druid Kafka streaming ingestion now optionally supports specifying group.id
on the connections Druid tasks make to the Kafka brokers. This is useful for accessing clusters which require this be set as part of authorization, and can be specified in the consumerProperties
section of the Kafka supervisor spec. See Kafka ingestion documentation for more details.
https://github.com/apache/druid/pull/11147
Druid native 'perfect rollup' 2-phase ingestion tasks now support using deep storage as a shuffle location, as an alternative to local disks on middle-managers or indexers. To use this feature, set druid.processing.intermediaryData.storage.type
to deepstore
, which uses the configured deep storage type.
Note - With "deepstore" type, data is stored in shuffle-data
directory under the configured deep storage path, auto clean up for this directory is not supported yet. One can setup cloud storage lifecycle rules for auto clean up of data at shuffle-data
prefix location.
https://github.com/apache/druid/pull/11507
Druid native batch ingestion has received a new configuration option, druid.indexer.task.batchProcessingMode
which introduces two new operating modes that should allow batch ingestion to operate with a smaller and more predictable heap memory usage footprint. The CLOSED_SEGMENTS_SINKS
mode is the most aggressive, and should have the smallest memory footprint, and works by eliminating in memory tracking and mmap of intermediary segments produced during segment creation, but isn't super well tested at this point so considered experimental. CLOSED_SEGMENTS
, which is the new default option, eliminates mmap
of intermediary segments, but still tracks the entire set of segments in heap, though it is relatively well tested at this point and considered stable. OPEN_SEGMENTS
will use the previous ingestion path, which is shared with streaming ingestion and performs a mmap
on intermediary segments and builds a timeline so that these segments can be queryable by realtime queries. This is not needed at all for batch, but OPEN_SEGMENTS
mode can be selected if any problems occur with the 2 newer modes.
https://github.com/apache/druid/pull/11123
https://github.com/apache/druid/pull/11294
https://github.com/apache/druid/pull/11536
Druid native batch ingestion tasks can now be optionally configured to not terminate until after the ingested segments are completely loaded by Historical servers. This can be useful for scenarios when the trade-off of keeping an extra task slot occupied is worth using the task state as a measure of if ingestion is complete and segments are available to query.
This can be enabled by adding awaitSegmentAvailabilityTimeoutMillis
to the tuningConfig
in the ingestion spec, which specifies the maximum amount of time that a task will wait for segments to be loaded before terminating. If not all segments become available by the time this timeout expires, the job will still succeed. However, in the ingestion report, segmentAvailabilityConfirmed
will be false. This indicates that handoff was not successful and these newly indexed segments may not all be available for query. On the other hand, if all segments become available for query on the Historical services before the timeout expires, the value for that key in the report will be true.
This tuningConfig
value is not supported for compaction tasks at this time. If a user tries to specify a value for awaitSegmentAvailabilityTimeoutMillis
for Compaction, the task will fail telling the user it is not supported.
https://github.com/apache/druid/pull/10676
Druid manual and automatic compaction can now be configured to change segment granularity, and manual compaction can also change query granularity. Additionally, compaction will preserve segment granularity by default. This allows operators to more easily perform options like changing older data to larger segment and query granularities in exchange for decreased data size. See compaction docs for details.
https://github.com/apache/druid/pull/10843
https://github.com/apache/druid/pull/10856
https://github.com/apache/druid/pull/10900
https://github.com/apache/druid/pull/10912
https://github.com/apache/druid/pull/11009
Druid auto-compaction will now by default temporarily skip locked intervals instead of waiting for the lock to become free, which should improve the rate at which datasources can be compacted. This is controlled by druid.coordinator.compaction.skipLockedIntervals
, and can be set to false if this behavior is not desired for some reason.
https://github.com/apache/druid/pull/11190
You can configure automated cleanup to remove records from the metadata store after you delete delete some entities from Druid:
This feature helps maintain performance when you have a high datasource churn rate, meaning you frequently create and delete many short-lived datasources or other related entities. You can limit the length of time to retain unused metadata records to prevent your metadata store from filling up. See automatic cleanup documentation for more information.
https://github.com/apache/druid/pull/11078
https://github.com/apache/druid/pull/11084
https://github.com/apache/druid/pull/11164
https://github.com/apache/druid/pull/11200
https://github.com/apache/druid/pull/11227
https://github.com/apache/druid/pull/11232
https://github.com/apache/druid/pull/11245
A new setting, dropExisting
has been added to the ioConfig
of Druid native batch ingestion tasks and compaction, which if set to true (and appendToExist
is false), then the ingestion task will transactionally mark all existing segments in the interval as unused, replacing them with the new set of segments. This can be useful in compaction use cases where normal overshadowing does not completely replace a set of segments in an interval, such as when changing segment granularity to a smaller size and some of the smaller granularity buckets would have no data, leaving the original segments only partially overshadowed.
Note that this functionality is still experimental, and can result in temporary data unavailability for data within the compacted interval
. Changing this config does not cause intervals to be compacted again.
Similarly, markAsUnused
has been added as an option to the Druid kill task, which will mark any segments in the supplied interval as 'unused' prior to deleting all of the unused segments. This is useful for allowing the mark unused -> delete sequence to happen with a single API call for the caller, as well as allowing the unmark action to occur under a task interval lock.
https://github.com/apache/druid/pull/11070
https://github.com/apache/druid/pull/11025
https://github.com/apache/druid/pull/11501
A new Druid coordinator dynamic configuration option allows controlling the behavior whenever a segment load action times out when using Zookeeper based segment management. replicateAfterLoadTimeout
when set to true, the coordinator will attempt to replicate the segment that failed to load to a different historical server. This helps improve the segment availability if there are a few slow historical servers in the cluster. However, the slow historical may still load the segment later and the coordinator may need to issue drop requests if the segment is over-replicated.
https://github.com/apache/druid/pull/10213
Another new coordinator dynamic configuration option, useBatchedSegmentSampler
, when set to true can potentially provide a large performance increase in the speed which the coordinator can process the segment balancing phase. This should be particularly notable at very large cluster sizes with many segments, but is disabled by default to err on the side of caution.
https://github.com/apache/druid/pull/11257
The Druid coordinator load status API now supports a new optional URL query parameter, computeUsingClusterView
, which when specified will cause the coordinator compute under-replication for segments based on the number of servers available within cluster that the segment can be replicated to, instead of the configured replication count configured in load rule. For example, if the load rules specify 2 replicas, but there is only 1 server which can hold segments, this API would not report as under-replicated because the segments are as replicated as is possible for the given cluster size.
https://github.com/apache/druid/pull/11056
A new coordinator dynamic configuration, maxNonPrimaryReplicantsToLoad
, with default value of Integer.MAX_VALUE
, lets operators to define a hard upper limit on the number of non-primary replicants that will be loaded in a single coordinator execution cycle. The default value will mimic the behavior that exists today.
Example usage: If you set this configuration to 1000, the coordinator will load a maximum of 1000 non-primary replicants in each run cycle execution. Meaning if you ingested 2000 segments with a replication factor of 2, the coordinator would load 2000 primary replicants and 1000 non-primary replicants on the first execution. Then the next execution, the last 1000 non-primary replicants will be loaded.
https://github.com/apache/druid/pull/11135
The Druid web-console 'services' tab will now display which coordinator and overlord servers are serving as the leader, displayed in the 'Detail' column of the table. This should help operators be able to more quickly determine which node is the leader and thus which likely has the interesting logs to examine.
The web-console now also supports using ASCII control characters, by entering them in the form of \uNNNN
where NNNN
is the unicode code point for the character.
https://github.com/apache/druid/pull/10951
https://github.com/apache/druid/pull/10795
The query view of the web-console has received a number of 'quality of life' improvements in Druid 0.22.0. First, the query view now provides an indicator of how long a query took to execute:
Also, queries will no longer auto-run when opening a fresh page, to prevent stale queries from being executed when opening a browser, the page will be reset to 0 if the query result changes and the query limit will automatically increase when the last page is loaded re-running the query.
Inline documentation now also should include Druid type information:
and should provide better suggestions whenever a query error occurs:
Finally, the web console query view now supports the hot-key combination command + enter
(on mac) and ctrl + enter
on Windows and Linux.
https://github.com/apache/druid/pull/11158
https://github.com/apache/druid/pull/11128
https://github.com/apache/druid/pull/11203
https://github.com/apache/druid/pull/11365
The web-console segments view timeline now has the ability to pick any time interval, instead of just the previous year!
The web-console segments view has also been improved to hopefully be more performant when interacting with the sys.segments
table, including providing the ability to 'force' the web-console to only use the native JSON API methods to display segment information:
The lookup view has also been improved, so that now 'poll period' and 'summary' are available as columns in the list view:
We have also added validation for poll period to prevent user error, and improved error reporting:
https://github.com/apache/druid/pull/11359
https://github.com/apache/druid/pull/10909
https://github.com/apache/druid/pull/11620
A new "contrib" extension has been added, prometheus-emitter
, which allows Druid metrics to be sent directly to a Prometheus server. See the extension documentation page for complete details: https://druid.apache.org/docs/0.22.0/development/extensions-contrib/prometheus.html
https://github.com/apache/druid/pull/10412
https://github.com/apache/druid/pull/11618
ingest/notices/queueSize
is a new metric added to provide monitoring for supervisor ingestion task control message processing queue sizes, to help in determining if a supervisor might be overloaded by a large volume of these notices. This metric is emitted by default for every running supervisor.
https://github.com/apache/druid/pull/11417
query/segments/count
is a new metric which has been added to track the number of segments which participate in a query. This metric is not enabled by default, so must be enabled via a custom extension to override which QueryMetrics
are emitted similar to other query metrics that are not emitted by default. (We know this is definitely not friendly, and hope someday in the future to make this easier, sorry).
https://github.com/apache/druid/pull/11394
Druid 0.22.0 adds AWS Web Identity Token Support, which allows for the use of IAM roles for service accounts on Kubernetes, if configured as the AWS credentials provider.
https://github.com/apache/druid/pull/10541
Druid native batch ingestion from S3 input sources can now use the AssumeRole
capability in AWS for cross-account file access. This can be utilized by setting assumeRoleArn
and assumeRoleExternalId
on the S3 input source specification in a batch ingestion task. See AWS documentation and native batch documentation for more details.
https://github.com/apache/druid/pull/10995
Druid lookups now support loading via Google Cloud Storage, similar to existing functionality available with S3. This requires the druid-google-extensions
must be loaded in addition to the lookup extensions, but beyond that it is as simple as using a Google Cloud Storage URI.
https://github.com/apache/druid/pull/11026
Avro ingestion using Druid batch or streaming ingestion now supports an alternative mechanism of extracting data for Avro Union types. This new option, extractUnionsByType
only works when utilizing a flattenSpec
to extract nested data from union types, and will cause the extracted data to be available with the type as part of the flatten path. For example, given a multi-typed union column someMultiMemberUnion
, with this option enabled a long value would be extracted by $.someMultiMemberUnion.long
instead of $.someMultiMemberUnion
, and would only extract long values from the union. See Avro documentation for complete information.
https://github.com/apache/druid/pull/10505
Druid MySQL extensions now supports using the MariaDB connector library as an alternative to the MySQL connector. This can be done by setting druid.metadata.mysql.driver.driverClassName
to org.mariadb.jdbc.Driver
and includes full support for JDBC URI parameter whitelists used by JDBC lookups and SQL based ingestion.
https://github.com/apache/druid/pull/11402
Druid now provides a DynamicConfigProvider
implementation that is backed by environment variables. For example:
druid.some.config.dynamicConfigProvider={"type": "environment","variables":{"secret1": "SECRET1_VAR","secret2": "SECRET2_VAR"}}
See dynamic config provider documentation for further information.
https://github.com/apache/druid/pull/11377
Ingestion formats which support Confluent Schema Registry now support supplying these parameters via a DynamicConfigProvider
which is the newer alternative to PasswordProvider
. This will allow ingestion tasks to use the config provider to supply this information instead of directly in the JSON specifications, allowing the potential for more secure manners of supplying credentials and other sensitive configuration information. See data format and dynamic config provider documentation for more details.
https://github.com/apache/druid/pull/11362
Druid 0.22.0 adds new facilities to control the set of allowed protocols used by HTTP and HDFS input sources in batch ingestion. druid.ingestion.hdfs.allowedProtocols
is configured by default to accept hdfs
as the protocol, and druid.ingestion.http.allowedProtocols
by default will allow http
and https
. This might cause issue with existing deployments since it is more restrictive than the current default behavior in older versions of Druid, but overall allows operators more flexibility in securing these input sources.
https://github.com/apache/druid/pull/10830
This version of Druid also fixes a flaw in druid-basic-security
extension when using LDAP, where the credentials cache would not correctly expire, potentially holding expired credential information after it should have expired, until another trigger was hit or the service was restarted. Druid clusters using LDAP for authorization should update to 0.22.0 whenever possible to fix this issue.
https://github.com/apache/druid/pull/11395
JOIN
queries by allowing some INNER JOIN
queries to be translated into native Druid filters: https://github.com/apache/druid/pull/11068
JOIN
queries, controlled by new query context parameter enableJoinLeftTableScanDirect
(default to false
): https://github.com/apache/druid/pull/10697
druid.sql.avatica.minRowsPerFrame
broker configuration which can be used to significantly improve JDBC performance by increasing the result batch size: https://github.com/apache/druid/pull/10880
sys.segments
: https://github.com/apache/druid/pull/11008
segmentMetadata
queries which are used to build SQL schema https://github.com/apache/druid/pull/10892
LONG
columns with 'auto' encoding (not the default): https://github.com/apache/druid/pull/11004
Druid 0.22.0 contains over 80 bug fixes, you can see the complete list here.
Consider the following changes and updates when upgrading from Druid 0.21.x to 0.22.0. If you're updating from an earlier version than 0.21.0, see the release notes of the relevant intermediate versions.
Following up to 0.21, which officially deprecated support for Zookeeper 3.4, which has been end-of-life for a while, support for ZooKeeper 3.4 is now removed in 0.22.0. Be sure to upgrade your Zookeeper cluster prior to upgrading your Druid cluster to 0.22.0.
https://github.com/apache/druid/issues/10780
https://github.com/apache/druid/pull/11073
Druid 0.22.0 includes an important bug-fix in native batch indexing where transient failures of indexing sub-tasks can result in non-contiguous partitions in the result segments, which will never become queryable due to logic which checks for the 'complete' set. This issue has been resolved in the latest version of Druid, but required a change in the protocol which batch tasks use to allocate segments, and this change can cause issues during rolling downgrades if you decide to roll back from Druid 0.22.0 to an earlier version.
To avoid task failure during a rolling-downgrade, set
druid.indexer.task.default.context={ "useLineageBasedSegmentAllocation" : false }
in the overlord runtime properties, and wait for all tasks which have useLineageBasedSegmentAllocation
set to true to complete before initiating the downgrade. After these tasks have all completed the downgrade shouldn't have any further issue and the setting can be removed from the overlord configuration (recommended, as you will want this setting enabled if you are running Druid 0.22.0 or newer).
https://github.com/apache/druid/pull/11189
Prior to Druid 0.22, an SQL group by query which is using a single universal grouping key (e.g. only aggregators) such as SELECT COUNT(*), SUM(x) FROM y WHERE z = 'someval'
would produce an empty result set instead of [0, null]
that might be expected from this query matching no results. This was because underneath this would plan into a timeseries query with 'ALL' granularity, and skipEmptyBuckets
set to true in the query context. This latter option caused the results of such a query to return no results, as there are no buckets with values to aggregate and so they are skipped, making an empty result set instead of a 'nil' result set. This behavior has been changed to behave in line with other SQL implementations, but the previous behavior can be obtained by explicitly setting skipEmptyBuckets
on the query context.
https://github.com/apache/druid/pull/11188
Batch tasks using a 'Druid' input source to reingest segment data will no longer accept the 'dimensions' and 'metrics' sections of their task spec, and now will internally use a new columns filter to specify which columns from the original segment should be retained. Additionally, timestampSpec is no longer ignored, allowing the __time column to be modified or replaced with a different column. These changes additionally fix a bug where transformed columns would be ignored and unavailable on the new segments.
https://github.com/apache/druid/pull/10267
Some things might still work, but it is no longer officially supported so that newer Javascript features can be used to develop the web-console.
https://github.com/apache/druid/pull/11357
Druid coordinator maxSegmentsInNodeLoadingQueue
dynamic configuration has been changed from unlimited (0
) to instead to 100
. This should make the coordinator behave in a much more relaxed manner during periods of cluster volatility, such as a rolling upgrade, but caps the total number of segments that will be loaded in any given coordinator cycle to 100 per server, which can slow down the speed at which a completely stopped cluster is started and loaded from deep storage.
https://github.com/apache/druid/pull/11540
druid-processing
to druid-core
The CacheKeyBuilder
class, which is annotated with @PublicAPI
has been moved from druid-processing
to druid-core
so that expressions can extend the Cacheable
interface to allow expressions to generate cache keys which depend on some external state, such as lookup version.
https://github.com/apache/druid/pull/11358
QueryProcessingPool
instead of ExecutorService
directlyThis impacts a handful of method signatures in the query processing engine, such as QueryRunnerFactory
and QuerySegmentWalker
to allow extensions to hook into various parts of the query processing pool and alternative processing pool scheduling strategies in the future.
https://github.com/apache/druid/pull/11382
This allows extensions to provide alternative segment loading implementations to customize how Druid segments are loaded from deep storage and made available to the query engine. This should be considered an unstable api, and is annotated as such in the code.
https://github.com/apache/druid/pull/11398
For a full list of open issues, please see https://github.com/apache/druid/labels/Bug.
Thanks to everyone who contributed to this release!
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@a2l007
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Published by clintropolis over 3 years ago
Apache Druid 0.21.1 is a bug fix release that fixes a few regressions with the 0.21 release. The first is an issue with the published Docker image, which causes containers to fail to start due to volume permission issues, described in #11166 as fixed in #11167. This release also fixes an issue caused by a bug in the upgraded Jetty version which was released in 0.21, described in #11206 and fixed in #11207. Finally, a web console regression related to field validation has been added in #11228.
https://github.com/apache/druid/pull/11167 fix docker volume permissions
https://github.com/apache/druid/pull/11207 Upgrade jetty version
https://github.com/apache/druid/pull/11228 Web console: Fix required field treatment
https://github.com/apache/druid/pull/11299 Fix permission problems in docker
Thanks to everyone who contributed to this release!
@a2l007
@clintropolis
@FrankChen021
@maytasm
@vogievetsky
Published by jihoonson over 3 years ago
Apache Druid 0.21.0 contains around 120 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 36 contributors. Refer to the complete list of changes and everything tagged to the milestone for further details.
The new Kubernetes extension supports service discovery and leader election based on Kubernetes. This extension works in conjunction with the HTTP-based server view (druid.serverview.type=http
) and task management (druid.indexer.runner.type=httpRemote
) to allow you to run a Druid cluster with zero ZooKeeper dependencies. This extension is still experimental. See Kubernetes extension for more details.
https://github.com/apache/druid/pull/10544
https://github.com/apache/druid/pull/9507
https://github.com/apache/druid/pull/10537
You can set the percentOfSegmentsToConsiderPerMove
to limit the number of segments considered when picking a candidate segment to move. The candidates are searched up to maxSegmentsToMove * 2
times. This new configuration prevents Druid from iterating through all available segments to speed up the segment balancing process, especially if you have lots of available segments in your cluster. See Coordinator dynamic configuration for more details.
https://github.com/apache/druid/pull/10284
status
and selfDiscovered
endpoints for IndexersThe Indexer now supports status
and selfDiscovered
endpoints. See Processor information APIs for details.
https://github.com/apache/druid/pull/10679
grouping
aggregator functionYou can use the new grouping
aggregator SQL function with GROUPING SETS
or CUBE
to indicate which grouping dimensions are included in the current grouping set. See Aggregation functions for more details.
https://github.com/apache/druid/pull/10518
Expression processing now can be vectorized when inputs are missing. For example a non-existent column. When an argument is missing in an expression, Druid can now infer the proper type of result based on non-null arguments. For instance, for longColumn + nonExistentColumn
, nonExistentColumn
is treated as (long) 0
instead of (double) 0.0
. Finally, in default null handling mode, math functions can produce output properly by treating missing arguments as zeros.
https://github.com/apache/druid/pull/10499
TIMESTAMPADD
TIMESTAMPADD
function now allows zero period. This functionality is required for some BI tools such as Tableau.
https://github.com/apache/druid/pull/10550
Parallel task no longer requires you to set explicit intervals in granularitySpec
. If intervals are missing, the parallel task executes an extra step for input sampling which collects the intervals to index.
https://github.com/apache/druid/pull/10592
https://github.com/apache/druid/pull/10647
Druid now supports Apache Kafka older than 0.11. To read from an old version of Kafka, set the isolation.level
to read_uncommitted
in consumerProperties
. Only 0.10.2.1 have been tested up until this release. See Kafka supervisor configurations for details.
https://github.com/apache/druid/pull/10551
A new tuningConfig, maxColumnsToMerge
, controls how many segments can be merged at the same time in the task. This configuration can be useful to avoid high memory pressure during the merge. See tuningConfig for native batch ingestion for more details.
https://github.com/apache/druid/pull/10689
Parallel tasks now sort segments by ID before assigning them to subtasks. This sorting minimizes the number of time chunks for each subtask to handle. As a result, each subtask is expected to use less memory, especially when a single Parallel task is issued to re-ingest segments covering a long time period.
https://github.com/apache/druid/pull/10646
The new web console styles make better use of the Druid brand colors and standardize paddings and margins throughout. The icon and background colors are now derived from the Druid logo.
https://github.com/apache/druid/pull/10515
The web console now shows datasource partitioning information on the new Segment granularity
and Partitioning
columns.
Segment granularity
column in the Datasources
tabPartitioning
column in the Segments
tabhttps://github.com/apache/druid/pull/10533
Schema
table matches the dimensionsSpec
The Schema
table now reflects the dimension ordering in the dimensionsSpec
.
https://github.com/apache/druid/pull/10588
The coordinator performs several 'duty' tasks. For example segment balancing, loading new segments, etc. Now there are two new metrics to help you analyze how fast the Coordinator is executing these duties.
coordinator/time
: the time for an individual duty to executecoordinator/global/time
: the time for the whole duties runnable to executehttps://github.com/apache/druid/pull/10603
A new metric provides the number of timed out queries. Previously timed out queries were treated as interrupted and included in the query/interrupted/count
(see Changed HTTP status codes for query errors for more details).
query/timeout/count
: the number of timed out queries during the emission period
https://github.com/apache/druid/pull/10567
Two new metrics provide shuffle statistics for MiddleManagers and Indexers. These metrics have the supervisorTaskId
as their dimension.
ingest/shuffle/bytes
: number of bytes shuffled per emission periodingest/shuffle/requests
: number of shuffle requests per emission periodTo enable the shuffle metrics, add org.apache.druid.indexing.worker.shuffle.ShuffleMonitor
in druid.monitoring.monitors
. See Shuffle metrics for more details.
https://github.com/apache/druid/pull/10359
The default metrics monitor scheduler is implemented based on ScheduledThreadPoolExecutor
which is prone to unbounded clock drift. A new monitor scheduler, ClockDriftSafeMonitorScheduler
, overcomes this limitation. To use the new scheduler, set druid.monitoring.schedulerClassName
to org.apache.druid.java.util.metrics.ClockDriftSafeMonitorScheduler
in the runtime.properties file.
https://github.com/apache/druid/pull/10448
https://github.com/apache/druid/pull/10732
A new PasswordProvider
type allows access to AWS RDS DB instances using temporary AWS tokens. This extension can be useful when an RDS is used as Druid's metadata store. See AWS RDS extension for more details.
https://github.com/apache/druid/pull/9518
sys.servers
table shows leadersA new long-typed column is_leader
in the sys.servers
table indicates whether or not the server is the leader.
https://github.com/apache/druid/pull/10680
druid-influxdb-emitter
extension supports the HTTPS protocolSee Influxdb emitter extension for new configurations.
https://github.com/apache/druid/pull/9938
The docker image size is reduced by half by eliminating unnecessary duplication.
https://github.com/apache/druid/pull/10506
DynamicConfigProvider
A new class DynamicConfigProvider
enables fetching consumer properties at runtime. For instance, you can use DynamicConfigProvider
fetch bootstrap.servers
from location such as a local environment variable if it is not static. Currently, only a map-based config provider is supported by default. See DynamicConfigProvider for how to implement a custom config provider.
https://github.com/apache/druid/pull/10309
Druid 0.21.0 contains 30 bug fixes, you can see the complete list here.
Before 0.21.0, the query fails with an error when you use post aggregators with sub-totals. Now this bug is fixed and you can use post aggregators with subtotals.
https://github.com/apache/druid/pull/10653
In 0.19.0 and 0.20.0, Indexers could not process queries against streaming data as they did not announce themselves as segment servers. They are fixed to announce themselves properly in 0.21.0.
https://github.com/apache/druid/pull/10631
Historicals now perform validity check after they download segment files and re-download automatically if those files are crashed.
https://github.com/apache/druid/pull/10650
StorageLocationSelectorStrategy
injection failure is fixedThe injection failure while reading the configurations of StorageLocationSelectorStrategy
is fixed.
https://github.com/apache/druid/pull/10363
Consider the following changes and updates when upgrading from Druid 0.20.0 to 0.21.0. If you're updating from an earlier version than 0.20.0, see the release notes of the relevant intermediate versions.
Before this release, Druid returned the "internal error (500)" for most of the query errors. Now Druid returns different error codes based on their cause. The following table lists the errors and their corresponding codes that has changed:
Exception | Description | Old code | New code |
---|---|---|---|
SqlParseException and ValidationException from Calcite | Query planning failed | 500 | 400 |
QueryTimeoutException | Query execution didn't finish in timeout | 500 | 504 |
ResourceLimitExceededException | Query asked more resources than configured threshold | 500 | 400 |
InsufficientResourceException | Query failed to schedule because of lack of merge buffers available at the time when it was submitted | 500 | 429, merged to QueryCapacityExceededException |
QueryUnsupportedException | Unsupported functionality | 400 | 501 |
There is also a new query metric for query timeout errors. See New query timeout metric for more details.
https://github.com/apache/druid/pull/10464
https://github.com/apache/druid/pull/10746
query/interrupted/count
no longer counts the queries that timed out. These queries are counted by query/timeout/count
.
context
dimension in query metricscontext
is now a default dimension emitted for all query metrics. context
is a JSON-formatted string containing the query context for the query that the emitted metric refers to. The addition of a dimension that was not previously alters some metrics emitted by Druid. You should plan to handle this new context
dimension in your metrics pipeline. Since the dimension is a JSON-formatted string, a common solution is to parse the dimension and either flatten it or extract the bits you want and discard the full JSON-formatted string blob.
https://github.com/apache/druid/pull/10578
As ZooKeeper 3.4 has been end-of-life for a while, support for ZooKeeper 3.4 is deprecated in 0.21.0 and will be removed in the near future.
https://github.com/apache/druid/issues/10780
sys.segments
tableAll columns in the sys.segments
table are now serialized in the JSON format to make them consistent with other system tables. Column names now use the same "snake case" convention.
https://github.com/apache/druid/pull/10481
The Thrift extension can be useful for ingesting files of the Thrift format into Druid. However, there is a known security vulnerability in the version of the Thrift library that Druid uses. The vulerability can be exploitable by ingesting maliciously crafted Thrift files when you use Indexers. We recommend granting the DATASOURCE WRITE
permission to only trusted users.
If you run the Druid docker cluster for the first time in your machine, using the 0.21.0 image can create internal directories with the root account. As a result, Druid services can fail due lack of permissions. This issue is filed in https://github.com/apache/druid/issues/11166.
If you are using docker compose, you can use the below commands to work around this issue. These commands will create internal directories first using an old image and then start services using the 0.21.0 image.
$ cd ${PREV_SRC_DIR}
$ docker-compose -f distribution/docker/docker-compose.yml create
$ cd ${0.21.0_SRC_DIR}
$ docker-compose -f distribution/docker/docker-compose.yml up
If you are not using docker compose, you can directly pass the volume parameter for /opt/druid/var
when you start services using the 0.21.0 image. For example, you can run the command below to start the coordinator service.
$ docker run -v /path/to/host/dir:/opt/druid/var apache/druid:0.21.0 coordinator
For a full list of open issues, please see https://github.com/apache/druid/labels/Bug.
Thanks to everyone who contributed to this release!
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Published by jihoonson over 3 years ago
Apache Druid 0.20.2 introduces new configurations to address CVE-2021-26919: Authenticated users can execute arbitrary code from malicious MySQL database systems. Users are recommended to enable new configurations in the below to mitigate vulnerable JDBC connection properties. These configurations will be applied to all JDBC connections for ingestion and lookups, but not for metadata store. See security configurations for more details.
druid.access.jdbc.enforceAllowedProperties
: When true, Druid applies druid.access.jdbc.allowedProperties
to JDBC connections starting with jdbc:postgresql:
or jdbc:mysql:
. When false, Druid allows any kind of JDBC connections without JDBC property validation. This config is set to false by default to not break rolling upgrade. This config is deprecated now and can be removed in a future release. The allow list will be always enforced in that case.druid.access.jdbc.allowedProperties
: Defines a list of allowed JDBC properties. Druid always enforces the list for all JDBC connections starting with jdbc:postgresql:
or jdbc:mysql:
if druid.access.jdbc.enforceAllowedProperties
is set to true. This option is tested against MySQL connector 5.1.48 and PostgreSQL connector 42.2.14. Other connector versions might not work.druid.access.jdbc.allowUnknownJdbcUrlFormat
: When false, Druid only accepts JDBC connections starting with jdbc:postgresql:
or jdbc:mysql:
. When true, Druid allows JDBC connections to any kind of database, but only enforces druid.access.jdbc.allowedProperties
for PostgreSQL and MySQL.Published by jihoonson over 3 years ago
Apache Druid 0.20.1 is a bug fix release that addresses CVE-2021-25646: Authenticated users can override system configurations in their requests which allows them to execute arbitrary code.
docker-compose.yml
The Druid version is specified as 0.20.0 in the docker-compose.yml
file. We recommend to update the version to 0.20.1 before you run a Druid cluster using docker compose.
Published by jon-wei about 4 years ago
Apache Druid 0.20.0 contains around 160 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 36 contributors. Refer to the complete list of changes and everything tagged to the milestone for further details.
A new combining InputSource has been added, allowing the user to combine multiple input sources during ingestion. Please see https://druid.apache.org/docs/0.20.0/ingestion/native-batch.html#combining-input-source for more details.
https://github.com/apache/druid/pull/10387
When hash partitioning is used in parallel batch ingestion, it is no longer necessary to specify numShards
in the partition spec. Druid can now automatically determine a number of shards by scanning the data in a new ingestion phase that determines the cardinalities of the partitioning key.
https://github.com/apache/druid/pull/10419
The size-based splitHintSpec
now supports a new maxNumFiles
parameter, which limits how many files can be assigned to individual subtasks in parallel batch ingestion.
The segment-based splitHintSpec
used for reingesting data from existing Druid segments also has a new maxNumSegments
parameter which functions similarly.
Please see https://druid.apache.org/docs/0.20.0/ingestion/native-batch.html#split-hint-spec for more details.
https://github.com/apache/druid/pull/10243
New task slot usage metrics have been added. Please see the entries for the taskSlot
metrics at https://druid.apache.org/docs/0.20.0/operations/metrics.html#indexing-service for more details.
https://github.com/apache/druid/pull/10379
A partitioning spec can now be defined for auto-compaction, allowing users to repartition their data at compaction time. Please see the documentation for the new partitionsSpec
property in the compaction tuningConfig
for more details:
https://druid.apache.org/docs/0.20.0/configuration/index.html#compaction-tuningconfig
https://github.com/apache/druid/pull/10307
A new coordinator API which shows the status of auto-compaction for a datasource has been added. The new API shows whether auto-compaction is enabled for a datasource, and a summary of how far compaction has progressed.
The web console has also been updated to show this information:
https://user-images.githubusercontent.com/177816/94326243-9d07e780-ff57-11ea-9f80-256fa08580f0.png
Please see https://druid.apache.org/docs/latest/operations/api-reference.html#compaction-status for details on the new API, and https://druid.apache.org/docs/latest/operations/metrics.html#coordination for information on new related compaction metrics.
https://github.com/apache/druid/pull/10371
https://github.com/apache/druid/pull/10438
Druid now supports query-time segment pruning (excluding certain segments as read candidates for a query) for hash partitioned segments. This optimization applies when all of the partitionDimensions
specified in the hash partition spec during ingestion time are present in the filter set of a query, and the filters in the query filter on discrete values of the partitionDimensions
(e.g., selector filters). Segment pruning with hash partitioning is not supported with non-discrete filters such as bound filters.
For existing users with existing segments, you will need to reingest those segments to take advantage of this new feature, as the segment pruning requires a partitionFunction
to be stored together with the segments, which does not exist in segments created by older versions of Druid. It is not necessary to specify the partitionFunction
explicitly, as the default is the same partition function that was used in prior versions of Druid.
Note that segments created with a default partitionDimensions
value (partition by all dimensions + the time column) cannot be pruned in this manner, the segments need to be created with an explicit partitionDimensions
.
https://github.com/apache/druid/pull/9810
https://github.com/apache/druid/pull/10288
To enable vectorization features, please set the druid.query.default.context.vectorizeVirtualColumns
property to true
or set the vectorize
property in the query context. Please see https://druid.apache.org/docs/0.20.0/querying/query-context.html#vectorization-parameters for more information.
Expression virtual columns now have vectorization support (depending on the expressions being used), which an results in a 3-5x performance improvement in some cases.
Please see https://druid.apache.org/docs/0.20.0/misc/math-expr.html#vectorization-support for details on the specific expressions that support vectorization.
https://github.com/apache/druid/pull/10388
https://github.com/apache/druid/pull/10401
https://github.com/apache/druid/pull/10432
Vectorization support has been added for several aggregation types: numeric min/max aggregators, variance aggregators, ANY aggregators, and aggregators from the druid-histogram
extension.
https://github.com/apache/druid/pull/10260 - numeric min/max
https://github.com/apache/druid/pull/10304 - histogram
https://github.com/apache/druid/pull/10338 - ANY
https://github.com/apache/druid/pull/10390 - variance
We've observed about a 1.3x to 1.8x performance improvement in some cases with vectorization enabled for the min, max, and ANY aggregator, and about 1.04x to 1.07x wuth the histogram aggregator.
offset
parameter for GroupBy and Scan queriesIt is now possible set an offset
parameter for GroupBy and Scan queries, which tells Druid to skip a number of rows when returning results. Please see https://druid.apache.org/docs/0.20.0/querying/limitspec.html and https://druid.apache.org/docs/0.20.0/querying/scan-query.html for details.
https://github.com/apache/druid/pull/10235
https://github.com/apache/druid/pull/10233
OFFSET
clause for SQL queriesDruid SQL queries now support an OFFSET
clause. Please see https://druid.apache.org/docs/0.20.0/querying/sql.html#offset for details.
https://github.com/apache/druid/pull/10279
Druid has added new substring search operators in its expression language and for SQL queries.
Please see documentation for CONTAINS_STRING
and ICONTAINS_STRING
string functions for Druid SQL (https://druid.apache.org/docs/0.20.0/querying/sql.html#string-functions) and documentation for contains_string
and icontains_string
for the Druid expression language (https://druid.apache.org/docs/0.20.0/misc/math-expr.html#string-functions).
We've observed about a 2.5x performance improvement in some cases by using these functions instead of STRPOS
.
https://github.com/apache/druid/pull/10350
Druid SQL queries now support the UNION ALL
operator, which fuses the results of multiple queries together. Please see https://druid.apache.org/docs/0.20.0/querying/sql.html#union-all for details on what query shapes are supported by this operator.
https://github.com/apache/druid/pull/10324
It is now possible to set cluster-wide default query context properties by adding a configuration of the form druid.query.override.default.context.*
, with *
replaced by the property name.
https://github.com/apache/druid/pull/10208
The retention rules UI in the web console has been improved. It now provides suggestions and basic validation in the period dropdown, shows the cluster default rules, and makes editing the default rules more accessible.
https://github.com/apache/druid/pull/10226
The Redis cache extension now supports Redis Cluster, selecting which database is used, connecting to password-protected servers, and period-style configurations for the expiration
and timeout
properties.
https://github.com/apache/druid/pull/10240
It is now possible to disable sending of server version information in Druid's response headers.
This is controlled by a new property druid.server.http.sendServerVersion
, which defaults to true
.
https://github.com/apache/druid/pull/9832
Druid now supports units for specifying byte-based configuration properties, e.g.:
druid.server.maxSize=300g
equivalent to
druid.server.maxSize=300000000000
Please see https://druid.apache.org/docs/0.20.0/configuration/human-readable-byte.html for more details.
https://github.com/apache/druid/pull/10203
Druid 0.20.0 fixes a query correctness issue when a broker issues a query expecting a historical to have certain segments for a datasource, but the historical when queried does not actually have any segments for that datasource (e.g., they were all unloaded before the historical processed the query). Prior to 0.20.0, the query would return successfully but without the results from the segments that were missing in the manner described previously. In 0.20.0, queries will now fail in such situations.
https://github.com/apache/druid/pull/10199
Druid 0.20.0 fixes an issue introduced in 0.19.0 (https://github.com/apache/druid/issues/10337) which can prevent query caches from being populated when result-level caching is enabled.
https://github.com/apache/druid/pull/10341
The variance aggregator previously used an incorrect comparator that compared using an aggregator's internal count
variable instead of the variance.
https://github.com/apache/druid/pull/10340
Druid 0.20.0 fixes an issues with groupBy queries and caching, where the limitSpec of the query was not considered in the cache key, leading to potentially incorrect results if queries that are identical except for the limitSpec are issued.
https://github.com/apache/druid/pull/10093
stringFirst
and stringLast
with rollup enabledhttps://github.com/apache/druid/issues/7243 has been resolved, the stringFirst
and stringLast
aggregators no longer cause an exception when used during ingestion with rollup enabled.
https://github.com/apache/druid/pull/10332
Please be aware of the following considerations when upgrading from 0.19.0 to 0.20.0. If you're updating from an earlier version than 0.19.0, please see the release notes of the relevant intermediate versions.
maxSize
druid.server.maxSize
will now default to the sum of maxSize
values defined within the druid.segmentCache.locations
. The user can still provide a custom value for druid.server.maxSize
which will take precedence over the default value.
https://github.com/apache/druid/pull/10255
Compaction and kill tasks issued by the coordinator will now have their task IDs prefixed by coordinator-issued
, while user-issued kill tasks will be prefixed by api-issued
.
https://github.com/apache/druid/pull/10278
The size-based and segment-based splitHintSpec
for parallel batch ingestion now apply a default file/segment limit of 1000 per subtask, controlled by the maxNumFiles
and maxNumSegments
respectively.
https://github.com/apache/druid/pull/10243
PostAggregator
and AggregatorFactory
methodsUsers who have developed an extension with custom PostAggregator
or AggregatorFactory
implementions will need to update their extensions, as these two interfaces have new methods defined in 0.20.0.
PostAggregator
now has a new method:
ValueType getType();
To support type information on PostAggregator
, AggregatorFactory
also has 2 new methods:
public abstract ValueType getType();
public abstract ValueType getFinalizedType();
Please see https://github.com/apache/druid/pull/9638 for more details on the interface changes.
Expr
-related methodsUsers who have developed an extension with custom Expr
implementions will need to update their extensions, as Expr
and related interfaces hae changed in 0.20.0. Please see the PR below for details:
https://github.com/apache/druid/pull/10401
query/cpu/time
metricIn 0.20.0, the accuracy of the query/cpu/time
metric has been improved. Previously, it did not account for certain portions of work during query processing, described in more detail in the following PR:
https://github.com/apache/druid/pull/10377
If you are using audit logging, please be aware that new columns have been added to the audit log service metric (comment
, remote_address
, and created_date
). An optional payload
column has also been added, which can be enabled by setting druid.audit.manager.includePayloadAsDimensionInMetric
to true
.
https://github.com/apache/druid/pull/10373
sqlQueryContext
in request logsIf you are using query request logging, the request log events will now include the sqlQueryContext
for SQL queries.
https://github.com/apache/druid/pull/10368
Hash-partitioned segments created by Druid 0.20.0 will now have additional partitionFunction
data in the metadata store.
Additionally, compaction tasks will now store additional per-segment information in the metadata store, used for tracking compaction history.
https://github.com/apache/druid/pull/10288
https://github.com/apache/druid/pull/10413
druid.segmentCache.locationSelectorStrategy
injection failureSpecifying a value for druid.segmentCache.locationSelectorStrategy
prevents services from starting due to an injection error. Please see https://github.com/apache/druid/issues/10348 for more details.
When a timeout occurs while sampling data in the web console, internal resources created to read from the input source are not properly closed. Please see https://github.com/apache/druid/pull/10467 for more information.
Thanks to everyone who contributed to this release!
@a2l007
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@ArvinZheng
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@capistrant
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@clintropolis
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@xvrl
Published by clintropolis about 4 years ago
Apache Druid 0.19.0 contains around 200 new features, bug fixes, performance enhancements, documentation improvements, and additional test coverage from 51 contributors. Refer to the complete list of changes and everything tagged to the milestone for further details.
Vectorized query engines for GroupBy and Timeseries queries were introduced in Druid 0.16, as an opt in feature. Since then we have extensively tested these engines and feel that the time has come for these improvements to find a wider audience. Note that not all of the query engine is vectorized at this time, but this change makes it so that any query which is eligible to be vectorized will do so. This feature may still be disabled if you encounter any problems by setting druid.query.vectorize
to false
.
https://github.com/apache/druid/pull/10065
New in Druid 0.19.0, native batch indexing now supports Apache Avro Object Container Format encoded files, allowing batch ingestion of Avro data without needing an external Hadoop cluster. Check out the docs for more details
https://github.com/apache/druid/pull/9671
An 'SqlInputSource' has been added in Druid 0.19.0 to work with the new native batch ingestion specifications first introduced in Druid 0.17, deprecating the SqlFirehose. Like the 'SqlFirehose' it currently supports MySQL and PostgreSQL, using the driver from those extensions. This is a relatively low level ingestion task, and the operator must take care to manually ensure that the correct data is ingested, either by specially crafting queries to ensure no duplicate data is ingested for appends, or ensuring that the entire set of data is queried to be replaced when overwriting. See the docs for more operational details.
https://github.com/apache/druid/pull/9449
A new extension in Druid 0.19.0 adds an Authorizer which implements access control for Druid, backed by Apache Ranger. Please see [the extension documentation]((https://druid.apache.org/docs/0.19.0/development/extensions-core/druid-ranger-security.html) and Authentication and Authorization for more information on the basic facilities this extension provides.
https://github.com/apache/druid/pull/9579
A new 'contrib' extension has been added for Alibaba Cloud Object Storage Service (OSS) to provide both deep storage and usage as a batch ingestion input source. Since this is a 'contrib' extension, it will not be packaged by default in the binary distribution, please see community extensions for more details on how to use in your cluster.
https://github.com/apache/druid/pull/9898
Another 'contrib' extension new in 0.19.0 has been added to support ingestion worker autoscaling, which allows a Druid Overlord to provision or terminate worker instances (MiddleManagers or Indexers) whenever there are pending tasks or idle workers, for Google Compute Engine. Unlike the Amazon Web Services ingestion autoscaling extension, which provisions and terminates instances directly without using an Auto Scaling Group, the GCE autoscaler uses Managed Instance Groups to more closely align with how operators are likely to provision their clusters in GCE. Like other 'contrib' extensions, it will not be packaged by default in the binary distribution, please see community extensions for more details on how to use in your cluster.
https://github.com/apache/druid/pull/8987
A new REGEXP_LIKE
function has been added to Druid SQL and native expressions, which behaves similar to LIKE
, except using regular expressions for the pattern.
https://github.com/apache/druid/pull/9893
Druid 0.19 also web console also includes some useful improvements to the lookup table management interface. Creating and editing lookups is now done with a form to accept user input, rather than a raw text editor to enter the JSON spec.
Additionally, clicking the magnifying glass icon next to a lookup will now allow displaying the first 5000 values of that lookup.
https://github.com/apache/druid/pull/9549
https://github.com/apache/druid/pull/9587
A coordinator API can make it easier to determine if the latest published segments are available for querying. This is similar to the existing coordinator 'loadstatus' API, but is datasource specific, may specify an interval, and can optionally live refresh the metadata store snapshot to get the latest up to date information. Note that operators should still exercise caution when using this API to query large numbers of segments, especially if forcing a metadata refresh, as it can potentially be a 'heavy' call on large clusters.
https://github.com/apache/druid/pull/9965
Part bug fix, part new feature, Druid native batch (once again) supports appending new data to existing time chunks when those time chunks were partitioned with 'hash' or 'range' partitioning algorithms. Note that currently the appended segments only support 'dynamic' partitioning, and when rolling back to older versions that these appended segments will not be recognized by Druid after the downgrade. In order to roll back to a previous version, these appended segments should be compacted with the rest of the time chunk in order to have a homogenous partitioning scheme.
https://github.com/apache/druid/pull/10033
Druid 0.19.0 contains 65 bug fixes, you can see the complete list here.
Druid 0.19.0 fixes an important query correctness issue, where 'dynamic' partitioned segments produced by a batch ingestion task were not tracking the overall number of partitions. This had the implication that when these segments came online, they did not do so as a complete set, but rather as individual segments, meaning that there would be periods of swapping where results could be queried from an incomplete partition set within a time chunk.
https://github.com/apache/druid/pull/10025
Prior to 0.19.0, Druid had a bug when using hash or ranged partitioning where if data skew was such that any of the buckets were 'empty' after ingesting, the partitions would never be recognized as 'complete' and so never become queryable. Druid 0.19.0 fixes this issue by adjusting the schema of the partitioning spec. These changes to the json format should be backwards compatible, however rolling back to a previous version will again make these segments no longer queryable.
https://github.com/apache/druid/pull/10012
A bug in Druid versions prior to 0.19.0 allowed for (incorrect) coordinator operation in the event druid.server.maxSize
was not set. This bug would allow segments to load, and effectively randomly balance them in the cluster (regardless of what balancer strategy was actually configured) if all historicals did not have this value set. This bug has been fixed, but as a result druid.server.maxSize
must be set to the sum of the segment cache location sizes for historicals, or else they will not load segments.
https://github.com/apache/druid/pull/10070
Please be aware of the following issues when upgrading from 0.18.1 to 0.19.0. If you're updating from an earlier version than 0.18.1, please see the release notes of the relevant intermediate versions.
A Coordinator bug fix as a side-effect now requires druid.server.maxSize
to be set for segments to be loaded. While this value should have been set correctly for previous versions, please be sure this value is configured correctly before upgrading your clusters or else segments will not be loaded.
https://github.com/apache/druid/pull/10070
The removal of the 'payload' column from the sys.segments
table should make queries on this table much more efficient, and the most useful fields from this, the list of 'dimensions', 'metrics', and the 'shardSpec', have been split out, and so are still available to devote to processing queries.
https://github.com/apache/druid/pull/9883
The druid.segmentCache.numLoadingThreads
configuration has had the default value changed from 'number of cores' to 'number of cores' divided by 6. This should make historicals a bit more well behaved out of the box when loading a large number of segments, limiting the impact on query performance.
https://github.com/apache/druid/pull/9856
A number of incomplete changes to facilitate more efficient join queries, based on the idea of utilizing broadcast load rules to propagate smaller datasources among the cluster so that join operations can be pushed down to individual segment processing, have been added to 0.19.0. While not a finished feature yet, as part of the changes to make this happen, 'broadcast' load rules no longer have the concept of 'colocated datasources', which would attempt to only broadcast segments to servers that had segments of the configured datasource. This didn't work so well in practice, as it was non-atomic, meaning that the broadcast segments would lag behind loads and drops of the colocated datasource, so we decided to remove it.
https://github.com/apache/druid/pull/9971
Another effect of the afforementioned preliminary work to introduce efficient 'broadcast joins', Brokers and realtime indexing tasks will now load segments loaded by 'broadcast' rules, if a segment cache is configured. Since the feature is not complete there is little reason to do this in 0.19.0, and it will not happen unless explicitly configured.
https://github.com/apache/druid/pull/9971
The lpad and rpad functions have gone through a slight behavior change in Druids default non-SQL compatible mode, in order to make them behave consistently with PostgreSQL. In the new behavior, if the pad expression is an empty string, then the result will be the (possibly trimmed) original characters, rather than the empty string being treated as a null and coercing the results to null.
https://github.com/apache/druid/pull/10006
A change to the Expr
interface in Druid 0.19.0 requires that any extension which provides custom expressions via ExprMacroTable
must also implement equals
and hashCode
methods to function correctly, especially with JOIN queries, which rely on filter and expression analysis for determining how to optimally process a query.
https://github.com/apache/druid/pull/9830
For a full list of open issues, please see https://github.com/apache/druid/labels/Bug.
Thanks to everyone who contributed to this release!
@2bethere
@a-chumagin
@a2l007
@abhishekrb19
@agricenko
@ahuret
@alex-plekhanov
@AlexanderSaydakov
@awelsh93
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@clintropolis
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@gianm
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Published by jihoonson over 4 years ago
Apache Druid 0.18.1 is a bug fix release that fixes Streaming ingestion failure with Avro, ingestion performance issue, upgrade issue with HLLSketch, and so on. The complete list of bug fixes can be found at https://github.com/apache/druid/pulls?q=is%3Apr+milestone%3A0.18.1+label%3ABug+is%3Aclosed.
datasketches-java
from 1.2.0 to 1.1.0 to workaround upgrade failure with HLLSketch.CloseableIterator
which potentially leads to resource leaks in Data loader.A nested groupBy query can result in an incorrect result when it is on top of a Join of subqueries and the inner and the outer groupBys have different filters. See https://github.com/apache/druid/issues/9866 for more details.
Thanks to everyone who contributed to this release!
@clintropolis
@gianm
@jihoonson
@maytasm
@suneet-s
@viongpanzi
@whutjs