A Time Series Data Browser
APACHE-2.0 License
binjr is a time series browser; it renders time series data produced by other applications as dynamically editable representations and provides advanced features to navigate the data smoothly and efficiently (drag & drop, zoom, history, detachable tabs, advanced time-range picker).
It is a standalone client application, that runs independently of the applications that produce the data; there are no server or server-side components dedicated to binjr that need to be installed on the source.
The user experience in binjr revolves around enabling users to compose a custom view by using any of the time-series exposed by the source, simply by dragging and dropping them on the view. That view then constantly evolves as users add or remove series from different sources, while navigating through it by changing the time range, the type of chart visualization and smaller aspects such as the color or transparency for each series. Users can navigate the change history for these views, using back and forward like in a web browser, and save the current state of their session at any time to a file, in order to reopen it later or to share it with someone else.
binjr also possesses the ability to visualize time series not only as charts of numeric values, but can be customized to support visualization for any data type; for instance it features out-of-the-box a source adapter for text based log files.
Log files, produced by applications to trace their lifecycle at runtime, typically contain timestamps for each event they contain; so we can think of them as time series, but with data points being textual information instead of numerical values. In practical terms, this means that a lot of the features built into binjr to compose and navigate time series visualizations can be applied to log files with great benefits.
Behind the scene, binjr uses Apache Lucene to index data from log files; meaning users can use its powerful query language to hack through vast quantities logged events. It also allows binjr to open log files of any size; unlike most text editors which will fail to load multi gigabytes-sized files as they try to fit it all in memory, binjr will happily index those and present a paginated view so that memory usage remains reasonable, while the backing index ensures that searches are fast and navigation snappy.
With these abilities, binjr aims to become the missing link between text editors and command line tools traditionally used to analyse monitoring data locally and full-blown log analytics platforms (e.g. Elastic/Logstash/Kibana stack) that centralizes logs for entire organizations. It provides many of the same powerful visualization and search features while still remaining a totally local solution (the data never needs to be pushed to the cloud - or anywhere else for that matter), and requiring no setup nor maintenance to speak of.
binjr can consume time series data provided by the following data sources:
Name | Description | Built-in[1] | Source type |
---|---|---|---|
JRDS | A performance monitoring application written in Java. | Remote | |
Netdata | Distributed, real-time performance and health monitoring for systems and applications. | Remote | |
RRD Files | Round-Robin Database files produced by RRDtool and RRD4J. | Local files | |
CSV Files | Comma Separated Values files. | Local files | |
Log Files | Text based, semi-structured log files. | Local files | |
JDK Flight Recoder | Low-overhead data collection framework for troubleshooting Java applications and the HotSpot JVM. | Local files | |
Demo Adapter | A plugin for binjr that provides data sources for demonstration purposes. | Local files |
[1]: Support for data sources not marked as 'Built-in' requires additional plugins.
There are several ways to get up and running with binjr:
The simplest way to start using binjr is to download an application bundle from the download page.
These bundles contain all the dependencies required to run the app, including a copy of the Java runtime specially crafted to only include the required components and save disk space. They are only ~80 MiB in size and there is one for each of the supported platform: Windows, Linux and macOS.
Simply download the one for your system, unpack it and run binjr
to start!
You can also build or run the application from the source code using the included Gradle wrapper. Simply clone the repo from Github and run:
./gradlew build
to build the JAR for the all the modules../gradlew run
to build and start the application straight away../gradlew clean packageDistribution
to build an application bundle for the platform on which you ran the build.Please note that it is mandatory to run the
clean
task in between two executions of thepackageDistribution
in the same environment.
If you'd like to experience binjr's visualization capabilities but do not have a compatible data source handy, you can use the demonstration data adapter.
It is a plugin which embeds a small, stand-alone data source that you can readily browse using binjr.
binjr-adapter-demo-1.x.x.zip
archive from https://github.com/binjr/binjr-adapter-demo/releases/latest
binjr-adapter-demo-1.x.x.jar
file contained in the zip file into the plugins
folder of yourdemo.bjr
Workspaces > Open...
, or press Ctrl+O)The documentation can be found here.
If you encounter an issue, or would like to suggest an enhancement or a new feature, you may do so here.
binjr is released under the Apache License version 2.0.
This project accepts contributions via GitHub pull requests.
By contributing to this project you agree to the Developer Certificate of Origin (DCO). This document was created by the Linux Kernel community and is a simple statement that you, as a contributor, have the legal right to make the contribution. See the DCO file for details.