Eventgen a modular set of Python code, structured as a Splunk application, which allows for a number of use cases:
- Stand-alone event generation
- Embedding in Splunk and serving multiple Splunk apps’ event generation needs by allowing those apps to specify an eventgen.conf in their Splunk default/local app directories.
- Generation built using sample files and configuration
- Generation using custom code to model a scenario
- Output to Splunk modular input or any number of pluggable output modes
Prior to version 4, Eventgen was a monolithic set of code with dozens of configuration tuneables all driving a complex set of conditional code. Over its life, Eventgen has grown so many tunables that the code had essentially become unmaintainable. This drove a desire by Brian Bingham (email@example.com), the primary Eventgen maintainer, to rearchitect the code to make it more modular.
A second set of requirements also drove the version 4 re-architecture. There continue to be a number of eventgens written internally to Splunk, and while most of those developers would be happy using Eventgen, either the config syntax is more arduous to learn than writing some python code or the scenario they want to model simply isn’t possible using relatively naive methods of looking for events and replacing regex identified tokens with substitutions from random selections. Replay mode is also insuffucient in that they do not want to have to base the eventgens on real data.
These requirements have led to this new architecture in verison 4. Eventgen is now completely modular, with the core code base handling a few tasks:
- Configuration management
- Config can come from Splunk if running as an app or via python’s ConfigParser if running standalones
- Handles flattening of configs from global defaults and individual config files
- Loading Plugins
- Load plugins from the
lib/pluginsdirectory or App’s
bindirectories, with three types of Plugins: Rating, Generator and Output
- For more detail on writing plugins, see the Plugins documentation
- Load plugins from the
- Creating timers for each sample
- For samples with queueable generators (meaning we can have multiple threads generating sample simultaneously), the timer wakes up every interval and puts a task in the queue to be generated by a generator worker
- For samples which are not queueable (meaning we can only run one simultaneous thread of the generator), the thread runs the generator plugin single threaded and handles sleeping the proper amount to manage intervals.
- Managing workers
- Generator and Output plugins run as separate threads or processes and can have a configurable number of workers running
That’s about it, everything else is handed in a plugin.
Events get generated through a series of processing queues. Everything in the core eventgen architecture is multi-threaded. In order to get around Python’s Global Interpreter Lock (GIL), we can also run in a multiprocess mode. Essentially:
Timers -> Generator Jobs in Generator Queue -> Output Jobs in Output Queue (Optional)
Depending on tunable parameters, these can be threads or processes and can either all run on one machine or multiple machines.
This queue architecture allows significant advantages by distributing processing. At each timer execution a generation job gets put in the queue. This queue by default is a Python Queue (either in queue.Queue or multiprocessing.Queue). For details, see the Performance guide
Given the complexity and the reimplementation of a number of features during refactors, we’ve also built out a number of test examples. We currently don’t have test scripts built with assertions etc, but there are a series of configs and samples under the
tests/ directory which should demonstrate and allow testing of basic functionality. Check out the
tests/sample_eventgen_conf directory in the project for commonly-used plugins and configurations. You can execute a test config by doing:
python -m splunk_eventgen generate tests/<dir>/<test>.conf
This is a new feature included in version >= 6.0 Traditionally, it has been difficult to configure multiple Eventgen instances at the same time. The performance of a single Eventgen instance is often limited in its architecture and compute power of the host machine. We introduce Controller-Server architecture to support scalable Eventgen deployment.
In this Architecture, we may have one or multiple servers (Eventgen instances), one controller and one instance of Redis (which lives inside of the same container as the controller). You don’t have to worry about setting up Redis yourself. Redis is used for communication between Controller and Servers.
In order to get setup, I would recommend building a Docker image locally using
make image command.
Then, start a single instance of Controller (using
controller as the container argument). You may start any number of servers (using
server as the container argument) with env var
REDIS_PORT that points to the controller’s host address and Redis port (by default
Users can also set up Standalone Eventgen servers without a controller (using
standalone as the container argument). Standalone mode allows users to use the same backend API calls.