Frequently Asked Questions

A curated summary of the top questions asked on our Slack community, often relating to implementation, functionality, and building better products generally.
Statsig FAQs

Does the Ruby Server SDK for Statsig have a local cache and what are its characteristics?

The Ruby Server SDK for Statsig is designed with an in-memory local cache to store configurations for gates and experiments. This cache enables the SDK to evaluate rules even in the event of Statsig service disruptions. The cache is updated by polling the Statsig server at a default interval of 10 seconds, which is configurable through the rulesets_sync_interval initialization option.

The memory footprint of the cache is typically small, as it consists of a JSON string representing the configurations. However, developers should be cautious when using large ID lists for targeting, as this can significantly increase the size of the cached data and is generally not recommended.

It is important to note that the SDK does not cache user-specific assignments and parameters, but rather the configuration specifications. Monitoring application memory usage is always advisable to ensure efficient operation. For more detailed guidance on managing large ID lists and memory usage, refer to the official documentation.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
SoundCloud SoundCloud
Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
Karandeep Anand
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
Don Browning
SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
Partha Sarathi
Director of Engineering
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