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GENERAL

How can we reduce the payload size of `getClientInitializeResponse` in Statsig, and convert Statsig IDs back to their names for gate cleanup?

Date of slack thread: 5/29/24

Anonymous: Hey team, question for you about the result of getClientInitializeResponse and piping that through to the react sdk. My team has been debugging some initial page load slowness and someone flagged that the resulting payload that comes back from getClientInitializeResponse is very very large. We have a lot of feature gates and dynamic configs so it’s not surprising that the evaluation results for a given user is large but I am curious if there are some ways to mitigate.

The most obvious thing that comes to mind is that we barely actually use any dynamic configs on the client side. We generally just use that feature on the server side. It would be ideal if we could denote which gates/configs are relevant for client sdks and have getClientInitializeResponse just pass us those instead of all flags. Assuming that isn’t a quick thing to do on your end, any other suggestions here?

Cooper Reid (Statsig): Which SDK is this? You can manage overall config size using a few different approaches which may help reduce latency associated with their method:

  1. https://docs.statsig.com/guides/using-environments#environment-specific-sdk-keys will allow you to control which gates/tests get downloaded by the SDK and persisted to Data Store. If you create a Gate that targets only “development”, the production-SDK key won’t download that gate.
  2. You can also consider using https://docs.statsig.com/sdk-keys/target-apps to target specific gates/tests by experiment key to keep the config size leaner.

Anonymous: We’re using the nodejs sdk on the backend and react sdk on the client

Anonymous: Target apps looks exactly like what we want. Thank you!

Anonymous: Hey all! As part of this work, I was hoping you might have some pointers on how to convert Statsig IDs back to their names. I’m trying to get a list of stale gates we can clean up, but I’m stuck on how to get our names out of an ID like this: DgM1zPA/TAsyalQaED1C/wvSMtIia/g9fdKBlcTqE50=

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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.
OpenAI
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.
Brex
Karandeep Anand
President
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.
Notion
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.
SoundCloud
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.
Ancestry
Partha Sarathi
Director of Engineering
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