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

Do we need to apply our own date filters for metrics ingestion or does Statsig handle it?

When integrating metrics ingestion with Statsig, it is not necessary for developers to apply their own date filters within their SQL queries. Statsig is designed to manage date filtering autonomously during the data backfill process.

Upon setting up data ingestion, developers provide a SQL query that generates a view for Statsig to ingest. This query is then utilized by Statsig to incrementally load data on a daily basis. For the purpose of backfilling, Statsig will inspect the data for the past three days from the current date to determine if there has been a significant change, defined as a variation exceeding 5% in the number of rows.

If such a change is detected, Statsig will automatically initiate a backfill. For data outside of this three-day window, it is expected that the developer will manually trigger a backfill for the desired date range. Therefore, the SQL query provided for data ingestion should not be constrained by row limits, as Statsig will handle the necessary date filtering to ensure efficient and accurate data ingestion.

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What builders love about us

OpenAI OpenAI
Brex Brex
Notion Notion
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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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