Evaluating Feature Gates in the Statsig SDK

Wed Jun 16 2021

Bringing conditional evaluation to a server near you

Background: This post assumes some prior knowledge of Statsig Feature Gates (Feature flags). You can read more in our article: What are Feature Flags?

To evaluate Feature Gates on your servers or in your clients, Statsig provides SDKs in a number of different languages. Let’s explore how they work.

Statsig SDKs target two different types of environments:

  1. Single-user/client-side environments (check Feature Gate values and experiments for the current user)
  2. Multi-user/server-side environments (check Feature Gate values and experiments for the user associated with the current request)

All SDKs expose a similar API:

  • initialize() — setup the SDK
  • checkGate() — gets the value of a Feature Gate for the user. The gate may be open to everyone, or in a partial rollout state where only certain users pass the gate, based on some predefined criteria or random assignment
  • getConfig() — gets the json blob of data we call a Dynamic Config for the user
  • logEvent() —send log data to Statsig, which is used to create metrics and produce A/B test results (e.g. Group A logs more purchase events)

The playbook for single-user, client-side environments is simple: fetch all the Feature Gates/Dynamic Configs for a user up front in initialize(), then serve checkGate() and getConfig() requests synchronously thereafter with the cached values. If they log in or log out, you can call updateUser() to refetch the Gates and Configs for the updated user. All subsequent calls are assumed to be associated with the same user.

Statsig client SDKs can cache Feature Gate and Dynamic Config values for the current user. Server SDKs don’t have this luxury

This works well for websites and mobile apps, where a single viewer is interacting with your product. But what about a server which is responding to requests from many different users? We cannot make the same assumptions and cache Feature Gate/Dynamic Config values up front for each user.

For multi-user environments, we started simple. We made checkGate and getConfig take a user, and then hit our REST API endpoints to check the value for that user. Using the REST API required an API request for every single call to check a gate or config:

The Statsig REST API. The Statsig REST API. Though it may be simple, building a server SDK on this model introduced too much latency for each API call

While this is simple, making an external request to Statsig servers for each Feature Gate check is unacceptable for performance.

Fortunately, we can improve this. Our server already evaluates gates and configs, based on a blob of data store in our database. Our server and first server SDK were both written in node.js even! So what if we could just pass the same blob of data down to our SDK, and evaluate conditions in the SDK as we did on our server?

We always knew we would get to this eventually — it turns out that “eventually” is now. Starting with v3.0.0 of our node.js SDK, we are proud to announce that Feature Gate and Dynamic Config conditions are evaluated locally, without a round trip to the server!

Now, instead of using the REST API for each request, the Statsig Server SDK can do the evaluation locally, and not hit Statsig servers for each request! In terms of our diagrams, it looks a bit like this:

It may seem more complicated, but local evaluation of Feature Gate and Dynamic Config conditions is fast

Let’s take a look at a Feature Gate that we use at Statsig, and how that condition is evaluated in code via the statsig-node SDK.

We have a number of features under development at any given time which we only want logged in Statsig employees to see. So we have a gate, is_statsig_employee, which helps us hide these features for everybody else. This gate has a single rule and condition:

The is_statsig_employee gate

If the current user is logged in, and their account has a verified email ending in @statsig.com or @statsig.io, they will pass this gate.

That gets passed down to our SDK as a str_contains_any condition, with an array target of ['@statsig.com', '@statsig.io']. When that gate is checked in code, the server calls statsig.checkGate(user, 'is_statsig_employee'). The user’s email is used as the value that is compared to the array target.

It looks like this:


If that condition evaluation passes, the rule (which is set to pass for 100% of users matching the condition) will pass as well. And thats how a single, string-based condition is evaluated. There are many more different comparisons baked in to power any and all of your advanced Feature Gates. Feel free to poke around the SDK code on github for more examples!

And there you have it. Though the is_statsig_employee example showcased our node SDK, we’ve now taken this playbook and applied it to a broader set languages: so far, we’ve added SDKs for .NET, java, and ruby! They are all open source and hosted on github. Waiting on a particular language? Let us know!

Try Statsig Today

Explore Statsig’s smart feature gates with built-in A/B tests, or create an account instantly and start optimizing your web and mobile applications. You can also schedule a live demo or chat with us to design a custom package for your business.


Recently published

My Summer as a Statsig Intern


This summer I had the pleasure of joining Statsig as their first ever product design intern. This was my first college internship, and I was so excited to get some design experience. I had just finished my freshman year in college and was still working on...

Read more

Long-live the 95% Confidence Interval


The 95% confidence interval currently dominates online and scientific experimentation; it always has. Yet it’s validity and usefulness is often questioned. It’s called too conservative by some [1], and too permissive by others. It’s deemed arbitrary...

Read more

Realtime Product Observability with Apache Druid


Statsig’s Journey with Druid This is the text version of the story that we shared at Druid Summit Seattle 2022. Every feature we build at Statsig serves a common goal — to help you better know about your product, and empower you to make good decisions for...

Read more

Quant vs. Qual


💡 How to decide between leaning on data vs. research when diagnosing and solving product problems Four heuristics I’ve found helpful when deciding between data vs. research to diagnose + solve a problem. Earth image credit of Moncast Drawing. As a PM, data...

Read more

The Importance of Default Values


Have you ever sent an email to the wrong person? Well I have. At work. From a generic support email address. To a group of our top customers. Facepalm. In March of 2018, I was working on the games team at Facebook. You may remember that month as a tumultuous...

Read more

CUPED on Statsig


Run experiments with more speed and accuracy We’re pleased to announce the rollout of CUPED for all our customers. Statsig will now automatically use CUPED to reduce variance and bias on experiments’ key metrics. This gives you access to a powerful experiment...

Read more

We use cookies to ensure you get the best experience on our website.

Privacy Policy