Performance and scalability

This page aims to give some background to help prioritize work on the Zulip’s server’s performance and scalability. By scalability, we mean the ability of the Zulip server on given hardware to handle a certain workload of usage without performance materially degrading.

First, a few notes on philosophy.

  • We consider it an important technical goal for Zulip to be fast, because that’s an important part of user experience for a real-time collaboration tool like Zulip. Many UI features in the Zulip web app are designed to load instantly, because all the data required for them is present in the initial HTTP response, and both the Zulip API and web app are architected around that strategy.

  • The Zulip database model and server implementation are carefully designed to ensure that every common operation is efficient, with automated tests designed to prevent the accidental introductions of inefficient or excessive database queries. We much prefer doing design/implementation work to make requests fast over the operational work of running 2-5x as much hardware to handle the same load.

See also scalability for production users.

Load profiles

When thinking about scalability and performance, it’s important to understand the load profiles for production uses.

Zulip servers typically involve a mixture of two very different types of load profiles:

  • Open communities like open source projects, online classes, etc. have large numbers of users, many of whom are idle. (Many of the others likely stopped by to ask a question, got it answered, and then didn’t need the community again for the next year). Our own Zulip development community is a good example for this, with more than 15K total user accounts, of which only several hundred have logged in during the last few weeks. Zulip has many important optimizations, including soft deactivation to ensure idle users have minimal impact on both server-side scalability and request latency.

  • Fulltime teams, like your typical corporate Zulip installation, have users who are mostly active for multiple hours a day and sending a high volume of messages each. This load profile is most important for self-hosted servers, since many of those are used exclusively by the employees of the organization running the server.

The load profile is effectively the sum of thousands of organizations from each of those two load profiles.

Major Zulip endpoints

It’s important to understand that Zulip has a handful of endpoints that result in the vast majority of all server load, and essentially every other endpoint is not important for scalability. We still put effort into making sure those other endpoints are fast for latency reasons, but were they to be 10x faster (a huge optimization!), it wouldn’t materially improve Zulip’s scalability.

For that reason, we organize this discussion of Zulip’s scalability around the several specific endpoints that have a combination of request volume and cost that makes them important.

That said, it is important to distinguish the load associated with an API endpoint from the load associated with a feature. Almost any significant new feature is likely to result in its data being sent to the client in page_params or GET /messages, i.e. one of the endpoints important to scalability here. As a result, it is important to thoughtfully implement the data fetch code path for every feature.

Furthermore, a snappy user interface is one of Zulip’s design goals, and so we care about the performance of any user-facing code path, even though many of them are not material to scalability of the server. But only with regard to the requests detailed below, is it worth considering optimizations which save a few milliseconds that would be invisible to the end user, if they carry any cost in code readability.

In Zulip’s documentation, our general rule is to primarily write facts that are likely to remain true for a long time. While the numbers presented here vary with hardware, usage patterns, and time (there’s substantial oscillation within a 24 hour period), we expect the rough sense of them (as well as the list of important endpoints) is not likely to vary dramatically over time.


Average time

Request volume

Average impact

POST /users/me/presence




GET /messages








GET /events




GET /user_uploads/*




POST /messages/flags




POST /messages




POST /users/me/*




The “Average impact” above is computed by multiplying request volume by average time; this tells you roughly that endpoint’s relative contribution to the steady-state total CPU load of the system. It’s not precise – waiting for a network request is counted the same as active CPU time, but it’s extremely useful for providing intuition for what code paths are most important to optimize, especially since network wait is in practice largely waiting for PostgreSQL or memcached to do work.

As one can see, there are two categories of endpoints that are important for scalability: those with extremely high request volumes, and those with moderately high request volumes that are also expensive. It doesn’t matter how expensive, for example, POST /users/me/subscriptions is for scalability, because the volume is negligible.


Zulip’s Tornado-based real-time push system, and in particular GET /events, accounts for something like 50% of all HTTP requests to a production Zulip server. Despite GET /events being extremely high-volume, the typical request takes 1-3ms to process, and doesn’t use the database at all (though it will access memcached and redis), so they aren’t a huge contributor to the overall CPU usage of the server.

Because these requests are so efficient from a total CPU usage perspective, Tornado is significantly less important than other services like Presence and fetching message history for overall CPU usage of a Zulip installation.

It’s worth noting that most (~80%) Tornado requests end the longpolling via a heartbeat event, which are issued to idle connections after about a minute. These heartbeat events are useless aside from avoiding problems with networks/proxies/NATs that are configured poorly and might kill HTTP connections that have been idle for a minute. It’s likely that with some strategy for detecting such situations, we could reduce their volume (and thus overall Tornado load) dramatically.

Currently, Tornado is sharded by realm, which is sufficient for arbitrary scaling of the number of organizations on a multi-tenant system like With a somewhat straightforward set of work, one could change this to sharding by user_id instead, which will eventually be important for individual large organizations with many thousands of concurrent users.


POST /users/me/presence requests, which submit the current user’s presence information and return the information for all other active users in the organization, account for about 36% of all HTTP requests on production Zulip servers. See presence for details on this system and how it’s optimized. For this article, it’s important to know that presence is one of the most important scalability concerns for any chat system, because it cannot be cached long, and is structurally a quadratic problem.

Because typical presence requests consume 10-50ms of server-side processing time (to fetch and send back live data on all other active users in the organization), and are such a high volume, presence is the single most important source of steady-state load for a Zulip server. This is true for most other chat server implementations as well.

There is an ongoing effort to rewrite the data model for presence that we expect to result in a substantial improvement in the per-request and thus total load resulting from presence requests.

Fetching page_params

The request to generate the page_params portion of GET / (equivalent to the response from GET /api/v1/register used by mobile/terminal apps) is one of Zulip’s most complex and expensive.

Zulip is somewhat unusual among web apps in sending essentially all of the data required for the entire Zulip web app in this single request, which is part of why the Zulip web app loads very quickly – one only needs a single round trip aside from cacheable assets (avatars, images, JS, CSS). Data on other users in the organization, streams, supported emoji, custom profile fields, etc., is all included. The nice thing about this model is that essentially every UI element in the Zulip client can be rendered immediately without paying latency to the server; this is critical to Zulip feeling performant even for users who have a lot of latency to the server.

There are only a few exceptions where we fetch data in a separate AJAX request after page load:

  • Message history is managed separately; this is why the Zulip web app will first render the entire site except for the middle panel, and then a moment later render the middle panel (showing the message history).

  • A few very rarely accessed data sets like message edit history are only fetched on demand.

  • A few data sets that are only required for administrative settings pages are fetched only when loading those parts of the UI.

Requests to GET / and /api/v1/register that fetch page_params are pretty rare – something like 0.3% of total requests, but are important for scalability because (1) they are the most expensive read requests the Zulip API supports and (2) they can come in a thundering herd around server restarts (as discussed in fetching message history.

The cost for fetching page_params varies dramatically based primarily on the organization’s size, varying from 90ms-300ms for a typical organization but potentially multiple seconds for large open organizations with 10,000s of users. There is also smaller variability based on a individual user’s personal data state, primarily in that having 10,000s of unread messages results in a somewhat expensive query to find which streams/topics those are in.

We consider any organization having normal page_params fetch times greater than a second to be a bug, and there is ongoing work to fix that.

It can help when thinking about this to imagine page_params as what in another web app would have been 25 or so HTTP GET requests, each fetching data of a given type (users, streams, custom emoji, etc.); in Zulip, we just do all of those in a single API request. In the future, we will likely move to a design that does much of the database fetching work for different features in parallel to improve latency.

For organizations with 10K+ users and many default streams, the majority of time spent constructing page_params is spent marshalling data on which users are subscribed to which streams, which is an area of active optimization work.

Fetching message history

Bulk requests for message content and metadata (GET /messages) account for ~3% of total HTTP requests. The zulip web app has a few major reasons it does a large number of these requests:

  • Most of these requests are from users clicking into different views – to avoid certain subtle bugs, Zulip’s web app currently fetches content from the server even when it has the history for the relevant stream/topic cached locally.

  • When a browser opens the Zulip web app, it will eventually fetch and cache in the browser all messages newer than the oldest unread message in a non-muted context. This can be in total extremely expensive for users with 10,000s of unread messages, resulting in a single browser doing 100 of these requests.

  • When a new version of the Zulip server is deployed, every browser will reload within 30 minutes to ensure they are running the latest code. For installations that deploy often like and, this can result in a thundering herd effect for both / and GET /messages. A great deal of care has been taking in designing this auto-reload system to spread most of that herd over several minutes.

Typical requests consume 20-100ms to process, much of which is waiting to fetch message IDs from the database and then their content from memcached. While not large in an absolute sense, these requests are expensive relative to most other Zulip endpoints.

Some requests, like full-text search for commonly used words, can be more expensive, but they are sufficiently rare in an absolute sense so as to be immaterial to the overall scalability of the system.

This server-side code path is already heavily optimized on a per-request basis. However, we have technical designs for optimizing the overall frequency with which clients need to make these requests in two major ways:

  • Improving client-side caching to allow caching of narrows that the user has viewed in the current session, avoiding repeat fetches of message content during a given session.

  • Adjusting the behavior for clients with 10,000s of unread messages to not fetch as much old message history into the cache. See this issue for relevant design work.

Together, it is likely that these changes will reduce the total scalability cost of fetching message history dramatically.

User uploads

Requests to fetch uploaded files (including user avatars) account for about 5% of total HTTP requests. Zulip spends consistently ~10-15ms processing one of these requests (mostly authorization logic), before handing off delivery of the file to nginx (which may itself fetch from S3, depending on the configured upload backend).

Sending and editing messages

Sending new messages (including incoming webhooks) represents less than 0.5% of total request volume. That this number is small should not be surprising even though sending messages is intuitively the main feature of a chat service: a message sent to 50 users triggers ~50 GET /events requests.

A typical message-send request takes 20-70ms, with more expensive requests typically resulting from Markdown rendering of more complex syntax. As a result, these requests are not material to Zulip’s scalability. Editing messages and adding emoji reactions are very similar to sending them for the purposes of performance and scalability, since the same clients need to be notified, and these requests are lower in volume.

That said, we consider the performance of these endpoints to be some of the most important for Zulip’s user experience, since even with local echo, these are some of the places where any request processing latency is highly user-visible.

Typing notifications are slightly higher volume than sending messages, but are also extremely cheap (~3ms).

Other endpoints

Other API actions, like subscribing to a stream, editing settings, registering an account, etc., are vanishingly rare compared to the requests detailed above, fundamentally because almost nobody changes these things more than a few dozen times over the lifetime of their account, whereas everything above are things that a given user might do thousands of times.

As a result, performance work on those requests is generally only important for latency reasons, not for optimizing the overall scalability of a Zulip server.

Queue processors and cron jobs

The above doesn’t cover all of the work that a production Zulip server does; various tasks like sending outgoing emails or recording the data that powers /stats are run by queue processors and cron jobs, not in response to incoming HTTP requests. In practice, all of these have been written such that they are immaterial to total load and thus architectural scalability, though we do from time to time need to do operational work to add additional queue processors for particularly high-traffic queues. For all of our queue processors, any serialization requirements are at most per-user, and thus it would be straightforward to shard by user_id or realm_id if required.

Service scalability

In addition to the above, which just focuses on the total amount of CPU work, it’s also relevant to think about load on infrastructure services (memcached, redis, rabbitmq, and most importantly postgres), as well as queue processors (which might get backlogged).

In practice, efforts to make an individual endpoint faster will very likely reduce the load on these services as well. But it is worth considering that database time is a more precious resource than Python/CPU time (being harder to scale horizontally).

Most optimizations to make an endpoint cheaper will start with optimizing the database queries and/or employing caching, and then continue as needed with profiling of the Python code and any memcached queries.

For a handful of the critical code paths listed above, we further optimize by skipping the Django ORM (which has substantial overhead) for narrow sections; typically this is sufficient to result in the database query time dominating that spent by the Python application server process.

Zulip’s server logs are designed to provide insight when a request consumes significant database or memcached resources, which is useful both in development and in production.