Sending messages

While sending a message in a chat product might seem simple, there’s a lot of underlying complexity required to make a professional-quality experience.

This document aims to explain conceptually what happens when a message is sent in Zulip, and why that is correct behavior. It assumes the reader is familiar with our real-time sync system for server-to-client communication and new application feature tutorial, and we generally don’t repeat the content discussed there.

Message lists

This is just a bit of terminology: A “message list” is what Zulip calls the frontend concept of a (potentially narrowed) message feed. There are 3 related structures:

  • A message_list_data just has the sequencing data of which message IDs go in what order.

  • A message_list is built on top of message_list_data and additionally contains the data for a visible-to-the-user message list (E.g. where trailing bookends should appear, a selected message, etc.).

  • A message_list_view is built on top of message_list and additionally contains rendering details like a window of up to 400 messages that is present in the DOM at the time, scroll position controls, etc.

(This should later be expanded into a full article on message lists and narrowing).

Compose area

The compose box does a lot of fancy things that are out of scope for this article. But it also does a decent amount of client-side validation before sending a message off to the server, especially around mentions (E.g. checking the stream name is a valid stream, displaying a warning about the number of recipients before a user can use @**all** or mention a user who is not subscribed to the current stream, etc.).

Backend implementation

The backend flow for sending messages is similar in many ways to the process described in our new application feature tutorial. This section details the ways in which it is different:

  • There is significant custom code inside the process_message_event function in zerver/tornado/event_queue.py. This custom code has a number of purposes:

    • Triggering email and mobile push notifications for any users who do not have active clients and have settings of the form “push notifications when offline”. In order to avoid doing any real computational work inside the Tornado codebase, this logic aims to just do the check for whether a notification should be generated, and then put an event into an appropriate queue to actually send the message. See maybe_enqueue_notifications and related code for this part of the logic.

    • Splicing user-dependent data (E.g. flags such as when the user was mentioned) into the events.

    • Handling the local echo details.

    • Handling certain client configuration options that affect messages. E.g. determining whether to send the plaintext/markdown raw content or the rendered HTML (e.g. the apply_markdown and client_gravatar features in our events API docs).

  • The webapp uses websockets for client/server interaction for sending messages.

  • Following our standard naming convention, input validation is done inside the check_message function, which is responsible for validating the user can send to the recipient, rendering the markdown, etc. – basically everything that can fail due to bad user input.

  • The core do_send_messages function (which handles actually sending the message) is one of the most optimized and thus complex parts of the system. But in short, its job is to atomically do a few key things:

    • Store a Message row in the database.

    • Store one UserMessage row in the database for each user who is a recipient of the message (including the sender), with appropriate flags for whether the user was mentioned, an alert word appears, etc. See the section on soft deactivation for a clever optimization we use here that is important for large open organizations.

    • Do all the database queries to fetch relevant data for and then send a message event to the events system containing the data it will need for the calculations described above. This step adds a lot of complexity, because the events system cannot make queries to the database directly.

    • Trigger any other deferred work caused by the current message, e.g. outgoing webhooks or embedded bots.

    • Every query is designed to be a bulk query; we carefully unit-test this system for how many database and memcached queries it makes when sending messages with large numbers of recipients, to ensure its performance.

Websockets

For the webapp only, we use WebSockets rather than standard HTTPS API requests for triggering message sending. This is a design feature we are very ambivalent about; it has some slight latency benefits, but is also features extra complexity and some mostly-unmaintained dependencies (e.g. sockjs-tornado). But in short, this system works as follows:

  • Requests are sent from the webapp to the backend via the sockjs library (on the frontend) and sockjs-tornado (on the backend). This ends up calling a handler in our Tornado codebase (zerver/tornado/socket.py), which immediately puts the request into the message_sender queue.

  • The message_sender queue processor reformats the request into a Django HttpRequest object with a fake SOCKET HTTP method (which is why these requests appear as SOCKET in our server logs), calls the Django get_response method on that request, and returns the response to Tornado via the tornado_return queue.

  • Tornado then sends the result (success or error) back to the client via the relevant WebSocket connection.

  • sockjs automatically handles for us a fallback to longpolling in the event that a WebSockets connection cannot be opened successfully (which despite WebSockets being many years old is still a problem on some networks today!).

Local echo

An essential feature for a good chat is experience is local echo (i.e. having the message appear in the feed the moment the user hits send, before the network round trip to the server). This is essential both for freeing up the compose box (for the user to send more messages) as well as for the experience to feel snappy.

A sloppy local echo experience (like Google Chat had for over a decade for emoji) would just render the raw text the user entered in the browser, and then replace it with data from the server when it changes.

Zulip aims for a near-perfect local echo experience, which requires is why our markdown system requires both an authoritative (backend) markdown implementation and a secondary (frontend) markdown implementation, the latter used only for the local echo feature. Read our markdown documentation for all the tricky details on how that works and is tested.

The rest of this section details how Zulip manages locally echoed messages.

  • The core function in the frontend codebase echo.try_deliver_locally. This checks whether correct local echo is possible (via markdown.contains_backend_only_syntax) and useful (whether the message would appear in the current view), and if so, causes Zulip to insert the message into the relevant feed(s).

  • Since the message hasn’t been confirmed by the server yet, it doesn’t have a message ID. The frontend makes one up, via local_message.next_local_id, by taking the highest message ID it has seen and adding the decimal 0.01. The use of a floating point value is critical, because it means the message should sort correctly with other messages (at the bottom) and also won’t be duplicated by a real confirmed-by-the-backend message ID. We choose just above the max_message_id, because we want any new messages that other users send to the current view to be placed after it in the feed (this decision is someone arbitrary; in any case we’ll resort it to its proper place once it is confirmed by the server. We do it this way to minimize messages jumping around/reordering visually).

  • The POST /messages API request to the server to send the message is passed two special parameters that clients not implementing local echo don’t use: queue_id and local_id. The queue_id is the ID of the client’s event queue; here, it is used just as a unique identifier for the specific client (e.g. a browser tab) that sent the message. And the local_id is, by the construction above, a unique value within that namespace identifying the message.

  • The do_send_messages backend code path includes the queue_id and local_id in the data it passes to the events system. The events system will extend the message event dictionary it delivers to the client containing the queue_id with local_message_id field, containing the local_id that the relevant client used when sending the message. This allows the client to know that the message event it is receiving is the same message it itself had sent.

  • Using that information, rather than adding the “new message” to the relevant message feed, it updates the (locally echoed) message’s properties (at the very least, message ID and timestamp) and rerenders it in any message lists where it appears. This is primarily done in exports.process_from_server in static/js/echo.js.

Putting it all together

This section just has a brief review of the sequence of steps all in one place:

  • User hits send in the compose box.

  • Compose box validation runs; if passes, it locally echoes the message and sends websocket message to Tornado

  • Tornado converts websocket message to a message_sender queue item

  • message_sender queue processor turns the queue item into a Django HttpRequest and calls Django’s main response handler

  • The Django URL routes and middleware run, and eventually calls the send_message_backend view function in zerver/views/messages.py. (Alternatively, for an API request to send a message via the HTTP API, things start here).

  • send_message_backend does some validation before triggering the check_message + do_send_messages backend flow.

  • That backend flow saves the data to the database and triggers a message event in the notify_tornado queue (part of the events system).

  • The events system processes, and dispatches that event to all clients subscribed to receive notifications for users who should receive the message (including the sender). As a side effect, it adds queue items to the email and push notification queues (which, in turn, may trigger those notifications).

    • Other receive the event and display the new message.

    • For the client that sent the message, it instead replaces its locally echoed message with the final message it received back from the server (it indicates this to the sender by adding a display timestamp to the message).

  • For an API client, the send_message_backend view function returns a 200 HTTP response; the client receives that response and mostly does nothing with it other than update some logging details. (This may happen before or after the client receives the event notifying it about the new message via its event queue.)

  • For a browser (websockets sender), the client receives the equivalent of the HTTP response via a websockets message from Tornado (which, in turn, got that via the tornado_return queue).

Error handling

When there’s an error trying to send a message, it’s important to not lose the text the user had composed. Zulip handles this with a few approaches:

  • The data for a message in the process of being sent are stored in browser local storage (see .e.g. _save_localstorage_requests in static/js/socket.js), so that the client can retransmit as appropriate, even if the browser reloads in the meantime.

  • Additionally, Zulip renders UI for editing/retransmitting/resending messages that had been locally echoed on top of those messages, in red.

Message editing

Message editing uses a very similar principle to how sending messages works. A few details are worth mentioning:

  • maybe_enqueue_notifications_for_message_update is an analogue of maybe_enqueue_notifications, and exists to handle cases like a user was newly mentioned after the message is edited (since that should trigger email/push notifications, even if the original message didn’t have one).

  • We use a similar technique to what’s described in the local echo section for doing client-side rerendering to update the message feed.

  • In the default configuration, Zulip stores the message edit history (which is useful for forensics but also exposed in the UI), in the message.edit_history attribute.

  • We support topic editing, including bulk-updates moving several messages between topics.

Inline URL previews

Zulip’s inline URL previews feature (zerver/lib/url_preview/) uses variant of the message editing/local echo behavior. The reason is that for inline URL previews, the backend needs to fetch the content from the target URL, and for slow websites, this could result in a significant delay in rendering the message and delivering it to other users.

  • For this case, Zulip’s backend markdown processor will render the message without including the URL embeds/previews, but it will add a deferred work item into the embed_links queue.

  • The queue processor for the embed_links queue will fetch the URLs, and then if they return results, rerun the markdown processor and notify clients of the updated message rendered_content.

  • We reuse the update_message framework (used for Zulip’s message editing feature) in order to avoid needing custom code to implement the notification-and-rerender part of this implementation.

Soft deactivation

This section details a somewhat subtle issue: How Zulip uses a user-invisible technique called “soft deactivation” to handle scalability to communities with many thousands of inactive users.

For background, Zulip’s threading model requires tracking which individual messages each user has received and read (in other chat products, the system either doesn’t track what the user has read at all, or just needs to store a pointer for “how far the user has read” in each room, channel, or stream).

We track these data in the backend in the UserMessage table, storing rows (message_id, user_id, flags), where flags is 32 bits of space for boolean data like whether the user has read or starred the message. All the key queries needed for accessing message history, full-text search, and other key features can be done efficiently with the database indexes on this table (with joins to the Message table containing the actual message content where required).

The downside of this design is that when a new message is sent to a stream with N recipients, we need to write N rows to the UserMessage table to record those users receiving those messages. Each row is just 3 integers in size, but even with modern databases and SSDs, writing thousands of rows to a database starts to take a few seconds.

This isn’t a problem for most Zulip servers, but is a major problem for communities like chat.zulip.org, where might be 10,000s of inactive users who only stopped by briefly to check out the product or ask a single question, but are subscribed to whatever the default streams in the organization are.

The total amount of work being done here was acceptable (a few seconds of total CPU work per message to large public streams), but the latency was unacceptable: The server backend was introducing a latency of about 1 second per 2000 users subscribed to receive the message. While these delays may not be immediately obvious to users (Zulip, like many other chat applications, local echoes messages that a user sends as soon as the user hits “send”), latency beyond a second or two significantly impacts the feeling of interactivity in a chat experience (i.e. it feels like everyone takes a long time to reply to even simple questions).

A key insight for addressing this problem is that there isn’t much of a use case for long chat discussions among 1000s of users who are all continuously online and actively participating. Streams with a very large number of active users are likely to only be used for occasional announcements, where some latency before everyone sees the message is fine. Even in giant organizations, almost all messages are sent to smaller streams with dozens or hundreds of active users, representing some organizational unit within the community or company.

However, large, active streams are common in open source projects, standards bodies, professional development groups, and other large communities with the rough structure of the Zulip development community. These communities usually have thousands of user accounts subscribed to all the default streams, even if they only have dozens or hundreds of those users active in any given month. Many of the other accounts may be from people who signed up just to check the community out, or who signed up to ask a few questions and may never be seen again.

The key technical insight is that if we can make the latency scale with the number of users who actually participate in the community, not the total size of the community, then our database write limited send latency of 1 second per 2000 users is totally fine. But we need to do this in a way that doesn’t create problems if any of the thousands of “inactive” users come back (or one of the active users sends a private message to one of the inactive users), since it’s impossible for the software to know which users are eventually coming back or will eventually be interacted with by an existing user.

We solved this problem with a solution we call “soft deactivation”; users that are soft-deactivated consume less resources from Zulip in a way that is designed to be invisible both to other users and to the user themself. If a user hasn’t logged into a given Zulip organization for a few weeks, they are tagged as soft-deactivated.

The way this works internally is:

  • We (usually) skip creating UserMessage rows for soft-deactivated users when a message is sent to a stream where they are subscribed.

  • If/when the user ever returns to Zulip, we can at that time reconstruct the UserMessage rows that they missed, and create the rows at that time (or, to avoid a latency spike if/when the user returns to Zulip, this work can be done in a nightly cron job). We can construct those rows later because we already have the data for when the user might have been subscribed or unsubscribed from streams by other users, and, importantly, we also know that the user didn’t interact with the UI since the message was sent (and thus we can safely assume that the messages has not been marked a read by the user). This is done in the add_missing_messages function, which is the core of the soft-deactivation implementation.

  • The “usually” above is because there are a few flags that result from content in the message (e.g., a message that mentions a user results in a “mentioned” flag in the UserMessage row), that we need to keep track of. Since parsing a message can be expensive (>10ms of work, depending on message content), it would be too inefficient to need to re-parse every message when a soft-deactivated user comes back to Zulip. Conveniently, those messages are rare, and so we can just create UserMessage rows which would have “interesting” flags at the time they were sent without any material performance impact. And then add_missing_messages skips any messages that already have a UserMessage row for that user when doing its backfill.

The end result is the best of both worlds:

  • Nobody’s view of the world is different because the user was soft-deactivated (resulting in no visible user-experience impact), at least if one is running the cron job. If one does not run the cron job, then users returning after being away for a very long time will potentially have a (very) slow loading experience as potentially 100,000s of UserMessage rows might need to be reconstructed at once.

  • On the latency-sensitive message sending and fanout code path, the server only needs to do work for users who are currently interacting with Zulip.

Empirically, we’ve found this technique completely resolved the “send latency” scaling problem. The latency of sending a message to a stream now scales only with the number of active subscribers, so one can send a message to a stream with 5K subscribers of which 500 are active, and it’ll arrive in the couple hundred milliseconds one would expect if the extra 4500 inactive subscribers didn’t exist.

There are a few details that require special care with this system:

  • Email and mobile push notifications. We need to make sure these are still correctly delivered to soft-deactivated users; making this work required careful work for those code paths that assumed a UserMessage row would always exist for a message that triggers a notification to a given user.

  • Digest emails, which use the UserMessage table extensively to determine what has happened in streams the user can see. We can use the user’s subscriptions to construct what messages they should have access to for this feature.