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<ol class="chapter"><li class="chapter-item expanded affix "><li class="part-title">Introduction</li><li class="chapter-item expanded "><a href="../../welcome_and_overview.html">Welcome and Overview</a></li><li class="chapter-item expanded affix "><li class="part-title">Setup</li><li class="chapter-item expanded "><a href="../../setup/installation.html">Installation</a></li><li class="chapter-item expanded "><a href="../../postgres.html">Using Postgres</a></li><li class="chapter-item expanded "><a href="../../reverse_proxy.html">Configuring a Reverse Proxy</a></li><li class="chapter-item expanded "><a href="../../setup/forward_proxy.html">Configuring a Forward/Outbound Proxy</a></li><li class="chapter-item expanded "><a href="../../turn-howto.html">Configuring a Turn Server</a></li><li><ol class="section"><li class="chapter-item expanded "><a href="../../setup/turn/coturn.html">coturn TURN server</a></li><li class="chapter-item expanded "><a href="../../setup/turn/eturnal.html">eturnal 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<h2 id="streams"><a class="header" href="#streams">Streams</a></h2>
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<p>Synapse has a concept of "streams", which are roughly described in <a href="https://github.com/element-hq/synapse/blob/develop/synapse/storage/util/id_generators.py"><code>id_generators.py</code></a>.
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Generally speaking, streams are a series of notifications that something in Synapse's database has changed that the application might need to respond to.
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For example:</p>
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<ul>
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<li>The events stream reports new events (PDUs) that Synapse creates, or that Synapse accepts from another homeserver.</li>
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<li>The account data stream reports changes to users' <a href="https://spec.matrix.org/v1.7/client-server-api/#client-config">account data</a>.</li>
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<li>The to-device stream reports when a device has a new <a href="https://spec.matrix.org/v1.7/client-server-api/#send-to-device-messaging">to-device message</a>.</li>
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</ul>
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<p>See <a href="https://github.com/element-hq/synapse/blob/develop/synapse/replication/tcp/streams/__init__.py"><code>synapse.replication.tcp.streams</code></a> for the full list of streams.</p>
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<p>It is very helpful to understand the streams mechanism when working on any part of Synapse that needs to respond to changes—especially if those changes are made by different workers.
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To that end, let's describe streams formally, paraphrasing from the docstring of <a href="https://github.com/element-hq/synapse/blob/a719b703d9bd0dade2565ddcad0e2f3a7a9d4c37/synapse/storage/util/id_generators.py#L96"><code>AbstractStreamIdGenerator</code></a>.</p>
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<h3 id="definition"><a class="header" href="#definition">Definition</a></h3>
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<p>A stream is an append-only log <code>T1, T2, ..., Tn, ...</code> of facts<sup class="footnote-reference"><a href="#1">1</a></sup> which grows over time.
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Only "writers" can add facts to a stream, and there may be multiple writers.</p>
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<p>Each fact has an ID, called its "stream ID".
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Readers should only process facts in ascending stream ID order.</p>
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<p>Roughly speaking, each stream is backed by a database table.
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It should have a <code>stream_id</code> (or similar) bigint column holding stream IDs, plus additional columns as necessary to describe the fact.
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Typically, a fact is expressed with a single row in its backing table.<sup class="footnote-reference"><a href="#2">2</a></sup>
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Within a stream, no two facts may have the same stream_id.</p>
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<blockquote>
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<p><em>Aside</em>. Some additional notes on streams' backing tables.</p>
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<ol>
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<li>Rich would like to <a href="https://github.com/matrix-org/synapse/issues/13456">ditch the backing tables</a>.</li>
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<li>The backing tables may have other uses.
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> For example, the events table serves backs the events stream, and is read when processing new events.
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> But old rows are read from the table all the time, whenever Synapse needs to lookup some facts about an event.</li>
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<li>Rich suspects that sometimes the stream is backed by multiple tables, so the stream proper is the union of those tables.</li>
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</ol>
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</blockquote>
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<p>Stream writers can "reserve" a stream ID, and then later mark it as having being completed.
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Stream writers need to track the completion of each stream fact.
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In the happy case, completion means a fact has been written to the stream table.
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But unhappy cases (e.g. transaction rollback due to an error) also count as completion.
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Once completed, the rows written with that stream ID are fixed, and no new rows
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will be inserted with that ID.</p>
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<h3 id="current-stream-id"><a class="header" href="#current-stream-id">Current stream ID</a></h3>
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<p>For any given stream reader (including writers themselves), we may define a per-writer current stream ID:</p>
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<blockquote>
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<p>A current stream ID <em>for a writer W</em> is the largest stream ID such that
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all transactions added by W with equal or smaller ID have completed.</p>
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</blockquote>
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<p>Similarly, there is a "linear" notion of current stream ID:</p>
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<blockquote>
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<p>A "linear" current stream ID is the largest stream ID such that
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all facts (added by any writer) with equal or smaller ID have completed.</p>
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</blockquote>
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<p>Because different stream readers A and B learn about new facts at different times, A and B may disagree about current stream IDs.
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Put differently: we should think of stream readers as being independent of each other, proceeding through a stream of facts at different rates.</p>
|
|
<p>The above definition does not give a unique current stream ID, in fact there can
|
|
be a range of current stream IDs. Synapse uses both the minimum and maximum IDs
|
|
for different purposes. Most often the maximum is used, as its generally
|
|
beneficial for workers to advance their IDs as soon as possible. However, the
|
|
minimum is used in situations where e.g. another worker is going to wait until
|
|
the stream advances past a position.</p>
|
|
<p><strong>NB.</strong> For both senses of "current", that if a writer opens a transaction that never completes, the current stream ID will never advance beyond that writer's last written stream ID.</p>
|
|
<p>For single-writer streams, the per-writer current ID and the linear current ID are the same.
|
|
Both senses of current ID are monotonic, but they may "skip" or jump over IDs because facts complete out of order.</p>
|
|
<p><em>Example</em>.
|
|
Consider a single-writer stream which is initially at ID 1.</p>
|
|
<table><thead><tr><th>Action</th><th>Current stream ID</th><th>Notes</th></tr></thead><tbody>
|
|
<tr><td></td><td>1</td><td></td></tr>
|
|
<tr><td>Reserve 2</td><td>1</td><td></td></tr>
|
|
<tr><td>Reserve 3</td><td>1</td><td></td></tr>
|
|
<tr><td>Complete 3</td><td>1</td><td>current ID unchanged, waiting for 2 to complete</td></tr>
|
|
<tr><td>Complete 2</td><td>3</td><td>current ID jumps from 1 -> 3</td></tr>
|
|
<tr><td>Reserve 4</td><td>3</td><td></td></tr>
|
|
<tr><td>Reserve 5</td><td>3</td><td></td></tr>
|
|
<tr><td>Reserve 6</td><td>3</td><td></td></tr>
|
|
<tr><td>Complete 5</td><td>3</td><td></td></tr>
|
|
<tr><td>Complete 4</td><td>5</td><td>current ID jumps 3->5, even though 6 is pending</td></tr>
|
|
<tr><td>Complete 6</td><td>6</td><td></td></tr>
|
|
</tbody></table>
|
|
<h3 id="multi-writer-streams"><a class="header" href="#multi-writer-streams">Multi-writer streams</a></h3>
|
|
<p>There are two ways to view a multi-writer stream.</p>
|
|
<ol>
|
|
<li>Treat it as a collection of distinct single-writer streams, one
|
|
for each writer.</li>
|
|
<li>Treat it as a single stream.</li>
|
|
</ol>
|
|
<p>The single stream (option 2) is conceptually simpler, and easier to represent (a single stream id).
|
|
However, it requires each reader to know about the entire set of writers, to ensures that readers don't erroneously advance their current stream position too early and miss a fact from an unknown writer.
|
|
In contrast, multiple parallel streams (option 1) are more complex, requiring more state to represent (map from writer to stream id).
|
|
The payoff for doing so is that readers can "peek" ahead to facts that completed on one writer no matter the state of the others, reducing latency.</p>
|
|
<p>Note that a multi-writer stream can be viewed in both ways.
|
|
For example, the events stream is treated as multiple single-writer streams (option 1) by the sync handler, so that events are sent to clients as soon as possible.
|
|
But the background process that works through events treats them as a single linear stream.</p>
|
|
<p>Another useful example is the cache invalidation stream.
|
|
The facts this stream holds are instructions to "you should now invalidate these cache entries".
|
|
We only ever treat this as a multiple single-writer streams as there is no important ordering between cache invalidations.
|
|
(Invalidations are self-contained facts; and the invalidations commute/are idempotent).</p>
|
|
<h3 id="writing-to-streams"><a class="header" href="#writing-to-streams">Writing to streams</a></h3>
|
|
<p>Writers need to track:</p>
|
|
<ul>
|
|
<li>track their current position (i.e. its own per-writer stream ID).</li>
|
|
<li>their facts currently awaiting completion.</li>
|
|
</ul>
|
|
<p>At startup,</p>
|
|
<ul>
|
|
<li>the current position of that writer can be found by querying the database (which suggests that facts need to be written to the database atomically, in a transaction); and</li>
|
|
<li>there are no facts awaiting completion.</li>
|
|
</ul>
|
|
<p>To reserve a stream ID, call <a href="https://www.postgresql.org/docs/current/functions-sequence.html"><code>nextval</code></a> on the appropriate postgres sequence.</p>
|
|
<p>To write a fact to the stream: insert the appropriate rows to the appropriate backing table.</p>
|
|
<p>To complete a fact, first remove it from your map of facts currently awaiting completion.
|
|
Then, if no earlier fact is awaiting completion, the writer can advance its current position in that stream.
|
|
Upon doing so it should emit an <code>RDATA</code> message<sup class="footnote-reference"><a href="#3">3</a></sup>, once for every fact between the old and the new stream ID.</p>
|
|
<h3 id="subscribing-to-streams"><a class="header" href="#subscribing-to-streams">Subscribing to streams</a></h3>
|
|
<p>Readers need to track the current position of every writer.</p>
|
|
<p>At startup, they can find this by contacting each writer with a <code>REPLICATE</code> message,
|
|
requesting that all writers reply describing their current position in their streams.
|
|
Writers reply with a <code>POSITION</code> message.</p>
|
|
<p>To learn about new facts, readers should listen for <code>RDATA</code> messages and process them to respond to the new fact.
|
|
The <code>RDATA</code> itself is not a self-contained representation of the fact;
|
|
readers will have to query the stream tables for the full details.
|
|
Readers must also advance their record of the writer's current position for that stream.</p>
|
|
<h1 id="summary"><a class="header" href="#summary">Summary</a></h1>
|
|
<p>In a nutshell: we have an append-only log with a "buffer/scratchpad" at the end where we have to wait for the sequence to be linear and contiguous.</p>
|
|
<hr />
|
|
<div class="footnote-definition" id="1"><sup class="footnote-definition-label">1</sup>
|
|
<p>we use the word <em>fact</em> here for two reasons.
|
|
Firstly, the word "event" is already heavily overloaded (PDUs, EDUs, account data, ...) and we don't need to make that worse.
|
|
Secondly, "fact" emphasises that the things we append to a stream cannot change after the fact.</p>
|
|
</div>
|
|
<div class="footnote-definition" id="2"><sup class="footnote-definition-label">2</sup>
|
|
<p>A fact might be expressed with 0 rows, e.g. if we opened a transaction to persist an event, but failed and rolled the transaction back before marking the fact as completed.
|
|
In principle a fact might be expressed with 2 or more rows; if so, each of those rows should share the fact's stream ID.</p>
|
|
</div>
|
|
<div class="footnote-definition" id="3"><sup class="footnote-definition-label">3</sup>
|
|
<p>This communication used to happen directly with the writers <a href="../../tcp_replication.html">over TCP</a>;
|
|
nowadays it's done via Redis's Pubsub.</p>
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