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59 lines
2.6 KiB
59 lines
2.6 KiB
9 years ago
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Replication Architecture
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========================
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Motivation
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----------
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We'd like to be able to split some of the work that synapse does into multiple
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python processes. In theory multiple synapse processes could share a single
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postgresql database and we'd scale up by running more synapse processes.
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However much of synapse assumes that only one process is interacting with the
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database, both for assigning unique identifiers when inserting into tables,
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notifying components about new updates, and for invalidating its caches.
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So running multiple copies of the current code isn't an option. One way to
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run multiple processes would be to have a single writer process and multiple
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reader processes connected to the same database. In order to do this we'd need
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a way for the reader process to invalidate its in-memory caches when an update
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happens on the writer. One way to do this is for the writer to present an
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append-only log of updates which the readers can consume to invalidate their
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caches and to push updates to listening clients or pushers.
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Synapse already stores much of its data as an append-only log so that it can
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correctly respond to /sync requests so the amount of code changes needed to
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expose the append-only log to the readers should be fairly minimal.
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Architecture
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------------
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The Replication API
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~~~~~~~~~~~~~~~~~~~
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Synapse will optionally expose a long poll HTTP API for extracting updates. The
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API will have a similar shape to /sync in that clients provide tokens
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indicating where in the log they have reached and a timeout. The synapse server
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then either responds with updates immediately if it already has updates or it
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waits until the timeout for more updates. If the timeout expires and nothing
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happened then the server returns an empty response.
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However until the /sync API this replication API is returning synapse specific
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data rather than trying to implement a matrix specification. The replication
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results are returned as arrays of rows where the rows are mostly lifted
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directly from the database. This avoids unnecessary JSON parsing on the server
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and hopefully avoids an impedance mismatch between the data returned and the
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required updates to the datastore.
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This does not replicate all the database tables as many of the database tables
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are indexes that can be recovered from the contents of other tables.
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The format and parameters for the api are documented in
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``synapse/replication/resource.py``.
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The Slaved DataStore
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~~~~~~~~~~~~~~~~~~~~
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There are read-only version of the synapse storage layer in
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``synapse/replication/slave/storage`` that use the response of the replication
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API to invalidate their caches.
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