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Synapse database schema files
Synapse's database schema is stored in the synapse.storage.schema
module.
Logical databases
Synapse supports splitting its datastore across multiple physical databases (which can be useful for large installations), and the schema files are therefore split according to the logical database they apply to.
At the time of writing, the following "logical" databases are supported:
state
- used to store Matrix room state (more specifically,state_groups
, their relationships and contents).main
- stores everything else.
Additionally, the common
directory contains schema files for tables which must be
present on all physical databases.
Synapse schema versions
Synapse manages its database schema via "schema versions". These are mainly used to help avoid confusion if the Synapse codebase is rolled back after the database is updated. They work as follows:
-
The Synapse codebase defines a constant
synapse.storage.schema.SCHEMA_VERSION
which represents the expectations made about the database by that version. For example, as of Synapse v1.36, this is59
. -
The database stores a "compatibility version" in
schema_compat_version.compat_version
which defines theSCHEMA_VERSION
of the oldest version of Synapse which will work with the database. On startup, ifcompat_version
is found to be newer thanSCHEMA_VERSION
, Synapse will refuse to start.Synapse automatically updates this field from
synapse.storage.schema.SCHEMA_COMPAT_VERSION
. -
Whenever a backwards-incompatible change is made to the database format (normally via a
delta
file),synapse.storage.schema.SCHEMA_COMPAT_VERSION
is also updated so that administrators can not accidentally roll back to a too-old version of Synapse.
Generally, the goal is to maintain compatibility with at least one or two previous releases of Synapse, so any substantial change tends to require multiple releases and a bit of forward-planning to get right.
As a worked example: we want to remove the room_stats_historical
table. Here is how it
might pan out.
-
Replace any code that reads from
room_stats_historical
with alternative implementations, but keep writing to it in case of rollback to an earlier version. Also, increasesynapse.storage.schema.SCHEMA_VERSION
. In this instance, there is no existing code which reads fromroom_stats_historical
, so our starting point is:v1.36.0:
SCHEMA_VERSION=59
,SCHEMA_COMPAT_VERSION=59
-
Next (say in Synapse v1.37.0): remove the code that writes to
room_stats_historical
, but don’t yet remove the table in case of rollback to v1.36.0. Again, we increasesynapse.storage.schema.SCHEMA_VERSION
, but because we have not broken compatibility with v1.36, we do not yet updateSCHEMA_COMPAT_VERSION
. We now have:v1.37.0:
SCHEMA_VERSION=60
,SCHEMA_COMPAT_VERSION=59
. -
Later (say in Synapse v1.38.0): we can remove the table altogether. This will break compatibility with v1.36.0, so we must update
SCHEMA_COMPAT_VERSION
accordingly. There is no need to updatesynapse.storage.schema.SCHEMA_VERSION
, since there is no change to the Synapse codebase here. So we end up with:v1.38.0:
SCHEMA_VERSION=60
,SCHEMA_COMPAT_VERSION=60
.
If in doubt about whether to update SCHEMA_VERSION
or not, it is generally best to
lean towards doing so.
Full schema dumps
In the full_schemas
directories, only the most recently-numbered snapshot is used
(54
at the time of writing). Older snapshots (eg, 16
) are present for historical
reference only.
Building full schema dumps
If you want to recreate these schemas, they need to be made from a database that has had all background updates run.
To do so, use scripts-dev/make_full_schema.sh
. This will produce new
full.sql.postgres
and full.sql.sqlite
files.
Ensure postgres is installed, then run:
./scripts-dev/make_full_schema.sh -p postgres_username -o output_dir/
NB at the time of writing, this script predates the split into separate state
/main
databases so will require updates to handle that correctly.
Delta files
Delta files define the steps required to upgrade the database from an earlier version. They can be written as either a file containing a series of SQL statements, or a Python module.
Synapse remembers which delta files it has applied to a database (they are stored in the
applied_schema_deltas
table) and will not re-apply them (even if a given file is
subsequently updated).
Delta files should be placed in a directory named synapse/storage/schema/<database>/delta/<version>/
.
They are applied in alphanumeric order, so by convention the first two characters
of the filename should be an integer such as 01
, to put the file in the right order.
SQL delta files
These should be named *.sql
, or — for changes which should only be applied for a
given database engine — *.sql.posgres
or *.sql.sqlite
. For example, a delta which
adds a new column to the foo
table might be called 01add_bar_to_foo.sql
.
Note that our SQL parser is a bit simple - it understands comments (--
and /*...*/
),
but complex statements which require a ;
in the middle of them (such as CREATE TRIGGER
) are beyond it and you'll have to use a Python delta file.
Python delta files
For more flexibility, a delta file can take the form of a python module. These should
be named *.py
. Note that database-engine-specific modules are not supported here –
instead you can write if isinstance(database_engine, PostgresEngine)
or similar.
A Python delta module should define either or both of the following functions:
import synapse.config.homeserver
import synapse.storage.engines
import synapse.storage.types
def run_create(
cur: synapse.storage.types.Cursor,
database_engine: synapse.storage.engines.BaseDatabaseEngine,
) -> None:
"""Called whenever an existing or new database is to be upgraded"""
...
def run_upgrade(
cur: synapse.storage.types.Cursor,
database_engine: synapse.storage.engines.BaseDatabaseEngine,
config: synapse.config.homeserver.HomeServerConfig,
) -> None:
"""Called whenever an existing database is to be upgraded."""
...
Background updates
It is sometimes appropriate to perform database migrations as part of a background process (instead of blocking Synapse until the migration is done). In particular, this is useful for migrating data when adding new columns or tables.
Pending background updates stored in the background_updates
table and are denoted
by a unique name, the current status (stored in JSON), and some dependency information:
- Whether the update requires a previous update to be complete.
- A rough ordering for which to complete updates.
A new background updates needs to be added to the background_updates
table:
INSERT INTO background_updates (ordering, update_name, depends_on, progress_json) VALUES
(7706, 'my_background_update', 'a_previous_background_update' '{}');
And then needs an associated handler in the appropriate datastore:
self.db_pool.updates.register_background_update_handler(
"my_background_update",
update_handler=self._my_background_update,
)
There are a few types of updates that can be performed, see the BackgroundUpdater
:
register_background_update_handler
: A generic handler for custom SQLregister_background_index_update
: Create an index in the backgroundregister_background_validate_constraint
: Validate a constraint in the background (PostgreSQL-only)register_background_validate_constraint_and_delete_rows
: Similar toregister_background_validate_constraint
, but deletes rows which don't fit the constraint.
For register_background_update_handler
, the generic handler must track progress
and then finalize the background update:
async def _my_background_update(self, progress: JsonDict, batch_size: int) -> int:
def _do_something(txn: LoggingTransaction) -> int:
...
self.db_pool.updates._background_update_progress_txn(
txn, "my_background_update", {"last_processed": last_processed}
)
return last_processed - prev_last_processed
num_processed = await self.db_pool.runInteraction("_do_something", _do_something)
await self.db_pool.updates._end_background_update("my_background_update")
return num_processed
Synapse will attempt to rate-limit how often background updates are run via the given batch-size and the returned number of processed entries (and how long the function took to run). See background update controller callbacks.
Boolean columns
Boolean columns require special treatment, since SQLite treats booleans the same as integers.
Any new boolean column must be added to the BOOLEAN_COLUMNS
list in
synapse/_scripts/synapse_port_db.py
. This tells the port script to cast
the integer value from SQLite to a boolean before writing the value to the
postgres database.
event_id
global uniqueness
event_id
's can be considered globally unique although there has been a lot of
debate on this topic in places like
MSC2779 and
MSC2848 which
has no resolution yet (as of 2022-09-01). There are several places in Synapse
and even in the Matrix APIs like GET /_matrix/federation/v1/event/{eventId}
where we assume that event IDs are globally unique.
When scoping event_id
in a database schema, it is often nice to accompany it
with room_id
(PRIMARY KEY (room_id, event_id)
and a FOREIGN KEY(room_id) REFERENCES rooms(room_id)
) which makes flexible lookups easy. For example it
makes it very easy to find and clean up everything in a room when it needs to be
purged (no need to use sub-select
query or join from the events
table).
A note on collisions: In room versions 1
and 2
it's possible to end up with
two events with the same event_id
(in the same or different rooms). After room
version 3
, that can only happen with a hash collision, which we basically hope
will never happen (SHA256 has a massive big key space).
Worked examples of gradual migrations
Some migrations need to be performed gradually. A prime example of this is anything
which would need to do a large table scan — including adding columns, indices or
NOT NULL
constraints to non-empty tables — such a migration should be done as a
background update where possible, at least on Postgres.
We can afford to be more relaxed about SQLite databases since they are usually
used on smaller deployments and SQLite does not support the same concurrent
DDL operations as Postgres.
We also typically insist on having at least one Synapse version's worth of backwards compatibility, so that administrators can roll back Synapse if an upgrade did not go smoothly.
This sometimes results in having to plan a migration across multiple versions of Synapse.
This section includes an example and may include more in the future.
Transforming a column into another one, with NOT NULL
constraints
This example illustrates how you would introduce a new column, write data into it based on data from an old column and then drop the old column.
We are aiming for semantic equivalence to:
ALTER TABLE mytable ADD COLUMN new_column INTEGER;
UPDATE mytable SET new_column = old_column * 100;
ALTER TABLE mytable ALTER COLUMN new_column ADD CONSTRAINT NOT NULL;
ALTER TABLE mytable DROP COLUMN old_column;
Synapse version N
SCHEMA_VERSION = S
SCHEMA_COMPAT_VERSION = ... # unimportant at this stage
Invariants:
old_column
is read by Synapse and written to by Synapse.
Synapse version N + 1
SCHEMA_VERSION = S + 1
SCHEMA_COMPAT_VERSION = ... # unimportant at this stage
Changes: 1.
ALTER TABLE mytable ADD COLUMN new_column INTEGER;
Invariants:
old_column
is read by Synapse and written to by Synapse.new_column
is written to by Synapse.
Notes:
new_column
can't have aNOT NULL NOT VALID
constraint yet, because the previous Synapse version did not write to the new column (since we haven't bumped theSCHEMA_COMPAT_VERSION
yet, we still need to be compatible with the previous version).
Synapse version N + 2
SCHEMA_VERSION = S + 2
SCHEMA_COMPAT_VERSION = S + 1 # this signals that we can't roll back to a time before new_column existed
Changes:
- On Postgres, add a
NOT VALID
constraint to ensure new rows are compliant. SQLite does not have such a construct, but it would be unnecessary anyway since there is no way to concurrently perform this migration on SQLite.ALTER TABLE mytable ADD CONSTRAINT CHECK new_column_not_null (new_column IS NOT NULL) NOT VALID;
- Start a background update to perform migration: it should gradually run e.g.
This background update is technically pointless on SQLite, but you must schedule it anyway so that theUPDATE mytable SET new_column = old_column * 100 WHERE 0 < mytable_id AND mytable_id <= 5;
portdb
script to migrate to Postgres still works. - Upon completion of the background update, you should run
VALIDATE CONSTRAINT
on Postgres to turn theNOT VALID
constraint into a valid one.
This will take some time but does NOT hold an exclusive lock over the table.ALTER TABLE mytable VALIDATE CONSTRAINT new_column_not_null;
Invariants:
old_column
is read by Synapse and written to by Synapse.new_column
is written to by Synapse and new rows always have a non-NULL
value in this field.
Notes:
- If you wish, you can convert the
CHECK (new_column IS NOT NULL)
to aNOT NULL
constraint free of charge in Postgres by adding theNOT NULL
constraint and then dropping theCHECK
constraint, because Postgres can statically verify that theNOT NULL
constraint is implied by theCHECK
constraint without performing a table scan. - It might be tempting to make version
N + 2
redundant by moving the background update toN + 1
and delaying adding theNOT NULL
constraint toN + 3
, but that would mean the constraint would always be validated in the foreground inN + 3
. Whereas if theN + 2
step is kept, the migration inN + 3
would be fast in the happy case.
Synapse version N + 3
SCHEMA_VERSION = S + 3
SCHEMA_COMPAT_VERSION = S + 1 # we can't roll back to a time before new_column existed
Changes:
- (Postgres) Update the table to populate values of
new_column
in case the background update had not completed. Additionally,VALIDATE CONSTRAINT
to make the check fully valid.-- you ideally want an index on `new_column` or e.g. `(new_column) WHERE new_column IS NULL` first, or perhaps you can find a way to skip this if the `NOT NULL` constraint has already been validated. UPDATE mytable SET new_column = old_column * 100 WHERE new_column IS NULL; -- this is a no-op if it already ran as part of the background update ALTER TABLE mytable VALIDATE CONSTRAINT new_column_not_null;
- (SQLite) Recreate the table by precisely following the 12-step procedure for SQLite table schema changes.
During this table rewrite, you should recreate
new_column
asNOT NULL
and populate any outstandingNULL
values at the same time. Unfortunately, you can't dropold_column
yet because it must be present for compatibility with the Postgres schema, as needed byportdb
. (Otherwise you could do this all in one go with SQLite!)
Invariants:
old_column
is written to by Synapse (but no longer read by Synapse!).new_column
is read by Synapse and written to by Synapse. Moreover, all rows have a non-NULL
value in this field, as guaranteed by a schema constraint.
Notes:
- We can't drop
old_column
yet, or even stop writing to it, because that would break a rollback to the previous version of Synapse. - Application code can now rely on
new_column
being populated. The remaining steps are only motivated by the wish to clean-up old columns.
Synapse version N + 4
SCHEMA_VERSION = S + 4
SCHEMA_COMPAT_VERSION = S + 3 # we can't roll back to a time before new_column was entirely non-NULL
Invariants:
old_column
exists but is not written to or read from by Synapse.new_column
is read by Synapse and written to by Synapse. Moreover, all rows have a non-NULL
value in this field, as guaranteed by a schema constraint.
Notes:
- We can't drop
old_column
yet because that would break a rollback to the previous version of Synapse.
TODO: It may be possible to relax this and drop the column straight away as long as the previous version of Synapse detected a rollback occurred and stopped attempting to write to the column. This could possibly be done by checking whether the database's schema compatibility version wasS + 3
.
Synapse version N + 5
SCHEMA_VERSION = S + 5
SCHEMA_COMPAT_VERSION = S + 4 # we can't roll back to a time before old_column was no longer being touched
Changes: 1.
ALTER TABLE mytable DROP COLUMN old_column;