synapse/docs/development/database_schema.md

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 is 59.

• The database stores a "compatibility version" in schema_compat_version.compat_version which defines the SCHEMA_VERSION of the oldest version of Synapse which will work with the database. On startup, if compat_version is found to be newer than SCHEMA_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.

1. 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, increase synapse.storage.schema.SCHEMA_VERSION. In this instance, there is no existing code which reads from room_stats_historical, so our starting point is:

   v1.36.0: SCHEMA_VERSION=59, SCHEMA_COMPAT_VERSION=59

2. 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 increase synapse.storage.schema.SCHEMA_VERSION, but because we have not broken compatibility with v1.36, we do not yet update SCHEMA_COMPAT_VERSION. We now have:

   v1.37.0: SCHEMA_VERSION=60, SCHEMA_COMPAT_VERSION=59.

3. 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 update synapse.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."""
    ...

Boolean columns

Boolean columns require special treatment, since SQLite treats booleans the same as integers.

There are three separate aspects to this:

• 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.

• Before SQLite 3.23, TRUE and FALSE were not recognised as constants by SQLite, and the IS [NOT] TRUE/IS [NOT] FALSE operators were not supported. This makes it necessary to avoid using TRUE and FALSE constants in SQL commands.

  For example, to insert a TRUE value into the database, write:

  txn.execute("INSERT INTO tbl(col) VALUES (?)", (True, ))
  

• Default values for new boolean columns present a particular difficulty. Generally it is best to create separate schema files for Postgres and SQLite. For example:

  # in 00delta.sql.postgres:
  ALTER TABLE tbl ADD COLUMN col BOOLEAN DEFAULT FALSE;
  

  # in 00delta.sql.sqlite:
  ALTER TABLE tbl ADD COLUMN col BOOLEAN DEFAULT 0;
  

  Note that there is a particularly insidious failure mode here: the Postgres flavour will be accepted by SQLite 3.22, but will give a column whose default value is the string "FALSE" - which, when cast back to a boolean in Python, evaluates to True.

event_id global uniqueness

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.

There are several places in Synapse and even Matrix APIs like GET /_matrix/federation/v1/event/{eventId} where we assume that event IDs are globally unique.

But hash collisions are still possible, and by treating event IDs as room scoped, we can reduce the possibility of a hash collision. When scoping event_id in the database schema, it should be also accompanied by room_id (PRIMARY KEY (room_id, event_id)) and lookups should be done through the pair (room_id, event_id).

There has been a lot of debate on this in places like https://github.com/matrix-org/matrix-spec-proposals/issues/2779 and MSC2848 which has no resolution yet (as of 2022-09-01).