Remove "single process" restrictions on SQLite in favour of using WAL mode by ashb · Pull Request #44839 · apache/airflow

ashb · 2024-12-11T11:26:12Z

Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling
which allos multiple concurrent readers and one writer. More than good enough
for us for "local" use.

The primary driver for this change was a realisation that it is possible and
to reduce the amount of code in complexity in DagProcessorManager before
reworking it for AIP-72 support :- we have a lot of code in the
DagProcessorManager to support if async_mode that makes understanding the
flow complex.

Some useful docs and articles about this mode:

This still keeps the warning against using SQLite in production, but it
greatly reduces the restrictions what combos and settings can use this. In
short, when using an SQLite db it is now possible to:

use LocalExecutor, including with more than 1 concurrent worker slot
have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to
that)

We execute the PRAGMA journal_mode every time we connect, which is more
often that is strictly needed as this is one of the few modes thatis
persistent and a property of the DB file just for ease and to ensure that it
it is in the mode we want.

I have tested this with breeze -b sqlite start_airflow and a kicking off a
lot of tasks concurrently.

Will this be without problems? No, not entirely, but due to the
scheduler+webserver+api server process we've already got the case where
multiple processes are operating on the DB file. This change just makes the
best use of that following the guidance of the SQLite project: Ensuring that
only a single process accesses the DB concurrently is not a requirement
anymore!

… mode Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling which allos multiple concurrent readers and one writer. More than good enough for us for "local" use. The primary driver for this change was a realisation that it is possible and to reduce the amount of code in complexity in DagProcessorManager before reworking it for AIP-72 support :- we have a lot of code in the DagProcessorManager to support `if async_mode` that makes understanding the flow complex. Some useful docs and articles about this mode: - [The offical docs](https://sqlite.org/wal.html) - [Simon Willison's TIL](https://til.simonwillison.net/sqlite/enabling-wal-mode) - [fly.io article about scaling read concurrency](https://fly.io/blog/sqlite-internals-wal/) This still keeps the warning against using SQLite in production, but it greatly reduces the restrictions what combos and settings can use this. In short, when using an SQLite db it is now possible to: - use LocalExecutor, including with more than 1 concurrent worker slot - have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to that) We execute the `PRAGMA journal_mode` every time we connect, which is more often that is strictly needed as this is one of the few modes thatis persistent and a property of the DB file just for ease and to ensure that it it is in the mode we want. I have tested this with `breeze -b sqlite start_airflow` and a kicking off a lot of tasks concurrently. Will this be without problems? No, not entirely, but due to the scheduler+webserver+api server process we've _already_ got the case where multiple processes are operating on the DB file. This change just makes the best use of that following the guidance of the SQLite project: Ensuring that only a single process accesses the DB concurrently is not a requirement anymore!

ashb · 2024-12-11T11:27:46Z

 def set_sqlite_pragma(dbapi_connection, connection_record):
 cursor = dbapi_connection.cursor()
 cursor.execute("PRAGMA foreign_keys=ON")
+ cursor.execute("PRAGMA journal_mode=WAL")


This is essentially the change, everything else is removing code that isn't needed anymore!

ashb · 2024-12-11T12:03:02Z

Looks like I left a load of validate_database_executor_compatibility in the tests.

potiuk · 2024-12-11T13:53:38Z

This is a fantastic improvement. And it will make "airflow standalone" finally getting really usefuil for "local experience".

… mode (apache#44839) Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling which allos multiple concurrent readers and one writer. More than good enough for us for "local" use. The primary driver for this change was a realisation that it is possible and to reduce the amount of code in complexity in DagProcessorManager before reworking it for AIP-72 support :- we have a lot of code in the DagProcessorManager to support `if async_mode` that makes understanding the flow complex. Some useful docs and articles about this mode: - [The offical docs](https://sqlite.org/wal.html) - [Simon Willison's TIL](https://til.simonwillison.net/sqlite/enabling-wal-mode) - [fly.io article about scaling read concurrency](https://fly.io/blog/sqlite-internals-wal/) This still keeps the warning against using SQLite in production, but it greatly reduces the restrictions what combos and settings can use this. In short, when using an SQLite db it is now possible to: - use LocalExecutor, including with more than 1 concurrent worker slot - have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to that) We execute the `PRAGMA journal_mode` every time we connect, which is more often that is strictly needed as this is one of the few modes thatis persistent and a property of the DB file just for ease and to ensure that it it is in the mode we want. I have tested this with `breeze -b sqlite start_airflow` and a kicking off a lot of tasks concurrently. Will this be without problems? No, not entirely, but due to the scheduler+webserver+api server process we've _already_ got the case where multiple processes are operating on the DB file. This change just makes the best use of that following the guidance of the SQLite project: Ensuring that only a single process accesses the DB concurrently is not a requirement anymore!

That import was removed in apache#44839, but apache#44710 wasn't up-to-date with main so static checks there didn't fail. This simply adds it back.

That import was removed in #44839, but #44710 wasn't up-to-date with main so static checks there didn't fail. This simply adds it back.

As part of Airflow 3 DAG definition files will have to use the Task SDK for all their classes, and anything involving running user code will need to be de-coupled from the database in the user-code process. This change moves all of the "serialization" change up to the DagFileProcessorManager, using the new function introduced in apache#44898 and the "subprocess" machinery introduced in apache#44874. **Important Note**: this change does not remove the ability for dag processes to access the DB for Variables etc. That will come in a future change. Some key parts of this change: - It builds upon the WatchedSubprocess from the TaskSDK. Right now this puts a nasty/unwanted depenednecy between the Dag Parsing code upon the TaskSDK. This will be addressed before release (we have talked about introducing a new "apache-airflow-base-executor" dist where this subprocess+supervisor could live, as the "execution_time" folder in the Task SDK is more a feature of the executor, not of the TaskSDK itself) - A number of classes that we need to send between processes have been converted to Pydantic for ease of serialization. - In order to not have to serialize everything in the subprocess and deserialize everything in the parent Manager process, we have created a `LazyDeserializedDAG` class that provides lazy access to much of the properties needed to create update the DAG related DB objects, without needing to fully deserialize the entire DAG structure. - Classes switched to attrs based for less boilerplate in constructors. - Internal timers convert to `time.monotonic` where possible, and `time.time` where not, we only need second diff between two points, not datetime objects - With the earlier removal of "sync mode" for SQLite in apache#44839 the need for separate TERMIANTE and END messages over the control socket can go Co-authored-by: Jed Cunningham <66968678+jedcunningham@users.noreply.github.com> Co-authored-by: Daniel Imberman <daniel.imberman@gmail.com>

As part of Airflow 3 DAG definition files will have to use the Task SDK for all their classes, and anything involving running user code will need to be de-coupled from the database in the user-code process. This change moves all of the "serialization" change up to the DagFileProcessorManager, using the new function introduced in apache#44898 and the "subprocess" machinery introduced in apache#44874. **Important Note**: this change does not remove the ability for dag processes to access the DB for Variables etc. That will come in a future change. Some key parts of this change: - It builds upon the WatchedSubprocess from the TaskSDK. Right now this puts a nasty/unwanted depenednecy between the Dag Parsing code upon the TaskSDK. This will be addressed before release (we have talked about introducing a new "apache-airflow-base-executor" dist where this subprocess+supervisor could live, as the "execution_time" folder in the Task SDK is more a feature of the executor, not of the TaskSDK itself.) - A number of classes that we need to send between processes have been converted to Pydantic for ease of serialization. - In order to not have to serialize everything in the subprocess and deserialize everything in the parent Manager process, we have created a `LazyDeserializedDAG` class that provides lazy access to much of the properties needed to create update the DAG related DB objects, without needing to fully deserialize the entire DAG structure. - Classes switched to attrs based for less boilerplate in constructors. - Internal timers convert to `time.monotonic` where possible, and `time.time` where not, we only need second diff between two points, not datetime objects. - With the earlier removal of "sync mode" for SQLite in apache#44839 the need for separate TERMIANTE and END messages over the control socket can go. Co-authored-by: Jed Cunningham <66968678+jedcunningham@users.noreply.github.com> Co-authored-by: Daniel Imberman <daniel.imberman@gmail.com>

As part of Airflow 3 DAG definition files will have to use the Task SDK for all their classes, and anything involving running user code will need to be de-coupled from the database in the user-code process. This change moves all of the "serialization" change up to the DagFileProcessorManager, using the new function introduced in #44898 and the "subprocess" machinery introduced in #44874. **Important Note**: this change does not remove the ability for dag processes to access the DB for Variables etc. That will come in a future change. Some key parts of this change: - It builds upon the WatchedSubprocess from the TaskSDK. Right now this puts a nasty/unwanted depenednecy between the Dag Parsing code upon the TaskSDK. This will be addressed before release (we have talked about introducing a new "apache-airflow-base-executor" dist where this subprocess+supervisor could live, as the "execution_time" folder in the Task SDK is more a feature of the executor, not of the TaskSDK itself.) - A number of classes that we need to send between processes have been converted to Pydantic for ease of serialization. - In order to not have to serialize everything in the subprocess and deserialize everything in the parent Manager process, we have created a `LazyDeserializedDAG` class that provides lazy access to much of the properties needed to create update the DAG related DB objects, without needing to fully deserialize the entire DAG structure. - Classes switched to attrs based for less boilerplate in constructors. - Internal timers convert to `time.monotonic` where possible, and `time.time` where not, we only need second diff between two points, not datetime objects. - With the earlier removal of "sync mode" for SQLite in #44839 the need for separate TERMINATE and END messages over the control socket can go. --------- Co-authored-by: Jed Cunningham <66968678+jedcunningham@users.noreply.github.com> Co-authored-by: Daniel Imberman <daniel.imberman@gmail.com>

… mode (apache#44839) Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling which allos multiple concurrent readers and one writer. More than good enough for us for "local" use. The primary driver for this change was a realisation that it is possible and to reduce the amount of code in complexity in DagProcessorManager before reworking it for AIP-72 support :- we have a lot of code in the DagProcessorManager to support `if async_mode` that makes understanding the flow complex. Some useful docs and articles about this mode: - [The offical docs](https://sqlite.org/wal.html) - [Simon Willison's TIL](https://til.simonwillison.net/sqlite/enabling-wal-mode) - [fly.io article about scaling read concurrency](https://fly.io/blog/sqlite-internals-wal/) This still keeps the warning against using SQLite in production, but it greatly reduces the restrictions what combos and settings can use this. In short, when using an SQLite db it is now possible to: - use LocalExecutor, including with more than 1 concurrent worker slot - have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to that) We execute the `PRAGMA journal_mode` every time we connect, which is more often that is strictly needed as this is one of the few modes thatis persistent and a property of the DB file just for ease and to ensure that it it is in the mode we want. I have tested this with `breeze -b sqlite start_airflow` and a kicking off a lot of tasks concurrently. Will this be without problems? No, not entirely, but due to the scheduler+webserver+api server process we've _already_ got the case where multiple processes are operating on the DB file. This change just makes the best use of that following the guidance of the SQLite project: Ensuring that only a single process accesses the DB concurrently is not a requirement anymore!

That import was removed in apache#44839, but apache#44710 wasn't up-to-date with main so static checks there didn't fail. This simply adds it back.

As part of Airflow 3 DAG definition files will have to use the Task SDK for all their classes, and anything involving running user code will need to be de-coupled from the database in the user-code process. This change moves all of the "serialization" change up to the DagFileProcessorManager, using the new function introduced in apache#44898 and the "subprocess" machinery introduced in apache#44874. **Important Note**: this change does not remove the ability for dag processes to access the DB for Variables etc. That will come in a future change. Some key parts of this change: - It builds upon the WatchedSubprocess from the TaskSDK. Right now this puts a nasty/unwanted depenednecy between the Dag Parsing code upon the TaskSDK. This will be addressed before release (we have talked about introducing a new "apache-airflow-base-executor" dist where this subprocess+supervisor could live, as the "execution_time" folder in the Task SDK is more a feature of the executor, not of the TaskSDK itself.) - A number of classes that we need to send between processes have been converted to Pydantic for ease of serialization. - In order to not have to serialize everything in the subprocess and deserialize everything in the parent Manager process, we have created a `LazyDeserializedDAG` class that provides lazy access to much of the properties needed to create update the DAG related DB objects, without needing to fully deserialize the entire DAG structure. - Classes switched to attrs based for less boilerplate in constructors. - Internal timers convert to `time.monotonic` where possible, and `time.time` where not, we only need second diff between two points, not datetime objects. - With the earlier removal of "sync mode" for SQLite in apache#44839 the need for separate TERMINATE and END messages over the control socket can go. --------- Co-authored-by: Jed Cunningham <66968678+jedcunningham@users.noreply.github.com> Co-authored-by: Daniel Imberman <daniel.imberman@gmail.com>

ashb · 2024-12-11T11:26:12Z

Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling
which allos multiple concurrent readers and one writer. More than good enough
for us for "local" use.

The primary driver for this change was a realisation that it is possible and
to reduce the amount of code in complexity in DagProcessorManager before
reworking it for AIP-72 support :- we have a lot of code in the
DagProcessorManager to support if async_mode that makes understanding the
flow complex.

Some useful docs and articles about this mode:

This still keeps the warning against using SQLite in production, but it
greatly reduces the restrictions what combos and settings can use this. In
short, when using an SQLite db it is now possible to:

use LocalExecutor, including with more than 1 concurrent worker slot
have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to
that)

We execute the PRAGMA journal_mode every time we connect, which is more
often that is strictly needed as this is one of the few modes thatis
persistent and a property of the DB file just for ease and to ensure that it
it is in the mode we want.

I have tested this with breeze -b sqlite start_airflow and a kicking off a
lot of tasks concurrently.

Will this be without problems? No, not entirely, but due to the
scheduler+webserver+api server process we've already got the case where
multiple processes are operating on the DB file. This change just makes the
best use of that following the guidance of the SQLite project: Ensuring that
only a single process accesses the DB concurrently is not a requirement
anymore!

… mode Since 2010(!) sqlite has had a WAL, or Write-Ahead Log mode of journalling which allos multiple concurrent readers and one writer. More than good enough for us for "local" use. The primary driver for this change was a realisation that it is possible and to reduce the amount of code in complexity in DagProcessorManager before reworking it for AIP-72 support :- we have a lot of code in the DagProcessorManager to support `if async_mode` that makes understanding the flow complex. Some useful docs and articles about this mode: - [The offical docs](https://sqlite.org/wal.html) - [Simon Willison's TIL](https://til.simonwillison.net/sqlite/enabling-wal-mode) - [fly.io article about scaling read concurrency](https://fly.io/blog/sqlite-internals-wal/) This still keeps the warning against using SQLite in production, but it greatly reduces the restrictions what combos and settings can use this. In short, when using an SQLite db it is now possible to: - use LocalExecutor, including with more than 1 concurrent worker slot - have multiple DAG parsing processes (even before AIP-72/TaskSDK changes to that) We execute the `PRAGMA journal_mode` every time we connect, which is more often that is strictly needed as this is one of the few modes thatis persistent and a property of the DB file just for ease and to ensure that it it is in the mode we want. I have tested this with `breeze -b sqlite start_airflow` and a kicking off a lot of tasks concurrently. Will this be without problems? No, not entirely, but due to the scheduler+webserver+api server process we've _already_ got the case where multiple processes are operating on the DB file. This change just makes the best use of that following the guidance of the SQLite project: Ensuring that only a single process accesses the DB concurrently is not a requirement anymore!

ashb · 2024-12-11T11:27:46Z

 def set_sqlite_pragma(dbapi_connection, connection_record):
 cursor = dbapi_connection.cursor()
 cursor.execute("PRAGMA foreign_keys=ON")
+ cursor.execute("PRAGMA journal_mode=WAL")


This is essentially the change, everything else is removing code that isn't needed anymore!

ashb · 2024-12-11T12:03:02Z

Looks like I left a load of validate_database_executor_compatibility in the tests.

potiuk · 2024-12-11T13:53:38Z