• Overview
• Release Notes
• Release 2.0
• Release 1.7
• Release 1.6
• Release 1.5
• Release 1.4
• Release 1.3
• Release 1.2
• Release 1.1
• High Level Description
• Features
• A Full Example
• Installation
• Configuration
• Permissions
• Deployment of Automatic DDL Replication
• Monitoring and Administration
• DDL involving multiple tables
• Unsupported Commands
• Multi-Statement Client SQL Limitations
• Native Logical Supported Configurations
• Resolving Failed DDL on Subscribers
• Resolving Unhandled DDL
• Disable DDL Replication on Subscriber
• Help Wanted Features
• SQL files
• Regression testing

As of Postgres 13, there is no native way to do "transparent DDL replication"
to other Postgres clusters alongside any logical replication technology, built
on standard Postgres.

This project is an attempt to do just that. The framework is built on the
following concepts:

• Event triggers always fire on DDL events, and thus give us immediate access to
  what we want
• Event triggers gives us access (from 9.5+) to what objects are being altered
• We can see what SQL the client is executing within an event trigger
• We can validate and choose to propagate that SQL statement to subscribers
• We can add new tables to replication at the point of creation, prior to any
  DML execution

In many environments, this may cover most if not all DDL statements that are
executed in an application environment. We know this doesn't cover 100% of edge
cases, but we believe the functionality and robustness is significant enough to
add great value in many Postgres environments. It will work especially well in
these two use cases:

• When you want to replicate all user tables
• When you want to replicate only a subset of tables in a schema that will not
  have any foreign key dependencies to other schemas

We also think it's possible to expand this concept by further leveraging the
Postgres parser. There is much detail below on what the Limitations and
Restrictions are of this framework.

Features

• Any DDL SQL statement can be propagated directly to subscribers without your
  developers needing to overhaul their migration process or know the intricacies
  of replication.

• Tables can be automatically added to replication upon creation (include_schema_regex option).

• Filtering by schema (regular expression) is supported. This allows you to
  selectively replicate only certain schemas within a publication/replication set.

• Filtering by a specific set of tables is supported, which is most useful to
  replicate a small set of tables and maintain things like columns added/dropped

• There is an option to deploy in a lock-safe way on subscribers. Note that
  this means replication will lag until all blockers finish running or are
  terminated.

• There is an option to fail certain events on the subscriber to be retried later.
  This is useful for example if you are replicating VIEW DDL but do not want that
  to block replication on failure.

• In some edge cases, alerting can be built around provided logging for the DBA
  to then handle possible manual deployments

• ALTER TABLE statements can be filtered by subcommand tags. For example, if you are using
  selective replication and want to ignore things like DISABLE TRIGGER which may not exist
  on the subscriber, this is useful to add robustness to DDL replication.

• Optional support for automatically killing blocking processes on the subscriber system that
  is preventing DDL execution.

A Full Example

Since we always look for documentation by example, we show this first. Assuming
logical replication is already setup with an active subscription, and given these publications/replication sets:

• default - replicate every event
• insert_update - replicate only inserts and updates

Provider:

Subscribers (run on both default and insert_update subscribers):

Here is a way to fix the subscriber table owner based on the tables already in
replication on provider for the same publication/replication set and ddl configuration you
just setup on provider (shell command - pglogical example):

Provider:

Subscriber - only required if using native in order
to add the pgl_ddl_deploy.queue table to replication.

Provider:

NOTE - after creating a table using schema-regex-based DDL replication with
native logical replication, it is necessary to manually execute on the subscriber
the command SELECT pgl_ddl_deploy.retry_all_subscriber_logs(); to complete the process
of adding the new table to replication. This is because
of the bug https://www.postgresql.org/message-id/CAMa1XUh7ZVnBzORqjJKYOv4_pDSDUCvELRbkF0VtW7pvDW9rZw@mail.gmail.com
preventing the safety of running ALTER SUBSCRIPTION ... REFRESH PUBLICATION in a replication
process. This will be updated as soon as a patch becomes available.

Subscriber to default:

Note that both tables are replicated based on configuration, as are all events
(inserts, updates, and deletes).

Subscriber to insert_update:

Note that the foo table (in public schema) was not replicated.
Also, because we are not replicating deletes here, happy.foo still has all
data.

Installation

The functionality of this requires postgres version 9.5+ and a working install
of pglogical.

DEB available on official PGDG repository as postgresql-${PGSQL_VERSION}-pgl-ddl-deploy
see installation instruction on https://wiki.postgresql.org/wiki/Apt

See the notes below on requirements to run the regression suite.

This extension requires pglogical to be installed before you can create the
extension in any database. Then the extension can be deployed as any postgres
extension:

This extension needs to be installed on provider and all subscribers.
As of version 1.5.0, you must have the same pgl_ddl_deploy version on both
the provider and subscriber (NOT necessarily the same Postgres version).

To update to pgl_ddl_deploy 1.5 from a previous version, install the latest version
packages on your server(s), then run in the database(s):

Setup and Deployment

Configuration

For native logical replication, DDL replication is configured on a per-publication basis.
For pglogical, DDL replication is configured on a per-replication set basis.

There are three basic types of configuration:

• include_only_repset_tables - Only tables already in a publication/replication set
  are maintained. This means only ALTER TABLE or COMMENT statements are replicated.
• include_schema_regex - Provide a regular expression to match both current
  and future schemas to be automatically added to replication. This supports all event
  types except for ones like GRANT which do not provide access to information about
  which schema an object exists in.
• include_everything - Propagate all DDL events regardless of schema. This is for cases
  like GRANT which do not provide access to information about which schema an object exists in

The above 3 options are mutually exclusive. You can, however, use an additional
option ddl_only_replication either with include_schema_regex or include_everything. This
only means tables are not automatically added to replication. Its use is if you want to keep the
schema of two systems in sync, but not necessarily replicate data for all tables.

Add rows to pgl_ddl_deploy.set_configs in order to configure (but not yet
deploy) DDL replication for a particular publication/replication set. Note especially that
driver controls which replication technology this is for, native or pglogical. For example:

The relevant settings:

• driver: native or pglogical (type pgl_ddl_deploy.driver) - DEFAULT pglogical
• set_name: publication name OR pglogical replication_set name
• include_schema_regex: a regular expression for which schemas to include in
  DDL replication. This can be used to auto-add new tables to replication. This
  option is incompatible with include_only_repset_tables.
• lock_safe_deployment: if true, DDL will execute in a low lock_timeout loop
  on subscriber
• allow_multi_statements: if true, multiple SQL statements sent by client can
  be propagated under certain conditions. See below for more details on caveats
  and edge cases. If false, only a single SQL statement (technically speaking - a
  SQL statement with a single node parsetree) will be eligible for propagation.
• include_only_repset_tables: if true, only tables that are in replication will
  be maintained by DDL replication. Thus only ALTER TABLE
  statements are permitted here. This option is incompatible with
  include_schema_regex.
• queue_subscriber_failures: if true, DDL will be allowed to fail on subscriber
  without breaking replication, and queued for retry using function
  pgl_ddl_deploy.retry_all_subscriber_logs(). This is useful for example if you
  are replicating VIEW DDL but do not want failures to block data replication. It
  is NOT recommendeded that you use this with any TABLE replication, since those
  events are likely to break data replication.
• create_tags: the set of command tags for which the create event triggers will
  fire. Change with caution. These are defaulted to the appropriate default set
  for either include_schema_regex or include_only_repset_tables.
• drop_tags: the set of command tags for which the drop event triggers will fire.
  Change with caution. These are defaulted to the appropriate default set
  for either include_schema_regex or include_only_repset_tables.
• blacklisted_tags: These are command tags that are never permitted to be propagated to
  subscribers. It is configurable, but the default is pgl_ddl_deploy.blacklisted_tags()
• exclude_alter_table_subcommands: if you want to exclude certain ALTER TABLE subcommand tags,
  here is the place to do it. The standard list can be found as the function
  pgl_ddl_deploy.common_exclude_alter_table_subcommands(). You can also simply choose
  only select tags from this list to exclude.
• ddl_only_replication: for use with include_schema_regex only. Allows you to
  only replicate the schema without auto-adding tables to replication. This is useful
  in particular if you want to keep the structure of two systems fully synchronized, but
  you don't necessarily want to replicate data for all tables.
• include_everything: Propagate all DDL events regardless of schema. This is for cases
  like GRANT which do not provide access to information about which schema an object exists in.
• signal_blocking_subscriber_sessions: Kill processes on the subscriber holding any kind of
  lock on the target table which would prevent DDL execution. cancel will use pg_cancel_backend,
  terminate will use pg_terminate_backend. cancel_then_terminate will try to cancel and if not
  successful, will go to terminate. NULL disables this feature. Killed sessions
  will be logged to the subscriber table pgl_ddl_deploy.killed_blockers, which has a field
  reported and reported_at which are designed for monitoring where you can notify users
  of killed queries, and then mark those queries as reported to users.
  NOTE - currently, we do not support figuring out dependent locks with native partitioning.
  You may miss killing blocking processes when native partitioning is involved.
• subscriber_lock_timeout: Only for use with signal_blocking_subscriber_sessions. This is an
  optional parameter for lock_timeout for DDL execution on subscriber in milliseconds before killing
  blockers. Default 3000 (3 seconds).

There is already a pattern of schemas excluded always that you need not worry
about. You can view them in this function:

You can use this query to check what your current configs will pull in
for schemas if you are using include_schema_regex:

You can use this query to test a new regex for what existing schemas it
matches:

There are no stored procedures to insert/update set_configs, which we don't
think would add much value at this point. There are check constraints and triggers in place
to ensure the regex is valid and the other conditions of uniqueness are met for config options.

Permissions

It is important to consider which role will be allowed to run DDL in a given
provider. As it stands, this role will need to exist on the subscriber as well,
because this same role will be used to try to deploy on the subscriber.
pgl_ddl_deploy provides a function to provide permissions needed for a given
role to use DDL deployment. This needs to run provider and all subscribers:

Note that in upgrading to version 1.5, we are automatically re-applying the permissions
you have already granted using add_role to the new tables in this version.

Deployment of Automatic DDL Replication

To deploy (meaning activate) DDL replication for a given replication set,
run:

• From this point on, the event triggers are live and will fire on the following
  events (by default, unless you have customized create_tags or drop_tags):

  command_tag ----------------- ALTER FUNCTION ALTER SEQUENCE ALTER TABLE ALTER TYPE ALTER VIEW CREATE FUNCTION CREATE SCHEMA CREATE SEQUENCE CREATE TABLE CREATE TABLE AS CREATE TYPE CREATE VIEW DROP FUNCTION DROP SCHEMA DROP SEQUENCE DROP TABLE DROP TYPE DROP VIEW SELECT INTO

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

  command_tag ----------------- ALTER FUNCTION ALTER SEQUENCE ALTER TABLE ALTER TYPE ALTER VIEW CREATE FUNCTION CREATE SCHEMA CREATE SEQUENCE CREATE TABLE CREATE TABLE AS CREATE TYPE CREATE VIEW DROP FUNCTION DROP SCHEMA DROP SEQUENCE DROP TABLE DROP TYPE DROP VIEW SELECT INTO

• Not all of these events are handled in the same way - see Limitations and
  Restrictions below

• Note that if, based on your configuration, you have tables that should be
  added to replication already, but are not, you will not be allowed to deploy.
  This is because DDL replication should only be expected to automatically add
  new tables to replication. To override this, add the tables to replication
  manually and sync as necessary.

DDL replication can be disabled/enabled (this will disable/enable event
triggers):

You can also undeploy DDL replication, which means dropping all event triggers
and functions for a given config:

If you want to change the configuration in set_configs, you can re-deploy
by again running pgl_ddl_deploy.deploy on the given set_name. There is
currently no enforcement/warning if you have changed configuration but not
deployed, but should be easy to add such a feature.

Note that you are able to override the event triggers completely, for example,
if you are an administrator who wants to run DDL and you know you don't want
that propagated to subscribers. You can do this with SESSION_REPLICATION_ROLE,
i.e.:

Monitoring and Administration

This framework will log all DDL changes that attempt to be propagated. It is
also generous in allowing DDL replication procedures to fail in order not to
prevent application deployments of DDL. An exception will never be allowed to
block a DDL statement. The most that will happen is log_level WARNING will be
raised. This feature is based on the assumption that we do not want replication
issues to ever block client-facing application functionality.

Several tables are setup to manage DDL replication and log exceptions, in
addition to server log warnings raised at WARNING level in case of issues:

• events - Logs replicated DDL events on the provider
• subscriber_logs - Logs replicated DDL events executed on subscribers
• commands - Logs detailed output from pg_event_trigger_ddl_commands() and
  pg_event_trigger_dropped_objects()
• unhandled - Any DDL that is captured but cannot be handled by this framework
  (see details below) is logged here.
• exceptions - Any unexpected exception raise by the event trigger functions are logged here

There are resolved fields on the unhandled and exceptions tables that can
be marked so that monitoring will show only new problems based on this table by
using functions:

• pgl_ddl_deploy.resolve_unhandled(unhandled_id INT, notes TEXT = NULL)
• pgl_ddl_deploy.resolve_exception(exception_id INT, notes TEXT = NULL)

Limitations and Restrictions

DDL involving multiple tables

A single DDL SQL statement which alters tables both replicated and
non-replicated cannot be supported. For example, if I have
include_schema_regex which includes only the regex '^replicated.*', this is
unsupported:

Likewise, the following can be problematic if you are using filtered replication:

Depending on your environment, such cases may be very rare, or possibly common.
For example, such edge cases are far less likely when you want to do 1:1
replication of just about all tables in your application database. Also, if you
are not likely to have relationships between schemas you both are and are not
replicating, then edge cases will be unlikely. As mentioned above, this
framework will work best with these two use cases:

• When you want to replicate all user tables
• When you want to replicate only a subset of tables in a schema that will not
  have any foreign key dependencies to other schemas

Some of these edge cases can be minimized with the exclude_alter_table_subcommands
option, which was developed precisely because for us, the most common failures were
things like DISABLE TRIGGER for a trigger that does not exist on the subscriber.

In this case, the DDL statement could fail on the subscriber. To resolve this,
see Resolving Failed DDL on Subscribers.

Unsupported Commands

CREATE TABLE AS and SELECT INTO are not supported to replicate DDL due to
limitations on transactional consistency. That is, if a table is created from a
set of data on the provider, to run the same SQL on the subscriber will in no
way guarantee consistent data. For example:

Not only is it possible that table_1 doesn't even exist on the subscriber,
even if it does it may not be fully up to date with the provider, in which case
the data created in the table on the subscriber would not match. Worse, is that
the now() function is basically guaranteed to be different on the subscriber.

It is recommended instead that the DDL statement creating the table and
the DML inserting into the table are separated. Continuing the above
example:

The above is completely supported by this framework, bearing in mind
some of the edge cases with multi-statements. The
CREATE TABLE will automatically be replicated by this framework, and
the table will be added to replication since it has a primary key. Then
the INSERT will be replicated by normal logical replication.

NOTE that temp tables are not affected by this limitation, since temp objects are
always excluded from DDL replication anyway.

To resolve these, see Resolving Unhandled DDL.

Multi-Statement Client SQL Limitations

It is important to understand that limitations on multi-statement client SQL has
nothing to do with executing multiple SQL statements at once, or in one
transaction. Of course, it is assumed that will often or even usually be the
case, and this framework can handle that just fine.

The complexities and limitations come when the client sends all SQL statements
as one single string to Postgres. Assume the following SQL statements:

If this was in a file that I called via psql, it would run as two separate SQL
command strings.

However, if in python or ruby's ActiveRecord I create a single string as above
and execute it, then it would be sent to Postgres as 1 single SQL command
string. In such a case, this framework is aware that a multi-statement is
being executed by using Postgres' parser to get the command tags of the full
SQL statement. You have a little freedom here with the
allow_multi_statements option:

• If false, pgl_ddl_deploy will only auto-replicate a client SQL statement
  that contains 1 command tag that matches the event trigger command tag.
  That's really safe, but it means you may have a lot more unhandled deployments.
• If true, pgl_ddl_deploy will only auto-replicate DDL that contains
  safe command tags to propagate. For example, mixed DDL and DML is
  forbidden, because executing such a statement would in effect double-execute
  DML on both provider and subscriber. However, if you have a CREATE TABLE and
  ALTER TABLE in one command, assuming the table should be included in
  replication based on your configuration, then such a statement will be sent to
  subscribers. But of course, we can't guarantee that all DDL statements in this
  command are on the same table - so there could be edge cases.

In any case that a SQL statement cannot be automatically run on the subscriber
based on these analyses, instead it will be logged as a WARNING and put into
the unhandled table for manual processing.

To resolve these, see Resolving Unhandled DDL.

The regression suite in the sql folder has examples of several of these cases.

Thus, limitations on multi-statement SQL is largely based on how your client
sends its messages in SQL to Postgres, and likewise how your developers tend to
write SQL. The good thing about this is that it ought to be much easier to
train developers to logically separate SQL in their migrations, as opposed to
far more work we need to do when we don't have any kind of transparent DDL
replication.

These limitations obviously have to be weighed against the cost of not using a
framework like this at all in your environment.

The unhandled table and WARNING logs are designed to be leveraged with
monitoring to create alerting around when manual intervention is required for
DDL changes.

Native Logical Supported Configurations

The only known configuration limitation for native logical DDL replication is that
only a publication that includes replicating inserts can support DDL replication. This relates
to how the DDL queueing mechanism works. The default CREATE PUBLICATION in postgres
includes publishing inserts. It can also be configured specifically with WITH (publish = 'insert').
For more details see https://www.postgresql.org/docs/current/sql-createpublication.html.

Resolving DDL Replication Issues

Resolving Failed DDL on Subscribers

In some cases, you may propagate DDL that fails on the subscriber, and
replication will break, unless you have enabled queue_subscriber_failures.
You will then need to:

• Manually deploy with the same SQL statement modified so that it excludes the
  failing portion. For the example above of both adding a column and adding a
  foreign key, assuming we don't have that unreplicated table, then you would
  run:
  
  ALTER TABLE replicated.foo ADD COLUMN foo_id INT;

  1	ALTER TABLE replicated.foo ADD COLUMN foo_id INT;

• Consume the change in affected replication slot using
  pg_logical_slot_get_changes up to specific LSN of the transaction which
  included the DDL statement to get replication working again.
• You could also "trick" the DDL into applying - for example, by creating a dummy
  object that will allow the DDL to apply even if you don't need it, then discarding
  those objects once replication is working again.
• Re-enable replication for affected subscriber(s)

If you are using queue_subscriber_failures, any DDL failures for your given
configuration will be logged as failed in pgl_ddl_deploy.subscriber_logs.
You can resolve issues, and attempt to re-deploy by using functions:

When you retry subscriber logs, new rows are created for the retries, thus the rows
with succeeded = f still remain.

Queries like this are helpful:

Then to check retry:

Resolving Unhandled DDL

At present, an unhandled DDL deployment may not break replication by itself.
If the DDL statement that could not be deployed doesn't actually affect any data
being replicated (for example, a brand new table is added), then replication
will continue. However, the situation should be resolved ASAP because it is
assumed that whatever table(s) that were involved in the DDL should be
propagated to subscribers. It is also possible that replication will break
immediately. For example, if a column is added to the table, and data is
replicating, it will fail immediately because of a column mismatch. In such
cases you will need to:

• Manually deploy the unhandled SQL statement modified so that it excludes the
  unhandled portion. For the example of a multi-statement SQL above, you would
  need to exclude the INSERT portion of the SQL and run:
  
  CREATE TABLE foo (id serial primary key, bla text);

  1	CREATE TABLE foo (id serial primary key, bla text);

• If a new table is involved, in this case you also will need to manually add
  the table to replication using ALTER PUBLICATION ADD TABLE
  or pglogical.replication_set_add_table depending on driver.
• If a new table is involved, you may need to resynchronize the table if data
  has been replicating for it
• If a new table is NOT involved (for example ALTER TABLE ADD COLUMN), then
  replication will simply continue where it broke
• Re-enable replication for affected subscriber(s)
• Mark your unhandled records as resolved using function:
  
  SELECT pgl_ddl_deploy.resolve_unhandled(unhandled_id INT, notes TEXT = NULL);

  1	SELECT pgl_ddl_deploy.resolve_unhandled(unhandled_id INT, notes TEXT = NULL);

To be more conservative, we may want a feature that forces replication to break
if there is an unhandled deployment, for example, by sending an exception
through replicate_ddl_command. But such a feature may cause additional and
unnecessary administration overhead, since it is likely the strictness of
replication will cause the system to break when it should.

Disable DDL Replication on Subscriber

The following applies to native logical replication only.

As a last resort, you might find some unexpected problem where you want to disable DDL
replication on the subscriber because you are in an error loop. This should be done
very cautiously and as a last resort.

Disable the trigger on the DDL queue table to ignore all DDL replication on subscriber:

Do what you need to do for manual fixes. If you suspect a bug, please report it. When
you are finished, you MUST ENABLE THE TRIGGER IN REPLICATION MODE ONLY:

For Developers

Help Wanted Features

We are currently using the parser only to get the list of command tags. One
big advantage to this is that it isn't going to be difficult to maintain as
Postgres develops. One disadvantage is that it tells us nothing about the
actual objects being modified, nor the type of modification.

We believe it is feasible to use the parser to actually only process the parts
of a multi-statement SQL command that we want to, but this far more ambitious,
and would be more work to maintain. We would need to leverage the different
structures of each DDL command's parsetree to determine if the table is in a
schema we care to replicate. Then, we would need to use the parser's lex code
to take out the piece(s) we want.

Theoretically speaking, don't we have all that we need in the SQL statement +
the parser to programatically use only what we want and send it to subscribers?
I think we do, but lots of work would be required, and we would welcome those
with more comfort in the parser code to help if interested.

SQL files

To build the higher version SQL files (i.e. 2.0) from the lower versions +
the upgrade patch SQL files, run pgl_ddl_deploy-sql-maker.sh.

Note the script correct_2.0_for_no_pglogical.py. This was used to edit the
long .sql files to safely remove the hard dependency on pglogical. This should
not be needed long-term (i.e. once moving to versions above 2.0).

Regression testing

The building of the regression suite has changed since adding native logical
support. There are several scripts involved to manage this. Basically, I am
duplicating the entire original test suite and making in-place changes in order
to create a separate test flow for native logical replication, but also supporting
the other pg versions prior to pg10. Here is a brief explanation:

1. The script duplicate_tests_for_native.py copies all of the tests, adding
   conditionals where needed. It's not pretty but was the most efficient way
   of managing the test suite with these 2 test flows. It is not perfect, but
   once it has been run, the test failures can be seen to be only white space
   issues that then can be easily resolved by re-copying the results file to
   expected. It is desirable to have as few changes as possible between the
   two test suites.
2. The script generate_new_native_tests.py is to generate numerically a brand
   new test only applicable to native logical replication.

You can run the regression suite, which must be on a server that has pglogical
packages available, and a cluster that is configured to allow creating the
pglogical extension (i.e. adding it to shared_preload_libraries). Note that
the regression suite does not do any cross-server replication testing, but it
does cover a wide variety of replication cases and the core of what is needed
to verify DDL replication.

As with any Postgres extension:

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