Partitioning and postgres_fdw optimisations for multi-tenancy - Mailing list pgsql-hackers

Hi Hackers,

The idea of achieving Postgres scaling via sharding using postgres_fdw + 
partitioning got a lot of attention last years. Many optimisations have 
been done in this direction: partition pruning, partition-wise 
aggregates / joins, postgres_fdw push-down of LIMIT, GROUP BY, etc. In 
many cases they work really nice.

However, still there is a vast case, where postgres_fdw + native 
partitioning doesn't perform so good — Multi-tenant architecture. From 
the database perspective it is presented well in this Citus tutorial 
[1]. The main idea is that there is a number of tables and all of them 
are sharded / partitioned by the same key, e.g. company_id. That way, if 
every company mostly works within its own data, then every query may be 
effectively executed on a single node without a need for an internode 
communication.

I built a simple two node multi-tenant schema for tests, which can be 
easily set up with attached scripts. It creates three tables (companies, 
users, documents) distributed over two nodes. Everything can be found in 
this Gist [2] as well.

Some real-life test queries show, that all single-node queries aren't 
pushed-down to the required node. For example:

SELECT
     *
FROM
     documents
     INNER JOIN users ON documents.user_id = users.id
WHERE
     documents.company_id = 5
     AND users.company_id = 5;

executed as following

                       QUERY PLAN
-------------------------------------------------------
  Nested Loop
    Join Filter: (documents.user_id = users.id)
    ->  Foreign Scan on users_node2 users
    ->  Materialize
          ->  Foreign Scan on documents_node2 documents

i.e. it uses two foreign scans and does the final join locally. However, 
once I specify target partitions explicitly, then the entire query is 
pushed down to the foreign node:

                        QUERY PLAN
---------------------------------------------------------
  Foreign Scan
    Relations: (documents_node2) INNER JOIN (users_node2)

Execution time is dropped significantly as well — by more than 3 times 
even for this small test database. Situation for simple queries with 
aggregates or joins and aggregates followed by the sharding key filter 
is the same. Something similar was briefly discussed in this thread [3].

IIUC, it means that push-down of queries through the postgres_fdw works 
perfectly well, the problem is with partition-wise operation detection 
at the planning time. Currently, partition-wise aggregate routines, 
e.g., looks for a GROUP BY and checks whether sharding key exists there 
or not. After that PARTITIONWISE_AGGREGATE_* flag is set. However, it 
doesn't look for a content of WHERE clause, so frankly speaking it isn't 
a problem, this functionality is not yet implemented.

Actually, sometimes I was able to push down queries with aggregate 
simply by adding an additional GROUP BY with sharding key, like this:

SELECT
     count(*)
FROM
     documents
WHERE
     company_id = 5
GROUP BY company_id;

where this GROUP BY obviously doesn't change a results, it just allows 
planner to choose from more possible paths.

Also, I have tried to hack it a bit and forcedly set 
PARTITIONWISE_AGGREGATE_FULL for this particular query. Everything 
executed fine and returned result was correct, which means that all 
underlying machinery is ready.

That way, I propose a change to the planner, which will check whether 
partitioning key exist in the WHERE clause and will set 
PARTITIONWISE_AGGREGATE_* flags if appropriate. The whole logic may look 
like:

1. If the only one condition by partitioning key is used (like above), 
then it is PARTITIONWISE_AGGREGATE_FULL.
2. If several conditions are used, then it should be 
PARTITIONWISE_AGGREGATE_PARTIAL.

I'm aware that WHERE clause may be extremely complex in general, but we 
could narrow this possible optimisation to the same restrictions as 
postgres_fdw push-down "only WHERE clauses using built-in operators and 
functions will be considered for execution on the remote server".

Although it seems that it will be easier to start with aggregates, 
probably we should initially plan a more general solution? For example, 
check that all involved tables are filtered by partitioning key and push 
down the entire query if all of them target the same foreign server.

Any thoughts?


[1] 
https://docs.citusdata.com/en/v9.3/get_started/tutorial_multi_tenant.html
[2] https://gist.github.com/ololobus/8fba33241f68be2e3765d27bf04882a3
[3] 
https://www.postgresql.org/message-id/flat/CAFT%2BaqL1Tt0qfYqjHH%2BshwPoW8qdFjpJ8vBR5ABoXJDUcHyN1w%40mail.gmail.com

Regards
-- 
Alexey Kondratov

Postgres Professional https://www.postgrespro.com
Russian Postgres Company