PostgreSQL's developments for high volumes processing


On the last few years, PostgreSQL received several enhancements to process high-volume databases.

This first post will try to list them. We will see that they can be splitted in different kinds:

  • Parallelism
  • Improvement of query processing
  • Partitioning
  • Access methods
  • Maintenance tasks
  • SQL features

In order to maintain clarity, the explanation of each feature will remain brief.

Note: this article was written during the development phase of version 11. I integrated new features of version 11. As long as it is not released, these features might be removed.

I thank Guillaume Lelarge for his review of this article ;).

Table of Contents

SQL features


TABLESAMPLE clause (since PG 9.5) allows to execute a query on a data sample. This gives an overview of the result. As in most things about statistics, the larger the sample, closer the result will be to the real result.


Still with version 9.5, PostgreSQL has new clauses allowing to do multiple aggregations called GROUPING SETS. New aggregates are: GROUPING SETS, ROLLUP, CUBE.

See this article by Depesz: Waiting for 9.5 – Support GROUPING SETS, CUBE and ROLLUP

Note that version 10 gives very significant gains, thanks to improvement in hash functions.

Foreign table inheritance (9.5)

Since version 9.5, you can use foreign tables as child tables. It is thus possible to distribute data on different servers and to access all of them from a single instance. This is similar to sharding.

See this article written by Michael Paquier: Postgres 9.5 feature highlight - Scale-out with Foreign Tables now part of Inheritance Trees


PostgreSQL being multi-processes, a query was executed on a single core. There are many posts in which users complain about this:

Since 9.6, Postgres forks multiple processes (called workers) to execute a single query. This major breakthrough was the culmination of several years of work. It took a lot of infrastructure to allow Postgres to use multiple processes. See this article from Robert Haas: Parallelism Progress

Sequential Scan (9.6)

Since 9.6, PostgreSQL is able to use multiple processes to parallelize read operation on tables. The related query node is Parallel Seq Scan.

Index Scan (10)

Version 10 has made it possible to extend the parallelization to index scans.

So Postgres is now able to use several processes for these types of scan (only with BTree):

  • Index Scan
  • Bitmap heap scan
  • Index Only Scan

Joins (9.6, 10, 11)

In addition to the parallelization of the sequential scan, Postgres 9.6 also has the capacity to parallelize join operations for the following nodes:

  • Nested-loop
  • Hash join

Postgres 10 extends the parallelization to the merge join node.

Finally, version 11 brings a big change with the parallel hash join. With previous versions, every worker had to build its own hash table. There was a big loss of efficiency:

  • Several workers were doing the same operation
  • The same hash table existed several times in memory

The parallel hash join allows workers to parallelize the creation of this hash table and share a single hash table.

The main author of this feature wrote a great article: Parallel Hash for PostgreSQL

Aggregation (9.6)

Still with version 9.6, Postgres can use multiple workers to perform aggregation operations (COUNT, SUM…).

Actually, each worker makes a partial aggregation (Partial Aggregate), then, a parent node is responsible for finalizing the operation (Finalize Aggregate).

Union of sets (11)

Version 11 provides the ability to parallelize unions (node append), for example when using UNION or when tables are inherited.

Access methods

Indexes and access methods are often confused by regularly using the term “index”.

In fact, in the broad sense, in the domain of databases, an index is an access method.

BRIN Indexes (9.5)

Since version 9.5, PostgreSQL offers a particular kind of index: BRIN for Block Range INdexes.

This type of index contains the summary of a set of blocks. They are very compact and can easily fit in RAM.

They are particularly suitable to high volumes with queries manipulating a large amount of data. Be careful, it is very important that there is a strong correlation between the data and their location.

I gave a talk on this kind of indexes during the PGDay France 2016 in Lille: BRIN indexes - How they works and usages?

BLOOM filters (9.6)

Since version 9.6, it is possible to use Bloom filters. Without going into details, this type of data structure used to say with certainty that the information sought is not in a set. Conversely, information may (with some probability) be in another set.

The advantage of bloom filters is that they are very compact and allow to respond to multi-column searches from a single index.

See this article by Depesz: Waiting for 9.6 – Bloom index contrib module


Partitioning existed as a table inheritance. However this approach has the disadvantage of requiring to set up triggers to route writes in the right tables. The performance impact was important.

Version 10 includes native partitioning management. So, it is no longer necessary to set up triggers. Maintenance operations are facilitated and performance is improved.

Forms of partitioning (10, 11)

PostgreSQL supports partition by:

  • List - LIST (10)
  • Interval - RANGE (10)
  • Hash - HASH (11)

See theses articles by Depesz:

Indexes (11)

Version 11 also provides easier index management on partition tables. Index creation on a partitioned table will also be created on all partitions. Similarly, it is possible to have a unique index (only if they include partition key). It is also possible to have a foreign key on a partitioned table.

Partition exclusion (11)

partition pruning consists of excluding unnecessary partitions. Postgres relies on exclusion constraints to exclude partitions from planning.

The algorithm was not designed to handle a large number of partitions. His complexity is linear depending on the number of partitions (How many table partitions is too many in Postgres?)

To fix this, version 11 includes a new, more efficient search algorithm: Faster Partition Pruning

Finally, Postgres could exclude partitions only during the planning. With version 11, the engine is able to exclude a partition during execution. This feature is called Runtime Partition Pruning.

Joins and aggregates (11)

Version 11 introduces new join and aggregation algorithms. The idea is to perform join operations and aggregates partition by partition during a join between two partitioned tables (See Partition and conquer large data with PostgreSQL 10).

Internal improvements

Hashing functions (10)

Hash functions have been improved in version 10. Thus, aggregate (GROUP BY, GROUPING SETS, CUBE …) as well as the nodes type bitmap scans benefit from these improvements. The execution time of some queries has been halved!

Executor improvements (10)

The executor has been improved in version 10, it is now more efficient with expressions processing. This thread mentions very significant gains: Faster Expression Processing

Sorts improvements

Abbreviated keys (9.5)

Sorting algorithm has been reworked with version 9.5, which makes better use of processor’s cache. Gains were reported between 40 and 70% (see Use abbreviated keys for faster sorting of text datums).

External sorts improvements (10)

Version 10 also brings very significant gains when sorts are made on disk. Some queries saw their execution time halved.

Just-In-time (11)

Version 11 includes an infrastructure for Just-In-Time (JIT). JIT compiles the query to generate bytecode which will be executed.

Once again, gains sound impressive as showed by these slides: JITing PostgreSQL using LLVM

Maintenance tasks


Before version 9.6, a VACUUM FREEZE reads the whole table, even if tuples had already been frozen. Version 9.6 adds additional information to the visibility map to see if a block has already been frozen. This information allows Postgres to skip blocks already frozen.

Reduce index scan during VACUUM (11)

During a simple vacuum (where postgres will clean the dead tuples), Postgres was able to skip blocks where it knew there was no line to deal with. However, it still had to scan the index, which can be expensive with a large table. Version 11 makes it possible to avoid unnecessary index scan.

A new parameter appeared: vacuum_cleanup_index_scale_factor.

Postgres can avoid index scan if two conditions are met:

  • No block should be deleted
  • Statistics are up to date

Postgres considers that the statistics are stalled if more than: [number of inserted lines] > vacuum_cleanup_index_scale_factor * [number of rows in the table]

Parallel create index (11)

When creating an index, Postgres can use multiple processes to perform sort operation. Depending on the number of processes, the creation time of the index can be divided by 2 to 4 approximately.

Foreign Data Wrapper pushdown (9.6, 10)

A reminder, Foreign Data Wrapper provides access to external data. This is the Postgres implementation of the SQL / MED standard for “Management of external Data”.

A FDW allows access to any type of external data when a FDW exists (see Foreign data wrappers).

The community maintains a FDW to connect to a Postgres instance: postgres_fdw.

But that’s not all, some operations can be “pushed” to the remote server. This is called pushdown

Prior to Version 9.6, a sort or join resulted in the download of all the data and the server performed sort and/or join locally.

Version 9.6 includes sort and join pushdown. Thus, sort or join operation can be performed by the remote server. On the one hand, there is less load on the local server running the query, on the other hand, the remote server will be able to use appropriate join algorithm or an index for sorting the data.

Finally, version 10 also includes the execution of FULL JOIN aggregations and joins on the remote server.


Feature freeze of version 11 ended on April 7: PostgreSQL 11 Release Management Team & Feature Freeze

This means that some features have not been implemented, some of which are considered not mature enough to be integrated. Others are still in the state of discussion or demonstration.

Note that some features might be removed after the feature freeze if the developers consider that they are not stable enough or that their implementation must be changed.

Luckily, the developers of the various companies that contribute to the development of Postgres, communicate about their roadmap. This gives an overview of the trends of the future features.

Extension of the storage system

The community works to make modular storage system (pluggable storage). Thus, Postgres could have different storage engines. In the work in progress, we can list:

  • Columnar storage (associated with vectorization)
  • Table compression
  • In-memory storage
  • zheap : this engine would update records directly in the heap, without duplicating tuples in the tables according to MVCC. And so, get rid of fragmentation and vacuum.

Extension of JIT

The author of the JIT plans to extend it to the rest of Postgres (aggregation, hashing, sorts …).

In the mentioned tracks, there would also be caching and sharing the bytecode between sessions. This would make it possible to use the JIT even in the case of OLTP traffic.


The idea behind vectorization would be to process data in batch to use the processors’s SIMD instructions.

Coupled with a column storage, gains can be impressive:

FDW and asynchronous execution

Sharding comes back regularly. In a way, the use of FDW partly meets this need. However, PostgreSQL still has some progress to make in this area. For example, if it performs an aggregate query on multiple remote tables, he must request each remote server sequentially. An improvement track would be to query all remote servers asynchronously. Thus, the operation would be parallelized on all remote servers. See this presentation: FDW-based Sharding Update and Future.


PostgreSQL rocks!

Guillaume Lelarge

This article has allowed us to see different developments of PostgreSQL in the world of big databases. Of course, there is still work to be done, but what has already been done is important and allows us to manage impressive databases.

Adrien Nayrat
Adrien Nayrat
PostgreSQL DBA Freelance

Postgres and opensource passionate