Parallelism, what next?

This blog post is about the journey of parallelism in PostgreSQL till now and what is in store for the future.  Since PostgreSQL 9.6 where the first feature of parallel query has arrived, each release improves it.  Below is a brief overview of the parallel query features added in each release.

PG9.6 has added Parallel execution of sequential scans, joins, and aggregates.

PG10 has added (a) Support parallel B-tree index scans, (b) Support parallel bitmap heap scans, (c) Allow merge joins to be performed in parallel, (d) Allow non-correlated subqueries to be run in parallel, (e) Improve ability of parallel workers to return pre-sorted data and (f) Increase parallel query usage in procedural language functions.

PG11 has added (a) Allow parallel building of a btree index, (b) Allow hash joins to be performed in parallel using a shared hash table, (c) Allow parallelization of commands CREATE TABLE ... AS, SELECT INTO, and CREATE MATERIALIZED VIEW, (d) Allow UNION to run each SELECT in parallel if the individual SELECTs cannot be parallelized, (e) Allow partition scans to more efficiently use parallel workers, (f) Allow LIMIT to be passed to parallel workers, this allows workers to reduce returned results and use targeted index scans, (g) Allow single-evaluation queries, e.g. WHERE clause aggregate queries, and functions in the target list to be parallelized.

PG12 has added Allow parallelized queries when in SERIALIZABLE isolation mode.

The progress for PG13 with respect to parallelism.  Some of the important advancements are:

(a) Parallel vacuum - This feature allows the vacuum to leverage multiple CPUs in order to process indexes.  This enables us to perform index vacuuming and index cleanup with background workers.  This adds a PARALLEL option to VACUUM command where the user can specify the number of workers that can be used to perform the command which is limited by the number of indexes on a table.  Specifying zero as a number of workers will disable parallelism.  For more information, see commit.

(b) Improve EXPLAIN's handling of per-worker details.  This allows displaying the worker information in a much better way.  The few visible side-effects as mentioned in the commit

* In text format, instead of something like

  Sort Method: external merge  Disk: 4920kB

  Worker 0:  Sort Method: external merge  Disk: 5880kB

  Worker 1:  Sort Method: external merge  Disk: 5920kB

  Buffers: shared hit=682 read=10188, temp read=1415 written=2101

  Worker 0:  actual time=130.058..130.324 rows=1324 loops=1

    Buffers: shared hit=337 read=3489, temp read=505 written=739

  Worker 1:  actual time=130.273..130.512 rows=1297 loops=1

    Buffers: shared hit=345 read=3507, temp read=505 written=744

you get

  Sort Method: external merge  Disk: 4920kB

  Buffers: shared hit=682 read=10188, temp read=1415 written=2101

  Worker 0:  actual time=130.058..130.324 rows=1324 loops=1

    Sort Method: external merge  Disk: 5880kB

    Buffers: shared hit=337 read=3489, temp read=505 written=739

  Worker 1:  actual time=130.273..130.512 rows=1297 loops=1

    Sort Method: external merge  Disk: 5920kB

    Buffers: shared hit=345 read=3507, temp read=505 written=744

(c) Avoid unnecessary shm writes in Parallel Hash Join.  This improves the performance of Parallel Hash Join by a significant amount on large systems running many-core joins.  Though this work has been back-patched to v11 where Parallel Hash Join was introduced, I mentioned it here as it is done during PG13 development.  For more information, see commit.

What is being discussed for the future:

(a) Parallel grouping sets - PostgreSQL already supports parallel aggregation by aggregating in two stages. First, each process participating in the parallel portion of the query performs an aggregation step, producing a partial result for each group of which that process is aware. Second, the partial results are transferred to the leader via the Gather node. Finally, the leader re-aggregates the results across all workers in order to produce the final result.

Next, there has been a discussion in the community to parallelize queries containing grouping sets in much the same way as we do parallel aggregation.

Basically, the aim is to parallelize queries like SELECT brand, size, sum(sales) FROM items_sold GROUP BY GROUPING SETS ((brand), (size), ());

This feature has been proposed for PG13, but yet not committed.

(b) Parallel copy - We also want to parallelize the Copy command, in particular "Copy <table_name> from .. ;" command.  This will help improve the bulk load operation in PostgreSQL.  Currently, we do a lot of work during the Copy command.  We read the file in 64KB chunks, then find the line endings and process that data line by line, where each line corresponds to one tuple.  We first form the tuple (in form of value/null array) from that line, check if it qualifies the where condition and if it qualifies, then perform constraint check and few other checks and then finally store it in local tuple array.  Once we reach 1000 tuples or consumed 64KB (whichever occurred first), we insert them together and then for each tuple insert into the index(es) and execute after row triggers.  The aim of this work is to parallelize as much as possible the work done during the copy.  There is an ongoing discussion in the community on this topic.

There is a small caveat here that to achieve parallel copy, we need to work on relation extension lock where parallel workers block each other while extending the relation which is not the case currently.  There is already a discussion on this topic in the community.

(c) Parallel file_fdw - The proposed work in this area allows file_fdw to divide its scan up for parallel workers, much like a parallel seq scan.

There are more areas where parallelism can be used like parallel DMLs (inserts, updates, deletes).  During a discussion with Thomas Munro, it came up that it would be beneficial if we can parallelize index creation and index scans for indexes other than btree especially gin and gist.  Note that we already support parallel index scans and parallel index creation for btree. I would not like to go in detail of these operations as till now we haven't seen any proposal for those.  Similarly, we can improve few things in our current parallel infrastructure (a) As of now, for each query the parallel workers are created and destroyed, instead we can have some pool of parallel query workers which can avoid the overhead of starting them for each query, (b) As of now, each worker can use up to work_mem of memory which might increase the overall memory usage of query.  We might want to improve this, but currently, there is no proposal for this.

Parallel Index Scans In PostgreSQL

There is a lot to say about parallelism in PostgreSQL. We have come a long way since I wrote my first post on this topic (Parallel Sequential Scans). Each of the past three releases (including PG-11, which is in its beta) have a parallel query as a major feature which in itself says how useful is this feature and the amount of work being done on this feature. You can read more about parallel query from the PostgreSQL docs or from a blog post on this topic by my colleague Robert Haas. The intent of this blog post is to talk about parallel index scans which were released in PostgreSQL 10. Currently, we have supported parallel scan for btree-indexes.

To demonstrate how the feature works, here is an example of TPC-H Q-6 at scale factor - 20 (which means approximately 20GB database). Q6 is a forecasting revenue change query. This query quantifies the amount of revenue increase that would have resulted from eliminating certain company-wide discounts in a given percentage range in a given year. Asking this type of "what if" query can be used to look for ways to increase revenues.

explain analyze

select sum(l_extendedprice * l_discount) as revenue
          from lineitem

          where l_shipdate >= date '1994-01-01' and

          l_shipdate < date '1994-01-01' + interval '1' year and

          l_discount between 0.02 - 0.01 and 0.02 + 0.01 and

          l_quantity < 24
          LIMIT 1;

Non-parallel version of plan
-------------------------------------
Limit
-> Aggregate
    -> Index Scan using idx_lineitem_shipdate on lineitem
         Index Cond: ((l_shipdate >= '1994-01-01'::date) AND (l_shipdate < '1995-01-01

         00:00:00'::timestamp without time zone) AND (l_discount >= 0.01) AND

         (l_discount <= 0.03)  AND  (l_quantity < '24'::numeric))

Planning Time: 0.406 ms
Execution Time: 35073.886 ms

Parallel version of plan
-------------------------------
Limit
-> Finalize Aggregate
    -> Gather

         Workers Planned: 2

         Workers Launched: 2
          -> Partial Aggregate
               -> Parallel Index Scan using idx_lineitem_shipdate on lineitem
                    Index Cond: ((l_shipdate >= '1994-01-01'::date) AND (l_shipdate < '1995-01-01 

                    00:00:00'::timestamp without time zone) AND (l_discount >= 0.01) AND

                    (l_discount <= 0.03) AND (l_quantity < '24'::numeric))

Planning Time: 0.420 ms
Execution Time: 15545.794 ms

We can see that the execution time is reduced by more than half for a parallel plan with two parallel workers. This query filters many rows and the work (CPU time) to perform that is divided among workers (and leader), leading to reduced time.

To further see the impact with a number of workers, we have used somewhat bigger dataset (scale_factor = 50). The setup has been done using TPC-H like benchmark for PostgreSQL. We have also created few additional indexes on columns (l_shipmode, l_shipdate, o_orderdate, o_comment)

Non-default parameter settings:
random_page_cost = seq_page_cost = 0.1
effective_cache_size = 10GB
shared_buffers = 8GB
work_mem = 1GB

The time is reduced almost linearly till 8 workers and then it reduced slowly. The further increase in workers won’t help unless the data to scan increases.

We have further evaluated the parallel index scan feature for all the queries in TPC-H benchmark and found that it is used in a number of queries and the impact is positive (reduced the execution time significantly). Below are results for TPC-H, scale factor - 20 with a number of parallel workers as 2. X-axis indicates (1: Q-6, 2: Q14, 3: Q18).

Under the Hood

The basic idea is quite similar to parallel heap scans where each worker (including leader whenever possible) will scan a block (all the tuples in a block) and then get the next block that is required to be scan. The parallelism is implemented at the leaf level of a btree. The first worker to start a btree scan will scan till it reaches the leaf and others will wait till the first worker has reached the leaf. Once, the first worker read the leaf block, it sets the next block to be read and wakes one of the workers waiting to scan blocks. Further, it proceeds scanning tuples from the block it has read. Henceforth, each worker after reading a block sets the next block to be read and wakes up the next waiting worker. This continues till no more pages are left to scan at which we end the parallel scan and notify all the workers.

A new guc min_parallel_index_scan_size has been introduced which indicates the minimum amount of index data that must be scanned in order for a parallel scan to be considered. Users can try changing the value of this parameter to see if the parallel index plan is effective for their queries. The number of parallel workers is decided based on the number of index pages to be scanned. The final cost of parallel plan considers the cost (CPU cost) to process the rows will be divided equally among workers.

In the end, I would like to thank the people (Rahila Syed and Robert Haas) who were involved in this work (along with me) and my employer EnterpriseDB who has supported this work. I would also like to thank Rafia Sabih who helped me in doing performance testing for this blog.

zheap: a storage engine to provide better control over bloat

In the past few years, PostgreSQL has advanced a lot in terms of features, performance, and scalability for many-core systems.  However, one of the problems that many enterprises still complain is that its size increases over time which is commonly referred to as bloat. PostgreSQL has a mechanism known as autovacuum wherein a dedicated process (or set of processes) tries to remove the dead rows from the relation in an attempt to reclaim the space, but it can’t completely reclaim the space in many cases.  In particular, it always creates a new version of a tuple on an update which must eventually be removed by periodic vacuuming or by HOT-pruning, but still in many cases space is never reclaimed completely.  A similar problem occurs for tuples that are deleted. This leads to bloat in the database.  My colleague Robert Haas has discussed some such cases in his blog DO or UNDO - there is no VACUUM where the PostgreSQL heap tends to bloat and has also mentioned the solution (zheap: a new storage format for PostgreSQL) on which EnterpriseDB is working to avoid the bloat whenever possible.  The intent of this blog post is to elaborate on that work in some more detail and show some results.

This project has three major objectives:

1. Provide better control over bloat.  zheap will prevent bloat (a) by allowing in-place updates in common cases and (b) by reusing space as soon as a transaction that has performed a delete or non-in-place update has committed.  In short, with this new storage, whenever possible, we’ll avoid creating bloat in the first place.

2. Reduce write amplification both by avoiding rewrites of heap pages and by making it possible to do an update that touches indexed columns without updating every index.

3. Reduce the tuple size by (a) shrinking the tuple header and (b) eliminating most alignment padding.

In this blog post, I will mainly focus on the first objective (Provide better control over bloat) and leave other things for future blog posts on this topic.

In-place updates will be supported except when (a) the new tuple is larger than the old tuple and the increase in size makes it impossible to fit the larger tuple onto the same page or (b) some column is modified which is covered by an index that has not been modified to support “delete-marking”.  Note that the work to support delete-marking in indexes is yet to start and we intend to support it at least for btree indexes. For in-place updates, we have to write the old tuple in the undo log and the new tuple in the zheap which help concurrent readers to read the old tuple from undo if the latest tuple is not yet visible to them.

Deletes write the complete tuple in the undo record even though we could get away with just writing the TID as we do for an insert operation. This allows us to reuse the space occupied by the deleted record as soon as the transaction that has performed the operation commits. Basically, if the delete is not yet visible to some concurrent transaction, it can read the tuple from undo and in heap, we can immediately (as soon as the transaction commits) reclaim the space occupied by the record.

Below are some of the graphs that compare the size of heap and zheap table when the table is constantly updated and there is a concurrent long-running transaction.  To perform these tests, we have used pgbench to initialize the data (at scale factor 1000) and then use the simple-update test (which comprises of one-update, one-select, one-insert) to perform updates.  You can refer to the PostgreSQL manual for more about how to use pgbench. These tests have been performed on a machine with an x86_64 architecture, 2-sockets, 14-cores per socket, 2-threads per-core and has 64-GB RAM.  The non-default configuration for the tests is shared_buffers=32GB, min_wal_size=15GB, max_wal_size=20GB, checkpoint_timeout=1200, maintenance_work_mem=1GB, checkpoint_completion_target=0.9, synchoronous_commit = off. The below graphs show the size of the table on which this test has performed updates.

In the above test, we can see that the initial size of the table was 13GB in heap and 11GB in zheap.  After running the test for 25 minutes (out of which there was an open transaction for first 15-minutes), the size in heap grows to 16GB at 8-client count test and to 20GB at 64-client count test whereas for zheap the size remains at 11GB for both the client-counts at the end of the test. The initial size of zheap is lesser because the tuple header size is smaller in zheap. Now, certainly for first 15 minutes, autovacuum can’t reclaim any space due to the open transaction, but it can’t reclaim it even after the open transaction is ended. On the other hand, the size of zheap remains constant and all the undo data generated is removed within seconds of the transaction ending.

Below are some more tests where the transaction has been kept open for a much longer duration.

After running the test for 40 minutes (out of which there was an open transaction for first 30-minutes), the size in heap grows to 19GB at 8-client count test and to 26GB at 64-client count test whereas for zheap the size remains at 11GB for both the client-counts at the end of test and all the undo generated during test gets discarded within a few seconds after the open transaction is ended.

After running the test for 55 minutes (out of which there was an open transaction for first 45-minutes), the size in heap grows to 22GB at 8-client count test and to 28GB at 64-client count test whereas for zheap the size remains at 11GB for both the client-counts at the end of test and all the undo generated during test gets discarded within few seconds after the open transaction is ended.

So from all the above three tests, it is clear that the size of heap keeps on growing as the time for a concurrent long-running transaction is increasing.  It was 13GB at the start of the test, grew to 20GB, then to 26GB, then to 28GB at 64-client count test as the duration of the open transaction has increased from 15-mins to 30-mins and then to 45-mins. We have done a few more tests on the above lines and found that as the duration of open-transaction increases, the size of heap keeps on increasing whereas zheap remains constant.  For example, similar to above, if we keep the transaction open 60-mins in a 70-min test, the size of heap increases to 30GB. The increase in size also depends on the number of updates that are happening as part of the test.

The above results show not only the impact on size, but we also noticed that the TPS (transactions per second) in zheap is also always better (up to ~45%) for the above tests.  In similar tests on some other high-end machine, we see much better results with zheap with respect to performance. I would like to defer the details about raw-performance of zheap vs. heap to another blog post as this blog has already become big. I would like to mention that the above results don't mean that zheap will be better in all cases than heap. For example, rollbacks will be costlier in zheap. Just to be clear, this storage format is proposed as another format alongside current heap, so that users can decide which storage they want to use for their use case.

The code for this project has been published and is proposed as a feature for PG-12 to PostgreSQL community.  Thanks to Kuntal Ghosh for doing the performance tests mentioned in this blog post.

Hash indexes are faster than Btree indexes?

PostgreSQL have supported Hash Index for a long time, but they are not much used in production mainly because they are not durable.  Now, with the next version of PostgreSQL, they will be durable.  The immediate question is how do they perform as compared to Btree indexes. There is a lot of work done in the coming version to make them faster. There are multiple ways in which we can compare the performance of Hash and Btree indexes, like the time taken for creation of the index, search or insertion in the index.  This blog will mainly focus on the search operation. By definition, hash indexes are O(1) and Btree indexes are O(log n), however with duplicates that is not exactly true.

To start with let us see the impact of work being done to improve the performance of hash indexes. Below is the performance data of the pgbench read-only workload to compare the performance difference of Hash indexes between 9.6 and HEAD on IBM POWER-8 having 24 cores, 192 hardware threads, 492GB RAM.

The workload is such that all the data fits in shared buffers (scale factor is 300 (~4.5GB) and shared_buffers is 8GB).  As we can see from the above graph, that the performance has increased at all client counts in the range of 7% to 81% and the impact is more pronounced at higher client counts. The main work which has led to this improvement is 6d46f478 (Improve hash index bucket split behavior.) and 293e24e5 (Cache hash index's metapage in rel->rd_amcache.).

The first commit 6d46f478 has changed the heavyweight locks (locks that are used for logical database objects to ensure the database ACID properties) to lightweight locks (locks to protect shared data structures) for scanning the bucket pages.  In general, acquiring the heavyweight lock is costlier as compare to lightweight locks.  In addition to reducing the locking cost, this also avoids locking out scans and inserts for the lifetime of the split.

The second commit 293e24e5 avoids a significant amount of contention for accessing metapage. Each search operation needs to access metapage to find the bucket that contains tuple being searched which leads to high contention around metapage.  Each access to metapage needs to further access buffer manager. This work avoids that contention by caching the metapage information in backend local cache which helps bypassing all the buffer manager related work and hence the major contention in accessing the metapage.


The next graph shows how the hash index performs as compared to the btree index.  In this run we have changed hash to btree index in pgbench read-only tests.

We can see here that the hash index performs better than the btree index and the performance difference is in the range of 10 to 22%.  In some other workloads we have seen a better performance like with hash index on varchar columns and even in the community, it has been reported that there is performance improvement in the range of 40-60% when hash indexes are used for unique index columns.


The important thing to note about the above data is that it is only on some of the specific workloads and it mainly covers Selects as that is the main area where performance improvement work has been done for PostgreSQL10.  The other interesting parameters to compare are the size of the index and update on the index which needs more study and experiments.

In the end, I would like to thank my colleagues who were directly involved in this work and my employer EnterpriseDB who has supported this work.  Firstly I would like to thank, Robert Haas who has envisioned all this work and is the committer of this work, and Mithun C Y who was the author of commit 293e24e5.  Also, I would like to extend sincere thanks to all the community members who are involved in this work and especially Jeff Janes and Jesper Pedersen who have reviewed and tested this work.

Troubleshooting waits in PostgreSQL

Currently when the PostgreSQL database becomes slow especially on systems with high load, it becomes difficult to find the exact reasons.  Currently one can use tools like perf, strace, dynamic tracing (http://www.postgresql.org/docs/devel/static/dynamic-trace.html), etc. to find out the reasons of slowdown, but most of the times they are quite inconvenient to use which lead to the development of the new feature to display wait events information in pg_stat_activity view.  Wait events are invented to capture the information of system blocks or waits to perform some action like waiting for another backend process to release the heavyweight or lightweight locks, waits to access data buffer when no other process can be examining the buffer, waits to read or write the data to disk, etc.  As part of initial feature, we have covered some of the common wait event types due to which there are waits in system, however it is designed such that it can be extended to capture other types of wait events as well.

I will briefly explain the wait event types covered as part of this feature and then explain with examples, how one can use this feature to find stalls or waits in the system.  First wait event type is lightweight lock which is used to protect a particular data structure in shared memory.  Second wait event type is named lightweight lock tranche, this indicates that the server process is waiting for one of a group of related lightweight locks. Third wait event type is heavyweight lock which is used to primarily protect SQL-visible objects such as tables.  Fourth type of wait event is BufferPin where the server process waits to access to a data buffer during a period when no other process can be examining that buffer.  For detail explanation, refer PostgreSQL documentation at http://www.postgresql.org/docs/devel/static/monitoring-stats.html#PG-STAT-ACTIVITY-VIEW

Now, let us try to understand with the help of simple examples, how to find waits in the system using this powerful tool.

Create table and insert data which will be used in below examples:

postgres=# create table wait_event_tbl(c1 int);

CREATE TABLE

postgres=# insert into wait_event_tbl values(1);

INSERT 0 1

wait event type - Lock (Heavyweight locks)

-------------------------------------------------

Scenario - 1

Let us try to examine the waits for a scenario where one of the session has acquired Access Exclusive Lock on a table and the other session wants to acquire Access Share Lock on the same table and is waiting for first session to complete it's transaction.

Session -1

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           6088

(1 row)

postgres=# begin;

BEGIN

postgres=# Lock wait_event_tbl in Access Exclusive Mode;

LOCK TABLE

Session-2

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           1152

(1 row)

postgres=# begin;

BEGIN

postgres=# Lock wait_event_tbl in Access Share Mode;

Session-3

postgres=# select pid, wait_event_type, wait_event from pg_stat_activity where wait_event is NOT NULL;

 pid  | wait_event_type | wait_event

------+-----------------+------------

 1152 | Lock            | relation

(1 row)

Here, via above statement, it is shown that session-2 is waiting for a Lock on a relation.  To know more information about relation, one can add "query" column in the above statement.

Scenario - 2

Three sessions try to update the same row, first one will be successful and the other two will be waiting.

Session -1

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           6088

(1 row)

postgres=# begin;

BEGIN

postgres=# update wait_event_tbl set c1 = 2 where c1=1;

UPDATE 1

Session - 2

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           1152

(1 row)

postgres=# begin;

BEGIN

postgres=# update wait_event_tbl set c1 = 3 where c1 = 1;

Session - 3

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           5404

(1 row)

postgres=# begin;

BEGIN

postgres=# update wait_event_tbl set c1 = 4 where c1 = 1;

Session - 4

postgres=# select pid, wait_event_type, wait_event from pg_stat_activity where wait_event is NOT NULL;

 pid  | wait_event_type |  wait_event

------+-----------------+---------------

 1152 | Lock            | transactionid

 5404 | Lock            | tuple

(2 rows)

Here, above statement indicates that session-2 and session-3 are waiting.

To find detailed information about locks, you can join this table information with pg_locks as described in link:https://wiki.postgresql.org/wiki/Lock_Monitoring or some other similar way.

wait event type - LWLockName (Lightweight Locks)

----------------------------------------------------

One session trying to execute the update statement and other session is trying to execute select statement can block each other for short time.

Session - 1

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           1152

(1 row)

postgres=# update wait_event_tbl set c1 = 2;

Session - 2

postgres=# select pg_backend_pid();

 pg_backend_pid

----------------

           6088

(1 row)

postgres=# select * from wait_event_tbl;

Session - 3

postgres=# select pid, wait_event_type, wait_event from pg_stat_activity where wait_event is NOT NULL;

 pid  | wait_event_type |  wait_event

------+-----------------+---------------

 1152 | LWLockNamed     | ProcArrayLock

(1 row)

I have created this scenario with the help of debugger, but it is quite possible to see such wait events during high load on the system.

One point to note for users who are using "waiting" column of pg_stat_activity to find blocking statements is that they need to change their queries for next version (presumably 9.6) of PostgreSQL  as waiting column is removed from pg_stat_activity.  This is an intentional decision taken by PostgreSQL community for the ease of use and or understanding of this feature especially for future versions.

This feature has been committed in PostgreSQL code.  For details, you can refer commit id - 53be0b1add7064ca5db3cd884302dfc3268d884e.  It took us approximately 9 months to complete this feature.  Thanks to all the PostgreSQL community members who have given their valuable feedback throughout the development of this feature and special thanks to Robert Haas and Ildus Kurbangaliev for giving tremendous support to me both by reviews and by helping in writing parts of code.  Also Thanks to Alexander Korotkov for review and inputs for this feature and last but not least Thanks to Thom Brown for inputs in documentation of this feature.

Parallel Sequential Scans in play

Parallelism is now reality in PostgreSQL.  With 9.6, I hope we will see many
different form of queries that can use parallelism to execute.  For now, I will
limit this discussion to what we can already do, which is Parallel Sequential
Scans.

Parallel Sequential Scans are used to scan a relation parallely with the help of
background workers which in turns improve the performance of such scans.  I
will discuss about the scenarios where user can expect a performance boost
due to this feature later in this blog, but first let us understand the basic feature
and how it works.  Three new GUC parameters have been added to tune the
usage of this feature.

max_parallel_degree - This is used to set the maximum number of workers that
can be used for an individual parallel operation.  It is very well possible that the
requested number of workers are not available at execution time.  Parallel workers
are taken from the pool of processes established by max_worker_processes which
means that value of max_parallel_degree should be lesser than max_worker_processes.
It might not be useful to set the value of this parameter more than the number of CPU
count on your system.

parallel_tuple_cost - This is used by planner to estimate the cost of transferring a
tuple from parallel worker process to master backend.  The default is 0.1.  The more
the number of tuples that needs to be passed from worker backend processes to
master backend process, the more this cost will be and more overall cost of
parallel sequential scan plan.

parallel_setup_cost - This is used by planner to estimate the cost of launching parallel
worker processes and setting up dynamic shared memory to communicate.
The default is 1000.

Now let us see the simple example to demonstrate how parallel sequential scan works:

 create table tbl_parallel_test(c1 int, c2 char(1000));   
 insert into tbl_parallel_test values(generate_series(1,1000000),'aaaaa');   
 Analyze tbl_parallel_test;   
 Explain analyze select * from tbl_parallel_test where c1 < 10000 and  
 c2 like '%bb%';   
               QUERY PLAN              
  -------------------------------------------------------------------------------------------------------------   
  Seq Scan on tbl_parallel_test  
          (cost=0.00..157858.09 rows=1 width=1008)  
          (actual time=378.414..378.414 rows=0 loops=1)   
   Filter: ((c1 < 10000) AND (c2 ~~ '%bb%'::text))   
   Rows Removed by Filter: 1000000   
  Planning time: 0.075 ms   
  Execution time: 378.431 ms   
  (5 rows)   

Set the max parallel degree to enable the use of parallelism in queries.

 set max_parallel_degree = 6;  
 Explain analyze select * from tbl_parallel_test where c1 < 10000  
 and c2 like '%bb%';  
                                QUERY PLAN                    
 -------------------------------------------------------------------------------------------------------------  
  Gather (cost=1000.00..29701.57 rows=1 width=1008)   
        (actual time=182.708..182.708 rows=0 loops=1)  
   Number of Workers: 5  
   -> Parallel Seq Scan on tbl_parallel_test  
         (cost=0.00..28701.47 rows=1 width=1008)  
         (actual time=179.496..1081.120 rows=0 loops=1)  
      Filter: ((c1 < 10000) AND (c2 ~~ '%bb%'::text))  
      Rows Removed by Filter: 1000000  
  Planning time: 0.078 ms  
  Execution time: 200.610 ms  
 (7 rows)  

Here, we can see how changing max_parallel_degree allows the usage of parallel workers
to perform parallel sequential scans.  We can notice in above example that even though we
have set max_parallel_degree as 6, still it uses 5 workers and the reason for same is that
currently the parallel workers are choosen based on size of relation.

Next, let us discuss about usage of functions in parallel query. A new clause PARALLEL
is added to the CREATE FUNCTION statement.  There are three valid values that can be
used by user with this clause.

1. PARALLEL Unsafe - This indicates that the function can't be executed in parallel mode
and the presence of such a function in a SQL statement forces a serial execution plan.
2. PARALLEL Restricted - This indicates that the function can be executed in parallel mode,
but the execution is restricted to parallel group leader.  As of now, if the qualification for any
particular relation has anything that is parallel restricted, that relation won't be chosen for
parallelism.
3. Parallel Safe - This indicates that the function is safe to run in parallel mode without
restriction.

The default value for function is PARALLEL Unsafe.

Now let us see the impact of using Parallel Safe and Unsafe function in the queries.  I will
continue using the query used in previous example to explain the concept.

Create a Parallel Safe function

 create or replace function calc_factorial(a integer, fact_val integer)  
 returns integer   
  as $$   
  begin   
    perform (fact_val)!;   
    return a;   
  end;   
  $$ language plpgsql PARALLEL Safe;  

Use it in query

 Explain analyze select * from tbl_parallel_test where  
               c1 < calc_factorial(10000, 10)   
               and c2 like '%bb%';   
        QUERY PLAN   
  --------------------------------------------------------------------------------   
  Gather (cost=1000.00..75154.99 rows=1 width=1008)   
     (actual time=120566.456..120566.456 rows=0 loops=1)   
   Number of Workers: 5   
   -> Parallel Seq Scan on tbl_parallel_test   
      (cost=0.00..74154.89 rows=1 width=1008)   
      (actual time=119635.421..359721.498 rows=0 loops=1)   
    Filter: ((c2 ~~ '%bb%'::text) AND (c1 < calc_factorial(10000, 10)))   
    Rows Removed by Filter: 1000000   
  Planning time: 54.904 ms   
  Execution time: 120622.631 ms   
  (7 rows)   

Here we can see that Parallel Plan is chosen and the parallel safe function
is pushed to workers for evaluation of quals.

Now lets change that function as Parallel Unsafe and see how the above
query behaves.

  Alter Function calc_factorial(integer, integer) PARALLEL Unsafe;   
  Explain analyze select * from tbl_parallel_test where  
              c1 < calc_factorial(10000, 10)   
              and c2 like '%bb%';   
         QUERY PLAN   
  --------------------------------------------------------------------------------   
  Seq Scan on tbl_parallel_test   
     (cost=0.00..407851.91 rows=1 width=1008)   
     (actual time=33166.138..33166.138 rows=0 loops=1)   
   Filter: ((c2 ~~ '%bb%'::text) AND (c1 < calc_factorial(10000, 10)))   
   Rows Removed by Filter: 1000000   
  Planning time: 0.162 ms   
  Execution time: 33166.208 ms   
  (5 rows)   

So using parallel unsafe functions in queries would lead to serial plans.

Next, let us see the Performance characteristics of Parallelism:

Non-default settings used to collect performance data:

 shared_buffers=32GB; min_wal_size=5GB; max_wal_size=10GB  
 checkpoint_timeout =30min; max_connections=300;  
 max_worker_processes=100;  

Test setup

 create table tbl_perf(c1 int, c2 char(1000));  
 insert into tbl_perf values(generate_series(1,30000000),'aaaaa');  
 Explain analyze select c1 from tbl_perf where  
              c1 > calc_factorial($1,10) and  
              c2 like '%aa%';  

The function calc_factorial is same as used in previous example and the values passed
to it are such that the desired percentage of rows can be selected.  Example

 --"to select 1% of rows, below query can be used"  
 Explain analyze select c1 from tbl_perf where  
              c1 > calc_factorial(29700000,10) and  
              c2 like '%aa%';"  
 --"to select 10% of rows, below query can be used"  
 Explain analyze select c1 from tbl_perf where  
              c1 > calc_factorial(27000000,10) and  
              c2 like '%aa%';"  
 --"to select 25% of rows, below query can be used"  
 Explain analyze select c1 from tbl_perf where  
              c1 > calc_factorial(22500000,10) and  
              c2 like '%aa%';"  

Performance Data -

1. With increase in degree of parallelism (more parallel workers), the time to complete
the execution reduces.
2. Along with workers, master backend also participates in execution due to which you
can see more time reduction in some cases.
3. After certain point, increasing max parallel degree won't help.

The cases we have seen in this blog are mostly the cases where parallel query helps by
using the workers, however there exists some cases like when qualification is very cheap
where it hurts or won't help even by employing more number of workers.  There is
more investigation needed to make sure that planner won't choose such plans for parallelism.

Improved Writes in PostgreSQL For 9.6 (Part - 1)

Lately, PostgreSQL has gained attention because of numerous performance

improvements that are being done in various areas (like for 9.5 the major

areas as covered in my PGCon presentation are Read operations, Sorting, 

plpgsql, new index type for data access, compression of full_page_writes),

however still there is more to be done to make it better than other commercial

RDBMS's and one of the important areas for improvements is Write

operations as shown in one of my previous posts (Write Scalability in

PostgreSQL). During my investigation of Write operations, I found that

there are locking bottlenecks during Write operations which is one of the

cause for limiting its performance and the one which contends most is

ProcArrayLock which is used during commit of transaction and for taking

Snapshots.

Removing the contention around ProcArrayLock gives a very good boost

in performance especially at higher client count and this work has been done

for PostgreSQL 9.6.  To start with let us first discuss the improvement

in-terms of TPS (transactions per second) after this work. I have ran a pgbench

read-write (sort of tpcb) workload to compare the performance difference

with and without this commit in PostgreSQL on Intel m/c having 8 sockets,

64 cores (128 hardware threads), 500GB RAM and here is performance data

(running same tests on IBM POWER-8 m/c also shows similar gain)

Non-default settings used in all the tests are:
max_connections = 300
shared_buffers = 8GB
wal_buffers = 256MB
min_wal_size=10GB
max_wal_size=15GB
checkpoint_timeout    =35min
maintenance_work_mem = 1GB
checkpoint_completion_target = 0.9

The data is taken when all the data fits in shared_buffers as this work mainly helps
such cases. The performance increase is visible at somewhat higher client count,
at 64 clients we will see 30% improvement and at 256 clients, the performance
improvement is 133%.  At lower client-count (8 or 16 clients), there is not much
difference (due to fluctuation, I see 1-2% difference, but I think for such cases this
work doesn't help).

Now coming to the work done to improve the performance, presently for the
correctness requirement of taking snapshot's in PostgreSQL, it enforces the strict
serialization of commits and rollbacks with snapshot-taking: it doesn't allow any
transaction to exit the set of running transactions while a snapshot is being taken.
To achieve the same, while taking snapshot it acquires ProcArrayLock in SHARED
mode and each exiting transaction acquires it in EXCLUSIVE mode.  So in this
protocol, there are two different types of contention, one is between a backend
which is trying to acquire a snapshot with backend trying to commit a transaction
and second is among backends  that are trying to commit a transaction at same
time.  The idea used in this work is to allow only one backend (we can call it as
a group leader) at-a-time to take a ProcArrayLock and complete the work for
all other transactions which are trying to commit the transactions at the same
time.  This helps in minimising the ProcArrayLock acquisition in EXCLUSIVE
mode and which intern greatly reduces the contention around it.

Apart from the benefit this patch brings, it also opens up the opportunity
to do more optimisations to reduce contention of various other locks like
CLogControlLock and WALWriteLock etc. in PostgreSQL which I see as a huge
benefit for Write operations. I hope to see more improvements for Write
operations and cover them in future Blogs.

Last but not least, I would like to thank all who were involved in this work.  Firstly
I would like to thank my employer EnterpriseDB and Robert Haas who not only
encouraged me to work in this area, but also helped in various stages of this
Patch development. When I was in-middle of this work and wanted feedback
and suggestions, a lot of people (during PGCon) shared their thoughts with me
and among them who really helped me to move this work to a level where it
can be presented to PostgreSQL community are Robert Haas, Andres Freund
and Simon Riggs.  In the end, I would also like to thank Pavan Deolasee who
has reviewed this patch.