Friday, 25 May 2018

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
-> 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
-> 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.

Monday, 5 March 2018

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.