Query Parallelism in PostgreSQL: What's coming next?

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Query Parallelism In PostgreSQL
What’s coming next?
Dilip Kumar
(Principle Software Engineer)

❏ Infrastructure for parallelism
❏ Intra-query parallelism in v9.6
❏ Parallel aware executor nodes
❏ Performance on TPC-H
❏ Parallelism enhancements in v10
❏ More executor-nodes
❏ Performance on TPC-H
❏ Take away

❏ Groundwork for parallelism
❏ Dynamic background workers
❏ Dynamic shared memory
❏ Shared messaging capabilities
❏ Group locking
❏ Parallel context
❏ Intra-query parallel support
❏ Parallel executor
❏ Parallel-aware nodes
❏ seq scan,
❏ joins, and
❏ aggregates

Gather
nodeGather.c
Parallel-Aware
Executor Nodes
nodeSeqScan.c
nodeForeignScan.c
nodeCustom.c
Parallel Executor
Support
execParallel.c
Tuple Queue Reader
and DestReceiver
tqueue.c
Dynamic
Background Workers
bgworker.c
Dynamic Shared
Memory
dsm.c, dsm_impl.c
Shared Memory
Message Queue
shm_mq.c
Parallel Context
parallel.c
Shared Memory
Table of Contents
shm_toc.c
Error/Notice
Forwarding
pqmq.c
State Synchronization
dfmgr.c, guc.c, combocid.c,
snapmgr.c, xact.c
Group Locking
lock.c

❏ Dynamic background worker (shm_mq)
❏ Postmaster can launch the background worker processes
at run time
❏ Dynamic shared memory
❏ Allocate a chunk of memory that can be shared among
co-operating processes
❏ Shared memory table of contents
❏ A simple scheme for carving DSM into numbered chunks
❏ Shared messaging capabilities
❏ Shared memory message queue
❏ For error/notice forwarding
❏ Tuple queue reader and DestReceiver

❏ Parallel context
❏ Core toolkit for parallel operations
❏ Launch a number of workers, establish “useful”
state, run C code you specify and ensure timely
shutdown.
❏ State synchronization
❏ To ensure same GUC values, libraries, and
transactions with same snapshot across workers
❏ Group locking
❏ To solve the issue of undetected deadlock
❏ Leader and its workers are treated as one entity for
locking purposes

❏ Parallel executor support
❏ Execute a plan by a set of worker
❏ Pass instrumentation information to each worker
❏ Parallel aware executor nodes
❏ Different behaviour when run in parallel or otherwise
❏ Gather
❏ Collect results across all workers and merge them
into a single result stream

❏ Parallel access methods
❏ Seq scan is the only parallel access method
❏ No support for parallel index, index-only or
bitmap-heap scan
❏ Parallel joins
❏ NestedLoop and Hash joins are supported for parallel
execution
❏ For hash-join, each worker prepares its own copy of
hash-table
❏ Merge join cannot execute in parallel
❏ Parallel aggregates
❏ Each worker performs partial aggregate and finalize
aggregate is done by leader

❏ Experimental setup
❏ IBM power7 box (popularly known as Hydra in
community)
❏ Parameter settings
❏ Max_parallel_degree = 4
❏ Work_mem = 64 MB
❏ Shared_buffers = 8 GB
❏ Database setup
❏ Scale factor = 10

❏ Need parallel-index scan
❏ Q6, Q14
❏ Need parallel bitmap-heap scan
❏ Q4, Q15
❏ Need parallel merge-join
❏ Q2, Q3, Q9, Q20
❏ Need parallel hash table build
❏ Q3, Q5, Q7, Q8, Q21
❏ Need parallel subquery handling
❏ Q2, Q22
…

❏ More parallel executor nodes
❏ Access methods
❏ Parallel index, index-only, bitmap-heap
❏ Join methods
❏ Merge join
❏ Hash join with shared hash
❏ Other
❏ Gather-merge
❏ Relaxation for nodes using uncorrelated sub-plan, init-plan
❏ Improvements in parallel-append
❏ Parallel DDL/maintenance commands
❏ Index-creation
❏ Vacuum

❏ Parallel index scan
❏ Firstly, a worker will process the intermediate pages
of B-tree and determine the starting leaf page where
scan is to be started
❏ Next, all the workers start scanning the leaf pages
block by block
❏ Finally, all the filtered tuples are gathered by the
leader process
❏ This operator improves the performance significantly
when the database is in-memory
❏ Similar mechanism is built for index-only scans

❏ Parallel bitmap heap scan
❏ A bitmap scan fetches all the pointers from index in
one go, sort them using in-memory “bitmap”, finally,
visits the tuple in physical order
❏ Bitmap will be created by a single worker
❏ Next, all the workers will jointly scan the heap,
page by page
❏ For further improvement, we can also build bitmap by
multiple workers

❏ Parallel bitmap heap scan
Gather
Workers Planned: 2
-> Parallel Bitmap Heap Scan on foo
Recheck Cond: ((a < 100000) OR (b <
10000))
-> BitmapOr
-> Bitmap Index Scan on idx1
Index Cond: (a < 100000)
-> Bitmap Index Scan on idx2
Index Cond: (b < 10000)

❏ Parallel Merge Join
❏ If outer node is using parallelism then we consider
parallel merge-join
❏ Outer node will be scanned in parallel by multiple workers
❏ Inner node will be processed completely by individual workers
❏ There is still scope of improvements in this strategy
❏ Parallelise inner sort or materialize nodes

❏ Parallel shared hash
❏ Previously, each worker builds its own copy of hash
table
❏ This is particularly favourable to cases when hash
table is small
❏ Improved mechanism is to employ the workers for
building hash-table in parallel
❏ Once, hash-table is ready, parallel probing can be
done
❏ This facilitates the usage of parallel operators on
either sides of joins

Parallel shared-hash
Gather
Workers Planned: 2
Workers Launched: 2
-> Hash Join
Hash Cond (foo.b = bar.b)
-> Parallel Seq Scan on foo
-> Parallel Shared Hash
-> Parallel Seq Scan on bar

❏ Gather-merge
❏ Previously, there was only one option to collect the
result from parallel operators i.e gather, it does
not maintain interesting order
❏ Therefore, extra sort node is required on top for
ordered output
❏ Now, if workers are providing sorted result
specifically, output from parallel index, parallel
merge join, etc. then gather-merge will maintain the
sort-order in the final result

❏ Experimental setup
❏ RAM = 512 GB
❏ Number of cores = 32
❏ Parameter settings
❏ Work_mem = 64 MB
❏ Shared_buffers = 8 GB
❏ Effective_cache_size = 10 GB
❏ Random_page_cost = seq_page_cost = 0.1
❏ Max_parallel_workers_per_gather = 4
❏ Database setup
❏ Scale factors = 20, 300
❏ Additional indexes: l_shipmode, l_shipdate,
o_orderdate, o_comment

Results on scale factor 20

❏ Tuning parameters
❏ Max_parallel_workers_per_gather
❏ Recommended value 1 to 4
❏ Reduce following costs
❏ Parallel_tuple_cost: planner's estimate of the cost of transferring one tuple from a
parallel worker process to another process
❏ Parallel_setup_cost: planner's estimate for launching parallel workers and initializing
dynamic shared memory
❏ Min_parallel_table_scan_size: the minimum size of relations to be considered for
parallel sequence scan
❏ Min_parallel_index_scan_size: the minimum size of index to be considered for parallel
scan
❏ Random_page_cost: estimated cost of accessing a random page in disk
❏ Increase following parameters
❏ Work_mem
❏ Effective_cache_size
❏ Shared_buffers

❏ Adding intra-query parallelism improves per query response time
❏ Previously, overall throughput was the only focus
❏ This makes it more suitable for OLAP environments
❏ Till version 9.6, parallel support for sequence scan, hash join, nestloop join,
and aggregates is available
❏ Out of 22 queries of TPC-H, performance improved for 15 queries
❏ In which 3 queries are at least 4 times faster and 11 queries are 2
times faster
❏ More parallel executor nodes are planned for upcoming versions
❏ More parallel access methods - index, index-only are already
committed
❏ Improved parallel join mechanisms
❏ Gather with interesting order
❏ Removed restrictions for nodes using SubPlans(already committed) or
InitPlans
❏ Around 10 of 22 TPC-H queries show significant improvement in
performance
❏ In which around 4 queries show more than 2x improvement

27
Output: Thank You
Gather
Workers Planned: 2
Workers Launched: 2
-> Parallel Index Scan on Common_phrases
Index Cond: ( value = ‘Thank You’ )
Filter: Language = ‘English’
Slide credits:
[1] https://www.pgcon.org/2016/schedule/events/913.en.html
[2] https://www.postgresql.eu/events/schedule/pgconfeu2016/session/1360-parallel-query-in-postgresql/
[3] http://pgconf.in/schedule/query-parallelism-in-postgresql-expectations-and-opportunities/

Query Parallelism in PostgreSQL: What's coming next?

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