PostgreSQL Performance
Presentation
9.5devel Edition
Mark Wong
Consultant, 2ndQuadrant &
Contributor, PostgreSQL
10 March 2015
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Agenda
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Background
Patches to test
The test suite
Compare results between difference
versions of PostgreSQL
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Background
This presentation made possible by the AXLE1
project:
• Advanced Analytics for Extremely Large
European Databases
• “The objectives of the AXLE project are to
greatly improve the speed and quality of
decision making on real-world data sets.
AXLE aims to make these improvements
generally available through high quality
open source implementations via the
PostgreSQL and Orange products.”
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http://axleproject.eu/
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A few AXLE details
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Aspects of the project include:
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Scalability engineering—autopartitioning,
compression
Security, privacy, and audit techniques
Visual analytics
Advanced architectures for hardware and
software
Partners
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2ndQuadrant
Barcelona Supercomputing Center
PortaVita
University of Manchester
University of Ljubljana
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The Patches
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Changes Evaluated
Patches tested in this presentation are intended
to help the performance of large database
systems:
• New feature already committed:
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BRIN indexes
Scalability related improvements still under
review:
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Concurrent hash table for buffer lookups
Wait free LWLocks
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Light Weight Locks
Currently provides mutually exclusive access to shared
memory by using spin locks or semaphores
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BRIN
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Block Range indexes are a
new-to-PostgreSQL type of index
Authored by Álvaro Herrera @ 2ndQuadrant
Originally proposed as MinMax indexes2
Intended for Big Data systems
Similar to Oracle Exadata ”storage indexes”
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http://goo.gl/yUwav9
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Why use BRIN?
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Much smaller and less costly to maintain
compared to B-tree indexes
Allowing use of an index on a large table
that would previously been impractical using
B-tree indexes without horizontal
partitioning
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When to use BRIN?
Best case scenario:
• On a column that has a linear sort order
• Example:
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A table recording sales orders
Use BRIN on the DATE column representing
when an order was placed
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Index Structure Comparison
B-tree
BRIN
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Concurrent Hash Table
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Authored by Robert Haas @ EnterpriseDB 3
Improve concurrency accessing the buffer
table between simultaneously executing
queries
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Lookups performed using memory barriers
instead of locks
Inserts and deletes (to the buffer table) use
atomic operations instead of locks
http://goo.gl/9naq5c
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Series of changes around
LWLocks
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Related to wait free LW SHARED acquisition
Authored by Andres Freund @
2ndQuadrant 4
Use atomic compare-and-exchange to
acquire LWLock
Lockless clock sweep buffer eviction
Reduce false sharing by increase padding
between LWLocks
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http://goo.gl/8bf3CN
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False sharing visual from Intel
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The Hardware
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Environment: Barcelona
Barcelona Supercomputing Center
• OS: SUSE Linux Enterprise Server 11 SP3
• Hardware
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2 x Intel Xeon E5-2670, Sandy Bridge DP,
2.6Ghz 8 Core/16 Threads, 3.3Ghz Turbo,
8GT/s QPI, 20MB cache, 115W
256 GB RAM
120TB storage connected via 10GigE
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Environment: Manchester
University of Manchester
• OS: Ubuntu 12.04.4 LTS
• Hardware
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4 x Intel Xeon E54620 Eight Core 2.2GHz
Processors, 8 Core/32 Threads
256 GB RAM
10TB storage, 17 x 600GB SSD RAID-0
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The Test
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TPC BenchmarkTMH
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http://www.tpc.org/tpch/
A suite of business oriented ad-hoc queries
against a parts supplier data warehouse
Composed of three tests:
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Load Test
Power Test
Throughput Test
Use smaller parts of the TPC-H to perform
microbenchmarks against BRIN indexes
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TPC-H Load Test
Measure the time to load data into 8 tables and
build all indexes
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TPC-H Load Test notes
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Load of each table starts simultaneously
All indexes created simultaneously as soon
as table is loaded
Expectation is that the combination of BRIN
indexes and the scalability patches will
reduce the index creation portion of the test
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TPC-H Power Test
Raw query execution for a single user:
• 22 different queries executed consecutively
• Order of queries randomized per test
• Scored by taking the geometric mean of the
execution time of each query
• Expectation is that the use of BRIN indexes
will be comparable to the use of B-tree
indexes
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A sample of Power
Test queries
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TPC-H Query 14
This query monitors the market response to a
promotion such as TV advertisements or a
special campaign.
select
100.00 ∗ sum( case
when p t y p e l i k e ’PROMO% ’
then l e x t e n d e d p r i c e ∗ ( 1 − l d i s c o u n t )
else 0
end ) / sum( l e x t e n d e d p r i c e ∗ ( 1 − l d i s c o u n t ) ) as promo revenue
from
lineitem ,
part
where
l partkey = p partkey
and l s h i p d a t e >= date ’1994−10−01 ’
and l s h i p d a t e < cast ( date ’1994−10−01 ’ + i n t e r v a l ’ 1 month ’ as date ) ;
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TPC-H Query 12
This query determines whether selecting less expensive
modes of shipping is negatively affecting the
critical-priority orders by causing more parts to be
received by customers after the committed date.
s e l e c t l shipmode ,
sum( case when o o r d e r p r i o r i t y = ’1−URGENT ’
or o o r d e r p r i o r i t y = ’2−HIGH ’
then 1
else 0
end ) as h i g h l i n e c o u n t ,
sum( case when o o r d e r p r i o r i t y <> ’1−URGENT ’
and o o r d e r p r i o r i t y <> ’2−HIGH ’
then 1
else 0
end ) as l o w l i n e c o u n t
from orders , l i n e i t e m
where o o r d e r k e y = l o r d e r k e y
and l shipmode i n ( ’FOB ’ , ’TRUCK ’ )
and l c o m m i t d a t e < l r e c e i p t d a t e
and l s h i p d a t e < l c o m m i t d a t e
and l r e c e i p t d a t e >= date ’1994−01−01 ’
and l r e c e i p t d a t e < date ’1994−01−01 ’ + i n t e r v a l ’ 1 year ’
group by l shipmode
order by l shipmode ;
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TPC-H Throughput Test
Measure the system’s ability to execute queries
from multiple users simultaneously:
• Execute multiple Power Tests at the same
time
• Each instance of a Power Test referred to
as a stream
• Scored as the ratio of the total number of
queries executed over the length of the test
• Expectation is that we can run more
streams and execute queries faster
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Initial BRIN Tests
But first design microbenchmarks around BRIN
in case their use is masked in the TPC-H
workload:
• Loading a table
• Creating an index
• Maintenance on growing a table
• Measure against the lineitem table, 6
million rows per scale factor
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Load tables with BRIN
1. Load data into the table when there are no
indexes
2. Load data into the table when there is only
a single B-tree index on a DATE column
3. Repeat after replaces the B-tree index with
a BRIN index
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Create a BRIN index
1. Load data into the table when there are no
indexes
2. Create a B-tree index on the same DATE
column
3. Start over and repeat with a BRIN index
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BRIN maintenance overhead
1. Create table without any indexes
2. Time the load of 8GB of data repeatedly
until table size reaches 1TB
3. Start over and repeat with a B-tree index on
the same DATE column
4. Start over and repeat with a BRIN index on
the same DATE column
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The Results
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BRIN
microbenchmarks
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BRIN index increases load time by 10% vs 84%
B-tree index
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Creating a BRIN index is 81% faster than
creating a B-tree index
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BRIN overhead a constant 10% vs B-tree
overhead growing slightly as the table grows
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Impact on TPC-H Tests
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Load Test at 30GB
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B-tree
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28 B-tree indexes across 8 tables
Total build time 46 minutes
BRIN
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29 total indexes across 8 tables
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21 B-tree
8 BRIN
Total build time 34 minutes
Use of BRIN indexes reduces load time by
26%
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Power Test notes at 30GB
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15 of 22 queries to use BRIN index
Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q10, Q11,
Q14, Q15, Q16, Q19, Q20, Q21
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Power Test results at 30GB
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Power Test results at 100GB
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Summary of Power Test
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At 30GB
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Overall score 2145 (B-tree) vs 1806 (BRIN)
Use of BRIN indexes within 15% of the B-tree
score
At 100Gb
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Overall score 1418 (B-tree) vs 1815 (BRIN)
Use of BRIN indexes improves score by 28%
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Index Comparison at 100GB5
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statement timeout set to 3000 seconds for no indexes
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Summary of Power Test with
BRIN vs No indexes
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At 100Gb
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Overall score 10616 (no indexes) vs 1815 BRIN
Use of BRIN indexes improves score by over
71%
Should be lower without statement timeout set
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BRIN objectives met
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BRIN index size is 612 times smaller:
1.7MB vs 1GB B-tree index on 8 GB of data
BRIN overhead, maintenance, size all
improved over B-tree
Performance of BRIN a little slower than
B-tree performance
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Scalability results
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Scalability effect on Load Test
Additional patches 20% improved over 9.2, 14%
over development branch:
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Scaling on a 2-socket system
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Scaling on a 4-socket system
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Power Test History
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Thank you!
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The research leading to these results has
received funding from the European Union’s
Seventh Framework Programme
(FP7/2007-2013) under grant agreement n◦
318633
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