Dumb Simple PostgreSQL Performance (NYCPUG)

Dumb Simple PostgreSQL Performance
Joshua D. Drake
Command Prompt, Inc.
United States PostgreSQL
Software in the Public Interest
jd@commandprompt.com
@cmdpromptinc
+joshua

I assume you all have
An Android, Iphone or Windows (really?) Phone?

A moment of silence
I would like to take a moment to observe a
moment of silence in honor:

Donation to PgUS
Of all of you donating to PgUS:
https://www.postgresql.us/donate

Start with Hard Drives
Hard drives are the slowest part of the system.

Rules of the Hard Drive
How fast the data can be retrieved or written to disk is the single largest bottleneck you
will experience.
Rule #1:
Thou shall have a hardware RAID controller with BBU
Rule #2:
There are only two RAID levels 1 and 10.
Rule #3:
It is better to purchase 14 small drives than 7 big drives.

BBU
Battery Backup Unit
Used on good RAID cards in case of power
outage or sudden crash. Allows for storage of
pending writes until the machine comes back on
line. A requirement if you are running any kind of
CACHE on the RAID.

RAID 1
Redundancy through
use of mirror
Increased performance
(sometimes) through
shared or partitioned
reads
If you have enough
spindles, RAID 1 is
great for pg_xlog.

RAID 1 + 0
Minimum 4 Spindles
Increased performance
through use of stripe
Increased reliability
through use of mirror

Hard Drive Technology
SATA
SATA is fine but you need at least twice as many spindles to get the same
performance of SAS. If you need a lot of space, the cost per megabyte can't be
beat.
SAS
The workhorse of the hard drive industry. Reasonably priced and high
performance.
SSD
A relative newcomer, SSD is extremely fast and fairly expensive. Higher
potential for two drive failure in RAID configurations. Insure that it is power
failure safe. Check write lifetime.

Power Loss Safe SSD
Intel
320: http://www.intel.com/content/www/us/en/solid-state-drives/ssd-320-brief.html
710: http://www.intel.com/content/www/us/en/solid-state-drives/ssd-710-brief.html
S3700:
http://www.intel.com/content/www/us/en/solid-state-drives/solid-state-drives-dc-s3700-series.html
OCZ R Series
RM84/88: http://ocz.com/enterprise/z-drive-r4-pcie-ssd/rs-specifications
Samsung
Samsung SM1625
Crucial[1]
M500 series: http://www.micron.com/products/solid-state-storage/client-ssd#m500
1. (Best price / Capacity)

DAS vs NAS vs SAN
●DAS is almost always faster
●DAS is almost always more cost effective
●DAS can be just as scalable (see Dell MD1220s)
●DAS can be just as manageable
●NAS is expensive
●NAS is not as reliable (for PostgreSQL) as it generally uses something like NFS
●NAS is highly configurable
●NAS is highly manageable
●NAS is a shared resource
●SAN is expensive
●Generally uses iSCSI
●Limited by network bandwidth which is almost always slower (excluding 10Gb) than DAS
●SAN is highly configurable
●SAN is highly manageable
●SAN is a shared resource

Lots of memory
●PostgreSQL is efficient.
●Memory is cheap (330.00 for 32GB)
●Most data sets are less than 4Gb.
●If you have more memory than data your active
data set can remain in file and or shared_buffer
cache.

Processor
●PostgreSQL is processed based.
●AMD shines in this arena.

Upgrade to 9.2
Source: http://rhaas.blogspot.com/2012/04/did-i-say-32-cores-how-about-64.html

Linux Kernel
If you are running Kernel 3.2 – 3.8.
Upgrade, NOW!

Numbers don't lie (before)
                CPU     %user     %nice   %system   %iowait    %steal     %idle
08:45:01 AM     all     30.91      0.00      5.66     40.05      0.00     23.38
08:55:02 AM     all     29.32      0.00      5.10     39.66      0.00     25.92
09:05:02 AM     all     31.71      0.00      6.24     40.99      0.00     21.06
09:15:01 AM     all     32.45      0.00      6.59     46.74      0.00     14.21
09:25:01 AM     all     20.62      0.00      5.39     60.00      0.00     14.00
09:35:01 AM     all     31.03      0.00      3.61     33.95      0.00     31.41
09:45:01 AM     all     36.54      0.00      3.22     34.13      0.00     26.11
09:55:02 AM     all     40.17      0.00      3.66     30.98      0.00     25.19
10:05:01 AM     all     33.49      0.00      3.04     32.28      0.00     31.19
10:15:01 AM     all     48.63      0.00      2.87     25.50      0.00     23.00
10:25:01 AM     all     51.34      0.00      3.56     26.06      0.00     19.04
10:35:01 AM     all     39.41      0.00      3.44     29.86      0.00     27.29
10:45:02 AM     all     36.07      0.00      8.79     30.94      0.00     24.20
10:55:03 AM     all     38.04      0.00      7.98     32.98      0.00     21.01
11:05:11 AM     all     39.25      0.00      8.81     36.75      0.00     15.19
11:15:02 AM     all     35.19      0.00      8.76     41.98      0.00     14.07
11:25:03 AM     all     38.21      0.00      9.65     38.86      0.00     13.28
11:35:02 AM     all     42.92      0.00     11.66     34.28      0.00     11.14
11:45:02 AM     all     39.40      0.00      9.96     39.03      0.00     11.61
11:55:01 AM     all     28.72      0.00      3.27     36.32      0.00     31.69

Numbers don't lie (3.9.x)
                CPU     %user     %nice   %system   %iowait    %steal     %idle
08:35:02 AM     all     40.08      0.00      4.46     10.66      0.00     44.80
08:45:01 AM     all     38.80      0.00      3.94      7.96      0.00     49.29
08:55:01 AM     all     31.48      0.00      3.03      2.58      0.00     62.91
09:05:01 AM     all     32.18      0.00      3.09      3.86      0.00     60.87
09:15:01 AM     all     26.71      0.00      2.39      3.52      0.00     67.39
09:25:01 AM     all     30.49      0.00      3.10      2.80      0.00     63.61
09:35:01 AM     all     32.50      0.00      3.49      3.42      0.00     60.60
09:45:01 AM     all     36.76      0.00      3.85      6.39      0.00     53.01
09:55:01 AM     all     44.45      0.00      4.63      9.23      0.00     41.69
10:05:02 AM     all     38.39      0.00      4.28      8.60      0.00     48.72
10:15:01 AM     all     33.57      0.00      3.53      4.10      0.00     58.80
10:25:01 AM     all     29.42      0.00      2.96      3.16      0.00     64.45
10:35:01 AM     all     32.90      0.00      3.37      5.33      0.00     58.40
10:45:01 AM     all     34.56      0.00      3.62      4.32      0.00     57.50
10:55:01 AM     all     34.84      0.00      3.37      4.27      0.00     57.52
11:05:02 AM     all     38.30      0.00      4.05      7.56      0.00     50.08
11:15:01 AM     all     36.80      0.00      3.54      9.51      0.00     50.16
11:25:01 AM     all     34.79      0.00      3.82      8.17      0.00     53.21
11:35:01 AM     all     32.68      0.00      3.07      4.97      0.00     59.28
11:45:02 AM     all     31.77      0.00      3.45      6.07      0.00     58.72
11:55:01 AM     all     33.58      0.00      3.92      6.39      0.00     56.10

VM settings
●vm.dirty_background_ratio
●vm.dirty_ratio
●vm.dirty_background_bytes
●vm.dirty_bytes
●I can't say it any better than Greg Smith:
http://www.westnet.com/~gsmith/content/linux-pdflush.htm

PostgreSQL memory settings
● shared_buffers
● work_mem
● maintenance_work_mem

What are shared_buffers
●The working cache of all hot tuples (and Index
entries) within PostgreSQL.
●Pre-allocated cache (buffers).
●On Linux sysctl.conf – kernel.shmmax
●Use 20% of available memory (up to 40%, as of
9.2 your mileage may vary)
●Watch out for IO Storms (extremely rare on 9.x+)

What is work_mem
●The working memory available for work
operations (sorts) before PostgreSQL will swap.
●Be aware of it, not afraid of it.
●Set reasonable amount globally
●Use per transaction for agressive allocation
●Use EXPLAIN ANALYZE to see if you are
overflowing

Example EXPLAIN ANALYZE
QUERY PLAN
--------------------------------------------------------------------------
Sort (cost=0.02..0.03 rows=1 width=0) (actual time=2270.744..2588.341
rows=1000000 loops=1)
Sort Key: (generate_series(1, 1000000))
Sort Method: external merge Disk: 13696kB
-> Result (cost=0.00..0.01 rows=1 width=0) (actual
time=0.006..144.720 rows=1000000 loops=1)
Total runtime: 3009.218 ms
(5 rows)

What is maintenance_work_mem
The amount of memory (RAM) allowed for
maintenance tasks before PostgreSQL swaps.
Typical tasks are ANALYZE, VACUUM, CREATE
INDEX, REINDEX
Without maintanence, performance will decrease.

maintenance_work_mem
Set to a reasonable amount for autovacuum
Use SET for per session changes such as
CREATE INDEX
SET maintenance_work_mem to '1GB';
CREATE INDEX foo ON bar(baz);
RESET maintenance_work_mem;

What is effective_cache_size
A pointer for the PostgreSQL planner to hint at
how much of the database will be cached. This is
not an allocation setting.

effective_cache_size
Take into account shared_buffers
total used free shared buffers cached
Mem: 6126208 3168356 2957852 0 480884 1258304
% of cached + shared_buffers = effective_cache_size
● % depends on workload. Generally between 40% and 70%
● Can be used to encourage index scans, use higher than normal
amounts if have fast IO.

Let's talk I/O
log_checkpoints
checkpoint_timeout
checkpoint_completion_target
checkpoint_segments
wal_sync_method
synchronous_commit

log_checkpoints
By default this is off. Turn on to correlate between
checkpoints and spikes in %IOWait from sar.

checkpoint_timeout
The amount of time PostgreSQL will wait
before it forces a checkpoint. Properly
configured it reduces IO utilization. Set
to 60 minutes. It is affected by:
● checkpoint_segments
● checkpoint_completion_target

checkpoint_completion_target
This paramater is used to reduce
spikes in IO by completing a
checkpoint over a period of time.
Do not change this paramater, increase
checkpoint_timeout instead.

checkpoint_segments
The number of transaction logs that will be
utilized before a checkpoint is forced. Each
segment is 16 Mb. The default is 3. Use
checkpoint_warning to see if you need more.
Change to at least 10.
Use checkpoint_warning and logging to get
accurate setting.

wal_sync_method
The type of fsync that will be called to flush file
modifications to disk. Leave commented to have
PostgreSQL figure it out. On Linux it should look
like:
postgres=# show wal_sync_method ;
wal_sync_method
-----------------
fdatasync

synchronous_commit
Specifies whether transaction commit will wait for
WAL records to be written to disk before the
command returns a "success" indication to the
client.
Depends on application. Turn off for faster
commits. Low risk of lost commits (but not
integrity).
Required to be on if you want synchronous
replicaiton.

Let's talk brains
● default_statistics_target
● seq_page_cost
● random_page_cost
● cpu_operator_cost
● cpu_tuple_cost

default_statistics_target
An arbitrary value used to determine the volume
of statistics collected on a relation. The larger the
value the longer analyze takes but generally the
better the plan. Can be set per column.

set default_statistics_target to 100;
pggraph_2_2=# analyze verbose pggraph_indexrollup;
INFO: analyzing "aweber_shoggoth.pggraph_indexrollup"
INFO: "pggraph_indexrollup": scanned 30000 of 1448084
pages, containing 1355449 live rows and 0 dead rows; 30000 rows in
sample, 65426800 estimated total rows
ANALYZE

set default_statistics_target to 300;
pggraph_2_2=# analyze verbose pggraph_indexrollup;
INFO: analyzing "aweber_shoggoth.pggraph_indexrollup"
INFO: "pggraph_indexrollup": scanned 90000 of 1448084
pages, containing 4066431 live rows and 137 dead rows; 90000 rows in
sample, 65428152 estimated total rows
ANALYZE
pggraph_2_2=#

Increasing per column
ALTER TABLE foo
ALTER COLUMN BAR
SET STATISTICS 120

How do I know to increase it?
Unique (cost=264.65..282.65 rows=100 width=2) (actual time=8.665..12.460
rows=100 loops=1)
-> Sort (cost=264.65..273.65 rows=3600 width=2) (actual
time=8.664..10.423 rows=3600 loops=1)
Sort Key: one
Sort Method: quicksort Memory: 265kB
-> Seq Scan on bar
(cost=0.00..52.00 rows=52 width=2) (actual time=0.007..1.894 rows=3600
loops=1)
Total runtime: 12.553 ms

seq_page_cost
Tells the planner how expensive a sequential scan
is. It directly relates to random_page_cost.

random_page_cost
Tells the planner the expense of fetching a
random page. If using RAID 10, the value should
be inverted with seq_page_cost (1.0 vs 4.0) or at
least made the same.
This can hurt data analysis queries, look into
cpu_tuple_cost as well.

cpu_operator_cost
Sets the planner's estimate of the cost of
processing each operator or function executed
during a query. The default is 0.0025.
In real world tests, a setting of 0.5 generally
provides a better plan. Test using SET in a
session.
SET cpu_operator_cost TO 0.5;
EXPLAIN ANALYZE SELECT ...

cpu_tuple_cost
Sets the planner's estimate of the cost of
processing each row during a query. The default
is 0.01.
In real world tests, a setting of 0.5 generally
provides a better plan. Test using SET in a
session.
SET cpu_tuple_cost TO 0.5;
EXPLAIN ANALYZE SELECT ...

Design
Connection Pooling
Load Balancing

Connection Pooling
●
Reduces CPU utilization
●
Keeps relations hot (in cache)
●
Pgbouncer (no ssl):
– http://pgfoundry.org/projects/pgbouncer
●
Pgpool2 (SSL capable on client to pool or pool to server):
●
http://www.pgpool.net

Load Balancing
Hot Standby + PgPool-II

Autovacuum
Just say no to disabling. If you are experiencing
”peformance problems” due to vacuum. You are
experiencing performance problems lack of
management/provisioning/planning. Just say no to
disabling.

Questions?
Questions / Comments?
Take your best shot!
I can speak about:
Hardware
Consulting
Open Source Communities
Non-Profits
PostgreSQL
Politics

Dumb Simple PostgreSQL Performance (NYCPUG)

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