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# How To Benchmark PostgreSQL Queries Well

###### May 18, 2022
Web Engineering

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As I was digging into writing the third article of my series on PostgreSQL Authorization ([part 1](https://www.tangramvision.com/blog/hands-on-with-postgresql-authorization-part-1-roles-and-grants), [part 2](https://www.tangramvision.com/blog/hands-on-with-postgresql-authorization-part-2-row-level-security)), I realized that, in order to measure the performance impacts of different Row-Level Security policies, I first needed to understand how to accurately time queries. This article will be a short detour from the aforementioned series — we’ll explore and discuss different approaches to timing queries in PostgreSQL and mention important implications to be aware of when using these different techniques.

I have a few goals and disclaimers to outline before we dive in:

- I want to measure time taken by a single SELECT query. I’m not looking to measure system throughput or real-world load scenarios (i.e. I won’t consider update/insert/delete performance, locking, contention, etc.).
- I want to measure overall query duration — not just parsing or planning or execution.
- I would *prefer* to have percentile or histogram results, not just an average.
- I’m aiming to learn how to compare one query to another query to determine which is faster, so whatever tool we use for timing queries shouldn’t introduce systematic error that might mislead us about which query is faster. This partly comes down to testing methodology, which we’ll discuss in more detail soon.

## Hands-On With an Example Schema

Of course, we need to actually try things out to measure performance! You can follow along (and let me know if you see different results!) using Docker and the repo at [https://gitlab.com/tangram-vision/oss/tangram-visions-blog/-/tree/main/2022.05.17_HowToBenchmarkPostgreSQLQueriesWell](https://gitlab.com/tangram-vision/oss/tangram-visions-blog/-/tree/main/2022.05.17_HowToBenchmarkPostgreSQLQueriesWell)

As described in the repo's README, you can run the command below, which uses the [official Postgres Docker image](https://hub.docker.com/_/postgres) to run a PostgreSQL database locally. The first volume mount will load the schema.sql file, which will create a simple table and fill it with 10 million rows. The -c config commands to postgres will make the [pg_stat_statements extension](https://www.postgresql.org/docs/current/pgstatstatements.html) available.

bash
docker run --name=postgres \
--rm \
--volume=$(pwd)/schema.sql:/docker-entrypoint-initdb.d/schema.sql \ --volume=$(pwd):/repo \
--env=PSQLRC=/repo/.psqlrc \
postgres:latest \
-c pg_stat_statements.track_planning=on


To open a psql prompt in that Docker container, run the following in another terminal:

bash
docker exec --interactive --tty \
--user=postgres \
--workdir=/repo \


## General Benchmarking Concerns

When measuring database/query performance, there are many things that can introduce systematic error or produce low-confidence results. Let’s talk about some!

- If possible, avoid connections and transferring data over the network. Even traffic on the loopback interface must travel through the network stack and suffer its associated copying and context switches, which will add noise to measurements. Prefer a UNIX domain socket, which is [faster/simpler than TCP](https://momjian.us/main/blogs/pgblog/2012.html#June_6_2012).
- Database performance depends heavily on caching. Avoid comparing cold caches and cache misses against warm caches and cache hits. Run the query a few times before measuring runtime or throw away a few measurement outliers or use a percentile statistic rather than a mean or maximum. You can enable the [track_io_timing setting in PostgreSQL](https://www.postgresql.org/docs/current/runtime-config-statistics.html#GUC-TRACK-IO-TIMING) and use [EXPLAIN with the BUFFERS option](https://www.postgresql.org/docs/14/sql-explain.html) to see if a query is reading from disk.
- On your test machine, limit background activity such as open applications, heavy cronjobs, or antivirus scans. I ran a ~1.5 second query 100 times on my machine with background apps (slack, firefox, chrome, and vscode) open and with them closed — see the histogram below for a comparison, which shows that having extra apps open added considerable noise to query durations.

- Use large enough data that queries take some time to finish. Also, run tests many times. Comparing queries that take <1 ms by running each of them 5 times will not give conclusive results — the measurements will be dominated by noise or happenstance error.
- Make sure the query planner has [up-to-date statistics](https://www.depesz.com/2013/05/30/explaining-the-unexplainable-part-5/) by running the [ANALYZE command](https://www.postgresql.org/docs/current/sql-analyze.html) before running tests.
- If your tests involve deleting rows, be consistent about [vacuuming](https://www.postgresql.org/docs/current/sql-vacuum.html), or removing deleted rows from the table. If a table is [auto-vacuumed](https://www.postgresql.org/docs/current/routine-vacuuming.html) in the middle of a test, that test could be thrown off significantly by the extra I/O caused by vacuuming, and all subsequent tests will operate on a database table with a different disk layout and size.

## Query Timing Approaches

There are a lot of different ways to time queries with PostgreSQL! I’ve picked a few different tools and approaches to demonstrate and discuss.

In the rest of this article, I’ll test different methods of timing queries. For all of these tests, I’ll be running 2 queries on a table called “artists”:

- SELECT * FROM artists
- SELECT count(1) FROM artists

The artists table contains 10 million rows. Each row is a 4-byte integer primary key column and a 13-byte text column (timings seem similar whether the text column uses [EXTENDED or PLAIN storage](https://www.postgresql.org/docs/current/sql-altertable.html#id-1.9.3.35.5.2.3.11.1.2)). I run each query 5 times to warm the cache (ignoring the results) and then 100 times for the actual test.

### psql \timing

A common first foray into timing queries is using psql and its [\timing option](https://www.postgresql.org/docs/current/app-psql.html). Here’s what it looks like:

sql
-- Query output is not shown for brevity
=# \timing
Timing is on.
=# SELECT * FROM artists;
Time: 1550.342 ms (00:01.550)
=# SELECT count(1) FROM artists;
Time: 175.526 ms


Simple! But note that the \timing option is a psql option and runs the timer in psql. That means the output time includes network latency (from the PostgreSQL server process to the psql client), which can vary a lot and throw off our comparisons. Network latency and speed are important concerns in real-world scenarios, but we don’t want spurious network-related noise throwing off our comparison of which versions of a query execute faster in PostgreSQL. If you’re connecting over a UNIX domain socket (as we are with the Docker approach described above), then network noise/overhead is less of a concern. Nevertheless, we’ll generally prefer other approaches in this article, most of which measure query duration server-side.

### EXPLAIN ANALYZE

[EXPLAIN](https://www.postgresql.org/docs/current/sql-explain.html) is a command that explains how PostgreSQL will execute a query. It has lots of useful options, such as:

- ANALYZE — actually runs the query and reports some stats
- SETTINGS — shows non-default settings that affect the query
- BUFFERS — shows caching info such as hits and misses and how much data involved disk I/O
- TIMING — shows time spent in each step of the query

There’s a lot written about EXPLAIN around the internet, so I won’t rehash the details of the command. If you want to learn more, check out the docs [about the command](https://www.postgresql.org/docs/current/sql-explain.html) and [using EXPLAIN](https://www.postgresql.org/docs/14/using-explain.html) and [depesz’s series on understanding EXPLAIN output](https://www.depesz.com/tag/unexplainable/).

For our purposes, we’ll use EXPLAIN ANALYZE to run the query and tell us how long it took.

sql
=# EXPLAIN ANALYZE SELECT * FROM artists;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------
Seq Scan on artists  (cost=0.00..163695.00 rows=10000000 width=17) (actual time=0.044..486.527 rows=10000000 loops=1)
Planning Time: 0.079 ms
Execution Time: 705.450 ms
(3 rows)

=# EXPLAIN ANALYZE SELECT count(1) FROM artists;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
Finalize Aggregate  (cost=116778.55..116778.56 rows=1 width=8) (actual time=298.815..302.724 rows=1 loops=1)
->  Gather  (cost=116778.33..116778.54 rows=2 width=8) (actual time=298.721..302.716 rows=3 loops=1)
Workers Planned: 2
Workers Launched: 2
->  Partial Aggregate  (cost=115778.33..115778.34 rows=1 width=8) (actual time=280.831..280.832 rows=1 loops=3)
->  Parallel Seq Scan on artists  (cost=0.00..105361.67 rows=4166667 width=0) (actual time=0.024..162.195 rows=3333333 loops=3)
Planning Time: 0.073 ms
JIT:
Functions: 8
Options: Inlining false, Optimization false, Expressions true, Deforming true
Timing: Generation 0.866 ms, Inlining 0.000 ms, Optimization 0.337 ms, Emission 5.465 ms, Total 6.668 ms
Execution Time: 303.638 ms
(12 rows)


Lots going on here! We see that the command provides an “Execution Time” measurement toward the bottom, which we could use for benchmarking. Comparing these times to the ones we got in the previous section using \timing is curious:

<table>
<tr>
<th>Query</th>
<th>SELECT * FROM artists</th>
<th>SELECT count(1) FROM artists</th>
</tr>
<tr>
<th>psql \timing</th>
<td>1550.342</td>
<td>175.526</td>
</tr>
<tr>
<th>EXPLAIN ANALYZE</th>
<td>705.450</td>
<td>303.638</td>
</tr>
</table>

We know that psql’s \timing includes network-related overhead, so we could claim that as the reason that EXPLAIN ANALYZE is faster for the first query. But, EXPLAIN ANALYZE is slower for the second query! Clearly, network-related overhead isn’t the whole story.

You may notice that the EXPLAIN ANALYZE output includes “actual time” measurements for every node. How does it do that? We might expect that a node tracks the time when it starts running and when it stops, and then it reports the difference as the time in that node. But, PostgreSQL doesn’t work like that — it follows [the Volcano model](https://www.ongres.com/blog/explain_analyze_may_be_lying_to_you/#the-volcano), meaning that each node produces a stream of rows and its parent node pulls rows as needed. That’s how a query like SELECT * FROM artists LIMIT 5 is efficient: the LIMIT node pulls 5 rows from its child node (a sequential scan of the table) and the query is complete — the sequential scan doesn’t scan the entire table before returning results to the LIMIT operation. This means that the “actual time” measurements for a node are not from timing the entire node (because the node doesn’t know when or if it might need to produce more rows), they’re from timing how long it takes the node to produce *each row*. So, clock_gettime is called before and after producing each row (i.e. twice per row) for each node in the query plan, which you can confirm with a command like sudo ltrace -p <postgres_pid> -c -e clock_gettime (thanks to [this article](https://www.ongres.com/blog/explain_analyze_may_be_lying_to_you/) for the tip). A flamegraph also does a good job of visualizing how much time is spent calling clock_gettime.

*[Perf flamegraph](https://github.com/brendangregg/FlameGraph) of executing EXPLAIN ANALYZE SELECT * FROM artists.*

bash
$pg_test_timing Testing timing overhead for 3 seconds. Per loop time including overhead: 23.40 ns Histogram of timing durations: < us % of total count 1 97.66239 125227359 2 2.33714 2996796 4 0.00021 272 8 0.00014 180 16 0.00011 146 32 0.00000 6 64 0.00000 0 128 0.00000 1  Different [time sources](http://btorpey.github.io/blog/2014/02/18/clock-sources-in-linux/) have different overhead, so EXPLAIN ANALYZE timing results can vary a lot across operating systems and virtualization setups. Luckily, we can get more consistent results by asking PostgreSQL to skip timing all the query nodes. To do so, we turn off the TIMING option: sql =# EXPLAIN (ANALYZE, TIMING FALSE) SELECT * FROM artists; -- Query plan output is not shown for brevity Execution Time: 385.025 ms =# EXPLAIN (ANALYZE, TIMING FALSE) SELECT count(1) FROM artists; -- Query plan output is not shown for brevity Execution Time: 198.411 ms  Now our table comparing query timings looks as follows: <table> <tr> <th>Query</th> <th>SELECT * FROM artists</th> <th>SELECT count(1) FROM artists</th> </tr> <tr> <th>psql \timing</th> <td>1550.342</td> <td>175.526</td> </tr> <tr> <th>EXPLAIN ANALYZE</th> <td>705.450</td> <td>303.638</td> </tr> <tr> <th>EXPLAIN (ANALYZE, TIMING FALSE)</th> <td>385.025</td> <td>198.411</td> </tr> </table> Disabling TIMING cut execution time of the first query almost in half, which matches what we would expect based on how much of the above flamegraph was taken up by calls to clock_gettime. ### Logging Options PostgreSQL provides a few different config options that log query duration. These are useful settings if you want to track or analyze query durations for an active database, but we can also use them to benchmark in a local development database. These approaches time the overall query (not each and every node in the query plan like EXPLAIN ANALYZE does!), so timing overhead should be minimal — we should get accurate timings that let us compare queries’ performance. #### log_duration To log the duration of all queries with the [log_duration option](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-DURATION), set log_duration = on in postgresql.conf or in your psql session. Example usage and output follows: sql -- Reminder: Lines starting with =# are commands entered into a psql prompt. =# SET log_duration = on; =# SELECT * FROM artists; -- In postgres process stdout/log: 2022-05-01 18:56:57.418 UTC [82] LOG: duration: 1642.956 ms =# SELECT count(1) FROM artists; -- In postgres process stdout/log: 2022-05-01 18:57:14.470 UTC [82] LOG: duration: 194.590 ms  > 💡 If you want to see database log messages inline in your psql process (rather than having to look at the postgres process’s stdout/log, you can change the [client_min_messages setting](https://www.postgresql.org/docs/current/runtime-config-client.html#GUC-CLIENT-MIN-MESSAGES) to LOG by running SET client_min_messages = LOG. However, all the log messages seem to be accompanied by lines like LOCATION: exec_simple_query, postgres.c:1297, so it’s a bit messier to read than the database log. #### log_min_duration_statement Use the [log_min_duration_statement option](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-MIN-DURATION-STATEMENT) to log queries that take longer than some threshold (say, 250 ms). To use this option, set log_min_duration_statement = 250 in postgresql.conf or in your psql session. By setting the threshold to zero, the duration of all queries will be logged (similar to using log_duration). However, log_duration only logs the query duration, whereas log_min_duration_statement will also log the text of the query. sql =# SET log_min_duration_statement = 0; =# SELECT * FROM artists; -- In postgres process stdout/log: 2022-05-01 19:08:44.395 UTC [82] LOG: duration: 1634.927 ms statement: SELECT * FROM artists; =# SELECT count(1) FROM artists; -- In postgres process stdout/log: 2022-05-01 19:08:56.390 UTC [82] LOG: duration: 191.741 ms statement: SELECT count(1) FROM artists;  To disable log_min_duration_statement, set it to -1. #### log_statement_stats If you want more detailed information about query duration, memory usage, page faults, context switches, etc. then try out the [log_statement_stats options](https://www.postgresql.org/docs/14/runtime-config-statistics.html#RUNTIME-CONFIG-STATISTICS-MONITOR), of which there are a few variations: - log_statement_stats — Log stats for the query overall (can’t be enabled if any of the following options are enabled) - log_parser_stats — Log stats for the query parsing stage - log_planner_stats — Log stats for the query planning stage - log_executor_stats — Log stats for the query execution stage The last 3 options can all be enabled together. Below is an example of using log_statement_stats. sql =# SET log_statement_stats = on; =# SELECT * FROM artists; -- In postgres process stdout/log: 2022-05-01 19:47:05.003 UTC [79] LOG: QUERY STATISTICS 2022-05-01 19:47:05.003 UTC [79] DETAIL: ! system usage stats: ! 1.447567 s user, 0.107958 s system, 1.573427 s elapsed ! [182.988793 s user, 16.027573 s system total] ! 187488 kB max resident size ! 0/0 [0/0] filesystem blocks in/out ! 0/0 [10892/6708] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 4/145 [2251/18917] voluntary/involuntary context switches 2022-05-01 19:47:05.003 UTC [79] STATEMENT: select * from artists; =# SELECT count(1) FROM artists; -- In postgres process stdout/log: 2022-05-01 19:54:26.231 UTC [79] LOG: QUERY STATISTICS 2022-05-01 19:54:26.231 UTC [79] DETAIL: ! system usage stats: ! 0.154934 s user, 0.015904 s system, 0.175565 s elapsed ! [199.261753 s user, 17.894312 s system total] ! 187516 kB max resident size ! 0/0 [0/0] filesystem blocks in/out ! 0/10 [10893/7761] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 5/28 [3132/20791] voluntary/involuntary context switches 2022-05-01 19:54:26.231 UTC [79] STATEMENT: select count(1) from artists;  Note that the stats inside square brackets are cumulative for the session. ### pg_stat_statements The [pg_stat_statements module](https://www.postgresql.org/docs/current/pgstatstatements.html) tracks statistics for all queries run by the PostgreSQL server. It must be loaded by setting shared_preload_libraries='pg_stat_statements' on the command line or in postgresql.conf. Then, run the SQL statement CREATE EXTENSION pg_stat_statements and you’ll have a pg_stat_statements view containing rows like the below: sql =# SELECT * FROM pg_stat_statements WHERE query = 'SELECT * FROM artists'; -[ RECORD 1 ]-------+---------------------- userid | 10 dbid | 13757 toplevel | t queryid | 2059596454762915818 query | SELECT * FROM artists plans | 100 total_plan_time | 4.6373750000000005 min_plan_time | 0.030738 max_plan_time | 0.07399 mean_plan_time | 0.04637375000000001 stddev_plan_time | 0.0066957018263584574 calls | 100 total_exec_time | 158575.761936 min_exec_time | 1570.0188819999998 max_exec_time | 1624.1295579999999 mean_exec_time | 1585.7576193599996 stddev_exec_time | 10.636442294647289 rows | 1000000000 shared_blks_hit | 292800 shared_blks_read | 6076700 shared_blks_dirtied | 0 shared_blks_written | 0 local_blks_hit | 0 local_blks_read | 0 local_blks_dirtied | 0 local_blks_written | 0 temp_blks_read | 0 temp_blks_written | 0 blk_read_time | 0 blk_write_time | 0 wal_records | 0 wal_fpi | 0 wal_bytes | 0 -- Irrelevant columns omitted from below output for brevity =# SELECT * FROM pg_stat_statements WHERE query = 'SELECT count($1) FROM artists';
-[ RECORD 1 ]-------+------------------------------
query               | SELECT count(\$1) FROM artists
plans               | 100
total_plan_time     | 4.789295
min_plan_time       | 0.035157
max_plan_time       | 0.056928
mean_plan_time      | 0.04789295
stddev_plan_time    | 0.0031252439596773894
calls               | 100
total_exec_time     | 17308.772363999997
min_exec_time       | 170.66001599999998
max_exec_time       | 176.27587
mean_exec_time      | 173.08772363999998
stddev_exec_time    | 1.3255601605010174
rows                | 100


> 💡 Enabling [track_io_timing](https://www.postgresql.org/docs/current/runtime-config-statistics.html#GUC-TRACK-IO-TIMING) will populate the blk_read_time and blk_write_time columns.

Similar to the logging options, pg_stat_statements is a handy tool to enable on an active database, so you can keep an eye on which queries run slowly and/or are called often.

For the purpose of benchmarking queries, we can warm the cache, call SELECT pg_stat_statements_reset() to reset the stats, call our query repeatedly, then see the resulting timing stats in the pg_stat_statements view. The view is convenient, because we don’t have to parse log data and calculate all the stats ourselves (which I’ve been doing in a [spreadsheet](https://gitlab.com/tangram-vision/oss/tangram-visions-blog/-/blob/main/2022.05.17_HowToBenchmarkPostgreSQLQueriesWell/data/workbook.ods) for all the other timing approaches so far!). But, we can’t produce percentile data or histograms when we only have aggregated values.

### pg_bench

PostgreSQL has a dedicated benchmarking tool called [pg_bench](https://www.postgresql.org/docs/current/pgbench.html), which we can configure to test specific queries against each other. First, we write scripts containing the queries we want pg_bench to run — this is simple in our case of testing a single query at a time:

sql
-- scripts/pg_bench_script.sql
SELECT * FROM artists;


sql
-- scripts/pg_bench_count_script.sql
SELECT count(1) FROM artists;


Then, we call the pg_bench binary (pgbench) that’s included with PostgreSQL, passing command-line arguments to log all transactions and to run 100 transactions. (As far as I can tell, pg_bench uses the word “transaction” to simply mean “one execution of the script” — it doesn’t wrap the script in a database transaction.)

sql
-- We use psql’s \! command to execute the pgbench binary in a shell,
-- rather than running a new docker command.

=# \! pgbench -f scripts/pg_bench_script.sql --log --transactions=100 --username=postgres postgres
pgbench (14.1 (Debian 14.1-1.pgdg110+1))
starting vacuum...pgbench: error: ERROR:  relation "pgbench_branches" does not exist
pgbench: (ignoring this error and continuing anyway)
pgbench: error: ERROR:  relation "pgbench_tellers" does not exist
pgbench: (ignoring this error and continuing anyway)
pgbench: error: ERROR:  relation "pgbench_history" does not exist
pgbench: (ignoring this error and continuing anyway)
end.
transaction type: scripts/pg_bench_script.sql
scaling factor: 1
query mode: simple
number of clients: 1
number of transactions per client: 100
number of transactions actually processed: 100/100
latency average = 1724.637 ms
initial connection time = 2.352 ms
tps = 0.579828 (without initial connection time)

=# \! pgbench -f scripts/pg_bench_count_script.sql --log --transactions=100 --username=postgres postgres
pgbench (14.1 (Debian 14.1-1.pgdg110+1))
-- Extra output omitted for brevity
latency average = 178.929 ms
initial connection time = 2.319 ms
tps = 5.588447 (without initial connection time)


You’ll notice some errors in the output — that’s because pg_bench expects to run against [a preset schema](https://github.com/postgres/postgres/blob/ed57cac84d1c5642737dab1e4c4b8cb4f0c4305f/src/bin/pgbench/pgbench.c#L4723-L4772) based on the [TPC-B benchmark](https://www.tpc.org/tpcb/) (first established back in 1990 and marked obsolete in 1995!). Other than the output noise, pg_bench is perfectly happy to run whatever script you give it, and we can see that it ran our query 100 times and reported the average latency (how long it took to run the query).

Similar to pg_stat_statements, pg_bench provides already-computed stats. But, with the --log command-line option we provided, pg_bench writes [the time taken for each transaction into a log file](https://www.postgresql.org/docs/current/pgbench.html#id-1.9.4.11.9.6), and we can use those measurements to derive more stats on our own.

In the end, pg_bench is somewhat useful in our quest to benchmark and compare queries. But, it’s a better fit for load testing than query comparison — it lets you provide a mix of queries (you can simulate a realistic load of reads and writes), run many clients in parallel, target a specific transaction rate, and much more.

### SQL Function with clock_timestamp

The final approach we’ll try is writing a [PL/pgSQL function](https://www.postgresql.org/docs/9.6/plpgsql.html) to compare [clock_timestamp](https://www.postgresql.org/docs/current/functions-datetime.html#FUNCTIONS-DATETIME-TABLE) values before and after running our query. I’ve modified [this StackOverflow answer](https://stackoverflow.com/a/9064100) to end up with the below function:

sql
-- scripts/clock_timestamp_function.sql
CREATE OR REPLACE FUNCTION bench(query TEXT, iterations INTEGER = 100)
RETURNS TABLE(avg FLOAT, min FLOAT, q1 FLOAT, median FLOAT, q3 FLOAT, p95 FLOAT, max FLOAT) AS $$DECLARE _start TIMESTAMPTZ; _end TIMESTAMPTZ; _delta DOUBLE PRECISION; BEGIN CREATE TEMP TABLE IF NOT EXISTS _bench_results ( elapsed DOUBLE PRECISION ); -- Warm the cache FOR i IN 1..5 LOOP EXECUTE query; END LOOP; -- Run test and collect elapsed time into _bench_results table FOR i IN 1..iterations LOOP _start = clock_timestamp(); EXECUTE query; _end = clock_timestamp(); _delta = 1000 * ( extract(epoch from _end) - extract(epoch from _start) ); INSERT INTO _bench_results VALUES (_delta); END LOOP; RETURN QUERY SELECT avg(elapsed), min(elapsed), percentile_cont(0.25) WITHIN GROUP (ORDER BY elapsed), percentile_cont(0.5) WITHIN GROUP (ORDER BY elapsed), percentile_cont(0.75) WITHIN GROUP (ORDER BY elapsed), percentile_cont(0.95) WITHIN GROUP (ORDER BY elapsed), max(elapsed) FROM _bench_results; DROP TABLE IF EXISTS _bench_results; END$$
LANGUAGE plpgsql;


We run the query many times, measuring the duration and storing results in a temporary table, which we use to calculate whatever statistics we want and return them to the function caller. Calling it looks like:

sql
=# SELECT * FROM bench('SELECT * FROM artists');
avg   |   min   |    q1     | median  |        q3         |    p95    |   max
----------+---------+-----------+---------+-------------------+-----------+---------
869.59934 | 868.069 | 869.15325 | 869.546 | 870.0382500000001 | 870.59285 | 872.134

=# SELECT * FROM bench('SELECT count(1) FROM artists');
avg    |   min   |    q1     |  median  |    q3    |   p95   |   max
-----------+---------+-----------+----------+----------+---------+---------
177.44266 | 175.186 | 176.81625 | 177.1125 | 177.8295 | 180.107 | 183.201


This approach is flexible and customizable — we don’t have to parse out individual measurements or calculate statistics by hand, and we can include whatever statistics we care about. We could even make the function output a graphical histogram as described in these articles:

- [https://tapoueh.org/blog/2014/02/postgresql-aggregates-and-histograms/](https://tapoueh.org/blog/2014/02/postgresql-aggregates-and-histograms/)

## Conclusion

Whew! PostgreSQL has a lot of different ways to time queries and we didn’t even cover some other options, like [pg_stat_monitor](https://github.com/percona/pg_stat_monitor) or [dynamic tracing](https://www.postgresql.org/docs/14/dynamic-trace.html).

For fun, let’s compare our average query duration (in ms) for each query and each timing approach:

<table>
<tr>
<th>Query</th>
<th>SELECT * FROM artists</th>
<th>SELECT count(1) FROM artists</th>
</tr>
<tr>
<th>psql \timing</th>
<td>1550.342</td>
<td>175.526</td>
</tr>
<tr>
<th>EXPLAIN ANALYZE</th>
<td>705.450</td>
<td>303.638</td>
</tr>
<tr>
<th>EXPLAIN (ANALYZE, TIMING FALSE)</th>
<td>385.025</td>
<td>198.411</td>
</tr>
<tr>
<th>log_duration</th>
<td>1642.956</td>
<td>194.590</td>
</tr>
<tr>
<th>log_min_duration_statement</th>
<td>1634.927</td>
<td>191.741</td>
</tr>
<tr>
<th>log_statement_stats</th>
<td>1573.427</td>
<td>175.565</td>
</tr>
<tr>
<th>pg_stat_statements</th>
<td>1585.758</td>
<td>173.088</td>
</tr>
<tr>
<th>pg_bench</th>
<td>1724.638</td>
<td>178.929</td>
</tr>
<tr>
<th>function with clock_timestamp</th>
<td>869.599</td>
<td>177.443</td>
</tr>
</table>

Some concluding observations:

- Despite taking precautions mentioned in the “General Benchmarking Concerns” section, there still appears to be quite a bit of noise! For example, we’d expect the client-side psql \timing approach to report longer query durations than approaches that measure time on the server, but these results show the opposite! This reinforces [the message in the pg_bench documentation](https://www.postgresql.org/docs/current/pgbench.html#id-1.9.4.11.9.9) about getting reproducible benchmark results: “*never* believe any test that runs for only a few seconds... In some cases you could need hours to get numbers that are reproducible”.
- In general, don’t compare query durations derived from different timing approaches — many use different timing mechanisms and start/stop timers in different locations in the code, so they’re not directly comparable.
- The 3 logging approaches are pretty similar. In fact, they all end their timers and print results in [the same location in the PostgreSQL code](https://github.com/postgres/postgres/blob/ed57cac84d1c5642737dab1e4c4b8cb4f0c4305f/src/backend/tcop/postgres.c#L1330-L1347), though they use slightly different timer start locations and timing methods (on Linux: [clock_gettime](https://linux.die.net/man/3/clock_gettime) for log_duration and log_min_duration_statement vs [getrusage](https://linux.die.net/man/2/getrusage) for log_statement_stats).
- The “function with clock_timestamp” and EXPLAIN approaches are much faster than all the other methods when a lot of data is returned to the database client, so there must be some allocation or serialization step that isn’t included in the times for these approaches.
- Why is the “EXPLAIN (ANALYZE, TIMING FALSE)” approach so much faster than “function with clock_timestamp”? They both execute the query and (presumably) skip allocating or serializing the results. I didn’t get a chance to delve into this question, but I’d love to know what the difference is — let me know if you have some insight!

In future articles, I’ll use one of these logging approaches to measure and compare queries. It’s not as convenient as the “function with clock_timestamp” approach, but it seems more representative of real world performance, because it includes the time needed to return data for the client (an important part of what a database does!).

---

Thanks for reading! I hope you learned something new and will return for the next article in the [PostgreSQL Authorization series](https://www.tangramvision.com/blog/hands-on-with-postgresql-authorization-part-1-roles-and-grants). If you have any corrections to this article or suggestions/questions for future articles, please [let me know](mailto:greg.schafer@tangramvision.com) or [send us a tweet](https://www.twitter.com/tangramvision). If you’re curious to learn more about how we improve perception sensors, visit us at [Tangram Vision](https://www.tangramvision.com/), and if saving perception engineers years of effort sounds like your calling, then I have even better news: we’re hiring! Check out our [careers page for open positions](http://www.tangramvision.com/careers).

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