PGBENCH(1)

🎀 `PGBENCH(1)` 🎀

PostgreSQL 15.19 Documentation, bestie ✨

🌸 NAME 🌸

pgbench — run a benchmark test on PostgreSQL

✨ SYNOPSIS ✨

pgbench -i [option...] [dbname]

pgbench [option...] [dbname]

💅 DESCRIPTION 💅

Okay so pgbench is literally a simple program for running benchmark tests on PostgreSQL. It runs the same sequence of SQL commands over and over, possibly in multiple concurrent database sessions, and then calculates the average transaction rate — that’s transactions per second, iykyk. By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. But it is easy to test other cases by writing your own transaction script files. Obsessed.

Typical output from pgbench looks like:

transaction type: <builtin: TPC-B (sort of)>
scaling factor: 10
query mode: simple
number of clients: 10
number of threads: 1
maximum number of tries: 1
number of transactions per client: 1000
number of transactions actually processed: 10000/10000
number of failed transactions: 0 (0.000%)
latency average = 11.013 ms
latency stddev = 7.351 ms
initial connection time = 45.758 ms
tps = 896.967014 (without initial connection time)

The first seven lines report some of the most important parameter settings. The sixth line reports the maximum number of tries for transactions with serialization or deadlock errors (see Failures and Serialization/Deadlock Retries for more information). The eighth line reports the number of transactions completed and intended (the latter being just the product of number of clients and number of transactions per client); these will be equal unless the run failed before completion or some SQL command(s) failed. (In -T mode, only the actual number of transactions is printed.) The next line reports the number of failed transactions due to serialization or deadlock errors (see Failures and Serialization/Deadlock Retries for more information). The last line reports the number of transactions per second.

The default TPC-B-like transaction test requires specific tables to be set up beforehand. pgbench should be invoked with the -i (initialize) option to create and populate these tables. (When you are testing a custom script, you don’t need this step, but will instead need to do whatever setup your test needs.) Initialization looks like:

pgbench -i [ other-options ] dbname

where dbname is the name of the already-created database to test in. (You may also need -h, -p, and/or -U options to specify how to connect to the database server.)

Caution, bestie: pgbench -i creates four tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers, destroying any existing tables of these names. Be very careful to use another database if you have tables having these names! 💀

At the default “scale factor” of 1, the tables initially contain this many rows:

table                   # of rows
---------------------------------
pgbench_branches        1
pgbench_tellers         10
pgbench_accounts        100000
pgbench_history         0

You can (and, for most purposes, probably should) increase the number of rows by using the -s (scale factor) option. The -F (fillfactor) option might also be used at this point.

Once you have done the necessary setup, you can run your benchmark with a command that doesn’t include -i, that is

pgbench [ options ] dbname

In nearly all cases, you’ll need some options to make a useful test. The most important options are -c (number of clients), -t (number of transactions), -T (time limit), and -f (specify a custom script file). See below for a full list.

🔥 OPTIONS 🔥

The following is divided into three subsections. Different options are used during database initialization and while running benchmarks, but some options are useful in both cases.

🧚 Initialization Options 🧚

pgbench accepts the following command-line initialization arguments:

dbname — Specifies the name of the database to test in. If this is not specified, the environment variable PGDATABASE is used. If that is not set, the user name specified for the connection is used.
-i, --initialize — Required to invoke initialization mode.
-I init_steps, --init-steps=init_steps — Perform just a selected set of the normal initialization steps. init_steps specifies the initialization steps to be performed, using one character per step. Each step is invoked in the specified order. The default is dtgvp. The available steps are:
- d (Drop) — Drop any existing pgbench tables.
- t (create Tables) — Create the tables used by the standard pgbench scenario, namely pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers.
- g or G (Generate data, client-side or server-side) — Generate data and load it into the standard tables, replacing any data already present. With g (client-side data generation), data is generated in pgbench client and then sent to the server. This uses the client/server bandwidth extensively through a COPY. pgbench uses the FREEZE option with version 14 or later of PostgreSQL to speed up subsequent VACUUM, unless partitions are enabled. Using g causes logging to print one message every 100,000 rows while generating data for the pgbench_accounts table. With G (server-side data generation), only small queries are sent from the pgbench client and then data is actually generated in the server. No significant bandwidth is required for this variant, but the server will do more work. Using G causes logging not to print any progress message while generating data. The default initialization behavior uses client-side data generation (equivalent to g).
- v (Vacuum) — Invoke VACUUM on the standard tables.
- p (create Primary keys) — Create primary key indexes on the standard tables.
- f (create Foreign keys) — Create foreign key constraints between the standard tables. (Note that this step is not performed by default.)
-F fillfactor, --fillfactor=fillfactor — Create the pgbench_accounts, pgbench_tellers and pgbench_branches tables with the given fillfactor. Default is 100.
-n, --no-vacuum — Perform no vacuuming during initialization. (This option suppresses the v initialization step, even if it was specified in -I.)
-q, --quiet — Switch logging to quiet mode, producing only one progress message per 5 seconds. The default logging prints one message each 100,000 rows, which often outputs many lines per second (especially on good hardware). This setting has no effect if G is specified in -I.
-s scale_factor, --scale=scale_factor — Multiply the number of rows generated by the scale factor. For example, -s 100 will create 10,000,000 rows in the pgbench_accounts table. Default is 1. When the scale is 20,000 or larger, the columns used to hold account identifiers (aid columns) will switch to using larger integers (bigint), in order to be big enough to hold the range of account identifiers.
--foreign-keys — Create foreign key constraints between the standard tables. (This option adds the f step to the initialization step sequence, if it is not already present.)
--index-tablespace=index_tablespace — Create indexes in the specified tablespace, rather than the default tablespace.
--partition-method=NAME — Create a partitioned pgbench_accounts table with NAME method. Expected values are range or hash. This option requires that --partitions is set to non-zero. If unspecified, default is range.
--partitions=NUM — Create a partitioned pgbench_accounts table with NUM partitions of nearly equal size for the scaled number of accounts. Default is 0, meaning no partitioning.
--tablespace=tablespace — Create tables in the specified tablespace, rather than the default tablespace.
--unlogged-tables — Create all tables as unlogged tables, rather than permanent tables.

🌈 Benchmarking Options 🌈

pgbench accepts the following command-line benchmarking arguments:

-b scriptname[@weight], --builtin=scriptname[@weight] — Add the specified built-in script to the list of scripts to be executed. Available built-in scripts are: tpcb-like, simple-update and select-only. Unambiguous prefixes of built-in names are accepted. With the special name list, show the list of built-in scripts and exit immediately. Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. See below for details.
-c clients, --client=clients — Number of clients simulated, that is, number of concurrent database sessions. Default is 1.
-C, --connect — Establish a new connection for each transaction, rather than doing it just once per client session. This is useful to measure the connection overhead.
-d, --debug — Print debugging output.
-D varname=value, --define=varname=value — Define a variable for use by a custom script (see below). Multiple -D options are allowed.
-f filename[@weight], --file=filename[@weight] — Add a transaction script read from filename to the list of scripts to be executed. Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. (To use a script file name that includes an @ character, append a weight so that there is no ambiguity, for example filen@me@1.) See below for details.
-j threads, --jobs=threads — Number of worker threads within pgbench. Using more than one thread can be helpful on multi-CPU machines. Clients are distributed as evenly as possible among available threads. Default is 1.
-l, --log — Write information about each transaction to a log file. See below for details.
-L limit, --latency-limit=limit — Transactions that last more than limit milliseconds are counted and reported separately, as late. When throttling is used (--rate=...), transactions that lag behind schedule by more than limit ms, and thus have no hope of meeting the latency limit, are not sent to the server at all. They are counted and reported separately as skipped.

Oh and bestie, real talk on --max-tries: when the --max-tries option is used, a transaction that fails because of a serialization anomaly or a deadlock will not be retried if the total time of all its tries is greater than limit ms. Want to limit only the time of the tries and not how many there are? Use --max-tries=0. By default, --max-tries is set to 1, which means transactions with serialization/deadlock errors just… don't get retried. See Failures and Serialization/Deadlock Retries for more info about retrying those transactions. 💅

-M querymode, --protocol=querymode — which protocol do we use to send queries to the server? Pick your fighter:
- simple: use the simple query protocol.
- extended: use the extended query protocol.
- prepared: use the extended query protocol with prepared statements.

In prepared mode, pgbench reuses the parse analysis result starting from the second query iteration, so pgbench runs faster than in the other modes. ✨

The default is the simple query protocol. (See Chapter 55 for more info, iykyk.)

-n, --no-vacuum — do zero vacuuming before running the test. You need this option if you're running a custom test scenario that doesn't include the standard tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers. 🧹🚫
-N, --skip-some-updates — run the built-in simple-update script. It's just shorthand for -b simple-update. 🎀
-P sec, --progress=sec — show a progress report every sec seconds. The report includes the time since the beginning of the run, the TPS since the last report, and the transaction latency average, standard deviation, and the number of failed transactions since the last report. Under throttling (-R), the latency is computed with respect to the transaction scheduled start time, not the actual transaction beginning time, so it also includes the average schedule lag time. When --max-tries is used to enable transaction retries after serialization/deadlock errors, the report includes the number of retried transactions and the sum of all retries. 📊💖
-r, --report-per-command — after the benchmark finishes, report these stats for each command: the average per-statement latency (execution time from the client's perspective), the number of failures, and the number of retries after serialization or deadlock errors in this command. The report only shows retry statistics if --max-tries is not equal to 1. 👑
-R rate, --rate=rate — execute transactions targeting the specified rate instead of going as fast as possible (which is the default). The rate is in transactions per second. If the targeted rate is above the maximum possible rate, the rate limit won't impact the results, no cap, it just sends it. 🔥

The rate is targeted by starting transactions along a Poisson-distributed schedule time line. The expected start time schedule moves forward based on when the client first started, not when the previous transaction ended. That approach means when transactions go past their original scheduled end time, it's possible for later ones to catch up again.

When throttling is active, the transaction latency reported at the end of the run is calculated from the scheduled start times, so it includes the time each transaction had to wait for the previous transaction to finish. That wait time is called the schedule lag time, and its average and maximum are also reported separately. The transaction latency with respect to the actual transaction start time — i.e., the time actually spent executing the transaction in the database — can be computed by subtracting the schedule lag time from the reported latency. Math girlie moment. 🧮

If --latency-limit is used together with --rate, a transaction can lag behind so much that it's already over the latency limit when the previous transaction ends, because the latency is calculated from the scheduled start time. Such transactions are not sent to the server — they're skipped altogether and counted separately.

A high schedule lag time is a sign that the system can't process transactions at the specified rate, with the chosen number of clients and threads. When the average transaction execution time is longer than the scheduled interval between each transaction, each successive transaction falls further behind, and the schedule lag time keeps increasing the longer the test run is. When that happens, you'll have to reduce the specified transaction rate. 😭

-s scale_factor, --scale=scale_factor — report the specified scale factor in pgbench's output. With the built-in tests this isn't necessary — the correct scale factor gets detected by counting the number of rows in the pgbench_branches table. But when you're testing only custom benchmarks (the -f option), the scale factor gets reported as 1 unless you use this option. 🌸
-S, --select-only — run the built-in select-only script. Shorthand for -b select-only. 💫
-t transactions, --transactions=transactions — number of transactions each client runs. Default is 10.
-T seconds, --time=seconds — run the test for this many seconds, rather than a fixed number of transactions per client. -t and -T are mutually exclusive, so pick one, bestie, you can't have both. 🙈
-v, --vacuum-all — vacuum all four standard tables before running the test. With neither -n nor -v, pgbench will vacuum the pgbench_tellers and pgbench_branches tables, and will truncate pgbench_history.
--aggregate-interval=seconds — length of the aggregation interval (in seconds). May only be used with the -l option. With this option, the log contains per-interval summary data, as described below. ⏱️
--failures-detailed — report failures in per-transaction and aggregation logs, as well as in the main and per-script reports, grouped by these types: 💀
- serialization failures;
- deadlock failures;

See Failures and Serialization/Deadlock Retries for more info.

--log-prefix=prefix — set the filename prefix for the log files created by --log. The default is pgbench_log. 🎀
--max-tries=number_of_tries — enable retries for transactions with serialization/deadlock errors and set the maximum number of these tries. This option can be combined with the --latency-limit option, which limits the total time of all transaction tries; and heads up, you cannot use an unlimited number of tries (--max-tries=0) without --latency-limit or --time. The default value is 1, and transactions with serialization/deadlock errors are not retried. See Failures and Serialization/Deadlock Retries for more info about retrying such transactions.
--progress-timestamp — when showing progress (option -P), use a timestamp (Unix epoch) instead of the number of seconds since the beginning of the run. The unit is in seconds, with millisecond precision after the dot. This helps compare logs generated by various tools. 🕰️
--random-seed=seed — set the random generator seed. It seeds the system random number generator, which then produces a sequence of initial generator states, one for each thread. Values for seed may be: time (the default, the seed is based on the current time), rand (use a strong random source, failing if none is available), or an unsigned decimal integer value. The random generator gets invoked explicitly from a pgbench script (random... functions) or implicitly (for instance, the --rate option uses it to schedule transactions). When explicitly set, the value used for seeding is shown on the terminal. Any value allowed for seed may also be provided through the environment variable PGBENCH_RANDOM_SEED. To ensure that the provided seed impacts all possible uses, put this option first or use the environment variable. 🌱

Setting the seed explicitly allows you to reproduce a pgbench run exactly, as far as random numbers are concerned. Since the random state is managed per thread, this means the exact same pgbench run for an identical invocation if there's one client per thread and there are no external or data dependencies. From a statistical viewpoint, reproducing runs exactly is a bad idea because it can hide the performance variability or improve performance unduly, e.g., by hitting the same pages as a previous run. However, it may also be of great help for debugging, for instance re-running a tricky case that leads to an error. Use wisely. 🦋

--sampling-rate=rate — sampling rate, used when writing data into the log, to reduce the amount of log generated. If this option is given, only the specified fraction of transactions are logged. 1.0 means all transactions will be logged, 0.05 means only 5% of the transactions will be logged. 🍓

Remember to take the sampling rate into account when processing the log file. For example, when computing TPS values you need to multiply the numbers accordingly (e.g., with 0.01 sample rate, you'll only get 1/100 of the actual TPS).

--show-script=scriptname — show the actual code of the builtin script scriptname on stderr, and exit immediately. 👀
--verbose-errors — print messages about all errors and failures (errors without retrying), including which limit for retries was exceeded and how far it was exceeded for the serialization/deadlock failures. (Note that in this case the output can be significantly increased — like, a lot.) See Failures and Serialization/Deadlock Retries for more info.

🌈 Common Options 🌈

pgbench also accepts these common command-line arguments for connection parameters, because of course it does, networking is a whole vibe:

-h hostname, --host=hostname — the database server's host name 🖥️
-p port, --port=port — the database server's port number
-U login, --username=login — the user name to connect as
-V, --version — print the pgbench version and exit.
-?, --help — show help about pgbench command line arguments, and exit. 🫶

🚪 EXIT STATUS 🚪

A successful run will exit with status 0, slay. 💅

Exit status 1 indicates static problems such as invalid command-line options or internal errors which are supposed to never occur. Early errors that occur when starting the benchmark, such as initial connection failures, also exit with status 1.

Errors during the run — like database errors or problems in the script — will result in exit status 2. In that latter case, pgbench will still print partial results, so you're not left completely empty-handed. 😭

🌍 ENVIRONMENT 🌍

PGDATABASE, PGHOST, PGPORT, PGUSER — default connection parameters, bestie. That’s the whole vibe of this block 💅

This utility, like most other PostgreSQL utilities, uses the environment variables supported by libpq (see Section 34.15). No cap, it’s just built different that way ✨

The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Pick your aesthetic 🎀

💭 NOTES 💭

🎲 What Is the “Transaction” Actually Performed in pgbench? 🎲

Okay so here’s the tea: pgbench executes test scripts chosen randomly from a specified list. Those scripts may include built-in scripts specified with -b and user-provided scripts specified with -f. Each script may be given a relative weight specified after an @ so as to change its selection probability. The default weight is 1. Scripts with a weight of 0 are ignored. Lowkey important, highkey iconic 👑

The default built-in transaction script (also invoked with -b tpcb-like) issues seven commands per transaction over randomly chosen aid, tid, bid and delta. The scenario is inspired by the TPC-B benchmark, but is not actually TPC-B, hence the name. It’s giving “inspired by, not a copy” energy 💀

BEGIN;
UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
END;

If you select the simple-update built-in (also -N), steps 4 and 5 aren’t included in the transaction. This will avoid update contention on these tables, but it makes the test case even less like TPC-B. So yeah, fewer updates, more “not TPC-B” energy 🫶

If you select the select-only built-in (also -S), only the SELECT is issued. Just the one query, main character behavior only 🌟

🛠️ Custom Scripts 🛠️

pgbench has support for running custom benchmark scenarios by replacing the default transaction script (described above) with a transaction script read from a file (-f option). In this case a “transaction” counts as one execution of a script file. Periodt 💅

A script file contains one or more SQL commands terminated by semicolons. Empty lines and lines beginning with -- are ignored. Script files can also contain “meta commands”, which are interpreted by pgbench itself, as described below. It’s like the script file has its own little backstage crew 🎬

Note 🫶 Before PostgreSQL 9.6, SQL commands in script files were terminated by newlines, and so they could not be continued across lines. Now a semicolon is required to separate consecutive SQL commands (though an SQL command does not need one if it is followed by a meta command). If you need to create a script file that works with both old and new versions of pgbench, be sure to write each SQL command on a single line ending with a semicolon. It is assumed that pgbench scripts do not contain incomplete blocks of SQL transactions. If at runtime the client reaches the end of the script without completing the last transaction block, it will be aborted. So please do not leave your transaction block hanging, bestie 😭

There is a simple variable-substitution facility for script files. Variable names must consist of letters (including non-Latin letters), digits, and underscores, with the first character not being a digit. Variables can be set by the command-line -D option, explained above, or by the meta commands explained below. In addition to any variables preset by -D command-line options, there are a few variables that are preset automatically, listed in Table 288. A value specified for these variables using -D takes precedence over the automatic presets. Once set, a variable’s value can be inserted into an SQL command by writing :variablename. When running more than one client session, each session has its own set of variables. pgbench supports up to 255 variable uses in one statement. That’s a whole lot of variable girlbossing, ngl ✨

Table 288. pgbench Automatic Variables

client_id — unique number identifying the client session (starts from zero)
default_seed — seed used in hash and pseudorandom permutation functions by default
random_seed — random generator seed (unless overwritten with -D)
scale — current scale factor

Script file meta commands begin with a backslash (\) and normally extend to the end of the line, although they can be continued to additional lines by writing backslash-return. Arguments to a meta command are separated by white space. These meta commands are supported:

\gset [prefix], \aset [prefix] — These commands may be used to end SQL queries, taking the place of the terminating semicolon (;).

When the \gset command is used, the preceding SQL query is expected to return one row, the columns of which are stored into variables named after column names, and prefixed with prefix if provided.

When the \aset command is used, all combined SQL queries (separated by \;) have their columns stored into variables named after column names, and prefixed with prefix if provided. If a query returns no row, no assignment is made and the variable can be tested for existence to detect this. If a query returns more than one row, the last value is kept.

\gset and \aset cannot be used in pipeline mode, since the query results are not yet available by the time the commands would need them. So don’t try to rush the pipeline, bestie 🚫

The following example puts the final account balance from the first query into variable abalance, and fills variables p_two and p_three with integers from the third query. The result of the second query is discarded. The result of the two last combined queries are stored in variables four and five.

UPDATE pgbench_accounts
  SET abalance = abalance + :delta
  WHERE aid = :aid
  RETURNING abalance \gset
-- compound of two queries
SELECT 1 \;
SELECT 2 AS two, 3 AS three \gset p_
SELECT 4 AS four \; SELECT 5 AS five \aset

\if expression, \elif expression, \else, \endif — This group of commands implements nestable conditional blocks, similarly to psql’s \if expression. Conditional expressions are identical to those with \set, with non-zero values interpreted as true. It’s literally if/else but make it SQL-adjacent 💅
\set varname expression — Sets variable varname to a value calculated from expression. The expression may contain the NULL constant, Boolean constants TRUE and FALSE, integer constants such as 5432, double constants such as 3.14159, references to variables :variablename, operators with their usual SQL precedence and associativity, function calls, SQL CASE generic conditional expressions and parentheses.

Functions and most operators return NULL on NULL input.

For conditional purposes, non zero numerical values are TRUE, zero numerical values and NULL are FALSE.

Too large or small integer and double constants, as well as integer arithmetic operators (+, -, * and /) raise errors on overflows. So maybe do not send it with the numbers, bestie 😭

When no final ELSE clause is provided to a CASE, the default value is NULL.

Examples:

\set ntellers 10 * :scale
\set aid (1021 * random(1, 100000 * :scale)) % \
           (100000 * :scale) + 1
\set divx CASE WHEN :x <> 0 THEN :y/:x ELSE NULL END

\sleep number [ us | ms | s ] — Causes script execution to sleep for the specified duration in microseconds (us), milliseconds (ms) or seconds (s). If the unit is omitted then seconds are the default. number can be either an integer constant or a :variablename reference to a variable having an integer value.

Example:

\sleep 10 ms

\setshell varname command [ argument ... ] — Sets variable varname to the result of the shell command command with the given argument(s). The command must return an integer value through its standard output.

command and each argument can be either a text constant or a :variablename reference to a variable. If you want to use an argument starting with a colon, write an additional colon at the beginning of argument.

Example:

\setshell variable_to_be_assigned command literal_argument :variable ::literal_starting_with_colon

\shell command [ argument ... ] — Same as \setshell, but the result of the command is discarded.

Example:

\shell command literal_argument :variable ::literal_starting_with_colon

\startpipeline, \endpipeline — These commands delimit the start and end of a pipeline of SQL statements. In pipeline mode, statements are sent to the server without waiting for the results of previous statements. See Section 34.5 for more details. Pipeline mode requires the use of extended query protocol. Pipeline girlies stay winning 🌈

🧮 Built-in Operators 🧮

The arithmetic, bitwise, comparison and logical operators listed in Table 289 are built into pgbench and may be used in expressions appearing in \set. The operators are listed in increasing precedence order. Except as noted, operators taking two numeric inputs will produce a double value if either input is double, otherwise they produce an integer result. Math but make it benchmark-core, obsessed 🎀

Table 289. pgbench Operators 🌸

okay bestie, this is the whole operator menu — every symbol your pgbench expressions can flex, with the description and a lil example for each so you can actually see it working. no gatekeeping 💅

boolean OR boolean → boolean — logical OR, the "either one is enough" queen — example: 5 or 0 → TRUE
boolean AND boolean → boolean — logical AND, both sides gotta show up — example: 3 and 0 → FALSE
NOT boolean → boolean — logical NOT, flips the vibe completely — example: not false → TRUE
boolean IS [NOT] (NULL|TRUE|FALSE) → boolean — boolean value tests, checking what something actually is — example: 1 is null → FALSE
value ISNULL|NOTNULL → boolean — nullness tests, i.e. "is this thing even here or nah" — example: 1 notnull → TRUE
number = number → boolean — equal, the classic — example: 5 = 4 → FALSE
number <> number → boolean — not equal (the first of two spellings, yes there's two, stay with me) — example: 5 <> 4 → TRUE
number != number → boolean — not equal again, same energy, different glyph — example: 5 != 5 → FALSE
number < number → boolean — less than — example: 5 < 4 → FALSE
number <= number → boolean — less than or equal to — example: 5 <= 4 → FALSE
number > number → boolean — greater than — example: 5 > 4 → TRUE
number >= number → boolean — greater than or equal to — example: 5 >= 4 → TRUE
integer | integer → integer — bitwise OR, bit-fiddling but make it fashion — example: 1 | 2 → 3
integer # integer → integer — bitwise XOR (yes, # does this here, not a comment, don't come for me) — example: 1 # 3 → 2
integer & integer → integer — bitwise AND — example: 1 & 3 → 1
~ integer → integer — bitwise NOT — example: ~ 1 → -2
integer << integer → integer — bitwise shift left — example: 1 << 2 → 4
integer >> integer → integer — bitwise shift right — example: 8 >> 2 → 2
number + number → number — addition, the moment we've all been waiting for 🎀 — example: 5 + 4 → 9
number - number → number — subtraction — example: 3 - 2.0 → 1.0
number * number → number — multiplication — example: 5 * 4 → 20
number / number → number — division, and heads up: it truncates the result towards zero if both inputs are integers (so no sneaky decimals, it just chops) — example: 5 / 3 → 1
integer % integer → integer — modulo (that's the remainder, the leftover crumbs) — example: 3 % 2 → 1
- number → number — negation, the unary minus, main character of flipping signs ✨

...and that's the last row of that operator table, bestie — here's how it lands:

       │               .PP - 2.0 → -2.0          │
       └─────────────────────────────────────────┘

🎀 That's the whole operator table, wrapped up. Now we get to the good stuff.

✨ Built-In Functions ✨

So. pgbench is not just out here doing math with operators like some calculator from 2004 — it also has a whole wardrobe of built-in functions, and they are listed in Table 290. 📋

Here's the vibe: these functions are built into pgbench itself (not something you load, not something you define — they're just there, like a bestie who always has gum 🫶). And you can use them in any expression that shows up in \set. That's it. That's the whole rule. If it's in a \set expression, these bad boys are fair game. ✨

Lowkey important: the expressions in \set is the exact place these functions live, so if you were trying to use them somewhere else and it's not working — yeah, that's why, ngl 😭

okay so this is Table 290, aka the pgbench Functions menu, and bestie it is stacked 💅 every single one of these is a little math/flavor gremlin you can call straight from your script. going down the list in order, no skipping, periodt:

abs ( number ) → same type as input — absolute value, so negatives get their glow-up. abs(-17) → 17
debug ( number ) → same type as input — prints the argument to stderr and hands it right back to you, like a friend who hypes you up and returns your hoodie. debug(5432.1) → 5432.1
double ( number ) → double — casts to double, that's it. double(5432) → 5432.0
exp ( number ) → double — exponential, aka e raised to the given power. exp(1.0) → 2.718281828459045
greatest ( number [, ... ] ) → double if any argument is double, else integer — picks the largest value among the arguments, main character energy only. greatest(5, 4, 3, 2) → 5
hash ( value [, seed ] ) → integer — this is literally just an alias for hash_murmur2, iykyk. hash(10, 5432) → -5817877081768721676
hash_fnv1a ( value [, seed ] ) → integer — computes FNV-1a hash. hash_fnv1a(10, 5432) → -7793829335365542153
hash_murmur2 ( value [, seed ] ) → integer — computes MurmurHash2 hash. hash_murmur2(10, 5432) → -5817877081768721676
int ( number ) → integer — casts to integer. int(5.4 + 3.8) → 9
least ( number [, ... ] ) → double if any argument is double, else integer — picks the smallest value among the arguments, humble queen behavior. least(5, 4, 3, 2.1) → 2.1
ln ( number ) → double — natural logarithm. ln(2.718281828459045) → 1.0
mod ( integer, integer ) → integer — modulo, aka the remainder. mod(54, 32) → 22
permute ( i, size [, seed ] ) → integer — this gives you the permuted value of i, in the range [0, size). translation: it's the new position of i (modulo size) in a pseudorandom permutation of the integers 0...size-1, parameterized by seed, see below 👀. permute(0, 4) → an integer between 0 and 3
pi () → double — the approximate value of π, our favorite irrational icon. pi() → 3.14159265358979323846
pow ( x, y ) → double, and power ( x, y ) → double — same thing, two names, both slay. it's x raised to the power of y. pow(2.0, 10) → 1024.0
random ( lb, ub ) → integer — computes a uniformly-distributed random integer in [lb, ub]. random(1, 10) → an integer between 1 and 10
random_exponential ( lb, ub, parameter ) → integer — computes an exponentially-distributed random integer in [lb, ub], see below for the deets. random_exponential(1, 10, 3.0) → an integer between 1 and 10
random_gaussian ( lb, ub, parameter ) → integer — computes a Gaussian-distributed random integer in [lb, ub], see below, it's giving bell curve 💀. random_gaussian(1, 10, 2.5) → an integer between 1 and 10
random_zipfian ( lb, ub, parameter ) → integer — the way this one slays is that it computes a Zipfian-distributed random integer in [lb, ub] — full deets below bestie, we get into it 💅
- random_zipfian(1, 10, 1.5) → an integer between 1 and 10 ✨
sqrt ( number ) → double — literally just the square root, no cap 🧮
- sqrt(2.0) → 1.414213562

So okay, the random function generates values using a uniform distribution — meaning all the values get drawn within the specified range with equal probability, super fair, everyone gets a turn 🌈. But random_exponential, random_gaussian and random_zipfian are the extra ones — they each require an additional double parameter which determines the precise shape of the distribution. Shape-shifters, honestly 🦋

exponential — the parameter controls the distribution by truncating a quickly-decreasing exponential distribution at parameter, and then projecting onto integers between the bounds. To be precise, with

f(x) = exp(-parameter * (x - min) / (max - min + 1)) / (1 - exp(-parameter))

Then value i between min and max inclusive is drawn with probability: f(i) - f(i + 1).

Intuitively? The larger the parameter, the more frequently values close to min are accessed, and the less frequently values close to max are accessed. The closer to 0 parameter is, the flatter (more uniform) the access distribution. A crude approximation of the distribution is that the most frequent 1% values in the range, close to min, are drawn parameter% of the time. The parameter value must be strictly positive ⚠️ like do not test her on that one.

gaussian — the interval is mapped onto a standard normal distribution (the classical bell-shaped Gaussian curve, you know the one 🛎️) truncated at -parameter on the left and +parameter on the right. Values in the middle of the interval are more likely to be drawn — main character energy in the center, truly. To be precise, if PHI(x) is the cumulative distribution function of the standard normal distribution, with mean mu defined as (max + min) / 2.0, with

f(x) = PHI(2.0 * parameter * (x - mu) / (max - min + 1)) /
       (2.0 * PHI(parameter) - 1)

then value i between min and max inclusive is drawn with probability: f(i + 0.5) - f(i - 0.5). Intuitively, the larger the parameter, the more frequently values close to the middle of the interval are drawn, and the less frequently values close to the min and max bounds. About 67% of values are drawn from the middle 1.0 / parameter, that is a relative 0.5 / parameter around the mean, and 95% in the middle 2.0 / parameter, that is a relative 1.0 / parameter around the mean; for instance, if parameter is 4.0, 67% of values are drawn from the middle quarter (1.0 / 4.0) of the interval (i.e., from 3.0 / 8.0 to 5.0 / 8.0) and 95% from the middle half (2.0 / 4.0) of the interval (second and third quartiles). The minimum allowed parameter value is 2.0 — go below that and it's not giving.

random_zipfian — generates a bounded Zipfian distribution. The parameter defines how skewed the distribution is. The larger the parameter, the more frequently values closer to the beginning of the interval are drawn. The distribution is such that, assuming the range starts from 1, the ratio of the probability of drawing k versus drawing k+1 is ((k+1)/k)**parameter. For example, random_zipfian(1, ..., 2.5) produces the value 1 about (2/1)**2.5 = 5.66 times more frequently than 2, which itself is produced (3/2)**2.5 = 2.76 times more frequently than 3, and so on. Math ate, honestly 🔥

pgbench's implementation is based on "Non-Uniform Random Variate Generation", Luc Devroye, p. 550-551, Springer 1986. Due to limitations of that algorithm, the parameter value is restricted to the range [1.001, 1000]. Respect the range bestie 🫶

Note 📌 When designing a benchmark which selects rows non-uniformly, be aware that the rows chosen may be correlated with other data such as IDs from a sequence or the physical row ordering, which may skew performance measurements. To avoid this, you may wish to use the permute function, or some other additional step with similar effect, to shuffle the selected rows and remove such correlations.

Hash functions hash, hash_murmur2 and hash_fnv1a accept an input value and an optional seed parameter. In case the seed isn't provided, the value of :default_seed is used, which is initialized randomly unless set by the command-line -D option 💖

permute accepts an input value, a size, and an optional seed parameter. It generates a pseudorandom permutation of integers in the range [0, size), and returns the index of the input value in the permuted values. The permutation chosen is parameterized by the seed, which defaults to :default_seed, if not specified. Unlike the hash functions, permute ensures that there are no collisions or holes in the output values — iconic behaviour, no crumbs left behind 👑. Input values outside the interval are interpreted modulo the size. The function raises an error if the size is not positive 🚨. permute can be used to scatter the distribution of non-uniform random functions such as random_zipfian or random_exponential so that values drawn more often are not trivially correlated. For instance, the following pgbench script simulates a possible real world workload typical for social media and blogging platforms where a few accounts generate excessive load:

\set size 1000000
\set r random_zipfian(1, :size, 1.07)
\set k 1 + permute(:r, :size)

In some cases several distinct distributions are needed which don't correlate with each other and this is when the optional seed parameter comes in handy 🎀

\set k1 1 + permute(:r, :size, :default_seed + 123)
\set k2 1 + permute(:r, :size, :default_seed + 321)

A similar behavior can also be approximated with hash:

\set size 1000000
\set r random_zipfian(1, 100 * :size, 1.07)
\set k 1 + abs(hash(:r)) % :size

However — and this is the catch, lowkey 💀 — since hash generates collisions, some values will not be reachable and others will be more frequent than expected from the original distribution. So like, it's giving approximation, not the real thing 🙈

As an example, the full definition of the built-in TPC-B-like transaction is:

\set aid random(1, 100000 * :scale)
\set bid random(1, 1 * :scale)
\set tid random(1, 10 * :scale)
\set delta random(-5000, 5000)
BEGIN;
UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
END;

This script allows each iteration of the transaction to reference different, randomly-chosen rows. (This example also shows why it's important for each client session to have its own variables — otherwise they'd not be independently touching different rows. Sharing variables is not the vibe 🌸)

🎀 Per-Transaction Logging 🎀

With the -l option (but without the --aggregate-interval option), pgbench writes information about each transaction to a log file 📝. The log file will be named prefix.nnn, where prefix defaults to pgbench_log, and nnn is the PID of the pgbench process. The prefix can be changed by using the --log-prefix option. If the -j option is 2 or higher, so that there are multiple worker threads, each will have its own log file. The first worker will use the same name for its log file as in the standard single worker case. The additional log files for the other workers will be named prefix.nnn.mmm, where mmm is a sequential number for each worker starting with 1. Everybody gets their own little diary ✨

Each line in a log file describes one transaction. It contains the following space-separated fields:

client_id — identifies the client session that ran the transaction
transaction_no — counts how many transactions have been run by that session
time — transaction's elapsed time, in microseconds
script_no — identifies the script file that was used for the transaction (useful when multiple scripts are specified with -f or -b)
time_epoch — transaction's completion time, as a Unix-epoch time stamp
time_us — fractional-second part of transaction's completion time, in microseconds
schedule_lag — transaction start delay, that is the difference between the transaction's scheduled start time and the time it actually started, in microseconds (present only if --rate is specified)
retries — count of retries after serialization or deadlock errors during the transaction (present only if --max-tries is not equal to one)

When both --rate and --latency-limit are used, the time for a skipped transaction will be reported as skipped. If the transaction ends with a failure, its time will be reported as failed. If you use the --failures-detailed option, the time of the failed transaction will be reported as serialization or deadlock depending on the type of failure (see Failures and Serialization/Deadlock Retries for more information). Labeled and accounted for 💅

Here is a snippet of a log file generated in a single-client run:

0 199 2241 0 1175850568 995598
0 200 2465 0 1175850568 998079
0 201 2513 0 1175850569 608
0 202 2038 0 1175850569 2663

Another example with --rate=100 and --latency-limit=5 (note the additional schedule_lag column 👀):

0 81 4621 0 1412881037 912698 3005
0 82 6173 0 1412881037 914578 4304
0 83 skipped 0 1412881037 914578 5217
0 83 skipped 0 1412881037 914578 5099
0 83 4722 0 1412881037 916203 3108
0 84 4142 0 1412881037 918023 2333
0 85 2465 0 1412881037 919759 740

In this example, transaction 82 was late, because its latency (6.173 ms) was over the 5 ms limit. The next two transactions were skipped, because they were already late before they were even started. Fumbled the timing, it happens 😭

Like, sometimes things go wrong and the log file says so — here's a snippet where stuff failed and got retried, with the max number of tries set to 10 (peep the extra retries column, she's new 💅):

3 0 47423 0 1499414498 34501 3
3 1 8333 0 1499414498 42848 0
3 2 8358 0 1499414498 51219 0
4 0 72345 0 1499414498 59433 6
1 3 41718 0 1499414498 67879 4
1 4 8416 0 1499414498 76311 0
3 3 33235 0 1499414498 84469 3
0 0 failed 0 1499414498 84905 9
2 0 failed 0 1499414498 86248 9
3 4 8307 0 1499414498 92788 0

And if you turn on --failures-detailed, the log gets all specific about what kind of failure it was, showing the type in that spot instead:

3 0 47423 0 1499414498 34501 3
3 1 8333 0 1499414498 42848 0
3 2 8358 0 1499414498 51219 0
4 0 72345 0 1499414498 59433 6
1 3 41718 0 1499414498 67879 4
1 4 8416 0 1499414498 76311 0
3 3 33235 0 1499414498 84469 3
0 0 serialization 0 1499414498 84905 9
2 0 serialization 0 1499414498 86248 9
3 4 8307 0 1499414498 92788 0

Real talk though — if you're running a long test on beefy hardware that can handle a lot of transactions, those log files are gonna get huge. Like, unhinged huge 😭. So use the --sampling-rate option to log just a random sample of transactions. Self-care, bestie 🫶.

✨ Aggregated Logging ✨

With --aggregate-interval, the log files switch to a whole different format. Now each log line describes one aggregation interval, and it's got these space-separated fields:

interval_start — when the interval started, as a Unix-epoch time stamp ⏰
num_transactions — how many transactions happened inside the interval
sum_latency — the sum of all the transaction latencies
sum_latency_2 — the sum of the squares of the transaction latencies
min_latency — the smallest transaction latency
max_latency — the biggest transaction latency
sum_lag — the sum of the transaction start delays (this is zero unless --rate is specified)
sum_lag_2 — the sum of the squares of the transaction start delays (zero unless --rate is specified)
min_lag — the minimum transaction start delay (zero unless --rate is specified)
max_lag — the maximum transaction start delay (zero unless --rate is specified)
skipped — how many transactions got skipped because they would've started too late (zero unless --rate and --latency-limit are specified)
retried — how many transactions got retried (zero unless --max-tries is not equal to one)
retries — how many retries happened after serialization or deadlock errors (zero unless --max-tries is not equal to one)
serialization_failures — how many transactions got a serialization error and were not retried afterwards (zero unless --failures-detailed is specified)
deadlock_failures — how many transactions got a deadlock error and were not retried afterwards (zero unless --failures-detailed is specified)

Here's, like, actual example output from running with these options 🔥:

pgbench --aggregate-interval=10 --time=20 --client=10 --log --rate=1000 --latency-limit=10 --failures-detailed --max-tries=10 test

1650260552 5178 26171317 177284491527 1136 44462 2647617 7321113867 0 9866 64 7564 28340 4148 0
1650260562 4808 25573984 220121792172 1171 62083 3037380 9666800914 0 9998 598 7392 26621 4527 0

Okay so heads up slightly: the plain (unaggregated) log format shows you which script was used for each transaction, but the aggregated format does not. So if you're out here needing per-script data, you're gonna have to aggregate that data yourself, babes. No shortcut, sorry 🫠.

🎀 Per-Statement Report 🎀

With the -r option, pgbench collects these stats for each and every statement:

latency — the elapsed transaction time for each statement. pgbench reports an average value of all the successful runs of the statement.
The number of failures in this statement. Peep Failures and Serialization/Deadlock Retries for the deets.
The number of retries after a serialization or a deadlock error in this statement. Again, Failures and Serialization/Deadlock Retries has your back.

The report only shows retry stats if the --max-tries option is not equal to 1. Iykyk.

All these values get computed for each statement executed by every client, and they're reported after the benchmark finishes 🏁.

For the default script, the output looks something like this:

starting vacuum...end.
transaction type: <builtin: TPC-B (sort of)>
scaling factor: 1
query mode: simple
number of clients: 10
number of threads: 1
maximum number of tries: 1
number of transactions per client: 1000
number of transactions actually processed: 10000/10000
number of failed transactions: 0 (0.000%)
number of transactions above the 50.0 ms latency limit: 1311/10000 (13.110 %)
latency average = 28.488 ms
latency stddev = 21.009 ms
initial connection time = 69.068 ms
tps = 346.224794 (without initial connection time)
statement latencies in milliseconds and failures:
   0.012  0  \set aid random(1, 100000 * :scale)
   0.002  0  \set bid random(1, 1 * :scale)
   0.002  0  \set tid random(1, 10 * :scale)
   0.002  0  \set delta random(-5000, 5000)
   0.319  0  BEGIN;
   0.834  0  UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
   0.641  0  SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
  11.126  0  UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
  12.961  0  UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
   0.634  0  INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
   1.957  0  END;

And here's another one, this time for the default script running with the serializable default transaction isolation level (PGOPTIONS='-c default_transaction_isolation=serializable' pgbench ...) — look at all that spicy retry data 😳:

starting vacuum...end.
transaction type: <builtin: TPC-B (sort of)>
scaling factor: 1
query mode: simple
number of clients: 10
number of threads: 1
maximum number of tries: 10
number of transactions per client: 1000
number of transactions actually processed: 6317/10000
number of failed transactions: 3683 (36.830%)
number of transactions retried: 7667 (76.670%)
total number of retries: 45339
number of transactions above the 50.0 ms latency limit: 106/6317 (1.678 %)
latency average = 17.016 ms
latency stddev = 13.283 ms
initial connection time = 45.017 ms
tps = 186.792667 (without initial connection time)
statement latencies in milliseconds, failures and retries:
  0.006     0      0  \set aid random(1, 100000 * :scale)
  0.001     0      0  \set bid random(1, 1 * :scale)
  0.001     0      0  \set tid random(1, 10 * :scale)
  0.001     0      0  \set delta random(-5000, 5000)
  0.385     0      0  BEGIN;
  0.773     0      1  UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
  0.624     0      0  SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
  1.098   320   3762  UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
  0.582  3363  41576  UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
  0.465     0      0  INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
  1.933     0      0  END;

If you specify multiple script files, then all the stats get reported separately for each script file. Every single one 💯.

Okay so real caveat time: collecting all that extra timing info for per-statement latency computation adds overhead, periodt. It'll slow down your average execution speed and lower the computed TPS. How much slowdown? Literally varies significantly depending on platform and hardware, so don't @ me. A good way to check if the timing overhead is significant is to compare average TPS values with and without latency reporting enabled. Science 🧪.

💀 Failures and Serialization/Deadlock Retries 💀

When pgbench is doing its thing, there are like three main types of errors. Buckle up:

Errors of the main program. These are the most serious ones and they always result in an immediate exit from pgbench with the corresponding error message. They include:
- errors at the beginning of pgbench (e.g. an invalid option value);
- errors in the initialization mode (e.g. the query to create tables for built-in scripts fails);
- errors before starting threads (e.g. could not connect to the database server, syntax error in the meta command, thread creation failure);
- internal pgbench errors (which are supposed to never occur...). 🙈
Errors when the thread manages its clients (e.g. the client could not start a connection to the database server / the socket for connecting the client to the database server has become invalid). In these cases, all clients of this thread stop while the other threads keep on working.
Direct client errors. These only lead to an immediate exit from pgbench with the corresponding error message in the case of an internal pgbench error (which are supposed to never occur...). Otherwise, in the worst case, they only lead to the abortion of the failed client while the other clients continue their run (but some client errors are handled without aborting the client and get reported separately, see below). For the rest of this section, just assume we're only talking about the direct client errors, and that they are not internal pgbench errors.

A client's run gets aborted in case of a serious error — for example, the connection with the database server was lost, or the end of script was reached without completing the last transaction. Also, if execution of an SQL or meta command fails for reasons other than serialization or deadlock errors, the client is aborted. Otherwise, if an SQL command fails with serialization or deadlock errors, the client is not aborted. In those cases the current transaction is rolled back, which also includes setting the client variables as they were before the run of this transaction (it's assumed that one transaction script contains only one transaction; see What Is the "Transaction" Actually Performed in pgbench? for more info). Transactions with serialization or deadlock errors get repeated after rollbacks until they complete successfully, or until they hit the maximum number of tries (specified by the --max-tries option) / the maximum time of retries (specified by the --latency-limit option) / the end of benchmark (specified by the --time option). And if that last trial run fails? The transaction gets reported as failed, but the client is not aborted and just keeps going. Resilient queen 👑.

Note

okay so bestie, if you don’t pass --max-tries, a transaction will never be retried after a serialization or deadlock error, because its default value is literally 1 😭. Wanna go unlimited? Use --max-tries=0 and let --latency-limit limit only the maximum time of tries. You can also use --time to limit the benchmark duration under an unlimited number of tries. slay.

⚠️ Be careful when repeating scripts that contain multiple transactions: the script is always retried completely, so successful transactions can be performed several times. not me finding that out the hard way 💀

⚠️ Also be careful when repeating transactions with shell commands. Unlike the results of SQL commands, the results of shell commands are not rolled back, except for the variable value of the \setshell command. periodt.

The latency of a successful transaction includes the entire time of transaction execution with rollbacks and retries. Latency is measured only for successful transactions and commands, but not for failed transactions or commands. iykyk.

The main report contains the number of failed transactions. If --max-tries is not equal to 1, the main report also contains statistics related to retries: the total number of retried transactions and the total number of retries. The per-script report inherits all these fields from the main report. The per-statement report displays retry statistics only if --max-tries is not equal to 1. ✨

If you want to group failures by basic types in per-transaction and aggregation logs, as well as in the main and per-script reports, use --failures-detailed. If you also want to distinguish all errors and failures (errors without retrying) by type — including which limit for retries was exceeded and how much it was exceeded by for the serialization/deadlock failures — use --verbose-errors. we love a detailed queen 💅

✨ Good Practices ✨

ngl, it is very easy to use pgbench to produce completely meaningless numbers 😭. Here are some guidelines to help you get useful results.

First things first: never believe any test that runs for only a few seconds. Use -t or -T to make the run last at least a few minutes, so as to average out noise. In some cases you could need hours to get numbers that are reproducible. It’s a good idea to try the test run a few times, to find out if your numbers are reproducible or not. 🕰️

For the default TPC-B-like test scenario, the initialization scale factor (-s) should be at least as large as the largest number of clients you intend to test (-c); else you’ll mostly be measuring update contention. There are only -s rows in the pgbench_branches table, and every transaction wants to update one of them, so -c values in excess of -s will undoubtedly result in lots of transactions blocked waiting for other transactions. the way that math is mathing 🔥

The default test scenario is also quite sensitive to how long it’s been since the tables were initialized: accumulation of dead rows and dead space in the tables changes the results. To understand the results you must keep track of the total number of updates and when vacuuming happens. If autovacuum is enabled it can result in unpredictable changes in measured performance. keep an eye on it bestie 👀

A limitation of pgbench is that it can itself become the bottleneck when trying to test a large number of client sessions. This can be alleviated by running pgbench on a different machine from the database server, although low network latency will be essential. It might even be useful to run several pgbench instances concurrently, on several client machines, against the same database server. main character energy, but distributed 🌈

🔒 Security 🔒

If untrusted users have access to a database that has not adopted a secure schema usage pattern, do not run pgbench in that database. pgbench uses unqualified names and does not manipulate the search path. this is the serious part, bestie — no cap 🫶

byeee 💖 source/version: PostgreSQL 15.19 · date: 2026 · and yes, this was the PGBENCH(1) page 🎀

📦 where this page comes from

opensuse-leap 16.0: postgresql15-contrib 15.19-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql15-contrib 15.19-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql15-contrib 15.19-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql15-contrib 15.19-160000.1.1 · PostgreSQL
💖 girlified by deepseek-ai/DeepSeek-V4.1-Flash-1017 on 2026-11-04: a language model's rewrite of the original, under its terms

PGBENCH(1)

🎀 PGBENCH(1) 🎀

🌸 NAME 🌸

✨ SYNOPSIS ✨

💅 DESCRIPTION 💅

🔥 OPTIONS 🔥

🧚 Initialization Options 🧚

🌈 Benchmarking Options 🌈

🌈 Common Options 🌈

🚪 EXIT STATUS 🚪

🌍 ENVIRONMENT 🌍

💭 NOTES 💭

🎲 What Is the “Transaction” Actually Performed in pgbench? 🎲

🛠️ Custom Scripts 🛠️

🧮 Built-in Operators 🧮

✨ Built-In Functions ✨

🎀 Per-Transaction Logging 🎀

✨ Aggregated Logging ✨

🎀 Per-Statement Report 🎀

💀 Failures and Serialization/Deadlock Retries 💀

✨ Good Practices ✨

🔒 Security 🔒

🎀 `PGBENCH(1)` 🎀