PGBENCH(1)

🎀 `PGBENCH(1)` — PostgreSQL 13.23 Documentation 🎀

okay bestie, buckle up, because this is the manual page for pgbench, and it's genuinely iconic. it lives in the PostgreSQL 13.23 docs, and by the end of this you're gonna know it like the back of your hand. 💅

✨ NAME ✨

pgbench — run a benchmark test on PostgreSQL. that's it. that's the vibe. 🏃‍♀️💨

🦋 SYNOPSIS 🦋

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

pgbench [option...] [dbname]

🌸 DESCRIPTION 🌸

So pgbench is, like, a simple program for running benchmark tests on PostgreSQL. not simple as in "barely does anything" — simple as in "she's straightforward and she delivers." she 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 here's the thing — it is so easy to test other cases by writing your own transaction script files. main character energy tbh.

Typical output from pgbench looks like this, and yes, copy it exactly:

transaction type: <builtin: TPC-B (sort of)>
scaling factor: 10
query mode: simple
number of clients: 10
number of threads: 1
number of transactions per client: 1000
number of transactions actually processed: 10000/10000
tps = 85.184871 (including connections establishing)
tps = 85.296346 (excluding connections establishing)

Let's break that down because it's giving ✨information✨:

The first six lines report some of the most important parameter settings.
The next 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. (in -T mode, only the actual number of transactions is printed.)
The last two lines report the number of transactions per second, figured with and without counting the time to start database sessions.

The default TPC-B-like transaction test requires specific tables to be set up beforehand. so pgbench should be invoked with the -i (initialize) option to create and populate these tables. (when you're testing a custom script, you don't need this step, but you'll 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 ⚠️ — 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! this is the "not me accidentally nuking my data" moment. please don't. 😭💀

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've 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. that's the whole job. slay.
-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 the pgbench client and then sent to the server. this uses the client/server bandwidth extensively through a COPY. 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. big numbers, big vibes. 🔢
--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. very "let me see how much this costs" of you. 🫶
-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. 😬
-M querymode, --protocol=querymode — protocol to use for submitting queries to the server:
- simple: use simple query protocol.
- extended: use extended query protocol.
- prepared: use extended query protocol with prepared statements.

In the prepared mode, pgbench reuses the parse analysis result starting from the second query iteration, so pgbench runs faster than in other modes. The default is simple query protocol. (see Chapter 52 for more information.) ⚡

-n, --no-vacuum — bestie, this one says no vacuuming before the test, okay? And it's not optional-optional, it's necessary if you're running a custom test scenario that doesn't include the standard tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers. Like, if your tables aren't there, don't go vacuuming ghosts. 👻
-N, --skip-some-updates — runs the built-in simple-update script. It's literally just shorthand for -b simple-update. That's it, that's the whole vibe. ✨
-P sec, --progress=sec — shows a progress report every sec seconds 🕐 and the report is stacked: time since the beginning of the run, TPS since the last report, and the transaction latency average + standard deviation 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 yeah, it also includes the average schedule lag time. Math girlies stay winning 💅
-r, --report-latencies — reports the average per-statement latency (that's execution time from the client's perspective, iykyk) of each command after the benchmark finishes. Details are down below. 👑
-R rate, --rate=rate — executes transactions targeting the specified rate instead of going as fast as physically possible (which is the default, goblin mode). The rate is in transactions per second. And if the targeted rate is above the maximum possible rate? The rate limit just… won't impact the results. It simply cannot make you faster than you are, no cap.

So how does the targeting work? Transactions are started 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. Which means — when transactions go past their original scheduled end time, it is possible for later ones to catch up again. Cute, right?

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. Wanna know the latency with respect to the actual transaction start time — i.e. the time actually spent executing the transaction in the database? Subtract the schedule lag time from the reported latency. Math. 🧮

And if --latency-limit is used together with --rate, a transaction can lag behind so much that it's already over the latency limit by the time the previous transaction ends — because the latency is calculated from the scheduled start time, remember? Those transactions are not sent to the server, they're skipped altogether and counted separately. Bye bestie 👋

A high schedule lag time is your system telling you it cannot 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 will have to reduce the specified transaction rate. Not a suggestion, an order. 💀

-s scale_factor, --scale=scale_factor — reports the specified scale factor in pgbench's output. With the built-in tests you don't need it, the correct scale factor gets detected automatically by counting the number of rows in the pgbench_branches table. But if you're testing only custom benchmarks (the -f option), the scale factor will be reported as 1 unless you use this option. Rude? Slightly.
-S, --select-only — runs the built-in select-only script. Shorthand for -b select-only. Look at you reading the whole thing. 🫶
-t transactions, --transactions=transactions — number of transactions each client runs. Default is 10.
-T seconds, --time=seconds — run the test for this many seconds instead of a fixed number of transactions per client. Heads up: -t and -T are mutually exclusive, they cannot both be main character. 😤
-v, --vacuum-all — vacuum all four standard tables before running the test. And with neither -n nor -v, pgbench will vacuum the pgbench_tellers and pgbench_branches tables, and will truncate pgbench_history. The default has lore, okay.
--aggregate-interval=seconds — length of the aggregation interval, in seconds. May be used only with the -l option. With this option, the log contains per-interval summary data, as described below. 🎀
--log-prefix=prefix — sets the filename prefix for the log files created by --log. Default is pgbench_log.
--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. Cross-tool comparison slay. 🔥
--random-seed=seed — sets 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
- an unsigned decimal integer value

The random generator is invoked explicitly from a pgbench script (random... functions) or implicitly (for instance option --rate 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. Order matters, bestie. 💅

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, that means the exact same pgbench run for an identical invocation if there is 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 which 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). Do the math or your numbers will lie to you. 😭

--show-script=scriptname — shows the actual code of builtin script scriptname on stderr, and exits immediately. Peek behind the curtain, queen. 🦋

🌈 Common Options 🌈

pgbench also accepts the following common command-line arguments for connection parameters:

-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. Bye! ✌️
-?, --help — show help about pgbench command line arguments, and exit. Helpful icon. 🎀

💖 EXIT STATUS 💖

A successful run exits with status 0. Iconic. ✨

Exit status 1 means static problems — stuff like invalid command-line options. Like, you typed it wrong, babe.

Errors during the run — database errors, problems in the script — result in exit status 2. And in that latter case, pgbench will print partial results. So you still get something, even when things go sideways. 🙈

🌸 ENVIRONMENT 🌸

PGDATABASE
PGHOST
PGPORT
PGUSER

Those are the default connection parameters. 🫶

This utility, like most other PostgreSQL utilities, uses the environment variables supported by libpq (see Section 33.14).

The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Aesthetic diagnostics, we love to see it. 🌈

👑 NOTES 👑

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

pgbench executes test scripts chosen randomly from a specified list. That list can 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. Zero weight = zero attention. 💀

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 it is not actually TPC-B, hence the name. Close enough to be sisters, not close enough to be twins. 👯

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 avoids update contention on those tables, but it makes the test case even less like TPC-B. Trade-offs, bestie. ⚖️

If you select the select-only built-in (also -S), only the SELECT is issued. Minimalist queen. 💅

🎀 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 (the -f option). In this case a “transaction” counts as one execution of a script file.

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. Spooky but useful. ✨

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 a 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. Backwards-compatible bestie privileges. 🫶

okay so there’s this simple variable-substitution facility for script files, bestie, and it’s honestly so useful 💅 Variable names have to be letters (non-Latin letters totally count), digits, and underscores, and the first character can’t be a digit. You can set variables with the command-line -D option (explained above) or with the meta commands below. Besides whatever you preset with -D, pgbench auto-presets a few variables — they’re in Table 273. If you give one of those a value with -D, your value wins over the automatic preset. Once a variable is set, you drop it into a SQL command by writing :variablename. When you’re running more than one client session, each session gets its own set of variables. And pgbench supports up to 255 variable uses in one statement — yes, 255, not infinity 😭

Table 273. pgbench Automatic Variables

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

Script file meta commands start with a backslash (\) and normally run to the end of the line. You can continue them onto more lines by writing backslash-return. Arguments to a meta command are separated by white space. These are the supported meta commands:

\gset [prefix], \aset [prefix] — these end SQL queries and take the place of the terminating semicolon (;). With \gset, the preceding SQL query is expected to return one row; its columns get stored into variables named after the column names, prefixed with prefix if you gave one. With \aset, all combined SQL queries (separated by \;) have their columns stored into variables named after the column names, prefixed with prefix if provided. If a query returns no row, no assignment is made, and you can test the variable for existence to detect that. If a query returns more than one row, the last value is kept. Obsessed with the flexibility ngl ✨

The example below 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 gives you nestable conditional blocks, kinda like psql's \if expression. Conditional expressions are identical to those with \set, and non-zero values are interpreted as true. 🧠
\set varname expression — sets variable varname to a value calculated from expression. The expression can 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, while 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. 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 ] — makes script execution sleep for the specified duration in microseconds (us), milliseconds (ms), or seconds (s). If you leave off the unit, seconds are the default. number can be an integer constant or a :variablename reference to a variable with 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 the 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

🧮 Built-in Operators

The arithmetic, bitwise, comparison, and logical operators listed in Table 274 are built into pgbench and can be used in expressions that appear in \set. The operators are listed in increasing precedence order. Except as noted, operators that take two numeric inputs will produce a double value if either input is double; otherwise they produce an integer result. 🔥

🎀 Table 274. pgbench Operators 🎀

okay bestie, this is the whole operator menu your pgbench scripts can snack on — every single one, with the exact type signature and the exact example from the docs. no cap, no skipping 💅

boolean OR boolean → boolean — logical OR ✨ example: 5 or 0 → TRUE
boolean AND boolean → boolean — logical AND 💖 example: 3 and 0 → FALSE
NOT boolean → boolean — logical NOT, the classic flip 🦋 example: not false → TRUE
boolean IS [NOT] (NULL|TRUE|FALSE) → boolean — boolean value tests, like a vibe check for your truthiness 🌸 example: 1 is null → FALSE
value ISNULL|NOTNULL → boolean — nullness tests, literally "is there anything there or not" 🙈 example: 1 notnull → TRUE
number = number → boolean — equal 🫶 example: 5 = 4 → FALSE
number <> number → boolean — not equal 💀 example: 5 <> 4 → TRUE
number != number → boolean — also not equal, yes there are two spellings and both ate 🔥 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, integer besties only 💜 example: 1 | 2 → 3
integer # integer → integer — bitwise XOR, yes the symbol is literally a hash 💅 example: 1 # 3 → 2
integer & integer → integer — bitwise AND 🧚 example: 1 & 3 → 1
~ integer → integer — bitwise NOT, it flips every bit and that's the whole personality 😭 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 everyone understood ✨ 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 bestie: it truncates the result towards zero if both inputs are integers 👀 example: 5 / 3 → 1
integer % integer → integer — modulo (the remainder) ⭐ example: 3 % 2 → 1
- number → number — negation, the unary minus, main character energy 💅
- 2.0 → -2.0 — okay so this is the last row of that operator table and it got a little mangled on the way in (the .PP up in there is just roff having a moment, ignore her 💅), but the vibe is clear: you slap the operator on the value and you get the result. That's the whole table, bestie, congrats 🎀

🧮 Built-In Functions 🧮

The functions listed in Table 275 are built into pgbench and may be used in expressions appearing in \set.

✨ iykyk — that means you don't gotta define em, you don't gotta load em, they're just there for you inside your \set expressions. main character functions fr fr 💖

🎀 Table 275. pgbench Functions 🎀

okay bestie, this is the function menu 💅 every one of these is built in and ready to be dropped straight into your script — each entry gives you its return type, what it actually does, and an example you can absolutely steal. no cap, this table ate.

abs ( number ) → same type as input
- Absolute value 🦋
- Example: abs(-17) → 17
debug ( number ) → same type as input
- Prints the argument to stderr, and returns the argument. (yeah she's a little snitch but she gives it right back 🫶)
- Example: debug(5432.1) → 5432.1
double ( number ) → double
- Casts to double ✨
- Example: double(5432) → 5432.0
exp ( number ) → double
- Exponential (e raised to the given power) 🔥
- Example: exp(1.0) → 2.718281828459045
greatest ( number [, ... ] ) → double if any argument is double, else integer
- Selects the largest value among the arguments 👑
- Example: greatest(5, 4, 3, 2) → 5
hash ( value [, seed ] ) → integer
- This is an alias for hash_murmur2. 💜
- Example: hash(10, 5432) → -5817877081768721676
hash_fnv1a ( value [, seed ] ) → integer
- Computes FNV-1a hash 🌸
- Example: hash_fnv1a(10, 5432) → -7793829335365542153
hash_murmur2 ( value [, seed ] ) → integer
- Computes MurmurHash2 hash 💫
- Example: hash_murmur2(10, 5432) → -5817877081768721676
int ( number ) → integer
- Casts to integer 🎀
- Example: int(5.4 + 3.8) → 9
least ( number [, ... ] ) → double if any argument is double, else integer
- Selects the smallest value among the arguments 🧚
- Example: least(5, 4, 3, 2.1) → 2.1
ln ( number ) → double
- Natural logarithm 🌿
- Example: ln(2.718281828459045) → 1.0
mod ( integer, integer ) → integer
- Modulo (remainder) ⭐
- Example: mod(54, 32) → 22
pi () → double
- Approximate value of π 🥧
- Example: pi() → 3.14159265358979323846
pow ( x, y ) → double
power ( x, y ) → double
- x raised to the power of y 🔥
- Example: pow(2.0, 10) → 1024.0
random ( lb, ub ) → integer
- Computes a uniformly-distributed random integer in [lb, ub]. 🎲
- Example: 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 👀
- Example: 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 👀
- Example: random_gaussian(1, 10, 2.5) → an integer between 1 and 10
random_zipfian ( lb, ub, parameter ) → integer
- Computes a Zipfian-distributed random integer in [lb, ub], see below 👀
- Example: random_zipfian(1, 10, 1.5) → an integer between 1 and 10
sqrt ( number ) → double

Square root is the last row of that little table, and it does exactly what it says on the tin: sqrt(2.0) → 1.414213562. Math slay 💅

The random function generates values using a uniform distribution — that means every value in the range is equally likely to get picked, no favourites, no drama. The random_exponential, random_gaussian and random_zipfian functions need one extra double parameter that decides the precise shape of the distribution. Let's break each one down, bestie 🫶

random_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 get accessed, and the less frequently values close to max get 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. And hey — the parameter value must be strictly positive, no zeroes, no negatives, okay? 🚫

random_gaussian — the interval gets mapped onto a standard normal distribution (the classical bell-shaped Gaussian curve) truncated at -parameter on the left and +parameter on the right. Values in the middle of the interval are more likely to be drawn. 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. Don't try to go lower, it's not that kind of party 🙈

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.

pgbench's implementation is based on "Non-Uniform Random Variate Generation", Luc Devroye, p. 550-551, Springer 1986. And because of limitations of that algorithm, the parameter value is restricted to the range [1.001, 1000]. That's the vibe check it has to pass 😌

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. Hash functions can be used to scatter the distribution of random functions such as random_zipfian or random_exponential. For instance, the following pgbench script simulates a possible real world workload typical for social media and blogging platforms where few accounts generate excessive load:

\set r random_zipfian(0, 100000000, 1.07)
\set k abs(hash(:r)) % 1000000

In some cases several distinct distributions are needed that don't correlate with each other, and that's exactly when the implicit seed parameter comes in handy:

\set k1 abs(hash(:r, :default_seed + 123)) % 1000000
\set k2 abs(hash(:r, :default_seed + 321)) % 1000000

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.) 🎀

🌸 Per-Transaction Logging 🌸

With the -l option (but without the --aggregate-interval option), pgbench writes info 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 with the --log-prefix option. If the -j option is 2 or higher, so that there are multiple worker threads, each one gets its own log file. The first worker uses the same name for its log file as in the standard single worker case. The extra log files for the other workers get named prefix.nnn.mmm, where mmm is a sequential number for each worker starting with 1.

The format of the log is:

client_id transaction_no time script_no time_epoch time_us [ schedule_lag ]

where client_id tells you which client session ran the transaction, transaction_no counts how many transactions have been run by that session, time is the total elapsed transaction time in microseconds, script_no identifies which script file was used (super useful when multiple scripts were specified with -f or -b), and time_epoch/time_us are a Unix-epoch time stamp and an offset in microseconds (suitable for creating an ISO 8601 time stamp with fractional seconds) showing when the transaction completed. The schedule_lag field is the difference between the transaction's scheduled start time and the time it actually started, in microseconds. It's only present when the --rate option is used. When both --rate and --latency-limit are used, the time for a skipped transaction will be reported as skipped.

Here's 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. Not them getting benched before they even stepped on the field 😭

When you're running a long test on hardware that can handle a lot of transactions, those log files can get very large. The --sampling-rate option can be used to log only a random sample of transactions. Your disk will thank you 🫶

💜 Aggregated Logging 💜

With the --aggregate-interval option, a different format gets used for the log files:

interval_start num_transactions sum_latency sum_latency_2 min_latency max_latency [ sum_lag sum_lag_2 min_lag max_lag [ skipped ] ]

where interval_start is the start of the interval (as a Unix epoch time stamp), num_transactions is the number of transactions within the interval, sum_latency is the sum of the transaction latencies within the interval, sum_latency_2 is the sum of squares of the transaction latencies within the interval, min_latency is the minimum latency within the interval, and max_latency is the maximum latency within the interval. The next fields — sum_lag, sum_lag_2, min_lag and max_lag — are only present if the --rate option is used. They give you statistics about the time each transaction had to wait for the previous one to finish, i.e., the difference between each transaction's scheduled start time and the time it actually started. The very last field, skipped, is only present if the --latency-limit option is used too. It counts the number of transactions skipped because they would have started too late. Each transaction is counted in the interval when it was committed.

Here's some example output:

1345828501 5601 1542744 483552416 61 2573
1345828503 7884 1979812 565806736 60 1479
1345828505 7208 1979422 567277552 59 1391
1345828507 7685 1980268 569784714 60 1398
1345828509 7073 1979779 573489941 236 1411

Heads up: while the plain (unaggregated) log file shows which script was used for each transaction, the aggregated log does not. So if you need per-script data, you gotta aggregate the data on your own. No shortcuts, bestie 💅

✨ Per-Statement Latencies ✨

With the -r option, pgbench collects the elapsed transaction time of each statement executed by every client. It then reports an average of those values — referred to as the latency for each statement — after the benchmark has finished.

For the default script, the output will look similar to this:

…and it wraps up with the per-statement receipts, like:

           starting vacuum...end.
           transaction type: <builtin: TPC-B (sort of)>
           scaling factor: 1
           query mode: simple
           number of clients: 10
           number of threads: 1
           number of transactions per client: 1000
           number of transactions actually processed: 10000/10000
           latency average = 15.844 ms
           latency stddev = 2.715 ms
           tps = 618.764555 (including connections establishing)
           tps = 622.977698 (excluding connections establishing)
           statement latencies in milliseconds:
                   0.002  \set aid random(1, 100000 * :scale)
                   0.005  \set bid random(1, 1 * :scale)
                   0.002  \set tid random(1, 10 * :scale)
                   0.001  \set delta random(-5000, 5000)
                   0.326  BEGIN;
                   0.603  UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
                   0.454  SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
                   5.528  UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
                   7.335  UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
                   0.371  INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
                   1.212  END;

Okay so if you pass multiple script files, the averages get reported separately for each script file — one set per file, not all mashed together. Cute, organized, we love that ✨

🫶 Real talk though: collecting that extra per-statement timing info adds overhead. It literally slows your average execution speed and drags the computed TPS down. How much slower? Depends massively on your platform and hardware — no universal number, bestie. Pro tip: run the same test with and without latency reporting and compare the average TPS. That's the honest way to find out whether the timing overhead is actually significant for you 💅

🌸 Good Practices 🌸

Okay so — and I say this with so much love — it is extremely easy to make pgbench spit out completely meaningless numbers. Like, embarrassingly easy. Here's how to actually get results that mean something 💀

First things first: never believe any test that runs for only a few seconds. Use the -t or -T option so the run lasts at least a few minutes, so the noise averages itself out. Sometimes you'll need hours to get numbers that are reproducible. And it's a genuinely good idea to run the test a few times just to find out whether your numbers are reproducible at all 🔥
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 plan to test (-c). Otherwise you're mostly measuring update contention, not performance. Explanation: the pgbench_branches table only has -s rows, and every transaction wants to update one of them. So -c values in excess of -s will, with basically zero doubt, result in a ton of transactions blocked waiting on other transactions 😭
The default test scenario is also super 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 actually understand what you're looking at, you have to keep track of the total number of updates and when vacuuming happens. And if autovacuum is enabled, it can cause unpredictable changes in the measured performance. 🙈
Lowkey a limitation of pgbench: it can itself become the bottleneck when you're trying to test a large number of client sessions. You can alleviate that by running pgbench on a different machine from the database server — though low network latency is absolutely essential. It might even be useful to run several pgbench instances concurrently, on several client machines, all against the same database server 👑

🔒 Security 🔒

Do not run pgbench in a database that hasn't adopted a secure schema usage pattern if untrusted users have access to it. Periodt. Why? Because pgbench uses unqualified names and does not manipulate the search path — so untrusted users could absolutely wreck your day. This one's not a vibe, it's a warning 🚨

And that's the whole page, bestie — you ate 💅 This little beauty is from PostgreSQL 13.23, dated 2025, and it lives in section PGBENCH(1) of the manual. Go forth and benchmark responsibly 🎀✨🦋

📦 where this page comes from

opensuse-leap 16.0: postgresql13-contrib 13.23-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql13-contrib 13.23-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql13-contrib 13.23-160000.1.1 · PostgreSQL
opensuse-leap 16.0: postgresql13-contrib 13.23-160000.1.1 · 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