pgbench

Initialization Options

pgbench accepts the following command-line initialization arguments:

Benchmarking Options

pgbench accepts the following command-line benchmarking arguments:

Common Options

pgbench accepts the following command-line common arguments:

What is the “Transaction” Actually Performed in pgbench?

pgbench executes test scripts chosen randomly from a specified list. They include built-in scripts with -b and user-provided custom scripts with -f. Each script may be given a relative weight specified after a @ so as to change its drawing probability. The default weight is 1. Scripts with a weight of 0 are ignored.

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.

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.

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

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.

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.

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.

There is a simple variable-substitution facility for script files. Variable names must consist of letters (including non-Latin letters), digits, and underscores. 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 241. A value specified for these variables using -D takes precedence over the automatic presets. Once set, a variable's value can be inserted into a SQL command by writing :variablename. When running more than one client session, each session has its own set of variables.

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:

Built-In Operators

The arithmetic, bitwise, comparison and logical operators listed in Table 242 are built into pgbench and may be used in expressions appearing in \set.

Built-In Functions

The functions listed in Table 243 are built into pgbench and may be used in expressions appearing in \set.

The random function generates values using a uniform distribution, that is all the values are drawn within the specified range with equal probability. The random_exponential, random_gaussian and random_zipfian functions require an additional double parameter which determines the precise shape of the distribution.

• For an exponential distribution, 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.

• For a Gaussian distribution, the interval is 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 parameter is 2.0 for performance of the Box-Muller transform.

• random_zipfian generates an approximated bounded Zipfian distribution. For parameter in (0, 1), an approximated algorithm is taken from "Quickly Generating Billion-Record Synthetic Databases", Jim Gray et al, SIGMOD 1994. For parameter in (1, 1000), a rejection method is used, based on "Non-Uniform Random Variate Generation", Luc Devroye, p. 550-551, Springer 1986. The distribution is not defined when the parameter's value is 1.0. The function's performance is poor for parameter values close and above 1.0 and on a small range.

  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 closer to 0 parameter is, the flatter (more uniform) the output distribution. 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.

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 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 which don't correlate with each other and this is when 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 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.

The format of the log is:

client_id transaction_no time script_no time_epoch time_us [ schedule_lag ]

where client_id indicates 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 (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 is 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 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.

When running a long test on hardware that can handle a lot of transactions, the log files can become very large. The --sampling-rate option can be used to log only a random sample of transactions.

Aggregated Logging

With the --aggregate-interval option, a different format is 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 provide 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 is 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

Notice that while the plain (unaggregated) log file shows which script was used for each transaction, the aggregated log does not. Therefore if you need per-script data, you need to aggregate the data on your own.

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:

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;

If multiple script files are specified, the averages are reported separately for each script file.

Note that collecting the additional timing information needed for per-statement latency computation adds some overhead. This will slow average execution speed and lower the computed TPS. The amount of slowdown varies significantly depending on platform and hardware. Comparing average TPS values with and without latency reporting enabled is a good way to measure if the timing overhead is significant.

Good Practices

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

In the first place, never believe any test that runs for only a few seconds. Use the -t or -T option 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 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.

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.

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.