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|PGBENCH(1)||PostgreSQL 13.4 Documentation||PGBENCH(1)|
pgbench - run a benchmark test on PostgreSQL
pgbench -i [option...] [dbname]
pgbench [option...] [dbname]
pgbench is 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 (transactions per second). By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. However, it is easy to test other cases by writing your own transaction script files.
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 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)
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. 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.)
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.
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.
pgbench accepts the following command-line initialization arguments:
t (create Tables)
g or G (Generate data, client-side or server-side)
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. 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).
p (create Primary keys)
f (create Foreign keys)
This setting has no effect if G is specified in -I.
pgbench accepts the following command-line benchmarking arguments:
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.
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.
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.
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.)
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 that when transactions go past their original scheduled end time, it is 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. The 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 spent executing the transaction in the database, can be computed by subtracting the schedule lag time from the reported latency.
If --latency-limit is used together with --rate, a transaction can lag behind so much that it is 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, but are skipped altogether and counted separately.
A high schedule lag time is an indication that the system 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 will fall further behind, and the schedule lag time will keep increasing the longer the test run is. When that happens, you will have to reduce the specified transaction rate.
Setting the seed explicitly allows to reproduce a pgbench run exactly, as far as random numbers are concerned. As the random state is managed per thread, this 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.
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).
pgbench also accepts the following common command-line arguments for connection parameters:
A successful run will exit with status 0. Exit status 1 indicates static problems such as invalid command-line options. Errors during the run such as database errors or problems in the script will result in exit status 2. In the latter case, pgbench will print partial results.
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.
What Is the “Transaction” Actually Performed in pgbench?¶
pgbench executes test scripts chosen randomly from a specified list. The 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.
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.
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.
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, 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 273. 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. pgbench supports up to 255 variable uses in one statement.
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 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]
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.
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.
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
\set varname expression
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.
When no final ELSE clause is provided to a CASE, the default value is NULL.
\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 ]
\sleep 10 ms
\setshell varname command [ argument ... ]
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.
\setshell variable_to_be_assigned command literal_argument :variable ::literal_starting_with_colon
\shell command [ argument ... ]
\shell command literal_argument :variable ::literal_starting_with_colon
The arithmetic, bitwise, comparison and logical operators listed in Table 274 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.
Table 274. pgbench Operators
|Operator .PP Description .PP Example(s)|
|boolean OR boolean → boolean .PP Logical OR .PP 5 or 0 → TRUE|
|boolean AND boolean → boolean .PP Logical AND .PP 3 and 0 → FALSE|
|NOT boolean → boolean .PP Logical NOT .PP not false → TRUE|
|boolean IS [NOT] (NULL|TRUE|FALSE) → boolean .PP Boolean value tests .PP 1 is null → FALSE|
|value ISNULL|NOTNULL → boolean .PP Nullness tests .PP 1 notnull → TRUE|
|number = number → boolean .PP Equal .PP 5 = 4 → FALSE|
|number <> number → boolean .PP Not equal .PP 5 <> 4 → TRUE|
|number != number → boolean .PP Not equal .PP 5 != 5 → FALSE|
|number < number → boolean .PP Less than .PP 5 < 4 → FALSE|
|number <= number → boolean .PP Less than or equal to .PP 5 <= 4 → FALSE|
|number > number → boolean .PP Greater than .PP 5 > 4 → TRUE|
|number >= number → boolean .PP Greater than or equal to .PP 5 >= 4 → TRUE|
|integer | integer → integer .PP Bitwise OR .PP 1 | 2 → 3|
|integer # integer → integer .PP Bitwise XOR .PP 1 # 3 → 2|
|integer & integer → integer .PP Bitwise AND .PP 1 & 3 → 1|
|~ integer → integer .PP Bitwise NOT .PP ~ 1 → -2|
|integer << integer → integer .PP Bitwise shift left .PP 1 << 2 → 4|
|integer >> integer → integer .PP Bitwise shift right .PP 8 >> 2 → 2|
|number + number → number .PP Addition .PP 5 + 4 → 9|
|number - number → number .PP Subtraction .PP 3 - 2.0 → 1.0|
|number * number → number .PP Multiplication .PP 5 * 4 → 20|
|number / number → number .PP Division (truncates the result towards zero if both inputs are integers) .PP 5 / 3 → 1|
|integer % integer → integer .PP Modulo (remainder) .PP 3 % 2 → 1|
|- number → number .PP Negation .PP - 2.0 → -2.0|
The functions listed in
Table 275 are built into pgbench and may be used in expressions appearing in \set.
Table 275. pgbench Functions
|Function .PP Description .PP Example(s)|
|abs ( number ) → same type as input .PP Absolute value .PP abs(-17) → 17|
|debug ( number ) → same type as input .PP Prints the argument to stderr, and returns the argument. .PP debug(5432.1) → 5432.1|
|double ( number ) → double .PP Casts to double. .PP double(5432) → 5432.0|
|exp ( number ) → double .PP Exponential (e raised to the given power) .PP exp(1.0) → 2.718281828459045|
|greatest ( number [, ... ] ) → double if any argument is double, else integer .PP Selects the largest value among the arguments. .PP greatest(5, 4, 3, 2) → 5|
|hash ( value [, seed ] ) → integer .PP This is an alias for hash_murmur2. .PP hash(10, 5432) → -5817877081768721676|
|hash_fnv1a ( value [, seed ] ) → integer .PP Computes FNV-1a hash. .PP hash_fnv1a(10, 5432) → -7793829335365542153|
|hash_murmur2 ( value [, seed ] ) → integer .PP Computes MurmurHash2 hash. .PP hash_murmur2(10, 5432) → -5817877081768721676|
|int ( number ) → integer .PP Casts to integer. .PP int(5.4 + 3.8) → 9|
|least ( number [, ... ] ) → double if any argument is double, else integer .PP Selects the smallest value among the arguments. .PP least(5, 4, 3, 2.1) → 2.1|
|ln ( number ) → double .PP Natural logarithm .PP ln(2.718281828459045) → 1.0|
|mod ( integer, integer ) → integer .PP Modulo (remainder) .PP mod(54, 32) → 22|
|pi () → double .PP Approximate value of π .PP pi() → 3.14159265358979323846|
|pow ( x, y ) → double .PP power ( x, y ) → double .PP x raised to the power of y .PP pow(2.0, 10) → 1024.0|
|random ( lb, ub ) → integer .PP Computes a uniformly-distributed random integer in [lb, ub]. .PP random(1, 10) → an integer between 1 and 10|
|random_exponential ( lb, ub, parameter ) → integer .PP Computes an exponentially-distributed random integer in [lb, ub], see below. .PP random_exponential(1, 10, 3.0) → an integer between 1 and 10|
|random_gaussian ( lb, ub, parameter ) → integer .PP Computes a Gaussian-distributed random integer in [lb, ub], see below. .PP random_gaussian(1, 10, 2.5) → an integer between 1 and 10|
|random_zipfian ( lb, ub, parameter ) → integer .PP Computes a Zipfian-distributed random integer in [lb, ub], see below. .PP random_zipfian(1, 10, 1.5) → an integer between 1 and 10|
|sqrt ( number ) → double .PP Square root .PP sqrt(2.0) → 1.414213562|
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.
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.
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.
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].
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.)
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.
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.
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)
0.603 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);
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.
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.
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.