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 latency average = 11.013 ms latency stddev = 7.351 ms initial connection time = 45.758 ms tps = 896.967014 (without initial connection time)
  The first 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 line reports the number of transactions per second.
 
   The default TPC-B-like transaction test requires specific tables to be
   set up beforehand.  pgbench should be invoked with
   the -i (initialize) option to create and populate these
   tables.  (When you are testing a custom script, you don't need this
   step, but will instead need to do whatever setup your test needs.)
   Initialization looks like:
pgbench -i [other-options]dbname
   where dbname is the name of the already-created
   database to test in.  (You may also need -h,
   -p, and/or -U options to specify how to
   connect to the database server.)
  
    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:
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--initializeRequired to invoke initialization mode.
-I init_steps--init-steps=init_steps
        Perform just a selected set of the normal initialization steps.
        init_steps specifies the
        initialization steps to be performed, using one character per step.
        Each step is invoked in the specified order.
        The default is dtgvp.
        The available steps are:
        
d (Drop)Drop any existing pgbench tables.
t (create Tables)
            Create the tables used by the
            standard pgbench scenario, namely
            pgbench_accounts,
            pgbench_branches,
            pgbench_history, and
            pgbench_tellers.
           
g or G (Generate data, client-side or server-side)Generate data and load it into the standard tables, replacing any data already present.
            With g (client-side data generation),
            data is generated in pgbench client and then
            sent to the server. This uses the client/server bandwidth
            extensively through a COPY.
            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--quietSwitch logging to quiet mode, producing only one progress message per 5 seconds. The default logging prints one message each 100,000 rows, which often outputs many lines per second (especially on good hardware).
        This setting has no effect if G is specified
        in -I.
       
-s scale_factor--scale=scale_factor
        Multiply the number of rows generated by the scale factor.
        For example, -s 100 will create 10,000,000 rows
        in the pgbench_accounts table. Default is 1.
        When the scale is 20,000 or larger, the columns used to
        hold account identifiers (aid columns)
        will switch to using larger integers (bigint),
        in order to be big enough to hold the range of account
        identifiers.
       
--foreign-keys
        Create foreign key constraints between the standard tables.
        (This option adds the f step to the initialization
        step sequence, if it is not already present.)
       
--index-tablespace=index_tablespaceCreate 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=tablespaceCreate tables in the specified tablespace, rather than the default tablespace.
--unlogged-tablesCreate all tables as unlogged tables, rather than permanent tables.
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=clientsNumber of clients simulated, that is, number of concurrent database sessions. Default is 1.
-C--connectEstablish a new connection for each transaction, rather than doing it just once per client session. This is useful to measure the connection overhead.
-d--debugPrint 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=threadsNumber 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--logWrite 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=querymodeProtocol 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 53 for more information.)
-n--no-vacuum
        Perform no vacuuming before running the test.
        This option is necessary
        if you are running a custom test scenario that does not include
        the standard tables pgbench_accounts,
        pgbench_branches, pgbench_history, and
        pgbench_tellers.
       
-N--skip-some-updates
        Run built-in simple-update script.
        Shorthand for -b simple-update.
       
-P sec--progress=sec
        Show progress report every sec seconds.  The report
        includes the time since the beginning of the run, the TPS since the
        last report, and the transaction latency average and 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, thus it also
        includes the average schedule lag time.
       
-r--report-latenciesReport the average per-statement latency (execution time from the perspective of the client) of each command after the benchmark finishes. See below for details.
-R rate--rate=rateExecute transactions targeting the specified rate instead of running as fast as possible (the default). The rate is given in transactions per second. If the targeted rate is above the maximum possible rate, the rate limit won't impact the results.
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.
-s scale_factor--scale=scale_factor
        Report the specified scale factor in pgbench's
        output.  With the built-in tests, this is not necessary; the
        correct scale factor will be detected by counting the number of
        rows in the pgbench_branches table.
        However, when testing only custom benchmarks (-f option),
        the scale factor will be reported as 1 unless this option is used.
       
-S--select-only
        Run built-in select-only script.
        Shorthand for -b select-only.
       
-t transactions--transactions=transactionsNumber of transactions each client runs. Default is 10.
-T seconds--time=seconds
        Run the test for this many seconds, rather than a fixed number of
        transactions per client. -t and
        -T are mutually exclusive.
       
-v--vacuum-all
        Vacuum all four standard tables before running the test.
        With neither -n nor -v, pgbench will vacuum the
        pgbench_tellers and pgbench_branches
        tables, and will truncate pgbench_history.
       
--aggregate-interval=seconds
        Length of aggregation interval (in seconds).  May be used only
        with -l option.  With this option, the log contains
        per-interval summary data, as described below.
       
--log-prefix=prefix
        Set the filename prefix for the log files created by
        --log.  The 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.
       
--random-seed=seed
        Set random generator seed.  Seeds the system random number generator,
        which then produces a sequence of initial generator states, one for
        each thread.
        Values for seed may be:
        time (the default, the seed is based on the current time),
        rand (use a strong random source, failing if none
        is available), or an unsigned decimal integer value.
        The random generator 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.
      
        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.
       
--sampling-rate=rateSampling rate, used when writing data into the log, to reduce the amount of log generated. If this option is given, only the specified fraction of transactions are logged. 1.0 means all transactions will be logged, 0.05 means only 5% of the transactions will be logged.
Remember to take the sampling rate into account when processing the log file. For example, when computing TPS values, you need to multiply the numbers accordingly (e.g., with 0.01 sample rate, you'll only get 1/100 of the actual TPS).
--show-script=scriptname
        Show the actual code of builtin script scriptname
        on stderr, and exit immediately.
       
pgbench also accepts the following common command-line arguments for connection parameters:
-h hostname--host=hostnameThe database server's host name
-p port--port=portThe database server's port number
-U login--username=loginThe user name to connect as
-V--versionPrint the pgbench version and exit.
-?--helpShow help about pgbench command line arguments, and exit.
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.
PGDATABASEPGHOSTPGPORTPGUSERDefault connection parameters.
This utility, like most other PostgreSQL utilities, uses the environment variables supported by libpq (see Section 34.15).
   The environment variable PG_COLOR specifies whether to use
   color in diagnostic messages. Possible values are
   always, auto and
   never.
  
   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.
  
BEGIN;
UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
END;
   If you select the simple-update built-in (also -N),
   steps 4 and 5 aren't included in the transaction.
   This will avoid update contention on these tables, but
   it makes the test case even less like TPC-B.
  
   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 an SQL command does not need one if it is followed by a meta command). If you need to create a script file that works with both old and new versions of pgbench, be sure to write each SQL command on a single line ending with a semicolon.
   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 282. A value specified for these
   variables using -D takes precedence over the automatic presets.
   Once set, a variable's
   value can be inserted into an SQL command by writing
   :variablename.  When running more than
   one client session, each session has its own set of variables.
   pgbench supports up to 255 variable uses in one
   statement.
  
Table 282. pgbench Automatic Variables
| Variable | Description | 
|---|---|
| client_id | unique number identifying the client session (starts from zero) | 
| default_seed | seed used in hash and pseudorandom permutation functions by default | 
| random_seed | random generator seed (unless overwritten with -D) | 
| scale | current scale factor | 
   Script file meta commands begin with a backslash (\) and
   normally extend to the end of the line, although they can be continued
   to additional lines by writing backslash-return.
   Arguments to a meta command are separated by white space.
   These meta commands are supported:
  
\gset [prefix]
     \aset [prefix]
    
      These commands may be used to end SQL queries, taking the place of the
      terminating semicolon (;).
     
      When the \gset command is used, the preceding SQL query is
      expected to return one row, the columns of which are stored into variables
      named after column names, and prefixed with prefix
      if provided.
     
      When the \aset command is used, all combined SQL queries
      (separated by \;) have their columns stored into variables
      named after column names, and prefixed with prefix
      if provided. If a query returns no row, no assignment is made and the variable
      can be tested for existence to detect this. If a query returns more than one
      row, the last value is kept.
     
      \gset and \aset cannot be used in
      pipeline mode, since the query results are not yet available by the time
      the commands would need them.
     
      The following example puts the final account balance from the first query
      into variable abalance, and fills variables
      p_two and p_three
      with integers from the third query.
      The result of the second query is discarded.
      The result of the two last combined queries are stored in variables
      four and five.
UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid RETURNING abalance \gset -- compound of two queries SELECT 1 \; SELECT 2 AS two, 3 AS three \gset p_ SELECT 4 AS four \; SELECT 5 AS five \aset
\if expression\elif expression\else\endif
      This group of commands implements nestable conditional blocks,
      similarly to psql's \if expression.
      Conditional expressions are identical to those with \set,
      with non-zero values interpreted as true.
     
\set varname expression
    
      Sets variable varname to a value calculated
      from expression.
      The expression may contain the NULL constant,
      Boolean constants TRUE and FALSE,
      integer constants such as 5432,
      double constants such as 3.14159,
      references to variables :variablename,
      operators
      with their usual SQL precedence and associativity,
      function calls,
      SQL CASE generic conditional
      expressions and parentheses.
     
      Functions and most operators return NULL on
      NULL input.
     
      For conditional purposes, non zero numerical values are
      TRUE, zero numerical values and NULL
      are FALSE.
     
      Too large or small integer and double constants, as well as
      integer arithmetic operators (+,
      -, * and /)
      raise errors on overflows.
     
      When no final ELSE clause is provided to a
      CASE, the default value is NULL.
     
Examples:
\set ntellers 10 * :scale
\set aid (1021 * random(1, 100000 * :scale)) % \
           (100000 * :scale) + 1
\set divx CASE WHEN :x <> 0 THEN :y/:x ELSE NULL END
\sleep number [ us | ms | s ]
    
      Causes script execution to sleep for the specified duration in
      microseconds (us), milliseconds (ms) or seconds
      (s).  If the unit is omitted then seconds are the default.
      number can be either an integer constant or a
      :variablename reference to a variable
      having an integer value.
     
Example:
\sleep 10 ms
\setshell varname command [ argument ... ]
    
      Sets variable varname to the result of the shell command
      command with the given argument(s).
      The command must return an integer value through its standard output.
     
      command and each argument can be either
      a text constant or a :variablename reference
      to a variable. If you want to use an argument starting
      with a colon, write an additional colon at the beginning of
      argument.
     
Example:
\setshell variable_to_be_assigned command literal_argument :variable ::literal_starting_with_colon
\shell command [ argument ... ]
    
      Same as \setshell, but the result of the command
      is discarded.
     
Example:
\shell command literal_argument :variable ::literal_starting_with_colon
\startpipeline\endpipelineThese commands delimit the start and end of a pipeline of SQL statements. In pipeline mode, statements are sent to the server without waiting for the results of previous statements. See Section 34.5 for more details. Pipeline mode requires the use of extended query protocol.
   The arithmetic, bitwise, comparison and logical operators listed in
   Table 283 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 283. pgbench Operators
| Operator Description Example(s) | 
|---|
| 
         Logical OR 
         | 
| 
         Logical AND 
         | 
| 
         Logical NOT 
         | 
| 
         Boolean value tests 
         | 
| 
         Nullness tests 
         | 
| 
         Equal 
         | 
| 
         Not equal 
         | 
| 
         Not equal 
         | 
| 
         Less than 
         | 
| 
         Less than or equal to 
         | 
| 
         Greater than 
         | 
| 
         Greater than or equal to 
         | 
| 
         Bitwise OR 
         | 
| 
         Bitwise XOR 
         | 
| 
         Bitwise AND 
         | 
| 
         Bitwise NOT 
         | 
| 
         Bitwise shift left 
         | 
| 
         Bitwise shift right 
         | 
| 
         Addition 
         | 
| 
         Subtraction 
         | 
| 
         Multiplication 
         | 
| 
         Division (truncates the result towards zero if both inputs are integers) 
         | 
| 
         Modulo (remainder) 
         | 
| 
         Negation 
         | 
   The functions listed in Table 284 are built
   into pgbench and may be used in expressions appearing in
   \set.
  
Table 284. pgbench Functions
| Function Description Example(s) | 
|---|
| 
         Absolute value 
         | 
| 
         Prints the argument to stderr, and returns the argument. 
         | 
| 
         Casts to double. 
         | 
| 
         
        Exponential ( 
         | 
| 
         Selects the largest value among the arguments. 
         | 
| 
         
        This is an alias for  
         | 
| 
         Computes FNV-1a hash. 
         | 
| 
         Computes MurmurHash2 hash. 
         | 
| 
         Casts to integer. 
         | 
| 
         Selects the smallest value among the arguments. 
         | 
| 
         Natural logarithm 
         | 
| 
 Modulo (remainder) 
         | 
| 
         
        Permuted value of  
         | 
| 
         Approximate value of π 
         | 
| 
         
         
         
         | 
| 
         
        Computes a uniformly-distributed random integer in  
         | 
| 
         
        Computes an exponentially-distributed random integer in  
         | 
| 
         
        Computes a Gaussian-distributed random integer in  
         | 
| 
         
        Computes a Zipfian-distributed random integer in  
         | 
| 
         Square root 
         | 
    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 allowed parameter
      value is 2.0.
     
      random_zipfian generates a bounded Zipfian
      distribution.
      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
      ((.
      For example, k+1)/k)**parameterrandom_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.  Due to limitations of that algorithm,
      the parameter value is restricted to
      the range [1.001, 1000].
     
When designing a benchmark which selects rows non-uniformly, be aware that the rows chosen may be correlated with other data such as IDs from a sequence or the physical row ordering, which may skew performance measurements.
      To avoid this, you may wish to use the permute
      function, or some other additional step with similar effect, to shuffle
      the selected rows and remove such correlations.
    
    Hash functions hash, hash_murmur2 and
    hash_fnv1a accept an input value and an optional seed parameter.
    In case the seed isn't provided the value of :default_seed
    is used, which is initialized randomly unless set by the command-line
    -D option.
  
    permute accepts an input value, a size, and an optional
    seed parameter.  It generates a pseudorandom permutation of integers in
    the range [0, size), and returns the index of the input
    value in the permuted values.  The permutation chosen is parameterized by
    the seed, which defaults to :default_seed, if not
    specified.  Unlike the hash functions, permute ensures
    that there are no collisions or holes in the output values.  Input values
    outside the interval are interpreted modulo the size.  The function raises
    an error if the size is not positive.  permute can be
    used to scatter the distribution of non-uniform random functions such as
    random_zipfian or random_exponential
    so that values drawn more often are not trivially correlated.  For
    instance, the following pgbench script
    simulates a possible real world workload typical for social media and
    blogging platforms where a few accounts generate excessive load:
\set size 1000000 \set r random_zipfian(1, :size, 1.07) \set k 1 + permute(:r, :size)
In some cases several distinct distributions are needed which don't correlate with each other and this is when the optional seed parameter comes in handy:
\set k1 1 + permute(:r, :size, :default_seed + 123) \set k2 1 + permute(:r, :size, :default_seed + 321)
    A similar behavior can also be approximated with hash:
\set size 1000000 \set r random_zipfian(1, 100 * :size, 1.07) \set k 1 + abs(hash(:r)) % :size
    However, since hash generates collisions, some values
    will not be reachable and others will be more frequent than expected from
    the original distribution.
  
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.nnnprefix 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.mmmmmm is a sequential number for each worker starting
   with 1.
  
The format of the log is:
client_idtransaction_notimescript_notime_epochtime_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_startnum_transactionssum_latencysum_latency_2min_latencymax_latency [sum_lagsum_lag_2min_lagmax_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 = 10.870 ms
latency stddev = 7.341 ms
initial connection time = 30.954 ms
tps = 907.949122 (without initial connection time)
statement latencies in milliseconds:
    0.001  \set aid random(1, 100000 * :scale)
    0.001  \set bid random(1, 1 * :scale)
    0.001  \set tid random(1, 10 * :scale)
    0.000  \set delta random(-5000, 5000)
    0.046  BEGIN;
    0.151  UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
    0.107  SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
    4.241  UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
    5.245  UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
    0.102  INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
    0.974  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.
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.