Provided by: postgresql-10_10.5-1_amd64 bug


       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, some options are useful in both

   Initialization Options
       pgbench accepts the following command-line initialization arguments:

           Required to invoke initialization mode.

       -F fillfactor
           Create the pgbench_accounts, pgbench_tellers and pgbench_branches tables with the
           given fillfactor. Default is 100.

           Perform no vacuuming after initialization.

           Switch logging to quiet mode, producing only one progress message per 5 seconds. The
           default logging prints one message each 100000 rows, which often outputs many lines
           per second (especially on good hardware).

       -s 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.

           Create foreign key constraints between the standard tables.

           Create indexes in the specified tablespace, rather than the default tablespace.

           Create tables in the specified tablespace, rather than the default tablespace.

           Create all tables as unlogged tables, rather than permanent tables.

   Benchmarking Options
       pgbench accepts the following command-line benchmarking arguments:

       -b scriptname[@weight]
           Add the specified built-in script to the list of executed scripts. An optional integer
           weight after @ allows to adjust the probability of drawing the script. If not
           specified, it is set to 1. Available built-in scripts are: tpcb-like, simple-update
           and select-only. Unambiguous prefixes of built-in names are accepted. With special
           name list, show the list of built-in scripts and exit immediately.

       -c clients
           Number of clients simulated, that is, number of concurrent database sessions. Default
           is 1.

           Establish a new connection for each transaction, rather than doing it just once per
           client session. This is useful to measure the connection overhead.

           Print debugging output.

       -D varname=value
           Define a variable for use by a custom script (see below). Multiple -D options are

       -f filename[@weight]
           Add a transaction script read from filename to the list of executed scripts. An
           optional integer weight after @ allows to adjust the probability of drawing the test.
           See below for details.

       -j 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.

           Write information about each transaction to a log file. See below for details.

       -L limit
           Transaction which 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 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.

           The default is simple query protocol. (See Chapter 52 for more information.)

           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.

           Run built-in simple-update script. Shorthand for -b simple-update.

       -P 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

           Report 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
           Execute 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
           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.

           Run built-in select-only script. Shorthand for -b select-only.

       -t transactions
           Number of transactions each client runs. Default is 10.

       -T seconds
           Run the test for this many seconds, rather than a fixed number of transactions per
           client.  -t and -T are mutually exclusive.

           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

           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.

           Set the filename prefix for the log files created by --log. The default is

           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.

           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).

   Common Options
       pgbench accepts the following command-line common arguments:

       -h hostname
           The database server's host name

       -p port
           The database server's port number

       -U login
           The user name to connect as

           Print the pgbench version and exit.

           Show help about pgbench command line arguments, and exit.


   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 balance. The scenario is
       inspired by the TPC-B benchmark, but is not actually TPC-B, hence the name.

        1. BEGIN;

        2. UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;

        3. SELECT abalance FROM pgbench_accounts WHERE aid = :aid;

        4. UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;

        5. UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;

        6. INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid,
           :delta, CURRENT_TIMESTAMP);

        7. 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.

   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.

           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

       There is a simple variable-substitution facility for script files. 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.

       Table 241. Automatic Variables
       │VariableDescription                   │
       │scale     │ current scale factor          │
       │client_id │ unique number identifying the │
       │          │ client session (starts from   │
       │          │ zero)                         │

       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

       \set varname expression
           Sets variable varname to a value calculated from expression. The expression may
           contain integer constants such as 5432, double constants such as 3.14159, references
           to variables :variablename, unary operators (+, -) and binary operators (+, -, *, /,
           %) with their usual precedence and associativity, function calls, and parentheses.


               \set ntellers 10 * :scale
               \set aid (1021 * random(1, 100000 * :scale)) % \
                          (100000 * :scale) + 1

       \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.


               \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.


               \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.


               \shell command literal_argument :variable ::literal_starting_with_colon

   Built-In Functions
       The functions listed in Table 242 are built into pgbench and may be used in expressions
       appearing in \set.

       Table 242. pgbench Functions
       │FunctionReturn TypeDescriptionExampleResult                 │
       │abs(a)                 │ same as a       │ absolute value            │ abs(-17)              │ 17                     │
       │debug(a)               │ same as a       │ print a to                │ debug(5432.1)         │ 5432.1                 │
       │                       │                 │ stderr,                   │                       │                        │
       │                       │                 │         and               │                       │                        │
       │                       │                 │ return a                  │                       │                        │
       │double(i)              │ double          │ cast to double            │ double(5432)          │ 5432.0                 │
       │greatest(a [,          │ double if any a │ largest value             │ greatest(5, 4,        │ 5                      │
       │... ] )                │ is double, else │ among arguments           │ 3, 2)                 │                        │
       │                       │ integer         │                           │                       │                        │
       │int(x)                 │ integer         │ cast to int               │ int(5.4 + 3.8)        │ 9                      │
       │least(a [, ... ]       │ double if any a │ smallest value            │ least(5, 4, 3,        │ 2.1                    │
       │)                      │ is double, else │ among arguments           │ 2.1)                  │                        │
       │                       │ integer         │                           │                       │                        │
       │pi()                   │ double          │ value of the              │ pi()                  │ 3.14159265358979323846 │
       │                       │                 │ constant PI               │                       │                        │
       │random(lb, ub)         │ integer         │ uniformly-distributed     │ random(1, 10)         │ an integer between 1   │
       │                       │                 │ random integer            │                       │ and 10                 │
       │                       │                 │ in [lb, ub]               │                       │                        │
       │random_exponential(lb, │ integer         │ exponentially-distributed │ random_exponential(1, │ an integer between 1   │
       │ub, parameter)         │                 │ random integer in         │ 10, 3.0)              │ and 10                 │
       │                       │                 │ [lb, ub],                 │                       │                        │
       │                       │                 │               see         │                       │                        │
       │                       │                 │ below                     │                       │                        │
       │random_gaussian(lb,    │ integer         │ Gaussian-distributed      │ random_gaussian(1,    │ an integer between 1   │
       │ub, parameter)         │                 │ random integer in [lb,    │ 10, 2.5)              │ and 10                 │
       │                       │                 │ ub],                      │                       │                        │
       │                       │                 │               see below   │                       │                        │
       │sqrt(x)                │ double          │ square root               │ sqrt(2.0)             │ 1.414213562            │

       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 and
       random_gaussian 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

       ·   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

       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)
           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);

       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

   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

           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

       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)
           script statistics:
            - 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.

       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.