pgbenchpgbench1Applicationpgbenchrun a benchmark test on PostgreSQLpgbenchoptiondbnamepgbenchoptiondbnameDescriptionpgbench 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
maximum number of tries: 1
number of transactions per client: 1000
number of transactions actually processed: 10000/10000
number of failed transactions: 0 (0.000%)
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 seven lines report some of the most important parameter
settings.
The sixth line reports the maximum number of tries for transactions with
serialization or deadlock errors (see
for more information).
The eighth 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 or some SQL command(s) failed. (In
mode, only the actual number of transactions is printed.)
The next line reports the number of failed transactions due to
serialization or deadlock errors (see
for more information).
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 (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-optionsdbname
where dbname is the name of the already-created
database to test in. (You may also need ,
, and/or 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 (scale factor) option. The
(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 , that is
pgbench optionsdbname
In nearly all cases, you'll need some options to make a useful test.
The most important options are (number of clients),
(number of transactions), (time limit),
and (specify a custom script file).
See below for a full list.
Options
The following is divided into three subsections. Different options are
used during database initialization and while running benchmarks, but some
options are useful in both cases.
Initialization Optionspgbench 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.
Required to invoke initialization mode.
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.
pgbench uses the FREEZE option with version 14 or later
of PostgreSQL to speed up
subsequent VACUUM, unless partitions are enabled.
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.)
fillfactorfillfactor
Create the pgbench_accounts,
pgbench_tellers and
pgbench_branches tables with the given fillfactor.
Default is 100.
Perform no vacuuming during initialization.
(This option suppresses the v initialization step,
even if it was specified in .)
Switch logging to quiet mode, producing only one progress message per 5
seconds. The default logging prints one message each 100,000 rows, which
often outputs many lines per second (especially on good hardware).
This setting has no effect if G is specified
in .
scale_factorscale_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.
(This option adds the f step to the initialization
step sequence, if it is not already present.)
Create indexes in the specified tablespace, rather than the default
tablespace.
Create a partitioned pgbench_accounts table with
NAME method.
Expected values are range or hash.
This option requires that is set to non-zero.
If unspecified, default is range.
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.
Create tables in the specified tablespace, rather than the default
tablespace.
Create all tables as unlogged tables, rather than permanent tables.
Benchmarking Optionspgbench accepts the following command-line
benchmarking arguments:
scriptname[@weight]=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.
clientsclients
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.
varname=valuevarname=value
Define a variable for use by a custom script (see below).
Multiple options are allowed.
filename[@weight]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.
threadsthreads
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.
limitlimit
Transactions that last more than limit milliseconds
are counted and reported separately, as late.
When throttling is used (), 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.
When the option is used, a transaction
which fails due to a serialization anomaly or from a deadlock will not
be retried if the total time of all its tries is greater than
limit ms. To limit only the time of tries
and not their number, use --max-tries=0. By
default, the option is set to 1 and
transactions with serialization/deadlock errors are not retried. See
for more information about
retrying such transactions.
querymodequerymode
Protocol to use for submitting queries to the server:
simple: use simple query protocol.extended: use extended query protocol.prepared: use extended query protocol with prepared statements.
In the prepared mode, pgbench
reuses the parse analysis result starting from the second query
iteration, so pgbench runs faster
than in other modes.
The default is simple query protocol. (See
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 .
secsec
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, standard deviation,
and the number of failed transactions since the last report. Under
throttling (), 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.
When is used to enable transaction retries
after serialization/deadlock errors, the report includes the number of
retried transactions and the sum of all retries.
Report the following statistics for each command after the benchmark
finishes: the average per-statement latency (execution time from the
perspective of the client), the number of failures, and the number of
retries after serialization or deadlock errors in this command. The
report displays retry statistics only if the
option is not equal to 1.
raterate
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 is used together with ,
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.
scale_factorscale_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 ( option),
the scale factor will be reported as 1 unless this option is used.
Run built-in select-only script.
Shorthand for .
transactionstransactions
Number of transactions each client runs. Default is 10.
secondsseconds
Run the test for this many seconds, rather than a fixed number of
transactions per client. and
are mutually exclusive.
Vacuum all four standard tables before running the test.
With neither nor , pgbench will vacuum the
pgbench_tellers and pgbench_branches
tables, and will truncate pgbench_history.
Length of aggregation interval (in seconds). May be used only
with option. With this option, the log contains
per-interval summary data, as described below.
Report failures in per-transaction and aggregation logs, as well as in
the main and per-script reports, grouped by the following types:
serialization failures;deadlock failures;
See for more information.
Set the filename prefix for the log files created by
. The default is pgbench_log.
Enable retries for transactions with serialization/deadlock errors and
set the maximum number of these tries. This option can be combined with
the option which limits the total time
of all transaction tries; moreover, you cannot use an unlimited number
of tries (--max-tries=0) without
or .
The default value is 1 and transactions with serialization/deadlock
errors are not retried. See
for more information about retrying such transactions.
When showing progress (option ), 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.
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
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, 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).
scriptname
Show the actual code of builtin script scriptname
on stderr, and exit immediately.
Print messages about all errors and failures (errors without retrying)
including which limit for retries was exceeded and how far it was
exceeded for the serialization/deadlock failures. (Note that in this
case the output can be significantly increased.).
See for more information.
Common Optionspgbench also accepts the following common command-line
arguments for connection parameters:
hostnamehostname
The database server's host name
portport
The database server's port number
loginlogin
The user name to connect as
Print the pgbench version and exit.
Show help about pgbench command line
arguments, and exit.
Exit Status
A successful run will exit with status 0. Exit status 1 indicates static
problems such as invalid command-line options or internal errors which
are supposed to never occur. Early errors that occur when starting
benchmark such as initial connection failures also exit with status 1.
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.
EnvironmentPGDATABASEPGHOSTPGPORTPGUSER
Default connection parameters.
This utility, like most other PostgreSQL utilities,
uses the environment variables supported by libpq
(see ).
The environment variable PG_COLOR specifies whether to use
color in diagnostic messages. Possible values are
always, auto and
never.
NotesWhat 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
and user-provided scripts specified with .
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 )
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 ),
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 ),
only the SELECT is issued.
Custom Scriptspgbench has support for running custom
benchmark scenarios by replacing the default transaction script
(described above) with a transaction script read from a file
( 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.
It is assumed that pgbench scripts do not contain incomplete blocks of SQL
transactions. If at runtime the client reaches the end of the script without
completing the last transaction block, it will be aborted.
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 option,
explained above, or by the meta commands explained below.
In addition to any variables preset by command-line options,
there are a few variables that are preset automatically, listed in
. A value specified for these
variables using 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.
pgbench Automatic VariablesVariableDescriptionclient_idunique number identifying the client session (starts from zero)default_seedseed used in hash and pseudorandom permutation functions by defaultrandom_seedrandom generator seed (unless overwritten with )scalecurrent 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
\ifexpression\elifexpression\else\endif
This group of commands implements nestable conditional blocks,
similarly to psql's .
Conditional expressions are identical to those with \set,
with non-zero values interpreted as true.
\set varnameexpression
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 varnamecommand [ 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\endpipeline
These 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
for more details.
Pipeline mode requires the use of extended query protocol.
Built-in Operators
The arithmetic, bitwise, comparison and logical operators listed in
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.
pgbench Operators
Operator
Description
Example(s)
booleanORbooleanboolean
Logical OR
5 or 0TRUEbooleanANDbooleanboolean
Logical AND
3 and 0FALSENOTbooleanboolean
Logical NOT
not falseTRUEbooleanIS [NOT] (NULL|TRUE|FALSE)boolean
Boolean value tests
1 is nullFALSEvalueISNULL|NOTNULLboolean
Nullness tests
1 notnullTRUEnumber=numberboolean
Equal
5 = 4FALSEnumber<>numberboolean
Not equal
5 <> 4TRUEnumber!=numberboolean
Not equal
5 != 5FALSEnumber<numberboolean
Less than
5 < 4FALSEnumber<=numberboolean
Less than or equal to
5 <= 4FALSEnumber>numberboolean
Greater than
5 > 4TRUEnumber>=numberboolean
Greater than or equal to
5 >= 4TRUEinteger|integerinteger
Bitwise OR
1 | 23integer#integerinteger
Bitwise XOR
1 # 32integer&integerinteger
Bitwise AND
1 & 31~integerinteger
Bitwise NOT
~ 1-2integer<<integerinteger
Bitwise shift left
1 << 24integer>>integerinteger
Bitwise shift right
8 >> 22number+numbernumber
Addition
5 + 49number-numbernumber
Subtraction
3 - 2.01.0number*numbernumber
Multiplication
5 * 420number/numbernumber
Division (truncates the result towards zero if both inputs are integers)
5 / 31integer%integerinteger
Modulo (remainder)
3 % 21-numbernumber
Negation
- 2.0-2.0
Built-In Functions
The functions listed in are built
into pgbench and may be used in expressions appearing in
\set.
pgbench Functions
Function
Description
Example(s)
abs ( number )
same type as input
Absolute value
abs(-17)17debug ( number )
same type as input
Prints the argument to stderr,
and returns the argument.
debug(5432.1)5432.1double ( number )
double
Casts to double.
double(5432)5432.0exp ( number )
double
Exponential (e raised to the given power)
exp(1.0)2.718281828459045greatest ( number, ... )
double if any argument is double, else integer
Selects the largest value among the arguments.
greatest(5, 4, 3, 2)5hash ( value, seed )
integer
This is an alias for hash_murmur2.
hash(10, 5432)-5817877081768721676hash_fnv1a ( value, seed )
integer
Computes FNV-1a hash.
hash_fnv1a(10, 5432)-7793829335365542153hash_murmur2 ( value, seed )
integer
Computes MurmurHash2 hash.
hash_murmur2(10, 5432)-5817877081768721676int ( number )
integer
Casts to integer.
int(5.4 + 3.8)9least ( number, ... )
double if any argument is double, else integer
Selects the smallest value among the arguments.
least(5, 4, 3, 2.1)2.1ln ( number )
double
Natural logarithm
ln(2.718281828459045)1.0mod ( integer, integer )
integer
Modulo (remainder)
mod(54, 32)22permute ( i, size [, seed ] )
integer
Permuted value of i, in the range
[0, size). This is the new position of
i (modulo size) in a
pseudorandom permutation of the integers 0...size-1,
parameterized by seed, see below.
permute(0, 4)an integer between 0 and 3pi ()
double
Approximate value of πpi()3.14159265358979323846pow ( x, y )
doublepower ( x, y )
doublex raised to the power of ypow(2.0, 10)1024.0random ( lb, ub )
integer
Computes a uniformly-distributed random integer in [lb,
ub].
random(1, 10)an integer between 1 and 10random_exponential ( lb, ub, parameter )
integer
Computes an exponentially-distributed random integer in [lb,
ub], see below.
random_exponential(1, 10, 3.0)an integer between 1 and 10random_gaussian ( lb, ub, parameter )
integer
Computes a Gaussian-distributed random integer in [lb,
ub], see below.
random_gaussian(1, 10, 2.5)an integer between 1 and 10random_zipfian ( lb, ub, parameter )
integer
Computes a Zipfian-distributed random integer in [lb,
ub], see below.
random_zipfian(1, 10, 1.5)an integer between 1 and 10sqrt ( number )
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,
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
((k+1)/k)**parameter.
For example, random_zipfian(1, ..., 2.5) produces
the value 1 about (2/1)**2.5 =
5.66 times more frequently than 2, which
itself is produced (3/2)**2.5 = 2.76 times more
frequently than 3, and so on.
pgbench's implementation is based on
"Non-Uniform Random Variate Generation", Luc Devroye, p. 550-551,
Springer 1986. 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.)
Per-Transaction Logging
With the option (but without
the 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 option.
If the 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.
Each line in a log file describes one transaction.
It contains the following space-separated fields:
client_id
identifies the client session that ran the transaction
transaction_no
counts how many transactions have been run by that session
time
transaction's elapsed time, in microseconds
script_no
identifies the script file that was used for the transaction
(useful when multiple scripts are specified
with or )
time_epoch
transaction's completion time, as a Unix-epoch time stamp
time_us
fractional-second part of transaction's completion time, in
microseconds
schedule_lag
transaction start delay, that is the difference between the
transaction's scheduled start time and the time it actually
started, in microseconds
(present only if is specified)
retries
count of retries after serialization or deadlock errors during the
transaction
(present only if is not equal to one)
When both and are used,
the time for a skipped transaction will be reported as
skipped.
If the transaction ends with a failure, its time
will be reported as failed. If you use the
option, the
time of the failed transaction will be reported as
serialization or
deadlock depending on the type of failure (see
for more information).
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.
The following example shows a snippet of a log file with failures and
retries, with the maximum number of tries set to 10 (note the additional
retries column):
3 0 47423 0 1499414498 34501 3
3 1 8333 0 1499414498 42848 0
3 2 8358 0 1499414498 51219 0
4 0 72345 0 1499414498 59433 6
1 3 41718 0 1499414498 67879 4
1 4 8416 0 1499414498 76311 0
3 3 33235 0 1499414498 84469 3
0 0 failed 0 1499414498 84905 9
2 0 failed 0 1499414498 86248 9
3 4 8307 0 1499414498 92788 0
If the option is used, the type of
failure is reported in the time like this:
3 0 47423 0 1499414498 34501 3
3 1 8333 0 1499414498 42848 0
3 2 8358 0 1499414498 51219 0
4 0 72345 0 1499414498 59433 6
1 3 41718 0 1499414498 67879 4
1 4 8416 0 1499414498 76311 0
3 3 33235 0 1499414498 84469 3
0 0 serialization 0 1499414498 84905 9
2 0 serialization 0 1499414498 86248 9
3 4 8307 0 1499414498 92788 0
When running a long test on hardware that can handle a lot of transactions,
the log files can become very large. The option
can be used to log only a random sample of transactions.
Aggregated Logging
With the option, a different
format is used for the log files. Each log line describes one
aggregation interval. It contains the following space-separated
fields:
interval_start
start time of the interval, as a Unix-epoch time stamp
num_transactions
number of transactions within the interval
sum_latency
sum of transaction latencies
sum_latency_2
sum of squares of transaction latencies
min_latency
minimum transaction latency
max_latency
maximum transaction latency
sum_lag
sum of transaction start delays
(zero unless is specified)
sum_lag_2
sum of squares of transaction start delays
(zero unless is specified)
min_lag
minimum transaction start delay
(zero unless is specified)
max_lag
maximum transaction start delay
(zero unless is specified)
skipped
number of transactions skipped because they would have started too late
(zero unless
and are specified)
retried
number of retried transactions
(zero unless is not equal to one)
retries
number of retries after serialization or deadlock errors
(zero unless is not equal to one)
serialization_failures
number of transactions that got a serialization error and were not
retried afterwards
(zero unless is specified)
deadlock_failures
number of transactions that got a deadlock error and were not
retried afterwards
(zero unless is specified)
Here is some example output generated with these options:
pgbench --aggregate-interval=10 --time=20 --client=10 --log --rate=1000 --latency-limit=10 --failures-detailed --max-tries=10 test
1650260552 5178 26171317 177284491527 1136 44462 2647617 7321113867 0 9866 64 7564 28340 4148 0
1650260562 4808 25573984 220121792172 1171 62083 3037380 9666800914 0 9998 598 7392 26621 4527 0
Notice that while the plain (unaggregated) log format shows which script
was used for each transaction, the aggregated format does not. Therefore if
you need per-script data, you need to aggregate the data on your own.
Per-Statement Report
With the option, pgbench
collects the following statistics for each statement:
latency — elapsed transaction time for each
statement. pgbench reports an average value
of all successful runs of the statement.
The number of failures in this statement. See
for more information.
The number of retries after a serialization or a deadlock error in this
statement. See for more information.
The report displays retry statistics only if the
option is not equal to 1.
All values are computed for each statement executed by every client and are
reported 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
maximum number of tries: 1
number of transactions per client: 1000
number of transactions actually processed: 10000/10000
number of failed transactions: 0 (0.000%)
number of transactions above the 50.0 ms latency limit: 1311/10000 (13.110 %)
latency average = 28.488 ms
latency stddev = 21.009 ms
initial connection time = 69.068 ms
tps = 346.224794 (without initial connection time)
statement latencies in milliseconds and failures:
0.012 0 \set aid random(1, 100000 * :scale)
0.002 0 \set bid random(1, 1 * :scale)
0.002 0 \set tid random(1, 10 * :scale)
0.002 0 \set delta random(-5000, 5000)
0.319 0 BEGIN;
0.834 0 UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
0.641 0 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
11.126 0 UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
12.961 0 UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
0.634 0 INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
1.957 0 END;
Another example of output for the default script using serializable default
transaction isolation level (PGOPTIONS='-c
default_transaction_isolation=serializable' pgbench ...):
starting vacuum...end.
transaction type: <builtin: TPC-B (sort of)>
scaling factor: 1
query mode: simple
number of clients: 10
number of threads: 1
maximum number of tries: 10
number of transactions per client: 1000
number of transactions actually processed: 6317/10000
number of failed transactions: 3683 (36.830%)
number of transactions retried: 7667 (76.670%)
total number of retries: 45339
number of transactions above the 50.0 ms latency limit: 106/6317 (1.678 %)
latency average = 17.016 ms
latency stddev = 13.283 ms
initial connection time = 45.017 ms
tps = 186.792667 (without initial connection time)
statement latencies in milliseconds, failures and retries:
0.006 0 0 \set aid random(1, 100000 * :scale)
0.001 0 0 \set bid random(1, 1 * :scale)
0.001 0 0 \set tid random(1, 10 * :scale)
0.001 0 0 \set delta random(-5000, 5000)
0.385 0 0 BEGIN;
0.773 0 1 UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
0.624 0 0 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
1.098 320 3762 UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
0.582 3363 41576 UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
0.465 0 0 INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
1.933 0 0 END;
If multiple script files are specified, all statistics 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.
Failures and Serialization/Deadlock Retries
When executing pgbench, there are three main types
of errors:
Errors of the main program. They are the most serious and always result
in an immediate exit from pgbench with the
corresponding error message. They include:
errors at the beginning of pgbench
(e.g. an invalid option value);
errors in the initialization mode (e.g. the query to create
tables for built-in scripts fails);
errors before starting threads (e.g. could not connect to the
database server, syntax error in the meta command, thread
creation failure);
internal pgbench errors (which are
supposed to never occur...).
Errors when the thread manages its clients (e.g. the client could not
start a connection to the database server / the socket for connecting
the client to the database server has become invalid). In such cases
all clients of this thread stop while other threads continue to work.
Direct client errors. They lead to immediate exit from
pgbench with the corresponding error message
only in the case of an internal pgbench
error (which are supposed to never occur...). Otherwise in the worst
case they only lead to the abortion of the failed client while other
clients continue their run (but some client errors are handled without
an abortion of the client and reported separately, see below). Later in
this section it is assumed that the discussed errors are only the
direct client errors and they are not internal
pgbench errors.
A client's run is aborted in case of a serious error; for example, the
connection with the database server was lost or the end of script was reached
without completing the last transaction. In addition, if execution of an SQL
or meta command fails for reasons other than serialization or deadlock errors,
the client is aborted. Otherwise, if an SQL command fails with serialization or
deadlock errors, the client is not aborted. In such cases, the current
transaction is rolled back, which also includes setting the client variables
as they were before the run of this transaction (it is assumed that one
transaction script contains only one transaction; see
for more information).
Transactions with serialization or deadlock errors are repeated after
rollbacks until they complete successfully or reach the maximum
number of tries (specified by the option) / the maximum
time of retries (specified by the option) / the end
of benchmark (specified by the option). If
the last trial run fails, this transaction will be reported as failed but
the client is not aborted and continues to work.
Without specifying the option, a transaction will
never be retried after a serialization or deadlock error because its default
value is 1. Use an unlimited number of tries (--max-tries=0)
and the option to limit only the maximum time
of tries. You can also use the option to limit the
benchmark duration under an unlimited number of tries.
Be careful when repeating scripts that contain multiple transactions: the
script is always retried completely, so successful transactions can be
performed several times.
Be careful when repeating transactions with shell commands. Unlike the
results of SQL commands, the results of shell commands are not rolled back,
except for the variable value of the \setshell command.
The latency of a successful transaction includes the entire time of
transaction execution with rollbacks and retries. The latency is measured
only for successful transactions and commands but not for failed transactions
or commands.
The main report contains the number of failed transactions. If the
option is not equal to 1, the main report also
contains statistics related to retries: the total number of retried
transactions and total number of retries. The per-script report inherits all
these fields from the main report. The per-statement report displays retry
statistics only if the option is not equal to 1.
If you want to group failures by basic types in per-transaction and
aggregation logs, as well as in the main and per-script reports, use the
option. If you also want to distinguish
all errors and failures (errors without retrying) by type including which
limit for retries was exceeded and how much it was exceeded by for the
serialization/deadlock failures, use the
option.
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 or 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
() should be at least as large as the largest number of
clients you intend to test (); else you'll mostly be
measuring update contention. There are only rows in
the pgbench_branches table, and every transaction wants to
update one of them, so values in excess of
will undoubtedly result in lots of transactions blocked waiting for
other transactions.
The default test scenario is also quite sensitive to how long it's been
since the tables were initialized: accumulation of dead rows and dead space
in the tables changes the results. To understand the results you must keep
track of the total number of updates and when vacuuming happens. If
autovacuum is enabled it can result in unpredictable changes in measured
performance.
A limitation of pgbench is that it can itself become
the bottleneck when trying to test a large number of client sessions.
This can be alleviated by running pgbench on a different
machine from the database server, although low network latency will be
essential. It might even be useful to run several pgbench
instances concurrently, on several client machines, against the same
database server.
Security
If untrusted users have access to a database that has not adopted a
secure schema usage pattern,
do not run pgbench in that
database. pgbench uses unqualified names and
does not manipulate the search path.