plProfiler: Performance Timings & Counters for PL/pgSQL CodeThe plProfiler extension is available for pg95 & pg96 in our Windows, OSX & Linux distributions. It is used to create performance profiles of PL/pgSQL functions and stored procedures. The included external Python class and command line utility can be used to easily control the extension, run arbitrary SQL commands (invoking PL/pgSQL functions), save and manage the resulting performance datasets and create HTML reports from them.
Installation on Linux & OSXOpen a Terminal window and navigate to the directory where PGC is installed. The below instructions assume you installed & started pg96 through our Package Manager Instructions in your $HOME directory.
$ cd $HOME/bigsql $ ./pgc update $ ./pgc install plprofiler3-pg96 $ cd pg96 $ source pg96.env $ plprofiler --help
Installation on WindowsOpen a Command Prompt window with Run as administrator. The below instructions assume you installed pg96 with our graphical installer in the default directory.
cd C:\Program Files\PostgreSQL\ pgc update pgc install plprofiler3-pg96 cd pg96 pg96-env.bat set PATH=%PATH%;%PGC_HOME%\python2 cd bin python plprofiler_util.py --help
Finding performance problems within PL/pgSQL functions and stored procedures can be difficult, especially when the code is nested. This is because PL/pgSQL creates a cloak over whatever is happening inside. The only thing visible in system or extension views, such as
pg_stat_statements is the query, sent from the client. In the case of invoking a stored procedure, that is just the outermost stored procedure call.
The plprofiler extension can be used to quickly identify the most time consuming functions and then drill down to find the individual statements within them.
The output, generated by the plprofiler, is a self-contained HTML document. The document starts with a FlameGraph at the top, followed by details about functions in the profile. Unlike usual CPU FlameGraphs, the plprofiler FlameGraph is based on the actual Wall-Clock time, spent in the PL/pgSQL functions. By default, the top ten functions, based on their self_time (total_time - children_time), are detailed. This can be overridden by the user.
Click on the screenshot below to see the actual, interactive report in your browser.
In this tutorial style set of examples, I mostly want to demonstrate the different ways, plprofiler allows to capture profiling data.
The examples are built on top of each other so it is best to at least once read over this document top to bottom.
It is assumed that anyone, interested in profiling complex PL/pgSQL code, is familiar with performance testing in general and performance testing of a PostgreSQL database in particular. Therefore it is also also assumed that the reader has a basic understanding of the pgbench utility.
- The example test case
- General command syntax
- Executing SQL using the plprofiler utility
- Analyzing the first profile
- Capturing profiling data by instrumenting the application
- Collecting statistics at a timed interval
- Collecting statistics via ALTER USER
- Profiling a live production system
- Fixing the performance problem
The example test case
All examples in this documentation are based on a modified pgbench database. The modifications are:
- The SQL queries, that make up the TPC-B style business transaction of pgbench, have been implemented in a set of PL/pgSQL functions. Each function essentially performs only one of the TPC-B queries. This is on purpose convoluted, since for the sake of demonstration we want a simple, yet nested example. The function definitions can be found in
- A custom pgbench profile, found in
examples/pgbench_pl.profile, is used with the -f option when invoking pgbench.
- The table pgbench_accounts is modified.
- The filler column is expanded and filled with 500 characters of data.
- A new column,
category intergeris added in front of the aid and made part of the primary key.
NOTE: The command syntax for pgbench custom profiles was changed in PostgreSQL 9.6. There are 9.6 specific profiles in the examples directory as well.
The modifications to the pgbench_accounts table are based on a real world case, encountered in a customer database. This pgbench example case of course is greatly simplified. In the real world case the access to the table in question was in a nested function, 8 call levels deep, the table had several indexes to choose from and the schema contained a total of >500 PL/pgSQL functions with >100,000 lines of PL code. In other words the author was looking for a needle in what once was a haystack, but had been eaten by an elephant.
Despite the simplification, the problem produced by these modifications simulates the original case surprisingly well. The TPC-B transaction accesses the pgbench_accounts table based on the aid column alone, so that is the only key part, available in the WHERE clause. However, since the table rows are now >500 bytes wide and the index is rather small, compared to the heap, the PostgreSQL query optimizer will still choose an index scan. This is the right choice, based on the available options, because a sequential scan would be worse.
pgbench_plprofiler=# explain select abalance from pgbench_accounts where aid = 1; QUERY PLAN -------------------------------------------------------------------------------------------------- Index Scan using pgbench_accounts_pkey on pgbench_accounts (cost=0.42..18484.43 rows=1 width=4) Index Cond: (aid = 1)
Since the first column of the index is not part of the WHERE clause and thus, the index condition, this results in a full scan of the entire index! Unfortunately that detail is nowhere visible except in this explain output. And then you will only notice it if you know the definition of that index. If we look at pg_stat_* tables after a benchmark run for example, they only tell us that all access to pgbench_accounts was done via index scans over the primary key and that all those scans returned a single row. One would normally think "nothing wrong here".
On top of that, since the queries accessing the table will never show up in any statistics, we will never see that each of them takes 30ms already on a 10x pgbench scaling factor. Imagine what that turns into when we scale out.
The full script to prepare the pgbench test database is found in
To get a performance baseline, the median result of 5 times 5 minutes pgbench with 24 clients reports 136 TPS on an 8-core machine with 32GB of RAM and the entire database fitting into the 8GB of shared buffers (yeah, it is that bad).
(venv)[wieck@localhost examples]$ pgbench -n -c24 -j24 -T300 -f pgbench_pl.profile transaction type: Custom query scaling factor: 1 query mode: simple number of clients: 24 number of threads: 24 duration: 300 s number of transactions actually processed: 40686 latency average: 176.965 ms tps = 135.580039 (including connections establishing) tps = 135.589426 (excluding connections establishing)
Time to create a profile.
General command syntax
The general syntax of the plprofiler utility is
plprofiler COMMAND [OPTIONS]
Common for all commands are options, that control the database connection. These are
||The host to connect to.|
||Port number the postmaster is listening on.|
||The database user name.|
||The database name, conninfo string or URI.|
plprofiler help [COMMAND] will show you more details than are explained in the examples, provided in this document.
In the examples below it is assumed that the environment variables
PGDATABASE have all been set to point to the pgbench_plprofiler database, that was created using the
examples/prepdb.sh script. The above connection parameters are left out to make the examples more readable. For security reasons, there is not way to specify a password on the command line. Please create the necessary
~/.pgpass entry if your database requires password authentication.
Executing SQL using the plprofiler utility
After having installed the plprofiler extension in the test database, the easiest way to generate a profile of PL/pgSQL functions is to run them using the plprofiler utility and let it create an HTML report directly from the local-data, collected in the backend.
plprofiler run --command "SELECT tpcb(1, 2, 3, -42)" --output tpcb-test1.html
Since not all information for the HTML report was actually specified on the command line, the utility will launch your
$EDITOR with a config file after the SQL statement finished, so you have a chance to change some of the defaults before it renders the HTML. At the end this will create the report
tpcb-test1.html in the current directory.
One thing to keep in mind about this style of profiling is that there is a significant overhead in PL/pgSQL on the first call to a function within a database session (connection). The PL/pgSQL function call handler must parse the entire function definition and create a saved PL execution tree for it. Certain types of SQL statements will also be parsed and verified. For these reasons calling a truly trivial PL/pgSQL example like this can give very misleading results.
To avoid this, the function should be called several times in a row. The file
examples/tpcb_queries.sql contains a set of 20 calls to the
tpcb() function and can be executed as
plprofiler run --file tpcb_queries.sql --output tpcb-test1.html
Analyzing the first profile
The report generated by the last
plprofiler command (the one with the --file option used) should look roughly like this (I narrowed the SVG FlameGraph from the default width of 1200 pixels to 800 to play nicer with embedding into markdown on bitbucket) and I set the tabstop to 4, which is how the SQL file for the PL functions is formatted:
Go ahead and open the actual HTML version in a separate window or tab to be able to interact with it.
What sticks out at the top of the FlameGraph are the two functions
tpcb_fetch_abalance() and its caller,
tpcb_upd_accounts(). When you hover over the FlameGraph entry for
tpcb_upd_accounts() you will see that it actually accounted for over 99% of the total execution time, spent inside of PL/pgSQL functions.
To examine this function closer we scroll down in the report to the details of
tpcb_upd_accounts() and click on the (show) link, we can see the source code of the function and the execution time spent in every single line of it.
Obviously there is a problem with accessing the pgbench_accounts table in that UPDATE statement. This function uses up 99% of our time and 50% of that is spent in a single row UPDATE statement? That cannot be right.
Likewise if we examine the details for function
tpcb_fetch_abalance(), we find that the same access path (single row SELECT via pgbench_accounts.aid) has the exact same performance problem.
Of course, this all was an excercise in Hunting an Elephant the Experienced Programmer's way. I deliberately placed an elephant in the middle of the room and found it. Not much of a surprise. It is what it is, the artificial reproduction of a real world problem encountered in the wild. You will have to take my word for it that it was almost as easy to find the problem in the real world case, this example is based on.
We're not going to fix the actual problem (missing/wrong index) just yet, but explore alternative methods of invoking the plprofiler instead. This way we can compare all the different methods based on the same broken schema.
Capturing profiling data by instrumenting the application
Sometimes it may be easier to add instrumentation calls to the application, than to extract stand alone queries, that can be run by the plprofiler via the --command or --file options. The way to do this is to add some plprofiler function calls at strategic places in the application code. In the case of pgbench, this application code is the custom profile
\set nbranches :scale \set ntellers 10 * :scale \set naccounts 100000 * :scale \setrandom aid 1 :naccounts \setrandom bid 1 :nbranches \setrandom tid 1 :ntellers \setrandom delta -5000 5000 SELECT pl_profiler_enable(true); SELECT tpcb(:aid, :bid, :tid, :delta); SELECT pl_profiler_collect_data(); SELECT pl_profiler_enable(false);
pl_profiler_enable(true) will cause the plprofiler extension to be loaded and start accumulating profiling data in the local-data hash tables. The function
pl_profiler_collect_data() copies that local-data over to the shared hash tables and resets the local-data counters to zero.
With this changed application code, we can run
plprofiler reset pgbench -n -c24 -j24 -T300 -fpgbench_pl.collect.profile
reset command deletes all data from the shared hash tables. After pgbench has finished, we use the shared-data (the data, that has been copied by the
pl_profiler_collect_data() function into the shared hash tables to generate a report.
plprofiler report --from-shared --name "tpcb-using-collect" --output "tpcb-using-collect.html"
There seems to be only a subtle change in the profile. The functions for updating the pgbench_branches and pgbench_tellers tables, which are almost invisible in the first profile, now used 5.81% and 2.60% of the time. That may not look like much, but with the access to pgbench_accounts being as screwed up as it is, this is in fact huge. The difference was caused by concurrency (24 clients).
Collecting statistics at a timed interval
Instead of collecting the local-data after each individual transaction, we can configure it to copy the local-data only every N seconds to the shared hash tables (and reset the local-data counters). The collecting happens at each transaction commit/rollback as well as when a PL/pgSQL function exits and the timer has elapsed.
For this we use a slightly different pgbench custom profile.
\set nbranches :scale \set ntellers 10 * :scale \set naccounts 100000 * :scale \setrandom aid 1 :naccounts \setrandom bid 1 :nbranches \setrandom tid 1 :ntellers \setrandom delta -5000 5000 SET plprofiler.enabled TO true; SET plprofiler.save_interval TO 10; SELECT tpcb(:aid, :bid, :tid, :delta);
I am not showing the resulting report for that because it is almost identical to the previous one.
Collecting statistics via ALTER USER
The above can also be done without changing the application code at all. Instead we can add the plprofiler to the
postgresql.conf file in
shared_preload_libraries = 'plprofiler'
(requires PostgreSQL server restart) and then configure the application user as follows:
ALTER USER myuser SET plprofiler.enabled TO on; ALTER USER myuser SET plprofiler.save_interval TO 10;
This has the exact same effect as the last example. It of course requires that the application reconnects after the
ALTER USER ... statements to start collecting data, and it better reconnect once more when we are done profiling and did the corresponding
ALTER USER ... RESET ... commands. So this is still not suitable for profiling a live production system since it is too disruptive.
Profiling a live production system
Debugging as well as profiling on a production system is a risky business and should be avoided if at all possible.
Unfortunately sometimes it is not avoidable. For that reason, plprofiler has options designed to minimize its impact on performance.
Like the previous example, the profiling method demonstrated below requires to have plprofiler pre-loaded from the
shared_preload_libraries = 'plprofiler'
This by itself is not a problem. The plprofiler will be loaded and place all callback functions into the PL instrumentation hooks. The first thing all these functions do is to check if profiling is enabled. If nothing is enabled, this check amounts to evaluating an
`if (!bool_var && int_var != ptr->int_var) return;`
at the beginning of all the callback functions. One of the callback functions is called at every function enter/exit and at every PL statement start/end (only the statements, that actually have runtime functionality). In the great scheme of things, this overhead is negligible.
shared_preload_libraries configured (and the database server restarted to let that take effect) and the shared-data empty (run
plprofiler reset) we launch
pgbench in the background. After a while we get one of the pgbench backend PIDs by examining the system view
pg_stat_activity. With that PID we run
plprofiler reset plprofiler monitor --pid <PID> --interval 10 --duration 300 plprofiler report --from-shared --name tpcb-using-monitor --output tpcb-using-monitor.html
plprofiler monitor command is using
ALTER SYSTEM ... and
SELECT pg_reload_conf() to enable profiling and turn it back off after the specified duration. This obviously will only work with a PostgreSQL database version 9.4 or newer. As with any database maintenance operations, this should only be done in a connection loss safe environment as losing the connection in the middle of the monitoring would leave those settings behind permanently.
Leaving out the --pid option will cause ALL active backends to save their stats at the specified interval.
Fixing the performance problem
In this final chapter of this tutorial we fix the artificially introduced performance problem as it was done in the real world case that stood model for it. We create the missing index.
CREATE INDEX pgbench_accounts_aid_idx ON pgbench_accounts (aid);
With that in place we use our last method of capturing profiling data once more to generate the last report for this tutorial.
plprofiler reset plprofiler monitor --pid <PID> --interval 10 --duration 300 plprofiler report --from-shared --name tpcb-problem-fixed --output tpcb-problem-fixed.html
The performance profile is now completely reversed. The access to pgbench_accounts is a small fraction (1.52% with 0.44% out of that accouting for fetching the new account balance) of the overall time spent. The access to pgbench_tellers and pgbench_branches completely dominates the picture. This is how a pgbench running inside of shared buffers is supposed to look like. Because the tellers and branches tables are so small, there is tremendous row level lock contention and constant bloat on them.
The overall performance of pgbench went from the original 136 TPS to a whooping
transaction type: Custom query scaling factor: 1 query mode: simple number of clients: 24 number of threads: 24 duration: 300 s number of transactions actually processed: 1086292 latency average: 6.628 ms tps = 3620.469364 (including connections establishing) tps = 3620.869051 (excluding connections establishing)
This is a performance boost by factor 27 for one additional index.
Not all performance problems are this easy to solve. But I hope the plprofiler will help you locating them quickly, so you have more time fixing them.