Once installed, the pgpro_stats extension starts collecting statistics on the executed statements. The collected data is similar to the one provided by pg_stat_statements, but also includes information on query plans and wait events for each query type. The statistics is saved into an in-memory ring buffer and is accessible through the pgpro_stats_statements view.

By default, pgpro_stats collects statistics on all the executed statements that satisfy the pgpro_stats.track and pgpro_stats.track_utility settings. If performance is a concern, you can set a sample rate for queries using the pgpro_stats.query_sample_rate parameter, and pgpro_stats will randomly select queries for statistics calculation at the specified rate.

To collect statistics on wait events, pgpro_stats uses time-based sampling. Wait events are sampled at the time interval specified by the pgpro_stats.profile_period parameter, which is set to 10ms by default. If the sampling shows that the process is waiting, the pgpro_stats.profile_period value is added to the wait event duration. Thus, time estimation for each wait event remains valid even if the pgpro_stats.profile_period parameter value has changed. If you are not interested in wait event statistics, you can disable wait event sampling by setting the pgpro_stats.enable_profile parameter to false.

pgpro_stats_statements.plans and pgpro_stats_statements.calls aren't always expected to match because planning and execution statistics are updated at their respective end phase, and only for successful operations. For example, if a statement is successfully planned but fails during the execution phase, only its planning statistics will be updated. If planning is skipped because a cached plan is used, only its execution statistics will be updated.

As an example, let's create a table with some random data and build an index on this table:

CREATE TABLE test AS (SELECT i, random() x FROM generate_series(1,1000000) i);
CREATE INDEX test_x_idx ON test (x);

Now run the following query several times using different values for :x_min and :x_max:

select * from test where x >= :x_min and x <= :x_max;

The collected statistics should appear in the pgpro_stats_statements view:

SELECT queryid, query, planid, plan, wait_stats FROM pgpro_stats_statements WHERE query LIKE 'select * from test where%';
-[ RECORD 1 ]----------------------------------------------------------------------------------------------------------
queryid    | 1109491335754870054
query      | select * from test where x >= $1 and x <= $2
planid     | 8287793242828473388
plan       | Gather
           |   Output: i, x
           |   Workers Planned: 2
           |   ->  Parallel Seq Scan on public.test
           |         Output: i, x
           |         Filter: ((test.x >= $3) AND (test.x <= $4))
           |
wait_stats | {"IO": {"DataFileRead": 10}, "IPC": {"BgWorkerShutdown": 10}, "Total": {"IO": 10, "IPC": 10, "Total": 20}}
-[ RECORD 2 ]----------------------------------------------------------------------------------------------------------
queryid    | 1109491335754870054
query      | select * from test where x >= $1 and x <= $2
planid     | -9045072158333552619
plan       | Bitmap Heap Scan on public.test
           |   Output: i, x
           |   Recheck Cond: ((test.x >= $3) AND (test.x <= $4))
           |   ->  Bitmap Index Scan on test_x_idx
           |         Index Cond: ((test.x >= $5) AND (test.x <= $6))
           |
wait_stats | {"IO": {"DataFileRead": 40}, "Total": {"IO": 40, "Total": 40}}
-[ RECORD 3 ]----------------------------------------------------------------------------------------------------------
queryid    | 1109491335754870054
query      | select * from test where x >= $1 and x <= $2
planid     | -1062789671372193287
plan       | Seq Scan on public.test
           |   Output: i, x
           |   Filter: ((test.x >= $3) AND (test.x <= $4))
           |
wait_stats | NULL
-[ RECORD 4 ]----------------------------------------------------------------------------------------------------------
queryid    | 1109491335754870054
query      | select * from test where x >= $1 and x <= $2
planid     | -1748292253893834280
plan       | Index Scan using test_x_idx on public.test
           |   Output: i, x
           |   Index Cond: ((test.x >= $3) AND (test.x <= $4))
           |
wait_stats | NULL

With pgpro_stats, you can define custom metrics to be monitored. The collected data will be saved into an in-memory ring buffer and then sent to a monitoring system. Unlike direct polling of a database by a monitoring system that can lose some data if the connection is interrupted, this approach allows to get all the collected data regardless of connection issues, as long as this data is still available in the ring buffer.

To set up a custom metric to collect, do the following:

The statistics gathered by the module are made available via a view named pgpro_stats_statements. This view contains one row for each distinct database ID, user ID and query ID (up to the maximum number of distinct statements that the module can track). The columns of the view are shown in Table F.88.

Take into account that like pg_stat_statements, pgpro_stats normalizes into one record those DML queries (containing SELECT, INSERT, UPDATE, DELETE and MERGE commands) that have equivalent structures according to some internal hash value. Being compared this way, two queries are normally considered equal if they are semantically equivalent up to constants included in the queries. All the other commands are, however, compared strictly as query texts. When the value of a constant in a query is ignored for comparison with other queries, this constant is replaced in the pgpro_stats output with a symbol of a parameter, such as, $k, where k is a positive integer. If a query already contains parameters, the initial value of k equals the number following the last number of a $n parameter in the original query text. If there are no parameters, the initial value of k equals 1. Note that sometimes hidden parameter symbols affect this numbering. For example, PL/pgSQL uses such hidden symbols to insert values of function local variables into queries, so a PL/pgSQL statement SELECT i + 1 INTO j will be represented as SELECT i + $2 in the normalized query text.

pgpro_stats uses a similar technique to normalize plan texts. When doing so, an attempt is made to associate numbers of constants in the plan text with the corresponding numbers of constants in the query text. If such an attempt appears unsuccessful for a certain constant in the plan text, it is assigned the number following the maximum number of a constant replaced in the query text. For example, consider the query:

SELECT 1::int, 'abc'::VARCHAR(3), 2::int;

pgpro_stats will replace numbers of constants in the query text and in the text of the corresponding plan as follows:

postgres=# SELECT query, plan FROM pgpro_stats_statements;
                     query                      |                                plan
------------------------------------------------+--------------------------------------------------
SELECT $1::int, $2::VARCHAR(3), $3::int         | Result                                           +
                                                |   Output: $1, $4, $3                             +

In this plan text, it appeared possible to associate constants numbered 1 and 3 from the query text, but not the constant numbered 2, and the latter was replaced with the number following the maximum number in the query text, that is, number 4.

Replacement of numbers in plan texts has an exception for version numbers of XML documents. If in the original query such a number is represented with a constant, e.g., '1.0', it is retained as is in the plan text rather than replaced with $k. If the version number of an XML document is represented with an expression, replacement of constants follows usual rules.

The aggregate statistics gathered by the module are made available via a view named pgpro_stats_totals. This view contains one row for each distinct object (up to the maximum number of distinct objects that the module can track). The columns of the view are shown in Table F.89.

The metrics gathered by pgpro_stats are displayed in the pgpro_stats_metrics view. The table below describes the columns of the view.

The pgpro_stats_archiver view will contain one row showing data about the archiver process of the cluster.

The pgpro_stats_vacuum_database view will contain one row for each database in the current cluster, showing statistics about vacuuming that database. These statistics are collected by the core system as explained in Section 26.2. The table below describes the columns of the view.

The pgpro_stats_vacuum_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about vacuuming that specific table. These statistics are collected by the core system as explained in Section 26.2. The table below describes the columns of the view.

Columns total_*, wal_* and blk_* include data on vacuuming indexes on this table, while columns system_time and user_time only include data on vacuuming the heap.

The pgpro_stats_vacuum_indexes view will contain one row for each index in the current database (including TOAST table indexes), showing statistics about vacuuming that specific index. These statistics are collected by the core system as explained in Section 26.2. The table below describes the columns of the view.

pgpro_stats_rusage is a record type that contains resource usage statistics of statement planning/execution. The fields of this type are shown in Table F.95.

In pgpro_stats, tracing of application sessions is implemented. It is based on filters, which trigger logging the execution of queries that match filtering conditions. Queries and their plans are logged in so called trace files specified by the user or in the system log file (if the trace file is not specified). Filters are stored in a table located in the shared memory. The rows of this table are filters, and the columns contain filtering conditions. You should fill this table with filters to start tracing queries.

Once a database administrator adds a filter in any session, all subsequent executions of queries that match the filter conditions will be traced by all sessions of the instance without a need in the server restart. In other words, filters can be added, deleted or updated “on the fly”, and tracing with these filters immediately starts for existing and future sessions.

Each filter includes:

Specialized functions enable creation, update and deletion of query filters:

SELECT pgpro_stats_trace_insert('alias', 'first', 'pid', pg_backend_pid(), 'explain_analyze', true);

SELECT pgpro_stats_trace_insert('alias', 'second', 'database_name', current_database(), 'explain_costs', false, 'tracefile', 'second_tf');

\x auto
SELECT * from pgpro_stats_trace_show();

 -[ RECORD 1 ]-------+----------
 filter_id           | 1
 active              | t
 alias               | first
 tracefile           | 
 pid                 | 243183
 database_name       | 
 client_addr         | 
 application_name    | 
 username            | 
 queryid             | 
 planid              | 
 duration            | 
 plan_time           | 
 exec_time           | 
 user_time           | 
 system_time         | 
 rows                | 
 shared_blks_hit     | 
 shared_blks_read    | 
 shared_blks_fetched | 
 shared_blks_dirtied | 
 shared_blks_written | 
 local_blks_hit      | 
 local_blks_read     | 
 local_blks_fetched  | 
 local_blks_dirtied  | 
 local_blks_written  | 
 temp_blks_read      | 
 temp_blks_written   | 
 wal_bytes           | 
 total_wait_time     | 
 total_inval_msgs    | 
 explain_analyze     | t
 explain_verbose     | f
 explain_costs       | t
 explain_settings    | f
 explain_buffers     | f
 explain_wal         | f
 explain_timing      | t
 explain_format      | text
 -[ RECORD 2 ]-------+----------
 filter_id           | 2
 active              | t
 alias               | second
 tracefile           | second_tf
 pid                 | 
 database_name       | postgres
 client_addr         | 
 application_name    | 
 username            | 
 queryid             | 
 planid              | 
 duration            | 
 plan_time           | 
 exec_time           | 
 user_time           | 
 system_time         | 
 rows                | 
 shared_blks_hit     | 
 shared_blks_read    | 
 shared_blks_fetched | 
 shared_blks_dirtied | 
 shared_blks_written | 
 local_blks_hit      | 
 local_blks_read     | 
 local_blks_fetched  | 
 local_blks_dirtied  | 
 local_blks_written  | 
 temp_blks_read      | 
 temp_blks_written   | 
 wal_bytes           | 
 total_wait_time     | 
 total_inval_msgs    | 
 explain_analyze     | f
 explain_verbose     | f
 explain_costs       | f
 explain_settings    | f
 explain_buffers     | f
 explain_wal         | f
 explain_timing      | f
 explain_format      | text

SELECT 1 as result;

2023-04-18 04:52:53.242 MSK [63112] LOG:  Filter 1 triggered explain of the plan:
Query Text: SELECT 1 as result;
Result  (cost=0.00..0.01 rows=1 width=4) (actual time=0.003..0.003 rows=1 loops=1)

Query Text: SELECT 1 as result;
Result

SELECT pgpro_stats_trace_delete(1);

SELECT pgpro_stats_trace_update(2, 'pid', 2);

SELECT 2 as result;

SELECT pgpro_stats_trace_reset();

pgpro_stats can create views similar to those available in pg_stat_statements and pg_stat_kcache extensions. Specifically, pg_stat_statements, pg_stat_statements_info, pg_stat_kcache and pg_stat_kcache_detail views can be created. Each view is only created in a Postgres Pro version if it is available in pg_stat_statements/pg_stat_kcache extension for the same version of Postgres Pro/PostgreSQL. For example, the pg_stat_statements_info view is only created in Postgres Pro versions starting with 14. The following functions enable creating these views:

Only superuser can call these functions.

To create pg_stat_statements* views, drop the pg_stat_statements extension if it was previously installed and call the function:

select pgpro_stats_create_pg_stat_statements_compatible_views();

To create pg_stat_kcache* views, drop the pg_stat_kcache extension if it was previously installed and call the function:

select pgpro_stats_create_pg_stat_kcache_compatible_views();

Once the views are created, you can work with them as if the pg_stat_statements/pg_stat_kcache extension is installed.

If you need to remove the views created earlier, do it in a regular way:

drop view pg_stat_statements;
drop view pg_stat_statements_info;
drop view pg_stat_kcache;
drop view pg_stat_kcache_detail;

The following parameters can be used to define a custom metric to collect. The N placeholder in the parameter name serves as a unique identifier of the metric to which this setting should apply; it must be set to a non-negative integer for each metric.

When you add these parameters for a new metric, you have to restart the server for the changes to take effect. Once the new metric is added, its parameters can be changed without a server restart by simply reloading the postgresql.conf configuration file.

The pgpro_stats_inval_status view shows one row with the current status of the cache invalidation global queue. The columns of the view are shown in Table F.97.

In a working system, num_inval_messages usually approximately equals 4000, which means that the queue is pretty full. The speed of the num_inval_queue_cleanups growth is determined by how fast invalidation messages are generated. Growth of num_inval_queue_resets is normally zero, and non-zero growth indicates either too fast generation of messages or delays in processing messages by backends. Monitoring num_inval_queue_cleanups and num_inval_queue_resets may in some cases allow you to detect problematic backend/backeds as described below.

If for a certain time interval, num_inval_queue_cleanups considerably increased, while num_inval_queue_resets did not, this indicates that invalidation messages are generated faster and/or backends process them more slowly, but backends still manage to process messages before the queue overflows.

If for a time interval, num_inval_queue_cleanups did not considerably increase, while num_inval_queue_resets did, this definitely indicates a delay in processing messages by backend(s), and the cache_resets column of the pgpro_stats_totals view allows you to figure out which backend(s) to blame.

If for a time interval, both counters considerably increased, this also indicates that invalidation messages are generated faster and/or backends process them more slowly, but this time backends fail to process messages before the queue overflows. The cache_resets column of the pgpro_stats_totals view allows you detect which backend(s) delay message processing. In this case, it is not possible to definitely conclude whether too fast generation of messages or a delay in message processing accounts for the growth of num_inval_queue_resets. However, the totals counter of the pgpro_stats_inval_msgs view may help here. If the change of this counter for that interval is pretty the same as for a previous interval of the same length, you can definitely conclude that the growth is caused by backend delays.

The pgpro_stats_statements and pgpro_stats_totals views for each corresponding object, show a record of the pgpro_stats_inval_msgs record type containing counters for cache invalidation messages. The fields of the type are shown in Table F.98.