Monitor Performance with Query Store
APPLIES TO: 
Azure Database for PostgreSQL - Flexible Server
The Query Store feature in Azure Database for PostgreSQL provides a way to track query performance over time. Query Store simplifies performance-troubleshooting by helping you quickly find the longest running and most resource-intensive queries. Query Store automatically captures a history of queries and runtime statistics, and it retains them for your review. It slices the data by time so that you can see temporal usage patterns. Data for all users, databases and queries is stored in a database named azure_sys in the Azure Database for PostgreSQL instance.
Important
Do not modify the azure_sys database or its schema. Doing so will prevent Query Store and related performance features from functioning correctly.
Enabling Query Store
Query Store is an opt-in feature, so it isn't enabled by default on a server. Query store is enabled or disabled globally for all databases on a given server and cannot be turned on or off per database.
Important
Do not enable Query Store on Burstable pricing tier as it would cause performance impact.
Enable Query Store
- Sign in to the Azure portal and select your Azure Database for PostgreSQL server.
- Select Server Parameters in the Settings section of the menu.
- Search for the
pg_qs.query_capture_modeparameter. - Set the value to
TOPorALLand Save. Allow up to 20 minutes for the first batch of data to persist in the azure_sys database.
Enable Query Store Wait Sampling
- Search for the
pgms_wait_sampling.query_capture_modeparameter. - Set the value to
ALLand Save.
Information in Query Store
Query Store has two stores:
- A runtime stats store for persisting the query execution statistics information.
- A wait stats store for persisting wait statistics information.
Common scenarios for using Query Store include:
- Determining the number of times a query was executed in a given time window
- Comparing the average execution time of a query across time windows to see large deltas
- Identifying longest running queries in the past few hours
- Identifying top N queries that are waiting on resources
- Understanding wait nature for a particular query To minimize space usage, the runtime execution statistics in the runtime stats store are aggregated over a fixed, configurable time window. The information in these stores can be queried using views.
Access Query Store information
Query Store data is stored in the azure_sys database on your Postgres server. The following query returns information about queries in Query Store:
SELECT * FROM query_store.qs_view;
Or this query for wait stats:
SELECT * FROM query_store.pgms_wait_sampling_view;
Finding wait queries
Wait event types combine different wait events into buckets by similarity. Query Store provides the wait event type, specific wait event name, and the query in question. Being able to correlate this wait information with the query runtime statistics means you can gain a deeper understanding of what contributes to query performance characteristics.
Here are some examples of how you can gain more insights into your workload using the wait statistics in Query Store:
| Observation | Action |
|---|---|
| High Lock waits | Check the query texts for the affected queries and identify the target entities. Look in Query Store for other queries modifying the same entity, which is executed frequently and/or have high duration. After identifying these queries, consider changing the application logic to improve concurrency, or use a less restrictive isolation level. |
| High Buffer IO waits | Find the queries with a high number of physical reads in Query Store. If they match the queries with high IO waits, consider introducing an index on the underlying entity, in order to do seeks instead of scans. This would minimize the IO overhead of the queries. Check the Performance Recommendations for your server in the portal to see if there are index recommendations for this server that would optimize the queries. |
| High Memory waits | Find the top memory consuming queries in Query Store. These queries are probably delaying further progress of the affected queries. Check the Performance Recommendations for your server in the portal to see if there are index recommendations that would optimize these queries. |
Configuration options
When Query Store is enabled it saves data in 15-minute aggregation windows, up to 500 distinct queries per window. The following options are available for configuring Query Store parameters.
| Parameter | Description | Default | Range |
|---|---|---|---|
| pg_qs.query_capture_mode | Sets which statements are tracked. | none | none, top, all |
| pg_qs.store_query_plans | Turns saving query plans on or off for pg_qs | off | on, off |
| pg_qs.max_plan_size | Sets the maximum number of bytes that will be saved for query plan text for pg_qs; longer plans will be truncated. | 7500 | 100 - 10k |
| pg_qs.max_query_text_length | Sets the maximum query length that can be saved. Longer queries will be truncated. | 6000 | 100 - 10K |
| pg_qs.retention_period_in_days | Sets the retention period. | 7 | 1 - 30 |
| pg_qs.index_generation_interval | Sets the index recommendation generating frequency for all databases when query store enabled. | 15 | 15 - 10080 |
| pg_qs.track_utility | Sets whether utility commands are tracked | on | on, off |
The following options apply specifically to wait statistics.
| Parameter | Description | Default | Range |
|---|---|---|---|
| pgms_wait_sampling.query_capture_mode | Sets which statements are tracked for wait stats. | none | none, all |
| Pgms_wait_sampling.history_period | Set the frequency, in milliseconds, at which wait events are sampled. | 100 | 1-600000 |
Note
pg_qs.query_capture_mode supersedes pgms_wait_sampling.query_capture_mode. If pg_qs.query_capture_mode is NONE, the pgms_wait_sampling.query_capture_mode setting has no effect.
Use the Azure portal to get or set a different value for a parameter.
Views and functions
View and manage Query Store using the following views and functions. Anyone in the PostgreSQL public role can use these views to see the data in Query Store. These views are only available in the azure_sys database. Queries are normalized by looking at their structure after removing literals and constants. If two queries are identical except for literal values, they will have the same queryId.
query_store.qs_view
This view returns all the data in Query Store. There is one row for each distinct database ID, user ID, and query ID.
| Name | Type | References | Description |
|---|---|---|---|
| runtime_stats_entry_id | bigint | ID from the runtime_stats_entries table | |
| user_id | oid | pg_authid.oid | OID of user who executed the statement |
| db_id | oid | pg_database.oid | OID of database in which the statement was executed |
| query_id | bigint | Internal hash code, computed from the statement's parse tree | |
| query_sql_text | varchar(10000) | Text of a representative statement. Different queries with the same structure are clustered together; this text is the text for the first of the queries in the cluster. The default query text length is 6000 and can be modified using query store parameter pg_qs.max_query_text_length. |
|
| plan_id | bigint | ID of the plan corresponding to this query | |
| start_time | timestamp | Queries are aggregated by time buckets - the time span of a bucket is 15 minutes by default. This is the start time corresponding to the time bucket for this entry. | |
| end_time | timestamp | End time corresponding to the time bucket for this entry. | |
| calls | bigint | Number of times the query executed | |
| total_time | double precision | Total query execution time, in milliseconds | |
| min_time | double precision | Minimum query execution time, in milliseconds | |
| max_time | double precision | Maximum query execution time, in milliseconds | |
| mean_time | double precision | Mean query execution time, in milliseconds | |
| stddev_time | double precision | Standard deviation of the query execution time, in milliseconds | |
| rows | bigint | Total number of rows retrieved or affected by the statement | |
| shared_blks_hit | bigint | Total number of shared block cache hits by the statement | |
| shared_blks_read | bigint | Total number of shared blocks read by the statement | |
| shared_blks_dirtied | bigint | Total number of shared blocks dirtied by the statement | |
| shared_blks_written | bigint | Total number of shared blocks written by the statement | |
| local_blks_hit | bigint | Total number of local block cache hits by the statement | |
| local_blks_read | bigint | Total number of local blocks read by the statement | |
| local_blks_dirtied | bigint | Total number of local blocks dirtied by the statement | |
| local_blks_written | bigint | Total number of local blocks written by the statement | |
| temp_blks_read | bigint | Total number of temp blocks read by the statement | |
| temp_blks_written | bigint | Total number of temp blocks written by the statement | |
| blk_read_time | double precision | Total time the statement spent reading blocks, in milliseconds (if track_io_timing is enabled, otherwise zero) | |
| blk_write_time | double precision | Total time the statement spent writing blocks, in milliseconds (if track_io_timing is enabled, otherwise zero) |
query_store.query_texts_view
This view returns query text data in Query Store. There is one row for each distinct query_text.
| Name | Type | Description |
|---|---|---|
| query_text_id | bigint | ID for the query_texts table |
| query_sql_text | Varchar(10000) | Text of a representative statement. Different queries with the same structure are clustered together; this text is the text for the first of the queries in the cluster. |
query_store.pgms_wait_sampling_view
This view returns wait events data in Query Store. There is one row for each distinct database ID, user ID, query ID, and event.
| Name | Type | References | Description |
|---|---|---|---|
| user_id | oid | pg_authid.oid | OID of user who executed the statement |
| db_id | oid | pg_database.oid | OID of database in which the statement was executed |
| query_id | bigint | Internal hash code, computed from the statement's parse tree | |
| event_type | text | The type of event for which the backend is waiting | |
| event | text | The wait event name if backend is currently waiting | |
| calls | Integer | Number of the same event captured |
query_store.query_plans_view
This view returns the query plan that was used to execute a query. There is one row per each distinct database ID, and query ID. This will only store query plans for non-utility queries.
| plan_id | db_id | query_id | plan_text |
|---|---|---|---|
| plan_id | bigint | The hash value from the query_text | |
| db_id | oid | pg_database.oid | OID of database in which the statement was executed |
| query_id | bigint | Internal hash code, computed from the statement's parse tree | |
| plan_text | varchar(10000) | Execution plan of the statement given costs=false, buffers=false, and format=false. This is the same output given by EXPLAIN. |
Functions
qs_reset discards all statistics gathered so far by Query Store. This function can only be executed by the server admin role.
staging_data_reset discards all statistics gathered in memory by Query Store (that is, the data in memory that has not been flushed yet to the database). This function can only be executed by the server admin role.
Limitations and known issues
- If a PostgreSQL server has the parameter
default_transaction_read_onlyon, Query Store will not capture any data.
Next steps
- Learn more about scenarios where Query Store can be especially helpful.
- Learn more about best practices for using Query Store.
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