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Statistics and Space Maintenance
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Last updated: 2024-11-27 15:36:05
Statistics and Space Maintenance
Last updated: 2024-11-27 15:36:05
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Background

Cluster statistics are critical to the use of clusters, and Tencent Cloud TCHouse-P's query optimizer is based on the dynamically calculated cost to determine how to make selections. How is the cost calculated? Usually, the calculation is based on the cost model and statistics. The cost model reasonableness and statistics accuracy will affect the effectiveness of query optimization.
The utilization of cluster tablespaces also affects the query cost. When a table has UPDATE operations (including INSERT VALUES, UPDATE, DELETE, ALTER TABLE, and ADD COLUMN), it will leave garbage data that is no longer used in the system table and the updated table, causing system performance degradation and taking up a lot of disk space. Therefore, you need to regularly monitor the data bloat of the table.
The following details the regular monitoring and maintenance of statistics and data bloat.

Statistics Collection

ANALYZE collects table statistics in a database and stores the results in the pg_statistic system directory. The query planner uses such statistics to help determine the most efficient execution plan for a query. The statistics contain various information such as the amount of data and indexes in the table. A good query plan is based on accurate table statistics.

ANALYZE description

ANALYZE is a command provided by Greenplum to collect statistics and supports column, table, and database granularities as shown below:
CREATE TABLE foo (id int NOT NULL, bar text NOT NULL) DISTRIBUTED BY (id); // Create the test table `foo`
ANALYZE foo(bar);  // Collect statistics of the `bar` column only
ANALYZE foo; // Collect statistics of the `foo` table
ANALYZE; // Collect statistics of all tables in the current database. You need to have the permission to do so

Use limits of ANALYZE

ANALYZE will put a SHARE UPDATE EXCLUSIVE lock on the target table, which will conflict with DDL statements.

ANALYZE speed

ANALYZE is a sampling statistical algorithm that usually does not scan all data in a table, but still consumes some time and computing resources for large tables.

Time to use ANALYZE

As mentioned above, ANALYZE locks tables and consumes system resources, so it is important to run it at the right time and as little as possible. We recommend you run ANALYZE in the following four scenarios.
After data is batch loaded; for example, after data is imported to a newly created table.
After an index is created.
After INSERT, UPDATE, and DELETE operations are performed on a large amount of data.
After VACUUM FULL is executed.

Analyzing partitioned table

As long as you keep the default value and do not modify the system parameter optimizer_analyze_root_partition, there is no difference in manipulating a partitioned table. Just run ANALYZE on the root table, and the system will automatically collect the statistics of partitioned tables on all leaf nodes.
If the number of partitioned tables is high, running ANALYZE on the root table can be time-consuming. Partitioned tables are usually time-dimensional, and historical partitioned tables are not modified; therefore, we recommend you run ANALYZE separately in partitions where data changes.

Data Bloat

The Greenplum database's heap table uses PostgreSQL's multiple version concurrency control (MVCC) storage implementation. The database will logically delete a deleted or updated row, but an invisible image of that row will remain in the table. As more operations are performed, the table will have more and more invisible data, which will take up a lot of storage space, thereby causing a serious performance degradation in table operations. Additionally, the data bloat can occupy a lot of space, which needs to be regularly fixed.

Data bloat monitoring

The gp_toolkit schema provides a gp_bloat_diag view that determines the table bloat by the ratio of the actual number of pages to the expected number of pages. To use this view, make sure that the latest statistics are collected for all tables in the database. Then, run the following SQL:
gpadmin=# SELECT * FROM gp_toolkit.gp_bloat_diag;
 bdirelid | bdinspname | bdirelname | bdirelpages | bdiexppages |                bdidiag                
----------+------------+------------+-------------+-------------+---------------------------------------
    21488 | public     | t1         |          97 |           1 | significant amount of bloat suspected
(1 row)
Here, bdirelpage is the actual number of pages and bdiexppages is the expected number of pages in the t1 table. A bloat ratio exceeding 4 will be recorded in the table, and there may also be a slight bloat even when no ratio is recorded. You can also compare the space of tables at different times to determine whether data has bloated.

Cleaning data bloat in table

The VACUUM <tablename> command adds expired rows to the shared free space map, so such space can be reused. When VACUUM is run periodically on a table with frequent UPDATE operations, the space occupied by expired rows can be reused quickly, easing table bloat. The cycle to run VACUUM is determined by the speed of UPDATE and DELETE on the table.
Note:
VACUUM holds a SHARE UPDATE EXCLUSIVE lock as ANALYZE does and may interlock with DDL operations.
When the table experiences a significant bloat, VACUUM can only slow down the process but not immediately reclaim the space. Therefore, you need to run VACUUM FULL to immediately reclaim all the bloat space. However, VACUUM FULL will add ACCESS EXCLUSIVE to the manipulated table, during which all other DDL and DML statements on the table will be locked. Proceed with caution when running VACUUM FULL as the operation may be time-consuming for large tables.
Another way to address bloat is to redistribute data in the table:
1. Record the distribution key of the table.
2. Change the distribution policy of the table to random distribution.
ALTER TABLE <tablename> SET WITH (REORGANIZE=false) 
             DISTRIBUTED randomly;
3. Change the distribution policy back to the initial one.
ALTER TABLE <table_name> SET WITH (REORGANIZE=true) 
DISTRIBUTED BY (<original distribution columns>);

Handling index bloat

The VACUUM FULL command will only reclaim spaces from tables. To reclaim spaces from indexes, you need to rebuild them with the REINDEX command.
REINDEX TABLE <table_name>    --- Rebuild all indexes in the table  
REINDEX INDEX <index_name> --- Rebuild a specified index
Note:
Note that both REINDEX and VACUUM FULL will add ACCESS EXCLUSIVE.

Regular Cluster Maintenance

When using a cluster, you need to eliminate data bloat and maintain statistics regularly so that the cluster can deliver an optimal performance.

Cleaning without table locking

As mentioned above, VACUUM <tablename> can mark reclaimable space without table locking and slow down data bloat. Cleaning without locking the table will not affect data table reads/writes, but DDL statements cannot be used, and some CPU and memory resources will be used.
Recommended frequency:
Once a day or at least twice a week if a large amount of data is updated in real time and many data records change every day.
Once a week under normal circumstances or once a month if data is not updated frequently.
Use the following script to clean a table with a cron job.
#!/bin/bash
export PGHOST=xxx.xxx.x.x
export PGPORT=5436
export PGUSER=test
export PGPASSWORD=test
db="test"
psql -d $db -e -c "VACUUM test_table;"

Full cleaning with table locking

If the data in a table is updated and deleted frequently, we recommend you plan a business pause window to run VACUUM FULL and REINDEX to reclaim all the bloat space in the table. Cleaning with table locking will put an ACCESS EXCLUSIVE lock on the manipulated table, during which you cannot perform any operations on the table.
1. Run VACUUM FULL <tablename>.
2. Run REINDEX TABLE <tablename> (you can skip this step for tables without indexes).
3. Run ANALYZE <tablename> .
Recommended frequency: Once a week or once a day if almost all data is updated daily.
Use the following script to regularly clean cluster tables, preferably during off hours in the early morning on the weekend.
#!/bin/bash
export PGHOST=xxx.xxx.x.x
export PGPORT=5436
export PGUSER=test
export PGPASSWORD=test
db="test"
psql -d $db -e -c "VACUUM FULL test_table;"
psql -d $db -e -c "REINDEX TABLE test_table;"
psql -d $db -e -c "ANALYZE test_table;"

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