Locate and analyze slow queries (II)

In a TP business, SQL queries are short and frequent, with execution times measured in microseconds or milliseconds. Therefore, it's difficult to identify slow queries based on execution time. For frequent short queries, the key is to calculate the number of rows read by each query.

Scenarios

If the IDX_C1 index is used for execution, each execution of the following query needs to read a very small number of rows, with the execution time measured in microseconds or milliseconds. However, if a full table scan is performed, each execution needs to read a large amount of data, with the execution time measured in tens to hundreds of milliseconds.

A query that takes only a few microseconds or milliseconds to execute is generally not considered a slow query. However, if this query is frequently executed, a large amount of computing resources will be consumed overall, even exhausting the CPU resources.

CREATE TABLE T1 (C1 INT, C2 INT);
CREATE INDEX IDX_C1 ON T1 (C1);

// insert 10K rows into T1 and C1 is almost unique

SELECT * FROM T1 WHERE C1 = 1;

Locate frequent short queries

To locate such slow queries, you need to perform some aggregate statistics on the resource consumption of similar queries. In OceanBase Database, you can use the GV$OB_SQL_AUDIT view to identify these slow queries. A sample diagnostic SQL query statement is as follows:

The following query statistics rank the top 10 requests of a certain type of SQL by the number of rows read. If it is found that the overall number of rows read by the top few requests is significantly higher by 1 to 2 orders of magnitude, then the value of optimizing these requests will be very high. If the execution plan of these requests can be optimized, significant overall benefits can be achieved.

During the actual diagnosis, the number of rows read is not the only factor that can cause high CPU usage. You can also sort the queries by other fields, such as the total CPU time, the number of retries (retry_cnt), and the number of RPCs (rpc_count), to identify potential slow queries from multiple dimensions.

select /*+READ_CONSISTENCY(WEAK), QUERY_TIMEOUT(100000000)*/  svr_ip, sql_id,
    tenant_id, tenant_name, user_name, db_name, plan_id,
    count(*) exections,
    max(event) event,
    max(table_scan) table_scan,
    sum(case when ret_code = 0 then 0 else 1 end) fail_times,
    sum(rpc_count) rpc_count,
    sum(retry_cnt) retry_cnt,
    sum(case when plan_type = 2 then 1 else 0 end)
    remote_plans,
    sum(case when is_hit_plan = 1 then 0 else 1 end) miss_plans,
    round(avg(elapsed_time)) elapsed_time,
    round(max(elapsed_time)) max_elapsed_time,
    round(avg(execute_time)) execute_time,
    round(avg(( execute_time - total_wait_time_micro + get_plan_time ))) cpu_time,
    round(max(( execute_time - total_wait_time_micro + get_plan_time ))) max_cpu_time,
    round(avg(queue_time)) queue_time,
    round(avg(net_wait_time)) netwait_time,
    round(avg(user_io_wait_time)) iowait_time,
    round(avg(get_plan_time)) getplan_time,
    round(avg(decode_time)) decode_time,
    round(avg(total_wait_time_micro)) total_wait_time,
    round(avg(application_wait_time)) app_wait_time,
    round(avg(concurrency_wait_time)) concurrency_wait_time,
    round(avg(schedule_time)) schedule_time,
    round(avg(return_rows)) return_rows,
    round(avg(affected_rows)) affected_rows,
    round(avg(row_cache_hit * 2 + bloom_filter_cache_hit * 2 + block_cache_hit +
    disk_reads)) logical_reads,
    round(avg(row_cache_hit)) row_cache_hit,
    round(avg(bloom_filter_cache_hit)) bloom_filter_cache_hit,
    round(avg(block_cache_hit)) block_cache_hit,
    round(avg(disk_reads)) disk_reads,
    round(avg(memstore_read_row_count)) memstore_read_row_count,
    round(avg(ssstore_read_row_count)) ssstore_read_row_count,
    sum(memstore_read_row_count + ssstore_read_row_count) as total_row_count
    from gv$ob_sql_audit
    where  is_inner_sql = 0
    group  by svr_ip, sql_id order by total_row_count desc limit 10;

Information about frequent short queries

After you locate the slow SQL queries, you can search for the execution plans of these queries.

1. Run the following command to obtain the execution plans of the slow SQL queries.

   In the following query, specify the tenant_id based on the business to be analyzed, and fill in the svr_ip and sql_id based on the results of locating the slow SQL queries.

   SELECT tenant_id,
      svr_ip,
      svr_port,
      sql_id,
      plan_id,
      last_active_time,
      first_load_time,
      outline_data
   FROM GV$OB_PLAN_CACHE_PLAN_STAT
   WHERE TENANT_ID = 1002
   AND SQL_ID = '3310A1D1D81D4BA92CEEF42538136DD1'
   AND SVR_IP = '11.xxx.x.xx'
   AND SVR_PORT = 35046;
   

   If the preceding command succeeds, you will find the execution plan ID (plan_id) of the query, the time when the execution plan was generated for the first time (first_load_time), and the time when the execution plan was last used (last_active_time). For batch processing, the time when the last active plan was used is very likely the time when the slow query was initiated.

   outline_data is a hint that describes the execution plan of the query.

   *************************** 1. row ***************************
       tenant_id: 1002
           svr_ip: 11.xxx.x.xx
           svr_port: 35046
           sql_id: 3310A1D1D81D4BA92CEEF42538136DD1
           plan_id: 741
   last_active_time: 2022-08-04 11:00:34.466037
   first_load_time: 2022-08-04 11:00:34.466037
       outline_data: /*+ BEGIN_OUTLINE_DATA FULL(@"SEL$1" "test.t1"@"SEL$1") END_OUTLINE_DATA*/
   

2. Obtain the plan form of slow SQL.

   Execute the following statement to obtain the tree-like structure of the execution plan.

   SELECT OPERATOR, NAME, ROWS, COST FROM GV$OB_PLAN_CACHE_PLAN_EXPLAIN
   WHERE TENANT_ID = 1002 AND
       SVR_IP = '11.xxx.x.xx' AND
       SVR_PORT = 35046 AND
       PLAN_ID = 741 ;
   

   Below is the physical plan returned by the preceding query.

   +----------------+------+------+------+
   | OPERATOR       | NAME | ROWS | COST |
   +----------------+------+------+------+
   | PHY_TABLE_SCAN | t1   |    1 |   45 |
   +----------------+------+------+------+
   

   In addition to the physical plan, you can also obtain the logical plan by simply executing the SQL statement with the EXPLAIN statement.

   explain select sleep(1000), rand() from t1;
   
   *************************** 1. row ***************************
   Query Plan: ===================================
   |ID|OPERATOR  |NAME|EST. ROWS|COST|
   -----------------------------------
   |0 |TABLE SCAN|t1  |1        |46  |
   ===================================
   
   Outputs & filters:
   -------------------------------------
   0 - output([sleep(?)], [rand()]), filter(nil),
       access([t1.__pk_increment]), partitions(p0)