8.7 Monitoring Parallel Execution Performance
You should perform the following types of monitoring when trying to diagnose parallel execution performance problems.
These types of monitoring include:
See Also:
Oracle Database Reference for information about dynamic views
8.7.1 Monitoring Parallel Execution Performance with Dynamic Performance Views
You can monitor parallel execution performance with dynamic performance views.
Oracle Database real-time monitoring feature enables you to monitor the performance of SQL statements while they are executing. SQL monitoring is automatically started when a SQL statement runs parallel or when it has consumed at least 5 seconds of CPU or I/O time for a single execution.
After your system has run for a few days, you should monitor parallel execution performance statistics to determine whether your parallel processing is optimal. Do this using any of the views discussed in this section.
In Oracle Real Application Clusters, global versions of the views described in this section aggregate statistics from multiple instances. The global views have names beginning with G, such as GV$FILESTAT for V$FILESTAT, and so on.
See Also:
Oracle Database SQL Tuning Guide for more information about monitoring performance
8.7.1.1 V$PX_BUFFER_ADVICE
You can monitor parallel execution performance with the V$PX_BUFFER_ADVICE dynamic performance view.
The V$PX_BUFFER_ADVICE view provides statistics on historical and projected maximum buffer usage by all parallel queries. You can consult this view to reconfigure SGA size in response to insufficient memory problems for parallel queries.
8.7.1.2 V$PX_SESSION
You can monitor parallel execution performance with the V$PX_SESSION dynamic performance view.
The V$PX_SESSION view shows data about query server sessions, groups, sets, and server numbers. It also displays real-time data about the processes working on behalf of parallel execution. This table includes information about the requested degree of parallelism (DOP) and the actual DOP granted to the operation.
8.7.1.3 V$PX_SESSTAT
You can monitor parallel execution performance with the V$PX_SESSTAT dynamic performance view.
The V$PX_SESSTAT view provides a join of the session information from V$PX_SESSION and the V$SESSTAT table. Thus, all session statistics available to a standard session are available for all sessions performed using parallel execution.
8.7.1.4 V$PX_PROCESS
You can monitor parallel execution performance with the V$PX_PROCESS dynamic performance view.
The V$PX_PROCESS view contains information about the parallel processes, including status, session ID, process ID, and other information.
8.7.1.5 V$PX_PROCESS_SYSSTAT
You can monitor parallel execution performance with the V$PX_PROCESS_SYSSTAT dynamic performance view.
The V$PX_PROCESS_SYSSTAT view shows the status of query servers and provides buffer allocation statistics.
8.7.1.6 V$PQ_SESSTAT
You can monitor parallel execution performance with the V$PQ_SESSTAT dynamic performance view.
The V$PQ_SESSTAT view shows the status of all current server groups in the system such as data about how queries allocate processes and how the multiuser and load balancing algorithms are affecting the default and hinted values.
You might need to adjust some parameter settings to improve performance after reviewing data from these views. In this case, refer to the discussion of Tuning General Parameters for Parallel Execution. Query these views periodically to monitor the progress of long-running parallel operations.
For many dynamic performance views, you must set the parameter TIMED_STATISTICS to TRUE in order for Oracle Database to collect statistics for each view. You can use the ALTER SYSTEM or ALTER SESSION statements to turn TIMED_STATISTICS on and off.
8.7.1.7 V$PQ_TQSTAT
You can monitor parallel execution performance with the V$PQ_TQSTAT dynamic performance view.
As a simple example, consider a hash join between two tables, with a join on a column with only two distinct values. At best, this hash function has one hash value to parallel execution server A and the other to parallel execution server B. A DOP of two is fine, but, if it is four, then at least two parallel execution servers have no work. To discover this type of deviation, use a query similar to the following example:
SELECT dfo_number, tq_id, server_type, process, num_rows FROM V$PQ_TQSTAT ORDER BY dfo_number DESC, tq_id, server_type, process;
The best way to resolve this problem might be to choose a different join method; a nested loop join might be the best option. Alternatively, if one join table is small relative to the other, a BROADCAST distribution method can be hinted using PQ_DISTRIBUTE hint. The optimizer considers the BROADCAST distribution method, but requires OPTIMIZER_FEATURES_ENABLE set to 9.0.2 or higher.
Now, assume that you have a join key with high cardinality, but one value contains most of the data, for example, lava lamp sales by year. The only year that had big sales was 1968, and the parallel execution server for the 1968 records is overwhelmed. You should use the same corrective actions as described in the previous paragraph.
The V$PQ_TQSTAT view provides a detailed report of message traffic at the table queue level. V$PQ_TQSTAT data is valid only when queried from a session that is executing parallel SQL statements. A table queue is the pipeline between query server groups, between the parallel execution coordinator and a query server group, or between a query server group and the coordinator. The table queues are represented explicitly in the operation column by PX SEND <partitioning type> (for example, PX SEND HASH) and PX RECEIVE.
V$PQ_TQSTAT has a row for each query server process that it reads from or writes to in each table queue. A table queue connecting 10 consumer processes to 10 producer processes has 20 rows in the view. Total the bytes column and group by TQ_ID, and the table queue identifier, to obtain the total number of bytes sent through each table queue. Compare this to the optimizer estimates; large variations might indicate a need to analyze the data using a larger sample.
Compute the variance of bytes grouped by TQ_ID. Large variances indicate workload imbalances. You should investigate large variances to determine whether the producers start out with unequal distributions of data, or whether the distribution itself is skewed. If the data itself is skewed, this might indicate a low cardinality, or low number of distinct values.
8.7.1.8 V$RSRC_CONS_GROUP_HISTORY
You can monitor parallel execution performance with the V$RSRC_CONS_GROUP_HISTORY dynamic performance view.
The V$RSRC_CONS_GROUP_HISTORY view displays a history of consumer group statistics for each entry in V$RSRC_PLAN_HISTORY that has a non-NULL plan, including information about parallel statement queuing.
8.7.1.9 V$RSRC_CONSUMER_GROUP
You can monitor parallel execution performance with the V$RSRC_CONSUMER_GROUP dynamic performance view.
The V$RSRC_CONSUMER_GROUP view displays data related to currently active resource consumer groups, including information about parallel statements.
8.7.1.10 V$RSRC_PLAN
You can monitor parallel execution performance with the V$RSRC_PLAN dynamic performance view.
The V$RSRC_PLAN view displays the names of all currently active resource plans, including the state of parallel statement queuing.
8.7.1.11 V$RSRC_PLAN_HISTORY
You can monitor parallel execution performance with the V$RSRC_PLAN_HISTORY dynamic performance view.
The V$RSRC_PLAN_HISTORY displays a history of when a resource plan was enabled, disabled, or modified on the instance. The history includes the state of parallel statement queuing
8.7.1.12 V$RSRC_SESSION_INFO
You can monitor parallel execution performance with the V$RSRC_SESSION_INFO dynamic performance view.
The V$RSRC_SESSION_INFO view displays resource manager statistics per session, including parallel statement queue statistics. Columns include PQ_SERVERS and PQ_STATUS.
The PQ_SERVERS column of the V$RSRC_SESSION_INFO view contains the number of active parallel servers if the session is active and running the parallel query. If the query is queued, the number of parallel servers that this query is trying to run with is shown.
The PQ_STATUS column maintains the reason that a parallel statement is queued
See Also:
Oracle Database Reference for information about the V$RSRC_SESSION_INFO view
8.7.1.13 V$RSRCMGRMETRIC
You can monitor parallel execution performance with the V$RSRCMGRMETRIC dynamic performance view.
The V$RSRCMGRMETRIC view displays statistics related to parallel statement queuing.
Statistics related to parallel statement queuing are added to the resource manager metrics that takes statistics for a given one-minute window and retains them for approximately one hour.
Columns include AVG_ACTIVE_PARALLEL_STMTS, AVG_QUEUED_PARALLEL_STMTS, AVG_ACTIVE_PARALLEL_SERVERS, AVG_QUEUED_PARALLEL_SERVERS, and PARALLEL_SERVERS_LIMIT.
See Also:
Oracle Database Reference for information about the V$RSRCMGRMETRIC view
8.7.2 Monitoring Session Statistics
You can monitor session statistics with the dynamic performance views to diagnose parallel execution performance.
Use GV$PX_SESSION to determine the configuration of the server group executing in parallel. In this example, session 9 is the query coordinator, while sessions 7 and 21 are in the first group, first set. Sessions 18 and 20 are in the first group, second set. The requested and granted DOP for this query is 2, as shown by the output from the following query:
SELECT QCSID, SID, INST_ID "Inst", SERVER_GROUP "Group", SERVER_SET "Set", DEGREE "Degree", REQ_DEGREE "Req Degree" FROM GV$PX_SESSION ORDER BY QCSID, QCINST_ID, SERVER_GROUP, SERVER_SET;
Your output should resemble the following:
QCSID SID Inst Group Set Degree Req Degree
---------- ---------- ---------- ---------- ---------- ---------- ----------
9 9 1
9 7 1 1 1 2 2
9 21 1 1 1 2 2
9 18 1 1 2 2 2
9 20 1 1 2 2 2
For a single instance, use SELECT FROM V$PX_SESSION and do not include the column name Instance ID.
The processes shown in the output from the previous example using GV$PX_SESSION collaborate to complete the same task. The next example shows the execution of a join query to determine the progress of these processes in terms of physical reads. Use this query to track any specific statistic:
SELECT QCSID, SID, INST_ID "Inst", SERVER_GROUP "Group", SERVER_SET "Set", NAME "Stat Name", VALUE FROM GV$PX_SESSTAT A, V$STATNAME B WHERE A.STATISTIC# = B.STATISTIC# AND NAME LIKE 'PHYSICAL READS' AND VALUE > 0 ORDER BY QCSID, QCINST_ID, SERVER_GROUP, SERVER_SET;
Your output should resemble the following:
QCSID SID Inst Group Set Stat Name VALUE
------ ----- ------ ------ ------ ------------------ ----------
9 9 1 physical reads 3863
9 7 1 1 1 physical reads 2
9 21 1 1 1 physical reads 2
9 18 1 1 2 physical reads 2
9 20 1 1 2 physical reads 2
Use the previous type of query to track statistics in V$STATNAME. Repeat this query as often as required to observe the progress of the query server processes.
The next query uses V$PX_PROCESS to check the status of the query servers.
SELECT * FROM V$PX_PROCESS;
Your output should resemble the following:
SERV STATUS PID SPID SID SERIAL# IS_GV CON_ID ---- --------- ------ --------- ------ ------- ----- ------- P002 IN USE 16 16955 21 7729 FALSE 0 P003 IN USE 17 16957 20 2921 FALSE 0 P004 AVAILABLE 18 16959 FALSE 0 P005 AVAILABLE 19 16962 FALSE 0 P000 IN USE 12 6999 18 4720 FALSE 0 P001 IN USE 13 7004 7 234 FALSE 0
See Also:
Monitoring Parallel Execution Performance with Dynamic Performance Views for descriptions of the dynamic performance views used in the examples
8.7.3 Monitoring System Statistics
You can monitor system statistics with the dynamic performance views to diagnose parallel execution performance.
The V$SYSSTAT and V$SESSTAT views contain several statistics for monitoring parallel execution. Use these statistics to track the number of parallel queries, DMLs, DDLs, data flow operators (DFOs), and operations. Each query, DML, or DDL can have multiple parallel operations and multiple DFOs.
In addition, statistics also count the number of query operations for which the DOP was reduced, or downgraded, due to either the adaptive multiuser algorithm or the depletion of available parallel execution servers.
Finally, statistics in these views also count the number of messages sent on behalf of parallel execution. The following syntax is an example of how to display these statistics:
SELECT NAME, VALUE FROM GV$SYSSTAT WHERE UPPER (NAME) LIKE '%PARALLEL OPERATIONS%' OR UPPER (NAME) LIKE '%PARALLELIZED%' OR UPPER (NAME) LIKE '%PX%';
Your output should resemble the following:
NAME VALUE -------------------------------------------------- ---------- queries parallelized 347 DML statements parallelized 0 DDL statements parallelized 0 DFO trees parallelized 463 Parallel operations not downgraded 28 Parallel operations downgraded to serial 31 Parallel operations downgraded 75 to 99 pct 252 Parallel operations downgraded 50 to 75 pct 128 Parallel operations downgraded 25 to 50 pct 43 Parallel operations downgraded 1 to 25 pct 12 PX local messages sent 74548 PX local messages recv'd 74128 PX remote messages sent 0 PX remote messages recv'd 0
The following query shows the current wait state of each worker (child process) and query coordinator process on the system:
SELECT px.SID "SID", p.PID, p.SPID "SPID", px.INST_ID "Inst",
px.SERVER_GROUP "Group", px.SERVER_SET "Set",
px.DEGREE "Degree", px.REQ_DEGREE "Req Degree", w.event "Wait Event"
FROM GV$SESSION s, GV$PX_SESSION px, GV$PROCESS p, GV$SESSION_WAIT w
WHERE s.sid (+) = px.sid AND s.inst_id (+) = px.inst_id AND
s.sid = w.sid (+) AND s.inst_id = w.inst_id (+) AND
s.paddr = p.addr (+) AND s.inst_id = p.inst_id (+)
ORDER BY DECODE(px.QCINST_ID, NULL, px.INST_ID, px.QCINST_ID), px.QCSID,
DECODE(px.SERVER_GROUP, NULL, 0, px.SERVER_GROUP), px.SERVER_SET, px.INST_ID;8.7.4 Monitoring Operating System Statistics
There is considerable overlap between information available in Oracle Database and information available though operating system utilities, such as sar and vmstat on UNIX-based systems.
Operating systems provide performance statistics on I/O, communication, CPU, memory and paging, scheduling, and synchronization primitives. The V$SESSTAT view provides the major categories of operating system statistics as well.
Typically, operating system information about I/O devices and semaphore operations is harder to map back to database objects and operations than is Oracle Database information. However, some operating systems have good visualization tools and efficient means of collecting the data.
Operating system information about CPU and memory usage is very important for assessing performance. Probably the most important statistic is CPU usage. The goal of low-level performance tuning is to become CPU bound on all CPUs. After this is achieved, you can work at the SQL level to find an alternate plan that might be more I/O intensive but use less CPU.
Operating system memory and paging information is valuable for fine tuning the many system parameters that control how memory is divided among memory-intensive data warehouse subsystems like parallel communication, sort, and hash join.