1 / 60

Advanced Performance Diagnostics for SQL in DB2 9.7

Advanced Performance Diagnostics for SQL in DB2 9.7. David Kalmuk IBM Platform: DB2 for Linux, Unix, Windows. Objectives. Learn how to leverage the latest DB2 performance monitoring features and time spent metrics to identify that you have an SQL problem

bernad
Télécharger la présentation

Advanced Performance Diagnostics for SQL in DB2 9.7

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Advanced Performance Diagnostics for SQL in DB2 9.7 David Kalmuk IBM Platform: DB2 for Linux, Unix, Windows

  2. Objectives • Learn how to leverage the latest DB2 performance monitoring features and time spent metrics to identify that you have an SQL problem • Learn how to pinpoint your most expensive statements using the package cache table functions • Learn about the new runtime explain capabilities and how you can perform an explain from the actual compiled section in the catalogs or the package cache to understand the query plan. • Learn how to leverage the new section actuals explain capabilities to examine the actual cardinalities from the execution of your problem queries • Take away practical examples you can try out in your own environment.

  3. Agenda • A quick review of the new Monitoring Capabilities introduced in DB2 9.7 • Time Spent Metrics • Identifying query related performance problems • Identifying high impact queries • Analyzing queries using Time Spent • Advanced diagnostics using Runtime Explain and Section Actuals • Final thoughts

  4. A Quick Review of the New Performance Monitoring Capabilities Introduced in DB2 9.7

  5. New Lightweight Monitoring Functions • New set of MON_* SQL functions introduced starting in DB2 9.7 • Less impact / more efficient then snapshot functions • Direct in-memory access through trusted routines (not fenced wrappers over snapshot) • Much less latch contention • Uses new infrastructure that pushes data up to accumulation points rather than forcing monitor queries to do extensive drilldown • Lower CPU consumption • Significantly faster response time • Less FCM resource usage / internode traffic • Monitoring data collection carries low overhead – is enabled by default on new databases

  6. New Monitoring Perspectives and Dimensions • Starting in 9.7, DB2 allows monitoring metrics to be accessed through a number of different dimensions • Allows more effective drilldown, and different perspectives on the data to help isolate problems • Three main dimensions, each consisting of a number of reporting points with corresponding SQL functions • System • Provide total perspective of application work being done by database system • Aggregated through the WLM infrastructure • Data objects • Provide perspective of impact of all activity occurring with the scope of data objects • Aggregated through data storage infrastructure • Activity • Provide perspective of work being done by specific SQL statements • Aggregated through the package cache infrastructure

  7. Connection ∑R Service Class ∑R Service Class ∑R Workload Occurrence (UOW) ∑R Workload Definition ∑R Request Metrics DB2 Agent Collects Data Database Request In-Memory Metrics: System Perspective Legend ΣR = Accumulation of request metrics collected by agent

  8. Access Points: System Perspective • MON_GET_UNIT_OF_WORK • MON_GET_WORKLOAD • MON_GET_CONNECTION • MON_GET_SERVICE_SUBCLASS • Also provide interfaces that produce XML output: • MON_GET_UNIT_OF_WORK_DETAILS • MON_GET_WORKLOAD_DETAILS • MON_GET_CONNECTION_DETAILS • MON_GET_SERVICE_SUBCLASS_DETAILS

  9. Bufferpool Metrics Bufferpool Container Metrics Temp Tablespace Tablespace Metrics Tablespace Tablespace Tablespace Container Row Data LOB /Data Index Table XML Data Table Metrics Database Request DB2 Agent Collects Data In-Memory Metrics: Data Object Perspective

  10. Access Points: Data Object Perspective • MON_GET_TABLE • MON_GET_INDEX • MON_GET_BUFFERPOOL • MON_GET_TABLESPACE • MON_GET_CONTAINER • MON_GET_EXTENT_MOVEMENT_STATUS • MON_GET_APPL_LOCKWAIT [9.7FP1] • MON_GET_LOCKS [9.7FP1] • MON_GET_FCM [9.7FP2] • MON_GET_FCM_CONNECTION_LIST [9.7FP2]

  11. In-Memory Metrics: Activity Perspective Package Cache ∑A Activity Metrics WLM Activity ∑A Activity Level DB2 Agent Collects Data Database Request Legend ΣA = Accumulation of metrics from activity execution portion of request

  12. Access Points: Activity Perspective • MON_GET_PKG_CACHE_STMT • Both static and dynamic SQL • MON_GET_PKG_CACHE_STMT_DETAILS • XML based output • MON_GET_ACTIVITY_DETAILS (XML) • Details for an activity currently in progress

  13. Time Spent Metrics

  14. Time Spent Metrics • A new set of metrics are being introduced into DB2 that represent a breakdown of where time is spent within DB2 • Represents sum of time spent by each agent thread in the system (foreground processing) • Provides user with a relative breakdown of time spent, showing which areas are the most expensive during request / query processing • Available in both system and activity perspectives • This presentation will focus on analysis from the activity perspective • Can be used for rapid identification and diagnosis of performance problems • Times are divided into: • Wait times • Time agent threads spend blocking on I/O, network communications, etc • Processing times (starting in 9.7FP1) • Time spent processing in different component areas when the agent was not stuck on a wait • Summary / total times • Total time spent in a particular component area including both processing + wait times

  15. “Time Spent” Metrics: Breakdown of Wait + Processing Times in DB2

  16. Activity Time Spent Hierarchy • “Time spent” metrics are mutually exclusive and in aggregate form a hierarchy (shown below) that breaks down the time spent executing queries in the database server on behalf of the client. Below we show the hierarchy for the activity perspective. STMT_EXEC_TIME TOTAL__ACT_WAIT_TIME LOCK_WAIT_TIME LOG_BUFFER_WAIT_TIME LOG_DISK_WAIT_TIME FCM_SEND/RECV_WAIT_TIME FCM_MESSAGE_SEND/RECV_WAIT_TIME FCM_TQ_SEND/RECV_WAIT_TIME DIAGLOG_WRITE_WAIT_TIME POOL_READ/WRITE_TIME DIRECT_READ/WRITE_TIME AUDIT_SUBSYSTEM_WAIT_TIME AUDIT_FILE_WRITE_TIME TOTAL_SECTION_PROC_TIME TOTAL_SECTION_SORT_PROC_TIME TOTAL_ROUTINE_NON_SECT_PROC_TIME TOTAL_ROUTINE_USER_CODE_PROC_TIME (Other – nested section execution) Time performing query plan execution

  17. Navigating the “time spent” hierarchy • The row based formatting functions introduced in 9.7 FP1 offer an easy way to navigate the time spent hierarchy in a generic fashion • MON_FORMAT_XML_TIMES_BY_ROW • Shows breakdown of waits + processing times • MON_FORMAT_XML_WAIT_TIMES_BY_ROW • Shows breakdown of just wait times • MON_FORMAT_XML_COMPONENT_TIMES_BY_ROW • Shows breakdown of processing time as well as overall time spent in each “component” of DB2 • MON_FORMAT_XML_METRICS_BY_ROW • Outputs all metrics in generic row based form

  18. Example Show me the full hierarchy of waits + processing times for a particular statement select r.metric_name, r.parent_metric_name, r.total_time_value as time, r.count, p.member, s.stmt_text as stmt from table(mon_get_pkg_cache_stmt_details(‘S',null,null,-2)) as p table(mon_get_pkg_cache_stmt(null,p.executable_id,null,-2)) as s, table(mon_format_xml_times_by_row(p.details)) as r, where s.stmt_text like ‘UPDATE stock%‘ order by total_time_value desc METRIC_NAME PARENT_METRIC_NAME TIME COUNT MEMBER STMT ------------------------- -------------------- ----------- --------- ------ -------------------- TOTAL_SECTION_PROC_TIME STMT_EXEC_TIME 1030980 2134223 0 UPDATE stock… STMT_EXEC_TIME - 1030980 2134223 0 UPDATE stock… WLM_QUEUE_TIME_TOTAL - 0 0 0 UPDATE stock… LOCK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… DIRECT_READ_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… DIRECT_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… LOG_BUFFER_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… LOG_DISK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… POOL_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… POOL_READ_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… AUDIT_FILE_WRITE_WAIT_TIM TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… AUDIT_SUBSYSTEM_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… DIAGLOG_WRITE_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… FCM_SEND_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… FCM_RECV_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock… TOTAL_ACT_WAIT_TIME STMT_EXEC_TIME 0 - 0 UPDATE stock… …

  19. Example 2 Show me the just the wait times perspective select r.metric_name, r.parent_metric_name, r.total_time_value as time, r.count, p.member, s.stmt_text as stmt from table(mon_get_pkg_cache_stmt_details(‘S',null,null,-2)) as p table(mon_get_pkg_cache_stmt(null,p.executable_id,null,-2)) as s, table(mon_format_xml_wait_times_by_row(p.details)) as r, where s.stmt_text like ‘UPDATE stock%‘ order by total_time_value desc METRIC_NAME PARENT_METRIC_NAME TIME COUNT MEMBER STMT ------------------------- -------------------- ----------- --------- ------ -------------------- WLM_QUEUE_TIME_TOTAL - 0 0 0 UPDATE stock LOCK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock DIRECT_READ_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock DIRECT_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock LOG_BUFFER_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock LOG_DISK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock POOL_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock POOL_READ_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock AUDIT_FILE_WRITE_WAIT_TIM TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock AUDIT_SUBSYSTEM_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock DIAGLOG_WRITE_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock FCM_SEND_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock FCM_RECV_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 UPDATE stock TOTAL_ACT_WAIT_TIME STMT_EXEC_TIME 0 - 0 UPDATE stock …

  20. Example 3 Show me the just the times for each individual component area select r.metric_name, r.parent_metric_name as parent_metric, r.total_time_value as total_time,r.proc_metric_name, r.parent_proc_metric_name as parent_proc_metric, r.proc_time_value as proc_time, r.count, p.member, s.stmt_text as stmt from table(mon_get_pkg_cache_stmt_details('S',null,null,-2)) as p, table (mon_get_pkg_cache_stmt(null,p.executable_id,null,-2)) as s, table(mon_format_xml_component_times_by_row(p.details)) as r where s.stmt_text like 'UPDATE stock%' order by total_time_value desc METRIC_NAME PARENT_METRIC TOTAL_TIME PROC_METRIC_NAME PARENT_PROC_METRIC PROC_TIME COUNT MEMBER STMT -------------------- ---------------- ---------- -------------------- ------------------ --------- ------ ------ ------- STMT_EXEC_TIME - 1031684 - - - 0 0 UPDATE… TOTAL_SECTION_TIME TOTAL_RQST_TIME 1031684 TOTAL_SECTION_PROC TOTAL_RQST_TIME 1031684 2134223 0 UPDATE… TOTAL_SECTION_SORT_T TOTAL_SECTION_TIME 0 TOTAL_SECTION_SORT_P TOTAL_SECTION_PROC 0 0 0 UPDATE… TOTAL_ROUTINE_NON_SE TOTAL_ROUTINE_TIME 0 TOTAL_ROUTINE_NON_SE TOTAL_ROUTINE_TIME 0 0 0 UPDATE… TOTAL_ROUTINE_TIME STMT_EXEC_TIME 0 - - - 0 0 UPDATE… TOTAL_ROUTINE_USER_C TOTAL_ROUTINE_NON_S 0 TOTAL_ROUTINE_USER_C TOTAL_ROUTINE_NON_S 0 0 0 UPDATE…

  21. Identifying Query Related Performance Problems

  22. Identifying Query Related Performance Problems • Generally we will want to try to identify and address any system level bottlenecks / non query related issues first before proceeding to more granular investigation at the statement or query level • We will look at some initial indicators here to help rule out broad system-wide or non query related problems • This will help us narrow down whether the performance issues we are observing are related to statement execution or plan issues specifically rather than other system bottlenecks, and that tuning individual queries will give us some bang for our buck • The following examples will show some of the basic indicators that may be useful in helping to make this determination

  23. “How much time on the database is spent doing query processing?” select sum(total_rqst_time) as rqst_time, sum(total_act_time) as act_time, sum(total_section_time) as sect_time, (case when sum(total_rqst_time) > 0 then (sum(total_act_time) * 100) / sum(total_rqst_time) else 0 end) as act_pct (case when sum(total_rqst_time) > 0 then (sum(total_section_time) * 100) / sum(total_rqst_time) else 0 end) as sect_pct from table(mon_get_connection(null,-2)) as t Added some percentages for readability About 46% of time spent executing query plans RQST_TIME ACT_TIME SECT_TIME ACT_PCT SECT_PCT ----------------- ----------------- ----------------- ----------------- ----------------- 18188462 8419265 8419265 46 46

  24. “Are there any system-wide bottlenecks?” • create view dbtimemetrics (metric_name, parent_metric_name, total_time_value, count) • as select u.metric_name, u.parent_metric_name, sum(u.total_time_value), sum(u.count) • from table(mon_get_service_subclass_details(null,null,-2)) as t, • table(mon_format_xml_times_by_row(t.details)) as u • group by metric_name, parent_metric_name • create global temporary table timesamples • as (select * from dbtimemetrics) definition only on commit delete rows • create view dbtimedelta (metric_name, parent_metric_name, total_time_value, count) • as select t2.metric_name, t2.parent_metric_name, • t2.total_time_value - t1.total_time_value, t2.count - t1.count • from dbtimemetrics as t2, timesamples as t1 • where t2.metric_name = t1.metric_name A bit of scripting / setup to help obtain deltas

  25. (continued) Get wait times on database sampled over 1 minute insert into timesamples select * from dbtimemetrics <sleep for 60s sampling period> select * from dbtimedelta order by total_time_value desc fetch first 5 rows only (Be sure to run with auto-commit disabled when using CLP) METRIC_NAME PARENT_METRIC_NAME TOTAL_TIME_VALUE COUNT MEMBER ------------------------- ------------------------- -------------------- -------------------- ------ TOTAL_RQST_TIME - 14694008 31778866 0 TOTAL_WAIT_TIME TOTAL_RQST_TIME 7471599 - 0 CLIENT_IDLE_WAIT_TIME - 7230313 - 0 TOTAL_SECTION_PROC_TIME TOTAL_RQST_TIME 7220409 29277233 0 LOG_DISK_WAIT_TIME TOTAL_WAIT_TIME 7001599 103341 0 …

  26. Identifying High Impact / Expensive Queries

  27. Identifying Problem Queries • Once we’ve ruled out general system-wide bottlenecks that might be impacting our performance we’ll want to identify the highest impact / worst performing queries on the system as possible candidates for tuning • Time spent metrics can be used to drill down in a number of different useful ways in order to help identify high impact queries. • Among the many things we can look at are: • Queries with the most execution time in server • Queries with the worst velocity in terms of processing vs wait • CPU intensive queries • Least efficient query plans • The following examples will show you several ways you can leverage the time spent metrics to drill down and identify your problem queries

  28. Finding High Impact Queries Top 5 queries by statement execution time in server select stmt_exec_time, num_executions, stmt_text from table(mon_get_pkg_cache_stmt(null,null,null,-2)) as s order by stmt_exec_time desc fetch first 5 rows only Statement with most execution time in the server STMT_EXEC_TIME NUM_EXECUTIONS STMT -------------------- -------------------- ----------------------------------------------- 3951764 2218111 SELECT s_quantity, s_dist_01, s_dist_02, … 902078 195866 SELECT c_balance, c_delivery_cnt … 619547 212999 DECLARE CUST_CURSOR1 CURSOR FOR SELEC … 480681 221873 SELECT w_tax, c_discount, c_last, c_credit … 441494 20124 SELECT count(distinct S_I_ID) INTO :H …

  29. More High Impact Queries Top 5 most CPU intensive queries select stmt_exec_time, num_executions, (total_cpu_time / 1000) as cpu_time, stmt_text from table(mon_get_pkg_cache_stmt(null,null,null,-2)) as s order by cpu_time desc fetch first 5 rows only select stmt_exec_time, num_executions, (pool_read_time + pool_write_time + direct_read_time + direct_write_time) as io_time from table(mon_get_pkg_cache_stmt(null,null,null,-2)) as t order by io_time desc fetch first 5 rows only Top 5 most I/O intensive queries

  30. Queries with the Worst Relative Velocity Relative velocity shows the degree to which progress of the query is impacted by waits select total_act_time, total_act_wait_time, (case when total_act_time > 0 then ((total_act_time – total_act_wait_time) * 100 / total_act_time) else 100 end) as relvelocity, stmt_text from table (mon_get_pkg_cache_stmt(null,null,null,-2)) as t order by relvelocity fetch first 5 rows only Compute percentage of query time where we’re processing Majority of query time spent in waits! TOTAL_ACT_TIME TOTAL_ACT_WAIT_TIME RELVELOCITY STMT_TEXT -------------------- -------------------- -------------------- -------------------- 1481597 1457690 1 DECLARE READ_ORDERLI … 228 223 2 create view dbtimeme … 28 27 3 alter table activity … 30 29 3 create event monitor … 35 33 5 create event monitor …

  31. Queries with the Least Efficient Plans This query shows us how much data we processed to produce a single row of results select rows_returned, rows_read, (case when rows_returned > 0 then rows_read / rows_returned else 0 end) as ratio, stmt_text as stmt from table(mon_get_pkg_cache_stmt(null,null,null,-2)) as p order by ratio desc fetch first 10 rows only Ratio of rows read to rows returned ROWS_RETURNED ROWS_READ RATIO STMT -------------------- -------------------- -------------------- ------------------------- 2 11137814 5568907 select count(*) from acti… 1 5568907 5568907 select min(time_completed 3 9 3 select * from syscat.WORK… 9 9 1 select substr(serviceclas… 9 9 1 select * from dbtimedelta… 2843729 2843729 1 DECLARE CUST_CURSOR1 CURS… 2843729 2843729 1 SELECT w_street_1, w_stre… 29599464 29599528 1 SELECT s_quantity, s_dist… 0 14 0 alter table control drop… 0 13 0 create view dbtimemetrics…

  32. The “Executable ID” • In order to facilitate tracking of individual statements, DB2 9.7 introduced the new concept of an “executable id” • Short identifier that uniquely identifies a section in the package cache (not an individual execution, but an individual statement in a particular compilation environment) • Can be obtained from activity monitoring interfaces such as MON_GET_PKG_CACHE_STMT / MON_GET_PKG_CACHE_STMT_DETAILS, MON_GET_ACTIVITY_DETAILS or the package cache and activity event monitors • Can be subsequently used as input to the package cache interfaces to retrieve data for that particular section without the statement text • In the next section we will leverage the executable ID that uniquely identifies our statements of interest in order to perform further drilldown

  33. Analyzing Individual Statements Using Time Spent

  34. Analyzing Individual Queries Using Time Spent • Once we have pinpointed our statements of interest, our next step is to drill down into these individual statements to understand where they are spending their time • By understanding where the time is being spent in the query we can identify where the database server is spending effort, and look for opportunities for tuning • Using the executable id from problem statements identified in the previous section we can now perform an analysis of the time spent breakdown for those statements

  35. “Where is my time being spent?” Show me the full hierarchy of waits + processing times for a particular statement select p.executable_id, r.metric_name, r.parent_metric_name, r.total_time_value as time, r.count, p.member from (select stmt_exec_time, executable_id from table(mon_get_pkg_cache_stmt(null,null,null,-2)) as s order by stmt_exec_time desc fetch first row only) as stmts, table(mon_get_pkg_cache_stmt_details(null, stmts.executable_id, null, -2)) as p, table(mon_format_xml_times_by_row(p.details)) as r order by stmts.executable_id, total_time_value desc Find statement with most time in server Executable ID for our statement(s) of interest Format XML details to produce row based format for time spent metrics

  36. (continued) Row based breakdown of where time is being spent EXEC_ID METRIC_NAME PARENT_METRIC_NAME TIME COUNT MEMBER ------------ -------------------------------- ---------------------- ------- ------ ------ x'00000001…' STMT_EXEC_TIME - 6676617 110191 0 x'00000001…' TOTAL_ROUTINE_NON_SECT_PROC_TIME TOTAL_ROUTINE_TIME 6008956 110191 0 x'00000001…' TOTAL_ROUTINE_USER_CODE_PROC_TIME TOTAL_ROUTINE_NON_S 6008956 110191 0 x'00000001…' POOL_READ_TIME TOTAL_ACT_WAIT_TIME 372754 52135 0 x'00000001…' TOTAL_ACT_WAIT_TIME STMT_EXEC_TIME 372754 - 0 x'00000001…' TOTAL_SECTION_PROC_TIME STMT_EXEC_TIME 294907 0 0 x'00000001…' WLM_QUEUE_TIME_TOTAL - 0 0 0 x'00000001…' FCM_TQ_RECV_WAIT_TIME FCM_RECV_WAIT_TIME 0 0 0 x'00000001…' FCM_MESSAGE_RECV_WAIT_TIME FCM_RECV_WAIT_TIME 0 0 0 x'00000001…' FCM_TQ_SEND_WAIT_TIME FCM_SEND_WAIT_TIME 0 0 0 x'00000001…' FCM_MESSAGE_SEND_WAIT FCM_SEND_WAIT_TIME 0 0 0 x'00000001…' LOCK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' DIRECT_READ_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' DIRECT_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…‘ LOG_BUFFER_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' LOG_DISK_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' POOL_WRITE_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' AUDIT_FILE_WRITE_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' AUDIT_SUBSYSTEM_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' DIAGLOG_WRITE_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' FCM_SEND_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' FCM_RECV_WAIT_TIME TOTAL_ACT_WAIT_TIME 0 0 0 x'00000001…' TOTAL_SECTION_SORT_PRO TOTAL_SECTION_PROC_T 0 0 0 …

  37. Understanding the Time Metrics: Wait Times • Wait Times represent the time an agent spends blocking / waiting on particular subsystems • Large % wait times in a particular subsystem may indicate is a bottleneck, and is preventing agents working on statement processing from making optimal progress • In some cases the presence of large wait times may indicate a potential query problem • For example, larger than expected bufferpool read time resulting from unexpected table scan activity might indicate a missing index rather than an I/O problem • It might also be an indication of a bad disk though  • In other cases just the presence of a wait time on its own may indicate a problem • For example, direct reads / writes when we are expecting LOBs to be inlined, or FCM TQ waits for a query that is expected to be co-located on a partitioned system would directly indicate a problem

  38. Wait Times (Activity)

  39. More Wait Times (Activity)

  40. Understanding the Time Metrics: Processing / Component Times • Processing / Component Times represent the time an agent thread spends processing / consuming CPU in a particular component area of DB2 • Broken down into two pieces - the processing portion of in the component as well as the total time in the component • Large % processing times in a particular component subsystem indicates that most of our processing is taking place there • Whether this is an issue depends on what the component is • For example, large times in sort might be expected in a particular query, whereas their presence in others might indicate a problem

  41. Processing/Component Times (Activity)

  42. Other Important Time Metrics • A few time (and time related) metrics that bear mention here: • STMT_EXEC_TIME • The total time spent executing a statement (included the time spent in any nested invocations) • TOTAL_ACT_WAIT_TIME • The total time spent blocking on waits while executing this particular section (does not include wait times for nested invocations however which are reported in the section entry for those individual statements) • TOTAL_ROUTINE_TIME • The total amount of time the statement spent executing within routines (UDFs or Stored Procedures) • TOTAL_CPU_TIME • Not a metric in the actual “time spent” hierarchy, but generally a critical time-related metric

  43. Advanced Diagnostics Using Runtime Explain and Section Actuals

  44. Introducing Runtime Explain • There may be cases when a more detailed analysis of query execution is required than can be provided with basic monitoring metrics such as time spent • In these cases the tool we typically turn to is the EXPLAIN feature of DB2 – which herein we will refer to as the SQL Compiler EXPLAIN • This capability compiles an input SQL statement and allows you to format and view the query plan • Expected to be a generally accurate approximation of the query you actually ran • May differ due to differences in compilation environment and/or table statistics from when your query was compiled • An exciting new feature introduced in DB2 9.7 FP1 is the ability to perform a “runtime explain” (or “Explain From Section”) which produces explain output directly from a compiled query plan (or section) in the engine • Allows you to obtain the plan from the actual section you are executing

  45. Explain from Section Procedures • A set of stored procedures provided in DB2 9.7 FP1 allow you to explain a runtime section into the explain tables • EXPLAIN_FROM_CATALOG • EXPLAIN_FROM_SECTION • EXPLAIN_FROM_ACTIVITY • EXPLAIN_FROM_DATA • Explain table content can then be processed using any existing explain tools (eg. db2exfmt) • Explain output can be generated from the following sources: • Static or dynamic statement entries in the package cache • Any cache entry (DETAILED) captured by the new package cache event monitor • Static statement from the catalog tables • Statement execution captured with section by the activity event monitor

  46. Section Actuals • One of the key benefits of the explain from section capability is the ability to capture and format “section actuals” • All Explain output will contain cardinality estimates for individual operators in the plan • When a section based explain is performed on a statement captured by the activity event monitor we can also capture the actual cardinalities for each operator for that execution • Examining this data gives you an indication of what actually happened during the query execution, and whether the estimates the optimizer used in generating the query plan were accurate • In order to examine the actuals we will need to capture the execution of our SQL statement of interest using the activity event monitor

  47. Capturing Activities to Obtain Actuals • The activity event monitor in DB2 allows the capture of execution details for individual SQL statements as well as several other recognized activities (eg. Load) • It can be configured to capture a variety of different metrics as well as the section data • Starting in DB2 9.7 FP1 the section data captured by the activity event monitor has been enhanced to now include section actuals • Since the capture of individual activities is quite granular we offer a fair degree of flexibility allowing the following data capture options: • Capture data for all activities running in a particular WLM workload • Capture data for all activities running in a particular WLM service class • Capture data for activities that violate a particular WLM threshold • We can also enable the capture of activities run by a specific application using the WLM_SET_CONN_ENV procedure first introduced in DB2 9.7 FP2 • Our final example will demonstrate how to capture a statement of interest using the activity event monitor and then obtain the section actuals using the new explain capabilities in DB2 9.7 FP1

  48. Step I: Required Setup Steps Create the explain tables… create event monitor actEvmon for activities write to table activity ( table activity, in monitorTBS), activityvals ( table activityvals, in monitorTBS ), activitystmt ( table activitystmt, in monitorTBS ), control ( table control, in monitorTBS ) manualstart Execute ~/sqllib/misc/EXPLAIN.DDL Create the activity event monitor

  49. Step II: Capturing the Activity Data Enable the event monitor and setup to capture a statement on my connection set event monitor actEvmon state 1 call wlm_set_conn_env(null, '<collectactdata>WITH DETAILS, SECTION</collectactdata> <collectactpartition>ALL</collectactpartition> <collectsectionactuals>BASE</collectsectionactuals>') select t1.ident, sum(t1.data) as data, sum(t2.moredata) as moredata from t1,t2 where t1.ident=t2.ident group by t1.ident Execute the statement I’m interested in Disable collection and the event monitoring once I am done call wlm_set_conn_env(null, '<collectactdata>NONE</collectactdata> <collectsectionactuals>BASE</collectsectionactuals>') set event monitor actEvmon state=0

  50. Step II: Another approach Enable the event monitor on the default subclass, and collect details and section data set event monitor actEvmon state 1 update db cfg using section_actuals base alter service class sysdefaultsubclass under sysdefaultuserclass collect activity data on all database partitions with details,section ( Queries of interest run and are captured… ) Disable the event monitor once I am done alter service class sysdefaultsubclass under sysdefaultuserclass collect activity data none update db cfg using section_actuals none set event monitor actEvmon state 0

More Related