Download
wait time based oracle performance management n.
Skip this Video
Loading SlideShow in 5 Seconds..
Wait-Time Based Oracle Performance Management PowerPoint Presentation
Download Presentation
Wait-Time Based Oracle Performance Management

Wait-Time Based Oracle Performance Management

150 Views Download Presentation
Download Presentation

Wait-Time Based Oracle Performance Management

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Wait-Time Based Oracle Performance Management Prepared for UNYOUGPresented by Matt Larson CTO, Confio Software

  2. Who am I? • Founder and CTO of database performance software company • Former DBA consultant specializing in Oracle performance tuning • Co-author of three Oracle books (Oracle Development Unleashed, Oracle Unleashed 2nd Edition, Oracle8 Server Unleashed) • Co-author of two other database related books

  3. Agenda • Foundation • Case Study One: PL/SQL Issue • Case Study Two: Full Table Scans • Case Study Three: Inefficient Indexes • Case Study Four: Locking Problems • Q&A

  4. Working the Wrong Problems • After spending an agonizing week tuning Oracle buffers to minimize I/O operations, management typically rewards you with: • A. An all expense paid vacation • B. A free lunch • C. A stale donut • D. Reward? Nobody even noticed!

  5. Tuning Success (or lack thereof) • Your role in the rollout of a new customer facing application results in: • A. Keys to drive the CEO’s Porsche • B. Keys to use the executive restroom • C. A mop to use in the executive restroom • D. Your office has been moved to the restroom

  6. Assumption: If I make the database healthy, users benefit Symptoms DBA finds “big” problem and fixes it, users report no impact Lots of data to review and things to fix, not sure which to do first Unclear view of performance leads to Finger-pointing Conventional Tools Measure System Health… It’s your Code! It’s your Database! IT staff Developer or vendor

  7. …RMM Focuses on User Wait-Time SQL Request SQL Response • Identify each bottleneck affecting the user • Rank bottlenecks by user impact • Implement proven suggestions • Set correct expectations on impact of fix • Show proof the fix helped users

  8. RMM: Confio’s Underlying Methodology • Resource Mapping Methodology: Industry best-practice optimizing performance tuning for maximum business impact • Three Key Principles of RMM 1. SQL View: All statistics atSQL statement level 2. Time View: MeasureTime, not number of times a resource is utilized 3. Full View: Separately measureevery resourceto isolate source of problems

  9. Illustrating example: SQL View Principle • Example: ‘CEO’ measuring ‘employee’ output • Averaging over entire company gives no useful data • Must measure each job separately • DBA must manage database similarly • Measure and identify bottlenecks for each SQL independently

  10. Illustrating example: Time View Principle • Example: ‘CEO’ counting ‘tasks’ vs. ‘time to complete’ • Counting system statistics not meaningful • Must measure Time to complete • System stats (buffer size, hit ratios, I/O counts) do not identify where database customers are waiting • Identify and optimize Wait Time for each SQL as best indicator of performance

  11. Illustrating example: Full View Principle • Example: ‘CEO’ measuring results with blind spot hiding key processes • Without direct visibility, valuable info is lost • Must have visibility to every process step • Distinctly identify and measure each Oracle resource for each distinct SQL

  12. RMM-compliant Performance Tool Types Two Primary Types of Tools • Session Specific Tools • Tools that focus on one session at a time often by tracing the process • Examples: tkprof (Oracle), OraSRP Profiler (open source) • Continuous DB Wide Monitoring Tools • Tools that focus on all sessions by sampling Oracle • Example: Confio Ignite • Both tools have a place in the organization

  13. Tracing • Tracing with wait events complies with RMM • Should be used cautiously in non-batch environments due to session statistics skew • 80 out of 100 sessions have no locking contention issues • 20 out of 100 have spent 99% of time waiting for locked rows • If you trace one of the “80” sessions, it appears as if you have no locking issues (and spend time trying to tune other items that may not be important) • If you trace one of the “20” sessions, it appears as if you could fix the locking problems and reduce your wait time by 99%

  14. Tracing (cont) • Very precise statistics, may be only way to get certain statistics • Bind variable information is available • Different types of tracing available providing detail analysis even deeper than wait events • Ideal if a known problem is going to occur in the future (and known session) • Difficult to see trends over time • Primary audience is technical user

  15. Continuous DB Wide Monitoring Tools • 24/7 sampling provides real-time and historical perspective • Allows DBA to go back in time and retrieve information even if problem was not expected • Not the level of detail provided by tracing • Most of these tools have trend reports that allow communication with others outside of the group • What is starting to perform poorly? • What progress have we made while tuning?

  16. Case Study OnePL/SQL Issue

  17. Problem Observed • Critical situation: application performance unsatisfactory • Response time between 240 and 900 seconds • Most times users shutdown application • Very high network traffic (3x—4x normal), indicating time-outs and user refreshes • “CritSit” declared: major effort to resolve problem

  18. Wait Events During Problem library cache lock library cache pin

  19. Investigation

  20. What does RMM tell us? • Which SQL: CERN_PROFILE Truncate • Which Resource: library cache pin library cache lock • How much time: up to 16 Hours of wait time per hour

  21. Results • Found an invalid trigger • Insert statement was trying to fire trigger • Truncate was locked behind it • Response time improvement from 60,000 seconds (worst case) to 0 seconds • Configured alert to notify DBA when the problem starts next time • Problem should not occur for 22 hours without anyone knowing

  22. Case Study Two DB File Scattered Reads

  23. Problem Observed • Problem: Login taking 4 minutes for each user everyday they started their day • High wait accumulation from 6:30 – 8:30 am • 600 Users X 4 Minutes = 40 Hours Every Day • 40 Hours lost productivity every day • Applied RMM approach to problem identification • Identify Wait Time, offending SQL, offending Resource

  24. Wait Events During Problem

  25. Investigation

  26. What does RMM tell us? • Which SQL: LoginLookup UpdateInventory • Which Resource: Scattered Read Buffer Busy Waits • How much time: 40+ Hour Every Day

  27. Hypotheses: Oracle Interpretations Two Alternative paths for optimization: • Eliminate Full Table Scan • There isn’t a need to read the whole table, so we need to find the right shortcut • Improve response time • We need to read most or all of the table anyway, so let’s just figure out how to do it faster • Key Questions: • Is full table scan necessary? • What causes a full table scan for this SQL Statement?

  28. I. Unnecessary Full Table Scan? Solutions: • Add / Modify index(es) on the table • Update table and/or index statistics if proper index not being used • Add hint to use existing index • Optimize the application

  29. Full Table Scan is Needed Two alternative paths for optimization: I. Eliminate Full Table Scan • There isn’t a need to read the whole table, so we need to find the right shortcut II. Improve response time • We need to read most or all of the table anyway, so let’s just figure out how to do it faster

  30. II. Improve Response Time for Db File Scattered Reads Solutions: • Use Parallel Reads • Set Database Parameters • Improve I/O Speed • Optimize the application • Larger Database Caches (64-bit)

  31. 1. Use Parallel Reads = Faster FTS • Parallel Reads • Can be set at the table level (use with caution) Alter table customer parallel degree 4; • Normally used by hinting in the SQL Statement select /*+ FULL(customer) PARALLEL(customer, 4) */ customer_namefrom  customer; • A delicate tradeoff • sacrifice the performance of others for the running query. • Not necessarily efficient, just faster • Parallel Reads may actually do twice the work of a sequential query but have four workers, thus finishing in half the time while using 8x resource

  32. 2. Set database parameters • DB_FILE_MULTIBLOCK_READ_COUNT • specifies the maximum number of blocks read in one I/O operation during a sequential scan • Impacts the optimizer • Reduces number of I/Os required • For OLTP, typically between 4 to 16 • Optimizer will more likely to FTS if set too high • Ensure that the database read requests are synced up with the O/S. • This gets tricky if different block sizes are used in different tablespaces

  33. 3. Improve I/O speed • Get your SA involved • Investigate I/O sub-system • Iostat, vmstat, sar, … for potential problems • Monitor during high activity • Investigate contention at the disk/controller level.   • Learn which disks share common resources • Use more disks to spread I/O and reduce hot spots • Investigate caching on disk sub-system and current memory usage

  34. 4. Optimizing the Application • Review application – do you have access to code for changes? • Understand the code around the problem SQL • Techniques to Optimize a statement: • Reduce the number of calls for a SQL • Caching the data in the application • Creating a summary table (perhaps via a materialized view) • Eliminating the need for the data • Retrieve Less Data with each statement • Add fields to the WHERE clause • Combine SQLs for fewer calls • Combine several SQLs with different bind variables into one large statement that retrieves all the data in one shot

  35. 5. Larger Database Caches (64-bit) • Larger cache means fewer disk reads • May need large increase to have significant impact Performance Gain % of database in memory

  36. Results • Added indexes to underlying tables • Added Materialized View Full Table Scan Fixed

  37. Case Study Three DB File Sequential Reads

  38. Problem Observed • Data Warehouse loads were taking too long • Noticed high wait times on db file sequential read wait event • DBAs were confused – why are data load inserts “reading” data • Applied RMM approach to problem identification • Identify Wait Time, offending SQL, offending Resource

  39. Investigation Sequential read time SQL Sequential read time by object for SQL

  40. What does RMM tell us? • Which SQL: 3 Insert Statements • Which Resource: DB File Sequential Read • How much time: 5 hour+ 90% of wait time

  41. Investigating db file sequential reads • Often considered a “good” read • DB file sequential reads normally occur during index lookups • Often a single-block read although it may retrieve more than one block. • Sequential Read may also be seen for reads from: • datafile headers • rebuilding the control file • dumping datafile headers

  42. Hypotheses: Oracle Interpretations of Sequential Reads Causes of excessive wait times: • Reading too many index leaf blocks • Not finding block in buffer cache forces disk read • Slow disk reads • Contention for certain blocks • High Read time on INSERT statements

  43. I. Reading too many index and table blocks (cont) • Rebuild Fragmented Indexes • alter index rebuild [online]; • Compress Indexes • alter index rebuild compress; • Uses more CPU • Multi-column indexes • Avoid the table lookup • Will create a larger index • Pre-sort Table data

  44. II. Not finding block in buffer cache forces disk read • Db File sequential reads occur because the block is not in the buffer cache. • How do we make sure more blocks are already in the cache? • Solutions • Increase the size of the buffer cache(s) • Put the object in a cache where it is less likely to get flushed out

  45. III. Slow disk reads • With databases, it often comes down to this – the disk just needs to be faster • Put certain objects on the fastest disk • O/S file caching using special software that makes normal files perform like raw files • Increase Storage System Caching – such as an EMC cache

  46. Results • Inserts were updating indexes that had low cardinality leading columns • Reordered columns in the index and got a 50% performance improvement • Log file sync wait event was then the largest wait event • Data was being committed too often • Tuning is an iterative process

  47. Case Study Four Enqueue

  48. Problem Observed • Problem: High Wait on CPPFPROD • Accumulated wait 9.5 hours (34,000 sec) during 3.00-4.00am hour • End users were complaining loudly • Applied RMM approach to problem identification: • Identify Wait Time, offending SQL, offending Resource

  49. Investigation: Drill down to Top SQL & Identify likely source of Problem