Parallelism in sql server


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In this session we will discuss about the parallelism in SQL Server. We will talk about configuration parameters, parallel execution plans, parallel operators and more. We also will talk about problems and best practices

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  • Thereis a lot of topicsonthis área and i tryedto concéntrate some of themostimportantparts in anhoursession
  • A quickexample:If i have 200 differentcoins and Iwanttogethowmuchmoney Ihave, i can addonebyone , I can give 100 coinstomypartnertoget a partialresult, orforexample Split mycoinsbetween 10 partnerstoget 10 partialresults and thenobtainhowmuchmoney I have … aftersome “specialfee” youknowThe real time expended gettingtheresultswillnot be thesame and obviouslythemuchpartners i use togetpartialresults, the more quicklyi´llgettheresult….butthisisnotalways true.
  • Typicalbennefit: the more CPU, the more performance…butitsthat true?It´stipicallon REBUILDING indexes, aggregations, tablescans,…CHART, GRAPHIC
  • The server iscomposedonmultiple NUMA nodes 2-4 typicallyonthe standard configurationsEach NUMA node has itsown CPU and memoryThe server seesthe sum of CPU and memory and all are accesible from SQL Server
  • The images show the detection of three-node NUMA hardware by SQL Server and the three lazy writer threads (one per each NUMA node).SQL Server is able to get the best performance in NUMA hardware by doing some special automatic configurations, such as having special threads for some internal components in each NUMA node. Note: Mention that as a common rule, you must configure the MAXDOP value lower than the number of physical cores per each NUMA node. With this configuration, if a query is executed in parallel, all the threads will be in the same node.
  • the SQLOS is a thin user-mode layer that sits between SQL Server and Windows. It is usedfor low-level operations such as scheduling, I/O completion, memory management, and resourceManagementWhen an execution request is made within a session, SQL Server divides the work into one or moretasks and then associates a worker thread to each task for its duration. Runs in user modeReduces context switchingBetter resource usageMultiprocessing is enhancedA task uses the same scheduler most of the timeMultiple tasks can be executed at the same timeData locality is enforcedBetter scalability on NUMA hardwareSQLOS works the same in each OS host (w2k3, w2k8r2, w2k12, etc.)
  • why would i do that?
  • This configuration is mainly for:Systems with more than one SQL Server instanceSystems with more than 32 heavily used CPUs on which you detected specific I/O congestion problemsWhen you don't use IO affinity the SQL Server worker handles (posts) the IO and takes care of the IO completion on the scheduler the worker was assigned to.The SQL Server GUI on SQL Server 2012 don´tletyoumakemistakesQUESTION: Whyiswrongtheconf “Bad”?REASON: By setting both at the same scheduler they will compete for resources, that is just what you want to avoid.
  • ENHANCE DATA LOCALITYOnlargesystems, bydoingthiskind of affinity, you can obtain a performance gain of 20%. QUESTION: Why?ANSWER: Becausestatistically, when a scheduler “touches” a datapage, the page isstored at NUMA memory X. if a schedulercommingfromanother NUMA nodeneedstoreadthatspecific page, ittakes 3-4 times the time togetthat page fromoutsideits NUMA node. So bydoingthis, we can forcé specificaplicationstoworkwithspecific NUMA nodes and doingthis, toincreasethepossibilitytoread-write data pagesonthesame NUMA nodes.
  • 26’
  • Degree of parallelism (DOP) is assigned at each parallel step of the execution planAll CPUs can be used by the schedulers, so threads can use all available CPUsNo special consideration for hyperthreaded CPUsBy limiting DOP, you can limit the number of available threads to solve a query DOP is determined when execution plan is retrieved from the plan cache
  • 28
  • This operator takes a single input stream of records and produces multiple output streams. The record contents and format are not changed. Each record from the input stream appears in one of the output streams. This operator automatically preserves the relative order of the input records in the output streams. Usually, hashing is used to decide to which output stream a particular input record belongs.
  • operator consumes multiple streams and produces multiple streams of records. The record contents and format are not changed. If the query optimizer uses a bitmap filter, the number of rows in the output stream is reduced. Each record from an input stream is placed into one output stream. If this operator is order preserving, all input streams must be ordered and merged into several ordered output streams. El cálculo si se mira el plan de eejcución en detalle, viene dado por una expresión que se puede obtener ya en el hash match. En el momento del gather se obtiene el valor 6.En la demo 2-exchange_operators.sql se puede ver con detenimiento
  • consumes several input streams and produces a single output stream of records by combining the input streamsPARALLEL PAGE SUPLIER to divide rowsacrossthreads in batches
  • 45
  • There are someenemies of theparalleliam and those are some of them
  • Paralleloperationsmust be synchronizedbeforeserializyng. So ifsomeworkerendsitsexecution and someotherisstillexecuting, he throws a CXPACKET signalto SQL Server announcingthat he iswaiting and finisheditsexecution. CXPACKET isnot a problemitselfbutitsanindicator of badparallel SQL Server configurationifweseelots of waitsignals of thistype
  • 50
  • Here are typical scenarios involving CXPACKET wait statistics.Note: It is very unusual to have a pure OLTP system because most customers uses their SQL Server instances for applications, reports, BI data loading solutions, and more.In the example at the bottom, note that 9 days of CPU time is wasted by CXPACKET (786556034 ms = 13109 minutes = 218 hours = 9 days) in threading synchronization due to a bad configuration. (This is a real example from one of the SolidQ’s customers.)Important: It is very important that the students really understand the degree of parallelism setting. It is very common for students to confuse MAXDOP with CPU AFFINITY. Furthermore, make sure that students understand what is a pure OLTP system.
  • Parallelism in sql server

    1. 1. Parallelism in SQL ServerEnrique Catala BañulsMentor, SolidQecatala@solidq.comTwitter: @enriquecatala
    2. 2. Enrique Catala Bañuls Computer engineer Mentor at SolidQ in the relational engine team Microsoft Technical Ranger Microsoft Active Professional since 2010 Microsoft Certified Trainer
    3. 3. Our Sponsors:
    4. 4. Volunteers: They spend their FREE time to give you this event. (2 months per person) Because they are crazy. Because they want YOU to learn from the BEST IN THE WORLD. If you see a guy with “STAFF” on their back – buy them a beer, they deserve it.
    5. 5. Paulo Matos:
    6. 6. Paulo Borges:
    7. 7. João Fialho:
    8. 8. Bruno Basto:
    9. 9. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices9 | 3/20/2013 |
    10. 10. Parallelism “Parallelism is the action of executing a single task across several CPUs” It enhances performance taking advance of newest HW configurations
    11. 11. Parallelism benefits SQL Server uses all CPU by default Generally the queries that qualify for parallelism are high IO queries
    12. 12. SMP Symmetric multiprocessing (SMP) system All the CPUs share the same main memory No hardware partitioning for memory access Typically used in smaller computers SMP architecture CPU CPU CPU CPU CPU CPU CPU CPU System bus CPU CPU F Main S Memory Memory B CPU CPU
    13. 13. NUMA Non-Uniform Memory Access Nodes connected by shared bus, cross-bar, ring Typically used in high-end computers CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU CPU Memory Memory Memory Memory Controller Controller Controller Controller Node Controller Node Controller Node Controller Node Controller Shared Bus
    14. 14. NUMA SQL Server is NUMA aware  Automatically detects NUMA configuration Minimizes the memory latency by using local memory in each node SQL Server must be properly configured to gain the best performance in NUMA systems
    15. 15. SQL Server Execution Model SQLOS  SQLOS creates a scheduler forMemory Node each logical CPU  A scheduler is like a logicalCPU Node CPU used by SQL Server Scheduler workers  Only one worker can be executed Worker by a scheduler at the same time  The unit of work for a worker is a Task task
    16. 16. Schedulers and concurrency Pre-emptive scheduler (Windows)  Windows uses pre-emptive scheduling because of its general operating system nature  It uses a priority-driven architecture  Each thread executes in a predetermined time slice  A thread can be preempted by a higher priority thread Cooperative scheduler (SQL Server)  Each task puts itself in the waiting list every time it needs a resource  The same scheduler executes until the end  This voluntary yielding by workers prevents context switching and improves performance
    17. 17. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices17 | 3/20/2013 |
    18. 18. Settings to adjust parallelism Hardware level  NUMA Instance level  Soft-NUMA (affinity mask)  Degree of parallelism  Cost threshold for parallelism  Max worker threads  -P parameter Connection level  Resource Governor by configuring MAXDOP Query level  MAXDOP clause  T-SQL patterns  CROSS APPLY  Functions…
    19. 19. CPU Affinity Mask• Used to set which processor(s) can be used by the SQL Server instance.• Setting a processor affinity will tie the threads to a particular processor
    20. 20. Affinity I/O Mask Used to affinitize the CPU usage to I/O operations Each I/O operation needs to be finalized  Byte checksum, number of transferred bytes, page number okay, etc.  CPU consumption Can be used to specify the lazy writer (in a new hidden scheduler) Bad Good
    21. 21. Network affinity 8000 8001 8002 8003
    22. 22. Threshold for parallelism Instance level configuration Change statistically the parallel execution  Changes the boundaries of when a serial plan should be changed to parallel plan if(best_plan_for_now.cost<1) return(best_plan_for_now) else if(MAXDOP>0 and best_plan.cost > threshold for parallelism) return(MIN(create_paralel_plan().cost, best_plan_for_now))
    23. 23. Demonstration 1 Affinity mask, cost threshold for parallelism
    24. 24. Degree of parallelism (DOP) Max degree of parallelism o Instance setting that affects the whole instance o Can be configured at resource governor´s workload level o Enforces the maximum number of CPUs that a single worker can use MAXDOP hint o Can be used at query level
    25. 25. Demonstration 2 MAXDOP
    26. 26. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices26 | 3/20/2013 |
    27. 27. Exchange operators Operators dedicated to moving rows between one or more workers, distributing individual rows among them
    28. 28. Distribute streams operator Row distribution based on  Hash  Each row computed a hash and each thread Works only with the rows that have the same hash  Round-robin  Each row is sent to the following thread of a round-robin  Broadcast  All rows are sent to all threads  Range  Each row is sent to a thread based on a range computation over a column  Rare and used in some parallel index creation operations  Demand  Pull mode  It SENDS the row to the operator is calling  It appears on partitioned tables
    29. 29. Repartition streams operator Takes rows from multiple sources and send rows to multiple destinations Doesn´t update any row
    30. 30. Gather streams operator It takes rows from multiple sources and send to a single destination (thread) Tipically increases CXPACKETS
    31. 31. Demonstration 3 OPERATORS
    32. 32. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices32 | 3/20/2013 |
    33. 33. Enemies of the parallelism Makes the whole plan serial  Modifying the contents of a table variable (reading is fine)  Any T-SQL scalar function  CLR scalar functions marked as performing data access (normal ones are fine)  Random intrinsic functions including OBJECT_NAME, ENCYPTBYCERT, and IDENT_CURRENT  System table access (e.g. sys.tables) Serial zones  TOP  Sequence project (e.g. ROW_NUMBER, RANK)  Multi-statement T-SQL table-valued functions  Backward range scans (forward is fine)  Global scalar aggregates  Common sub-expression spools  Recursive CTEs
    34. 34. Demonstration 4 ENEMIES OF THE PARALLELISM
    35. 35. CXPACKETSerial Parallel Serial
    36. 36. Demonstration 5 CXPACKET
    37. 37. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices37 | 3/20/2013 |
    38. 38. Best practices Never trust the default configuration for the degree of parallelism  By default, MAXDOP = 0 As a general rule  Pure OLTP should use MAXDOP = 1  MAXDOP not to exceed the number of physical cores  If NUMA architecture, MAXDOP <= #physical_cores_numa_node wait type name wait time (ms) requests CXPACKET 786556034 128110444 LATCH_EX 255701441 155553913 ASYNC_NETWORK_IO 129888217 19083082 PAGEIOLATCH_SH 83672746 2813207 WRITELOG 70634742 48398646 SOS_SCHEDULER_YIELD 47697175 176871743
    39. 39. Best practices When to apply MAXDOP?  ALTER INDEX operations  Typically set MAXDOP = #_physical_cores When to set max degree of parallelism?  When you see high CXPACKET waits  OLTP pure systems should set its value to 1 When to set cost threshold for parallelism?  When you want to change the number of parallel operations statistically
    40. 40. Objectives of this session  Basics on parallelism  Settings to adjust parallelism  Exchange operators  Enemies of the parallelism  Best practices41 | 3/20/2013 |
    41. 41. Thank you!
    42. 42. Parallelism in SQL ServerEnrique Catala BañulsMentor, SolidQecatala@solidq.comTwitter: @enriquecatala