Thursday, June 15, 2023

More Bang for your Buck in the Cloud with Resource Manager

Much of the cost in database IT is tied to the number of CPUs.  Oracle database licencing is priced per CPU.  The dominant factor in determining your cloud subscription cost is also CPU, although, disk, memory, and network can also be a cost factor. 

That incentivises you to minimise your CPU.  I believe it is inevitable that cloud systems will be configured with fewer CPUs and it will become more common to see them running either close to or beyond the point of having 0% idle CPU.  In fact, I'll go further:  

In the cloud, if your system is not constrained by CPU, at least some of the time, you are probably spending too much money on renting too many CPUs.

What happens to an Oracle database when it runs out of CPU?

The resource manager has been part of the Oracle database since 8i, but in my experience, it is rarely used.

Every process has to demand CPU and if necessary wait on the CPU run queue.  If you don't have a resource manager plan, then all the Oracle processes will have equal priority on that queue.  The resource manager will not intervene.  

However, not all processes are created equal.  Instead, the users of an application will consider some things more important or urgent than others.  Some processes are on a critical path to delivering something by a deadline, while others can wait.  That implies a hierarchy of priority.  A resource manager plan allocates CPU to higher priority processes over low priority within the constraint of a minimum guaranteed CPU allocation and can restrict the degree of parallelism.  

Note that "By default, all predefined maintenance windows use the resource plan DEFAULT_MAINTENANCE_PLAN".  When you introduce your own resource manager plan you don't need to alter the predefined windows.

A resource manager plan that reflects the business priorities can enable a system to meet its objectives with fewer resources, particularly CPU resources.  In a cloud system, using fewer resources, particularly CPU resources, will tend to save money on cloud subscription costs.

A User Story

Let me tell you about a PeopleSoft Financials system at an insurance company.  Like all insurance companies, they like to slice and dice their General Ledger in lots of different ways and produce lots of reports every night.

Data flows through the system from GL transaction processing via summary ledgers on which materialized views are built and then reports are run

Transactions -> Post to Ledger -> Summary Ledgers -> Materialised Views -> Reports

A fundamentally important thing this company did was to provide a quantitative definition of acceptable performance.  

  • "GL reports must be finished by the time continental Europe starts work at 8am CET / 2am EST"
  • "Without making the system unavailable to Asia/Pac users" 
  • "At night (in the US), some other things can wait, but need to be available at the start of the US working day."

They were running on a two-node RAC database on an engineered system, on-premises.  When the overnight GL batch was designed and configured on the old hardware, parallelism was increased until it consumed the entire box.

The system has now moved to an Exadata cloud-at-customer machine.  It is still a two-node RAC cluster.  We have a choice of up to 10 OCPUs (20 virtual CPUs) per node.  During testing, we progressively reduced the CPU count until we could only just meet that target.  Every time we reduce the CPU by 1 OCPU on each of the two nodes, we reduced the cost of the cloud subscription by approximately US$2000/month.

Implicit in that statement of adequate performance is also a statement of what is important to the business.  We started to create a hierarchy of processes.  

  • If the business is waiting on the output of a process then that is a high-priority process that is guaranteed a high proportion of available CPU. 
  • If a process is finished before the business needs it then it has a lower priority.  For example, a set of processes was building reporting tables that were not needed until the start of the US working day, so their start time was pushed back, and they were put in a lower prior consumer group that also restricted their degree of parallelism. 

Sometimes, it can be hard to determine whether the users are waiting and whether the performance is adequate, but usually, they will tell you!  However, with an overnight batch process, it is straightforward.  If it is outside office hours, then the users aren't waiting for it, but it needs to be there when they come into the office in the morning.

Like so many physical things in life, nearly everything that happens in computing involves putting a task on a queue and waiting for it to come back.  Most computer systems are chains of inbound and outbound queues.  On the way other requests for resources may be invoked that also have to be queued.  Ultimately, every system is bound by its resources.  On a computer that is CPU, memory, disk, and network.  A critical process whose performance is degraded, because it is not getting enough of the right kind of resource, becomes a bottleneck.

"Time lost at a bottleneck is lost across the system." 

One of my favourite books on Oracle performance is Optimizing Oracle Performance by Cary Millsap & Jeff Holt.  It introduced me to another book, The Goal by Eli Goldratt and Jeff Cox.  Its central theme is the nature of bottlenecks, otherwise called constraints.   "A bottleneck is any resource whose capacity is equal to or less than the demand placed upon it."

It is all about performance, without being anything to do with computers.  It is a Socratic case study of how to implement the 5-step strategy dubbed "The Theory of Constraints" to improve the performance of a system.  The five steps are set out plainly and then again in another book by Goldratt "What is This Thing Called Theory of Constraints and How Should It Be Implemented?"

  • IDENTIFY the system's constraint(s).
  • Decide how to EXPLOIT the system's constraint(s).
  • SUBORDINATE everything else to the above decision.
  • ELEVATE the system's constraint(s).
  • WARNING!!!! If in the previous steps, a constraint has been broken, go back to step 1, but do not allow INERTIA to cause a system's constraint.
  • In the factory in The Goal, the goal is to increase throughput while simultaneously reducing inventory and operating expense.

    In the cloud, the goal is to increase system throughput while simultaneously reducing response time and the cost of resources.

    The Resource Manager Plan

    The hierarchy of processes then determines who should get access to the CPU in preference to whom.  It translates into a database resource manager plan.  This is the 4th of Goldratt's 5 steps.  The higher priority processes are on the critical processing path get precedence for CPU so that they can make process.  The lower-priority processes may have to wait for CPU so they don't impede higher-priority processes (this is the 3rd step).

    The resource plan also manages the degree of parallelism that can be used within each consumer group, so that we don't run out of parallel query servers.  Higher-priority processes may not have a high PQ limit because there are more processes that run concurrently.  Processes are mostly allocated to consumer groups through mappings of module, action, and program name, some are mapped explicitly using triggers.

    Over the years, the resource manager plan for this particular system has gone through three main design iterations.  The 4 lowest-priority consumer groups were added to restrict the consumption of these groups when the higher groups were active.

    Priority1st Iteration2nd Iteration3rd IterationDescription of Consumer Group
    1PSFT_GROUPGeneral group for PeopleSoft application and batch processes.
    PQ limit = ½ of CPU_COUNT (3rd iteration)
    2HIGH_GROUPFor weekly stats collection process of 2 multi-billion row tables (LEDGER and JRNL_LN).
    PQ limit = 2x CPU_COUNT
    3SUML_GROUPProcess that refresh summary ledger tables, and MVs on summary ledgers.
    PQ limit = ¾ of CPU_COUNT
    nVision General Ledger reporting processes.
    PQ limit ≈ 3/8 of CPU_COUNT
    5GLXX_GROUPProcesses that build GLXX reporting tables, and do some reporting. Parallelism disabled. Run concurrently with nVision, but more important to complete GL reporting.
    PQ limit = 1. No parallelism
    Other queries submitted via PeopleSoft ad-hoc query tool and ad-hoc nVision
    PQ limit = 3 - 4
    Essbase processes.
    PQ limit = 2 - 4
    Other Processes.
    Also deals with an Oracle bug that causes AQ$_PLSQL_NTFN% jobs to run continuously consuming CPU.
    Actual/Estimated Time Limit

    This approach has certainly prevented the processes in GLXX_GROUP, ad-hoc queries in the PSQUERY_GROUP, and other processes in the LOW_GROUP from taking CPU away from critical processes in PSFT_GROUP, NVISION_GROUP and SUML_GROUP.  We also adjusted the configuration of the application to reduce the number of processes that can run concurrently.

    What if we decide to change the number of CPUs 

    When this system ran on an on-premises machine we had a single resource plan because the number of CPUs was fixed.  

    Now it has moved to the cloud, we can choose how many CPUs to pay for.  Performance was tested with various configurations.  Consequently, we have created several different resource plans for different numbers of CPUs with different PQ limits.  When we change the number of CPUs we just specify the corresponding resource manager plan.  

    Some other database parameters have been set to lower non-default values to restrict overall SQL parallelism and the number of concurrent processes on the database job scheduler. These are also changed in line with the number of CPUs.

    alter system set RESOURCE_MANAGER_PLAN=PSFT_PLAN_CPU8 scope=both sid='*';
    alter system set JOB_QUEUE_PROCESSES=8 scope=both sid='*';
    alter system set PARALLEL_MAX_SERVERS=40 scope=both sid='*';
    alter system set PARALLEL_SERVERS_TARGET=40 scope=both sid='*';

    It is possible that in the future we might automate changing the number of CPUs by schedule.  It is then easy to switch resource manager plans by simply setting an initialisation parameter.  

    At the moment, we have one plan in force at all times.  It is also possible to change plans on a schedule using scheduler windows, and you can still intervene manually by opening a window.

    TL;DR In the Cloud, Performance is Instrumented as Cost

    You can have as much CPU and performance as you are willing to pay for.

    By configuring the resource manager to prioritise CPU allocation to high-priority processes, ones for which users are waiting, over lower-priority ones, a system can achieve its performance objectives while consuming fewer resources.

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