Beyond The Numbers Baron Schwartz
Who Am I? ● baron@percona.com ● @xaprb ● linkedin.com/in/xaprb ● xaprb.com/blog
Who Am I? ● Maatkit ● Percona T oolkit ● Innotop ● Monitoring Plugins ● Aspersa ● Online T ools ● JavaScript Libraries
● Consulting ● Percona Server ● Support ● Percona XtraBackup ● Remote DBA ● Percona XtraDB Cluster ● Engineering ● Percona T oolkit ● Conferences & ● Many More Training
Today's Agenda ● Benchmarks ● Aggregation and Distributions ● Performance, Capacity & Utilization ● Rules of Thumb ● Queueing Theory and Scalability
Benchmarks
What's Missing? ● Distribution ● Time Series ● Response Times ● Parameters ● Goals ● System Specs
What's Misleading? ● Logarithmic X-Axis ● Interpolation
What's Good? ● Y-Axis Reaches 0 ● No Fake-Smoothing
Behind a Single Dot
Look At All That Data...
What's With The Grid Lines?!?!?
Better Benchmarks What does an ideal benchmark report look like?
Clear Benchmark Goals ● Validating hardware configuration ● Comparing two systems ● Checking for regressions ● Capacity planning ● Reproducing bad behavior to solve it ● Stress-testing to find bottlenecks
Hardware and Software ● Specs for CPU, disk, memory, network ● Software versions (OS, SUT, benchmark) ● Filesystem, RAID controller ● Disk queue scheduler
Presenting Results ● Ideally, make raw results available ● Include metrics from OS (CPU, RAM, IO, network) ● Generate some plots to summarize ● This is where the rubber meets the road!
Better Aggregate Measures ● Average ● Percentiles ● 95th ● 99th ● Maximum ● Observation Duration ● Question: how bad can 95th percentile be?
More Aggregate Measures ● Median (50th Percentile) ● Standard Deviation ● Index of Dispersion
Better...
Better Still...
Keep It Coming...
Throughput AND Response Time
Performance ● What is Performance? ● T wo Metrics ● Response Time (time per task) ● Throughput (tasks per time) ● They're not reciprocals ● More on this later
What Performance Isn't ● CPU Usage ● Load Average ● Other metrics of resource consumption
Performance ● I often focus on response time ● It represents user experience ● Throughput indicates capacity rather than performance ● For benchmarking, throughput is primary
Utilization ● The portion of time during which the resource is busy ● i.e. there is at least one thing in progress
Utilization is Confusing ● Be very careful with tools that report utilization ● From the Linux iostat man page: ● “%util: Percentage of CPU time during which I/O requests were issued to the device (bandwidth utilization for the device). Device saturation occurs when this value is close to 100%.” ● Can you parse that? Is it true?
Capacity ● What is Capacity?
Capacity
Capacity – My Definition Capacity is the maximum throughput ... at achievable concurrency ... with acceptable performance ... as defined by response time ... meeting specified constraints ... over specified observation intervals.
Capacity Example ● What is capacity of the system at a concurrency of 32 with 10-second 95th- percentile response time not to exceed 2ms over a 60-minute duration? ● T o determine this, we need goal-seeking benchmark software ● Most benchmark software can't do this
Benchmarks, etc Recap ● Most benchmarks reveal very little ● Benchmark reports reveal even less ● It's good to go beyond the surface
Amdahl's Law ● “The speedup of a program using multiple processors in parallel computing is limited by the time needed for the sequential fraction of the program.” - Wikipedia ● It's basically a law of diminishing returns.
Should I Defragment My Disk? ● Method 1: Google “defragment” ● Method 2: Try it and see ● Method 3: Measure if the disk is a bottleneck
Spolsky -vs- Millsap
Spolsky -vs- Millsap
Amdahl's Law ● Don't try to optimize little things.
Little's Law ● N = XR ● That is, ● Concurrency = Throughput * Response Time ● This holds regardless of queueing, arrival rate distribution, response time distribution, etc.
Little's Law Example ● If disk IOs average 4ms... ● And there are 280 IOs per second... ● Then the disk's average concurrency is: ● N = 280 * .004 ● N = 1.12 ● Do you believe this? ● When might it not be true?
Little's Law Example #2 ● If disk utilization is 98% ● And there are 280 IOs per second ● What do we know?
Utilization Law ● U = SX ● Also independent of distributions, etc... ● That is, ● Utilization = Service Time * Throughput ● Utilization = 98% and Throughput = 280 ● S = U/X ● Service Time = .98 / 280 = .0035
Queueing Theory ● How can we predict the amount of queueing in a system? ● How can we predict its response times? ● How can we predict capacity?
Erlang Queueing ● Erlang's formulas model the probability of queueing for a given arrival rate, service time, and number of servers. ● A “server” is anything capable of serving a request. ● CPUs ● Disks
CPU -vs- Disk Queueing ● Scenario: 4-CPU, 4-disk (RAID0) server ● Thought experiment: ● How do processes queue for CPU? ● How do I/O requests queue on disks?
Notation ● T ypically see something like M/M/1 ● Each letter is a placeholder in A/S/n ● A = Arrival distribution ● S = Service-time distribution ● n = Number of servers ● A and S can be one of: ● Markov ● Deterministic ● General
CPUs -vs- Disks ● CPUs: M/M/4 ● Disks: 4 x {M/M/1}
M/M/1 Queueing cmg.org
M/M/n Queueing cmg.org
Erlang C Function ● M/M/n queueing is modeled by Erlang C ● See http://en.wikipedia.org/wiki/Erlang_(unit)
What's Wrong With Erlang C? ● You must validate your arrival times. ● You must validate your service times. ● The equation is hard to work with. ● In practice, it's hard to use Erlang C.
Scalability ● Queueing causes non-linear scaling. ● But first, let's talk about linearity.
System Scalability Throughput Why? Concurrency
Universal Scalability Law Linear Amdahl Throughput USL Concurrency
Amdahl Scalability
USL Scalability
USL Scalability Modeling
USL Performance Modeling
Scalability Limitations ● Locks ● Synchronization points ● Shared resources ● Duplicated data to be kept in sync ● Weakest-link problems
RAID10 On EBS ● Which is faster? ● RAID 10 over 10 EBS volumes ● RAID 10 over 20 EBS volumes ● Hint: http://goo.gl/Xm92Y ● Also, http://goo.gl/fAEIL
Debunking “Linear” ● Ask to see the actual numbers. ● They shouldn't be rounded off suspiciously. ● They must be truly linear. ● They must intersect the point (0, 0).
Debunking, Example #1
Is it Linear?
It's Not Linear
Resources ● Naomi Robbins' Blog ● http://blogs.forbes.com/naomirobbins/ ● Percona White Papers ● http://www.percona.com/ ● Neil J. Gunther ● Guerrilla Capacity Planning ● http://www.contextneeded.com/
Questions?
baron@percona.com @xaprb
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