Understanding Application Hiccups
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1 Understanding Application Hiccups and what you can do about them An introduction to the Open Source jhiccup tool Gil Tene, CTO & co-founder, Azul Systems
2 About me: Gil Tene co-founder, Systems Have been working on think different GC approaches since 2002 Created Pauseless & C4 core GC algorithms (Tene, Wolf) A Long history building Virtual & Physical Machines, Operating Systems, Enterprise apps, etc... * working on real-world trash compaction issues, circa 2004
3 About Azul We make scalable Virtual Machines Vega Have built whatever it takes to get job done since generations of custom SMP Multi-core HW (Vega) Now Pure software for commodity x86 (Zing) C4 Industry firsts in Garbage collection, elastic memory, Java virtualization, memory scale
4 A classic look at response time behavior Key Assumption: Response time is a function of load Average? Max? Median? 90%? 99.9% source: IBM CICS server docuementation, understanding response times
5 Common fallacies Computers run application code continuously CPUs stop processing application code for all sorts of reasons e.g: Interrupts. Scheduling of other work, swapping, etc. Modern system architectures add more: Power management, Virtualization (cross-image context switching, physical VM motion), Garbage Collection, etc. Response time can be measured as work units/time. Response time exhibits a normal distribution Leading to attempts to represent with average + std. deviation
6 Response time over time When we measure behavior over time, we often see: Hiccups source: ZOHO QEngine White Paper: performance testing report analysis
7 Application Hiccups Where do they come from? Usually a factor of outside of the individual transaction work E.g: Queueing, accumulated work, platform inconsistency Do they matter? That depends. What are your end-user s expectations? Hiccups often dominate response time behavior How can/should we measure them? Average? Max? 99.9%? Mean with Std. deviation? Hiccup magnitude is often not a function of load
8 What happened here? Hiccups Source: Gil running an idle program and suspending it five times in the middle
9 Pitfall: Calulating % iles naïvely Common Example: build/buy simple load tester to measure throughput issue requests one by one at a certain rate measure and log response time for each request results log used to produce histograms, percentiles, etc. So what s wrong with that? works well only when all responses fit within in rate interval technique includes automatic backoff and coordination But requirements interested in random, uncoordinated requests Bad how bad can this get, really?
10 Example of naïve % ile System easily handles 100 requests/sec Responds to each in 1msec System Stalled for 100 Sec How would you characterize this system? Naïve results: 1msec 100 second 1 msec Elapsed Time Naïve characterization: 99.99% below 1 sec!!!
11 Proper measurement System easily handles 100 requests/sec System Stalled for 100 Sec Responds to each in 1msec 10,000 1 msec each 10,000 results Varying linearly from 100 sec to 10 msec Elapsed Time Proper characterization: 50% below 1 second
12 jhiccup A tool for capturing and displaying platform hiccups Records any observed non-continuity of the underlying platform plots results in simple, consistent format Simple, non-intrusive As simple as adding the word jhiccup to your java launch line % jhiccup java myflags myapp Adds a background thread that samples 1000/sec Open Source released to the public domain, creative commons CC0
13 what jhiccup measures jhiccup measures the platform, not the application Experiences whatever delays application threads would see Highlights inconsistency in platform execution of nothing No attempt to identify cause (e.g. scheduling, GC, swapping, etc.) An application cannot behave better than it s platform If the platform stalls, the application stalled as well. jhiccup provides a lower bound for best application behavior Useful control measurements Comparing jhiccup results for an idle application running on the same platform, at the same time, narrows down behavior to process-specific artifacts (-c option)
14 The anatomy of Hiccup Charts
15 Telco App Example Max Time per interval Optional SLA plotting Hiccups&by&Percen*le&Distribu*on& Hiccup duration at percentile levels 0%" 90%" 99%" 99.9%" & & 99.99%" %" %" Percen*le& Hiccups"by"Percen?le" SLA"
16 Max=967.68& Hiccups&by&Percen*le&Distribu*on& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le&
17 How jhiccup works Background thread, measures time to do nothing Sleeps for 1 milliseconds, allocates 1 object, records time gap Collects detailed floating point histogram of observed times Constant, small memory footprint, virtually no cpu load No effect on application threads Outputs two files A time-based log with 1 line per interval (default 5 sec interval). A percentile distribution log of accumulated histogram data Spreadsheet imports files and plots data A standard, simple chart format
18 Some fun with jhiccup
19 Examples
20 Idle App on Quiet System Idle App on Busy System 25" " 1 5" Hiccups&by&Percen*le&Distribu*on& Hiccups&by&Percen*le&Distribu*on& 25" " 1 5" Max=22.336& Max=49.728& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le&
21 Idle App on Quiet System Idle App on Dedicated System 25" 0.45" 2 15" 1 5" 0.4" 0.35" 0.3" 0.25" 0.2" 0.15" 0.1" 0.05" " 2 15" 1 5" Max=22.336& Hiccups&by&Percen*le&Distribu*on& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le& 0.45" 0.4" 0.35" 0.3" 0.25" 0.2" 0.15" 0.1" 0.05" Max=0.411& Hiccups&by&Percen*le&Distribu*on& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le&
22 A 1GB Java Cache under load Hiccups&by&Percen*le&Distribu*on& Max= & 0%" 90%" 99%" 99.9%" & & 99.99%" %" %" Percen*le&
23 Telco App Max= & Hiccups&by&Percen*le&Distribu*on& 0%" 90%" 99%" 99.9%" & & 99.99%" %" Percen*le&
24 Fun with jhiccup
25 Obviously, we can use it for comparison purposes
26 Zing 5, 1GB in an 8GB heap Oracle HotSpot CMS, 1GB in an 8GB heap 25" " 1 5" Hiccups&by&Percen*le&Distribu*on& Hiccups&by&Percen*le&Distribu*on& 25" " 1 5" Max=20.384& Max= & 0%" 90%" 99%" 99.9%" & & 99.99%" %" %" Percen*le& 0%" 90%" 99%" & 99.9%" & 99.99%" %" Percen*le&
27 Zing 5, 1GB in an 8GB heap Oracle HotSpot CMS, 1GB in an 8GB heap Hiccups&by&Percen*le&Distribu*on& Hiccups&by&Percen*le&Distribu*on& Max= & 0%" Max=20.384& 90%" 99%" 99.9%" & 99.99%" %" %" & Percen*le& 0%" 90%" 99%" & 99.9%" & 99.99%" %" Percen*le&
28 Oracle HotSpot CMS, 4GB in a 18GB heap Oracle HotSpot CMS, 1GB in an 8GB heap Hiccups&by&Percen*le&Distribu*on& Max= & 0%" 90%" 99%" & 99.9%" 99.99%" %" & Percen*le& Hiccups&by&Percen*le&Distribu*on& Max= & 0%" 90%" 99%" & 99.9%" & 99.99%" %" Percen*le&
29 Q & A
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