Enabling Java in Latency Sensitive Applications by Gil Tene, CTO, Azul Systems

©2013 Azul Systems, Inc.

Enabling Java
in
Latency Sensitive
Applications
Gil Tene, CTO & co-Founder, Azul Systems


About me: Gil Tene
co-founder, CTO
@Azul Systems
Have been working on
a “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


About Azul
We make scalable Virtual
Machines
Have built “whatever it takes
to get job done” since 2002
3 generations of custom SMP
Multi-core HW (Vega)
Zing: Pure software for
commodity x86
Known for Low Latency,
Consistent execution, and
Large data set excellence
Vega
C4


High level agenda
Java in a low latency application world
The (historical) fundamental problems
What people have done to try to get around them
What if the fundamental problems were eliminated?
What 2013 looks like for Low latency Java developers
What’s next?


Java in the low latency world?


Why do people use Java for low latency apps?
Are they crazy?
No. There are good, easy to articulate reasons
Projected lifetime cost
Developer productivity
Time-to-product, Time-to-market, ...
Leverage, ecosystem, ability to hire
Java in the low latency world?


E.g. Customer answer to:
“Why do you use Java in Algo Trading?”
Strategies have a shelf life
We have to keep developing and deploying new ones
Only one out of N is actually productive
Proﬁtability therefore depends on ability to
successfully deploy new strategies, and on the cost
of doing so
Our developers seem to be able to produce 2x-3x as
much when using a Java environment as they would
with C/C++ ...


So what is the problem?
Is Java Slow?
No...
A good programmer will get roughly the same speed
from both Java and C++
A bad programmer won’t get you fast code on either
The 50%‘ile and 90%‘ile are typically excellent...
It’s those pesky occasional stutters and stammers
and stalls that are the problem...
Ever hear of Garbage Collection?


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Hiccup&Dura*on&(msec)&
&Elapsed&Time&(sec)&
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Max"per"Interval" 99%" 99.90%" 99.99%" Max"
25"
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Is “jitter” even the right word for this?
99%‘ile is ~60 usec
Max is ~30,000%
higher than “typical”


Stop-The-World Garbage Collection:
Java’s Achilles heel
Let’s ignore the bad multi-second pauses for now...
Low latency applications regularly experience “small”,
“minor” GC events that range in the 10s of msec
Frequency directly related to allocation rate
So we have great 50%, 90%. Maybe even 99%
But 99.9%, 99.99%, Max, all “suck”
So bad that it affects risk, proﬁtability, service
expectations, etc.

One way to deal with Stop-The-World GC

A common way to “deal” with STW-GC
Averages and Standard Deviation


Reality: Latency is usually
strongly “multi-modal”
Usually does’t look anything like a normal distribution
In software systems, usually sees periodic freezes
Complete shifts from one mode/behavior to another
Mode A: “good”.
Mode B: “Somewhat bad”
Mode C: “terrible”, ...
....

Another way to deal with STW-GC


Another way to cope: “Creative Language”
“Guarantee a worst case of 5 msec, 99% of the time”
“Mostly” Concurrent, “Mostly” Incremental
Translation: “Will at times exhibit long monolithic stop-
the-world pauses”
“Fairly Consistent”
Translation: “Will sometimes show results well outside
this range”
“Typical pauses in the tens of milliseconds”
Translation: “Some pauses are much longer than tens of
milliseconds”


What do actual low latency developers
do about it?
They use “Java” instead of Java
They write “in the Java syntax”
They avoid allocation as much as possible
E.g. They build their own object pools for everything
They write all the code they use (no 3rd party libs)
They train developers for their local discipline
In short: They revert to many of the practices that
hurt productivity. They loose out on much of Java.


What do low latency (Java) developers
with all this effort?
They still see pauses (usually ranging to tens of msec)
They do get fewer (as in less frequent) pauses
And they see fewer people able to do the job
And they have to write EVERYTHING themselves
And they get to debug malloc/free patterns again
And they can only use memory in certain ways
...
Some call it “fun”... Others “duct tape engineering”...


It is an industry-wide problem
It’s 2013... We now have Zing.
was
Stop-The-World GC mechanisms
contradict the fundamental
requirements of
low latency & low jitter apps


The common GC behavior across ALL
currently shipping (non-Zing) JVMs
ALL use a Monolithic Stop-the-world NewGen
“small” periodic pauses (small as in 10s of msec)
pauses more frequent with higher throughput or allocation rates
Development focus for ALL is on Oldgen collectors
Focus is on trying to address the many-second pause problem
Usually by sweeping it farther and farther the rug
“Mostly X” (e.g. “mostly concurrent”) hides the fact that they refer
only to the OldGen part of the collector
E.g. CMS, G1, Balanced.... all are OldGen-only efforts
ALL use a Fallback to Full Stop-the-world Collection
Used to recover when other mechanisms (inevitably) fail
Also hidden under the term “Mostly”...


A Recipe: address STW-GC head-on
At Azul, we decided to focus on the core problems
Scale & productivity limited by responsiveness/latency
And it’s not the “typical” latency, it’s the outliers...
Even “short” GC pauses must be considered a problem
Responsiveness must be unlinked from key metrics:
Transaction Rate, Concurrent users, Data set size, etc.
Heap size, Live Set size, Allocation rate, Mutation rate
Responsiveness must be continually sustainable
Can’t ignore “rare but periodic” events
Eliminate ALL Stop-The-World Fallbacks


The Zing “C4” Collector
Continuously Concurrent Compacting Collector
Concurrent, compacting old generation
Concurrent, compacting new generation
No stop-the-world fallback
Always compacts, and always does so concurrently


Benefits


An example of “First day’s run” behavior
E-Commerce application


An example of behavior after 4 days of system tuning
Low latency application


Measuring Theory in Practice
jHiccup:
A tool that measures and reports
(as your application is running)
if your JVM is actually running
all the time


Incontinuities in Java platform execution
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Discontinuities in Java platform execution - Easy To Measure
A telco
App with
a bit of a
“problem”
I call
these
“hiccups”


Fun with jHiccup


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Oracle HotSpot (pure newgen) Zing
Low latency trading application


Low latency - Drawn to scale
Oracle HotSpot (pure newgen) Zing
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Lets not forget about GC tuning


Java GC tuning is “hard”…
Examples of actual command line GC tuning parameters:
Java -Xmx12g -XX:MaxPermSize=64M -XX:PermSize=32M -XX:MaxNewSize=2g
-XX:NewSize=1g -XX:SurvivorRatio=128 -XX:+UseParNewGC
-XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=0
-XX:CMSInitiatingOccupancyFraction=60 -XX:+CMSParallelRemarkEnabled
-XX:+UseCMSInitiatingOccupancyOnly -XX:ParallelGCThreads=12
-XX:LargePageSizeInBytes=256m …
Java –Xms8g –Xmx8g –Xmn2g -XX:PermSize=64M -XX:MaxPermSize=256M
-XX:-OmitStackTraceInFastThrow -XX:SurvivorRatio=2 -XX:-UseAdaptiveSizePolicy
-XX:+UseConcMarkSweepGC -XX:+CMSConcurrentMTEnabled
-XX:+CMSParallelRemarkEnabled -XX:+CMSParallelSurvivorRemarkEnabled
-XX:CMSMaxAbortablePrecleanTime=10000 -XX:+UseCMSInitiatingOccupancyOnly
-XX:CMSInitiatingOccupancyFraction=63 -XX:+UseParNewGC –Xnoclassgc …

A
few
GC
tuning
ﬂags
Source:
Word
Cloud
created
by
Frank
Pavageau
in
his
Devoxx
FR
2012
presentaFon
Ftled
“Death
by
Pauses”


The complete guide to
Zing GC tuning
java -Xmx40g


GC is only the biggest problem...
So what’s next?


JVMs make many tradeoffs
often trading speed vs. outliers
Some speed techniques come at extreme outlier costs
E.g. (“regular”) biased locking
E.g. counted loops optimizations
Deoptimization
Lock deﬂation
Weak References, Soft References, Finalizers
Time To Safe Point (TTSP)


Time To Safepoint (TTSP)
Your new #1 enemy
(Once GC itself was taken care of)
Many things in a JVM (still) use a global safepoint
All threads brought to a halt, at a “safe to analuze”
point in code, and then released after work is done.
E.g. GC phase shifts, Deoptimization, Class unloading,
Thread Dumps, Lock Deﬂation, etc. etc.
A single thread with a long time-to-safepoint path can
cause an effective pause for all other threads
Many code paths in the JVM are long...


Time To Safepoint (TTSP)
the most common examples
Array copies and object clone()
Counted loops
Many other other variants in the runtime...
Measure, Measure, Measure...
Zing has a built-in TTSP proﬁler
At Azul, I walk around with a 0.5msec stick...


OS related stuff
(once GC and TTSP are taken care of)
OS related hiccups tend to dominate once GC and TTSP
are removed as issues.
Take scheduling pressure seriously (Duh?)
Hyper-threading (good? bad?)
Swapping (Duh!)
Power management
Transparent Huge Pages (THP).
...


Takeaway: In 2013, “Real” Java is finally
viable for low latency applications
GC is no longer a dominant issue, even for outliers
2-3msec worst observed case with “easy” tuning
< 1 msec worst observed case is very doable
No need to code in special ways any more
You can finally use “real” Java for everything
You can finally 3rd party libraries without worries
You can finally use as much memory as you want
You can finally use regular (good) programmers


One-liner Takeaway:
Zing: A cure for the Java hiccups


One-liner Takeaway:
Zing: A cure for the Java hiccups
Q & A
jHiccup:
http://www.azulsystems.com/dev_resources/jhiccup

Enabling Java in Latency Sensitive Applications by Gil Tene, CTO, Azul Systems

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