GC free coding in @Java presented @Geecon

GC Free coding
Peter Lawrey
CEO, Principal Consultant
Higher Frequency Trading

Agenda
Who are we?
When to optimise.
What can be achieved in extreme cases.
Examples of object replacement.
Libraries designed to be ultra-low GC.

Who are we
Higher Frequency Trading is a small consulting
and software development house specialising in

Low latency, high throughput software

8 developers in Europe and USA.

Sponsor HFT related open source projects

Core Java engineering

Who am I?
Peter Lawrey
- CEO and Principal Consultant
- 3rd on Stackoverflow for Java,
most Java Performance answers.
- Founder of the Performance Java User's Group
- An Australian, based in the U.K.

What is our OSS
Key OpenHFT projects

Chronicle, low latency logging, event store and
IPC. (record / log everything)

HugeCollections, cross process embedded
persisted data stores. (only need the latest)
Millions of operations per second.
Micro-second latency.

Why do we optimise for memory
The level required depends on the application

Maximise throughput

Remove worst pauses (Full GC, tenured)

Remove small pauses

Optimise CPU cache utilisation

Maximise Throughput
Latency is not important, only the amount of time
it takes to perform a batch.
If you have a system which clean up do say 10
GB/s of garbage, it might be acceptable to
spend 20% of your time GC-ing
In which case 2 GB/s of garbage might
acceptable

Remove worst pauses
Stop the world, Full GCs and Tenure space
collections.
An allocation rate of around 500 MB/s

GC to below 5% of the CPU time

reduce pressure on your tenured space

Tuning the GC will do the rest

Remove small pauses
Reduce your garbage over a day to your Eden
size.
Say you have a 24 GB Eden space and an
allocation rate of 1 GB/hour, you can clean up
once per day.

Time scales every developer
should know.
Operation Latency In human terms
L1 Cache hit 1 ns A blink of an eye (~20 ms)
L2 Cache hit 3 ns Noticeable flicker
L3 Cache hit 10 – 20 ns Time to say “A”
Main memory 70 – 100 ns Time to say a ten word sentence
Signal down a 200m
fibre cable
1 μsec One slide (speaking quickly)
SSD access 5 – 25 μsec Time to reheat a meal (3 mins)
HDD access 8 msec Time to flight around the world. (1.8
days)
Network packet from
Germany to the USA
45 msec Waiting for a 7 working day delivery

Improve CPU cache utilisation
Your L1 cache is a small 32 KB of data, and L2
is 256 KB shared.
If you are producing 32 MB/s of garbage in a
thread, you are filling your L1 cache with
garbage every milli-seconds.
This makes it harder for the CPU to keep useful
stuff in the cache. It doesn't know what is
garbage.
Improving CPU Cache efficiency can speed up
an application by 2-5x.

Why use Java?
A rule of thumb is that 90% of the time is spent in
10% of the code.
Writing in Java will mean that 10% of your code
might mean optimising heavily.
Writing in C or C++ will mean that 100% of your
code will be harder to write, or you have to use
JNI, JNA, JNR-FFI.
Low level Java works well with natural Java.

When to optimise
Premature optimisation is the root of all evil
– Donald Knuth
Measure, Don't Guess
– Jack Shirazi & Kirk Pepperdine

When to optimise
You will need

Key portion of code working correctly.

A representative, reproducible test case.

An idea of the optimisation requirements

Throughput or average latency.

Latency profile at a given throughput.

Where to start
With representative and correct test case, I start
with a commercial profiler with

CPU Profiling enabled

Memory Profiling enabled.
With both on I look at the CPU profiling. Having
the memory profiling on, gives memory
allocation more weight.

An example: busy waiting for files
List<File> files = Arrays.asList(new
File("dir/a.txt"),
new File("dir/b.txt"), new
File("dir/c.txt"));
// waiting for all files to exists
boolean allFound;
do {
allFound = true;
for (File file : files) {
if (!file.exists()) {

Green Level Optimisation
List<File> files = Arrays.asList(new
File("dir/a.txt"),
new File("dir/b.txt"), new
File("dir/c.txt"));
// waiting for all files to exists
boolean allFound;
do {
allFound = true;
for (int i = 0; i < files.size();
i++) {

Red Level Optimisation
static class MyFile extends File {
String name;
public MyFile(String pathname) {
super(pathname);
}
@Override
public boolean exists() {
return super.exists();
}
@Override

Brown Level Optimisation
private GetBytes getBytesCache;
public byte[] getBytes(String
charsetName)
throws
UnsupportedEncodingException {
if (charsetName == null) throw new
NullPointerException();
GetBytes getBytes = getBytesCache;
if (getBytes != null &&
getBytes.charsetName.equals(charsetNam
e))

Common techniques to reduce
garbage
Use primitives instead of wrappers.
Double → double
BigDecimal → double or long
Use primitive collections
Trove4j
Guava collections and utilities

Thread local mutable data
You can recycle data structures yourself if it is
mutable.
Sharing mutable state between threads is hard.
Using mutable state which is local to a thread is
much simpler and can be very low GC.
A quick win can be reusing buffers like
ByteBuffer (and it can be off heap too)

SAX Parser instead of DOM
DOM is easier to work with, but requires reading
in the whole document and building a tree of
data.
SAX is harder to use, but you can build just the
data structure you need as you go. You can
populate a mutable structure for re-use.

Example, GC-free FIX parser
SAXophone is a library for SAX parsers

FIX parser

JSon parser

BSon parser

Yaml parser

SAX Parser interface
public interface BytesSaxParser {
/**
* reset any state
*/
void reset();
/**
* Parse as much of the Bytes as
possible.
*
* @param bytes
*/

FIX SAX Handler interface
public interface BytesSaxParser {
/**
* reset any state
*/
void reset();
/**
* Parse as much of the Bytes as
possible.
*
* @param bytes
*/

FIX Handler
final StringBuilder sender, target,
clOrdId, symbol;
double quantity, price;
int ordType;
@Override
public void onField(long fieldNumber,
Bytes value) {
switch ((int) fieldNumber) {
case 8: resetAll(); break;
case 35: assert value.readByte(
== 'D'; break;
case 49: value.parseUTF(sender,
StopCharTesters.ALL); break;

FIX Message to Decode
String s = "8=FIX.4.2|9=130|35=D|
34=659|49=BROKER04|56=REUTERS|
52=20070123-19:09:43|38=1000|59=1|
100=N|40=1|11=ORD10001|
60=20070123-19:01:17|55=HPQ|54=1|
21=2|10=004|";
NativeBytes nb = new
DirectStore(s.length()).bytes();
nb.append(s.replace('|', 'u0001'));
nb.flip();

Test harness
final AtomicInteger count = new
AtomicInteger();
FixSaxParser parser = new
FixSaxParser(new
MyFixHandler(count));
int runs = 200000;
for (int t = 0; t < 5; t++) {
count.set(0);
long start = System.nanoTime();
for (int i = 0; i < runs; i++) {
nb.position(0);

Performance
Run with -verbose:gc -Xmx32m
Average parse time was 0.96 us,
fields per message 17.00

Q & A
https://github.com/OpenHFT/OpenHFT
@PeterLawrey
peter.lawrey@higherfrequencytrading.com

What is HFT?

No standard definition.

Trading faster than a human can see.

Being fast can make the difference between
making and losing money.

For different systems this means typical
latencies of between
− 10 micro-seconds and
− 10 milli-second.
(Latencies external to the provider)

Event driven processing
Trading system use event driven processing to
minimise latency in a system.

Any data needed should already be loaded in
memory, not go off to a slow SQL database.

Each input event triggers a response, unless
there is a need to limit the output.

Critical Path
A trading system is designed around the critical
path. This has to be as short in terms of
latency as possible.

Critical path has a tight latency budget which
excludes many traditional databases.

Even the number of network hops can be
minimised.

Non critical path can use tradition databases

Critical Path databases

Time Series databases
− Kdb, kona
− InfluxDB
− OpenTSDB
Designed for millions of writes per second.
Column based database => 100 Million
operations per second e.g. sum a column.

Critical Path data store
HFT strategies are;

described using graphs.

handle events in real time ~10 – 100 μsec.

cache state rather than query a database.

all custom written libraries AFAIK.

Critical Path data store
Logging is performed by appending to memory
mapped files.
OpenHFT's Java Chronicle makes this easier to
do in Java in a GC-free, off heap, lock less
way.
Such low level coding is relatively easy in C or
C++.

Non-critical Datastore
Configuration management

ZooKeeper, etcd

Plain files with Version control

LDAP

Any distributed key-value store. e.g. MongoDB

Big Data
Back testing a HFT system is critical and a
number of solutions are available

Hadoop

Matlab

Time series

R

Operational Infrastructure
Control and management infrastructure

JMS, JMX

Tibco RV, LBM

Terracotta

MongoDB

Reliable persistence
Trades and Orders are high value data and less
voluminous than Market data or strategy
results.

Typically SQL Database.

Sometimes multiple databases for different
applications.

Why use more exotic database?

Mostly for high throughput.
− Million per second in one node.

Often for low latency.
− Latencies well below a milli-second.

Why wouldn't you use exotic DB

Not easy to learn, high knowledge investment.
(!R)@&{&/x!/:2_!x}'!R

Often harder to use.
− Less management tools.
− Not designed to work with web applications.

More sensitive to the details of the hardware
and what else is running on the same
machine.

Low latency at high throughput
Java Chronicle is designed as a low latency
logger and IPC.
At one million small messages per second

Almost zero garbage

Latency between processes around 1 micro-
second

Concurrent readers and writers
Supports bursts of 10 million messages/sec.

Chronicle and replication
Replication is point to point (TCP)
Server A records an event
– replicates to Server B
Server B reads local copy
– B processes the event
Server B stores the result.
– replicates to Server A
Server A replies.
Round trip
25 micro-seconds
99% of the time
GC-free
Lock less
Off heap
Unbounded

HugeCollections
HugeCollections provides key-value storage.

Persisted (by the OS)

Embedded in multiple processes

Concurrent reads and writes

Off heap accessible without serialization.

HugeCollections and throughput
SharedHashMap tested on a machine with 128
GB, 16 cores, 32 threads.
String keys, 64-bit long values.

10 million key-values updated at 37 M/s

500 million key-values updated at 23 M/s

On tmpfs, 2.5 billion key-values at 26 M/s

HugeCollections and latency
For a Map of small key-values (both 64-bit longs)
With an update rate of 1 M/s, one thread.
Percentile 100K
entries
1 M entries 10 M entries
50% (typical) 0.1 μsec 0.2 μsec 0.2 μsec
90% (worst 1 in 10) 0.4 μsec 0.5 μsec 0.5 μsec
99% (worst 1 in 100) 4.4 μsec 5.5 μsec 7 μsec
99.9% 9 μsec 10 μsec 10 μsec
99.99% 10 μsec 12 μsec 13 μsec
worst 24 μsec 29 μsec 26 μsec

Bonus topic: Units
A peak times an application writes 49 “mb/s” to a
disk which supports 50 “mb/s” and is replicated
over a 100 “mb/s” network.
What units were probably intended and where
would you expect buffering if any?

Bonus topic: Units
A peak times an application writes 49 MiB/s to a
disk which supports 50 MB/s and is replicated
over a 100 Mb/s network.
MiB = 1024^2 bytes
MB = 1000^2 bytes
Mb = 125,000 bytes
The 49 MiB/s is the highest rate and 100 Mb/s is
the lowest.

Bonus topic: Units
Unit bandwidth Used for
mb - miili-bit mb/s – milli-bits per second ?
mB - milli-byte mB/s – milli-bytes per second ?
kb – kilo-bit (1000) kb/s – kilo-bits (baud) per second Dial up bandwidth
kB – kilo-byte (1000) kB/s – kilo-bytes per second ?
Mb – mega-bit (1000^2) Mb/s – mega-bits (baud) per second Cat 5 ethernet
MB - mega-byte (1000^2) MB/s – mega bytes per second Disk bandwidth
Mib – mibi-bit (1024^2) Mib – Mibi-bits per second ?
MiB – mibi-byte (1024^2) MiB – Mibi-bytes per second Memory bandwidth
Gb – giga-bit (1000^3) Gb/s – giga-bit (baud) per second High speed networks
GB – giga-byte (1000^3) GB/s – giga-byte per second -
Gib – gibi-bit (1024^3) Gib/s – gibi-bit per second -
GiB – gibi-byte (1024^3) GiB/s – gibi-byte per second. Memory Bandwidth

GC free coding in @Java presented @Geecon

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