Discusses the processes and measurement procedures to identify the factors that affect streaming performance

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SPRINGONE2GX
WASHINGTON, DC
High Performance Stream
Processing
By Stephane Maldini, Glenn Renfro, David Turanski
@smaldini, @cppwfs, @dturanski

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Performance as it pertains to:
Message flow
Serialization
Processing
2

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Message Flow: The Myth?
3
“1 million events a second?”

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Message Flow: The Myth?
4
“It Depends”
https://spring.io/blog/2015/06/17/spring-xd-benchmarks-part-1

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Message Flow: The Myth?
5

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Hardware
• Check Network speed
• iperf
• Check Disk Read Write speed
• dd
• Processor Speed (specs)
6

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Hardware: Network
7
1 Gb Ethernet
Msg Size Msgs/Sec
100 1,250,000
1,000 125,000
10,000 12,500
100,000 1,250
10 Gb Ethernet
Msg Size Msgs/Sec
100 12,500,000
1,000 1,250,000
10,000 125,000
100,000 12,500

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Hardware: Disk
• dd bs=1M count=256 if=/dev/zero of=test
• will just commit your 128 MB of data into a RAM buffer
• initially fast but server is still writing to disk after test
• dd bs=1M count=256 if=/dev/zero of=/tmp/testfile
conv=fdatasync
• This tells dd to require a complete “sync” once, right before it
exits.
• Ensures all data is on the disk before calculating result
8
https://romanrm.net/dd-benchmark

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Message Size
9

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Message Size
10

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Transports
• Rabbit
• perftest
• https://www.rabbitmq.com/java-tools.html
• Kafka
• ProducerPerformance
• kafka-consumer-perf-test.sh
• https://engineering.linkedin.com/kafka/benchmarking-apache-
kafka-2-million-writes-second-three-cheap-machines
11

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Testing Tools
• Spring XD
• load-generator-source
• throughput
• https://github.com/spring-projects/spring-xd-modules
12

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Configuration
• Kafka
• BatchSize
o Default is 16384
o .0.8.2.1 vs .0.8.1.1
• Rabbit
• prefetch
o Default for XD is 1
13

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Batching
• https://github.com/cppwfs/rabbitmqsink
• https://github.com/cppwfs/rabbitmqsource
14
Spring AMQP
Batch
Size
Msgs
Per Sec
1 18,465
10 158,564
100 453,926

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Network Hops
• Adds a cost per hop
• Options:
• Direct Binding
• Composed Modules
• Custom Module
15

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One last little thing
• JMX is disabled by default
• When enabled it took a performance hit
because how SI was capturing stats via its
exporters
16

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Object Serialization
POJO ⇔ byte[] required for transporting data
between remote processes
• XD uses Kryo except when the payload type is
byte[] or String
• XD supports optimizing Kryo for known payload
types
17

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Serialization Benchmarks
18
An excellent comparative JVM serializers benchmark:
https://github.com/eishay/jvm-serializers/wiki
Best case: ~1500 ns

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Serialization Benchmarks
19
{"media":
{"uri": "http://javaone.com/keynote.ogg", "title":"Javaone Keynote", "width":640,
"height":480,
"format":"video/mpg4","duration":18000000,"size":58982400,"bitrate":262144,"
persons":["Bill Gates", "Steven Jobs"],
"player":"JAVA","copyright":"" },
"images":[
{"uri": "http://javaone.com/keynote_large.jpg","title":"Javaone Keynote","width":
1024,"height":768,"size":"LARGE"},
{ "uri": "http://javaone.com/keynote_small.jpg", "title":"Javaone
Keynote","width": 320,"height":240, "size":"SMALL"}]
}
Domain Object (as JSON):

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Object Serialization
• Size matters: YMMV
• Manually optimized Kryo ser/deser ~ 1500 ns = 1.5 µs = .0015
ms.
• Kafka XD;1000B messages ~ 500,000 msg/sec
• 2000 ns per message
• Serialization overhead
• ~ 285174 msg/sec (source|sink)
• At 50,000 msg/sec, the overhead may still be significant
(source|p1|p2|p3|sink)
20

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Optimizing Kryo in XD
• Disable references - If you know payload
types do not contain cyclic references.
xd.codec.kryo.references=false (in servers.yml)
• This is a global setting for all streams
• Register a custom serializer for a known
payload type
• Install a jar with containing the required beans in xd/lib. XD
will auto-configure these
21

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Custom Serializers in XD
22
public PojoCodec(java.util.List<KryoRegistrar> kryoRegistrars, boolean useReferences)
package spring.xd.bus.ext;
...
@Configuration
public class CustomKryoRegistrarConfig {
@Bean
public KryoRegistrar myCustomRegistration() {
List<Registration> registrations = new ArrayList<>();
registrations.add(new Registration(MyObject.class, new MySerializer(),62));
return new KryoRegistrationRegistrar(registrations);
}
}
XD scans this
package for beans of
type KryoRegistrar
Each Registration associates
a type to a serializer and a
unique ID

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Custom Serializers in XD
23
public class AddressSerializer extends Serializer<Address> {
@Override
public void write(Kryo kryo, Output output, Address address) {
output.writeString(address.getStreet());
output.writeString(address.getCity());
output.writeString(address.getCountry());
}
@Override
public Address read(Kryo kryo, Input input, Class<Address> type) {
return new Address(input.readString(),input.readString(),input.readString());
}
}

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Serializable Domain Object
24
public class Address implements KryoSerializable {
...
@Override
public void write(Kryo kryo, Output output) {
output.writeString(this.street);
output.writeString(this.city);
output.writeString(this.country);
}
@Override
public void read(Kryo kryo, Input input) {
this.street = input.readString();
this.city = input.readString();
this.country = input.readString();
}
}
• (+) Simple: This
works out of the box
with no additional
configuration
• (-) Requires access
to source or wrapping
• (-) Internal
benchmarks indicate
this is not as fast as

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Benchmarking Your Custom Serializers
25
Sample Results Ser (ns) Desr (ns)
Baseline 2710.5 4590.9
Serializable Domain
Object
1663.3 2579.1
Custom Serializers 1126.1 2873.8
The spring-xd-samples repo includes a serialization-
benchmarks project
• https://github.com/spring-projects/spring-xd-
samples/tree/master/serialization-benchmarks
Let’s look at some code…

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What about Processing ?
• Source Msg/s > Sink Msg/s ?
• Rate limited by Sink
• Blocking transformation (http, file…) ?
• Rate limited by blocking Processor
• Polling Sources Pausing ?
• Rate limited by small Prefetch properties
26

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Mitigating Cost of IO
• Negative impact ?
• Scale Out ?
o Loosing Data Affinity (colocation…)
o Network cost
• Scale Up ?
o Message passing Overhead
o More In-Flight Data
27

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Mitigating Cost of IO
• Negative impact ?
• Scale Out ?
o Loosing Data Affinity (colocation…)
o Network cost
• Scale Up ?
o Message passing Overhead
o More In-Flight Data
28

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Blocking IO
29

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Asynchronous IO
30

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Asynchronous IO
• Mitigate temporarily slow processors/sink
• Back to degraded mode when queue full
• Async Hand-Off generates Garbage
31

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Reactor Core:
Efficient Asynchronous
• Trade-off Memory vs Garbage generation
• Pre-Allocated Ring Buffer
• Concurrent consuming without duplicating
buffer content
• Ring Buffer Consumer Sequences
32

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Ring
What?
33

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Module Teasing
34
@EnableReactorModule(concurrency = 4) /* Invoke accept() 4 times */
public class Sample implements ReactiveProcessor<Message, Message> {
@Override
public void accept(
Publisher<Message> messageStream, /* A Ring Buffer Consumer */
ReactiveOutput<Message> output) { /* A Channel Output bridge */
output.writeOutput(messageStream);
}
}

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Questions
35

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Microservices to Fast Data
John T. Davies
Learn More. Stay Connected.
@springcentral Spring.io/video

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USING THIS TEMPLATE
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public class TransferServiceImpl implements TransferService {
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}
…
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1st Qtr
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0
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North
South
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2007 2008 2009 2010
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Category 5 4.3 5.6 7.1 3.4