3. Stream Processing: Processing events as soon as they happen..
● Stateless Processing
■ Transformation etc.
■ Lookup adjunct data (lookup databases/call services )
■ Producing results for every event
● Stateful Processing
■ Triggering/Producing results periodically (time-windows)
● Maintain intermediate state
■ E.g. Joining across multiple streams of events.
4. Stream Processing: State of the Union
MillwheelStorm
Heron
Spark Streaming
S4
Dempsey
Samza
Flink
Beam
Dataflow
Azure Stream Analytics
AWS Kinesis AnalyticsGearPump
Kafka Streams
Orleans
Not meant to be an
accurate timeline..
Yes It is CROWDED !!
5. Apache Samza
● Top level Apache project since Dec 2014
● 5 big Releases (0.7, 0.8, 0.9, 0.10, 0.11)
● 62 Contributors
● 14 Committers
● Companies using : LinkedIn, Uber, MetaMarkets, Netflix,
Intuit, TripAdvisor, MobileAware, Optimizely ….
https://cwiki.apache.org/confluence/display/SAMZA/Powered+By
● Applications at LinkedIn : from ~20 to ~200 in 2 years.
6. Key Differentiators for Apache Samza
● Performance !!
● Stability
● Support for a variety of input sources
● Stream processing as a service AND as an embedded library
7. Performance : Accessing Adjunct Data
Samza Processor
Database
Remote-read
Samza Processor
Capture changes
Databus,
Brooklin
Rocks-
DB
Local read
Database
Local data access
Remote data access
Input stream
Input stream
8. Performance : Maintaining Temporary State
Samza Processor Remote
Database
Read-Write
Samza Processor
Backup changes
Kafka Change
Log(Log
compacted)Rocks-
DB
Local read/write
Local data access
Remote data access
Input stream
Input stream
In Memory
Store
9. Performance : Let us talk numbers !
● 100x Difference between using Local State vs Remote No-Sql store
● Local State details:
○ 1.1 Million TPS on a single processing machine (SSD)
○ Used a 3 node Kafka cluster for storing the durable changelog
● Remote State details:
○ 8500 TPS when the Samza job was changed to accessing a remote No-
Sql store
○ No-Sql Store was also on a 3 node (ssd) cluster
10. Remote State : Asynchronous Event Processing
Event Loop
(Single thread)
ProcessAsync
Remote
DB
/Services
Asynchronous I/O calls,
using Java Nio, Netty...
Responses sent to
main thread via
callback
Event loop is woken up to process next message
Task.max.concurrency >1 to
enable pipelining
Available with Samza 0.11
11. Remote State: Synchronous Processing on Multiple
Threads
Event Loop
(Single thread)
Schedule
Process()
Remote
DB/
Services
Built-In
Thread pool
Blocking I/O
calls
Event loop is
woken up by
the worker
thread
job.container.thread.pool.size = N
Available with Samza 0.11
12. Incremental Checkpointing : MVP for stateful apps
Some applications
have ~ 2 TB state in
production
Stateful apps don’t
really work without
incremental
checkpointing
Samza Task
State
changelog
checkpoint
Host 1
Input stream(e.g. Kafka)
13. Key Differentiators for Apache Samza
● Performance !!
● Stability
● Support for a variety of input sources
● Stream processing as a service AND as an embedded library
14. Speed Thrills .. but can kill
● Local State Considerations:
○ State should NOT be reseeded under normal operations (e.g.
Upgrades, Application restarts)
○ Minimal State should be reseeded
- If a container dies/removed
- If a container is added
16. Stream A
Stream
B
Stream C
Job 1
Job 2
● Kafka or durable intermediate
queues are leveraged to
avoid backpressure issues in
a pipeline.
● Allows each stage to be
independent of the next stage
Backpressure in a Pipeline
17. Key Differentiators for Apache Samza
● Performance !!
● Stability
● Support for a variety of input sources
● Stream processing as a service AND as an embedded library
18. Pluggable system consumers
Samza Processor
Mongo
DB
DynamoDB
Streams
Kafka
Databus/
Brooklin
Kinesis
ZeroMQ
… Azure EventHub,
Azure Document DB,
Google Pub-Sub etc.
Oracle,
Espresso
Dynamo-
DB
19. Batch processing in Samza!! (NEW)
● HDFS system consumer for Samza
● Same Samza processor can be used for processing
events from Kafka and HDFS with no code changes
● Scenarios :
○ Experimentation and Testing
○ Re-processing of large datasets
○ Some datasets are readily available on HDFS
20. Samza - HDFS support
HDFS input
(Samza)
Processor HDFS
output
(Samza)
Processor HDFS
output
HDFS input
(Samza)
Processor
Kafka
Kafka
New
Available since
Samza 0.10
The batch job
auto-terminates
when the input is
fully processed.
24. Samza- HDFS Early Performance Results !!
Benchmark : Count number
of records grouped by <Field>
DataSize (bytes): 250 GB
Number of files : 487
Samza
Map/Reduce
Spark
Number of Containers
T
i
m
e
-
s
e
c
o
n
d
s
25. Key Differentiators for Apache Samza
● Performance !!
● Stability
● Support for a variety of input sources (batch and streaming)
● Stream processing as a service AND (coming soon) as an
embedded library
26. Stream Processing as a Service
● Based on YARN
○ Yarn-RM high availability
○ Work preserving RM
○ Support for Heterogenous hardware with Node Labels (NEW)
● Easy upgrade of Samza framework :
Use the Samza version deployed on the machine instead of packaging it with the application.
● Disk Quotas for local state (e.g. rocksDB state)
● Samza Management Service(SAMZA-REST)-> Next Slide
27. YARN
Resource
Managers
Nodes
in the
YARN
cluster
RM SRR RM SRR RM SRR
NM SRN
Samza Management Service (Samza REST) (NEW)
NM SRN NM SRN NM SRN
NM SRN NM SRN NM SRN NM SRN
/v1/jobs /v1/jobs /v1/jobs
Samza Containers
1. Exposes /jobs
resource to
start, stop, get
status of jobs
etc.
1. Cleans up
stores from
dead jobs
Samza
REST
YARN
processes(RM/NM)
29. Coming Soon : Samza as a Library
Stream Processor
Code
Job
Coordinator
Stream Processor
Code
Job
Coordinator
Stream Processor
Code
Job
Coordinator
...
Leader
● No YARN dependency
● Will use ZK for leader
election
● Embed stream processing
into your bigger application
StreamProcessor processor = new StreamProcessor (config, “job-name”,
“job-id”);
processor.start();
processor.awaitStart();
…
processor.stop();
30. Coming Soon: High Level API and Event Time (SAMZA-
914/915)
Count the number of PageViews by Region, every 30 minutes.
@Override public void init(Collection<SystemMessageStream> sources) {
sources.forEach(source -> {
Function<PageView, String> keyExtractor = view -> view.getRegion();
source.map(msg -> new PageViewMessage(msg))
.window(Windows.<PageViewMessage, String>intoSessionCounter(keyExtractor,
WindowType.Tumbling, 30*60 ))
});
}
31. Coming Soon: First class support for Pipelines
(Samza- 1041)
public class MyPipeline implements PipelineFactory {
public Pipeline create(Config config) {
Processor myShuffler = getShuffle(config);
Processor myJoiner = getJoin(config);
Stream inStream = getStream(config, “inStream1”);
// … omitted for brevity
PipelineBuilder builder = new PipelineBuilder();
return builder.addInputStreams(myShuffler, inStream1)
.addOutputStreams(myShuffler, intermediateOutStream)
.addInputStreams(myJoiner, intermediateOutStream, inStream2)
.addOutputStreams(myJoiner, finalOutStream)
.build();
}
}
Shuffle
Join
input output
32. Future: Miscellaneous
● Exactly once processing
● Making it easier to auto-scale even with Local State (on-
demand Standby containers)
● Turnkey Disaster Recovery for stateful applications
○ Easy Restore of changelog and checkpoints from
some other datacenter.
● Improved support for Batch jobs
● SQL over Streams
● A default Dashboard :)
Looking up data.. Sometimes people call it a Stream Table join.
Talk about bootstrapping feature of Samza.
Talk about bootstrapping feature of Samza.
Sure .. We can add caches.. But then how do the caches get populated and kept in sync.
Take-away:
Samza REST is designed to be a “man on the ground” for every node in your cluster. It can play different roles on different hosts.
Legend:
Blue boxes are YARN processes
Green boxes are Samza REST processes
SRR - Samza REST with a Resource Manager config
Exposes /jobs resource to start, stop, get status of jobs
SRN - Samza REST with a Node Manager config
Monitors node manager process
Cleans host affinity stores
Red boxes are Samza containers (jobs)
