apache samza * reliable stream processing atop apache kafka and yarn
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Apache Samza*Reliable Stream Processing atop
Apache Kafka and Yarn
Sriram SubramanianMe on Linkedin
Me on twitter - @sriramsub1
* Incubating
Agenda• Why Stream Processing?• What is Samza’s Design ?• How is Samza’s Design
Implemented? • How can you use Samza ?• Example usage at Linkedin
Why Stream Processing?
Response latency0 ms
Response latency
RPC
Synchronous
0 ms
Response latency
RPC
Synchronous Later. Possibly much later.
0 ms
Response latency
Samza
Milliseconds to minutes
RPC
Synchronous Later. Possibly much later.
0 ms
Newsfeed
Ad Relevance
Search Index
Metrics and Monitoring
What is Samza’s Design ?
Stream A
JOB
Stream B
Stream C
Stream A
JOB 1
Stream B
Stream C
Stream D
JOB 2
Stream E
Stream F
JOB 3
Stream G
StreamsPartition 0 Partition 1 Partition 2
StreamsPartition 0 Partition 1 Partition 2
123456
12345
1234567
StreamsPartition 0 Partition 1 Partition 2
123456
12345
1234567
StreamsPartition 0 Partition 1 Partition 2
123456
12345
1234567
StreamsPartition 0 Partition 1 Partition 2
123456
12345
1234567
StreamsPartition 0 Partition 1 Partition 2
123456
12345
1234567
StreamsPartition 0 Partition 1 Partition 2
next append
123456
12345
1234567
JobsStream A Stream B
Task 1 Task 2 Task 3
Stream C
JobsAdViews AdClicks
Task 1 Task 2 Task 3
AdClickThroughRate
Tasks
AdViewsCounterTask
Partition 0 Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
Ad Views - Partition 0
1234
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0 Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
Tasks
AdViewsCounterTask
Partition 0Partition 1
1234
2
Partition 1Checkpoint
Stream
Ad Views - Partition 0
Output Count Stream
DataflowStream A Stream B Stream C
Stream E
Stream B
Job 1 Job 2
Stream D
Job 3
DataflowStream A Stream B Stream C
Stream E
Stream B
Job 1 Job 2
Stream D
Job 3
Stateful Processing• Windowed Aggregation
– Counting the number of page views for each user per hour
• Stream Stream Join– Join stream of ad clicks to stream of ad views to identify the
view that lead to the click
• Stream Table Join– Join user region info to stream of page views to create an
augmented stream
• In memory state with checkpointing
– Periodically save out the task’s in memory data
– As state grows becomes very expensive
– Some implementation checkpoints diffs but adds complexity
How do people do this?
• Using an external store
– Push state to an external store
– Performance suffers because of remote queries
– Lack of isolation
– Limited query capabilities
How do people do this?
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Stateful TasksStream A
Task 1 Task 2 Task 3
Stream B Changelog Stream
Key-Value Store• put(table_name, key, value)• get(table_name, key)• delete(table_name, key)• range(table_name, key1, key2)
How is Samza’s Design Implemented?
Apache Kafka• Persistent,
reliable,distributed message queue
At LinkedIn
10+ billionwrites per day
172kmessages per second
(average)
60+ billionmessages per day
to real-time consumers
Apache Kafka• Models streams as topics
• Each topic is partitioned and each partition is replicated
• Producer sends messages to a topic
• Messages are stored in brokers
• Consumers consume from a topic (pull from broker)
YARN- Yet another resource negotiator
• Framework to run your code on a grid of machines
• Distributes our tasks across multiple machines
• Notifies our framework when a task has died
• Isolates our tasks from each other
JobsStream A
Task 1 Task 2 Task 3
Stream B
Containers
Task 1 Task 2 Task 3
Stream B
Stream A
Containers
Stream B
Stream A
Samza Container 1 Samza Container 2
Containers
Samza Container 1 Samza Container 2
YARN
Samza Container 1 Samza Container 2
Host 1 Host 2
YARN
Samza Container 1 Samza Container 2
NodeManager NodeManager
Host 1 Host 2
YARN
Samza Container 1 Samza Container 2
NodeManager NodeManager
Samza YARN AM
Host 1 Host 2
YARN
Samza Container 1 Samza Container 2
NodeManager
Kafka Broker
NodeManager
Samza YARN AM
Kafka Broker
Host 1 Host 2
YARN
MapReduceContainer
MapReduce Container
NodeManager
HDFS
NodeManager
MapReduce YARN AM
HDFS
Host 1 Host 2
YARN
Samza Container 1
NodeManager
Kafka Broker
Host 1
Stream C
Stream A
Samza Container 1 Samza Container 2
YARN
Samza Container 1
NodeManager
Kafka Broker
Host 1
Stream C
Stream A
Samza Container 1 Samza Container 2
YARN
Samza Container 1
NodeManager
Kafka Broker
Host 1
Stream C
Stream A
Samza Container 1 Samza Container 2
YARN
Samza Container 1
NodeManager
Kafka Broker
Host 1
Stream C
Stream A
Samza Container 1 Samza Container 2
YARN
Samza Container 1 Samza Container 2
NodeManager
Kafka Broker
NodeManager
Samza YARN AM
Kafka Broker
Host 1 Host 2
CGroups
Samza Container 1 Samza Container 2
NodeManager
Kafka Broker
NodeManager
Samza YARN AM
Kafka Broker
Host 1 Host 2
How can you use Samza ?
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
TasksPartition 0
class PageKeyViewsCounterTask implements StreamTask { public void process(IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = ((GenericRecord) envelope.getMsg()); String pageKey = record.get("page-key").toString(); int newCount = pageKeyViews.get(pageKey).incrementAndGet(); collector.send(countStream, pageKey, newCount); }}
Stateful Stream Taskpublic class SimpleStatefulTask implements StreamTask, InitableTask { private KeyValueStore<String, String> store; public void init(Config config, TaskContext context) { this.store = context.getStore("mystore"); } public void process( IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = (GenericRecord) envelope.getMessage(); String memberId = record.get("member_id"); String name = record.get("name"); System.out.println("old name: " + store.get(memberId)); store.put(memberId, name); }}
Stateful Stream Taskpublic class SimpleStatefulTask implements StreamTask, InitableTask { private KeyValueStore<String, String> store; public void init(Config config, TaskContext context) { this.store = context.getStore("mystore"); } public void process( IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = (GenericRecord) envelope.getMessage(); String memberId = record.get("member_id"); String name = record.get("name"); System.out.println("old name: " + store.get(memberId)); store.put(memberId, name); }}
Stateful Stream Taskpublic class SimpleStatefulTask implements StreamTask, InitableTask { private KeyValueStore<String, String> store; public void init(Config config, TaskContext context) { this.store = context.getStore("mystore"); } public void process( IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = (GenericRecord) envelope.getMessage(); String memberId = record.get("member_id"); String name = record.get("name"); System.out.println("old name: " + store.get(memberId)); store.put(memberId, name); }}
Stateful Stream Taskpublic class SimpleStatefulTask implements StreamTask, InitableTask { private KeyValueStore<String, String> store; public void init(Config config, TaskContext context) { this.store = context.getStore("mystore"); } public void process( IncomingMessageEnvelope envelope, MessageCollector collector, TaskCoordinator coordinator) {
GenericRecord record = (GenericRecord) envelope.getMessage(); String memberId = record.get("member_id"); String name = record.get("name"); System.out.println("old name: " + store.get(memberId)); store.put(memberId, name); }}
Example usage at Linkedin
Call graph assemblyget_unread_msg_count()
get_PYMK()
get_Pulse_news()
get_relevant_ads()
get_news_updates()
Lots of calls == lots of machines, logs
get_unread_msg_count()
get_PYMK()
get_Pulse_news()
get_relevant_ads()
get_news_updates()
unread_msg_service_call
get_PYMK_service_call
pulse_news_service_call
add_relevance_service_call
news_update_service_call
TreeID: Unique identifier
page_view_event (123456)
unread_msg_service_call (123456)
another_service_call (123456)
silly_service_call (123456)
get_PYMK_service_call (123456) counter_service_call (123456)
unread_msg_service_call (123456)count_invites_service_call (123456)
count_msgs_service_call (123456)
OK, now lots of streams with TreeIDs…
all_service_calls(partitioned by TreeID)
Samza job:Repartition-By-TreeID
*_service_call
Samza job:Assemble Call Graph
service_call_graphs
• Near real-time holistic view of how we’re actually serving data• Compare day-over-day, cost, changes, outages
Thank you• Quick start: bit.ly/hello-samza• Project homepage: samza.incubator.apache.org
• Newbie issues: bit.ly/samza_newbie_issues• Detailed Samza and YARN talk: bit.ly/samza_and_yarn
• A must-read: http://bit.ly/jay_on_logs• Twitter: @samzastream• Me on Twitter: @sriramsub1
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