Introduction to Stateful Stream Processing with Apache Flink.
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1 Kostas Kloudas @kkloudas HUG @ Warsaw JULY 3, 2017 Stateful Stream Processing with Apache Flink®
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Transcript of Introduction to Stateful Stream Processing with Apache Flink.
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Kostas Kloudas@kkloudas
HUG @ WarsawJULY 3, 2017
Stateful Stream Processingwith Apache Flink®
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Original creators of ApacheFlink®
Providers of thedA Platform
3 questions and some history▪ What is stateful stream processing?
▪ Why care about it?
▪ How does Flink do it?
▪ The evolution of Flink.
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Stateful Stream Processing
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Stateful Stream Processing
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Continuous Processingfor Continuously Arriving Data
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Batchjobs
t
2017-06-1401:00am
2017-06-1400:00am
2017-06-1311:00pm
2017-06-1310:00pm
... ● Continuously ingestingdata
● Time-bounded batchfiles
● Periodic batch jobs
The ol’ traditional batch way
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intermediatestate
t
2017-06-1401:00am
2017-06-1400:00am
2017-06-1311:00pm
2017-06-1310:00pm
...
● Compute a counter:#(A) per hour / 2 min
● What if:
● interval crosses batch boundaries?→ carry intermediate results to next batch
● events out of order?→ ???
The ol’ traditional batch way
▪ So, for a simple counting program:• Custom logic for handling state• Custom logic for handling time• Custom logic for fault tolerance
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The ol’ traditional batch way
▪ So, for a simple counting program:• Custom logic for handling state• Custom logic for handling time• Custom logic for fault tolerance
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The ol’ traditional batch way
Difficult and has nothing to do with your program.
Why should we care?▪...this is just for continuous data, right?
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Why should we care?▪...this is just for continuous data, right?
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Most datasets are continuously arriving streams.
Stream Processing
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Computation over an endless stream of data
YourCode
...
Distributed Stream Processing
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YourCode
... ... ...
YourCode
YourCode
● Partitions input by some key
● Distributes computationacross multiple instances
● Each instance is responsiblefor some keys
qwe
Stateful Stream Processing
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... ...
YourCode
YourCode
update localvariables/structures
var x = …
if (condition(x)) {…
}
Stateful Stream Processing
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... ...
YourCode
YourCode
qweupdate local
variables/structures
var x = …
if (condition(x)) {…
}● Embedded local state
● State co-partitioned withthe input stream by key
A practical stream processor
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state●Fault-tolerance
●Scalability
●Efficiency
●Event-time (out-of-order events)
●Allows you to work in event-time (e.g. timers)
time
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Stateful Stream Processorthat handles
consistently, robustly, and efficiently
LargeDistributed State
Time / Order /Completeness
● Stateful stream processing asa new paradigm tocontinuously processcontinuously arriving data
● Produce accurate results
● Real-time is only a naturalconsequence of the model
A practical stream processor
This is where Flink shines...▪ Supports out-of-order streams▪ Manages state transparently
• exactly-once processing
▪ Offers high throughput and low latency▪ Scales to large deployments
• https://data-artisans.com/blog/blink-flink-alibaba-search
• https://data-artisans.com/blog/rbea-scalable-real-time-analytics-at-king
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Apache Flink®
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About time ...
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... ...
YourCode
YourCode
When are my results complete?
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... ...
YourCode
YourCode
When are my results complete?
Processing Time drawbacks:• Incorrect results• Irreproducible results
About time ...
About time ...
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Event Time: Watermarks
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● Special markers, called Watermarks
● Flow with elements
● A watermark oftimestamp t meansthat no records withtimestamp < t shouldbe expected
Event Time: Watermarks
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Documentation:https://ci.apache.org/projects/flink/flink-docs-release-
1.3/dev/event_time.html
Event Time
Fault tolerance▪ How to ensure exactly-once semantics?
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Fault tolerance simple case
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event log
single process
main memoryperiodically take a Snapshot of the memory
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event log
single process
main memoryRecoveryrestore snapshot and replay
events since snapshot
persists events(temporarily)
Fault tolerance simple case
Fault tolerance distributed
▪ How to create consistent snapshots ofdistributed state?
▪ How to do it efficiently?
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Distributed Snapshots
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Coordination via markers, injected into the streams
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State index(Hash Table
or RocksDB)
Events flow without replication or synchronous writes
statefuloperation
source
Distributed Snapshots
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Trigger checkpointInject checkpoint barrier
statefuloperation
source
Distributed Snapshots
33stateful
operationsource
Take state snapshotTrigger state
copy-on-write
Distributed Snapshots
34stateful
operationsource
DFS
Durably persistsnapshots
asynchronouslyProcessing pipeline continues
Distributed Snapshots
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... YourCode
YourCode
YourCode
State
State
State
YourCode
State
● Consistent snapshotting:
Fault tolerance
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... YourCode
YourCode
YourCode
State
State
State
YourCode
State
checkpointedstate
checkpointedstate
checkpointedstate
File System Checkpoint
● Consistent snapshotting:
Fault tolerance
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... YourCode
YourCode
YourCode
State
State
State
YourCode
State
checkpointedstate
checkpointedstate
checkpointedstate
File System Restore
● Recover all embedded state● Reset position in input stream
Fault tolerance
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Documentation:https://ci.apache.org/projects/flink/flink-docs-release-
1.3/internals/stream_checkpointing.html
Fault tolerance
State Management: misc.
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▪ Savepoints
▪ Rescaling
▪ Queryable State
Apache Flink Ecosystem
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Integration
POSIX Java/ScalaCollections
POSIX
Apache Flink Stack
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DataStream APIStream Processing
DataSet APIBatch Processing
RuntimeDistributed Streaming Data Flow
Libraries
Streaming and batch as first class citizens.
Levels of abstraction
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Process Function (events, state, time)
DataStream API (streams, windows)
Table API (dynamic tables)
Stream SQL
low-level (statefulstream processing)
stream processing &analytics
declarative DSL
high-level langauge
API and Execution
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SourceDataStream<String> lines = env.addSource(new FlinkKafkaConsumer010(…));
DataStream<Event> events = lines.map(line -> parse(line));
DataStream<Statistic> stats = stream.keyBy("id").timeWindow(Time.seconds(5)).sum(new MyAggregationFunction());
stats.addSink(new BucketingSink(path));
map()[1]
keyBy()/window()/
apply()[1]
Transformation
Transformation
Sink
StreamingDataflowkeyBy()/
window()/apply()
[2]
map()[1]
map()[2]
Source[1]
Source[2]
Sink[1]
Evolution of Flink
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Programming APIs
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Large State Handling
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Conclusion
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TL;DR▪ Stateful stream processing as a paradigm for
continuous data processing
▪ Flink is a sophisticated and tested stateful streamprocessor
▪ Efficiency, management, and operational issues forstate are taken very seriously
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Thank you!@kkloudas@ApacheFlink@dataArtisans
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Stream Processingand Apache Flink®'s approach to it
@StephanEwen Apache Flink PMC
CTO @ data ArtisansFLINK FORWARD IS COMING BACK TO BERLINSEPTEMBER 11-13, 2017
BERLIN.FLINK-FORWARD.ORG -
We are hiring!data-artisans.com/careers
