from apache flink® 1.3 to 1.4
TRANSCRIPT
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Original creators of Apache
Flink®
Providers of
dA Platform 2, including
open source Apache Flink +
dA Application Manager
Overview
Apache Flink 1.3 – Previously on Apache
Flink
Apache Flink 1.4 – What’s happening now?
Apache Flink 1.5+ – Next on Apache Flink
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Previously on Apache Flink
Apache Flink 1.3
Apache Flink 1.3 in Numbers
141 contributors (no deduplication)
1400 commits
>= 680 resolved JIRA issues
+261813 / -65646 LOC
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Evolution of Flink’s API
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Flink 1.0.0
State API (ValueState
ReducingState, ListState)
Flink 1.1.0
Session Windows
Late arriving events
Flink 1.2.0
ProcessFunction (access
to state, timers, events)
Flink 1.3.0
Side outputs
Access to per-window state
Side Outputs
Additional outputs for a stream
Late events
Corrupted input data
More expressive APIs
FLINK-4460
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Process
Function
Main output
Side output
Side Outputs: Example
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DataStream<Integer> input = ...;final OutputTag<String> outputTag = new OutputTag<String>("side-output"){};
SingleOutputStreamOperator<Integer> mainDataStream = input.process(new ProcessFunction<Integer, Integer>() {
@Override public void processElement(Integer value,Context ctx,Collector<Integer> out) throws Exception {
// emit data to regular outputout.collect(value);
// emit data to side outputctx.output(outputTag, "sideout-" + String.valueOf(value));
}});
DataStream<String> sideOutputStream = mainDataStream.getSideOutput(outputTag);
Evolution of Large State Handling
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Flink 1.0.0
RocksDB for out-of-core
state support
Flink 1.1.0
Fully async RocksDB
snapshots
Flink 1.2.0
Rescalable keyed and
non-partitioned state
Flink 1.3.0
Incremental checkpoints
Fine-grained recovery
GHCD
Full Checkpoints
10Checkpoint 1 Checkpoint 2 Checkpoint 3
IE
ABCD
ABCD
AFCDE
@t1 @t2 @t3
AFC
DE
GHC
DIE
GHCD
Incremental Checkpoints
11Checkpoint 1 Checkpoint 2 Checkpoint 3
IE
ABCD
ABCD
AFCDE
EF
GHI
@t1 @t2 @t3
Incremental Checkpoints
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Checkpoint 1 Checkpoint 2 Checkpoint 3 Checkpoint 4
C1 C3C1 C1
Chunk
1
Chunk
2
Chunk
3Chunk
4Storage
C2 C4C3
Incremental Checkpointing Contd.
Currently supported for RocksDBstate backend
FLINK-5053
Faster and smaller checkpoints
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Full checkpoint Incremental checkpoint
Size 60 GB 1 – 30 GB
Time 180 s 3 – 30 s
“A Look at Flink’s Internal
Data Structures and
Algorithms for Efficient
Checkpointing” by Stefan
Richter, Tomorrow @
12:20 pm Maschinenhaus
Evolution of High Level APIs
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Flink 1.0.0
CEP library added
Table API v1
Flink 1.1.0
Table API overhaul
Integration with Apache Calcite
Flink 1.2.0
Tumbling, sliding and session
group-windows for Table API
Flink 1.3.0
Rescalable CEP operators
Retractions in Table API/SQL
Enriched CEP Language
Support for quantifiers (+, *, ?)
FLINK-3318
Iterative conditions
FLINK-6197
Not operator
FLINK-3320
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“Complex Event Processing With
Flink: The State of FlinkCEP” by
Kostas Kloudas, Today @ 2:30
pm Maschinenhaus
CEP: Detect Dipping Stocks
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DataStream<Stock> stocks = …;
Pattern<Stock, ?> pattern = Pattern.<Stock>begin("rising").where(new IterativeCondition<Stock>() {
@Overridepublic boolean filter(Stock stock, Context<Stock> ctx) throws Exception {
// calculate the average pricedouble sum = 0.0; int count = 0;for (Stock previousStock : ctx.getEventsForPattern("rising")) {
sum += previousStock.getPrice(); count++;}// only accept if the price is higher or equal than the average pricereturn stock.getPrice() >= sum / count;
}).oneOrMore().next("falling");
PatternStream<Stock> dippingStocks = new PatternStream<>(stocks.keyBy("name"), pattern);DataStream<String> namesOfDippingStocks = dippingStocks.select(…);
What’s Happening Now?
Apache Flink 1.4
Event Driven I/O
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Rework of Flink’s network stack
Event driven network I/O
Use full available capacity
Near perfect latency behaviour
TCP
Buffer
capacity left
flush
Flow Control
Flow control for TaskManager communication
Single channel no longer stalls other multiplexed channels
Fine-grained backpressure control
Improves checkpoint alignments
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“Building a Network Stack
for Optimal Throughput /
Low-Latency Trade-Offs”
by Nico Kruber, Today @
2:00 pm Palais Atelier
Receiver
Sender #1
Sender #2
Give credit
Send
credited data
New Deployment Model
Rework of Flink’s distributed architecture
Ready for multitude of deployment scenarios
Support for dynamic scaling
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“Flink in Containerland” by
Patrick Lucas, Tomorrow
@ 3:20 pm Maschinenhaus
Producing Exactly Once with Kafka 0.11
Support for Kafka 0.11
First Kafka producer with exactly once processingguarantees
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“Hit Me, Baby, Just One Time
– Building End-to-End Exactly
Once Applications With Flink”
by Piotr Nowojski, Today @
3:20 pm Palais Atelier
Consuming Producing
End-to-End exactly once processing
StreamSQL and Table API
Support for retractions
Extended aggregation support
Support for external table catalogs
Window joins
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“Unified Stream and Batch
Processing With Apache
Flink’s Relational APIs” by
Fabian Hüske, Tomorrow
@ 11:00 am Kesselhaus
“From Streams to Tables
and Back Again: A Demo
of Flink’s Table & SQL
API” by Timo Walther,
Tomorrow @ 11:50 am
Kesselhaus
Operational Robustness
Drop Java 7
Support Scala 2.12
Avoid dependency hell
Child first class loading
Relocation of dependencies
De-Hadoopification
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Next on Apache Flink
Apache Flink 1.5+
Side Inputs
Additional input for operator
Join with static data set
Feeding of externally trained ML model
Window joins
Flip-17 design document: https://goo.gl/W4yMEu
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Process
Function
Main input
Side input
State Management & Evolution
Eager state declaration
State type, serializer and name known at pre-flight time
Flip-22 design document: https://goo.gl/trFiSi
Evolving existing state
Schema updates
Serializer upgrades
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“Managing State in
Apache Flink” by
Tzu-Li Tai, Today @
4:30 pm Kesselhaus
State Replication
Replicate state between
TaskManagers
Faster recovery in
case of failures
High throughput
queryable state
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TaskManager
TaskManager
Change log stream
Input
State
Programmatic Job Control
Improve client to give better job control
Run concurrent jobs from the same
program
Trigger savepoints programmatically
Better testing facilities
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JobClient & ClusterClient
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StreamExecutionEnvironment env = ...;// define program
JobClient jobClient = env.execute();
CompletableFuture<Acknowledge> savepointFuture = jobClient.takeSavepoint(savepointPath);
// wait for the savepoint completionsavepointFuture.get();
CompletableFuture<JobExecutionResult> resultFuture = jobClient.getResultFuture();
// cancel the jobjobClient.cancelJob();
// get the execution result --> should be canceledJobExecutionResult result = resultFuture.get();
// get list of all still running jobs on the clusterClusterClient clusterClient = jobClient.getClusterClient();CompletableFuture<List<JobInfo>> jobInfosFuture = clusterClient.getJobInfos();List<JobInfo> jobInfos = jobInfosFuture.get();
TL;DL
Apache Flink one of the most innovative open source stream processing platforms
Stay tuned what’s happening next
Visit the in depths talks to learn more about Flink’s internals
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Thank you!
@stsffap
@ApacheFlink
@dataArtisans
We are hiring!
data-artisans.com/careers
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