Flink currently features different APIs for bounded/batch (DataSet) and streaming (DataStream) programs. And while the DataStream API can handle batch use cases, it is much less efficient in that compared to the DataSet API. The Table API was built as a unified API on top of both, to cover batch and streaming with the same API, and under the hood delegate to either DataSet or DataStream. In this talk, we present the latest on the Flink community's efforts to rework the APIs and the stack for better unified batch & streaming experience. We will discuss: - The future roles and interplay of DataSet, DataStream, and Table API - The new Flink stack and the abstractions on which these APIs will build - The new unified batch/streaming sources - How batch and streaming optimizations differ in the runtime, and what the future interplay of batch and streaming execution could look like

1. © 2019 Ververica
Aljoscha Krettek – Software Engineer, Flink PMC, Beam PMC
Towards Flink 2.0: Unified Batch & Stream
Processing

2. © 2019 Ververica2
This is joint work with many members of
the Apache Flink community

3. © 2019 Ververica3
Some of this presents work that is in
progress in the Flink community. Other
things are planned and/or have design
documents. Some were discussed at one
point or another on the mailing lists or in
person.
This represents our understaning of the
current state, this is not a fixed roadmap,
Flink is an open-source Apache project.

4. © 2019 Ververica4
Agenda
How does Flink
execute Pipelines?
How do users write
and operate
Pipelines?
API
s
Configuration
Execution/OpsExecution
Operators
Connectors

5. © 2019 Ververica
The Relationship between
Batch and Streaming

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Batch Processing is a special case of Stream Processing
A batch is just a bounded stream.
That is about 60% of the truth…

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The remaining 40% of the truth
*from the excellent Streaming 101 by Tyler Akidau: https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-
101)
The (Event-time) Watermark
… never seen this in
Batch Processing,
though.

8. © 2019 Ververica8
The remaining 40% of the truth
Continuous
Streaming
Batch
Processing
Data is incomplete
Latency SLAs
Completeness and
Latency is a tradeoff
Data is as complete
as it gets within the job
No Low Latency SLAs

9. © 2019 Ververica9
Real-time Stream Processing
older more recent
watermark

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Stream Re-Processing
older more recent
watermark
unprocesse
d

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Batch-style Processing
older more recent
watermark
unprocesse
d

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Batch and Streaming Processing Styles
S S S
M M M
R R R
S S S
S S S
M M M
R R R
S S S
more batch-y more stream-y
running
not running
can do things one-by-one everything is always-on
running

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Batch vs. Stream Processing
Continuous
Streaming
Batch
Processing
Watermarks to model
Completeness/Latency tradeoff No Watermarks
Incremental results &
Proc.-Time Timers
Results at end-of-
program only
In-receive-order
ingestion with low parallelism
Massively parallel
out-of-order ingestion

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In Flink
DataSet API
DataStream API

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Unify what? And why should I care?

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Unify what? And why would I care?
API:
- DataSet
- DataStream
Connectors:
- Source/FinkFunction
- Input/OutputFormat
Table API/SQL:
- Unified APIs
- Connectors
Runtime:
- Operators
- Optimization

17. © 2019 Ververica18
A Typical “Unified” Use Case: Bootstrapping State
“stream
”
source
“batch”
source
Stateful
operatio
n
batch-y partstream-y part
• We have a streaming use
case
• We want to bootstrap the
state of some operations
from a historical source
• First execute bounded parts
of the graph, then start the
rest

18. © 2019 Ververica19
Future of the DataStream API
• DataStream is already supporting Bounded and Unbounded Streams
• Not exploiting batch optimizations so far
– Bounded batch-style execution still faster on DataSet API
• After Flink 1.10:
– Introduce BoundedDataStream and non-incremental mode to exploit
optimizations for bounded data
– Watermarks "jump" from -∞ to +∞ at end of program
– Processing time timers deactivated or deferred (end of key)
– DataStream translation and runtime (operators) need to be enhanced to use the
added optimization potential

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Exploiting the Batch Special Case
Planner/Optimizer
Continuous Operators
Streaming
Scheduler Rules
Additional Bounded
Operators
Additional
Scheduling Strategies
if (bounded && non-incremental)
activates additional
optimizer choices
Core operators,
cover all cases
Optimized operators
for subset of cases

20. © 2019 Ververica21
Unify what? And why would I care?
API:
- DataSet
- DataStream
Connectors:
- Source/FinkFunction
- Input/OutputFormat
Table API/SQL:
- Unified APIs
- Connectors
Runtime:
- Operators
- Optimization

21. © 2019 Ververica22
Current Source Interfaces
Batch: InputFormat
Enumerate
splits
pull()
read
splits
pull()
Streaming: SourceFunction
JobManager
Source
Thread
push()

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A New (unified) Source Interface FLIP-27
Enumerate
splits
read
splits/
thread
mgmt
?()
JobManager

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Unify what? And why would I care?
API:
- DataSet
- DataStream
Connectors:
- Source/FinkFunction
- Input/OutputFormat
Table API/SQL:
- Unified APIs
- Connectors
Runtime:
- Operators
- Optimization

24. © 2019 Ververica25
Table API / SQL
• Unified API: yes
• Unified runtime: yes, with the new Blink-based Table Runner: FLINK-
11439
– but: still runs in either “batch” or “streaming” mode
– More work required to make the system automatically decide and
optimize
• Unified sources: no, eventually yes with FLIP-27

25. © 2019 Ververica26
Table API / SQL in Flink 1.9
Table API / SQL
Classic Query Processor
Flink Task Runtime
DataSet StreamTransformation
Driver
(Pull)
StreamOperator
(selectable push)
New Query Processor*
batch env. stream env. batch & stream
* Based on the Blink query processor FLINK-11439

26. © 2019 Ververica27
Unify what? And why would I care?
API:
- DataSet
- DataStream
Connectors:
- Source/FinkFunction
- Input/OutputFormat
Table API/SQL:
- Unified APIs
- Connectors
Runtime:
- Operators
- Optimization

27. © 2019 Ververica28
Push-based and Pull-based Operators
accept data from any input immediately
(like actor messages)
minimize latency
supports checkpoint alignment
pull data from one input at a time
(like reading streams)
control over data flow,
high-latency, breaks checkpoints
pull() pull()
Push Operators Pull Operators

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Flink 1.9 - Selectable Push-based Operators
subscribe to inputs (select)
and receive pushed events
 Operators control data flow by selecting active data paths
 Among active data paths, fully asynchronous data flow
driven by network, data sources (and timers)
similar to non-blocking-I/O model
Java NIO, Linux Epoll, or Select
select() select()
FLINK-11875

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Scheduling Strategies
• Build pipelined regions
– Incremental results: everything pipelines
– Non-incremental results: break pipelines once in a while
• Recovery: Restart the pipelined region from latest checkpoint (or beginning)
– replay input since checkpoint or beginning
✘✘

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Configuration/Ops Vision

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How can I enable checkpointing?
Only via StreamExecutionEnv.enableCheckpointing()
How can I restore from a Savepoint/Checkpoint?
Only via bin/flink run -s …
How can I run a job on a “remote” cluster?
Either bin/flink run or RemoteEnvironment
How can I run a job on YARN?
Only via bin/flink run*
* you could hand-roll other stuff but that’s complicated
Let’s do a Quiz

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Configuration All the Way Down
bin/flink run -c com.mystuff.MyProgram myjar.jar
--config my-config.yaml
--checkpointing.enabled true
--state.backend rocksdb
FLIP-59 is the beginning of this effort

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A new Executor Abstraction for Executing Jobs
bin/flink run -c com.mystuff.MyProgram myjar.jar
--executor yarn-per-job
--executor.yarn.some-option true
FLIP-73 tracks this effort, and then FLIP-74 for the JobClient API
java -jar myjar.jar
--executor kubernetes-per-job
--executor.kubernetes.discombobulator true
 Using Flink as a library

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Wrapping Up

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Preview of new Blink SQL Engine
Python Table API
Hive support
…and lot's more
Analytics over Checkpoints/Savepoints
Atomic stop-with-savepoint
What else is new in Flink 1.9?
🥳

36. © 2019 Ververica37
Cross-Batch-Streaming
Machine Learning
Unaligned Checkpoints
Python Table UDFs
…and lot's more
Interactive multi-job programs a big documentation overhaul
What else is the community working on?
DDL and Clients for
Streaming SQL
Full support of Blink SQL Engine
and TPC-DS coverage

37. © 2019 Ververica38
Thank you!
• Try Flink and help us improve it
• Contribute docs, code, tutorials
• Share your use cases and ideas
• Join a Flink Meetup
aljoscha@apache.org @ApacheFlink @VervericaData
https://flink.apache.org/

38. © 2019 Ververica
Thank you!
Questions?

39. © 2019 Ververica
Backup

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Latency vs. Completeness (for geeks)
1977 1980 1983 1999 2002 2005 2015
Processing Time
Episode
IV
Episode
V
Episode
VI
Episode
I
Episode
II
Episode
III
Episode
VII
Event Time
2016
Rogue
One
III.5
2017
Episode
VIII

41. © 2019 Ververica
FLIP-27: New Source Interface

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Current Source Interfaces
InputFormat
createInputSplits(): splits
openSplit(split)
assignInputSplit()
nextRecord(): T
closeCurrentSplit()
SourceFunction
run(OutputContext)
close()
batch streaming

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Batch InputFormat Processing
TaskManager TaskManager TaskManager
JobManager
(1) request split
(2) send split
(3) process split
• Splits are assigned to TaskManagers by the JobManager, which runs a copy of
the InputFormat  Flink knows about splits and can be clever about scheduling,
be reactive
• Splits can be processed in arbitrary order
• Split processing pulls records from the InputFormat
• InputFormat knows nothing about watermarks, timestamps, checkpointing  bad
for streaming

44. © 2019 Ververica45
Stream SourceFunction Processing
• Source have a run-loop that they manage completely on their own
• Sources have flexibility and can efficiently work with the source system: batch
accesses, dealing with multiple topics from one consumer, threading model,
etc…
• Flink does not know what’s going on inside and can’t be clever about it
• Sources have to implement their own per-partition watermarking, idleness
tracking, what have you
TaskManagerTaskManager TaskManager
(1) do your thing

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A New (unified) Source Interface
• This must support both batch and streaming use cases, allow Flink to be clever, be
able to deal with event-time, watermarks, source idiosyncrasies, and enable
snapshotting
• This should enable new features: generic idleness detection, event-time
alignment*
FLIP-27
Source
createSplitEnumerator()
createSplitReader
SplitEnumerator
discoverNewSplits()
nextSplit()
snapshotState()
isDone()
SplitReader
addSplit()
hasAvailable(): Future
snapshotState()
emitNext(Context): Status
* FLINK-10886: Event-time alignment for sources; Jamie Grier (Lyft) contributed the first parts of this

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A New (unified) SourceInterface: Execution Style I
TaskManager TaskManager TaskManager
JobManager
(1) request split
(2) send split
(3) process split
• Splits are assigned to TaskManagers by the JobManager, which runs a copy of
the SplitEnumerator  Flink knows about splits and can be clever about
scheduling, be reactive
• Splits can be processed in arbitrary order
• Split processing is driven by the TaskManager working with SplitReader
• SplitReader emits watermarks but Flink deals with idleness, per-split
watermarking

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A New (unified) SourceInterface: Execution Style II

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Current Stack

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Runtime
Tasks / JobGraph / Network Interconnect / Fault Tolerance
DataSet
“Operators“ and Drivers / Operator Graph / Visitors
DataStream
StreamOperators / StreamTransformation Graph* /
Monolithic Translators
Table API / SQL
Logical Nodes* / Different translation paths

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What could be improved?
• Each API has its own internal graph representation  code duplication
• Multiple translation components between the different graphs  code duplication
– DataStream API has an intermediate graph structure: StreamTransformation  StreamGraph 
JobGraph
• Separate (incompatible) operator implementations
– DataStream API has StreamOperator, DataSet API has Drivers  two map operators, two flatMap
operators
– These are run by different lower-level Tasks
– DataSet operators are optimized for different requirements than DataSet operators
• Table API is translated to two distinct lower-level APIs  two different translation
stacks
– ”project operator” for DataStream and for DataSet
• Connectors for each API are separate  a whole bunch of connectors all over the
From a system design/code quality/architecture/development perspective

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What does this mean for users?
• You have to decide between DataSet and DataStream when writing a job
– Two (slightly) different APIs, with different capabilities
– Different set of supported connectors: no Kafka DataSet connector, no HBase DataStream connector
– Different performance characteristics
– Different fault-tolerance behavior
– Different scheduling logic
• With Table API, you only have to learn one API
– Still, the set of supported connectors depends on the underlying execution API
– Feature set depends on whether there is an implementation for your underlying API
• You cannot combine more batch-y with more stream-y sources/sinks
• A “soft problem”: with two stacks of everything, less developer power will go into
each one individual stack  less features, worse performance, more bugs that are
fixed slower

52. © 2019 Ververica
Future Stack

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DataSet
“Operators“ and Drivers / Operator
Graph / Visitors
DataStream
StreamOperator /
StreamTransformation Graph* /
Monolithic Translators
Batch is a subset of streaming!
Can’t we just?
✅❌
Done!
🥳

54. © 2019 Ververica55
Unifying the Batch and Streaming APIs
• DataStream API functionality is already a superset of DataSet API functionality
• We need to introduce BoundedStream to harness optimization potential, semantics
are clear from earlier:
–No processing-time timers
–Watermark “jumps” from –Infinity to +Infinity at end of processing
• DataStream translation and runtime (operators) need to be enhanced to use the
added optimization potential
• Streaming execution is the generic case that always works, “batch” enables
additional “optimization rules”: bounded operators, different scheduling  we get
feature parity automatically ✅
• Sources need to be unified as well  see later

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Under-the-hood Changes
• StreamTransformation/StreamGraph
need to be beefed up to carry the
additional information about
boundedness
• Translation, scheduling, deployment,
memory management and network stack
needs to take this into account
Graph Representation / DAG Operator / Task
• StreamOperator needs to support
batch-style execution  see next slide
• Network stack must eventually support
blocking inputs

56. © 2019 Ververica57
Selective Push Model Operator FLINK-11875
batch: pull-based operator (or Driver) streaming: push-based StreamOperator
• StreamOperator needs additional API
to tell the runtime which input to
consume
• Network stack/graph needs to be
enhanced to deal with blocking inputs,
😱

57. © 2019 Ververica58
Table API / SQL
• API: easy, it’s already unified ✅
• Translation and runtime (operators) need to be enhanced to use the added
optimization potential but use the StreamOperator for both batch and streaming
style execution
• Streaming execution is the generic case that always works, “batch” enables
additional “optimization rules”: bounded operators, different scheduling  we get
feature parity automatically ✅
• Sources will be unified from the unified source interface ✅
• This is already available in the Blink fork (by Alibaba), FLINK-11439 is the effort of
getting that into Flink

58. © 2019 Ververica59
StreamTransformation DAG / StreamOperator
DataStream
“Physical“ Application API
Table API / SQL
Declarative API
Runtime
The Future
Stack