Introduction to Apache Flink™: How Stream Processing is Shaping the Data Engineering Space [email protected] Tzu-Li (Gordon) Tai @tzulitai
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Introduction to Apache Flink™:How Stream Processing is Shaping
the Data Engineering Space
Tzu-Li (Gordon) Tai
@tzulitai
● 戴資力（Gordon）● Apache Flink Committer● Co-organizer of Apache Flink Taiwan User Group● Software Engineer @ VMFive● Java, Scala● Enjoy developing distributed systems
Who am I?
Data Streaming is becomingincreasingly popular
1
Stream processing is enabling the obvious: continuous processing on data that is continuously produced
2
Streaming is the next programming paradigm for data applications, and you need to start thinking in terms
of streams
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01 The Traditional Batch Wayt
...
HDFSFile
MapReduce /Spark / Flink
Jobs
● Continouslyingesting data
● Periodicbatch files
● Periodicbatch jobs
4
01 The Traditional Batch Wayt
...
cross boundary
intermediateresults
● Jobs often has “dangling” results near batch boundaries
● Need to save them, and input into the next batch job
5
02 Key Observations for Batch
6
● Way too many moving parts
● Implicit treatment of time (the batch boundaries)
● Treating continuous state as discrete
● Troublesome to get accurate, correct results
03 The “Ideal” Streaming Wayt
...
Streaming processor that handles …
(1) continuous state(2) out-of-order events
scalably, robustly, and efficiently
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04 Apache Flink
Apache Flinkan open-source platform for distributed stream and batch data processing
● Apache Top-Level Project since Jan. 2015
● Streaming Dataflow Engine at its core○ Low latency○ High Throughput○ Stateful○ Accurate○ Distributed
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04 Apache Flink
Apache Flinkan open-source platform for distributed stream and batch data processing
● ~260 contributors, ~25 Committers / PMC
● Used adoption:○ Alibaba - realtime search optimization○ Uber - ride request fulfillment marketplace○ Netflix - Stream Processing as a Service (SPaaS)○ Kings Gaming - realtime data science dashboard○ ...
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04 Apache Flink
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05 Scala Collection-like APIcase class Word (word: String, count: Int)
val lines: DataSet[String] = env.readTextFile(...)
lines.flatMap(_.split(“ ”)).map(word => Word(word,1)).groupBy(“word”).sum(“count”).print()
val lines: DataStream[String] = env.addSource(new KafkaSource(...))
lines.flatMap(_.split(“ ”)).map(word => Word(word,1)).keyBy(“word”).timeWindow(Time.seconds(5)).sum(“count”).print()
DataSet API
DataStream API
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05 Scala Collection-like API
.filter(...).flatmap(...).map(...).groupBy(...).reduce(...)
● Becoming the de facto standard for new generation API to express data pipelines
● Apache Spark, Apache Flink, Apache Beam ...
12
06 What does Flink’s Engine do?
YourCode
process records one-at-a-time
...
● Computation on a never-ending stream of data records
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06 What does Flink’s Engine do?
YourCode...
● System distributes the computation across the cluster
YourCode...
YourCode...
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07 Streaming Dataflow Runtime
JobManager
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager
TaskManager
ExecutionGraph
(parallel) TaskManager
TaskManager
TaskManager
Application Code
(DataSet /DataStream)
Optimizer / Graph
Generator
JobGraph(logical)
Client
concurrently executed
distributed queues as push-based data shipping channels
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07 Streaming Dataflow Runtime
1. Record “A” enters Task 1, and is processed
2. The record is serialized into an output buffer at Task 1
3. The buffer is shipped to Task 2’s input buffer
Observation: Buffers need to be available throughout the process (think blocking queues used between threads)
● A slightly closer look into the transmission of data ...
Taken from anoutput buffer pool
Taken from aninput buffer pool
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07 Streaming Dataflow Runtime● Natural, built-in backpressure
● Receiving data at a higher rate than a system can process during a temporary load spike
○ ex. GC talls at processing tasks○ ex. data source natural load spike
Normal stable case:
Temporary load spike:
Ideal backpressure handling:
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● Due to one-at-a-time processing, Flink has very powerful built-in windowing (certainly among the best in the current streaming framework solutions)
○ Time-driven: Tumbling window, Sliding window○ Data-driven: Count window, Session window
07 Flexible Windows
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07 Time Windows
Tumbling Time Window Sliding Time Window
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07 Count-Triggered Windows
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07 Session Windows
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08 What does Flink’s Engine do?
YourCode
...
State
● Computation and state, ex.:○ counters○ in-progress
windows○ state machines○ trained ML
models
● Results depend onhistory of stream
● A stateful streamprocessor gives tools to manage state
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09 What does Flink’s Engine do?
YourCode
...
State
● Processingdepends on timestamps of whenevents were generated
● Core mechanics is called watermarks: basically a way to measure and advance clock time, instead of relying on machine time
t1t2 t3t4 t1 - t2 t3 - t4
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09 Different Kinds of “Time”
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09 Why Wall Time is Incorrect
● Think Twitter hash-tag count every 5 minutes
○ We would want the result to reflect the number of Twitter tweets actually tweeted in a 5 minute window
○ Not the number of tweet events the stream processor receives within 5 minutes
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09 Why Wall Time is Incorrect
● Think replaying a Kafka topic on a windowed streaming application …
○ If you’re replaying a queue, windows are definitely wrong if using a wall clock
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10 Flink’s Streaming Fault Tolerance
YourCode
YourCode
YourCode
State
State
State
...
...
...
...
...
...
...
...
...
YourCode
YourCode
YourCode
State
State
State
● Any operator in a Flink streaming topology can be stateful● How to ensure that the states are correct upon failure?
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10 Flink’s Streaming Fault Tolerance
● First, a recap of some guarantee concepts:
○ At-least-once: records may be processed more than once.Think counting: may over count, resulting in wrong state
○ Exactly-once “state”: records appear to be processed only once, with respect to the state.Think counting: even on failure, each record is counted exactly once
○ End-to-end exactly-once: records appear to be processed only once, even to external systemsThink counting: for results stored externally, even after failure, the results remain correct
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11 Flink’s Streaming Fault Tolerance
YourCode
YourCode
YourCode
State
State
State
...
...
...
...
...
...
...
...
...
YourCode
YourCode
YourCode
State
State
State
● Flink checkpoints: a combined snapshot of all operator state, with the corresponding position in the source
● Based on Chandly-Lamport Algorithm: does not haltany computation while taking consistent snapshots
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12 Flink’s Savepoints
● Flink checkpoints: consistent snapshots of the whole topology state that the system periodically takes
● Flink savepoints: manually triggered checkpoints that can be persisted, and used to initialize state for a new streaming job
tt1 t2 t3
savepointstate at t1
savepointstate at t2
savepointstate at t3
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13 So, back to this ...
t
...
HDFSFile
MapReduce /Spark / Flink
Jobs
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13 So, back to this ...
t
...
Streaming processor that handles …
(1) continuous state(2) out-of-order events
scalably, robustly, and efficiently
32
14 What Flink provides, in a nutshell example
● No stateless point-in-time
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14 What Flink provides, in a nutshell example
● Processing, or re-processing, in the batch way
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14 What Flink provides, in a nutshell example
● Batch is inherently unsuitable for the nature of continuously generated data
● State is corrupt at boundaries
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14 What Flink provides, in a nutshell example
● Flink’s Stateful streaming naturally treats state continuously as it processes your continuous data, and continuously generates results
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14 What Flink provides, in a nutshell example
● On reprocessing: initial state for the job reflects all previous history data in the stream
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14 What Flink provides, in a nutshell example
● On reprocessing: event-time processing guarantees correct results, even when fast-forwarding to the head of stream
event-time processing
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15 Final Takeaways
● Stateful Streaming correctly embraces the nature of continuously generated data, and is the new programming paradigm for their applications.
● Streaming isn’t only about real-time. Realtime is only a natural advantage of streaming.
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15 Final Takeaways● The choice is all about your data, and your code.
● Think:○ Is your data unbounded, or bounded?
■ Unbounded: click streams, page visits, impressions …■ Bounded: (???)
● Think:○ Does your code change faster than your data?
■ Data exploration, data mining, feature engineering …■ In this case, it doesn’t really matter whether you use batch or streaming
○ Or does your data change faster than your code?■ Production ETL pipelines, warehousing, serving, etc.■ For accuracy and robustness, definitely think and design in terms of
streaming
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15 Final Takeaways
● Upcoming features in Flink:
○ Dynamic scaling, with stateful streaming○ Queryable state○ Incremental state checkpointing○ Even more savepoint functionality
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15 Final Takeaways
● How Flink’s technology covers the application space:
Application
Realtime applications
Continuous applications
Analytics on historical data
Request/Response Apps
Technology
Low-latency stateful streaming
High-latency stateful streaming
Batch as special case of streaming
Queryable state
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