Apache Flink and what it is used for

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Aljoscha Krettek is the PMC chair of Apache Flink and Apache Beam, and co-founder of data Artisans. Apache Flink is an open-source platform for distributed stream and batch data processing. It allows for stateful computations over data streams in real-time and historically. Flink supports batch and stream processing using APIs like DataSet and DataStream. Data Artisans originated Flink and provides an application platform powered by Flink and Kubernetes for building stateful stream processing applications.

Aljoscha Krettek – PMC of Apache Flink and Apache Beam, Co-Founder at data Artisans
Apache Flink® and what it is used for

© 2018 data Artisans2
About Data Artisans
Original Creators of
Apache Flink®
RealTime Stream Processing
Enterprise Ready
2008 2009 2011
Studying and working at IBM
Development on Stratosphere begins
I joinTU Berlin research group with other co-founders
2014
data Artisans is founded

© 2018 data Artisans3
What is Apache Flink?
Queries
Applications
Devices
etc.
Database
Stream
File / Object
Storage
Historic
Data
Streams
Application
Stateful computations over streams
real-time and historic
fast, scalable, fault tolerant, in-memory,
event time, large state, exactly-once

© 2018 data Artisans4
Overview of Flink Use Cases
Event-driven
applications
Stream
Processing
Batch
processing

© 2018 data Artisans5
Batch Processing
a.k.a. collect now, figure
out later

© 2018 data Artisans6
Batch Processing Technologies Supported by Flink
DataSet
API

© 2018 data Artisans7
Observation 1
The origin of data are streams

© 2018 data Artisans8
Stream Processing
we can build applications directly
on data streams

© 2018 data Artisans9
Stream Processing Technologies Supported by Flink
DataStream
API

© 2018 data Artisans10
Observation 2
Stream Processing changes the database-centric
architecture

© 2018 data Artisans11
Changing the Two-Tier Architecture
reads/writes across
tier boundary
asynchronous writes
of large blobs
all modifications
are local
Classic tiered architecture Streaming architectureClassic tiered architecture
database
layer
compute
layer
application working state
+ historic state
compute
+
stream storage
and
snapshot storage
(backup)
application state
Streaming architecture

© 2018 data Artisans12
Keystone Routing Pipeline at Netflix
(as presented at Flink Forward San Francisco, 2018)
@
 Athena X
 SQL to define metrics
 Thresholds and actions to trigger
 Blends analytics and
actions
Streams from
Hadoop, Kafka, etc
SQL, thresholds,
actions
Analytics
Alerts
Derived streams

© 2018 data Artisans13
Observation 3
Stream Processing is about building applications,
not platforms

© 2018 data Artisans14
Internal streaming data platforms
built with Apache Flink

© 2018 data Artisans15
KubernetesResource Manager
Logging
Metrics
CI / CD
Application Platforms
deploying new
applications
scaling
applications
Kubernetes

© 2018 data Artisans16
Kubernetes
Database
Kubernetes
• Example: Scaling down a replicated database
• 3 replicas, 4 node scale down
need to move or
reorganize data
before container
shutdown
Kubernetes & stateful applications What about stateful containers?

© 2018 data Artisans17
• consistent stateful upgrades
‒application evolution and bug fixes
• migration of application state
‒cluster migration, A/B testing
• re-processing and reinstatement
‒fix corrupt results, bootstrap new applications
• state evolution (schema evolution)
A B
Stateful Questions
Container-based
Resource Orchestration
Stateful Stream
Processing & Snapshots
Kubernetes Apache Flink
Container-based
platform for stateful
data-driven applications
dA Platform
Code, Resource, Config, and
Snapshot Management
Application
Manager

© 2018 data Artisans18
Architecture
Apache Flink
Stateful stream processing
Kubernetes
Container platform
Logging
Metrics
dA Application
Manager
Application
lifecycle
management
Storage
In Action
App
Manager
Kubernetes
Resource
Allocation
Job Control
Snapshot
Management
CI/CDWeb
interface

© 2018 data Artisans19
Powered by Apache Flink

© 2018 data Artisans20
Free Trial Download of data Artisans Platform
data-artisans.com/download

THANK YOU! WE ARE HIRING
data-artisans.com/careers
@aljoscha
@dataArtisans
@ApacheFlink

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The document describes requirements for a platform to detect suspicious behavior in an organization. It involves three patterns: 1) Time-based aggregations to detect behaviors like many login failures within a short time. Windowing and aggregating events is needed. 2) Data enrichment to report details of alerts, like fetching user profiles to identify users. Side inputs allow querying external databases during event processing. 3) Dynamic processing since rules change over time. Broadcast state stores evolving rules and connects them to user event streams for continuous checking.

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If you are in the business of processing a stream of events you sooner or later come upon different notions of time. There is processing time, the current time of the machine your program is running on and event time, the local time at which an event occurred. In this talk we will look at why this distinction is relevant and also how Flink manages to work with these different ideas of time. We will look at how Flink tracks the progress of time and how you can employ windows to perform aggregating operations on an infinite stream of events.

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Apache Flink and what it is used for

1. Aljoscha Krettek – PMC of Apache Flink and Apache Beam, Co-Founder at data Artisans Apache Flink® and what it is used for

2. © 2018 data Artisans2 About Data Artisans Original Creators of Apache Flink® RealTime Stream Processing Enterprise Ready 2008 2009 2011 Studying and working at IBM Development on Stratosphere begins I joinTU Berlin research group with other co-founders 2014 data Artisans is founded

3. © 2018 data Artisans3 What is Apache Flink? Queries Applications Devices etc. Database Stream File / Object Storage Historic Data Streams Application Stateful computations over streams real-time and historic fast, scalable, fault tolerant, in-memory, event time, large state, exactly-once

11. © 2018 data Artisans11 Changing the Two-Tier Architecture reads/writes across tier boundary asynchronous writes of large blobs all modifications are local Classic tiered architecture Streaming architectureClassic tiered architecture database layer compute layer application working state + historic state compute + stream storage and snapshot storage (backup) application state Streaming architecture

12. © 2018 data Artisans12 Keystone Routing Pipeline at Netflix (as presented at Flink Forward San Francisco, 2018) @  Athena X  SQL to define metrics  Thresholds and actions to trigger  Blends analytics and actions Streams from Hadoop, Kafka, etc SQL, thresholds, actions Analytics Alerts Derived streams

16. © 2018 data Artisans16 Kubernetes Database Kubernetes • Example: Scaling down a replicated database • 3 replicas, 4 node scale down need to move or reorganize data before container shutdown Kubernetes & stateful applications What about stateful containers?

17. © 2018 data Artisans17 • consistent stateful upgrades ‒application evolution and bug fixes • migration of application state ‒cluster migration, A/B testing • re-processing and reinstatement ‒fix corrupt results, bootstrap new applications • state evolution (schema evolution) A B Stateful Questions Container-based Resource Orchestration Stateful Stream Processing & Snapshots Kubernetes Apache Flink Container-based platform for stateful data-driven applications dA Platform Code, Resource, Config, and Snapshot Management Application Manager

18. © 2018 data Artisans18 Architecture Apache Flink Stateful stream processing Kubernetes Container platform Logging Metrics dA Application Manager Application lifecycle management Storage In Action App Manager Kubernetes Resource Allocation Job Control Snapshot Management CI/CDWeb interface

21. THANK YOU! WE ARE HIRING data-artisans.com/careers @aljoscha @dataArtisans @ApacheFlink

Editor's Notes

#3: • data Artisans was founded by the original creators of Apache Flink • We provide dA Platform, a complete stream processing infrastructure with open-source Apache Flink
#13: Alibaba 472 mio records / second at peak Largest job: thousands of subtasks, tens of TBs of state Thousands of jobs >5k nodes >500k CPU cores Netflix ~ 3 trillion events/day ~2000 routing jobs ~10k containers ~200k parallel operator instances Uber Athena X SQL to define metrics Thresholds and actions to trigger Blends analytics and actions All have in common: built internal platforms
#21: • Also included is the Application Manager, which turns dA Platform into a self-service platform for stateful stream processing applications. • dA Platform is generally available, and you can download a free trial today!

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