Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads

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http://flink-forward.org/kb_sessions/dynamic-scaling-how-apache-flink-adapts-to-changing-workloads/ Modern stream processing engines not only have to process millions of events per second at sub-second latency but also have to cope with constantly changing workloads. Due to the dynamic nature of stream applications where the number of incoming events can strongly vary with time, systems cannot reliably predetermine the amount of required resources. In order to meet guaranteed SLAs as well as utilizing system resources as efficiently as possible, frameworks like Apache Flink have to adapt their resource consumption dynamically. In this talk, we will take a look under the hood and explain how Flink scales stateful application in and out. Starting with the concept of key groups and partionable state, we will cover ways to detect bottlenecks in streaming jobs and discuss efficient strategies how to scale out operators with minimal down-time.

Daten & Analysen

Till Rohrmann
trohrmann@apache.org
@stsffap
Dynamic Scaling: How Apache
Flink® Adapts to Changing
Workloads

Resource Adaption
3
+
time
Workload
Resources
time
Workload
Resources

What Is This Talk About?
§ Flink’s approach to dynamic scaling
§ Current state with demo
§ Outlook on next development steps
4

Basic Idea
6
• Spread work across more workers to decrease workload

Scaling Stateless Jobs
7
Scale Up Scale Down
Source
Mapper
Sink
• Scale up: Deploy new tasks
• Scale down: Cancel running tasks

Scaling Stateful Jobs
8
?
• Problem: Which state to assign to new task?

Keyed vs. Non-keyed State
10
• State bound to a key
• E.g. Keyed UDF and window state
• State bound to a subtask
• E.g. Source state
Keyed Non-keyed

Repartitioning Keyed State
§ Similar to consistent
hashing
§ Split key space into
key groups
§ Assign key groups to
tasks
11
Key space
Key group #1 Key group #2
Key group #3Key group #4

Repartitioning Keyed State contd.
§ Rescaling changes
key group
assignment
§ Maximum parallelism
defined by #key
groups
12

Repartitioning Non-keyed state
§ User defined merge and
split functions
• Most general approach
§ Breaking non-keyed state
up into finer granularity
• State has to contain
multiple entries
• Automatic repartitioning
wrt granularity
13
#1 #2
#3

Repartitioning Non-keyed State contd.
§ Non-keyed state entries gathered at the
job manager
§ Repartitioning schemes
• Repartition & send
• Union & broadcast
14

Example: Kafka Source
15
partitionId: 1, offset: 42
partitionId: 3, offset: 10
partitionId: 6, offset: 27
• Store offset for each partition
• Individual entries are repartitionable
partitionId: 1, offset: 42
partitionId: 3, offset: 10
partitionId: 6, offset: 27

Rescaling: Why is That so Hard?
§ Handling of state
§ Repartitioning of keyed & non-keyed
state
§ Unique among open source stream
processors, afaik
16

Demo Topology
18
Kafka Source Counter
KeyBy

Current State
§ Manual rescaling
1. Take savepoint
2. Stop the job
3. Restart job with adjusted parallelism and
savepoint
20

Next Steps
§ Integrate savepoint with stop signal
§ Rescaling individual operators w/o restart
§ Dynamic container de-/allocation
• “Running Flink Everywhere” by Stephan
Ewen, 16:45 at Kesselhaus
21

Auto Scaling Policies
22
• Latency
• Throughput
• Resource utilization
• Kubernetes on GCE, EC2 and Mesos (marathon-
autoscale) already support auto-scaling

Conclusion
§ Scaling of keyed and non-keyed state
§ Flink supports manual rescaling with
restart
(WIP branch: https://github.com/tillrohrmann/flink/tree/partitionable-op-state)
§ Future versions might support scaling on
the fly and automatic rescaling policies
23

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Flink Forward San Francisco 2022. The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples." by Thomas Weise

Introducing the Apache Flink Kubernetes Operator

Flink Forward

Apache Airflow (incubating) NL HUG Meetup 2016-07-19

Bolke de Bruin

2017 big data landscape and cutting edge innovations public

Evans Ye

Kafka streams fifth elephant 2018

Giridhar Addepalli

Moving from Lambda and Kappa Architectures to Kappa+ at Uber Kappa+ is a new approach developed at Uber to overcome the limitations of the Lambda and Kappa architectures. Whether your realtime infrastructure processes data at Uber scale (well over a trillion messages daily) or only a fraction of that, chances are you will need to reprocess old data at some point. There can be many reasons for this. Perhaps a bug fix in the realtime code needs to be retroactively applied (aka backfill), or there is a need to train realtime machine learning models on last few months of data before bringing the models online. Kafka's data retention is limited in practice and generally insufficient for such needs. So data must be processed from archives. Aside from addressing such situations, enabling efficient stream processing on archived as well as realtime data also broadens the applicability of stream processing. This talk introduces the Kappa+ architecture which enables the reuse of streaming realtime logic (stateful and stateless) to efficiently process any amounts of historic data without requiring it to be in Kafka. We shall discuss the complexities involved in such kind of processing and the specific techniques employed in Kappa+ to tackle them.

Flink Forward San Francisco 2019: Moving from Lambda and Kappa Architectures ...

Flink Forward

SolrCloud in Public Cloud: Scaling Compute Independently from Storage - Ilan ...

Lucidworks

The Fn project is a container-native Apache 2.0 licensed serverless platform that you can run anywhere – on any cloud or on-premise. It’s easy to use, supports every programming language, and is extensible and performant. This YourStory-Oracle Developer Meetup covers various design aspects of Serverless for polyglot programming, implementation of Saga pattern, etc. It also emphasizes on the monitoring aspect of Fn project using Prometheus and Grafana

Serverless design with Fn project

Siva Rama Krishna Chunduru

When Streaming Needs Batch With Konstantin Knauf | Current 2022 A streaming application is started once and then continuously ingests endless, fairly steady streams of events. That's as far as the theory goes. Unfortunately, reality is more complicated. Over time your application's ability to process large historical data sets robustly, efficiently and correctly will be critical: - for exploratory data analysis during development - for bootstrapping the initial state of an application - for back-filling following an outage or bugfix - for keeping up with bursty input streams These scenarios call for batch processing techniques. Apache Flink is as streaming-first as it gets. Yet over the last releases, the community has invested significant resources into unifying stream- and batch processing on all layers of the stack: scheduler to APIs. In this talk, I'll introduce Apache Flink's approach to unified stream and batch processing and discuss - by example - how these scenarios can already be addressed today and what might be possible in the future.

When Streaming Needs Batch With Konstantin Knauf | Current 2022

HostedbyConfluent

Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms. Apache Apex is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data. Apache Apex is a native Hadoop data-in-motion platform. We will discuss architectural differences between Apache Apex features with Spark Streaming. We will discuss how these differences effect use cases like ingestion, fast real-time analytics, data movement, ETL, fast batch, very low latency SLA, high throughput and large scale ingestion. We will cover fault tolerance, low latency, connectors to sources/destinations, smart partitioning, processing guarantees, computation and scheduling model, state management and dynamic changes. We will also discuss how these features affect time to market and total cost of ownership.

Intro to Apache Apex (next gen Hadoop) & comparison to Spark Streaming

Apache Apex

Spark Overview and Performance Issues

Antonios Katsarakis

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Version control 101

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Apache Flink is a distributed stream processing framework that allows users to process and analyze data in real-time. At LinkedIn, we developed a fully managed stream processing platform on Flink running on K8s to power hundreds of stream processing pipelines in production. This platform is the backbone for other infra systems like Search, Espresso (internal document store) and feature management etc. We provide a rich authoring and testing environment which allows users to create, test, and deploy their streaming jobs in a self-serve fashion within minutes. Users can focus on their business logic, leaving the Flink platform to take care of management aspects such as split deployment, resource provisioning, auto-scaling, job monitoring, alerting, failure recovery and much more. In this talk, we will introduce the overall platform architecture, highlight the unique value propositions that it brings to stream processing at LinkedIn and share the experiences and lessons we have learned.

Building a fully managed stream processing platform on Flink at scale for Lin...

Flink Forward

Flink Forward San Francisco 2022. When running Flink jobs, skew is a common problem that results in wasted resources and limited scalability. In the past years, we have helped our customers and users solve various skew-related issues in their Flink jobs or clusters. In this talk, we will present the different types of skew that users often run into: data skew, key skew, event time skew, state skew, and scheduling skew, and discuss solutions for each of them. We hope this will serve as a guideline to help you reduce skew in your Flink environment. by Jun Qin & Karl Friedrich

Evening out the uneven: dealing with skew in Flink

Flink Forward

Flink Forward San Francisco 2022. To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy. by Aansh Shah

“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...

Flink Forward

Flink Forward San Francisco 2022. Probably everyone who has written stateful Apache Flink applications has used one of the fault-tolerant keyed state primitives ValueState, ListState, and MapState. With RocksDB, however, retrieving and updating items comes at an increased cost that you should be aware of. Sometimes, these may not be avoidable with the current API, e.g., for efficient event-time stream-sorting or streaming joins where you need to iterate one or two buffered streams in the right order. With FLIP-220, we are introducing a new state primitive: BinarySortedMultiMapState. This new form of state offers you to (a) efficiently store lists of values for a user-provided key, and (b) iterate keyed state in a well-defined sort order. Both features can be backed efficiently by RocksDB with a 2x performance improvement over the current workarounds. This talk will go into the details of the new API and its implementation, present how to use it in your application, and talk about the process of getting it into Flink. by Nico Kruber

Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...

Flink Forward

Flink Forward San Francisco 2022. Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo. by Robert Metzger

Autoscaling Flink with Reactive Mode

Flink Forward

Flink Forward San Francisco 2022. Flink consumers read from Kafka as a scalable, high throughput, and low latency data source. However, there are challenges in scaling out data streams where migration and multiple Kafka clusters are required. Thus, we introduced a new Kafka source to read sharded data across multiple Kafka clusters in a way that conforms well with elastic, dynamic, and reliable infrastructure. In this presentation, we will present the source design and how the solution increases application availability while reducing maintenance toil. Furthermore, we will describe how we extended the existing KafkaSource to provide mechanisms to read logical streams located on multiple clusters, to dynamically adapt to infrastructure changes, and to perform transparent cluster migrations and failover. by Mason Chen

Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...

Flink Forward

Flink Forward San Francisco 2022. Next time you want to integrate with a new destination for a demo, concept or production application, the Async Sink framework will bootstrap development, allowing you to move quickly without compromise. In Flink 1.15 we introduced the Async Sink base (FLIP-171), with the goal to encapsulate common logic and allow developers to focus on the key integration code. The new framework handles things like request batching, buffering records, applying backpressure, retry strategies, and at least once semantics. It allows you to focus on your business logic, rather than spending time integrating with your downstream consumers. During the session we will dive deep into the internals to uncover how it works, why it was designed this way, and how to use it. We will code up a new sink from scratch and demonstrate how to quickly push data to a destination. At the end of this talk you will be ready to start implementing your own Flink sink using the new Async Sink framework. by Steffen Hausmann & Danny Cranmer

One sink to rule them all: Introducing the new Async Sink

Flink Forward

Flink Forward San Francisco 2022. In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you! by Olena Babenko

Tuning Apache Kafka Connectors for Flink.pptx

Flink Forward

Flink Forward San Francisco 2022. Pinterest is a visual discovery engine that serves over 433MM users. Stream processing allows us to unlock value from realtime data for pinners. At Pinterest, we adopt Flink as the unified streaming processing engine. In this talk, we will share our journey in building a stream processing platform with Flink and how we onboarding critical use cases to the platform. Pinterest has supported 90+near realtime streaming applications. We will cover the problem statement, how we evaluate potential solutions and our decision to build the framework. by Rainie Li & Kanchi Masalia

Flink powered stream processing platform at Pinterest

Flink Forward

Flink Forward San Francisco 2022. This talk will take you on the long journey of Apache Flink into the cloud-native era. It started all the way from where Hadoop and YARN were the standard way of deploying and operating data applications. We're going to deep dive into the cloud-native set of principles and how they map to the Apache Flink internals and recent improvements. We'll cover fast checkpointing, fault tolerance, resource elasticity, minimal infrastructure dependencies, industry-standard tooling, ease of deployment and declarative APIs. After this talk you'll get a broader understanding of the operational requirements for a modern streaming application and where the current limits are. by David Moravek

Apache Flink in the Cloud-Native Era

Flink Forward

Flinkn Forward San Francisco 2022. In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times. by Piotr Nowojski

Where is my bottleneck? Performance troubleshooting in Flink

Flink Forward

Flink Forward San Francisco 2022. Running natively on Kubernetes, using the new Apache Flink Kubernetes Operator is a great way to deploy and manage Flink application and session deployments. In this presentation, we provide: - A brief overview of Kubernetes operators and their benefits. - Introduce the five levels of the operator maturity model. - Introduce the newly released Apache Flink Kubernetes Operator and FlinkDeployment CRs - Dockerfile modifications you can make to swap out UBI images and Java of the underlying Flink Operator container - Enhancements we're making in: - Versioning/Upgradeability/Stability - Security - Demo of the Apache Flink Operator in-action, with a technical preview of an upcoming product using the Flink Kubernetes Operator. - Lessons learned - Q&A by James Busche & Ted Chang

Using the New Apache Flink Kubernetes Operator in a Production Deployment

Flink Forward

Flink Forward San Francisco 2022. The Table API is one of the most actively developed components of Flink in recent time. Inspired by databases and SQL, it encapsulates concepts many developers are familiar with. It can be used with both bounded and unbounded streams in a unified way. But from afar it can be difficult to keep track of what this API is capable of and how it relates to Flink's other APIs. In this talk, we will explore the current state of Table API. We will show how it can be used as a batch processor, a changelog processor, or a streaming ETL tool with many built-in functions and operators for deduplicating, joining, and aggregating data. By comparing it to the DataStream API we will highlight differences and elaborate on when to use which API. We will demonstrate hybrid pipelines in which both APIs interact with one another and contribute their unique strengths. Finally, we will take a look at some of the most recent additions as a first step to stateful upgrades. by David Andreson

The Current State of Table API in 2022

Flink Forward

Flink Forward San Francisco 2022. Based on the new Flink-Pulsar connector, we implemented Flink's TableAPI and Catalog to help users to interact with the Pulsar cluster via Flink SQL easily. We would like to go through the design and implementation of the SQL connector in the following aspects: 1. Two different modes of use Pulsar as a metadata store 2. Data format transformation and management 3. SQL semantics support within Pulsar context by Sijie Guo & Neng Lu

Flink SQL on Pulsar made easy

Flink Forward

Flink Forward San Francisco 2022. At Bloomberg, we deal with high volumes of real-time market data. Our clients expect to be notified of any anomalies in this market data, which may indicate volatile movements in the markets, notable trades, forthcoming events, or system failures. The parameters for these alerts are always evolving and our clients can update them dynamically. In this talk, we'll cover how we utilized the open source Apache Flink and Siddhi SQL projects to build a distributed, scalable, low-latency and dynamic rule-based, real-time alerting system to solve our clients' needs. We'll also cover the lessons we learned along our journey. by Ajay Vyasapeetam & Madhuri Jain

Dynamic Rule-based Real-time Market Data Alerts

Flink Forward

Flink Forward San Francisco 2022. At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing. by Xiang Zhang & Pratyush Sharma & Xiaoman Dong

Exactly-Once Financial Data Processing at Scale with Flink and Pinot

Flink Forward

Flink Forward San Francisco 2022. What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases. by Patrick Lucas

Processing Semantically-Ordered Streams in Financial Services

Flink Forward

Flink Forward San Francisco 2022. In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling. by Gang Ye & Steven Wu

Tame the small files problem and optimize data layout for streaming ingestion...

Flink Forward

Flink Forward San Francisco 2022. Goldman Sachs's Data Lake platform serves as the firm's centralized data platform, ingesting 140K (and growing!) batches per day of Datasets of varying shape and size. Powered by Flink and using metadata configured by platform users, ingestion applications are generated dynamically at runtime to extract, transform, and load data into centralized storage where it is then exported to warehousing solutions such as Sybase IQ, Snowflake, and Amazon Redshift. Data Latency is one of many key considerations as producers and consumers have their own commitments to satisfy. Consumers range from people/systems issuing queries, to applications using engines like Spark, Hive, and Presto to transform data into refined Datasets. Apache Iceberg allows our applications to not only benefit from consistency guarantees important when running on eventually consistent storage like S3, but also allows us the opportunity to improve our batch processing patterns with its scalability-focused features. by Andreas Hailu

Batch Processing at Scale with Flink & Iceberg

Flink Forward

Flink Forward San Francisco 2022. At Flink Forward, we get to hear creative, unique use cases, often on the bleeding edge of some of the most exciting current technologies. This talk will give you a chance to get to open up the hood on our driven and innovative Open Source community. I will cover what our community has been working on this past year, and how this work relates to our (Ververica's) exciting new Flink engineering roadmap! I will also go through some best practices and upcoming opportunities for getting involved in this community! by Caito Scherr

Welcome to the Flink Community!

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Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads

1. Till Rohrmann trohrmann@apache.org @stsffap Dynamic Scaling: How Apache Flink® Adapts to Changing Workloads

2. Changing Workloads And SLAs 2

3. Resource Adaption 3 + time Workload Resources time Workload Resources

4. What Is This Talk About? § Flink’s approach to dynamic scaling § Current state with demo § Outlook on next development steps 4

5. Dynamic Scaling 5

6. Basic Idea 6 • Spread work across more workers to decrease workload

7. Scaling Stateless Jobs 7 Scale Up Scale Down Source Mapper Sink • Scale up: Deploy new tasks • Scale down: Cancel running tasks

8. Scaling Stateful Jobs 8 ? • Problem: Which state to assign to new task?

9. State in Apache Flink 9

10. Keyed vs. Non-keyed State 10 • State bound to a key • E.g. Keyed UDF and window state • State bound to a subtask • E.g. Source state Keyed Non-keyed

11. Repartitioning Keyed State § Similar to consistent hashing § Split key space into key groups § Assign key groups to tasks 11 Key space Key group #1 Key group #2 Key group #3Key group #4

12. Repartitioning Keyed State contd. § Rescaling changes key group assignment § Maximum parallelism defined by #key groups 12

13. Repartitioning Non-keyed state § User defined merge and split functions • Most general approach § Breaking non-keyed state up into finer granularity • State has to contain multiple entries • Automatic repartitioning wrt granularity 13 #1 #2 #3

14. Repartitioning Non-keyed State contd. § Non-keyed state entries gathered at the job manager § Repartitioning schemes • Repartition & send • Union & broadcast 14

15. Example: Kafka Source 15 partitionId: 1, offset: 42 partitionId: 3, offset: 10 partitionId: 6, offset: 27 • Store offset for each partition • Individual entries are repartitionable partitionId: 1, offset: 42 partitionId: 3, offset: 10 partitionId: 6, offset: 27

16. Rescaling: Why is That so Hard? § Handling of state § Repartitioning of keyed & non-keyed state § Unique among open source stream processors, afaik 16

17. Demo Time 17

18. Demo Topology 18 Kafka Source Counter KeyBy

19. Current State and next Steps 19

20. Current State § Manual rescaling 1. Take savepoint 2. Stop the job 3. Restart job with adjusted parallelism and savepoint 20

21. Next Steps § Integrate savepoint with stop signal § Rescaling individual operators w/o restart § Dynamic container de-/allocation • “Running Flink Everywhere” by Stephan Ewen, 16:45 at Kesselhaus 21

22. Auto Scaling Policies 22 • Latency • Throughput • Resource utilization • Kubernetes on GCE, EC2 and Mesos (marathon- autoscale) already support auto-scaling

23. Conclusion § Scaling of keyed and non-keyed state § Flink supports manual rescaling with restart (WIP branch: https://github.com/tillrohrmann/flink/tree/partitionable-op-state) § Future versions might support scaling on the fly and automatic rescaling policies 23

Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads

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Till Rohrmann - Dynamic Scaling - How Apache Flink adapts to changing workloads