How to manage large amounts of data with akka streams

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Igor Mielientiev

How to manage large amounts of data with akka streams

How to manage large amounts
of data with Akka Streams
by Ihor Mielientiev

Agenda
1. Reactive Streams
2. Backpressure
3. Handling big amount of data (Demos)

Reactive Streams
http://www.reactive-streams.org/

Reactive Streams: Goals
1. Backpressured asynchronous stream processing
2. Standard interfaces

Reactive Streams
Reactive Streams is a standard and specification for Stream-oriented
libraries for the JVM that
process a potentially unbounded number of elements
asynchronously passing elements between components
with mandatory non-blocking backpressure.

Reactive Streams: Implementations
Akka Streams @ Lightbend (Typesafe)
Reactor @ Pivotal (Spring 5 will adopt “reactive” programming)
RxJava @ Netflix
Vert.x @ RedHat
Ratpack
Slick 3.0
MongoDB
Reactive Rabbit (driver)

Reactive Streams (interfaces)

Backpressure

What is backpressure?

What is backpressure?

What is backpressure?

Solutions
1. Just Increase Buffer Size :)
2. Bounded buffer (drop messages + require resending)
3. Negative ACK (tell Publisher to slow down)
4. speed(publisher) < speed(subscriber) (Dynamic Push/Pull)

Handling big files (HTTP)
In many cases the request stored in memory (gather/parse http
request and then analyzing)
It’s a bad, bad Idea to put the body in memory. (Try to upload
10 GB file and store it)

Classic Java Stream Management

Problems
Low Performance (if no buffer)
Buffer Issues (buffer management/overflow/...)
Error Handling
Mixing IO management with business logic
Not Modular
Synchronous (Blocking)
Ugly Code

Better Way: Akka Streams
Data Creator (Producer) Data ConsumerPipe and transfor data (Connector)

Akka Streams
Pure Functional
Composable
Type Safe

Let’s manage HTTP body stream with Akka
Stream/HTTP

Process (Upload)
1. File is post by client (browser etc.)
2. Normalize stream (chunks)
3. Async save each chunk in K/V storage (Amazon S3)
4. Store metadata

Process (Download)
1. Get a fileId from client
2. Stream each chunk to client :)

Questions?

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Artyushina_Guest lecture_YorkU CS May 2024.pptx

Artyushina_Guest lecture_YorkU CS May 2024.pptx

Artyushina_Guest lecture_YorkU CS May 2024.pptx

AnnaArtyushina1

Investing in AI transformation today The modern business advantage: Uncovering deep insights with AI Organizations around the world have come to recognize AI as the transformative technology that enables them to gain real business advantage. AI’s ability to organize vast quantities of data allows those who implement it to uncover deep business insights, augment human expertise, drive operational efficiency, transform their products, and better serve their customers

Microsoft AI Transformation Partner Playbook.pdf

Microsoft AI Transformation Partner Playbook.pdf

Microsoft AI Transformation Partner Playbook.pdf

Willy Marroquin (WillyDevNET)

ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Papp Krisztián

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

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WSO2CON 2024 - Building the API First Enterprise – Running an API Program, fr...

WSO2CON 2024 - Building the API First Enterprise – Running an API Program, fr...

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VTU technical seminar 8Th Sem on Scikit-learn

VTU technical seminar 8Th Sem on Scikit-learn

AmarnathKambale

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WSO2CON 2024 - Cloud Native Middleware: Domain-Driven Design, Cell-Based Arch...

WSO2CON 2024 - Cloud Native Middleware: Domain-Driven Design, Cell-Based Arch...

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Love witchcraft +27768521739 Binding love spell in Sandy Springs, GA |psychic...

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Artyushina_Guest lecture_YorkU CS May 2024.pptx

Artyushina_Guest lecture_YorkU CS May 2024.pptx

Artyushina_Guest lecture_YorkU CS May 2024.pptx

Microsoft AI Transformation Partner Playbook.pdf

Microsoft AI Transformation Partner Playbook.pdf

Microsoft AI Transformation Partner Playbook.pdf

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Architecture decision records - How not to get lost in the past

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

Announcing Codolex 2.0 from GDK Software

WSO2CON 2024 - Building the API First Enterprise – Running an API Program, fr...

WSO2CON 2024 - Building the API First Enterprise – Running an API Program, fr...

WSO2CON 2024 - Building the API First Enterprise – Running an API Program, fr...

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VTU technical seminar 8Th Sem on Scikit-learn

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How to manage large amounts of data with akka streams

1. How to manage large amounts of data with Akka Streams by Ihor Mielientiev

2. Agenda 1. Reactive Streams 2. Backpressure 3. Handling big amount of data (Demos)

3. Reactive Streams http://www.reactive-streams.org/

4. Reactive Streams: Goals 1. Backpressured asynchronous stream processing 2. Standard interfaces

5. Reactive Streams Reactive Streams is a standard and specification for Stream-oriented libraries for the JVM that process a potentially unbounded number of elements asynchronously passing elements between components with mandatory non-blocking backpressure.

6. Reactive Streams: Implementations Akka Streams @ Lightbend (Typesafe) Reactor @ Pivotal (Spring 5 will adopt “reactive” programming) RxJava @ Netflix Vert.x @ RedHat Ratpack Slick 3.0 MongoDB Reactive Rabbit (driver)

7. Reactive Streams (interfaces)

8. Backpressure

9. What is backpressure?

10. What is backpressure?

11. What is backpressure?

12. Solutions 1. Just Increase Buffer Size :) 2. Bounded buffer (drop messages + require resending) 3. Negative ACK (tell Publisher to slow down) 4. speed(publisher) < speed(subscriber) (Dynamic Push/Pull)

13.

14. Handling big files (HTTP) In many cases the request stored in memory (gather/parse http request and then analyzing) It’s a bad, bad Idea to put the body in memory. (Try to upload 10 GB file and store it)

15. Classic Java Stream Management

16.

17. Problems Low Performance (if no buffer) Buffer Issues (buffer management/overflow/...) Error Handling Mixing IO management with business logic Not Modular Synchronous (Blocking) Ugly Code

18. Better Way: Akka Streams Data Creator (Producer) Data ConsumerPipe and transfor data (Connector)

19.

20. Akka Streams Pure Functional Composable Type Safe

21. Let’s manage HTTP body stream with Akka Stream/HTTP

22. Process (Upload) 1. File is post by client (browser etc.) 2. Normalize stream (chunks) 3. Async save each chunk in K/V storage (Amazon S3) 4. Store metadata

23. Process (Download) 1. Get a fileId from client 2. Stream each chunk to client :)