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Mulesoft meetup 29.06
1.
29th Jun,2019 Hyderabad MuleSoft
Meetup Group
2.
All contents ©
MuleSoft Inc. Agenda 2 • 15:30 Introduction • 15:45 Session 1 • 16:45 Break for Snacks • 17:05 Session 2 • 17:40 Q&A
3.
Introductions
4.
All contents ©
MuleSoft Inc. Introductions 4 About the organizer/speaker : – Ravishankar Mishra – Integration Architect , Accenture – Satwinder Singh Banipal - Integration Architect, Accenture Partner: • Consulting services • Professional Services
5.
All contents ©
MuleSoft Inc. Introductions 5 Introduce yourselves ! – Name – Background • What’s your experience with Anypoint platform/ Mule ESB? • Why are you here ? Expectations?
6.
New Foundation of
Mule 4
7.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 7 • What’s In-Scope -Reactive Programming Model -Non Blocking Operations -Thread Management in Mule 4.x -Streaming in Mule 4.x
8.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 8 • We build integration solutions which cater to high volumes of real-time and batch data – Connectivity, Transformation, Enrichment, Validation, Routing, Error Handling, Security • Horizontal Scalability: High rates of API calls per second(More workers) • Vertical Scalability :Memory intensive processing (More vCore) • In Mule 4 vertical scaling is done differently using reactive programming. The aim is to maximize throughput and higher concurrency using non-blocking operations. This leads to more efficient use of CPU, memory and disk space.
9.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 9 • Non Blocking Operations -Fundamental to Reactive Programming (eventually Mule 4) -No Thread waits for any operation -Increases Concurrency however Latency is also more -Manual and E toll on highways
10.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 10 • Reactive Programming -Appetite for fast and responsive app (Picture Link) -Rx in .NET==JavaRx ==Reactor in Spring==Heart of Mule 4 -Merging Iterator and Observer Design Patterns • Giving consumer the power when to consume the data • Giving publisher the power when to push data -Back Pressure • Chain of (mostly) non blocking pub/sub pairs • Subscriber to signal to producer “slow down” -Automatic Back Pressure (No free threads available signal) • HTTP Listener 503-”Server Busy” • JMS Listener would not acknowledge the message • Avoid Out of Memory errors -Manual Back Pressure • maxConcurrency feature (Controls number of events)
11.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 11 • Thread Management -Mule 3 flow processing Strategies • Sync (Default) • Thread-per-processor(each processor in sequential different thread) • Qd-thread-per-processor(Induce a SEDA Q in previous), • QdAsync (Induce Q so no distribution across nodes) • Non-blocking (Picture Link) -One flow is getting executed by multiple threads in Mule 4 (Picture link) -Typical thread switching (Picture Link) -Transactional Scenario(Picture Link) -Transactional with Async (Picture Link) -Try Scope with transaction (Picture Link)
12.
All contents ©
MuleSoft Inc. Streaming: To process larger-than-memory Mule events, they are data structures that helps to process large data objects such as files, documents, and records by processing data continuously as it arrives. - Does not hold all the values in memory for processing unlike In-memory data structures - Avoids Out-Of-Memory while processing large data objects Mule 4 runtime by default streams large data payloads without any explicit configuration. All the Mule4 components that returns an InputStream or a Streamable collections support repeatable streams Some Connectors that support repeatable streams: -File -FTP -DB -HTTP -Sockets -Salesforce New Foundation of Mule 4
13.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 13 • Streaming options in Mule 4 - File based Repeatable Streams • Only available for Enterprise Edition • By Default store 500 objects in its In-Memory buffer and can be configured further • Excess objects get written to disk via Kyro Serializer - In-Memory Repeatable Streams • Default for Mule Community Edition • Uses default max buffer size of 500 objects • Increment size is 100 Objects until it reaches max buffer size • In case stream exceed max buffer limit, then application fails -Non Repeatable Streams • The input stream is only Read Once • No extra memory/performance overhead compared with repeatable streams
14.
All contents ©
MuleSoft Inc. New Foundation of Mule 4 14 • Types of Repeatable Streams Binary Streaming: Stream does not understand its data structure for modules like HTTP, SFTP, and File.(Picture Link) Object Streaming: Generally used for operations which support paging. Eg: Database, Salesforce select operations.(Picture Link)
15.
Thank You!!!
16.
Open discussion
17.
All contents ©
MuleSoft Inc. Pic-1 Non Blocking 17 • Back
18.
All contents ©
MuleSoft Inc. Pic-2 Thread Pools 18 • Back
19.
All contents ©
MuleSoft Inc. Pic-3 Typical Thread Switching 19 • Back
20.
All contents ©
MuleSoft Inc. Pic-4 Transactional with JMS 20 • Back
21.
All contents ©
MuleSoft Inc. Pic-5 Transactional with Async 21 • Back
22.
All contents ©
MuleSoft Inc. Pic-6 Transactional with Try scope 22 • Back
23.
All contents ©
MuleSoft Inc. Pic-7 Pub-Sub model 23 • Back
24.
All contents ©
MuleSoft Inc. Pic-8 Binary Streaming 24 • Back
25.
All contents ©
MuleSoft Inc. Pic-9 Object Streaming 25 • Back
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