Make it fast for everyone - performance and middleware design
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This document discusses various techniques for optimizing application performance, including reducing latency and increasing throughput. It covers strategies like using data structures like linked lists, bloom filters, and Merkle trees efficiently. Other topics include removing contention through approaches like the disruptor pattern, optimizing for network performance, and leveraging the reactor pattern. Performance of transports like XML/JSON and SOAP/REST is also evaluated. Monitoring tools like Java Flight Recorder are also mentioned.
![Sactificing Accuracy for
Performance (Bloom filters)
http://lkozma.net/blog/sketching-data-structures/
Initial configuration : 0000000000
Insert("Sally") : 0100000001
# h1("Sally") = 2, h2("Sally") = 10
Insert("Jane") : 1110000001
# h1("Jane") = 1, h2("Jane") = 3
Insert("Mary") : 1110100001
# h1("Mary") = 5, h2("Mary") = 2 [collision]
Query("Sally")
# bits 2 and 10 are set,
# return HIT
Query("John")
# h1("John") = 10 set, but h2("John") = 4 not set
# return MISS
Query("Bob")
# h1("Bob") = 5 set, h2("Bob") = 1 set
# return HIT (false positive)](https://image.slidesharecdn.com/makeitfastforeveryone-performanceandmiddlewaredesign-150329131619-conversion-gate01/85/Make-it-fast-for-everyone-performance-and-middleware-design-7-320.jpg)
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Make it fast for everyone - performance and middleware design
- 1. Make it fast for everyone Performance and Middleware design S. Suhothayan Technical Lead, WSO2 Inc.
- 3. Latency vs Throughput Latency measures the end-to-end time processing time. In a messaging environment, teams determine latency by measuring the time between sending a request and receiving the response. Latency is measured from the client machine and includes the network overhead as well. Throughput measures the amount of messages that a server processes during a specific time interval (e.g. per second). Throughput is calculated by measuring the time taken to processes a set of messages.
- 5. WSO2 CEP
- 6. Data structures o Linked List vs Array List vs Map CEP window processor : o Average temperature of last 10 min. o Inmemory o Average temperature of last 24 hour. o Disc Based implementation
- 7. Sactificing Accuracy for Performance (Bloom filters) http://lkozma.net/blog/sketching-data-structures/ Initial configuration : 0000000000 Insert("Sally") : 0100000001 # h1("Sally") = 2, h2("Sally") = 10 Insert("Jane") : 1110000001 # h1("Jane") = 1, h2("Jane") = 3 Insert("Mary") : 1110100001 # h1("Mary") = 5, h2("Mary") = 2 [collision] Query("Sally") # bits 2 and 10 are set, # return HIT Query("John") # h1("John") = 10 set, but h2("John") = 4 not set # return MISS Query("Bob") # h1("Bob") = 5 set, h2("Bob") = 1 set # return HIT (false positive)
- 8. Syncing Nodes (Merkle tree) Working with untrusted peer to peer networks (Torrents, Cassandra ring) hash 0 = hash( hash 0-0 + hash 0-1 ) http://en.wikipedia.org/wiki/Merkle_tree
- 9. Improve performance by Multi threading Use correct number of threads too much or too little is not good. Scaling Vertical : Improve Throughput reduce Latency Horizontal : Improve both Throughput & Latency Commonly used data structure : ArrayBlockingQueue
- 11. Removing contingency (disruptor) o Minimise thread contention o Maximise the use of single threaded algorithms and data structures. https://lmax-exchange.github.io/disruptor/
- 13. Blocking vs Non Blocking o Handling of thousands of connections simultaneously (significant number of connections may be in idle state as well) o Handling high latency connections o Request/response handling needs to be decoupled o Minimize latency, maximize throughput and avoiding unnecessary CPU cycles
- 14. WSO2 ESB Routing: Header based, content based, rule-based and priority- based routing Mediation: EIPs (including scatter/gather, message filters, recipient list, dead-letter channels, guaranteed delivery and message enrichment), database integration, event publishing, logging & auditing, validation Transformation: XSLT 1.0/2.0, XPath, XQuery, Smooks
- 15. Reactor Pattern Uses Readiness selection: It is the ability to choose a socket that will not block when read or written. This is a very efficient way of handling thousands of concurrent clients and scales well. http://wso2.com/library/articles/2013/12/demystifying-wso2-esb-pass-through-transport-part-i/
- 16. Message Flow
- 17. NHTTP
- 18. Message Relay Why we need to read the whole message if the necessary info is only at the message headers? Read only the headers and stream the message as bytes !
- 19. Passthrough Transport Going beyond design patterns & conventions
- 21. XML vs JSON & SOAP vs REST XML vs JSON o Serialization and Deserialization Performance o Data Storage o Data Transfer o Interoperability SOAP vs REST o REST has minimal overhead on top of HTTP o REST easy to scale don't have any server side sessions o REST Provides easier interface for client o SOAP is better when it comes to security o SOAP has better tools for schema, code generation and validation
- 22. Binary Transports (Thrift, Protocol buffers) http://ganges.usc.edu/pgroupW/images/a/a9/Serializarion_Framework.pdf
- 23. Binary Transports (Thrift, Protocol buffers) http://ganges.usc.edu/pgroupW/images/a/a9/Serializarion_Framework.pdf
- 24. Monitor all times (Java Flight Recorder)
- 25. Monitor all times (Java Flight Recorder)
- 26. Monitor all times (Java Flight Recorder)
- 27. Contact us !