Voxxed Days Cluj - 3 performance improvements with Hazelcast IMDG in your microservices architecture
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The document discusses the use of Hazelcast in-memory data grid for enhancing performance and scalability in microservices architectures. It highlights the benefits of caching and distributed computing while addressing the challenges of latency and stale data. The author emphasizes the importance of integration with various systems, including database access and session data management.
Voxxed Days Cluj - 3 performance improvements with Hazelcast IMDG in your microservices architecture
- 1. @nicolas_frankel With Hazelcast In-Memory Data Grid 3 performances improvement in your microservices architecture
- 2. @nicolas_frankel • Previously developer, team lead, architect, solutions architect • Developer Advocate • Pragmatic but curious Me, myself and I
- 3. @nicolas_frankel Hazelcast HAZELCAST IMDG is an operational, in-memory, distributed computing platform that manages data using in-memory storage, and performs parallel execution for breakthrough application speed and scale. HAZELCAST JET is the ultra fast, application embeddable, 3rd generation stream processing engine for low latency batch and stream processing.
- 4. @nicolas_frankel • Componentization via Services • Smart endpoints and dumb pipes • Decentralized Governance • Decentralized Data Management • Infrastructure Automation • Design for failure • Evolutionary Design • Organized around Business Capabilities • Products not Projects Microservices: a tentative definition https://martinfowler.com/articles/microservices.html
- 6. @nicolas_frankel Do you spot the difference?
- 7. @nicolas_frankel « You have to be in a really unusual spot to see in- process function calls turn into a performance hot spot these days, but remote calls are slow. If your service calls half-a-dozen remote services, each which calls another half-a-dozen remote services, these response times add up to some horrible latency characteristics. » -- https://martinfowler.com/articles/microservice-trade-offs.html Distributed systems
- 8. @nicolas_frankel • The network is reliable • Latency is zero • Bandwidth is infinite • The network is secure • Topology doesn't change • There is one administrator • Transport cost is zero • The network is homogeneous Fallacies of distributed computing https://yourlogicalfallacyis.com/
- 12. @nicolas_frankel
- 13. @nicolas_frankel Let’s use a hash map! • Unbounded • No eviction strategy • No TTL • etc. Caching?
- 14. @nicolas_frankel • A distributed object store • Think distributed hash map In-Memory Data Grid
- 15. @nicolas_frankel • Database access • HTTP call • Session data Caching use-cases in µservices
- 16. @nicolas_frankel • Object-Relational Mapping framework • Quite widespread • JPA implementation Hibernate
- 17. @nicolas_frankel • Level 1 cache • Implemented by default • Related to the Session object • Level 2 cache • Optional • Multiple integrations available Hibernate
- 18. @nicolas_frankel • The cache doesn’t contain the key 1. Load from the database 2. Put it in the cache • The cache contains the key 1. Return it How it reads
- 19. @nicolas_frankel Create or update the cached value How it writes
- 20. @nicolas_frankel
- 21. @nicolas_frankel • The code only interacts with Hazelcast • Registered listeners allow to write to the database • Sync or async Alternative
- 22. @nicolas_frankel • Catalog service • Stock service • Pricing service • Cart service • Recommendation service • Payment service • etc. E-commerce architecture
- 23. @nicolas_frankel HTTP and cache • Could be implemented manually • But there’s a Java API for that!
- 24. @nicolas_frankel • Specification • Multiple implementations • Integrated with Spring JCache
- 25. @nicolas_frankel
- 26. @nicolas_frankel Session data in cluster nodes
- 28. @nicolas_frankel 1. Filter-based 2. Spring Session integration 3. Direct Tomcat integration • Per-node configuration • Or through Spring Boot embedded 4. Direct Jetty integration Hazelcast to the rescue!
- 29. @nicolas_frankel
- 30. @nicolas_frankel Takeaways • Scalability and performance are not the same • Caching helps performance • The cost is stale data • Hazelcast IMDG provides several integration-points for caching across different areas • Database access • HTTP call • Session data
- 31. @nicolas_frankel • https://blog.frankel.ch/ • @nicolas_frankel • https://git.io/JenXz Thanks
Editor's Notes
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