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Core Terracotta: Scaling your Java Application with Terracotta
Ari Zilka teaches you how to use Terracotta's JVM level clustering technology with your application. Leverage Terracotta to make your Java Applications highly available and scalable with minimal effort.We'll discuss usage of Terracotta technology in System Implementations. Well hig
HLI
ght real world use cases where Terracotta was employed and as compared to alternatives provided better Scale and HA with semantic correctness whilst simultaneously providing low time to market and in depth visibility into the Java Cluster. We will cover the following use cases Http sessions Distributed Caches clustering POJOs e.g. Publisher Consumer Batch Processing etc. Spring singleton Beans/Context Events JVM Co ordination and clustering other development frameworks.LEARN HOW TO Apply Common Use Cases Use Session Clustering Apply POJO Clustering Write a TIM Co ordinate Clusters Manage Clusters Configure Distributed Cache Tune for performance Roll out for Production PROGRAMMEDAY 1Introduction Terracotta the Company Terracotta Services Terracotta Flag ship Technology Distributed Shared Objects o Philosophy o Concepts o Architecture Overview Terracotta Installation Terracotta Samples and Examples Common Use Cases Introduction Real life Examples and what Terracotta provides as compared with alternatives Session Clustering POJO Clustering The De Container. JVM Co ordination Cluster Management. Distributed Cache Various topologies o Cache Aside o Write through Cache o Write behind Cache o Partitioned Data Grid o Other configurationsSession Clustering Sample Application/Lab Clustering a sample app Scale HA Correctness characteristics POJO Clustering Concepts and Considerations Sample Application/Lab Clustering a LinkedBlockingQueue The De Container TIMs Concept Terracotta TIMs for EHCache Hibernate 2L Cache Wicket Struts Quartz Lucene Compass. Sample Applications/Labs Quartz Compass Writing a TIM. Cluster Co ordination Distributed Wait Notify Distributed Method Invocation Master/Worker Cluster Management JMX Events Sample Application DAY 2Distributed Cache Cache Configurations Distributed Cache Cache configurations o Read Only o Write Through o Write Behind o Cache Aside o Partitioned Data Grid Scale HA Correctness Simplicity characteristics of each configuration. Sample Application/Lab Clustering a ConcurrentHashMap EHCache Performance Tuning Memory Tuning o Garbage Collection o Virtual Memory Manager o Distributed Garbage Collection Lock Tuning o Striping o Lock Granularity o Lock Types o Lock Pessimism/Optimism Instrumentation Scope Other Tc config.xml tc.properties tuning Tools o Admin Console Lock Profiler DGC Tab etc. o Statistics Visualization Tab o Debug options tc config.xml LAB Improve Throughput/Latency of a poorly performing app Production Roll out Deployment Options Failure Analysis Monitoring the cluster in production Upgrades with minimal downtime Other post production best practices Summary Conclusions
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