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Distributed Cache Content on InfoQ


Latest featured content about Distributed Cache

Asynchronous Memcached with a Side of Ketchup and Membase

Topics
Membase,
Memcached,
Couchbase,
Distributed Document Oriented Database,
Distributed Cache,
Caching,
Companies,
Mono,
NoSQL,
.NET,
Clustering & Caching,
Programming,
Infrastructure,
Performance & Scalability,
Monospace,
Asynchronous Programming,
Database

Jason Sirota explains with code samples how to combine caching with asynchronous IO using memcached, Membase and Ketchup in order to maximize the throughput of an application.

News about Distributed Cache

CouchDB versus Couchbase: What are the differences, and what happened to Membase?

Topics
Membase,
CouchDB,
Memcached,
Couchbase,
Caching,
Distributed Cache,
Distributed Document Oriented Database,
Companies,
Clustering & Caching,
NoSQL,
Database,
Infrastructure,
Performance & Scalability

Recently Couchbase published a comparison of Couchbase and CouchDB to denote the differences and simlarities between the two. This document addresses a common question: "What is the difference between CouchDB and Couchbase?", and what happened to Membase? InfoQ caught up with James Phillips, a Couchbase founder, to discuss the comparison and the merger of the two products Membase and CouchDB.

Hazelcast 2.0 Released with Off-Heap Storage and Distributed Backups

Topics
Distributed Cache,
Java,
Caching,
Big Data,
Languages,
Clustering & Caching,
Database Design,
Performance & Scalability,
Programming,
Database,
Infrastructure

Version 2.0 of Hazelcast, a Java-based caching, clustering and data distribution solution, has recently been released. As part of this, the product is now offered in both commercial Enterprise and free open-source Community Editions.

MySQL Cluster 7.2 Released with 70x Increased Performance and NoSQL Features

Topics
Memcached,
Distributed Cache,
MySQL,
Caching,
NoSQL,
Clustering & Caching,
Relational Databases,
Oracle,
Performance & Scalability,
Database,
Infrastructure,
Companies

Oracle fires a new round for the heart of the NoSQL market. This 7.2 release of MySQL Cluster has new features putting it head to head with other NoSQL solutions including REST, memcached wire protocol, NoSQL C++, and standard MySQL interfaces. Oracle boasts 70x speed gains for complex queries using MapReduce like distributed joins. Is the world ready for a MySQL/NoSQL hybrid from Oracle?

Articles about Distributed Cache

Implementing Master-Worker with Terracotta

Topics
Terracotta,
Distributed Cache,
Companies,
Java,
Caching,
Languages,
Clustering & Caching,
Programming,
Performance & Scalability,
Infrastructure,
Grid Computing

A real world case study of a consultancy that distributed the load & increased scalability of its applications using Terracotta using the Master/Worker pattern.

Web Applications with Spring Web Flow and Terracotta for Spring

Topics
Terracotta,
Distributed Cache,
Spring,
Java,
Web Frameworks,
Dependency Injection,
Caching,
SpringSource,
Clustering & Caching,
Languages,
WOA,
Design Pattern,
VMWare,
Infrastructure,
Programming,
Performance & Scalability,
Patterns,
Object Oriented Design,
Architecture,
Design,
Companies

In this article we will first give you an overview of Spring Web Flow and Terracotta for Spring, and after that show you how you can use these technologies together to enter a new dimension in writing stateful, conversational, scalable and highly available web applications.

Introduction to OpenTerracotta

Topics
JVM,
Virtual Machines,
Terracotta,
Companies,
Runtimes,
Distributed Cache,
Java,
Caching,
Languages,
Clustering & Caching,
Performance & Scalability,
Programming,
Infrastructure

OpenTerracotta is an open source enterprise-class JVM clustering solution that can take multi-threaded single-JVM apps and have them run across multiple JVMs with no code changes. Orion Letizi goes super-indepth on Terracotta and how it works, explaining how to do session replication, distributed caching, master/worker, and more.

Presentations about Distributed Cache

Scaling Australia's Most Popular Online News Sites with Ehcache

Topics
Terracotta,
Ehcache,
Companies,
Distributed Cache,
QCon San Francisco 2010,
Caching,
Java,
Clustering & Caching,
Languages,
QCon,
Programming,
Performance & Scalability,
Infrastructure,
Conferences

Matthias Matook and Ari Zilka share the real-world experience of implementing Enterprise Ehcache at Australia's most visited online news sites. The talk will focus on the challenges and technical solutions to deal with massive page hits, high concurrency and how to achieve linear scalability without additional hardware.

Scaling Your Cache & Caching at Scale

Topics
Terracotta,
Ehcache,
Distributed Cache,
Companies,
Java,
QCon San Francisco 2009,
Caching,
Languages,
Clustering & Caching,
QCon,
Infrastructure,
Conferences,
Architecture,
Programming,
Performance & Scalability

Alex Miller explains shortly why caching is useful, followed by examples of typical difficulties encountered when setting up a cache, like large datasets, data eviction, stale data, replication, loading, duplication. Miller also discusses available choices for designing a distributed caching architecture, and ways to test a cache for performance.

Interviews about Distributed Cache

Ari Zilka on RAM is the New Disk & BigMemory

Topics
Terracotta,
Distributed Cache,
Ehcache,
Companies,
Java,
Caching,
GarbageCollection,
Deployment / Datacenter,
Operations,
Clustering & Caching,
Languages,
Performance & Scalability,
Infrastructure,
Programming

Terracotta creator Ari Zilka talks about about the RAM is the new disk and argues for scaling up before scaling out, comparing the architectural approaches of lots of VMs with small heaps vs. a few JVMs with very large heaps. Ari introduces BigMemory, a Java add-on to Enterprise Ehcache, which allows app designs with huge amounts of memory accessible in-process, with minimal garbage collection.

Billy Newport Discusses Parallel Programming in Java

Topics
WebSphere eXtreme Scale,
Websphere,
Distributed Cache,
Application Servers,
Caching,
IBM,
Clustering & Caching,
Java,
Companies,
QCon San Francisco 2009,
Languages,
Performance & Scalability,
Infrastructure,
QCon,
Architecture,
PIG,
Programming,
Cloud Computing,
Conferences,
Cascading,
Parallel Programming,
Hadoop

Billy Newport talks to InfoQ about the need for higher level abstraction to do parallel programming with multi-core systems effectively. The interview explores some approaches taken with MapReduce products such as Cascading and Pig for a Hadoop cluster, explores the limitations of the actor model and message passing, and touches on IBM's WebSphere eXtreme Scale (ObjectGrid) product.