On 23 October 2012 the JCP posted the Early Draft specification and API for JSR107. See http://jcp.org/en/jsr/detail?id=107. This is almost 12 years since the JSR kicked off. Note that this material was uploaded to the JCP in February this year but was delayed while the legal complications of having two companies as shared spec leads got sorted out. That is now done and will not be an issue going forward in the process.
We will now be working intensively to drive this to completion in its own right and for inclusion in Java ## 7. We expect to be final in early 2013.
We need your review
In the meantime the early draft review period is open until 22 November. Please visit the home of the project at https://github.com/jsr107 and send your comments to jsr107@googlegroups.com or create issues at https://github.com/jsr107/jsr107spec/issues. For a quick into see  https://github.com/jsr107/jsr107spec.
We have also just added a few new artifacts up on GitHub:
- A very simple demo which can be used when giving talks. https://github.com/jsr107/demo
- ehcache-jcache – an implementation of the 0.5 specification that works with the latest version of ehcache. https://github.com/jsr107/ehcache-jcache
Remaining JCP 2.7 Process Steps
The review period ends 22 November. Once we have a public draft, we will submit that for 30 days’ review. A EC ballot will be held in the last week of the public draft period before we move on to complete the RI and TCK and seek final approval.
Java EE 7 Deadline
We have sought clarification from the EE JSR on their deadline. It is fast approaching. We therefore intend to go hard.

Hi, at JavaOne 2012 last week a reader of this blog asked me why I hadn’t posted much lately. It was a good question and it stimulated, amonst other things, this post :)

There are a couple of reasons:

  1. Firstly, since December 2011 I have been CTO of Terracotta. Before that I looked after Ehcache at Terracotta. The stuff I am working on is pretty much inseparable from the what the company is doing. A lot of the work I am doing these days is research around in-memory. Some of these might become products and some not. It could cause a level of confusion (it has done in the past) to blog about these things. Care needs to be taken and blogs coordinated with Product Management and Marketing. This increases the time cost of doing it and makes it much less of a spontaneous activity, which is how I did in the past.
  2. Secondly, we do press and analyst briefings around important news and new product releases, with embargoes in place until publication date. Then once the journalists and analysts write it up, the news is already out so it’s old news.
  3. Thirdly, in June 2011 Terracotta became a wholly owned business unit of Software AG which is a German public company. Once again, care needs to be taken around publicising research under way.
  4. Fourthly, the role of the blog itself is less popular. On Twitter you can follow @gregrluck, @ehcache and @bigmemory. On LinkedIn we have a Big Data + In-Memory = BigMemory community we are kicking off which most of the technical people will be members of.

So I will be blogging less and participating a bit more on Twitter and LinkedIn.

See http://www.h-online.com/open/features/The-H-Half-Hour-Cache-back-Greg-Luck-and-the-return-of-JSR-107-1413380.html

A few weeks ago I attended a ThoughtWorks Technology Radar seminar. I worked at ThoughtWorks for years and think if anyone knows what is trending up and down in software development these guys do. At number 17 in Techniques with a rising arrow is what they called Thoughtful Caching. At drinks with Scott Shaw, I asked him what it meant.

What the trend is about is the movement from reactive caching to a new style. By reactive I mean you find out your system doesn’t perform or scale after you build it and it is already in production. Lots of Ehcache users come to it that way. This is a trend I am very happy to see.

Deliberate Caching

The new technique is:

  • proactive
  • planned
  • implemented before the system goes live
  • deliberate
  • is more than turning on caching in your framework and hoping for the best – this is the Thoughtful part
  • uses an understanding of the load characteristics and data access patterns
We kicked around a few names for this and came up with Deliberate Caching to sum all of this up.
The work we are doing standardising Caching for Java and JVM based languages, JSR107, will only aid with this transition. It will be included in Java EE 7 which even for those who have lost interest in following EE specifically will still send a signal that this is an architectural decision which should be made deliberately.

Why it has taken this long?

So, why has it taken until 10 years after Ehcache and Memcache and plenty of others came along for this “new” trend to emerge?  I think there are a few reasons.

Some people think caching is dirty

I have met plenty of developers who think that caching is dirty. And caching is cheating. They think it indicates some architectural design failure that is best of being solved some other way.
One of the causes of this is that many early and open source caches (including Ehcache) placed limits on the data safety that could be achieved. So the usual situation is that the data in the cache might but was not sure to be correct. Complicated discussions with Business Analysts were required to find out whether this was acceptable and how stale data was allowed to be. This has been overcome by the emergence of enterprise caches, such as Enterprise Ehcache, so named because they are feature rich and contain extensive data safety options, including in Ehcache’s case: weak consistency, eventual consistency, strong consistency, explicitly locking, Local and XA transactions and atomic operations.  So you can use caching even in situations where the data has to be right.

Following the lead of giant dotcom

The other thing that has happened is that as giant dotcoms it cannot have escaped anyone’s notice that they all use tons of caching. And that they won’t work if the caching layer is down. So much so that if you are building a big dot com app it is clear that you need to build a caching layer in.

Early Performance Optimisation is seen as an anti -pattern

Under Agile we focus on the simplest thing that can possibly work. Requirements are expected to keep changing. Any punts you take on future requirements may turn out to be wrong and your effort wasted. You only add things once it is clear they are needed. Performance and scalability tend to get done this way as well. Following this model you find out about the requirement after you put the app in production and it fails. This same way of thinking causes monolithic systems with single data stores to be built which later turn out to need expensive re-architecting.

I think we need to look at this as Capacity Planning. If we get estimated numbers at the start of the project for number of users, required response times, data volumes, access patterns etc then we can capacity plan the architecture as well as the hardware. And in that architecture planning we can plan to use caching. Because caching affects how the system is architected and what the hardware requirements are, it makes sense to do it then.

 

 

I have done a Q&A with InfoQ on similarities between Distributed Caches and NoSQL Stores. Read it here.

This post explores the new Java caching standard: javax.cache.

How it Fits into the Java Ecosystem

This standard is being developed by JSR107, of which the author is co-spec lead. JSR107 is included in Java EE 7, being developed by JSR342. Java EE 7 is due to be finalised at the end of 2012. But in the meantime javax.cache will work in Java SE 6 and higher and Java EE 6 environments as well aswith Spring and other popular environments.

JSR107 has draft status. We are currently at release 0.3 of the API, the reference implementation and the TCK. The code samples in this article work against this version.

Adoption

Vendors who are either active members of the expert group or have expressed interest in implementing the specification are:

  • Terracotta – Ehcache
  • Oracle – Coherence
  • JBoss – Infinispan
  • IBM – ExtemeScale
  • SpringSource – Gemfire
  • GridGain
  • TMax
  • Google App Engine Java

Terracotta will be releasing a module for Ehcache to coincide with the final draft and then updating that if required for the final version.

Features

From a design point of view, the basic concepts are a CacheManager that holds and controls a collection of Caches. Caches have entries. The basic API can be thought of map-­like with the following additional features:

  • atomic operations, similar to java.util.ConcurrentMap
  • read-through caching
  • write-through caching
  • cache event listeners
  • statistics
  • transactions including all isolation levels
  • caching annotations
  • generic caches which hold a defined key and value type
  • definition of storage by reference (applicable to on heap caches only) and storage by value

Optional Features

Rather than split the specification into a number of editions targeted at different user constituencies such as Java SE and Spring/EE, we have taken a different approach.

Firstly, for Java SE style caching there are no dependencies. And for Spring/EE where you might want to use annotations and/or transactions, the dependencies will be satisfied by those frameworks.

Secondly we have a capabilities API via ServiceProvider.isSupported(OptionalFeature feature)so that you can determine at runtime what the capabilities of the implementation are.  Optional features are:

  • storeByReference – storeByValue is the default
  • transactional
  • annotations

This makes it possible for an implementation to support the specification without necessarily supporting all the features, and allows end users and frameworks to discover what the features are so they can dynamically configure appropriate usage.

Good for Standalone and Distributed Caching

While the specification does not mandate a particular distributed cache topology it is cognizant that caches may well be distributed. We have one API that covers both usages but it is sensitive to distributed concerns. For example CacheEntryListener has a NotificationScope of events it listens for so that events can be restricted to local delivery. We do not have high network cost map-like methods such as keySet() and values(). And we generally prefer zero or low cost return types. So while Map has V put(K key, V value) javax.cache.Cache has void put(K key, V value).

Classloading

Caches contain data shared by multiple threads which may themselves be running in different container applications or OSGi bundles within one JVM and might be distributed across multiple JVMs in a cluster. This makes classloading tricky.

We have addressed this problem. When a CacheManager is created a classloader may be specified. If none is specified the implementation provides a default. Either way object de-serialization will use the CacheManager’s classloader.

This is a big improvement over the approach taken by caches like Ehcache that use a fall-back approach. First the thread’s context classloader is used and it that fails, another classloader is tried. This can be made to work in most scenarios but is a bit hit and miss and varies considerably by implementation.

Getting the Code

The spec is in Maven central. The Maven snippet is:

<dependency>
     <groupId>javax.cache</groupId>
     <artifactId>cache-api</artifactId>
     <version>0.3</version>
</dependency>

A Cook’s Tour of the API

Creating a CacheManager

We support the Java 6 java.util.ServiceLoader creational approach. It will automaticaly detect a cache implementation in your classpath. You then create a CacheManager with:

CacheManager cacheManager = Caching.getCacheManager();

which returns a singleton CacheManager called “__default__”. Subsequent calls return the same CacheManager.

CacheManagers can have names and classloaders configured in. e.g.

CacheManager cacheManager = Caching.getCacheManager(“app1”, Thread.currentThread().getContextClassLoader());

Implementations may also support direct creation with new for maximum flexibility:

CacheManager cacheManager = new RICacheManager(“app1”, Thread.currentThread().getContextClassLoader());

Or to do the same thing without adding a compile time dependency on any particular implementation:

String className = "javax.cache.implementation.RIServiceProvider";
Class<ServiceProvider> clazz =(Class<ServiceProvider>)Class.forName(className);
ServiceProvider provider = clazz.newInstance();
return provider.createCacheManager(Thread.currentThread().getContextClassLoader(), "app1");
We expect implementations to have their own well-known configuration files which will be used to configure the CacheManager. The name of the CacheManager can be used to distinguish the configuration file. For ehcache, this will be the familiar ehcache.xml placed at the root of the classpath with a hyphenated prefix for the name of the CacheManager. So, the default CacheManager will simply be ehcache.xml and “myCacheManager” will be app1-ehcache.xml.

Creating a Cache

The API supports programmatic creation of caches. This complements the usual convention of configuring caches declaratively which is left to each vendor.

To programmatically configure a cache named “testCache” which is set for read-through

cacheManager = getCacheManager();
CacheConfiguration cacheConfiguration = cacheManager.createCacheConfiguration()
cacheConfiguration.setReadThrough(true);
Cache testCache = cacheManager.createCacheBuilder(“testCache”)
.setCacheConfiguration(cacheConfiguration).build();

Getting a reference to a Cache

You get caches from the CacheManager. To get a cache called “testCache”:

Cache<Integer, Date> cache = cacheManager.getCache(“testCache”);

Basic Cache Operations

To put to a cache:

Cache<Integer, Date> cache = cacheManager.getCache(cacheName);

Date value1 = new Date();

Integer key = 1;

cache.put(key, value1);

 

To get from a cache:

Cache<Integer, Date> cache = cacheManager.getCache(cacheName);
Date value2 = cache.get(key);

 

To remove from a cache:

Cache<Integer, Date> cache = cacheManager.getCache(cacheName);
Integer key = 1;
cache.remove(key);

Annotations

JSR107 introduces a standardised set of caching annotations, which do method level caching interception on annotated classes running in dependency injection containers. Caching annotations are becoming increasingly popular, starting with Ehcache Annotations for Spring, which then influenced Spring 3’s caching annotations.

The JSR107 annotations cover the most common cache operations including:

  • @CacheResult – use the cache
  • @CachePut – put into the cache
  • @CacheRemoveEntry – remove a single entry from the cache
  • @CacheRemoveAll – remove all entries from the cache

When the required cache name, key and value can be inputed they are not required. See the JavaDoc for the details. To allow greater control, you can specify all these and more. In the following example, the cacheName attribute is specified to be “domainCache”, index is specified as the key and domain as the value.

public class DomainDao {
     @CachePut(cacheName="domainCache")
     public void updateDomain(String domainId, @CacheKeyParam int index,
          @CacheValue Domain domain) {
     ...
     }
}

The reference implementation includes an implementation for both Spring and CDI. CDI is the standardised container driven injection introduced in Java EE 6. The implementation is nicely modularised for reuse, uses an Apache license, and we therefore expect several open source caches to reuse them. While we have not done an implementation for Guice, this could be easily done.

Annotation Example

This example shows how to use annotations to keep a cache in sync with an underlying data structure, in this case a Blog manager, and also how to use the cache to speed up responses, done with @CacheResult

public class BlogManager {
@CacheResult(cacheName="blogManager")
public Blog getBlogEntry(String title) {...}
@CacheRemoveEntry(cacheName="blogManager")
public void removeBlogEntry(String title) {...}
@CacheRemoveAll(cacheName="blogManager")
public void removeAllBlogs() {...}
@CachePut(cacheName=”blogManager”)
public void createEntry(@CacheKeyParam String title,
@CacheValue Blog blog) {...}
@CacheResult(cacheName="blogManager")
public Blog getEntryCached(String randomArg,
@CacheKeyParam String title){...}
}

Wiring Up Spring

For Spring the key is the following config line, which adds the caching annotation interceptors into the Spring context:

<jcache-spring:annotation-driven proxy-target-class="true"/>

A full example  is:

<beans ...>
<context:annotation-config/>
<jcache-spring:annotation-driven proxy-target-class="true"/>
<bean id="cacheManager" factory-method="getCacheManager" />
</beans>

Spring has it’s own caching annotations based on earlier work from JSR107 contributor Eric Dalquist. Those annotations and JSR107 will happily co-exist.

Wiring Up CDI

First create an implementation of javax.cache.annotation.BeanProvider and then tell CDI where to find it  declaring a resource named javax.cache.annotation.BeanProvider in the classpath at /META-INF/services/.

For an example using the Weld implementation of CDI, see the CdiBeanProvider in our CDI test harness.

Further Reading

For further reading visit the JSRs home page at https://github.com/jsr107/jsr107spec.

0.3 of the JSR107 spec, RI and TCK have been released.

Changes in this release:

  • Numerous changes across the spec, TCK and RI
  • Annotations implementations in the RI for Spring and CDI
  • Transactions API finalised
The release is in Maven central so the snippet for the API is:
<dependency>
<groupId>javax.cache</groupId>
<artifactId>cache-api</artifactId>
<version>0.3</version>
</dependency>

We are pretty much on the home run with this now. Work on Ehcache, Infinispan and Coherence implementations are starting. Work will now shift to closing open issues and dealing with review comments as they come in.

We welcome community involvement. The jumping off point for all things JSR107 is the GitHub Page.

I will be speaking at JavaOne 2011. My sessions are:

24241 – The Essence of Caching, Parc 55 – Divisidero at 10:30am Tuesday 4 October

This presentation distills what the speaker has learned from 10 years of scaling Java. It starts with a performance problem and leads you through solving it with caching, discovering the problems of distributed caching and their solution along the way. It will equip you with the tools to analyze a performance situation and see whether a cache will help and what type of cache to apply.

Topics include
• The nature of system load
• Desirable properties of scalable systems
• Caching as a solution for offload, scale-out, and performance
• Why caching works
• Tiered cache design
• SOR coherency problem and solutions
• N * problem and solutiond
• Cache cluster topologies
• CAP and PACELC constraints
• Resulting design trade-offs

24223 – The New JSR 107 Caching Standard, Imperial Ballroom A , Hilton San Francisco at 1:30 pm Tuesday 4 October

In this session, the two spec leads for JSR 107 walk you through this important new caching standard, which will form part of Java EE 7.

You will learn how to
• Abstract your caching implementation, much as with JDBC
• Use the rich and modern API
• Use the new caching annotations
• Use the API before Java EE 7 is released within the Java SE, Java EE 6, and Spring environments
• Apply JCache to common caching scenarios

 

Come along and feel free to ask me any questions after my sessions.

JCache is rapidly nearing completion and we would like the community to start using it. The API is becoming quite stable.

The home for all things JCache is: https://github.com/jsr107/jsr107spec. Today I updated that page with the following details so that you can all get started.

We expect to release our first non-snapshot release in a few week’s time with further releases leading up to JavaOne.

I am doing two sessions on caching at JavaOne. If you are attending please come along to learn more. My sessions are:

Session ID: 24223

Session Title: The New JSR 107 Caching Standard

Session ID: 24241

Session Title: The Essence of Caching

For the uninitiated JCache is the API being defined in JSR107. It defines a standard Java Caching API for use by developers and a standard SPI (“Service Provider Interface”) for use by implementers.

Release

The stable releases of this software are tagged with version numbers, starting with 0.1. Eventually, when the specification is further along releases will match the specification number.

We expect out first stable release early August 2011.

Snapshot Releases

Snapshot releases of jars for binaries, source and javadoc are available.

Download the cache-api from https://oss.sonatype.org/index.html#nexus-search;quick~javax-cache

or use the following Maven snippet:

<repository>
    <id>sonatype-nexus-snapshots</id>
    <name>Sonatype Nexus Snapshots</name>
    <url>https://oss.sonatype.org/content/repositories/snapshots</url>
    <releases>
        <enabled>false</enabled>
    </releases>
    <snapshots>
        <enabled>true</enabled>
    </snapshots>
</repository>

<dependency>
  <groupId>javax.cache</groupId>
  <artifactId>cache-api</artifactId>
  <version>0.2-SNAPSHOT</version>
</dependency>

Javadoc

The JavaDoc is available as a jar with the releases. We also have the latest JavaDoc online.

Specification

The evolving specification is available online on as a Google Doc.

Reference Implementation

The reference implementation (“RI”) source is available on GitHub.

This implementation is not meant for production use. For that we would refer you to one of the many open source and commercial implementations of JCache.

The RI is there to ensure that the specification and API works.

For example, some things that we leave out:

  • implementation of transactions.
  • eviction does not use an LRU or similar algorithm it just evicts an entry when full.
  • concurrency. The RI is not exhaustively tested for thread safety.
  • tiered storage. A simple on heap store is used.
  • replicated or distributed caching
  • cache sizing. All caches are hard coded to be of size 100 entries.

Why did we do this? Because a much greater engineering effort, which gets put into the open source and commercial caches which implement this API, is required to accomplish these things.

Having said that, the RI is Apache 2 and is a correct implementation of the spec. It can be used to create new cache implementations.

Building From Source

mvn clean install

Mailing list

Please join the mailing list if you’re interested in using or developing the software: http://groups.google.com/group/jsr107

IRC

We will be using the #jsr107 channel on Freenode for chat.

We also have set up a commit hook which publishes commits to the channel.

Issue tracker

Please log issues to: https://github.com/jsr107/jsr107spec/issues

Contributing

Right now code contribution is limited to the Expert Group, but please feel free to post to the mailing list.

License

The API is available under the JPA license and may be freely used.

The TCK is available under a restricted TCK license although the tests.

The reference implementation is available under an Apache 2 license.

For details please read the license in each source code file.

Contributors

This free, open source software was made possible by the JSR107 Expert Group who put many hours of hard work into it.

 

This How To is too well hidden for it’s or end users’ good:

https://cloudbees.zendesk.com/entries/438491-sharing-files-with-build-executors-such-as-maven-settings-xml