Showing posts with label json. Show all posts
Showing posts with label json. Show all posts

Tuesday, August 13, 2013

Optimizing Java Serialization - Java vs XML vs JSON vs Kryo vs POF

Perhaps I'm naive, but I always thought Java serialization must surely be the fastest and most efficient way to serialize Java objects into binary form. After all, Java is on it's 7th major release, so this is not new technology, and since every JDK seems to be faster than the last, I incorrectly assumed serialization must be very fast and efficient by now. I thought, since Java serialization is binary, and language dependent, it must be much faster and more efficient than XML or JSON. Unfortunately, I was wrong, if you concerned about performance, I would recommend avoiding Java serialization.

Now, don't get me wrong, I'm not trying to dis Java. Java serialization has many requirements, the main one being able to serialize anything (or at least anything that implements Serializable), into any other JVM (even a different JVM version/implementation), even running a different version of the classes being serialized (as long as you set a serialVersionUID). The main thing is it just works, and that is really great. Performance is not the main requirement, and the format is standard and must be backward compatible, so optimization is very difficult. Also, for many types of use cases, Java serialization performs very well.

I got started on this journey into the bowels of serialization while working on a three tier concurrency benchmark. I noticed a lot of the CPU time being spent inside Java serialization, so I decided to investigate. I started by serializing a simple Order object that had a couple of fields. I serialized the object and output the bytes. Although the Order object only had a few bytes of data, I was not that naive to think it would serialize to only a few bytes, I knew enough about serialization that it would at least need to write out the full class name, so it knew what it had serialized, so it could read it back. So I expected, maybe 50 bytes or so. The result was over 600 bytes, that's when I realized Java serialization was not as simple as I had imagined.

Java serialization bytes for Order object

----sr--model.Order----h#-----J--idL--customert--Lmodel/Customer;L--descriptiont--Ljava/lang/String;L--orderLinest--Ljava/util/List;L--totalCostt--Ljava/math/BigDecimal;xp--------ppsr--java.util.ArrayListx-----a----I--sizexp----w-----sr--model.OrderLine--&-1-S----I--lineNumberL--costq-~--L--descriptionq-~--L--ordert--Lmodel/Order;xp----sr--java.math.BigDecimalT--W--(O---I--scaleL--intValt--Ljava/math/BigInteger;xr--java.lang.Number-----------xp----sr--java.math.BigInteger-----;-----I--bitCountI--bitLengthI--firstNonzeroByteNumI--lowestSetBitI--signum[--magnitudet--[Bxq-~----------------------ur--[B------T----xp----xxpq-~--xq-~--
(note "-" means an unprintable character)

As you may have noticed, Java serialization writes out not only the full class name of the object being serialized, but also the entire class definition of the class being serialized, and all of the referenced classes. The class definition can be quite large, and seems to be the main performance and efficiency issue, especially when writing out a single object. If you are writing out a large number of objects of the same class, then the class definition overhead is not normally a big issue. One other thing that I noticed, is that if your object has a reference to a class (such as a meta-data object), then Java serialization will write the entire class definition, not just the class name, so using Java serialization to write out meta-data is very expensive.

Externalizable

It is possible to optimize Java serialization through implementing the Externalizable interface. Implementing this interface avoids writing out the entire class definition, just the class name is written. It requires that you implement the readExternal and writeExternal methods, so requires some work and maintenance on your part, but is faster and more efficient than just implementing Serializable.

One interesting note on the results for Externalizable is that it was much more efficient for a small number of objects, but actually output slightly more bytes than Serializable for a large number of objects. I assume the Externalizable format is slightly less efficient for repeated objects.

Externalizable class

public class Order implements Externalizable {
    private long id;
    private String description;
    private BigDecimal totalCost = BigDecimal.valueOf(0);
    private List orderLines = new ArrayList();
    private Customer customer;

    public Order() {
    }

    public void readExternal(ObjectInput stream) throws IOException, ClassNotFoundException {
        this.id = stream.readLong();
        this.description = (String)stream.readObject();
        this.totalCost = (BigDecimal)stream.readObject();
        this.customer = (Customer)stream.readObject();
        this.orderLines = (List)stream.readObject();
    }

    public void writeExternal(ObjectOutput stream) throws IOException {
        stream.writeLong(this.id);
        stream.writeObject(this.description);
        stream.writeObject(this.totalCost);
        stream.writeObject(this.customer);
        stream.writeObject(this.orderLines);
    }
}

Externalizable serialization bytes for Order object

----sr--model.Order---*3--^---xpw---------psr--java.math.BigDecimalT--W--(O---I--scaleL--intValt--Ljava/math/BigInteger;xr--java.lang.Number-----------xp----sr--java.math.BigInteger-----;-----I--bitCountI--bitLengthI--firstNonzeroByteNumI--lowestSetBitI--signum[--magnitudet--[Bxq-~----------------------ur--[B------T----xp----xxpsr--java.util.ArrayListx-----a----I--sizexp----w-----sr--model.OrderLine-!!|---S---xpw-----pq-~--q-~--xxx

Other Serialization Options

I started to investigate what other serialization options there were in Java. I started with EclipseLink MOXy, which supports serializing objects to XML or JSON through the JAXB API. I was not expecting XML serialization to outperform Java serialization, so was quite surprised when it did for certain use cases. I also found a product Kryo, which is an open source project for optimized serialization. I also investigated the Oracle Coherence POF serialization format. There are pros and cons with each product, but my main focus was on how their performance compared, and how efficient they were.

EclipseLink MOXy - XML and JSON

The main advantage of using EclipseLink MOXy to serialize to XML or JSON is that both are standard, portable formats. You can access the data from any client using any language, so are not restricted to Java, as with Java serialization. You can also integrate your data with web services and REST services. Both formats are also text based, so human readable. No coding or special interfaces are required, only meta-data. The performance is quite acceptable, and outperforms Java serialization for small data-sets.

The draw backs are that the text formats are less efficient than optimized binary formats, and JAXB requires meta-data. So you need to annotate your classes with JAXB annotations, or provide an XML configuration file. Also, circular references are not handled by default, you need to use an @XmlIDREF to handle cycles.

JAXB annotated classes

@XmlRootElement
public class Order {
    @XmlID
    @XmlAttribute
    private long id;
    @XmlAttribute
    private String description;
    @XmlAttribute
    private BigDecimal totalCost = BigDecimal.valueOf(0);
    private List orderLines = new ArrayList();
    private Customer customer;
}

public class OrderLine {
    @XmlIDREF
    private Order order;
    @XmlAttribute
    private int lineNumber;
    @XmlAttribute
    private String description;
    @XmlAttribute
    private BigDecimal cost = BigDecimal.valueOf(0);
}

EclipseLink MOXy serialization XML for Order object

<order id="0" totalCost="0"><orderLines lineNumber="1" cost="0"><order>0</order></orderLines></order>

EclipseLink MOXy serialization JSON for Order object

{"order":{"id":0,"totalCost":0,"orderLines":[{"lineNumber":1,"cost":0,"order":0}]}}

Kryo

Kryo is a fast, efficient serialization framework for Java. Kryo is an open source project on Google code that is provided under the New BSD license. It is a small project, with only 3 members, it first shipped in 2009 and last shipped the 2.21 release in Feb 2013, so is still actively being developed.

Kryo works similar to Java serialization, and respects transient fields, but does not require a class be Serializable. I found Kryo to have some limitations, such as requiring classes to have a default constructor, and encountered some issues in serializing java.sql.Time, java.sql.Date and java.sql.Timestamp classes.

Kryo serialization bytes for Order object

------java-util-ArrayLis-----model-OrderLin----java-math-BigDecima---------model-Orde-----

Oracle Coherence POF

The Oracle Coherence product provides its own optimized binary format called POF (portable object format). Oracle Coherence is an in-memory data grid solution (distributed cache). Coherence is a commercial product, and requires a license. EclipseLink supports an integration with Oracle Coherence through the Oracle TopLink Grid product that uses Coherence as the EclipseLink shared cache.

POF provides a serialization framework, and can be used independently of Coherence (if you already have Coherence licensed). POF requires that your class implement a PortableObject interface and read/write methods. You can also implement a separate Serializer class, or use annotations in the latest Coherence release. POF requires that each class be assigned an constant id ahead of time, so you need some way determine this id. The POF format is a binary format, very compact, efficient, and fast, but does require some work on your part.

The total bytes for POF was 32 bytes for a single Order/OrderLine object, and 1593 bytes for 100 OrderLines. I'm not going to give the results, as POF is part of a commercially licensed product, but is was very fast.

POF PortableObject

public class Order implements PortableObject {
    private long id;
    private String description;
    private BigDecimal totalCost = BigDecimal.valueOf(0);
    private List orderLines = new ArrayList();
    private Customer customer;

    public Order() {
    }
    
    public void readExternal(PofReader in) throws IOException {
        this.id = in.readLong(0);
        this.description = in.readString(1);
        this.totalCost = in.readBigDecimal(2);
        this.customer = (Customer)in.readObject(3);
        this.orderLines = (List)in.readCollection(4, new ArrayList());
    }

    public void writeExternal(PofWriter out) throws IOException {
        out.writeLong(0, this.id);
        out.writeString(1, this.description);
        out.writeBigDecimal(2, this.totalCost);
        out.writeObject(3, this.customer);
        out.writeCollection(4, this.orderLines);
    }
}

POF serialization bytes for Order object

-----B--G---d-U------A--G-------

Results

So how does each perform? I made a simple benchmark to compare the different serialization mechanisms. I compared the serialization of two different use cases. The first is a single Order object with a single OrderLine object. The second is a single Order object with 100 OrderLine objects. I compared the average serialization operations per second, and measure the size in bytes of the serialized data. Different object models, use cases, and environments will give different results, but this gives you a general idea of the performance differences in different serializers.

The results show that Java serialization is slow for a small number of objects, but good for a large number of objects. Conversely, XML and JSON can outperform Java serialization for a small number of objects, but Java serialization is faster for a large number of objects. Kryo and other optimized binary serializers outperform Java serialization with both types of data.

You may wonder why it is relevant that something that takes less than a millisecond is of any relevance to performance, and you may be right. In general you would only have a real performance problem if you wrote out a very large number of objects, and then Java serialization performs quite well, so is the fact that it performs very poorly for a small number of object relevant? For a single operation, this is probably true, but if you execute many small serialization operations, then the cost is relevant. A typical server servicing many clients will typically send out many small requests, so while the cost of serialization is not great enough to make any of these single requests take a long time, it will dramatically effects the scalability of the server.

Order with 1 OrderLine

SerializerSize (bytes)Serialize (operations/second)Deserialize (operations/second)% Difference (from Java serialize)% Difference (deserialize)
Java Serializable636128,63419,1800%0%
Java Externalizable435160,54926,67824%39%
EclipseLink MOXy XML101348,05647,334170%146%
Kryo90359,368346,984179%1709%

Order with 100 OrderLines

SerializerSize (bytes)Serialize (operations/second)Deserialize (operations/second)% Difference (from Java serialize)% Difference (deserialize)
Java Serializable2,71516,47010,2150%0%
Java Externalizable2,81116,20611,483-1%12%
EclipseLink MOXy XML6,6287,3042,731-55%-73%
Kryo121622,86231,49938%208%

EclipseLink JPA

In EclipseLink 2.6 development builds, and to some degree 2.5, we have added the ability to choose your serializer anywhere that EclipseLink does serialization.

One such place is in serialized @Lob mappings. You can now use the @Convert annotation to specify a serializer such as @Convert(XML), @Convert(JSON), @Convert(Kryo). In addition to optimizing performance, this provides an easy mechanism to write XML and JSON data to your database.

Also for EclipseLink cache coordination you can choose your serializer using the "eclipselink.cache.coordination.serializer" property.

The source code for the benchmarks used in this post can be found here, or downloaded here.

Monday, April 2, 2012

EclipseLink JPA supports MongoDB

EclipseLink 2.4 will support JPA access to NoSQL databases. This support is already part of the EclipseLink development trunk and can be tried out using the milestone or nightly builds.  Initial support is provided for MongoDB and Oracle NoSQL. A plug-able platform and adapter layer allows for other databases to be supported.

NoSQL is a classification of database systems that do not conform to the relational database or SQL standard. They have various roots, from distributed internet databases, to object databases, XML databases and even legacy databases. They have become recently popular because of their use in large scale distributed databases in Google, Amazon, and Facebook.

There are various NoSQL databases including:
  • Mongo DB
  • Oracle NoSQL
  • Cassandra
  • Google BigTable
  • Couch DB

EclipseLink's NoSQL support allows the JPA API and JPA annotations/xml to be used with NoSQL data. EclipseLink also supports several NoSQL specific annotations/xml including @NoSQL that defines a class to map NoSQL data.

EclipseLink's NoSQL support is based on previous EIS support offered since EclipseLink 1.0. EclipseLink's EIS support allowed persisting objects to legacy and non-relational databases. EclipseLink's EIS and NoSQL support uses the Java Connector Architecture (JCA) to access the data-source similar to how EclipseLink's relational support uses JDBC. EclipseLink's NoSQL support is extendable to other NoSQL databases, through the creation of an EclipseLink EISPlatform class and a JCA adapter.

Let's walk through an example of using EclipseLink's NoSQL support to persist an ordering system's object model to a MongoDB database.

The source for the example can be found here, or from the EclipseLink SVN repository.

Ordering object model

The ordering system consists of four classes, Order, OrderLine, Address and Customer. The Order has a billing and shipping address, many order lines, and a customer.

public class Order implements Serializable {
private String id;
private String description;
private double totalCost = 0;
private Address billingAddress;
private Address shippingAddress;
private List orderLines = new ArrayList();
private Customer customer;
...
}
public class OrderLine implements Serializable {
private int lineNumber;
private String description;
private double cost = 0;
...
}
public class Address implements Serializable {
private String street;
private String city;
private String province;
private String country;
private String postalCode;
....
}
public class Customer implements Serializable {
private String id;
private String name;
...
}

Step 1 : Decide how to store the data

There is no standard on how NoSQL databases store their data. Some NoSQL databases only support key/value pairs, others support structured hierarchical data such as JSON or XML.

MongoDB stores data as BSON (binary JSON) documents. The first decision that must be made is how to store the objects. Normally each independent object would compose a single document, so a single document could contain Order, OrderLine and Address. Since customers can be shared amongst multiple orders, Customer would be its own document.

Step 2 : Map the data

The next step is to map the objects. Each root object in the document will be mapped as an @Entity in JPA. The objects that are stored by being embedded within their parent's document are mapped as @Embeddable. This is similar to how JPA maps relational data, but in NoSQL embedded data is much more common because of the hierarchical nature of the data format. In summary, Order and Customer are mapped as @Entity, OrderLine and Address are mapped as @Embeddable.

The @NoSQL annotation is used to map NoSQL data. This tags the classes as mapping to NoSQL data instead of traditional relational data. It is required in each persistence class, both entities and embeddables. The @NoSQL annotation allows the dataType and the dataFormat to be set.

The dataType is the equivalent of the table in relational data, its meaning can differ depending on the NoSQL data-source being used. With MongoDB the dataType refers to the collection used to store the data. The dataType is defaulted to the entity name (as upper case), which is the simple class name.

The dataFormat depends on the type of data being stored. Three formats are supported by EclipseLink, XML, Mapped, and Indexed. XML is the default, but since MongoDB uses BSON, which is similar to a Map in structure, Mapped is used. In summary, each class requires the @NoSql(dataFormat=DataFormatType.MAPPED) annotation.

@Entity
@NoSql(dataFormat=DataFormatType.MAPPED)
public class Order

@Embeddable
@NoSql(dataFormat=DataFormatType.MAPPED)
public class OrderLine

Step 3 : Define the Id

JPA requires that each Entity define an Id. The Id can either be a natural id (application assign id) or a generated id (id is assign by EclipseLink). MongoDB also requires an _id field in every document. If no _id field is present, then Mongo will auto generate and assign the _id field using an OID (object identifier) which is similar to a UUID (universally unique identifier).

You are free to use any field or set of fields as your Id in EclipseLink with NoSQL, the same as a relational Entity. To use an application assigned id as the Mongo id, simply name its field as "_id". This can be done through the @Field annotation, which is similar to the @Column annotation (which will also work), but without all of the relational details, it has just a name. So, to define the field Mongo will use for the id include @Field(name="_id") in your mapping.

To use the generated Mongo OID as your JPA Id, simply include @Id, @GeneratedValue, and @Field(name="_id") in your object's id field mapping. The @GeneratedValue tells EclipseLink to use the Mongo OID to generate this id value. @SequenceGenerator and @TableGenerator are not supported in MongoDB, so these cannot be used. Also the generation types of IDENTITY, TABLE and SEQUENCE are not supported. You can use the EclipseLink @UUIDGenerator if you wish to use a UUID instead of the Mongo OID. You can also use your own custom generator. The id value for a Mongo OID or a UUID is not a numeric value, it can only be mapped as String or byte[].

@Id
@GeneratedValue
@Field(name="_id")
private String id;

Step 4 : Define the mappings

Each attribute in your object has too be mapped. If no annotation/xml is defined for the attribute, then it mapping will be defaulted. Defaulting rules for NoSQL data, follow the JPA defaulting rules, so most simple mappings do not require any configuration if defaults are used. The field names used in the Mongo BSON document will mirror the object attribute names (as uppercase). To provide a different BSON field name, the @Field annotation is used.

Any embedded value stored in the document is persisted using the @Embedded JPA annotation. An embedded collection will use the JPA @ElementCollection annotation. The @CollectionTable of the @ElementCollection is not used or supported in NoSQL, as the data is stored within the document, no separate table is required. The @AttributeOverride is also not required nor supported with NoSQL, as the embedded objects are nested in the document, and do not require unique field names. The @Embedded annoation/xml is normally not required, as it is defaulted, the @ElementCollection is required, as defaulting does not currently work for @ElementCollection in EclipseLink.

The relationship annotations/xml @OneToOne, @ManyToOne, @OneToMany, and @ManyToMany are only to be used with external relationships in NoSQL. Relationships within the document use the embedded annotations/xml. External relationships are supported to other documents. To define an external relationship a foreign key is used. The id of the target object is stored in the source object's document. In the case of a collection, a collection of ids is stored. To define the name of the foreign key field in the BSON document the @JoinField annotation/xml is used.

The mappedBy option on relationships is not supported for NoSQL data, for bi-directional relationships, the foreign keys would need to be stored on both sides. It is also possible to define a relationship mapping using a query, but this is not currently supported through annotations/xml, only through a DescriptorCustomizer.

@Basic
private String description;
@Basic
private double totalCost = 0;
@Embedded
private Address billingAddress;
@Embedded
private Address shippingAddress;
@ElementCollection
private List orderLines = new ArrayList();
@ManyToOne(fetch=FetchType.LAZY)
private Customer customer;

Step 5 : Optimistic locking

Optimistic locking is supported with MongoDB. It is not required, but if locking is desired, the @Version annotation can be used.

Note that MongoDB does not support transactions, so if a lock error occurs during a transaction, any objects that have been previously written will not be rolled back.

@Version
private long version;

Step 6 : Querying

MongoDB has is own JSON based query by example language. It does not support SQL (i.e. NoSQL), so querying has limitations.

EclipseLink supports both JPQL and the Criteria API on MongoDB. Not all aspects of JPQL are supported. Most basic operations are supported, but joins are not supported, nor sub-selects, group bys, or certain database functions. Querying to embedded values, and element collections are supported, as well as ordering, like, and selecting attribute values.

Not all NoSQL database support querying, so EclipseLink's NoSQL support only supports querying if the NoSQL platform supports it.

Query query = em.createQuery("Select o from Order o where o.totalCost > 1000");
List orders = query.getResultList();

Query query = em.createQuery("Select o from Order o where o.description like 'Pinball%'");
List orders = query.getResultList();

Query query = em.createQuery("Select o from Order o join o.orderLines l where l.description = :desc");
query.setParameter("desc", "shipping");
List orders = query.getResultList();

Native queries are also supported in EclipseLink NoSQL. For MongoDB the native query is in MongoDB's command language.

Query query = em.createNativeQuery("db.ORDER.findOne({\"_id\":\"" + oid + "\"})", Order.class);
Order order = (Order)query.getSingleResult();

Step 7 : Connecting

The connection to a Mongo database is done through the JPA persistence.xml properties. The "eclipselink.target-database" property must define the Mongo platform "org.eclipse.persistence.nosql.adapters.mongo.MongoPlatform". A connection spec must also be defined through "eclipselink.nosql.connection-spec" to be "org.eclipse.persistence.nosql.adapters.mongo.MongoConnectionSpec". Other properties can also be set such as the "eclipselink.nosql.property.mongo.db", "eclipselink.nosql.property.mongo.host" and "eclipselink.nosql.property.mongo.port". The host and port can accept a comma separated list of values to connect to a cluster of Mongo databases.

<persistence-unit name="mongo-example" transaction-type="RESOURCE_LOCAL">
<class>model.Order</class>
<class>model.OrderLine</class>
<class>model.Address</class>
<class>model.Customer</class>
<properties>
<property name="eclipselink.target-database" value="org.eclipse.persistence.nosql.adapters.mongo.MongoPlatform">
<property name="eclipselink.nosql.connection-spec" value="org.eclipse.persistence.nosql.adapters.mongo.MongoConnectionSpec">
<property name="eclipselink.nosql.property.mongo.port" value="27017">
<property name="eclipselink.nosql.property.mongo.host" value="localhost">
<property name="eclipselink.nosql.property.mongo.db" value="mydb">
<property name="eclipselink.logging.level" value="FINEST">
</property>
</property>

Summary

The full source code to this demo is available from SVN.

To run the example you will need a Mongo database, which can be downloaded from, http://www.mongodb.org/downloads.

EclipseLink also support NoSQL access to other data-sources including:
  • Oracle NoSQL
  • XML files
  • JMS
  • Oracle AQ

Thursday, March 29, 2012

NoSQL

In the beginning data was free and wild. It was not confined to rows and columns and not bounded to standardization. Data access was unruly and proprietary. These were the first "NoSQL" databases. They consisted of flat file, hierarchical and network databases such as VSAM, IMS and ADABASE.

Then there was SQL, and things were good.

SQL was developed during the golden age of data in the 1970s. Database access became standardized through the SQL language and the relational model. The 1970s saw the birth of relational database products such as RDBMS, Ingres, Oracle and DB2. The 1980s saw ANSI standardization of the SQL language, and the adoption of client-server computing.

However, the legacy databases still existed, as well as the legacy applications that accessed them. New applications needed to access the old data, and this was in general a very painful experience.

Back in the good old Smalltalk days during the 1990s, Smalltalk was unofficially adopted as the programming language of choice for large corporate projects. It was the beginning of the commercial adoption of object-oriented programming, both Smalltalk, C++ and other OO languages. Things were great, but there was a dark side. All of the data was stored in relational databases, or worse legacy mainframe databases. Fitting round objects into square relational tables was difficult and cumbersome. Two solutions emerged, object-oriented databases, and object-relational mapping.

New commercial object-oriented database management systems (OODBMS) emerged in the 1990s including Versant, Gemstone and ObjectStore. They were integrated with their respective languages, Smalltalk and C++, and stored data as it was represented in memory, instead of in the relational model. These were the 2nd generation of "NoSQL" databases. There was little standardization and solutions were mainly proprietary. Access to the data from non object-oriented languages was difficult. The world did not adopt this new model, as it had previously adopted the relational model. The worlds data remained in the trusted, standardized and universally accessible relational model.

Object-relational mapping allowed objects to be used in the programming model, but have them converted to relational rows and SQL when persisted. A lot of OR mapping frameworks were built, including many corporate in-house solutions. TopLink, a product from The Object People became the leading OR mapper in the Smalltalk language. In C++ there was Persistence, as well as various other products in various languages.

Although the relational model was the industry standard for any new applications, much of the worlds data remained in mainframe databases. The data was slowly being migrated, but most corporations still had mainframe data. Consulting at TopLink clients in the 90s I found most clients were building applications on relational database, but still had to get some data from the mainframe. This is when we created the first version of TopLink's "NoSQL" support. Of coarse NoSQL was not a buzz word at the time, so the offering was called TopLink for the Mainframe. The main problem was that everyone's mainframe data and access was different, so the product involved lots of consulting.

When Java came along, TopLink moved from Smalltalk to Java. OR mapping became very popular in Java and many new products came to market. The first real OR standard came in the form on EJB CMP. It had is "issues" to say the least, and was coupled with the J2EE platform. A new competing standard of JDO was created in retaliation to CMP. To reconcile the issue of having two competing Java standards, JPA was created to replace them both, and was adopted by most OR mapping products.

In response to the popularity of object-oriented computing, the relational database vendors created the object-relational paradigm. This allowed storage of structured object types and collections in relational tables. SQL3 (SQL 1999) defined new query syntax to access this data. Despite some initial hype, the object-relational paradigm was not successful, and although the features remain in Oracle, DB2 and Postgres, the world stayed with the trusted relational model.

The panic around Y2K had the good fortune of getting most corporations and governments off mainframe databases, and into relational databases. Some legacy data still remained, so we also offered TopLink for the Mainframe in Java. At that time the Internet was taking off, and XML was becoming popular. Since XML is hierarchical data that you could convert any mainframe data to, it became part of our solution for accessing legacy data and the TopLink SDK was born.

With the explosion of the Internet, XML was becoming increasingly popular. This lead to once again the questioning of the relational model, and the creation of XML databases (the 3rd generation of NoSQL). There were several XML databases that achieved much hype, but limited market success. The relational database vendors responded by adding XML support for storage of XML in relational tables.

Again the world stayed with the relational model.

The TopLink SDK also provided simple object to XML mapping, perhaps the first such product to do so. As XML usage in Java became mainstream, the TopLink SDK was split into two products. TopLink Moxy become TopLink's object to XML mapping solution. TopLink EIS became TopLink's legacy data persistence solution.

Around 2009 the term NoSQL was used to categories the new distributed databases being used at Google, Amazon and Facebook. The databases categorized themselves as
being highly scalable, not adhering to ACID transaction semantics, and having limited querying. The NoSQL term grew to include the various other non-relational databases that have emerged throughout the ages.

Is the relational model dead? Will the world switch to the NoSQL model, and will data once again be free? Only time will tell. If history teaches us anything, one would expect the relational model to persist. NoSQL has already been renamed in some circles to "Not Only SQL", to leave room for the NoSQL databases to support the SQL standard. In fact, some NoSQL databases already have support for JDBC drivers. My intuition is a union of the two models, perhaps this has already begun with some NoSQL databases adding SQL support, and some relational databases extended their clustering support such as MySQL cluster.

EclipseLink 2.4 will contain JPA support for NoSQL databases. Initial support with include MongoDB and Oracle NoSQL. This support is already available in the EclipseLink nightly builds. Technically, this is not new functionality, as EclipseLink (formerly TopLink) has been supporting non-relational data for over a decade, but the JPA support is new.

In the upcoming months I will be blogging about some of the new features in EclipseLink to support NoSQL. This blog post is solely an introduction, so sorry to those expecting hard content.