Welcome!

Java IoT Authors: Stackify Blog, Pat Romanski, Elizabeth White, Liz McMillan, Mehdi Daoudi

Related Topics: Java IoT

Java IoT: Article

An Introduction to Service Data Objects

Integrating relational data into Web applications

Late last year, IBM Corp., and BEA Systems, Inc., introduced Service Data Objects (SDO), a new data programming specification that complements existing Java 2 Enterprise Edition technologies and enables service-oriented architectures by providing uniform data access for a wide variety of service and resource types. Not only does SDO enable a consistent approach to data access, but it also provides features that simplify common application tasks, such as allowing data browsing and update while the application is disconnected from the data source.

In this article we explain how SDO works and where it may fit into your own applications. We also take a closer look at how you can use SDO to retrieve and modify data stored in a relational database.

SDO Concepts
At the core of SDO are DataGraphs and Data Mediator Services (DMS). In simple terms, an application program obtains a DataGraph from a DMS that is specific to some back-end data store. The program can then examine and/or update the data contained in the graph. Optionally the program can employ a DMS to propagate the entire set of updates back to the original data source.

DataGraph
As a program object, a DataGraph provides an in-memory, nonpersistent copy of data. A program can work with this data even when there's no connection to the original data source. The data in a DataGraph is organized as a group of DataObjects, which may be linked together (i.e., the data is structured as a "graph"). A DataGraph also contains a schema that describes the structure of the DataObject type(s) contained in the DataGraph. To handle updates, a DataGraph also maintains a change history to track all modifications made to the graph.

Each DataObject contains a set of properties, which can be primitive values or references to other DataObjects contained in the DataGraph. If a DataObject's schema is known at development time, a developer can use automated tools to generate typed interfaces that simplify DataObject access. Alternatively, the application can define the schema at runtime, allowing dynamic access of DataObjects. With either static or dynamic access, linked data can be accessed in either a breadth-first or depth-first manner. For example, if a DataGraph contains customers and related orders, then orders can be obtained directly from the DataGraph or from their parent customer DataObject. SDO also allows for accessing data through XML Path Language (XPath) subset expressions.

Disconnected Programming Model
The DataGraph's disconnected, data source-independent nature provides a simple programming model, supports common application patterns, and offers a potential performance advantage.

Today, J2EE application developers have a wide variety of persistence frameworks to choose from, such as JDBC, EJB, or JDO. These frameworks have different APIs and are often complex, requiring developers to spend a great deal of time learning multiple APIs rather than developing applications. Since SDO provides a single data access API regardless of the persistence mechanism, developers can choose the framework that best fits an application without using different APIs.

Some developers strive for similar independence by developing custom Java objects to encapsulate data from different data sources. This tactic makes use of the Data Access Object design pattern. SDO inherently supports this pattern, freeing developers from the need to develop their own infrastructure.

To improve performance, some types of applications can exploit the DataGraph's support for applying multiple updates in one method call to reduce the number of connections and/or database operations. By storing data from multiple database rows and tables in a DataGraph, applications can make changes to the data without making additional round-trips to the database.

Mediators
An SDO DataGraph must be populated from a data source or a service. The SDO component that populates a DataGraph is called a data mediator service (DMS). A DMS also propagates changes to the in-memory DataGraph back to the originating data source. Note that the current SDO specification does not define a specific API for DMSs - beyond a few basic requirements, each DMS provider is free to design the DMS that best suits the associated data source.

Typically, a DMS accesses a single type of data source, for example, JDBC resources or entity EJBs. All DMSs require the developer to provide a description of the data to be accessed. This data description (or metadata) typically consists of a schema and a query over the associated data source.

Figure 1, from the SDO specification, illustrates the flow of data during a typical interaction between an SDO client and a DMS. The client makes a request to the DMS to return a DataGraph. The DMS reads the requested data from the data source, constructs a DataGraph of related DataObjects, and returns the DataGraph to the application. The SDO client makes changes to the DataGraph in-memory and then sends the modified DataGraph back to the DMS. The DMS examines the ChangeSummary contained in the graph and propagates the changes back to the original data source.

Because the DataGraph is disconnected from the data source, it's possible that another application will update the data (in the data source) that was used to populate a DataGraph before the application requests the DMS to propagate the application's changes back to the data source. To handle such potential update conflicts, a DMS typically implements some form of "optimistic" concurrency control and throws an exception to the application when a data collision occurs. At that point, it is the application's responsibility to recover from the collision, for example, by rereading the data and restarting the transaction.

Too-frequent collisions under an optimistic concurrency approach can degrade performance as well as aggravate end users. In applications where multiple applications will often attempt concurrent changes to the same data, optimistic concurrency control may not be a good choice. However, for applications without this behavior, optimistic concurrency control can improve performance by reducing lock contention.

Metamodel
The SDO specification assumes the presence of a metamodel and metadata API for the DataGraph, but does not specify one explicitly. Today, SDO could be implemented with a variety of metamodels and schema languages such as XML Schema or the Essential Meta Object Facility (EMOF). The metamodel implementation does not affect SDO end users.

XML Serialization
SDO defines the XML format for DataGraphs and DataObjects, and specifies that the format can be customized by an XSD. This same format is used for Java serialization. The serialized form of a DataGraph includes the DataObjects as well as the schema and change summary. This capability allows data to be easily transferred over the wire as would be required by a Web service invocation.

Relationship to Other J2EE Technologies
SDO can complement or simplify existing J2EE technologies. SDO complements JDBC by providing a more powerful framework and API for data access. In a typical relational database, data is normalized into multiple tables. When this data is read using a join query through JDBC, it's returned to the application in a tabular format that includes some data redundantly (e.g., an order number may be repeated with all individual line items for the same order). This format doesn't directly correspond to Java's object-oriented data model and can complicate navigation and update operations. A JDBC DMS can restructure this tabular data into a graph of related DataObjects. For example, an order might be represented by a DataObject that contains a list of references to other DataObjects containing line-item data. This allows an application to use standard Java techniques to access and modify the data.

Data access via EJBs can also be enhanced by using SDO. To implement a disconnected Data Access Object design pattern with EJBs alone, an application must use some combination of copy helper objects, session beans, and EJB access beans. An EJB DMS provides a ready-to-use disconnected architecture and frees developers from having to implement their own framework or custom artifacts.

SDO could also be used to complement other related technologies. For example:

  • JSR 227: Declaratively binding and accessing data in J2EE applications. SDO could be used as the mechanism to return results from the data source.
  • JSR 225: XQuery API for Java (XQJ). A Data Mediator Service could use the provided API to return SDOs.
  • JDO 2.0: SDO could provide data transfer objects from JDO persistent objects.
  • WEB UI Data Binding: JSF can use SDOs as a data binding. JSTL can use an SDO DataObject impl that implements the map interface.
Security
Security is not part of the current SDO model, so security in an SDO-based application is provided at the edges. For example, if an SDO-based application is developed that employs an EJB session bean and a JDBC connection, then security is provided at the boundaries of the application by these two J2EE components.

SDO with JDBC
SDO provides a uniform model for accessing data from a variety of services or data sources, including JDBC. Figure 2 shows interactions among the main artifacts involved when an application uses SDO over JDBC. Notice how the application code calls mediator and DataGraph methods, while the mediator calls JDBC and DataGraph methods, thus insulating the application from JDBC.

There are three central aspects to using a JDBC mediator: metadata, connections, and transaction handling.

Metadata
The application must supply the JDBC DMS with a metadata object that specifies the data to be retrieved. For a JDBC mediator, the metadata contains an augmented relational database schema that defines a set of tables, their relationships, and selection and ordering criteria. The JDBC DMS creates a DataGraph that corresponds to the provided schema definition. Each DataObject type within the DataGraph corresponds to a table definition in the schema, and each DataObject property corresponds to a table column.

The JDBC DMS uses the metadata to generate a SQL Select statement to retrieve data for the DataGraph. The simplest metadata example would describe a single table and no selection or ordering. For this specification, the JDBC mediator would retrieve all rows of the table and create a DataGraph that contains a list of DataObjects, with each DataObject containing the data from one row in the table. Each DataObject will have a set of values corresponding to the values from each column.

A more complex example might involve two related tables; say Customers and their respective Orders. In this case, the metadata must specify the relationship between the two tables, which will subsequently be reflected in a corresponding relationship between two types of DataObjects.

The DataGraph returned in this case would contain a list of Customer DataObjects and each of these Customer DataObjects would have a list of related Order DataObjects. The DataGraph will contain all Customers and Orders; they are organized as a tree with Customers at the "root" of the tree and related Orders branching off of each Customer.

Applications will frequently want data only from specified rows of a table. In this case, the metadata for a JDBC DMS specifies selection criteria. For example, customers might be selected from a particular zip code or with a particular last name. Also, the DataObjects in the graph can optionally be ordered by specifying "order by" columns in the metadata.

Normally the JDBC DMS generates SQL select, insert, update, and delete statements to read and update the associated relational database. However, an application can optionally provide an explicit Select statement for the mediator to use. If this option is used, the DMS will then generate only the complementary insert, update, and delete statements and will use the provided select statement as is.

Connections
In addition to specifying what data to retrieve, an application must specify which data source the DMS should access. For a JDBC DMS, this can be done by specifying a JDBC Connection object. The DMS will use this connection for all database interactions.

Transactions
As mentioned earlier, SDO provides a disconnected programming model and, accordingly, DMSs will typically connect to a data store only to read data for graph creation or to write data to reflect changes back to the store.

When an application requests the JDBC DMS to retrieve data and produce a DataGraph, the DMS starts a transaction, reads the data, creates the graph, and ends the transaction. The DataGraph is returned to the application and is "disconnected" in the sense that it is not associated with any connection or transaction; there are no locks held on the data.

The client can now read data from the DataGraph and make changes to it while it is in memory and disconnected from the data source. All changes made to the graph are recorded by the DataGraph. At some point the client will want to push these changes back to the data source and call the JDBC DMS "applyChanges" API.

As part of the "applyChanges" function, the JDBC DMS will reflect to the data store all changes made to the graph as part of a single transaction; this is true whether there is a single change to the graph or an entire batch of changes.

The disconnected programming model generally implies the use of an optimistic concurrency control scheme to push changes back to the data store; this is the approach taken by the JDBC DMS.

When the DMS attempts to apply DataGraph changes back to the data store, each row being updated is checked to ensure it has not been modified since it was originally read. If no intervening modifications have taken place, the update proceeds. If a row has been modified since the data was read, a collision has occurred; the update transaction is rolled back and an exception is thrown to the client.

There is also an option to use the DMS within a larger transaction. If this option is used, the DMS will assume that the client is controlling the transaction and will not perform any commit or rollback operations.

An Example
The following simple example demonstrates JDBC database access with SDO employing the JDBC DMS. This example has six steps, each illustrated by a code snippet.

Step 1: Create the JDBC mediator metadata instance
Create the metadata instance to represent the Customer table. This example demonstrates the creation of the JDBC DMS metadata programmatically. This is an obvious candidate for tooling support. Remember that the JDBC DMS uses a simple mapping scheme whereby each table definition results in a DataObject type and each table column results in a DataObject type property (see Listing 1).

Step 2: Create the DMS instance as in Listing 2

Step 3: Read the DataGraph from the database


//Create the "lastName" argument for the filter predicate
DataObject arguments = mediator.getParameterDataObject();
arguments.put("CUSTLASTNAME", "Pavick");
DataObject graph = mediator.getGraph(arguments);

Step 4 : Retrieve data from the graph


//Iterate through all returned customers and print the first name
Iterator i = graph.getList("CUSTOMER").iterator();
	while (i.hasNext()) {
	DataObject cust = (DataObject) i.next();
	System.out.println(cust.getString("CUSTFIRSTNAME"));
	}

Step 5: Change the DataGraph


List customers = graph.getList("CUSTOMER");
//Get the first customer in the graph and update the name
DataObject customer = (DataObject)customers.get(0);
	customer.setString("CUSTFIRSTNAME", "Kevin");

Step 6: Apply DataGraph changes back to the database

mediator.applyChanges(graph);

Variations on the Example
Metadata File

In Step 1 we created the mediator metadata programmatically. An alternative is to provide the metadata in the form of an XML file. Listing 3 is the XML representation of the Customer metadata.

Using this file, Step 1 would not be necessary and Step 2 would become:

Step 2: Create the mediator instance as shown in Listing 4

Static Types
The example provided above uses the dynamic access APIs of DataOb-ject. The JDBC DMS also supports the use of static SDO types. To use the static API access to DataObjects, a set of static types is generated at development time and tools are provided for this purpose. Using static types provides a cleaner user API as well as a performance boost at runtime. The generation step is beyond the scope of this article, but this is what Step 4 looks like when using a static customer DataObject.

Step 4: Retrieve data from the graph


//Iterate through all returned customers and print the first name
Iterator i = graph.getCustomers().iterator();
	while (i.hasNext()) {
	Customer cust = (Customer) i.next();
	System.out.println(cust.getFirstName());
	}

Paging
The JDBC Data Mediator Service also provides a paging capability that can be useful for marching through large data sets. A pager interface provides a cursor-like next() capability. The next() function returns a graph representing the next page of data from the entire data set specified by the mediator metadata; a previous() function is also available. A CountingPager is also provided that allows the retrieval of a specified page from the data set.

Listing 5 illustrates paging through a large set of customer instances using a Counting Pager.

Conclusion
In this article we have explored some of the key SDO concepts and also drilled down into a specific use of the technology for relational database access employing a JDBC Data Mediator.

SDO is a standard from IBM and BEA and there is a reference implementation under development at www.eclipse.org/emf. This EMF-based implementation of SDO will also be delivered with WebSphere Application Server 6.0 and will be complemented by:

  • JDBC Data Mediator Service
  • EJB Data Mediator Service
It is anticipated that the 6.0 version of WebSphere Studio will contain complete support for creating applications that leverage SDO; this will include visual tooling to configure the JDBC DMS. With the power of SDO, relational data can be integrated into Web applications more easily than ever.

Acknowledgments
We would like to thank Stephen Brodsky and Tom Schneider for their assistance with this article.

More Stories By Kevin Williams

Kevin Williams is a software developer with IBM and is leading IBM’s participation in the DAS subproject of the Apache Tuscany incubator.

More Stories By Brent Daniel

Brent Daniel is a software developer with IBM. He currently works on a JDBC data mediator service for WebSphere Application Server.

Comments (2) View Comments

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


Most Recent Comments
Brahm van Niekerk 10/12/04 08:41:38 AM EDT

JeeWiz implemented a similar technology last year in 2003 known as DataViews. DataViews became necessary for performance reasons when reading large data sets from Data Sources in Web Systems. JeeWiz is an MDA implementation that generates code for MS .NET and J2EE Application Servers, such as WebSphere, WebLogic, Oracle and JBoss. True portability as a design pattern was proven for us when we implemented it in both J2EE (JDBC) and in MS .NET implementation as Datasets. We have found that being able to generate systems for both J2EE and .NET from the same specification is hugely advantageous especially for vendors having to support these platforms. More information can be seen at http://www.jeewiz.com.

Rost Vashevnik 10/11/04 07:43:53 PM EDT

The capabilities described in this article are already implemented in MetaBoss - an Open Source MDA tool suite. It includes generation of Data Objects, data base schemas etc. from the UML model. The produced code is "technology independent" - meaning that it will work with WebSphere, JBoss or even just non-J2EE Tomcat deployment. You can see it for yourself at www.metaboss.com

@ThingsExpo Stories
SYS-CON Events announced today that App2Cloud will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct. 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. App2Cloud is an online Platform, specializing in migrating legacy applications to any Cloud Providers (AWS, Azure, Google Cloud).
IoT is at the core or many Digital Transformation initiatives with the goal of re-inventing a company's business model. We all agree that collecting relevant IoT data will result in massive amounts of data needing to be stored. However, with the rapid development of IoT devices and ongoing business model transformation, we are not able to predict the volume and growth of IoT data. And with the lack of IoT history, traditional methods of IT and infrastructure planning based on the past do not app...
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. Jack Norris reviews best practices to show how companies develop, deploy, and dynamically update these applications and how this data-first...
Intelligent Automation is now one of the key business imperatives for CIOs and CISOs impacting all areas of business today. In his session at 21st Cloud Expo, Brian Boeggeman, VP Alliances & Partnerships at Ayehu, will talk about how business value is created and delivered through intelligent automation to today’s enterprises. The open ecosystem platform approach toward Intelligent Automation that Ayehu delivers to the market is core to enabling the creation of the self-driving enterprise.
Internet-of-Things discussions can end up either going down the consumer gadget rabbit hole or focused on the sort of data logging that industrial manufacturers have been doing forever. However, in fact, companies today are already using IoT data both to optimize their operational technology and to improve the experience of customer interactions in novel ways. In his session at @ThingsExpo, Gordon Haff, Red Hat Technology Evangelist, shared examples from a wide range of industries – including en...
Consumers increasingly expect their electronic "things" to be connected to smart phones, tablets and the Internet. When that thing happens to be a medical device, the risks and benefits of connectivity must be carefully weighed. Once the decision is made that connecting the device is beneficial, medical device manufacturers must design their products to maintain patient safety and prevent compromised personal health information in the face of cybersecurity threats. In his session at @ThingsExpo...
"We're a cybersecurity firm that specializes in engineering security solutions both at the software and hardware level. Security cannot be an after-the-fact afterthought, which is what it's become," stated Richard Blech, Chief Executive Officer at Secure Channels, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
SYS-CON Events announced today that Massive Networks will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Massive Networks mission is simple. To help your business operate seamlessly with fast, reliable, and secure internet and network solutions. Improve your customer's experience with outstanding connections to your cloud.
SYS-CON Events announced today that Grape Up will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct. 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Grape Up is a software company specializing in cloud native application development and professional services related to Cloud Foundry PaaS. With five expert teams that operate in various sectors of the market across the U.S. and Europe, Grape Up works with a variety of customers from emergi...
Detecting internal user threats in the Big Data eco-system is challenging and cumbersome. Many organizations monitor internal usage of the Big Data eco-system using a set of alerts. This is not a scalable process given the increase in the number of alerts with the accelerating growth in data volume and user base. Organizations are increasingly leveraging machine learning to monitor only those data elements that are sensitive and critical, autonomously establish monitoring policies, and to detect...
Everything run by electricity will eventually be connected to the Internet. Get ahead of the Internet of Things revolution and join Akvelon expert and IoT industry leader, Sergey Grebnov, in his session at @ThingsExpo, for an educational dive into the world of managing your home, workplace and all the devices they contain with the power of machine-based AI and intelligent Bot services for a completely streamlined experience.
Because IoT devices are deployed in mission-critical environments more than ever before, it’s increasingly imperative they be truly smart. IoT sensors simply stockpiling data isn’t useful. IoT must be artificially and naturally intelligent in order to provide more value In his session at @ThingsExpo, John Crupi, Vice President and Engineering System Architect at Greenwave Systems, will discuss how IoT artificial intelligence (AI) can be carried out via edge analytics and machine learning techn...
When shopping for a new data processing platform for IoT solutions, many development teams want to be able to test-drive options before making a choice. Yet when evaluating an IoT solution, it’s simply not feasible to do so at scale with physical devices. Building a sensor simulator is the next best choice; however, generating a realistic simulation at very high TPS with ease of configurability is a formidable challenge. When dealing with multiple application or transport protocols, you would be...
With tough new regulations coming to Europe on data privacy in May 2018, Calligo will explain why in reality the effect is global and transforms how you consider critical data. EU GDPR fundamentally rewrites the rules for cloud, Big Data and IoT. In his session at 21st Cloud Expo, Adam Ryan, Vice President and General Manager EMEA at Calligo, will examine the regulations and provide insight on how it affects technology, challenges the established rules and will usher in new levels of diligence a...
An increasing number of companies are creating products that combine data with analytical capabilities. Running interactive queries on Big Data requires complex architectures to store and query data effectively, typically involving data streams, an choosing efficient file format/database and multiple independent systems that are tied together through custom-engineered pipelines. In his session at @BigDataExpo at @ThingsExpo, Tomer Levi, a senior software engineer at Intel’s Advanced Analytics ...
In the enterprise today, connected IoT devices are everywhere – both inside and outside corporate environments. The need to identify, manage, control and secure a quickly growing web of connections and outside devices is making the already challenging task of security even more important, and onerous. In his session at @ThingsExpo, Rich Boyer, CISO and Chief Architect for Security at NTT i3, discussed new ways of thinking and the approaches needed to address the emerging challenges of security i...
SYS-CON Events announced today that Dasher Technologies will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Dasher Technologies, Inc. ® is a premier IT solution provider that delivers expert technical resources along with trusted account executives to architect and deliver complete IT solutions and services to help our clients execute their goals, plans and objectives. Since 1999, we'v...
There is only one world-class Cloud event on earth, and that is Cloud Expo – which returns to Silicon Valley for the 21st Cloud Expo at the Santa Clara Convention Center, October 31 - November 2, 2017. Every Global 2000 enterprise in the world is now integrating cloud computing in some form into its IT development and operations. Midsize and small businesses are also migrating to the cloud in increasing numbers. Companies are each developing their unique mix of cloud technologies and service...
SYS-CON Events announced today that IBM has been named “Diamond Sponsor” of SYS-CON's 21st Cloud Expo, which will take place on October 31 through November 2nd 2017 at the Santa Clara Convention Center in Santa Clara, California.
SYS-CON Events announced today that Datera, that offers a radically new data management architecture, has been named "Exhibitor" of SYS-CON's 21st International Cloud Expo ®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Datera is transforming the traditional datacenter model through modern cloud simplicity. The technology industry is at another major inflection point. The rise of mobile, the Internet of Things, data storage and Big...