Spring for Apache Hadoop, provides a dedicated XML namespace for configuring Hadoop jobs with embedded scripting features and support for Hive and Pig. In addition, Spring for Apache Hadoop allows you to take advantage of core Spring Framework features such as task scheduling, Quartz integration, and property placeholders to reduce lines of code, improve testability and maintainability, and simplify the development proces.
What about cloud based data services? David Turanski: While there are currently no plans to support cloud based services such as Amazon 3S , Spring Data provides a flexible architecture upon which these may be implemented. Additionally, Spring provides excellent support for declarative transactions. With Spring Data, things get even easier. How can use Spring to perform: — Data ingestion from various data sources into Hadoop, — Orchestrating Hadoop based analysis workflow, — Exporting data out of Hadoop into relational and non-relational databases.
David Turanski: As previously mentioned, a complete big data processing pipeline involving all of these steps will require Spring for Apache Hadoop in conjunction with Spring Integration and Spring Batch. Spring Integration greatly simplifies enterprise integration tasks by providing a light weight messaging framework, based on the well known Patterns of Enterprise Integration by Hohpe and Woolf.
Spring Batch provides a robust framework for any type of batch processing and is be used to configure and execute scheduled jobs composed of the coarse-grained processing steps. Individual steps may be implemented as Spring Integration message flows or Hadoop jobs. While this was, and still is, developed independently as an open source Spring project, the GemFire product team recognized the value to its customers of developing with Spring and has increased its commitment to Spring Data GemFire.
As of the recent GemFire 7. At the same time, the project was moved under the Spring Data umbrella.
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Could you give a technical example on how do you simplify the development of building highly scalable applications? David Turanski: GemFire is a fairly mature distributed, memory oriented data grid used to build highly scalable applications. As a consequence, there is inherent complexity involved in configuration of cache members and data stores known as regions a region is roughly analogous to a table in a relational database.
GemFire supports peer-to-peer and client-server topologies, and regions may be local, replicated, or partitioned. In addition, GemFire provides a number of advanced features for event processing, remote function execution, and so on. This works well but is relatively limited in terms of flexibility.http://data.flinttworks.kayak.rocks/im-her-doctor.php
Spring Data Modern Data Access for Enterprise Java - PDF
Today, configuration of core components can be done entirely in Spring, making simple things simple and complex things possible. In a client-server scenario, an application developer may only be concerned with data access. In GemFire, a client application accesses data via a client cache and a client region which act as a proxies to provide access to the grid. Such components are easily configured with Spring and the application code is the same whether data is distributed across one hundred servers or cached locally.
The cache resources are configured in Spring XML:. Here we see the deployed application default profile depends on a remote GemFire locator process. The client region does not store data locally by default but is connected to an available cache server via the locator. The region is distributed among the cache server and its peers and may be partitioned or replicated. The test profile sets up a self contained region in local memory, suitable for unit integration testing. Additionally, applications may by further simplified by using a GemFire backed Spring Data Repository. The key difference from the example above is that the entity mapping annotations are replaced with GemFire specific annotations:.
The Region annotation maps the Person type to an existing region of the same name. The Region annotation provides an attribute to specify the name of the region if necessary. The project uses GemFire as a distributed data management platform. David Turanski: Customers choose GemFire primarily for performance. As an in memory grid , data access can be an order of magnitude faster than disk based stores. Many disk based systems also cache data in memory to gain performance. This is a major advantage for a certain class of high volume, low latency, distributed systems. Additionally, GemFire is extremely reliable, providing disk-based backup and recovery.
This includes a number of advanced tuning parameters to balance performance and reliability, synchronous or asynchronous replication, advanced object serialization features, flexible data partitioning with configurable data colocation, WAN gateway support, continuous queries,. Net interoperability, and remote function execution. David Turanski: Given all its capabilities and proven track record supporting many mission critical systems, I would certainly characterize GemFire as such. He is also a committer on the Spring Integration project. David has extensive experience as a developer, architect and consultant serving a variety of industries.
In addition he has trained hundreds of developers how to use the Spring Framework effectively.
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