This course explores how you can build memory data grid applications with quick and easy steps in just a few minutes. These step-by-step videos will allow you to watch and learn just how simple it is to scale mission-critical applications and provide fast and reliable access to frequently used data.
This course covers everything that you’ll need to know to develop, manage, deploy, and monitor Coherence applications with ease.
Beginning with installation and application development in Eclipse, we'll then go over topics such as cache functions for your servers, using Coherence facets, and monitoring applications with JMX. We'll also discuss different cache topologies and what sets them apart. We'll then wrap up with an overview of the Coherence API’s extensive features, as well as using the Coherence*Extend client.
Ahmet Fuat Sungur is an experienced computer engineer with Global Maksimum Data and Information Technologies, a company that provides consultancy services on many Oracle products, particularly Cep, Coherence, Exadata, Exalogic, Big Data, stream processing, data warehousing, business intelligence solutions, and data mining.
He has six years of experience in working with Oracle products. Since 2008, he has been working in the telecommunication industry. In his professional career, data processing technologies are his favorite subjects. He participated in several data-oriented applications, which have been developed using Java and Oracle technologies. Software architecture, distributed processing, Big Data, and NoSQL databases are his other main interests. He is also the reviewer of Packt’s Getting Started with Oracle Event Processing 11g
There are many files in the zip archive; we'll cover what they are and how you'll find them useful.
Quickly access the Coherence data grid without any IDE.
Configuring Coherence config files is not easy, unless you use Coherence Facelets in Eclipse.
There are several cache servers and thousands of operations in Coherence; with JMX, it can be easily monitored.
All cache servers need to access the same data; use replicated caches to reduce processing time.
A Partitioned Cache can hold much more data than a replicated cache in a clustered environment.
All cache servers can access the most frequently used data by using its local cache; they can also access least frequently used data by accessing the network.
Backing maps hold data not just in memory, but also in databases or files to hold cache data.
Refreshing data with the underlying data stage when there is a cache miss exception.
Implementing the write-through and write-behind mechanisms when you need to write Coherence objects into the underlying data stage.
There are different kinds of serialization methods; use POF to get increased performance.
Loading thousands of objects into the cache can be a slow process; we'll examine how you can speed it up.
We'll cover returning Coherence objects that match predefined criteria and also speeding up filtering operations, filters, and indexes.
While modifying thousands of objects, getting them into the client can be a real performance degrader; entry processor can be used to offset this by processing objects in the storage node instead of the client.
A cluster contains many cache servers, and each server should keep track of cache modifications: update, insert, and remove.
Map triggers are useful to perform some operations just before cache modifications.
ContinuousQueryCache is useful where it's necessary to use filtered data across many servers to keep them up-to-date. It does so by listening to all events and adds objects that match a set filter into the continuous query cache
Data in Coherence is available to cluster members, but sometimes there's a need to use objects outside your cluster.
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