Oracle recently announced a new free base level for the Oracle Database In-Memory Option, their real-time analytics capability. With the base level, you can allocate up to 16GB of memory to the In-Memory column store, per instance without having to have an addition In-Memory license. Larger column stores will still need the additional license. Continue reading “How to use Oracle Database In-Memory for free!”
Tag: In-Memory
Data-Driven Apps – What are they and the easiest way to develop them
At the moment, we hear a lot about how businesses need to become data-driven to remain competitive, how business need to understand their customer’s needs and quickly deliver value to those customers.
But how do you do that?
You take advantage of data-driven apps that allow users to create value or insights from data in real-time.
What are Data-Driven Apps?
Data-driven apps operate on a diverse set of data (spatial, documents, sensor, transactional, etc.) pulled from multiple different sources, often in real-time and create value from that data in very different ways to traditional applications. For example, they may use Machine Learning to make real-time recommendations to customers or detect fraudulent transactions. Or use Graph analytics to identify influencers in a community and target them with specific promotions or perhaps use spatial data to keep track of deliveries.
These apps are also frequently deployed on multiple platforms, including mobile devices as well as standard web browsers, which means they need a flexible, scalable and reliability deployment platform. Given the demands on these apps, they need to be continuously developed to adapt to new use cases or user needs, and all updates must happen online as they have to be available 24×7.
When building data-driven apps, developers need to leverage an ever-increasing set of data processing and machine learning algorithms to meet these requirements.
So how should you go about developing and deploying data-driven apps quickly, efficiently, and more importantly, in a maintainable way?
Data-Driven Techniques and Technology
You take a data-first approach, or as A. Neil Pappalardo put it ‘A Minimize Code, Maximize Data‘ approach. In other words, you bring the algorithms to the data, not the data to the algorithms.
In the video below, I explain how to take advantage of the built-in features and functionality of the Oracle Database to develop and deploy data-driven apps efficiently. I also share some easy to follow code examples to demonstrate how much simpler your application code can be if you use this approach! Continue reading “Data-Driven Apps – What are they and the easiest way to develop them”
Oracle Launches TimesTen Scaleout!
What a week for announcements! First we had Oracle Database 18c available on-premises and now Oracle TimesTen Scaleout.
If you are not familiar with TimesTen, let me reminder you that it’s Oracle’s In-Memory Database. In fact, it’s been a leading in-memory database for mission critical applications for over 20 years, and today they just launched an exciting new scalability feature called TimesTen Scaleout.
So, What is Scaleout?
TimesTen Scaleout is a distributed database, with a shared nothing architecture specifically designed to address real-time, transaction processing workloads. Think IoT, real-time trading, telecommunications billing or real-time fraud detection.
Controlling where objects are populated into memory on RAC
Last week I spent some time beta testing Oracle Database 12c RAC on docker (more on this in a future post) and decided to take a trip down memory lane and play around with Oracle Database In-Memory.
One of the things I looked at was the new FOR SERVICE sub-clause of the INMEMORY DISTRIBUTE clause.
ALTER TABLE customers INMEMORY PRIORITY HIGH DISTRIBUTE FOR SERVICE IM1;
This new sub-clause allows you to control exactly where an object (table or partition) is populated into memory in a RAC environment based on where a database service is allowed to run. If the service is stopped then the objects distributed for that service are automatically removed from the IM column store(s).
If you recall each RAC node has its own In-Memory column store (IM column store). By default, when a table is populated into memory in a RAC environment it will be automatically distributed across all of the IM column stores in the cluster. That is to say, a piece of the table will appear in each RAC node, effectively making the IM column store a shared-nothing architecture.
This is not always desirable, especially if you only run certain applications or workloads on a subset of RAC node.
The video below demonstrates how to use the new syntax and what gv$ performances views you should look at to monitor what is going on in the IM column store in a RAC environment. You’ll find a copy of the script I used in the video at the bottom of this post.
PLEASE NOTE: That the service names are case sensitive, so make sure you keep them consistent. If your service name is defined all in lower case and then you specify the name in upper case in the ALTER TABLE command, you will not get an error. Remember this command is only changing the INMEMORY attribute on the table. It’s only when we begin populating the table into memory will Oracle discover that the service you specified doesn’t exist, i.e. doesn’t match the case of the service defined.
Continue reading “Controlling where objects are populated into memory on RAC”
What’s New With Database In-Memory in 12.2?
My favorite marquee feature in Oracle Database 12c is Database In-Memory. With the introduction of Database In-Memory in 12.1.0.2, data can now be populated into memory both in a row format (the buffer cache) and a new in-memory optimized column format, simultaneously.
The database maintains full transactional consistency between the row and columnar formats, just as it maintains consistency between tables and indexes. The Oracle Optimizer is fully aware of what data exists in the column format and automatically routes analytic queries to the column format and OLTP operations to the row format, ensuring both outstanding performance and complete data consistency for all workloads without any application changes.
So, what can you expect from Database In-Memory in 12.2?
Continue reading “What’s New With Database In-Memory in 12.2?”
Oracle Database 12c Release 2 Available Everywhere!
Today Oracle Database 12c Release 2 became available for download on Oracle.com for both Linux & SPARC. So anyone who wasn’t ready to try 12.2 in the Cloud can now play around with it, in the comfort of their own environment.
With each new release of the Oracle Database come fundamental architectural changes, driven by new technologies and user requirements. This has never been more evident than with Oracle Database 12c, which has 3 marquee features:
Today Oracle Database 12c Release 2 became available for download on Oracle.com for both Linux & SPARC. So anyone who wasn’t ready to try 12.2 in the Cloud can now play around with it, in the comfort of their own environment.
With each new release of the Oracle Database come fundamental architectural changes, driven by new technologies and user requirements. This has never been more evident than with Oracle Database 12c, which has 3 marquee features:
Continue reading “Oracle Database 12c Release 2 Available Everywhere!”
Oracle Storage Index
If you are on Exadata or taking advantage of Database In-Memory it’s possible your queries will benefit for the automatically created and maintained Storage Indexes.
But what exactly are Storage Indexes and why don’t I always see a benefit from them?
Let me start by describing what Storage Indexes are in relation to Database In-Memory but remember they behavior in exactly the same way on the Exadata storage cell.
A Storage Index keeps track of minimum and maximum values for each column in an In-Memory Compression Unit (IMCU) or 1MB chunk on the Exadata storage cells. When a query specifies a WHERE clause predicate, the In-Memory Storage Index on the referenced column(s) is examined to determine if any entries with the specified value exist.
If you are on Exadata or taking advantage of Database In-Memory it’s possible your queries will benefit for the automatically created and maintained Storage Indexes.
What are Storage Indexes?
Let me start by describing what Storage Indexes are in relation to Database In-Memory but remember they behavior in exactly the same way on the Exadata storage cell.
A Storage Index keeps track of minimum and maximum values for each column in an In-Memory Compression Unit (IMCU) or 1MB chunk on the Exadata storage cells. When a query specifies a WHERE clause predicate, the In-Memory Storage Index on the referenced column(s) is examined to determine if any entries with the specified value exist.