Introduction to Oracle Big Data Cloud Service – Compute Edition (Part II) – Services

In my previous post, I gave a list of installed services on a “Oracle Big Data Cloud Service – Compute Edition” when you select “full” as deployment profile. In this post, I’ll explain these services and software.

HDFS: HDFS is a distributed, scalable, and portable file system written in Java for Hadoop. It stores data so it is the main component of the our cluster. A Hadoop (big data) cluster has nominally a single namenode plus a cluster of datanodes, but there are redundancy options available for the namenode due to its criticality. Both namenode and datanode services can run in same server (although it’s not recommended on a production environment). In our small cluster, we have 1 active namenode, 1 standby namenode and 3 datanodes – distributed to 3 servers.

YARN + MapReduce (v2): MapReduce is a programming model popularized by Google to process large datasets in a parallel and scalable way. is a framework for cluster resource management and job scheduling. YARN contains a Resource Manager and Node Managers (for redundancy we can create a standby Resource Manager). The Resource Manager tracks how many live nodes and resources are available on the cluster and coordinates which applications submitted by users should get these resources. Each datanode should have a nodemanager to run MapReduce jobs.

Introduction to Oracle Big Data Cloud Service – Compute Edition (Part I)

Over the last few years, Oracle has dedicated to cloud computing and they are in a very tough race with its competitors. In order to stand out in this race, Oracle provides more services day by day. One of the services Oracle offers to the end user is “Oracle Big Data Cloud Service – Compute Edition”. I examined this service by creating a trial account, and I decided to write a series of blog posts for those who would like to use this service.

In my opinion, the most difficult part of creating a Big Data ecosystem is to run many open source software projects together, and integrate them with each another. There are 3 major players on the market to help end-users to build an integrated and tested solution for big data: Cloudera, Hortonworks and MapR. Oracle has partnered with Cloudera to build the Oracle Big Data Appliance and Oracle Big Data Cloud Service. They also offer “Oracle Big Data Cloud Service – Compute Edition” based on Hortonworks. Creating “Oracle Big Data Cloud Service – Compute Edition” is simple. You get a ready-to-use big data cluster in about 15 minutes after giving the basic information such as the name of the cluster, the number of servers (nodes), CPU and disk sizes for each node, and the administrator password.

First, let’s create an “Oracle Big Data Cloud Service – Compute Edition”. After you create our test account for Oracle Cloud, you are log in to the “Oracle Cloud” dashboard. Using this dashboard you can see all your services and add new services at the same time.

Oracle Database on the Docker Store

You probably know that there’s an official github repository storing Dockerfiles and samples to build Docker images for Oracle products and Open Source projects. Now Oracle takes one more step to support Docker. Oracle Brings Oracle’s Flagship Databases and Developer Tools to the Docker Store (you can read the official announcement). Oracle Linux was already on the Docker Store. Oracle Database 12.1.0.2 Enterprise Edition, Oracle client tools, Oracle Weblogic, Oracle Coherence, Java 8 SE are also released on the Docker Store. Now you don’t need to build the image, you can download and use the image from the Docker Store.

Using EMCLI for Mass Update a Property Value

Here’s a quick and simple script which can help you to update a target property value of all targets running on a specific host. The script will expect you to enter 3 parameters: host name, property name and property value. Then it will search for the targets running on the given host name, and set the property to the given value:

It’s better to enter the property name in double quotes, because property names can contain spaces (for example “Line of Business”).

The script first checks the number of parameters, then login to OMS (enter your credentials – if you enter username but do not enter password, EMCLI will ask you to enter password when you run the script), gets the targets running on the given host (using EMCLI list command), and then executes set_target_property_value for each target. You may notice I give a non-default separator because some target names can contain colon (:) sign.

How Enterprise Manager Detects the Version of Oracle Databases

You know that patch numbering has been changed since November 2015, and the new format replaces the numeric 5th digit of the bundle version with a release date in the form “YYMMDD”. Let’s say you applied PSU 160719 to your 11.2.0.4 database, the exact version of your database becomes 11.2.0.4.160799. We also know that PSUs do not change the Oracle release version information that can be queried from v$version (Doc ID 861152.1), so when you query your database, you still see 11.2.0.4.0:

On the other hand, when you list your database targets on Oracle Enterprise Manager, it shows the exact version of Oracle Database.