Overview of Oracle Container Cloud Service

Oracle launch Oracle Container Cloud Service finally last year. Oracle Container Cloud Service – based on the technology and developed by the team that came from the StackEngine acquisition by Oracle – is a cloud-native and Docker compatible container as a service (CaaS) solution integrated into the Oracle Cloud. It is enterprise ready and with comprehensive tooling to compose, deploy, orchestrate, schedule, and operate Docker container-based applications on the Oracle Cloud. Designed for Dev, Dev/Test, DevOps, and Container Native use cases, OCCS enables self-healing scheduling, built-in service discovery, and CI/CD integrations via webhooks and REST API..It also comes in competition with the following competitor in world

– Docker for AWS or Azure
– Amazon Elastic Container Service
– Google Container Engine
– Azure Container Service
– DC/OS by Mesosphere
– OpenShift by Red Hat

Oracle Container Cloud Service offers Development and Operations teams the benefits of easy and secure Docker containerization when building and deploying applications.OCCS is the newest addition in the Oracle Cloud landscape. It’s a PaaS service that addresses those additional requirements for running Docker in production.

Manager and Worker Services
To get started with the Oracle Container Cloud Service you first define an OCCS service that represents a set of hosts used for OCCS. A service always consists of a manager node and one or more worker nodes.
The manager node orchestrates the deployment of containers to the worker nodes. The worker nodes host the containers or stacks of containers. The set of worker nodes for a service can later be further subdivided into pools that build a resource group.

Every configured OCCS service has its own admin user and password. To set up an OCCS service, you define its service name and either create a new SSH key or specify an existing one. Using this SSH key you can connect to the service from the command-line.

Oracle Container Cloud Service:

– provides an easy-to-use interface to manage the Docker environment

– provides out-of-the-box examples of containerized services and application stacks that can be deployed in one click

– enables developers to easily connect to their private Docker registries (so they can ‘bring their own containers’)

– enables developers to focus on building containerized application images and Continuous Integration/Continuous Delivery (CI/CD) pipelines, not on learning complex orchestration technologies

You can request trial account for 30 days for from Oracle.com. For learn how to use trail account please see below video.

Container cloud service uses docker.Let learn about Docker,
Docker has been a tremendous success over the last three years. From an almost unknown and rather technical open source technology in 2014, it has evolved into a standardized runtime environment now officially supported for many Oracle enterprise products.

The core concepts of Docker are images and containers. A Docker image contains everything that is needed to run your software: the code, a runtime (e.g. the JVM), drivers, tools, scripts, libraries, deployments, etc.

A Docker container is a running instance of a Docker image. However, unlike in traditional virtualization with a type 1 or type 2 hypervisor, a Docker container runs on the kernel of the host operating system. Within a Docker image there is no separate operating system,


Oracle Container Service is built upon Oracle’s StackEngine acquisition .You can run docker on container services.OCCS comes with several predefined OCCS container services. An OCCS container service defines a Docker service together with the necessary configuration settings for running a Docker image and its deployment directives.Oracle Container Cloud Service (OCCS) comes with some popular service examples like apache, nginx, jenkins, logstash, mariadb etc.

cccccon a host, plus default deployment directives. Service is neither container nor image running in containers. It is a high-level configuration objects that you can create, deploy, and manage using Oracle Container Cloud Service. Think of a service as a container ‘template’, or as a set of instructions to follow to deploy a running container.

Stack – Stack is all the necessary configuration for running a set of services as Docker containers in a coordinated way and managed as a single entity, plus default deployment directives. Think of it as multi-container application. Stacks themselves are neither containers nor images running in containers, but rather are high-level configuration objects that you can create, deploy, and manage using Oracle Container Cloud Service. For example, a stack might be one or more WildFly containers and a Couchbase container. Likewise, a cluster of database or application nodes can be built as a stack.

Deployment – A Deployment comprises a service or stack in which Docker containers are managed, deployed, and scaled according to a set of orchestration rules that you’ve defined. A single deployment can result in the creation of one or many Docker containers, across one or many hosts in a resource pool.

Resource Pool – Resource pools are a way to organize hosts and combine them into isolated groups of compute resources. Resource pools enable you to manage your Docker environment more effectively by deploying services and stacks efficiently across multiple hosts.Three resource pools are defined out of the box.

OCCS does not only define and deploy single services. You can also link services together and start them as a stack. The OCCS console already comes with several predefined stack examples, such as WordPress with a database or a Redis cluster with master and slave.

Stacks are defined by a YAML file that lists the contained services. You can define environment variables for every service in a stack.

Please give a trying OCCS.Happy Oracle Cloud learning.

Elastic Search-Beginner Tutorial for Oracle FMW users

ElasticSearch is an Open Source (Apache 2), Distributed Search Engine built on top of Apache Lucene.

Elasticsearch is a NOSQL, distributed full text database. Which means that this database is document based instead of using tables or schema, we use documents. Elasticsearch is much more than just Lucene and much more than “just” full text search. It is also:A distributed real-time document store where every field is indexed and searchable. A distributed search engine with real-time analytics. Capable of scaling to hundreds of servers and petabytes(Figure -1) of structured and unstructured data .

History – The project was started in 2010 by Shay Banon. Shay wanted to create a storage and search engine that would be easy to operate. Elasticsearch is based on the Lucene engine on top of which Shay added an http rest interface which resulted in a distributed search engine that is incredibly easy to scale and returns results at lightning speed

Need of Elasticsearch As a developer or business guy who is used to traditional relational databases, we often face challenges to find information in millions of record in rdbms table. Suppose developer had to search in millions of record in table with 100’s of column. Think about the search time. I am sure, who tried, and they frustrated to build a fast system. Situation gets worst, when it needed to search tables that had millions of records, resulting in overly complex database views/stored-procedures and adding full text search on relational database fields. Something which I personally dislike, as it made the database twice the size and the speed was not optimal either. Relational databases are simply not built for such operations.

In normal RDBMS table, we try searching like searchParam

Select * from tableName WHERE columnName LIKE ‘%searchParam %’;

I am sure, by like this, you can’t search everything and it’s not performance optimal.
Now with Elasticsearch we can achieve the speed we would like, as it lets us index millions of documents. Now definitely, we need a system of something, in which we can make search faster.

Real usecase-

Elasticsearch can be used for various usage, for example it can be used as a blog storage engine in case you would like your blog to be searchable. Traditional SQL doesn’t readily give you the means to do that.
How about Analytics tools? Most software generates tons of data that is worth analyzing, Elasticsearch comes with Logstash and Kibanato give you a full analytics system.
Finally, I like to see Elasticsearch as Data ware house, where you have documents with many different attributes and non-predictable schemas. Since Elasticsearch is schemaless, it won’t matter that you store various documents there, you will still be able to search them easily and quickly.On the other hand having a powerful tool like Kibana would allow you to have a custom dashboard that gives the opportunity for non-technical managers to view and analyze this data.

For me real use case to build search engine with over 5 erp for my organization. A search engine, where we can search almost everything within company different ERP system. In my organization,we have 5 different ERP system, which caters different use case for different business unit. So there are 5 data source, in which I need to make search. User will have one search field, on which they can search anything without mentioning what they want to search it.Portal should display all result based on search param to user. Sounds interesting? J Believe me it’s more challenging than, it sounds interesting 😛

ElasticSearch can be good fit here. We can get all information from 5 different ERP and do data indexing and after that search will be very faster and awesome , same as google.

How Elasticsearch saves data?
Elasticsearch does not have tables, and a schema is not required. Elasticsearch stores data documents that consist of JSON strings inside an index.

The field is like the columns of the SQL database and the value represents the data in the row cells.

When you save a document in Elasticsearch, you save it in an index. An

ElasticSearch is a great open source search engine built on top of Apache Lucene. Its features and upgrades allow it to basically function just like a schema-less JSON datastore that can be accessed using both search queries and regular database CRUD commands.

Here are the main “disadvantages” I see:

  • Transactions – There is no support for transactions or processing on data manipulation.
  • Data Availability – ES makes data available in “near real-time” which may require additional considerations in your application (ie: comments page where a user adds new comment, refreshing the page might not actually show the new post because the index is still updating).
  • Durability – ES is distributed and fairly stable but backups and durability are not as high priority as in other data stores. ElasticSearch has come a long way in the past few years since this original answer and now has better features, backup methods and even realtime indexing. Please review the official site for more information.

If you can deal with these issues then there’s certainly no reason why you can’t use ElasticSearch as your primary data store. It can actually lower complexity and improve performance by not having to duplicate your data but again this depends on your specific use case.

index is like a database in relational database. An index is saved across multiple shards and shards are then stored in one or more servers which are called nodes, multiple nodes form a cluster.

You can download the latest version of Elasticsearch from elasticsearch.org/download.

curl -L -O http://download.elasticsearch.org/PATH/TO/VERSION.zip
unzip elasticsearch-$VERSION.zip
cd  elasticsearch-$VERSION

Elasticsearch is now ready to run. You can start it up in the foreground with:


Add -d if you want to run it in the background as a daemon.

Test it out by opening another terminal window and running:

curl ‘http://localhost:9200/?pretty’

You should see a response like this:


   “status”: 200,

   “name”: “Shrunken Bones”,

   “version”: {

      “number”: “1.4.0”,

      “lucene_version”: “4.10”


   “tagline”: “You Know, for Search”


This means that your Elasticsearch cluster is up and running, and we can start experimenting with it.

Clusters and nodes

A node is a running instance of Elasticsearch. A cluster is a group of nodes with the same cluster.name that are working together to share data and to provide failover and scale, although a single node can form a cluster all by itself.

You should change the default cluster.name to something appropriate to you, like your own name, to stop your nodes from trying to join another cluster on the same network with the same name!

You can do this by editing the elasticsearch.yml file in the config/ directory, then restarting Elasticsearch. When Elasticsearch is running in the foreground, you can stop it by pressing Ctrl-C, otherwise you can shut it down with the api

curl –XPOST ‘http://localhost:9200/_shutdown’

Some of common terminologies in elastic search-


A cluster is a collection of one or more nodes (servers) that together holds your entire data and provides federated indexing and search capabilities across all nodes. A cluster is identified by a unique name which by default is “elasticsearch”. This name is important because a node can only be part of a cluster if the node is set up to join the cluster by its name.


A node is a single server that is part of your cluster, stores your data, and participates in the cluster’s indexing and search capabilities. Just like a cluster, a node is identified by a name which by default is a random Marvel character name that is assigned to the node at startup. You can define any node name you want if you do not want the default. This name is important for administration purposes where you want to identify which servers in your network correspond to which nodes in your Elasticsearch cluster.


An index is a collection of documents that have somewhat similar characteristics. For example, you can have an index for customer data, another index for a product catalog, and yet another index for order data. You will provide different index name for different data. An index is identified by a name and this name is used to refer to the index when performing indexing, search, update, and delete operations against the documents in it.

Shards & Replicas

An index can potentially store a large amount of data that can exceed the hardware limits of a single node. For example, a single index of a billion documents taking up 1TB of disk space may not fit on the disk of a single node or may be too slow to serve search requests from a single node alone.

To solve this problem, Elasticsearch provides the ability to subdivide your index into multiple pieces called shards. When you create an index, you can simply define the number of shards that you want. Each shard is in itself a fully-functional and independent “index” that can be hosted on any node in the cluster.

Now talking about how elastic search can help in Oracle ADF/WebCenter or fusion middleware technologies.

I will be publishing series of tutorial for elasticSearch. Will try to show, how we can use with ADF/Webcenter portal as well.Following architecture can be used in ADF/WebCenter Portal Application.


Elastic Search is very fast in comparison to ADF Search. In ADF, you can search using query panel or custom search box. With this you can combine all data on backend and search with one inputText to all tables schema or all backend data. Search response time is in ms. In WebCenter Portal Oracle Secure Enterprise Search is also going to replace with Elastic Search. So its good time to get into this.

Elastic Search can also be used in WebCenter content to searching documents.  See one of great demo by Team Informatics in this youtube channel.

We can also use Apache Kafka with elastic search for real time data ingestion and searching. I will cover in coming post.

Till then, Happy searching by elasticSearch with Vinay in techartifact. Data source can be webservices, data base or live streaming data. In this, we have schedular to bring data or ingest data in elastic search server. ADF/WebCenter Portal application can consume data using querying into Elastic Search Server.

Ref – https://www.elastic.co/guide/en/elasticsearch/reference/2.2/_basic_concepts.html  http://joelabrahamsson.com/elasticsearch-101/