I have downloaded the source code of Hue and have built it locally on my Ubuntu (16.04) system. I want to configure it to point to my HDInsight HDP cluster head-node so that I can access my hive databases. I am aware of the script action, but want the Hue on remote system and point it to the cluster. How can I go about?
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For study project requirement, I am selecting following technology because source of data is SQL SERVER
Initial data size is 100Gb and 10 growth#quarter
Information
Hadoop – Multi node cluster (1Namenode + 3 DataNode)
Hadoop 3.1.2,
Apache Maven 3.6.0
Ubuntu 18.04
Ambari
Above setup is ready now following item remaining
Sqoop: 1.4.7
Hive: 2.3.5
Oozie 5.0.0
Should they all be installed on separate machines?
What is the deployment strategy once development completed?
If you have the hardware available, then yes, every master service should be on separate machines for fault tolerance purposes.
Meaning, Oozie server, Hive server, Hive metastore are all separate.
Sqoop and Hive client are only clients and can be on any NodeManager
I am aware of installing Hue for HDInsight HDP cluster by deploying it on an edge node of the cluter (using a script action, link), it works fine but asks for the cluster credentials first and then directs me to the Hue login page. Is there a way to get rid of those credentials?
Else, is it possible to deploy Hue on a remote system and then point it to my HDInsight HDP cluster? If so how do I go about?
And which of the above two approaches is better?
Based on my understanding & experience, to answer your questions as below.
There is not any way to get rid of those credentials, due to the credential is to authenticate for Resource Management Template deployment, not only for cluster.
It's not possible to deploy Hue on a remote system, because of "Hue consists of a web service that runs on a special node in your cluster." as the Hue offical manual said from here.
Hope it helps.
We are currently using Apache Hadoop (Vanilla Version) in our org. We are planning to migrate to AWS EMR. I'm trying to understand how AWS EMR Hadoop works internally (not how to use it), I'm mainly interested in Hadoop administration steps and how master and slave communicates and various configuration configurations. I already checked the AWS EMR documentation but I don't see detailed comparison.
Can someone recommend me a link/tutorial for migrating to AWS EMR from an Apache Hadoop.
During EMR cluster creation, it will ask you to specify Master and Node. a default settings will provision 1 master and two nodes for you. You can also specify what all applications you want to be in the cluster (e.g.: hadoop, hive, spark, zeppelin, hue, etc.).
Once the cluster is created, it will provision all the services. you can click on these services and access them via web, or using ssh into the master. For e.g: to access the ambari interface, go to the service within EMR and click it. a new window will be launched with the ambari monitoring service interface.
Installing these applications is very easy. all you have to do is specify all the services while cluster creation.
Amazon Elastic MapReduce uses a mostly standard implementation of Hadoop and associated tools.
See: AMI Versions Supported in Amazon EMR
The benefits of using EMR are in the automated deployment of instances. For example, launching a cluster with an appropriate AMI means that software is already loaded on each instance and HDFS is configured across the core nodes.
The Master and Slave (Core/Task) nodes communicate in exactly the normal way that they communicate in any Hadoop cluster. However, only one Master is supported (with no backup Master).
When migrating to EMR, check that you are using compatible versions of software (eg Hadoop, Hive, Pig, Impala, etc). Also consider using Amazon S3 for storage of data instead of HDFS, especially for storing source data, since data on S3 persists even after the EMR cluster is terminated.
Technically, Hadoop provided with EMR, can be few releases back. You should check EMR release notes for detailed application provided with each version. EMR takes care application provisioning, setup and configuration. Based on EC2 instance type, Hadoop (and other application configuration) will change. You can override default settings using configure application.
Other than this Hadoop you have on premises and EMR should be the same.
I have a 10 node existing cluster in RHEL 6.6 which was prepared by plain apache Hadoop configuration XMLs. Now I wanted to check the cluster status by Ambari. Would it be possible to install Hortonworks Ambari just to monitor only not to install Hadoop.
No, Ambari must provision the cluster it's monitoring.
Ambari is designed around a Stack concept where each stack consists of several services. A stack definition is what allows Ambari to install, manage and monitor the services in the cluster.
In order for you to use Ambari with the hadoop core that you built you would have to provide your own Ambari stack definition.
Specifically in your case your existing Hadoop installation would not have the necessary alert.json descriptors used by Ambari to provide alerts for any given service.
I successfully built a 5 node cluster of HortonWorks HDP 2.2 using Ambari.
However I don't see Apache Spark in the installed services list.
I did some research and found that Ambari does not install certain components like hue etc. ( Spark was not in that list, but I guess its not installed).
How do I do a manual install of Apache spark on my 5 node HDP 2.2?
Or should I delete my cluster and perform a fresh install without using Ambari?
Hortonworks support for Spark is arriving but not fully complete (details and blog).
Instructions for how to integrate Spark with HDP can be found here.
You could build your own Ambari Stack for Spark. I recently did just that, but I cannot share that code :(
What I can do is share a tutorial I did on how to do any stack for Ambari, including Spark. There are many interesting issues with Spark that need to be addressed and are not covered through the tutorial. Anyways hope it helps. http://bit.ly/1HDBgS6
There is also a guide from the Ambari people here: https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=38571133.
1) Ambari 1.7x does not install Accumulo, Hue, Ranger, or Solr services for the HDP 2.2 Stack.
For Installing Accumulo, Hue, Knox, Ranger, and Solr services, install
HDP Manually.
2) Apache Spark 1.2.0 on YARN with HDP 2.2 : here .
3)
Spark and Hadoop: Working Together :
Standalone deployment: With the standalone deployment one can statically allocate resources on all or a subset of machines in a Hadoop cluster and run Spark side by side with Hadoop MR. The user can then run arbitrary Spark jobs on her HDFS data. Its simplicity makes this the deployment of choice for many Hadoop 1.x users.
Hadoop Yarn deployment: Hadoop users who have already deployed or are planning to deploy Hadoop Yarn can simply run Spark on YARN without any pre-installation or administrative access required. This allows users to easily integrate Spark in their Hadoop stack and take advantage of the full power of Spark, as well as of other components running on top of Spark.
Spark In MapReduce : For the Hadoop users that are not running YARN yet, another option, in addition to the standalone deployment, is to use SIMR to launch Spark jobs inside MapReduce. With SIMR, users can start experimenting with Spark and use its shell within a couple of minutes after downloading it! This tremendously lowers the barrier of deployment, and lets virtually everyone play with Spark.