I am having trouble in deploying h2o.ai in a cluster in EMR.
I am trying to installing with flatfile but it seems to be probing some issues in communicating with each other
Require help in this.
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I've installed Hadoop via a helm chart on my microk8s kubernetes cluster.
I would like to know how to create a dask cluster on my different machines on this hadoop cluster. I tried following the the tutorials on the Dask websites, but I keep getting errors because it is looking for the local yarn/hadoop. How do I point to the hadoop on kubernetes so I can create the cluster?
If you want to launch Dask on Yarn we recommend using https://yarn.dask.org
However, if you are using Kubernetes already you might consider https://kubernetes.dask.org, which is more commonly used today.
We are searching a viable way for provisioning a Hadoop ecosystem cluster with OpenShift (based on Docker). We look to build up a cluster using the services of the Hadoop ecosystem, i.e. HDFS, YARN, Spark, Hive, HBase, ZooKeeper etc.
My team has been using Hortonworks HDP for on-premise hardware but will now switch into a OpenShift-based infrastructure. Hortonworks Cloudbreak seems not to be suitable for OpenShift-based infrastructures. I have found this article that describes the integration of YARN into OpenShift but it seems like there are no further information available.
What is the easiest way to provision a Hadoop ecosystem cluster on OpenShift? Manually adding all the services feels error-prone and hard to administer. I have stumbled upon the Docker images of these separate services, but it is not comparable to the automated provisioning you get with a platform like Hortonworks HDP. Any guidance is appreciated.
If you install Openstack within Openshift, Sahara allows provisioning of Openstack Hadoop clusters
Alternatively, Cloudbreak is Hortonwork's tool for provisioning container based cloud deployments
Both provides Ambari, allowing you the same interface for cluster administration as HDP.
FWIW, I personally don't find the reason for putting Hadoop in containers. Your datanodes are locked to specific disks. There's no improvement in running several smaller ResourceManagers on a single host. Plus, for YARN, you'd be running containers within containers. And for the namenode, you must have a replicated Fsimage + Editlog because the container could be placed on any system
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.
Am using CHD3 on centos 5.6 in vmware virtual machine and am facing so many problems while configuring the multinode cluster.
Can anybody provide complete steps to configure multinode cluster??
Configuring a distributed cluster by yourself is a bit of a headache, can be done but it won't worth it if you're just testing and doing some research.
Since you're using Cloudera, you should take a look at Cloudera Manager, which automates the installation and management of Hadoop clusters and should play nice with VMs as well (as long as you have some serious RAM):
http://www.cloudera.com/content/cloudera/en/documentation/cloudera-manager/v4-8-2/Cloudera-Manager-Introduction/cmi_primer.html
This is the installation guide:
http://www.cloudera.com/content/cloudera/en/documentation/cloudera-manager/v4-8-2/Cloudera-Manager-Installation-Guide/Cloudera-Manager-Installation-Guide.html
Can anyone suggest me that whether I can read data from amazon hbase using the org.apache.hadoop.conf.Configuration and org.apache.hadoop.hbase.client.HTablePool.
We are migrating to Amazon's EMR framework having hbase running on top of it.
The present implementation is based on pure Apache hadoop and hbase distributions. I'm trying to verify that no code changes needed even we migrate to amazon's EMR.
Please share your thoughts.
While it should not happen, I would expect the problems and changes related to the nature of EC2 and its networking.
HBase relay on Regions able to renew their leases in timely manner. If Region servers are two busy - because of some massive operations over them, they can not do so and get kicked off the cluster.
In amazon performance of the EC2 instances are much less predictable then in dedicated cluster (unless you use cluster instances), so adjusting timeout parameters and/or nature of your loads might be needed to get cluster to work properly