My application is launched from remotely pc via spark-submit in yarn-cluster mode with Kerberos keytab and principals by this guide: https://spark.apache.org/docs/latest/running-on-yarn.html. The advantages of this approach are that I have my own version of the spark at any cluster.
Is it possible to automatically deploy Ignite/Hazelcast/Accumulo/Kudu or other NoSQL DB with random access on read/write into a Hadoop YARN cluster without sftp/ssh only by running a bash-script with HADOOP_CONF_DIR/YARN_CONF_DIR configs?
Deploying Hazelcast on a YARN cluster is possible and easy, take a look at https://github.com/hazelcast/hazelcast-yarn
Related
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
I have a hadoop cluster setup in AWS EC2, but my development setup(spark) is in local windows system. When I am trying to connect AWS Hive thrift server I able to connect , but it is showing some connection refused error when trying to submit a job from my local spark configuration. Please note in windows my user name is different that the user name for which Hadoop eco system is running in AWS server. Can any one explain me how the underlying system works in this setup?
1) When I am submitting a job from my local Spark to HIVE thrift , if it is associated any MR job , ASW Hive setup will submit that job NN with its own identity or it will carry forward my spark setup identity.
2) In my configuration do I need to run spark in local with same user name as I have for hadoop cluster in AWS ?
3) Do I need to configure SSL also to authenticate my local system?
Please note , my local system is not part of hadoop cluster and I can not include also in AWS Hadoop cluster.
Please let me know what will be actual setup for environment where my hadoop cluster is in AWS and spark is running on my local.
To simplify the problem, you are free to compile your code locally, produce an uber/shaded JAR, SCP to any spark-client in AWS, then run spark-submit --master yarn --class <classname> <jar-file>.
However, if you want to just Spark against EC2 locally, then you can set a few properties programmatically.
Spark submit YARN mode HADOOP_CONF_DIR contents
Alternatively, as mentioned in that post, the best way would be getting your cluster's XML files from HADOOP_CONF_DIR, and copying them over into your application's classpath. This is typically src/main/resources for a Java/Scala application.
Not sure about Python, R, or the SSL configs.
And yes, you need to add a remote user account for your local Windows username on all the nodes. This is how user impersonation will be handled by Spark executors.
I want to ask about "Cloudera" to run Hadoop MapReduce.
Does the cloudera support to run the application over cluster of machine?
What does cloudera actually over, when I use it with Virtual Machine to run the Hadoop MapReduce application?
If Cloudera doesn't support to run over cluster of machine? How can I run Hadoop app in cluster of machine?
I really confused about that:(
Thanks in advance.
I made a spark application that analyze file data. Since input file data size could be big, It's not enough to run my application as standalone. With one more physical machine, how should I make architecture for it?
I'm considering using mesos for cluster manager but pretty noobie at hdfs. Is there any way to make it without hdfs (for sharing file data)?
Spark maintain couple cluster modes. Yarn, Mesos and Standalone. You may start with the Standalone mode which means you work on your cluster file-system.
If you are running on Amazon EC2, you may refer to the following article in order to use Spark built-in scripts that loads Spark cluster automatically.
If you are running on an on-prem environment, the way to run in Standalone mode is as follows:
-Start a standalone master
./sbin/start-master.sh
-The master will print out a spark://HOST:PORT URL for itself. For each worker (machine) on your cluster use the URL in the following command:
./sbin/start-slave.sh <master-spark-URL>
-In order to validate that the worker was added to the cluster, you may refer to the following URL: http://localhost:8080 on your master machine and get Spark UI that shows more info about the cluster and its workers.
There are many more parameters to play with. For more info, please refer to this documentation
Hope I have managed to help! :)
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.