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.
Related
We are currently using Confluent HDFS Sink Connector platform within docker container to write data from Kafka(separate Kafka cluster) to HDFS(separate Hadoop cluster). By default the connector platform writes data to HDFS with root user and wheel group.
How can i configure connector to use a specific hadoop user/group ? Is there an environment variable I need to set in docker ?
Thanks.
The Java process in the Docker container runs as root.
You need to either make your own container with your own user account or run the Connect Workers as a different Unix account in some other way.
You could try setting HADOOP_IDENT_USER or HADOOP_USER_NAME environment variables, but I think these are only pulled by the Hadoop scripts, not the Java API
Keep in mind that user accounts in Hadoop don't really matter if you're not using a Kerberized cluster
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
I have a hadoop cluster setup using Ambari which has services like HDFS,YARN,spark running on the hosts.
When i run the sample spark pi in cluster mode as master yarn, the application gets successfully executed and I can view the same from resource manager logs.
But when i click on the history link, it does not show the spark history UI. How to enable/view the same?
First, check if your spark-history server is already configured by looking for spark.yarn.historyServer.address in spark-defaults.conf file.
If not configured, this link should help you configure the server: https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.3.6/bk_installing_manually_book/content/ch19s04s01.html
If already configured, check if the history server host is accessible from all the nodes in the cluster, and also the port is open.
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.