I have been using a Hadoop cluster, created using Google's script, for a few months.
Every time I boot the machines I have to manually start Hadoop using:
sudo su hadoop
cd /home/hadoop/hadoop-install/sbin
./start-all.sh
Besides scripting, how can I resolve this?
Or is this just the way it is by default?
(The first boot after cluster creation always starts Hadoop automatically, why not always?)
You have to configure using init.d.
Document provide more details and sample script for datameer. You need to follow similar steps. Script should be smart enough to check all the nodes in the cluster are up before invoking this script using ssh.
While different third-party scripts and "getting started" solutions like Cloud Launcher have varying degrees of support for automatic restart of Hadoop on boot, the officially supported tools are bdutil as a do-it-yourself deployment tool, and Google Cloud Dataproc as a managed service, both of which are already configured with init.d and/or systemd to automatically start Hadoop on boot.
More detailed instructions on using bdutil here.
Related
I´m not sure that someone can help me but I´ll take a try.
I´m running Jenkins on an Openshift-Cluster to use it for Deployment and as a jobserver for running ETL-Jobs. These jobs are transferring data from flatfiles to databases and from db to db.
Now, I should expand the system to transfer data to a hadoop cluster using MapR.
What I would like to know is, how can I use a new Jenkins-Slave as a jobserver on an EdgeNode from the hadoop-cluster using MapR. Do I need the Jenkins on the EdgeNode or am I able to use MapR from my existing Jenkins-Jobserver?
Mabye, someone is able to help me or has some informations/links how to solve it.
Thx to all....
"Use MapR" isn't quite clear to me because I just view it as Hadoop at the end of the day, but you can effectively make your Jenkins slave an "edge node" by installing only the Hadoop Java (maybe also MapR) client utilities plus any XML configuration files from the other edge nodes that define how to communicate with the cluster.
Then, Jenkins would be able to run sh("hadoop jar app.jar"), for example
If you're using Openshift, you might also try putting a Hadoop client inside a Docker image that could run in Jenkins, or anywhere else
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! :)
I trying to set up a hive environment on my google compute engine hadoop clusters which was deployed from one click deployment.
When I try to switch to hdfs user(su hdfs), I get below error message.
No passwd entry for user 'hdfs'
The "one-click deployment" is an older sample which perhaps showcases installation from shell scripts and tarballs, but isn't intended for use as a supported Hadoop service, and doesn't set up typical Hadoop installation configurations like an hdfs user or adding commands to /usr/bin.
If you want a more Hadoop (and Pig+Hive+Spark) specialized service, you may want to consider using Google Cloud Dataproc, which is Google's managed Hadoop solution. You can create clusters from the cloud console UI in Dataproc just like click-to-deploy, and you'll get a more fully installed Hadoop/Hive environment, including a per-cluster persistent MySQL-based Hive metastore which is shared with SparkSQL to make it easy to play with Spark without modifying your Hive environment if you so choose.
We are looking for the possibility of an automation script which we can give how many master and data nodes we need and it would configure a cluster. Probably giving the credentials in a properties file.
Currently our approach is to login to the console and configure the Hadoop cluster. It would be great if there could be an automated way around it.
I've seen this done very nicely using Foreman, Chef, and Ambari Blueprints. Foreman was used to provision the VMs, Chef scripts were used to install Ambari, configure the Ambari blueprint, and to create the cluster using the Blueprint.
i am new to Hadoop ,i likes to go in hadoop administration line so studied basics of hadoop and tried to install hadoop in pseudo distribution mode and installed successfully and run some basic examples also, now i need to improve me further,so i need to try a way to learn hadoop installation and configuration in real time so decided to go for Amazon micro instance ,can any one please tell how to install and configure hadoop in Amazon cloud.
Thanks in Advance.
I have tried this personally and you will not really be able to use hadoop on a single micro instance due to memory restrictions. IMHO you should atleast try a medium instance to run hadoop or better yet use their elastic-mapreduce api which is a modified version of hadoop. You can run a 3 node cluster for around 00.25 cents an hour. If you really want to learn big data this is the way I went.
You should check out their documentation here
http://aws.amazon.com/documentation/elasticmapreduce/