I Have already configured this setup using heartbeat and virtual IP mechanism on the Non VM setup.
I am using hadoop 1.0.3 and using shared directory for the Namenode metadata sharing. Problem is that on the amazon cloud there is nothing like virtual Ip to get the High Availibility using Linux-ha.
Has anyone been able to achieve this. Kindly let me the know the steps required?
For now I am using the Hbase replication WAL on hbase. Hbase later than 0.92 supports this.
For the hadoop clustering on cloud , I will wait for the 2.0 release getting stable.
Used the following
http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/replication/package-summary.html#requirements
On the client Side I added the logic to have 2 master servers , used alternatively to reconnect in case of network disruption.
This thing worked for a simple 2 machines backking up each other , not recommended for higher number of server.
Hope it helps.
Well, there's 2 parts to Hadoop to make it highly available. The first and more important is, of course, the NameNode. There's a secondary/checkpoint NameNode that can you startup and configure. This will help keep HDFS up and running in the event that your primary NameNode goes down. Next is the JobTracker, which runs all the jobs. To the best of my (outdated by 10 months) knowledge, there is no backup to the JobTracker that you can configured, so it's up to you to monitor and start up a new one with the correct configuration in the event that it goes down.
Related
I'm trying to fetch some data from Cloudera's Quick Start Hadoop distribution (a Linux VM for us) on our SAP HANA database using SAP Spark Controller. Every time I trigger the job in HANA, it gets stuck and I see the following warning being logged continuously every 10-15 seconds in SPARK Controller's log file, unless I kill the job.
WARN org.apache.spark.scheduler.cluster.YarnScheduler: Initial job has not accepted any resources; check your cluster UI to ensure that workers are registered and have sufficient resources
Although it's logged like a warning it looks like it's a problem that prevents the job from executing on Cloudera. From what I read, it's either an issue with the resource management on Cloudera, or an issue with blocked ports. In our case we don't have any blocked ports so it must be the former.
Our Cloudera is running a single node and has 16GB RAM with 4 CPU cores.
Looking at the overall configuration I have a bunch of warnings, but I can't determine if they are relevant to the issue or not.
Here's also how the RAM is distributed on Cloudera
It would be great if you can help me pinpoint the cause for this issue because I've been trying various combinations of things over the past few days without any success.
Thanks,
Dimitar
You're trying to use the Cloudera Quickstart VM for a purpose beyond it's capacity. It's really meant for someone to play around with Hadoop and CDH and should not be used for any production level work.
Your Node Manager only has 5GB of memory to use for compute resources. In order to do any work, you need to create an Application Master(AM) and a Spark Executor and then have reserve memory for your executors which you won't have on a Quickstart VM.
I'm using Cloudera (Apache Hadoop), so I have a pretty good idea about it.
However, I just found out about Linux HA project and
I cannot find out what is the difference between Linux HA and Apache Hadoop.
When should we use Apache Hadoop and when should we use Linux HA?
Thank you!
Linux HA is a software based High-availability cluster services which are used to improve the ability of many kinds of services. That means - This Linux HA is used to keep desired services up and running with no downtime. This uses the concept of heartbeat to identify the service state in the cluster. For example if you have a web server running on hostA, it is replicated to run on hostB also. Whenever the hostA is down, hostB starts and serves requests. i.e there is no downtime provided by the server.
Whereas, Apache Hadoop is a Framework that solves the problem of storing large amount of data and processing it.
So I am still a bit new to hadoop and am currently in the process of setting up a small test cluster on Amazonaws. So my question relates to some tips on the structuring of the cluster so it is possible to work submit jobs from remote machines.
Currently I have 5 machines. 4 are basically the Hadoop cluster with the NameNodes, Yarn etc. One machine is used as a manager machine( Cloudera Manager). I am gonna describe my thinking process on the setup and if anyone can chime in the points I am not clear with, that would be great.
I was thinking what was the best setup for a small cluster. So I decided to expose only one manager machine and probably use that to submit all the jobs through it. The other machines will see each other etc, but not be accessible from the outside world. I am have conceptual idea on how to do this,but I am not sure how to properly go about doing this though, if anyone could point me in the right direction that would great.
Also another big point is, I want to be able to submit jobs to the cluster through exposed machine from a client machine (might be Windows). I am not so clear on this setup as well. Do I need to have Hadoop installed on the machine in order to use the normal hadoop commands, and to write/submit jobs say from Eclipse or something similar.
So to sum it up my questions are,
Is this an ok setup for a small test cluster
How can I go about using one exposed machine to submit/route jobs to the cluster, without having any of the Hadoop nodes on it.
How do I setup a client machine to submit jobs to a remote cluster, and an example on how to do it on Windows. Also if there are any reason not to use Windows as a client machine in this setup.
Thanks I would greatly appreciate any advice or help on this.
Since this is not answered I will attempt to answer it.
1. Rest api to submit an application:
Resource 1(Cluster Applications API(Submit Application)): https://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/ResourceManagerRest.html#Cluster_Applications_APISubmit_Application
Resource 2: https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.6.5/bk_yarn-resource-management/content/ch_yarn_rest_apis.html
Resource 3: https://hadoop-forum.org/forum/general-hadoop-discussion/miscellaneous/2136-how-can-i-run-mapreduce-job-by-rest-api
Resource 4: Run a MapReduce job via rest api
2. Submitting hadoop job from client machine
Resource 1: https://pravinchavan.wordpress.com/2013/06/18/submitting-hadoop-job-from-client-machine/
3. Sending program to remote hadoop cluster
It is possible to send the program to a remote Hadoop cluster for running it. All you need to ensure is that you have set the resource manager address, fs.defaultFS, library files, and mapreduce.framework.name correctly before running the actual job.
Resource 1: (how to submit mapreduce job with yarn api in java)
I have 4 windows machines, On which i have installed hadoop on 3 out of 4.
One machine having the Harton work Sandbox ( say for 4-th machine) , Now i need to make the 4th machine as my server ( Name node )
and rest of the machine as slaves.
Whether it will work if i update the configuration files in the rest of 3 machines
Or is there any other way to do this ?
Any other suggestions ?
Thanks
finally i got this but i could not find any help
Hadoop cluster configuration with Ubuntu Master and Windows slave
A non-secure cluster will work (non-secure in the sense that you do not enable Hadoop Kerberos based auth and security, ie. hadoop.security.authentication is left as simple). You need to update all nodes config to point to the new 4th node as the master for various services you plan to host on it. You mention namenode, but I assume you really mean to make the 4th node the 'head' node, meaning it will probably also run resourcemanager and historyserver (or the jobtracker for old-style Hadoop). And that is only core, w/o considering higher level components like Hive, Pig, Oozie etc, and not even mentioning Ambari or Hue.
Doing a post-install configuration of existing Windows (or Linux, makes no difference) nodes is possible, editing the various xx-site.xml files. You'll have to know what to change and is not trivial. Probably it would be much easier to just deploy again the windows machines, with an appropriate clusterproperties.txt config file. See Option III - Manual Install One Node At A Time.
I have a 5 node Cassandra cluster set up on EC2, all in the same region.
If I connect over cqlsh (9160), queries respond in under a second.
When I connect via Dev Center, or using the native Java Driver, both of which use port 9042, the queries take over 20 seconds to respond.
They consistently respond in the same 21 second region. Never fast and then slow.
I have set up a few Cassandra Clusters on EC2 and have seen this before but do not know how to fix the problem. The last time, I scrapped the cluster and built a new one and the response time on port 9042 was fine.
Any help in how to debug or fix this problem would be appreciated, thanks.
The current version of DevCenter was designed to support as main scenario running (longish) CQL scripts (vs an interactive console with queries executed one after another). DevCenter is using as an underlying connector the DataStax Java driver for Cassandra.
For the above mentioned scenario, in order to ensure there are no "conflicts", a new Session is created for each execution. When a Session is initialized, the driver performs an auto-node discovery, creates connection pools, etc. Basically it does a lot of preparation work. Depending on the latency from your client machine to the EC2 nodes, the size of the cluster and also the configuration of these nodes (see the connection requirements), this initialization phase can be quite expensive.
As you can imagine the time spent preparing wouldn't represent a large percentage of running a DDL script and a decent size of inserts/updates. But for an interactive scenario, it will result in a suboptimal behavior (the one you are describing)
The next version(s) of DevCenter will address the interactive scenario and optimize for it so the user experience would be what you'd expect. And supporting this scenario is pretty high on our list of priorities.
The underlying Java driver obtains the whole cluster topology when it initially connects. This enables it to automatically connect to any node in the cluster. On EC2 it only obtains the private addresses, tries each one, and then times out. It then sends the request over the initial connection