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Re: Shared Cluster between HBase and MapReduce

These are general recommendations and definitely change based on the access patterns and the way you will be using HBase and MapReduce. In general, if you are building a latency sensitive application on top of HBase, running a MapReduce job at the same time will impact performance due to I/O contention. If your main access patterns is going to be running MapReduce over HBase tables, you should absolutely consider collocating the two frameworks. Now, these recommendations might change based on the resources you have on your nodes (CPU, disk, memory).

Having a single HDFS cluster and using some hosts for HBase and others for MapReduce only gets you a common storage fabric. It doesn't solve the problem of reading data into MapReduce tasks from remote hosts (region servers in this case) and is pretty much the same as having two separate clusters. In case of two separate clusters, you'll be running your MapReduce jobs to talk to a remote HBase instance. You don't have to export data out of that cluster manually onto the MapReduce cluster to run jobs on it.

Hope that makes it clearer.

On Tuesday, June 5, 2012 at 5:00 PM, Atif Khan wrote:

> During a recent Cloudera course we were told that it is "Best practice" to
> isolate a MapReduce/HDFS cluster from an HBase/HDFS cluster as the two when
> sharing the same HDFS cluster could lead to performance problems. I am not
> sure if this is entirely true given the fact that the main concept behind
> Hadoop is to export computation to the data and not import data to the
> computation. If I were to segregate HBase and MapReduce clusters, then when
> using MapReduce on HBase data would I not have to transfer large amounts of
> data from HBase/HDFS cluster to MapReduce/HDFS cluster?
> Cloudera on their best practice page
> (http://www.cloudera.com/blog/2011/04/hbase-dos-and-donts/) has the
> following:
> "Be careful when running mixed workloads on an HBase cluster. When you have
> SLAs on HBase access independent of any MapReduce jobs (for example, a
> transformation in Pig and serving data from HBase) run them on separate
> clusters. HBase is CPU and Memory intensive with sporadic large sequential
> I/O access while MapReduce jobs are primarily I/O bound with fixed memory
> and sporadic CPU. Combined these can lead to unpredictable latencies for
> HBase and CPU contention between the two. A shared cluster also requires
> fewer task slots per node to accommodate for HBase CPU requirements
> (generally half the slots on each node that you would allocate without
> HBase). Also keep an eye on memory swap. If HBase starts to swap there is a
> good chance it will miss a heartbeat and get dropped from the cluster. On a
> busy cluster this may overload another region, causing it to swap and a
> cascade of failures."
> All my initial investigation/reading lead me believe that I should a create
> a common HDFS cluster and then I can run MapReduce and HBase against the
> common HDFS cluster. But from the above Cloudera best practice it seems
> like I should create two HDFS clusters, one for MapReduce and one for HBase
> and then move data around when required. Something does not make sense with
> this best practice recommendation.
> Any thoughts and/or feedback will be much appreciated.
> --
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