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Sunday, January 23, 2011

Bulk Indexing With ElasticSearch and Hadoop

At Infochimps we recently indexed over 2.5 billion documents for a total of 4TB total indexed size. This would not have been possible without ElasticSearch and the Hadoop bulk loader we wrote, wonderdog. I'll go into the technical details in a later post but for now here's how you can get started with ElasticSearch and Hadoop:

Getting Started with ElasticSearch



The first thing is to actually install elasticsearch:


$: wget http://github.com/downloads/elasticsearch/elasticsearch/elasticsearch-0.14.2.zip
$: sudo mv elasticsearch-0.14.2 /usr/local/share/
$: sudo ln -s /usr/local/share/elasticsearch-0.14.2 /usr/local/share/elasticsearch


Next you'll want to make sure there is an 'elasticsearch' user and that there are suitable data, work, and log directories that 'elasticsearch' owns:


$: sudo useradd elasticsearch
$: sudo mkdir -p /var/log/elasticsearch /var/run/elasticsearch/{data,work}
$: sudo chown -R elasticsearch /var/{log,run}/elasticsearch


Then get wonderdog (you'll have to git clone it for now) and go ahead and copy the example configuration in wonderdog/config:


$: sudo mkdir -p /etc/elasticsearch
$: sudo cp config/elasticsearch-example.yml /etc/elasticsearch/elasticsearch.yml
$: sudo cp config/logging.yml /etc/elasticsearch/
$: sudo cp config/elasticsearch.in.sh /etc/elasticsearch/


Make changes to 'elasticsearch.yml' such that it points to the correct data, work, and log directories. Also, you'll want to change the number of 'recovery_after_nodes' and 'expected_nodes' in elasticsearch.yml to however many nodes (machines) you actually expect to have in your cluster. You'll probably also want to do a quick once-over of elasticsearch.in.sh and make sure the jvm settings, etc are sane for your particular setup. Finally, to startup do:


sudo -u elasticsearch /usr/local/share/elasticsearch/bin/elasticsearch -Des.config=/etc/elasticsearch/elasticsearch.yml


You should now have a happily running (reasonably configured) elasticsearch data node.

Index Some Data



Prerequisites:


  • You have a working hadoop cluster

  • Elasticsearch data nodes are installed and running on all your machines and they have discovered each other. See the elasticsearch documentation for details on making that actually work.

  • You've installed the following rubygems: 'configliere' and 'json'



Get Data



As an example lets index this UFO sightings data set from Infochimps here. (You should be familiar with this one by now...) It's mostly raw text and so it's a very reasonable thing to index. Once it's downloaded go ahead and throw it on the HDFS:

$: hadoop fs -mkdir /data/domestic/ufo
$: hadoop fs -put chimps_16154-2010-10-20_14-33-35/ufo_awesome.tsv /data/domestic/ufo/


Index Data



This is the easy part:


$: bin/wonderdog --rm --field_names=sighted_at,reported_at,location,shape,duration,description --id_field=-1 --index_name=ufo_sightings --object_type=ufo_sighting --es_config=/etc/elasticsearch/elasticsearch.yml /data/domestic/aliens/ufo_awesome.tsv /tmp/elasticsearch/aliens/out


Flags:

'--rm' - Remove output on the hdfs if it exists
'--field_names' - A comma separated list of the field names in the tsv, in order
'--id_field' - The field to use as the record id, -1 if the record has no inherent id
'--index_name' - The index name to bulk load into
'--object_type' - The type of objects we're indexing
'--es_config' - Points to the elasticsearch config*

*The elasticsearch config that the hadoop machines need must be on all the hadoop machines and have a 'hosts' entry listing the ips of all the elasticsearch data nodes (see wonderdog/config/elasticsearch-example.yml). This means we can run the hadoop job on a different cluster than the elasticsearch data nodes are running on.

The other two arguments are the input and output paths. The output path in this case only gets written to if one or more index requests fail. This way you can re-run the job on only those records that didn't make it the first time.

The indexing should go pretty quickly.
Next is to refresh the index so we can actually query our newly indexed data. There's a tool in wonderdog's bin directory for that:

$: bin/estool --host=`hostname -i` refresh_index



Query Data



Once again, use estool

$: bin/estool --host=`hostname -i` --index_name=ufo_sightings --query_string="ufo" query


Hurray.

135 comments:

  1. I am curious to know, how long did it take for indexing ? How many nodes were present in cluster which were indexing?

    ReplyDelete
  2. Also, Is 4 TB is raw data (for 2.5 Billion documents) or index size ? Also, are these text files ?

    ReplyDelete
  3. @About Trust, We plan on a blog post with just those technical details soon. For now, the 4TB was after indexing and the data was raw text. We used 16 m2.xlarge ec2 nodes for the elasticsearch cluster and 5 m1.large hadoop nodes. Took 2-5 minutes per input GB.

    --thedatachef

    ReplyDelete
  4. "2.5 minutes per input GB"? Is that correct?

    ReplyDelete
  5. @Michael Yes. Indexing speed varied from 2 minutes per input GB (at best) to 5 minutes per input GB (at worse). That is all given the setup explained in the previous comment.

    ReplyDelete
  6. I have done same thing that you did. and I checked wonderful speed. But I didn't use hadoop storage.
    I wonder how to use hadoop.
    I tried to use hadoop many times. but I failed

    please show hadoop setting about elasticsearch.yml

    ReplyDelete
  7. I am trying to understand whats going on. Is the indexing occuring in hadoop or are you just creating the necessary structures and posting them to the ES cluster for indexing?

    ReplyDelete
  8. Hey there I'm experimenting w/wonderdog and not sure I understand the basic architecture of it. I am trying to run a pig script and use ElasticStorage to write the data into my ElasticSearch cluster. I have 3 nodes running on the same machines as my hadoop datanodes.

    When I ran the pig job from that cluster (same one running hadoop and elastic search) it fails with a unable to bind 9300-9400 exception.

    Then I ran from a different hadoop cluster but didn't install elastic search to every node just the machine that submitted the job. This looks like it was going to work. It started the job and then started adding nodes to my ES cluster dynamically. It appears to be running but I am not sure why it needs to add nodes to the cluster to index.

    Could you explain what the purpose of this adding nodes is? Also what is the ideal setup for something like this? Run the pig job on hadoop cluster that doesn't have ES nodes on it or install ES on all hadoop nodes? Also the article stats we should install ES on all hadoop nodes?

    ReplyDelete
  9. @Corbin wonderdog as a ruby tool to launch a hadoop job to index documents to elasticsearch (which is what this article is about) is fairly out of date. ElasticSearchStorage, a pig store udf as part of the wonderdog repo on github (http://github.com/infochimps-labs/wonderdog) is maintained by the folks at infochimps and should be fairly work-y.

    Now, I haven't touched that code for a while but the basic idea is this:

    (1) You've got _elastic search_ data nodes up and running somewhere. What that typically means, in practice, is that you have a working elastic search cluster that can index documents, search for documents, etc. It is completely independent of hadoop and there is no shared configuration. You _could_, but it is by no means a requirement, run elastic search data nodes on your hadoop data nodes but I wouldn't recommend it if your hadoop cluster is doing any real work and you want your elastic search queries to have as little latency as possible...

    (2) You have a working hadoop cluster with pig installed. I will not go into details here as there is a cornucopia of information on how to do this elsewhere.

    (3) On the machine that you launch your pig script from, this could, for example, be the same machine as the jobtracker, the elastic search jars, plugins, and elasticsearch.yml are available _locally_. The elasticsearch.yml file I refer to here should have entries under the 'discovery' key for the elastic search data nodes from step (1).

    Now, once you launch your pig job, here's what happens:

    * In the frontend, on the machine you launched the pig script from, the jars (you have to "register" them), the plugins, and elasticsearch.yml are shipped to the hdfs and made available in the distributed cache.

    * In the backend, when a task starts on an arbitrary hadoop tasktracker (this could be a map or a reduce task depending on your pig script), the jars, plugins, and elasticsearch.yml are located in the distributed cache. These resources are then used to create a single elastic search client (so that's one elastic search indexing client per hadoop task). This is where you should see the "adding nodes" messages. The reason is that the client joins the elastic search cluster, not as a node capable of actually storing data, but just as a node capable of indexing data. Joining the cluster seems unnecessary and adds some complexity but at the time of writing I couldn't see another way.

    * As documents are received in the hadoop task the elastic search index client buffers them (see the github repo for how to change the number of documents buffered) and builds a BulkRequest object to send to the elastic search data nodes to be stored.

    So really there's nothing fancy going on here at all. Hadoop is merely being used as a convenient way of creating many elastic search clients to index documents in parallel. Putting the complexity of this into a pig store udf, for me, meant that it was possible to do this by writing easy to read, no nonsense, pig scripts. That's all.

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