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From "Alexander Kanarsky (JIRA)" <>
Subject [jira] Updated: (SOLR-1301) Solr + Hadoop
Date Mon, 20 Sep 2010 08:42:26 GMT


Alexander Kanarsky updated SOLR-1301:

    Attachment: SOLR-1301.patch

The latest SOLR-1301-hadoop-0-20 patch is repackaged to be placed under the contrib, as it
was initially (build.xml is included). and tested against the current trunk. As usual, after
applying the patch put the 4 lib jars (hadoop, log4j, and two commons-logging) to the contrib/hadoop/lib.
No unit tests as for now :) but I hope to add some soon. Here is the big question: as Andrzej
once mentioned, the unit tests require a running Hadoop cluster. One approach is to make the
patch and unit tests working with the Hadoop mini--cluster (ClusterMapReduceTestCase), however
this will bring some extra dependencies needed to run the cluster (like jetty). Another idea
is to use "your own" cluster and just configure access to this cluster in untt tests; this
approach seems to be logical but potentially may give different test results on different
clusters, and also may not give some low-level access to the execution, needed for tests.
So what is your opinion on how the tests for solr-hadoop should be run? I am not really happy
with the idea of starting and running the Hadoop cluster while performing the Solr unit tests,
but this still could be the better option than no unit tests at all.  

> Solr + Hadoop
> -------------
>                 Key: SOLR-1301
>                 URL:
>             Project: Solr
>          Issue Type: Improvement
>    Affects Versions: 1.4
>            Reporter: Andrzej Bialecki 
>             Fix For: Next
>         Attachments: commons-logging-1.0.4.jar, commons-logging-api-1.0.4.jar, hadoop-0.19.1-core.jar,
hadoop-0.20.1-core.jar, hadoop.patch, log4j-1.2.15.jar, README.txt, SOLR-1301-hadoop-0-20.patch,
SOLR-1301-hadoop-0-20.patch, SOLR-1301.patch, SOLR-1301.patch, SOLR-1301.patch, SOLR-1301.patch,
SOLR-1301.patch, SOLR-1301.patch, SOLR-1301.patch, SOLR-1301.patch,
> This patch contains  a contrib module that provides distributed indexing (using Hadoop)
to Solr EmbeddedSolrServer. The idea behind this module is twofold:
> * provide an API that is familiar to Hadoop developers, i.e. that of OutputFormat
> * avoid unnecessary export and (de)serialization of data maintained on HDFS. SolrOutputFormat
consumes data produced by reduce tasks directly, without storing it in intermediate files.
Furthermore, by using an EmbeddedSolrServer, the indexing task is split into as many parts
as there are reducers, and the data to be indexed is not sent over the network.
> Design
> ----------
> Key/value pairs produced by reduce tasks are passed to SolrOutputFormat, which in turn
uses SolrRecordWriter to write this data. SolrRecordWriter instantiates an EmbeddedSolrServer,
and it also instantiates an implementation of SolrDocumentConverter, which is responsible
for turning Hadoop (key, value) into a SolrInputDocument. This data is then added to a batch,
which is periodically submitted to EmbeddedSolrServer. When reduce task completes, and the
OutputFormat is closed, SolrRecordWriter calls commit() and optimize() on the EmbeddedSolrServer.
> The API provides facilities to specify an arbitrary existing solr.home directory, from
which the conf/ and lib/ files will be taken.
> This process results in the creation of as many partial Solr home directories as there
were reduce tasks. The output shards are placed in the output directory on the default filesystem
(e.g. HDFS). Such part-NNNNN directories can be used to run N shard servers. Additionally,
users can specify the number of reduce tasks, in particular 1 reduce task, in which case the
output will consist of a single shard.
> An example application is provided that processes large CSV files and uses this API.
It uses a custom CSV processing to avoid (de)serialization overhead.
> This patch relies on hadoop-core-0.19.1.jar - I attached the jar to this issue, you should
put it in contrib/hadoop/lib.
> Note: the development of this patch was sponsored by an anonymous contributor and approved
for release under Apache License.

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