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Posted to dev@lucene.apache.org by "ASF subversion and git services (JIRA)" <ji...@apache.org> on 2016/07/17 03:18:20 UTC
[jira] [Commented] (SOLR-9240) Support parallel ETL with the topic
expression
[ https://issues.apache.org/jira/browse/SOLR-9240?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15381066#comment-15381066 ]
ASF subversion and git services commented on SOLR-9240:
-------------------------------------------------------
Commit fc3894e837701b78a4704cf27529c34c15666586 in lucene-solr's branch refs/heads/master from jbernste
[ https://git-wip-us.apache.org/repos/asf?p=lucene-solr.git;h=fc3894e ]
SOLR-9240:Support running the topic() Streaming Expression in parallel mode.
> Support parallel ETL with the topic expression
> ----------------------------------------------
>
> Key: SOLR-9240
> URL: https://issues.apache.org/jira/browse/SOLR-9240
> Project: Solr
> Issue Type: Improvement
> Reporter: Joel Bernstein
> Assignee: Joel Bernstein
> Fix For: 6.2
>
> Attachments: SOLR-9240.patch, SOLR-9240.patch
>
>
> It would be useful for SolrCloud to support large scale *Extract, Transform and Load* work loads with streaming expressions. Instead of using MapReduce for ETL, the topic expression can be used which allows SolrCloud to be treated like a distributed message queue filled with data to be processed. The topic expression works in batches and supports retrieval of stored fields, so large scale *text ETL* will work perfectly with this approach.
> This ticket makes two small changes to the topic() expression that makes this possible:
> 1) Changes the topic expression so it can operate in parallel.
> 2) Adds the initialCheckpoint parameter to the topic expression so a topic can start pulling records from anywhere in the queue.
> Daemons can be sent to worker nodes that each work on processing a partition of the data from the same topic. The daemon() function's natural behavior is perfect for iteratively calling a topic until all records in the topic have been processed.
> The sample code below pulls all records from one collection and indexes them into another collection. A Transform function could be wrapped around the topic() to transform the records before loading. Custom functions can also be built to load the data in parallel to any outside system.
> {code}
> parallel(
> workerCollection,
> workers="2",
> sort="_version_ desc",
> daemon(
> update(
> updateCollection,
> batchSize=200,
> topic(
> checkpointCollection,
> topicCollection,
> q=*:*,
> id="topic1",
> fl="id, to , from, body",
> partitionKeys="id",
> initialCheckpoint="0")),
> runInterval="1000",
> id="daemon1"))
> {code}
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