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https://issues.apache.org/jira/browse/HCATALOG-42?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13064840#comment-13064840
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Sushanth Sowmyan commented on HCATALOG-42:
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A couple of points of feedback and results from testing:
+ Need to disable the max_partitions check as it's not clear if we want to keep
it or not. So we can disable it by default(through code), but we'll revisit
this if we see the need to change it.
+ Issues with HAR - In a secure cluster, with security enabled, our trying to
launch a HAR job in OutputCommitter fails because we can't launch a job from a
task without having a JobTracker delegation token. Code needs to be fixed to
fetch one, pass it on, use it to launch HAR and to cancel it.
> Storing across partitions(Dynamic Partitioning) from
> HCatStorer/HCatOutputFormat
> --------------------------------------------------------------------------------
>
> Key: HCATALOG-42
> URL: https://issues.apache.org/jira/browse/HCATALOG-42
> Project: HCatalog
> Issue Type: Improvement
> Affects Versions: 0.2
> Reporter: Sushanth Sowmyan
> Assignee: Sushanth Sowmyan
> Attachments: HCATALOG-42.6.patch, hadoop_archive-0.3.1.jar
>
>
> HCatalog allows people to abstract away underlying storage details and refer
> to them as tables and partitions. To this notion, the storage abstraction is
> more about classifying how data is organized, rather than bothering about
> where it is stored. A user thus then specifies partitions to be stored and
> leaves the job to HCatalog to figure out how and where it needs to do so.
> When it comes to reading the data, a user is able to specify that they're
> interested in reading from the table and specify various partition key value
> combinations to prune, as if specifying a SQL-like where clause. However,
> when it comes to writing, the abstraction is not so seamless. We still
> require of the end user to write out data to the table
> partition-by-partition. And these partitions require fine-grained knowledge
> of what key-value-pairs they require, and we require this knowledge in
> advance, and we require the writer to have already grouped the requisite data
> accordingly before attempting to store.
> For example, the following pig script illustrates this:
> --
> A = load 'raw' using HCatLoader();
> ...
> split Z into for_us if region='us', for_eu if region='eu', for_asia if
> region='asia';
> store for_us into 'processed' using HCatStorage("ds=20110110, region=us");
> store for_eu into 'processed' using HCatStorage("ds=20110110, region=eu");
> store for_asia into 'processed' using HCatStorage("ds=20110110,
> region=asia");
> --
> This has a major issue in that MapReduce programs and pig scripts need to be
> aware of all the possible values of a key, and that needs to be maintained,
> and modified if needed when new values are introduced, which may/may not
> always be easy or even possible. With more partitions, scripts begin to look
> cumbersome. And if each partition being written launches a separate HCatalog
> store, we are increasing the load on the HCatalog server and launching more
> jobs for the store by a factor of the number of partitions
> It would be much more preferable if HCatalog were to be able to figure out
> all the partitions required from the data being written, which would allow us
> to simplify the above script into the following:
> --
> A = load 'raw' using HCatLoader();
> ...
> store Z into 'processed' using HCatStorage("ds=20110110");
> --
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