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Posted to issues@hbase.apache.org by "Zhihong Yu (Updated) (JIRA)" <ji...@apache.org> on 2012/01/06 22:34:39 UTC
[jira] [Updated] (HBASE-5139) Compute (weighted) median using
AggregateProtocol
[ https://issues.apache.org/jira/browse/HBASE-5139?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Zhihong Yu updated HBASE-5139:
------------------------------
Description:
Suppose cf:cq1 stores numeric values and optionally cf:cq2 stores weights. This task finds out the median value among the values of cf:cq1 (See http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/R.basic/html/weighted.median.html)
This can be done in two passes.
The first pass utilizes AggregateProtocol where the following tuple is returned from each region:
(start-rowkey, partial-sum-of-values, partial-sum-of-weights)
The start-rowkey is used to sort the tuples. This way we can determine which region (called R) contains the (weighted) median. partial-sum-of-weights can be 0 if unweighted median is sought
The second pass involves scanning the table, beginning with startrow of region R and computing partial (weighted) sum until the threshold of S/2 is crossed. The (weighted) median is returned.
However, this approach wouldn't work if there is mutation in the underlying table between pass one and pass two.
In that case, sequential scanning seems to be the solution which is slower than the above approach.
was:
Suppose cf:cq1 stores numeric values and optionally cf:cq2 stores weights. This task finds out the median value among the values of cf:cq1 (See http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/R.basic/html/weighted.median.html)
This can be done in two passes.
The first pass utilizes AggregateProtocol where the following tuple is returned from each region:
(start-rowkey, partial-sum-of-values, partial-sum-of-weights)
The start-rowkey is used to sort the tuples. This way we can determine which region (called R) contains the (weighted) median. partial-sum-of-weights can be 0 if unweighted median is sought
The second pass involves scanning the region R and computing partial (weighted) sum until the threshold of S/2 is crossed. The (weighted) median is returned.
However, this approach wouldn't work if there is mutation in the underlying table between pass one and pass two.
In that case, sequential scanning seems to be the solution which is slower than the above approach.
> Compute (weighted) median using AggregateProtocol
> -------------------------------------------------
>
> Key: HBASE-5139
> URL: https://issues.apache.org/jira/browse/HBASE-5139
> Project: HBase
> Issue Type: Sub-task
> Reporter: Zhihong Yu
>
> Suppose cf:cq1 stores numeric values and optionally cf:cq2 stores weights. This task finds out the median value among the values of cf:cq1 (See http://www.stat.ucl.ac.be/ISdidactique/Rhelp/library/R.basic/html/weighted.median.html)
> This can be done in two passes.
> The first pass utilizes AggregateProtocol where the following tuple is returned from each region:
> (start-rowkey, partial-sum-of-values, partial-sum-of-weights)
> The start-rowkey is used to sort the tuples. This way we can determine which region (called R) contains the (weighted) median. partial-sum-of-weights can be 0 if unweighted median is sought
> The second pass involves scanning the table, beginning with startrow of region R and computing partial (weighted) sum until the threshold of S/2 is crossed. The (weighted) median is returned.
> However, this approach wouldn't work if there is mutation in the underlying table between pass one and pass two.
> In that case, sequential scanning seems to be the solution which is slower than the above approach.
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