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From "sunfl@certusnet.com.cn" <su...@certusnet.com.cn>
Subject Performance options for doing Phoenix full table scans to complete some data statistics and summary collection work
Date Tue, 06 Jan 2015 02:42:53 GMT
Hiļ¼Œall
Currently we are using Phoenix to store and query large datasets of KPI for our projects.
Noting that we definitely need
to do full table scan of phoneix KPI tables for data statistics and summary collection, e.g.
from five minutes data table to
summary hour based data table, and to day based and week based data tables, and so on. 
The approaches now we used currently are as follows:
1. using Phoenix upsert into ... select ... grammer , however, the query performance would
not satisfy our expectation.
2. using Apache Spark with the phoenix_mr integration to read data from phoenix tables and
create rdd, then we can transform 
these rdds to summary rdd, and bulkload to new Phoenix data table.    This approach can satisfy
most of our application requirements, but 
in some cases we cannot complete the full scan job.

Here are my questions:
1. Is there any more efficient approaches for improving performance of Phoenix full table
scan of large data sets? Any kindly share are greately
appropriated.
2. Noting that full table scan is not quite appropriate for hbase tables, is there any alternative
options for doing such work under current hdfs and
hbase environments? Please kindly share any good points.

Best regards,
Sun.





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