With 100+ columns, using multiple column families will help a lot if your full scan uses only few columns.
Hi Sun,Can you give us a sample DDL and upsert/select query for #1? What's the approximate cluster size and what does the client look like? How much data are you scanning? Are you using multiple column families? We should be able to help tune things to improve #1.Thanks,James
On Monday, January 5, 2015, firstname.lastname@example.org <email@example.com> wrote:We had firstly done the test using #1 and the result didnot satisfy our expectation.Unfortunately I had not saved the log copy, but under same conditions of datasets,#2 is better than #1.Thanks,Sun.
Region server fails consistently? Can you provide logs from the failing process?
On Monday, January 5, 2015, firstname.lastname@example.org <email@example.com> wrote:Hi, LarsThanks for your reply and advice. You are right, we are considering about sort of aggregates work.Our requirements need to assure full scan over table with approximately 50 million rows while containingnearly 100+ columns. We are using the latest 4.2.2 release, actually we are using Spark to read and write toPhoenix tables. We apply the schema of mapreduce over Phoenix tables to do full table scan in Spark, andthen we shall use the created rdd to write or bulkload to new Phoenix tables. Thats' just our production flow.Specifying the #1 vs #2 performance, we found that #1 shall always failed to complete and we can see regionserverfalling down during the job. #2 would cause some kind of ScannerTimeOutExecption, then we configure parametersfor our hbase cluster and such problems gone. However, we are still expecting more efficient approaches for doingsuch full table scan over Phoenix datasets.Thanks,Sun.
Hi Sun,assuming that you are mostly talking about aggregates (in the sense of scanning a lot of data, but the resulting set is small), it's interesting that option #1 would not satisfy your performance expectations, but #2 would.Which version of Phoenix are you using? From 4.2 Phoenix is well aware of the distribution of the data and will farm out full scans in parallel chunks.In number you would make a copy of the entire dataset in order to be able to "query" it via Spark?What kind of performance do you see with option #1 vs #2?Thanks.-- Lars
From: "firstname.lastname@example.org" <email@example.com>
To: user <firstname.lastname@example.org>; dev <email@example.com>
Sent: Monday, January 5, 2015 6:42 PM
Subject: Performance options for doing Phoenix full table scans to complete some data statistics and summary collection work
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
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.