Re:Re: Re: What is the purpose of these system tables(CATALOG, STATS, and SEQUENCE)?

2014-12-23 Thread chenwenhui
Hi James, Thanks for your help. The SYSTEM.SEQUENCE has contained 256 regions in the production cluster, if modify phoenix.sequence.saltBuckets to the less value, will the region number reduce after restarting hbase service? Sorry for my chatter, after all restarting the productive cluster is

sqlline.py failed to work

2014-12-23 Thread guxiaobo1982
Hi, I am trying phoenix 4.2.2 against hbase 0.98.4.2.2.0.0 installed by ambari, the hbase shell command works probably, but ./sqlline.py localhost failed to work with the following error messages: [xiaobogu@lix1 bin]$ ./sqlline.py lix1.bh.com Setting property: [isolation,

Re: sqlline.py failed to work

2014-12-23 Thread Nicolas Maillard
Hello Try ./sqlline.py localhost:2181:/hbase-unsecure This should help the znode for hbase in ambari is by default hbase-unsecure On Tue, Dec 23, 2014 at 12:14 PM, guxiaobo1982 guxiaobo1...@qq.com wrote: Hi, I am trying phoenix 4.2.2 against hbase 0.98.4.2.2.0.0 installed by ambari, the

Re: CSV bulk loading using map reduce

2014-12-23 Thread Gabriel Reid
Hi Noam, I think that the things that most typically can affect MR loading performance are: * number of regions (as this affects the number of reducers used to create the HFiles) * amount of memory used for sort buffers * use of compression on map output With your 32-region salted table, it

RE: CSV bulk loading using map reduce

2014-12-23 Thread Bulvik, Noam
Thanks for the answer we will look in to it and update The impala is impala parquet table -Original Message- From: Gabriel Reid [mailto:gabriel.r...@gmail.com] Sent: Tuesday, December 23, 2014 2:27 PM To: user@phoenix.apache.org Subject: Re: CSV bulk loading using map reduce Hi Noam, I

Re: CSV bulk loading using map reduce

2014-12-23 Thread Gabriel Reid
Ok, thanks for confirming that the Impala table is backed by Parquet. It's not surprising to hear that you can get higher throughput while loading a Parquet table compared to Phoenix. Even when using Parquet, the file structure used by Impala is quite lightweight compared to HFiles. For example,

array of tuples

2014-12-23 Thread Alex Kamil
is there a way to represent Map.EntryK,V[] as a column value in phoenix, i.e. store an array of tuples K,V instead of creating two arrays: K VARCHAR ARRAY[] and V VARCHAR ARRAY[]

Re:Re: What is the purpose of these system tables(CATALOG, STATS, and SEQUENCE)?

2014-12-23 Thread chenwenhui
Hi James, Too many regions can easily burden the hbase cluster heavily, even if empty region, and also i think the SYSTEM.SEQUENCE table indeed need not so much regions. After modify the phoenix.sequence.saltBuckets, and then restart hbase, the region number of SYSTEM.SEQUENCE table does not

Re: Re: What is the purpose of these system tables(CATALOG, STATS, and SEQUENCE)?

2014-12-23 Thread James Taylor
Hi David, bq. Too many regions can easily burden the hbase cluster heavily, even if empty region Is that true, HBase-committers? You'll need to delete the SYSTEM.SEQUENCE, make sure to set the phoenix.sequence.saltBuckets property, and bounce your cluster. Then the next time you try to establish

Re: array of tuples

2014-12-23 Thread James Taylor
No, that's currently not possible. You'd may be able to leverage one of the following to help you, though: - parallel arrays as you've mentioned - different tables with an FK (and likely an index) between them - dynamic columns (http://phoenix.apache.org/dynamic_columns.html) - on-the-fly

Re: sqlline.py failed to work

2014-12-23 Thread guxiaobo1982
This works, thanks. -- Original -- From: Nicolas Maillard;nmaill...@hortonworks.com; Send time: Tuesday, Dec 23, 2014 7:26 PM To: useruser@phoenix.apache.org; Subject: Re: sqlline.py failed to work Hello Try ./sqlline.py localhost:2181:/hbase-unsecure

Re: array of tuples

2014-12-23 Thread Alex Kamil
I'll use parallel arrays for now, but STRUCT would be ideal it's a case for nested tables, each attribute can have multiple values, and several attributes are grouped into sub-rows by group id, e.g. in the table below the group *id: name, price* corresponds to * id1: apple1, id1:1* that form one