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From Willem Conradie <willem.conra...@pbtgroup.co.za>
Subject RE: Telco HBase POC
Date Mon, 18 Jan 2016 10:47:28 GMT
Hi Pari,

My comments in blue.

Few notes from my experience :
1. Use bulk load rather than psql.py. Load larger files(merge) instead of small files.
Are you referring to native HBase bulk load or Phoenix MapReduce bulk load? Unfortunately
we can’t change how the files are received from source. Must we pre-process to merge the
files before running the bulk load utility?

2. Increase HBase block cache
3. Turn off HBase auto compaction
4. Select primary key correctly
5. Don't use salting . As table will be huge, your phoenix query will fork may scanners. Try
something like hash on userid.
6. Define TTL to purge data periodically


Regards,
Willem

From: Pariksheet Barapatre [mailto:pbarapatre@gmail.com]
Sent: 15 January 2016 03:17 PM
To: user@phoenix.apache.org
Subject: Re: Telco HBase POC

Hi Willem,
Looking at your use case. Phoenix would be a handy client.
Few notes from my experience :
1. Use bulk load rather than psql.py. Load larger files(merge) instead of small files.
2. Increase HBase block cache
3. Turn off HBase auto compaction
4. Select primary key correctly
5. Don't use salting . As table will be huge, your phoenix query will fork may scanners. Try
something like hash on userid.
6. Define TTL to purge data periodically

Cheers
Pari


On 15 January 2016 at 17:48, Pedro Gandola <pedro.gandola@gmail.com<mailto:pedro.gandola@gmail.com>>
wrote:
Hi Willem,

Just to give you my short experience as phoenix user.

I'm using Phoenix4.4 on top of a HBase cluster where I keep 3 billion entries.
In our use case Phoenix is doing very well and it saved a lot of code complexity and time.
If you guys have already decided that HBase is the way to go then having phoenix as a SQL
layer it will help a lot, not only in terms of code simplicity but It will help you to create
and maintain your indexes and views which can be hard&costly tasks using the plain HBase
API. Joining tables it's just a simple SQL join :).

And there are a lot of more useful features that make your life easier with HBase.

In terms of performance and depending on the SLAs that you have you need to benchmark, however
I think your main battles are going to be with HBase, JVM GCs, Network, FileSystem, etc...

I would say to give Phoenix a try, for sure.

Cheers
Pedro

On Fri, Jan 15, 2016 at 9:12 AM, Willem Conradie <willem.conradie@pbtgroup.co.za<mailto:willem.conradie@pbtgroup.co.za>>
wrote:



Hi,



I am currently consulting at a client with the following requirements.



They want to make available detailed data usage CDRs for customers to verify their data usage
against the websites that they visited. In short this can be seen as an itemised bill for
data usage.  The data is currently not loaded into a RDBMS due to the volumes of data involved.
The proposed solution is to load the data into HBase, running on a HDP cluster, and make it
available for querying by the subscribers.  It is critical to ensure low latency read access
to the subscriber data, which possibly will be exposed to 25 million subscribers. We will
be running a scaled down version first for a proof of concept with the intention of it becoming
an operational data store.  Once the solution is functioning properly for the data usage CDRs
other CDR types will be added, as such we need  to build a cost effective, scalable solution
.



I am thinking of using Apache Phoenix for the following reasons:



1.      1. Current data loading into RDBMS is file based (CSV) via a staging server using
the RDBMS file load drivers

2.      2.  Use Apache Phoenix   bin/psql.py script to mimic above process to load to HBase

3.       3. Expected data volume :  60 000 files per day
                                                  1 –to 10 MB per file
                                                  500 million records per day
                                                   500 GB total volume per day


4.        4. Use Apache Phoenix client for low latency data retrieval



Is Apache Phoenix a suitable candidate for this specific use case?



Regards,

Willem





--
Cheers,
Pari
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