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From Alok Singh <aloksi...@gmail.com>
Subject Re: data partitioning and data model
Date Fri, 20 Feb 2015 20:19:41 GMT
You don't want a lot of columns in a write heavy table. HBase stores
the "row key" along with each cell/column (Though old, I find this
still useful: http://www.larsgeorge.com/2009/10/hbase-architecture-101-storage.html)
 Having a lot of columns will amplify the amount of data being stored.

That said, if there are only going to be a handful of alert_ids for a
given "user_id+timestamp" row key, then you should be ok.

The query "Select * from table where user_id = X and timestamp > T and
(alert_id = id1 or alert_id = id2)" can be accomplished with either
design. See QualifierFilter and FuzzyRowFilter docs to get some ideas.


On Fri, Feb 20, 2015 at 11:21 AM, Marcelo Valle (BLOOMBERG/ LONDON)
<mvallemilita@bloomberg.net> wrote:
> Hi Alok,
> Thanks for the answer. Yes, I have read this section, but it was a little too abstract
for me, I think I was needing to check my understanding. Your answer helped me to confirm
I am on the right path, thanks for that.
> One question: if instead of using user_id + timestamp + alert_id  I use user_id + timestamp
as row key, I would still be able to store alert_id + alert_data in columns, right?
> I took the idea from the last section of this link: http://www.appfirst.com/blog/best-practices-for-managing-hbase-in-a-high-write-environment/
> But I wonder which option would be better for my case. It seems column scans are not
so fast as row scans, but what would be the advantages of one design over the other?
> If I use something like:
> Row key: user_id + timestamp
> Column prefix: alert_id
> Column value: json with alert data
> Would I be able to do a query like the one bellow?
> Select * from table where user_id = X and timestamp > T and (alert_id = id1 or alert_id
= id2)
> Would I be able to do the same query using user_id + timestamp + alert_id as row key?
> Also, I know Cassandra supports up to 2 billion columns per row (2 billion rows per partition
in CQL), do you know what's the limit for HBase?
> Best regards,
> Marcelo Valle.
> From: aloksingh@gmail.com
> Subject: Re: data partitioning and data model
> You can use a key like (user_id + timestamp + alert_id) to get
> clustering of rows related to a user. To get better write throughput
> and distribution over the cluster, you could pre-split the table and
> use a consistent hash of the user_id as a row key prefix.
> Have you looked at the rowkey design section in the hbase book :
> http://hbase.apache.org/book.html#rowkey.design
> Alok
> On Fri, Feb 20, 2015 at 8:49 AM, Marcelo Valle (BLOOMBERG/ LONDON)
> <mvallemilita@bloomberg.net> wrote:
>> Hello,
>> This is my first message in this mailing list, I just subscribed.
>> I have been using Cassandra for the last few years and now I am trying to create
a POC using HBase. Therefore, I am reading the HBase docs but it's been really hard to find
how HBase behaves in some situations, when compared to Cassandra. I thought maybe it was a
good idea to ask here, as people in this list might know the differences better than anyone
>> What I want to do is creating a simple application optimized for writes (not interested
in HBase / Cassandra product comparisions here, I am assuming I will use HBase and that's
it, just wanna understand the best way of doing it in HBase world). I want to be able to write
alerts to the cluster, where each alert would have columns like:
>> - alert id
>> - user id
>> - date/time
>> - alert data
>> Later, I want to search for alerts per user, so my main query could be considered
to be something like:
>> Select * from alerts where user_id = $id and date/time > 10 days ago.
>> I want to decide the data model for my application.
>> Here are my questions:
>> - In Cassandra, I would partition by user + day, as some users can have many alerts
and some just 1 or a few. In hbase, assuming all alerts for a user would always fit in a single
partition / region, can I just use user_id as my row key and assume data will be distributed
along the cluster?
>> - Suppose I want to write 100 000 rows from a client machine and these are from 30
000 users. What's the best manner to write these if I want to optimize for writes? Should
I batch all 100 k requests in one to a single server? As I am trying to optimize for writes,
I would like to split these requests across several nodes instead of sending them all to one.
I found this article: http://hortonworks.com/blog/apache-hbase-region-splitting-and-merging/
But not sure if it's what I need
>> Thanks in advance!
>> Best regards,
>> Marcelo.

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