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From Karthikeyan Muthukumarasamy <mkarthik.h...@gmail.com>
Subject Re: Questions on Table design for time series data
Date Fri, 05 Oct 2012 04:52:51 GMT
Jacques: I think you got me wrong on my statement. I was only requesting
you to think again about my questions assuming that I have seen the jive
video, since there are some differences in our case compared to jive. I
completely understand that all this is voluntary effort and my sincere
thanks for your suggestions. I will work through them and get back with
updates. Thanks again!


On Thu, Oct 4, 2012 at 12:29 AM, Jacques <whshub@gmail.com> wrote:

> We're all volunteers here so we don't always have the time to fully
> understand and plan others' schemas.
>
> In general your questions seemed to be worried about a lot of things that
> may or may not matter depending on the specifics of your implementation.
>  Without knowing those specifics it is hard to be super definitive.  You
> seem to be very worried about the cost of compactions and retention.  Is
> that because you're having issues now?
>
> Short answers:
>
> q1: Unless you have a good reason for splitting up into two tables, I'd
> keep as one.  Pros: Easier to understand/better matches intellectual
> understanding/allows checkAndPuts across both families/data is colocated
> (server, not disk) on retrieval if you want to work with both groups
> simultaneously using get, MR, etc.  Con: There will be some extra
> merge/flush activity if the two columns grow at substantially different
> rates.
>
> q2: 365*1000 regions is problematic (if that is what you're suggesting).
>  Even with HFilev2 and partially loaded multi-level indexes, there is still
> quite a bit of overhead per region.  I pointed you at the Jive thing in
> part since hashing that value as a bucket seems a lot more reasonable.
>  Additional Random idea: if you know retention policy on insert and your
> data is immutable post insertion, consider shifting the insert timestamp
> and maintain a single ttl.  Would require more client side code but would
> allow configurable ttls while utilizing existing HBase infrastructure.
>
> q3: Sounds like you're prematurely optimizing here.  Maybe others would
> disagree.  I'd use ttl until you find that isn't performant enough.  The
> tension between flexibility and speed is clear here.  I'd say you either
> need to pick specific ttls and optimize for that scenario via region
> pruning (e.g. separate tables for each ttl type) or you need to use a more
> general approach that leverages the per value ttl and compaction
> methodology.  There is enough operational work managing an HBase/HDFS
> cluster without having to worry about specialized region management.
>
> Jacques
>
> On Wed, Oct 3, 2012 at 11:31 AM, Karthikeyan Muthukumarasamy <
> mkarthik.here@gmail.com> wrote:
>
> > Hi Jacques,
> > Thanks for the response!
> > Yes, I have seen the video before. It suggets usage of TTL based
> retention
> > implementation. In their usecase, Jive has a fixed retention say 3 months
> > and so they can pre-create regions for so many buckets, their bucket id
> is
> > DAY_OF_YEAR%retention_in_days. But, in our usecase, the retention period
> is
> > configurable, so pre-creationg regions based on retention will not work.
> > Thats why we went for MMDD based buckets which is immune to retention
> > period changes.
> > Now that you know that Ive gone through that video from Jive, I would
> > request you to re-read my specific questions and share your suggestions.
> > Thanks & Regards
> > MK
> >
> >
> >
> > On Wed, Oct 3, 2012 at 11:51 PM, Jacques <whshub@gmail.com> wrote:
> >
> > > I would suggest you watch this video:
> > >
> > >
> >
> http://www.cloudera.com/resource/video-hbasecon-2012-real-performance-gains-with-real-time-data/
> > >
> > > The jive guys solved a lot of the problems you're talking about and
> > discuss
> > > it in that case study.
> > >
> > >
> > >
> > > On Wed, Oct 3, 2012 at 6:27 AM, Karthikeyan Muthukumarasamy <
> > > mkarthik.here@gmail.com> wrote:
> > >
> > > > Hi,
> > > > Our usecase is as follows:
> > > > We have time series data continuously flowing into the system and has
> > to
> > > be
> > > > stored in HBase.
> > > > Subscriber Mobile Number (a.k.a MSISDN) is the primary identifier
> based
> > > on
> > > > which data is stored and later retrieved.
> > > > There are two sets of parameters that get stored in every record in
> > > HBase,
> > > > lets call them group1 and group2. The number of records that would
> have
> > > > group1 parameters would be approx. 6 per day and the same for group2
> > > > parameters is approx. 1 per 3 days (their cardinality is different).
> > > >
> > > > Typically, the retention policy for group1 parameters is 3 months and
> > for
> > > > group2 parameters is 1 year. The read-pattern is as follows: An
> online
> > > > query would ask for records matching an MSISDN for a given date
> range,
> > > and
> > > > the system needs to respond with all available data (both from group1
> > and
> > > > group2) satifying the MSISDN and data range filters.
> > > >
> > > > Question1:
> > > > Alternative1: Create a single table with G1 and G2 as two column
> > > families.
> > > > Alternative2: Create two tables one for each group
> > > > Which is the better alternative and what are the pros and cons?
> > > >
> > > >
> > > > Question2:
> > > > To achieve max. distribution during write and reasonable complexity
> > > during
> > > > read, we decided on the following row key design:
> > > > <last 3 digits of MSISDN>,<MMDD>,<full MSISDN>
> > > > We will manually pre-split regions for the table based on the <last
3
> > > > digits of MSISDN>,<MMDD> part of row key
> > > > So there are 1000 (from 3 digits of MSISDN) * 365 (from MMDD) buckets
> > > that
> > > > would translate to as many regions
> > > > In this case, when retention is configured as < 1 year, the design
> > looks
> > > > optimal
> > > > When retention is configured > 1 year, one region might store data
> for
> > > more
> > > > than 1 day (feb 1 of 2012 and also feb 1 of 2013), which means more
> > data
> > > is
> > > > to be handled by hbase during compactions and read.
> > > > An alternative Key design, which does not have the above disadvantage
> > is:
> > > > <last 3 digits of MSISDN>,<YYYYMMDD>,<full MSISDN>
> > > > this way, in one region, there will be only 1 days data at any point,
> > > > regardless of retention
> > > > What are other pros & cons of the two key designs?
> > > >
> > > > Question3:
> > > > In our usecase, delete happens only based on retention policy, where
> > one
> > > > days full data has to be deleted when rention period is crossed (for
> > eg,
> > > if
> > > > retention is 30 days, on Apr 1 all the data for Mar 1 is deleted)
> > > > What is the most optimal way to implement this retention policy?
> > > > Alternative 1: TTL for column famil is configured and we leave it to
> > > HBase
> > > > to delete data during major compaction, but we are not sure of the
> cost
> > > of
> > > > this major compaction happening in all regions at same time
> > > > Alternative 2: Through key design logic mentioned before, if we
> ensure
> > > data
> > > > for one day goes into one set of regions, can we use HBase APIs like
> > > > HFileArchiver to programatically archive and drop regions?
> > > >
> > > > Thanks & Regards
> > > > MK
> > > >
> > >
> >
>

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