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 <[email protected]> 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 < > [email protected]> 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 <[email protected]> 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 < > > > [email protected]> 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 > > > > > > > > > >
