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From Damien Hardy <dha...@viadeoteam.com>
Subject Re: Is it necessary to set MD5 on rowkey?
Date Tue, 17 Dec 2013 09:21:13 GMT

yes you need 256 scans range or a full (almost) scan with combination of
filters for each 256 ranges

For mapreduce, the getsplit() method should be modified from
TableInputFormatBase to handle salt values.
This is what is done in
(to return on HBaseWD example)

So a mapper (several if a salt value cover many regions) is dedicated
for each salt value like simple TableInoutFormart would do without salt.

Best regards.


Le 17/12/2013 09:36, bigdata a écrit :
> Hello,
> @Alex Baranau
> Thanks for your salt solution. In my understanding, the salt solution is divide the data
into several partial(if 2 letters,00~FF, then 255 parts will be devided). My question is when
I want to scan data, do I need scan 256 times for the following situation:rowkey:  salt prefix
(00~FF) + date + xxx
> And If I want do mapreduce on this table, if the initTableMapperJob(List<Scan>,...)
is OK?
> If example of scan the salted table is appreciated!
> Thanks.
>> Date: Tue, 18 Dec 2012 12:12:37 -0500
>> Subject: Re: Is it necessary to set MD5 on rowkey?
>> From: alex.baranov.v@gmail.com
>> To: user@hbase.apache.org
>> Hello,
>> @Mike:
>> I'm the author of that post :).
>> Quick reply to your last comment:
>> 1) Could you please describe why "the use of a 'Salt' is a very, very bad
>> idea" in more specific way than "Fetching data takes more effort". Would be
>> helpful for anyone who is looking into using this approach.
>> 2) The approach described in the post also says you can prefix with the
>> hash, you probably missed that.
>> 3) I believe your answer, "use MD5 or SHA-1" doesn't help bigdata guy.
>> Please re-read the question: the intention is to distribute the load while
>> still being able to do "partial key scans". The blog post linked above
>> explains one possible solution for that, while your answer doesn't.
>> @bigdata:
>> Basically when it comes to solving two issues: distributing writes and
>> having ability to read data sequentially, you have to balance between being
>> good at both of them. Very good presentation by Lars:
>> http://www.slideshare.net/larsgeorge/hbase-advanced-schema-design-berlin-buzzwords-june-2012,
>> slide 22. You will see how this is correlated. In short:
>> * having md5/other hash prefix of the key does better w.r.t. distributing
>> writes, while compromises ability to do range scans efficiently
>> * having very limited number of 'salt' prefixes still allows to do range
>> scans (less efficiently than normal range scans, of course, but still good
>> enough in many cases) while providing worse distribution of writes
>> In the latter case by choosing number of possible 'salt' prefixes (which
>> could be derived from hashed values, etc.) you can balance between
>> distributing writes efficiency and ability to run fast range scans.
>> Hope this helps
>> Alex Baranau
>> ------
>> Sematext :: http://blog.sematext.com/ :: Hadoop - HBase - ElasticSearch -
>> Solr
>> On Tue, Dec 18, 2012 at 8:52 AM, Michael Segel <michael_segel@hotmail.com>wrote:
>>> Hi,
>>> First, the use of a 'Salt' is a very, very bad idea and I would really
>>> hope that the author of that blog take it down.
>>> While it may solve an initial problem in terms of region hot spotting, it
>>> creates another problem when it comes to fetching data. Fetching data takes
>>> more effort.
>>> With respect to using a hash (MD5 or SHA-1) you are creating a more random
>>> key that is unique to the record.  Some would argue that using MD5 or SHA-1
>>> that mathematically you could have a collision, however you could then
>>> append the key to the hash to guarantee uniqueness. You could also do
>>> things like take the hash and then truncate it to the first byte and then
>>> append the record key. This should give you enough randomness to avoid hot
>>> spotting after the initial region completion and you could pre-split out
>>> any number of regions. (First byte 0-255 for values, so you can program the
>>> split...
>>> Having said that... yes, you lose the ability to perform a sequential scan
>>> of the data.  At least to a point.  It depends on your schema.
>>> Note that you need to think about how you are primarily going to access
>>> the data.  You can then determine the best way to store the data to gain
>>> the best performance. For some applications... the region hot spotting
>>> isn't an important issue.
>>> Note YMMV
>>> HTH
>>> -Mike
>>> On Dec 18, 2012, at 3:33 AM, Damien Hardy <dhardy@viadeoteam.com> wrote:
>>>> Hello,
>>>> There is middle term betwen sequecial keys (hot spoting risk) and md5
>>>> (heavy scan):
>>>>  * you can use composed keys with a field that can segregate data
>>>> (hostname, productname, metric name) like OpenTSDB
>>>>  * or use Salt with a limited number of values (example
>>>> substr(md5(rowid),0,1) = 16 values)
>>>>    so that a scan is a combination of 16 filters on on each salt values
>>>>    you can base your code on HBaseWD by sematext
>>> http://blog.sematext.com/2012/04/09/hbasewd-avoid-regionserver-hotspotting-despite-writing-records-with-sequential-keys/
>>>>       https://github.com/sematext/HBaseWD
>>>> Cheers,
>>>> 2012/12/18 bigdata <bigdatabase@outlook.com>
>>>>> Many articles tell me that MD5 rowkey or part of it is good method to
>>>>> balance the records stored in different parts. But If I want to search
>>> some
>>>>> sequential rowkey records, such as date as rowkey or partially. I can
>>> not
>>>>> use rowkey filter to scan a range of date value one time on the date
>>>>> MD5. How to balance this issue?
>>>>> Thanks.

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