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From "Edward J. Yoon" <edwardy...@apache.org>
Subject Re: Why is scaling HBase much simpler then scaling a relational db?
Date Wed, 27 Aug 2008 15:06:49 GMT

Planet-scale data explorations and data mining operations will almost
always need to include some sequential scans. Then, How can we speed
up sequential scans? BigTable paper shows that.

* Column-oriented storage (it reduces I/O)
* Data compression
* PDP (parallel distributed processing) using Map/Reduce

Also, Matrices which are column major typically perform better with
column-oriented operations, and likewise for row major matrices. See
the Hama/Heart project (http://incubator.apache.org/hama,
http://wiki.apache.org/incubator/HeartProposal) on Hadoop + Hbase.

Salesman, Edward :)

On Wed, Aug 27, 2008 at 4:57 PM, Mork0075 <mork0075@googlemail.com> wrote:
> I'am still really interested in these three questions :)
>> You example with the friends makes perfectly sense. Can you imagine a
>> scenario where storing the data in column oriented instead of row
>> oriented db (so if you will an counterexample) causes such a huge
>> performance mismatch, like the friends one in row/column comparison?
>> Can you please provide an example of "good de-normalization" in HBase
>> and how its held consitent (in your friends example in a relational db,
>> there would be a cascadingDelete)? As i think of the users table: if i
>> delete an user with the userid='123', then if have to walk through all
>> of the other users column-family "friends" to guranty consitency?! Is
>> de-normalization in HBase only used to avoid joins? Our webapp doenst
>> use joins at the moment anyway.
>> As you describe it, its a problem of implementation. BigTable is
>> designed to scale, there are routines to shard the data, desitribute
>> it to the pool of connected servers. Could MySQL perhaps decide
>> tomorrow to implement something similar or does the relational model
>> avoids this?
>> Jonathan Gray schrieb:
>>> A few very big differences...
>>> - HBase/BigTable don't have "transactions" in the same way that a
>>> relational database does.  While it is possible (and was just recently
>>> implemented for HBase, see HBASE-669) it is not at the core of this
>>> design.  A major bottleneck of distributed multi-master relational
>>> databases is distributed transactions/locks.
>>> - There's a very big difference between storage of
>>> relational/row-oriented databases and column-oriented databases.  For
>>> example, if I have a table of 'users' and I need to store friendships
>>> between these users... In a relational database my design is something
>>> like:
>>> Table: users(pkey = userid)
>>> Table: friendships(userid,friendid,...) which contains one (or maybe
>>> two depending on how it's impelemented) row for each friendship.
>>> In order to lookup a given users friend, SELECT * FROM friendships
>>> WHERE userid = 'myid';
>>> This query would use an index on the friendships table to retrieve all
>>> the necessary rows.  Depending on the relational database you might
>>> also be fetching each and every row (entirely) off of disk to be
>>> read.  In a sharded relational database, this would require hitting
>>> every node to get whichever friendships were stored on that node.
>>> There's lots of room for optimizations here but any way you slice it,
>>> you're likely pulling non-sequential blocks off disk.  When you add in
>>> the overhead of ACID transactions this can get slow.
>>> The cost of this relational query continues to increase as a user adds
>>> more friends.  You also begin to have practical limits.  If I have
>>> millions of users, each with many thousands of potential friends, the
>>> size of these indexes grow exponentially and things get nasty
>>> quickly.  Rather than friendships, imagine I'm storing activity logs
>>> of actions taken by users.
>>> In a column-oriented database these things scale continuously with
>>> minimal difference between 10 users and 10,000,000 users, 10
>>> friendships and 10,000 friendships.
>>> Rather than a friendships table, you could just have a friendships
>>> column family in the users table.  Each column in that family would
>>> contain the ID of a friend.  The value could store anything else you
>>> would have stored in the friendships table in the relational model.
>>> As column families are stored together/sequentially on a per-row
>>> basis, reading a user with 1 friend versus a user with 10,000 friends
>>> is virtually the same.  The biggest difference is just in the shipping
>>> of this information across the network which is unavoidable.  In this
>>> system a user could have 10,000,000 friends.  In a relational database
>>> the size of the friendship table would grow massively and the indexes
>>> would be out of control.
>>> It's certainly possible to make relational databases "scale".  What
>>> that is about is usually massive optimizations, manual sharding, being
>>> very clever about how you query things, and often de-normalizing.
>>> Index bloat and table bloat can thrash a relational db.
>>> In HBase, de-normalizing is usually a good thing.  Storage space is
>>> often considered free (not a large cost associated with storing
>>> something in multiple places).  In a relational database this is not
>>> so much the case.
>>> In HBase, the sharding and distribution come for free out of the box.
>>> With a relational database you must jump through hoops and often end
>>> up implementing your own sharding/distributed hashing algorithms so
>>> you can distribute across machines.
>>> Yes, you lose the relational primitives.  Sometimes you wish you could
>>> do a simple join.  But if you get in the right mindset, you learn how
>>> to put your data into this new data model.  And in the end the payoffs
>>> are huge.
>>> In addition to all this, with HBase/Hadoop you get MapReduce.  It's
>>> feasible to implement something like that on top of a distributed
>>> relational database but again the complexity is enormous.  With
>>> HBase/Hadoop it's a built-in part of the system, a system which is
>>> very intelligent at keeping logic close to data, etc.
>>> To directly answer your question, it's "simpler" to scale HBase
>>> because the scaling comes for free out of the box.  You get automatic
>>> sharding/distribution of storage and queries.  There's nothing simpler
>>> than that.  Distributing a relational database is never simple.
>>> I hope this starts to shed some light on what the differences are.
>>> Jonathan Gray
>>> Streamy Inc.
>>> -----Original Message-----
>>> From: Mork0075 [mailto:mork0075@googlemail.com] Sent: Thursday, August
>>> 21, 2008 8:48 AM
>>> To: core-user@hadoop.apache.org; hbase-user@hadoop.apache.org
>>> Subject: Re: Why is scaling HBase much simpler then scaling a
>>> relational db?
>>> Thank you, but i still don't got it.
>>> I've read tons of websites and papers, but there's no clear und
>>> founded answer "why use BigTable instead of relational databases".
>>> MySQL Cluster seams to offer the same scalabilty and level of
>>> abstraction, whithout switching to a non relational pardigm. Lots of
>>> blog posts are highly emotional, without answering the core question:
>>> "Why RDBMS don't scale and why something like BigTable do". Often you
>>> read something like this:
>>> "They have also built a system called BigTable, which is a Column
>>> Oriented Database, which splits a table into columns rather than rows
>>> making is much simpler to distribute and parallelize."
>>> Why?
>>> Really confusing ... ;)
>>> Stuart Sierra schrieb:
>>>> On Tue, Aug 19, 2008 at 9:44 AM, Mork0075 <mork0075@googlemail.com>
>>>> wrote:
>>>>> Can you please explain, why someone should use HBase for horizontal
>>>>> scaling instead of a relational database? One reason for me would be,
>>>>> that i don't have to implement the sharding logic myself. Are there
>>>>> other?
>>>> A slight tangent -- there are various tools that implement sharding
>>>> over relational databases like MySQL.  Two that I know of are
>>>> DBSlayer,
>>>> http://code.nytimes.com/projects/dbslayer
>>>> and MySQL Proxy,
>>>> http://forge.mysql.com/wiki/MySQL_Proxy
>>>> I don't know of any formal comparisons between sharding traditional
>>>> database servers and distributed databases like HBase.
>>>> -Stuart

Best regards, Edward J. Yoon

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