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02:23 Insider’s Guide to ODA Performance (400 Bytes) » The Pythian Blog

I have a confession to make: I hate webinars. I find it difficult to concentrate on a disembodies voice. I typically get distracted and find myself checking email and blogs even during the best webinars. Watching a webinar is a bit like watching DVD of a live show – not as fun as live show, [...]

23:06 Debugging IN vs OR performance in MySQL (371 Bytes) » The Pythian Blog

I was recently puzzled by the question, “Which query will be faster?”: SELECT * FROM table WHERE pk = x OR pk = x1 OR pk = x2 ... Or SELECT * FROM table WHERE pk IN (x1, x2,...); There are 50k values in both IN and OR clauses and lookup is done via primary [...]

22:59 Possible Hadoop Trajectories (2547 Bytes) » myNoSQL

According to Michael Stonebraker and Jeremy Kepner the future of Hadoop is doomed:

Computational space	Data Management
Adopt Hadoop for pilot projects	Adopt Hadoop for pilot projects
Scale Hadoop to production use	Scale Hadoop to production use
Hit the wall, as the above problems become big issues	Observer an unacceptable performance penalty
Morph to something that deals with our issues	Morph to real parallel DBMS

Let me see if I get this right: you take 2 problem spaces, you generalize these to complete fields, try to use Hadoop, identify the mismatch but still go in production, ignore the solutions built on top of Hadoop/HDFS to address these problem spaces (Apache Hama or Twister) , then conclude by scientific generalization that these problems apply to everyone else, thus Hadoop is dead.

What’s wrong with all these companies using Hadoop for solving their problems? A bunch of stubborn people.

Original title and link: Possible Hadoop Trajectories (NoSQL database©myNoSQL)

21:53 Test-driving Reflex (384 Bytes) » The Pythian Blog

At $work we have a need for a little job daemon that would poll jobs and process them. If there was only one kind of job involved, the solution could be nothing more complicated than while ( my @jobs = poll_jobs() ) { process( $_ ) for @jobs; sleep $a_wee_bit; } But there are more [...]

16:29 持续学习 (4096 Bytes) » DBA Notes

by Fenng@dbanotes.net

知乎上有人说起「科班出身」这个话题，我大致写了一个回复。其实也是前几天我和前同事们分享提到的观点。很多人认为「科班出身」更加专业，而有些野路子半路出家也能做差不多的事情来，于是大家都疑惑，真的是这些人天赋异禀？

以计算机技术来说，大学本科学习的时间，不过四年而已，如果投入工作后，不能持续学习不能持续实践不能开拓思维的话，那么他的专业背景很可能停留在大学毕业那一刻而不再增长。而有些非科班的人，尽管起步阶段的积累不如科班的多，但他可能持续数年依然在学习实践、不停的开拓智域，那么你说，学了四年的人能和学了十年的人相比么？

如果读过《异类》这本书中，应该会对其中提到的「一万小时定律」，要成为某个领域的专家，需要一万小时的训练。大意也是如此。你想尽快成为众人仰慕的牛人，那么只有每天花更多的时间，下更大的功夫。那些牛人也不是一夜之间冒出来的，都是数年积累才可厚积薄发。就拿做产品来说，国内被人津津乐道的人物中，无论是搜索时代的俞军还是移动互联网时代的张小龙，最大的特点就是都够勤奋，肯下功夫。

无他，持续学习尔。跟是否科班没什么关系。只是这个环境中有耐心有恒心的人越来越少了。

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15:00 Log Buffer #272, A Carnival of the Vanities for DBAs (419 Bytes) » The Pythian Blog

It is evident and beyond doubt now that the new media technologies like Twitter and Facebook are not going to wipe-out the blogs, rather they are complimenting each other very nicely and it seems they were made for each other. This Log Buffer Edition enhances this match, and presents you Log Buffer #272. Oracle: It [...]

10:41 Tools that make your work with Oracle VM easier (434 Bytes) » The Pythian Blog

After completing your Oracle VM and Oracle VM Manager installation (see my previous blog posts here) you are ready to start your friendship with Oracle VM technology. However to make your life and experience even more enjoyable I would suggest you to follow a few simple steps listed bellow. Configure Public Oracle YUM and install [...]

05:35 Big Data: Transactions Plus Interactions Plus Observations (1596 Bytes) » myNoSQL

Big Data: Transactions Plus Interactions Plus Observations:

A Hortonworks post listing the 7 key drivers for the Big Data market from the business, technical, and financial perspective:

Original title and link: Big Data: Transactions Plus Interactions Plus Observations (NoSQL database©myNoSQL)

04:16 HBase 0.94 Released: What's New (1840 Bytes) » myNoSQL

With over 350 enhancements and bug fixes, 0.94 is the new major release of HBase. This Cloudera blog post does a good summary of the most interesting improvements:

Read caching improvements
Seek optimizations
WAL writes optimizations
added functionality to HBck: fixing orphaned regions, region holes, overlapping regions
simplified region sizing
atomic Put & Delete in a single transaction

Original title and link: HBase 0.94 Released: What’s New (NoSQL database©myNoSQL)

03:52 Hadoop Weaknesses and Where Teradata Aster Sees the Big Data Money (3184 Bytes) » myNoSQL

Hadoop Weaknesses and Where Teradata Aster Sees the Big Data Money:

An interesting post on Teradata Aster blog which is indirectly emphasizing the weaknesses of the Hadoop platform:

Make platform and tools to be easier to use to manage and curate data. Otherwise, garbage in = garbage out, and you will get garbage analytics.

Provide rich analytics functions out of the box. Each line of programming cuts your reachable audience by 50%.

Provide tools to update or delete data. Otherwise, data consistency will drift away from truth as history accumulates.

Provide applications to leverage data and find answers relevant to business. Otherwise the cost of DIY applications is too high to influence business – and won’t be done.

It’s difficult to argue against these points, but they are not insurmountable. I’d even say that once the operational complexity of Hadoop deployments will get simpler—I think the Apache community, Cloudera, and Hortonworks are already working on these aspects—, Hadoop will see even more adoption and with that contributions addressing points 2 to 4 will follow shortly.

Yet another interesting part of the post is the two “equations” describing the two environments:

big clusters = big administration = big programs = big friction = low influence (Hadoop)
big data = small clusters = easy administration = big analytics = big influence (ideal/Teradata Aster)

I think these are revealing how Teradata Aster is positioning their solutions and where they see themselves making money in the Big Data market. It goes like this: “we can make a lot of money if we offer a platform with lower complexity and operational costs and higher productivity leading to better business results”. This is a sound strategy and the competitors from the Hadoop space should better focus on these same aspects which are essential to wide adoption.