Warning [Rant]: YAML is an incredible piece of turd

I spent, hay wasted, an hour of my time today trying to figure out the reason for the following error message from Puppet Hiera:

vmhost05-hbase3: Error: syntax error on line 30, col -1: `’ at /root/bigtop/bigtop-deploy/puppet/manifests/site.pp:17 on node ….
The relevant part of the Hiera site.yaml file is

bigtop::bigtop_yumrepo_uri:  “http://archive.hostname.com/redhat/6/x86_64/7.3.0/”
bigtop::jdk_package_name: ‘jdk-1.7.0_55’

Firstly, as a former compiler developer it hurts every bit of my brain when I see error message like above. Huge “compliment” to the Hiera developers – learn how to write code, dammit.

Secondly, after investigating this literally for an hour I figured out that the separator in uri:  “http was a TAB (ASCII 9) instead of a whitespaces.

Seriously dudes – it’s 21st century. What’s the reason to use formats and parsers that fail so badly on separator terminals? Just imaging if Java or Groovy compiler would be so picky about tabs vs. spaces? I guarantee – the half of the development community would be screaming bloody murder right there. Yet – with frigging YAML POS it is just ok ;(



Hadoop genealogy simplified

I have decided to simplify the elephant genealogy tree by separating pre-Hadoop 2.x part out of it. The new supported version will only be reflecting Hadoop 2.x. The last updated full version of the diagram is available for anyone from my github workspace under the tag WDD4

Annual review of Bigdata software; what’s in store for 2014

In the couple of days left before the year end I wanted to look back and reflect on what has happened so far in the IT bubble 2.0 commonly referred to as “BigData”. Here are some of my musings.

Let’s start with this simple statement: BigData is misnomer. Most likely it has been put forward by some PR or MBA schmuck with no imagination whatsoever, who thought that terabyte consists of 1000 megabytes 😉 The word has been picked up by pointy-haired bosses all around the world as they need buzzwords to justify their existence to people around. But I digressed…

So what has happened in the last 12 months in this segment of software development? Well, surprisingly you can count real interesting events on one hand. To name a few:

  • Fault tolerance in the distributed systems got to the new level with NonStop Hadoop, introduced by WANdisco earlier this year. The idea of avoiding complex screw-ups by agreeing on the operations up-front is leaving things like Linux HA, Hadoop QJM, and NFS based solutions rolling in the dust in the rear-view mirror.
  • Hadoop HDFS is clearly here to stay: you can see customers shifting from platforms like Teradata towards cheaper and widely supported HDFS network storage; with EMC (VMWare, Greenplum, etc.) offering it as the storage layer under Greenplum’s proprietary PostegSQL cluster and many others.
  • While enjoying a huge head start, HDFS has a strong while not very obvious competitor – CEPH. As some know, there’s a patch that provides CEPH drop-in replacement for HDFS. But where it get real interesting is how systems like Spark (see next paragraph) can work directly on top of CEPH file-system with a relatively small changes in the code. Just picture it:

    distributed Linux file-system high-speed data analytic 

    Drawing conclusions is left as an exercise to the readers.

  • With the recent advent and fast rise of new in memory analytic platform – Apache Spark (incubating) – the traditional, two bit, MapReduce paradigm is loosing the grasp very quickly. The gap is getting wider with new generation of the task and resource schedulers gaining momentum by the day: Mesos, Spark standalone scheduler, Sparrow. The latter is especially interesting with its 5ms scheduling guarantees. That leaves the latest reincarnation of the MR in the predicament.
  • Shark – SQL layer on top of Spark – is winning the day in the BI world, as you can see it gaining more popularity. It seems to have nowhere to go but up, as things like Impala, Tez, ASF Drill are still very far away from being accepted in the data-centers.
  • With all above it is very exciting to see my good friends from AMPlab spinning up a new company that will be focusing on the core platform of Spark, Shark and all things related. All best wishes to Databricks in the coming year!
  • Speaking of BI, it is interesting to see that Bigdata BI and BA companies are still trying to prove their business model and make it self-sustainable. The case in point, Datameer with recent $19M D-round; Platfora’s last year $20M B-round, etc. I reckon we’ll see more fund-raisers in the 107 or perhaps 108 of dollars in the coming year among the application companies and platform ones. Also new letters will be added to the mix: F-rounds, G-rounds, etc. as cheap currency keeps finding its way from the Fed through the financial sector to the pockets of VCs and further down to high-risk sectors like IT and software development. This will lead to over-heated job market in the Silicon Valley and elsewhere followed by a blow-up similar to but bigger than 2000-2001. It will be particularly fascinating to watch big companies scavenging the pieces after the explosion. So duck to avoid shrapnel.
  • Stack integration and validation has became a pain-point for many. And I see the effects of it in shark uptake of the interest and growth of Apache Bigtop community. Which is no surprise, considering that all commercial distributions of Hadoop today are based or directly using Bigtop as the stack producing framework.

While I don’t have a crystal ball (would be handy sometimes) I think a couple of very strong trends are emerging in this segment of the technology:

  • HDFS availability – and software stack availability in general – is a big deal: with more and more companies adding HDFS layer into their storage stack more strict SLAs will emerge. And I am not talking about 5 nines – an equivalent of 5 minutes downtime per year – but rather about 6 and 7 nines. I think Zookeeper based solutions are in for a rough ride.
  • Machine Learning has a huge momentum. Spark summit was a one big evidence of it. With this comes the need to incredibly fast scheduling and hardware utilization. Hence things like Mesos, Spark standalone and Sparrow are going to keep gaining the momentum.
  • Seasonal lemming-like migration to the cloud will continue, I am afraid. The security will become a red-hot issue and an investment opportunity. However, anyone who values their data is unlikely to move to the public cloud, hence – private platforms like OpenStack might be on the rise (if the providers can deal with “design by committee” issues of course).
  • Storage and analytic stack deployment and orchestration will be more pressing than ever (no, I am talking about real orchestration, not cluster management software). That’s why I am looking very closely on that companies like Reactor8 are doing in this space.

So, last year brought a lot of excitement and interesting challenges. 2014, I am sure, will be even more fun. However “living in the interesting times” might a curse and a blessing. Stay safe, my friends!

High Availability is the past; Continuous Availability is the future

Do you know what are SiliconAngle and Wikibon project? If not – check them out soon. These guys have a vision about next generation media coverage. I would call it ‘#1 no-BS Silicon Valley media channel’. These guys are running professional video journalism with a very smart technical setup. And they aren’t your typical loudmouth from the TV: they use and grok technologies they are covering. Say, they run Apache Solr in house for real-time trends processing and searches. Amazing. And they don’t have teleprompters. Nor screenplay writers. How cool is that?

At any rate, I was invited on their show, theCube, last week at the last day of Hadoop Summit. I was talking about High Availability issues in Hadoop. Yup, High Availability has issues, you’ve heard me right. The issue is the lesser than 100% uptime. Basically, even if someone claims to provide 5-9s (that is 99.999% uptime) you still looking at about 6 minutes a year downtime of the mission critical infrastructure.

If you need 100% uptime for you Hadoop, then you should be looking for Continuous Availability. Curiously enough, the solution is found in the past (isn’t that always the case?) in so called Paxos algorithm that has been published by Leslie Lamport back in 1989. However, original Paxos algorithm has some performance issues and generally never been fully embraced by the industry and it is rarely used besides of just a few tech savvy companies. One of them – WANdisco – has applied it first for Subversion replication and now for Hadoop HDFS SPOF problem and made it generally available is the commercial product.

And just think what can be done if the same technology is applied to mission critical analytical platforms such as AMPlab Spark? Anyway, watch the recording of my interview on theCube and learn more.

YDN has posted the video from my Aug’12 talk about Hadoop distros

As the follow up on my last year post I just found the the video of the talk has been posted on YDN website. I apologies for the audio quality – echo and all, but you still should be able to make it out with a higher volume.

And in a bit you should be able to see another talk from May’13 about Hadoop stabilization.

We just invented a new game: "Whack a Hadoop namenode"

I just came back from Strata 2013 BigData conference. A pretty interesting event, considering that Hadoop wars are apparently over. It doesn’t mean that the battlefield is calm. On the contrary!

But this year’s war banner is different. Now it seems to be about Hadoop stack distributions. If I only had an artistic talent, the famous

would be saying something like “Check out how big is my Hadoop distro!”

But judge for yourself: WANdisco announced their WDD about 4 weeks ago, followed yesterday by Intel and Greenplum press releases. WDD has some uniquely cool stuff in it like non-stop namenode, which is the only ‘active-active’ technology for Namenode metadata replication on the market based on full implementation of Paxos algorithm,

And I was having fun during the conference too: we were playing the game ‘whack-a-namenode’. The setup includes a rack of blade Supermicro servers, running WDD cluster with three active namenodes.
While running stock TeraSort load, one of the namenode is killed dead with SIGKILL. Amazingly, TeraSort can’t care less and just keep going without a wince. We played about a 100 rounds of this “game” over the course of two days using live product, with people dropping by all the time to watch.

Looks like it isn’t easy to whack an HDFS cluster anymore.

And nice folks from SiliconAngle and WikiBon stopped at our booth to do the interview with me and my colleagues. Enjoy 😉