Hadoop
Discussion of Hadoop. Related subjects include:
Highlights of a busy news week
I put up 14 posts over the past week, so perhaps you haven’t had a chance yet to read them all. 🙂 Highlights included:
- My most important post of the week was a general guide to IT vendor strategy. That one has already spawned discussion at many companies, from the tiny to the multi-billion-dollar.
- The best comment thread of the week was probably on my post about scale-out relational OLTP choices, in which people discussed the merits of various particular alternatives.
- I recommended that people strongly consider attending XLDB 5 in Menlo Park on October 18-19.
Most of the posts, however, were reactions to news events. In particular:
- Teradata announced that Teradata 14 will be hybrid-columnar, more in Vertica’s way than in Greenplum’s or Aster Data’s. (Pay no attention to the Wall Street Journal’s apparent belief that no other analytic DBMS is hybrid-columnar at all.)
- Aster announced the unsurprising news that there will be a Teradata Aster appliance. Also, Aster talked about greater analytic flexibility in the forthcoming Aster 5.0.
- With Oracle OpenWorld coming up, Oracle decided to get some of its announcing out of the way early. In particular, it announced the Oracle Database Appliance, which is small-business-friendly hardware for running the Oracle DBMS. However, the Oracle Database Appliance doesn’t seem to do much about the complexity of running the Oracle DBMS software.
- In a catch-all Hadoop post, I noted that:
- Oracle has now clearly said it has a Hadoop appliance coming, no doubt next week at OpenWorld.
- I still can’t see why Hadoop appliances would succeed, but a lot of smart folks seem to disagree with me.
- Greenplum announced what looks like a nice but unimportant little product upgrade.
- It’s a really good thing that previously reported plans to revamp Hadoop are underway.
- DataStax announced that it really is a Cassandra company after all. Pay no attention to previous marketing that seemed to put DataStax in the same Hadoop-alternative category as, say, MapR.
- Ingres has changed its name to Actian. The announcement seems like a confession that Ingres and VectorWise are going nowhere.
Categories: Actian and Ingres, Aster Data, Data warehousing, DataStax, Greenplum, Hadoop, Teradata, VectorWise | Leave a Comment |
Some notes on Hadoop (mainly) and appliances
1. EMC Greenplum has evolved its appliance product line. As I read that, the latest announcement boils down to saying that you can neatly network together various Greenplum appliances in quarter-rack increments. If you take a quarter rack each of four different things, then Greenplum says “Hooray! Our appliance is all-in-one!” Big whoop.
2. That said, the Hadoop part of EMC ‘s story is based on MapR, which so far as I can tell is actually a pretty good Hadoop implementation. More precisely, MapR makes strong claims about performance and so on, and Apache Hadoop folks don’t reply “MapR is full of &#$!” Rather, they say “We’re going to close the gap with MapR a lot faster than the MapR folks like to think — and by the way, guys, thanks for the butt-kick.” A lot more precision about MapR may be found in this M. C. Srivas SlideShare.
3. On its latest earnings call, Oracle clearly said it would introduce a Hadoop appliance, versus just hinting at a Hadoop appliance the prior quarter. The money quote was: Read more
Categories: Data warehouse appliances, eBay, EMC, Greenplum, Hadoop, MapR, MapReduce, Open source, Oracle | 2 Comments |
Hadoop notes
I visited California recently, and chatted with numerous companies involved in Hadoop — Cloudera, Hortonworks, MapR, DataStax, Datameer, and more. I’ll defer further Hadoop technical discussions for now — my target to restart them is later this month — but that still leaves some other issues to discuss, namely adoption and partnering.
The total number of enterprises in the world paying subscription and license fees that they would regard as being for “Hadoop or something Hadoop-related” probably is not much over 100 right now, but I’d expect to see pretty rapid growth. Beyond that, let’s divide customers into three groups:
- Internet businesses.
- Traditional enterprises ‘ internet operations.
- Traditional enterprises’ other operations.
Hadoop vendors, in different mixes, claim to be doing well in all three segments. Even so, almost all use cases involve some kind of machine-generated data, with one exception being a credit card vendor crunching a large database of transaction details. Multiple kinds of machine-generated data come into play — web/network/mobile device logs, financial trade data, scientific/experimental data, and more. In particular, pharmaceutical research got some mentions, which makes sense, in that it’s one area of scientific research that actually enjoys fat for-profit research budgets.
Categories: Cloudera, Hadoop, Health care, Hortonworks, Investment research and trading, Log analysis, MapR, MapReduce, Market share and customer counts, Scientific research, Web analytics | 5 Comments |
Data management at Zynga and LinkedIn
Mike Driscoll and his Metamarkets colleagues organized a bit of a bash Thursday night. Among the many folks I chatted with were Ken Rudin of Zynga, Sam Shah of LinkedIn, and D. J. Patil, late of LinkedIn. I now know more about analytic data management at Zynga and LinkedIn, plus some bonus stuff on LinkedIn’s People You May Know application. 🙂
It’s blindingly obvious that Zynga is one of Vertica’s petabyte-scale customers, given that Zynga sends 5 TB/day of data into Vertica, and keeps that data for about a year. (Zynga may retain even more data going forward; in particular, Zynga regrets ever having thrown out the first month of data for any game it’s tried to launch.) This is game actions, for the most part, rather than log files; true logs generally go into Splunk.
I don’t know whether the missing data is completely thrown away, or just stashed on inaccessible tapes somewhere.
I found two aspects of the Zynga story particularly interesting. First, those 5 TB/day are going straight into Vertica (from, I presume, memcached/Membase/Couchbase), as Zynga decided that sending the data to some kind of log first was more trouble than it’s worth. Second, there’s Zynga’s approach to analytic database design. Highlights of that include: Read more
Categories: Aster Data, Couchbase, Data models and architecture, Games and virtual worlds, Greenplum, Hadoop, Petabyte-scale data management, Specific users, Vertica Systems, Zynga | 27 Comments |
Hadoop evolution
I wanted to learn more about Hadoop and its futures, so I talked Friday with Arun Murthy of Hortonworks.* Most of what we talked about was:
- NameNode evolution, and the related issue of file-count limitations.
- JobTracker evolution.
Arun previously addressed these issues and more in a June slide deck.
Read more
Categories: Hadoop, MapReduce, Parallelization, Workload management, Yahoo | 7 Comments |
Introduction to Zettaset
Zettaset is confusing, but as best I understand:
- Zettaset sells Hadoop add-on/enhancement software, with what might be called an enterprise-friendly Hadoop management focus.
- “Business intelligence” gets mentioned prominently in Zettaset’s marketing, but not in what executives now say. Apparently the BI focus is old news, predating a hard pivot.
- Zettaset’s marketing also mentions NoSQL, for little reason that I can discern, except insofar as Zettaset relies on HBase.
- CEO Brian Christian told me that Zettaset has been around since December, 2007; on the other hand, Zettaset press release boilerplate says Zettaset was founded in 2009. Apparently, the distinction is that Zettaset was founded in 2007 as a consultancy, but turned its efforts to software development in 2009.
- Zettaset has fewer than 20 people but is “hiring like mad.”
- Zettaset just did a $3 million Series A round — or maybe is just announcing it now; the latter interpretation might explain how those 20ish people are getting paid.
- Zettaset’s product was just launched and made generally available, notwithstanding that Version 2 of Zettaset’s product was shipped last year to fanfare on Zettaset’s blog.
- Zettaset’s pricing is based on how many terabytes of compressed data it is being used to manage.
- Until very recently, Zettaset was called GOTO Metrics; I imagine the name change is connected to the strategy pivot.
- Zettaset told me of one big customer — with an almost-petabyte Hadoop cluster before compression — namely Zions Bancorporation.
- Zettaset has “a number of paying customers” overall.
Categories: Hadoop, HBase, MapReduce, Market share and customer counts, Specific users, Zettaset | 4 Comments |
HBase is not broken
It turns out that my impression that HBase is broken was unfounded, in at least two ways. The smaller is that something wrong with the HBase/Hadoop interface or Hadoop’s HBase support cannot necessarily be said to be wrong with HBase (especially since HBase is no longer a Hadoop subproject). The bigger reason is that, according to consensus, HBase has worked pretty well since the .90 release in January of this year.
After Michael Stack of StumbleUpon beat me up for a while,* Omer Trajman of Cloudera was kind enough to walk me through HBase usage. He is informed largely by 18 Cloudera customers, plus a handful of other well-known HBase users such as Facebook, StumbleUpon, and Yahoo. Of the 18 Cloudera customers using HBase that Omer was thinking of, 15 are in HBase production, one is in HBase “early production”, one is still doing R&D in the area of HBase, and one is a classified government customer not providing such details. Read more
Categories: Cloudera, Derived data, Facebook, Hadoop, HBase, Log analysis, Market share and customer counts, Open source, Specific users, Web analytics | 6 Comments |
Hadoop futures and enhancements
Hadoop is immature technology. As such, it naturally offers much room for improvement in both industrial-strengthness and performance. And since Hadoop is booming, multiple efforts are underway to fill those gaps. For example:
- Cloudera’s proprietary code is focused on management, set-up, etc.
- The “Phase 1” plans Hortonworks shared with me for Apache Hadoop are focused on industrial-strengthness, as are significant parts of “Phase 2”.*
- MapR tells a performance story versus generic Apache Hadoop HDFS and MapReduce. (One aspect of same is just C++ vs. Java.)
- So does Hadapt, but mainly vs. Hive.
- Cloudera also tells me there’s a potential 4-5X performance improvement in Hive coming down the pike from what amounts to an optimizer rewrite.
(Zettaset belongs in the discussion too, but made an unfortunate choice of embargo date.)
Categories: Cloudera, Greenplum, Hadapt, Hadoop, HBase, MapR, MapReduce, Parallelization, Zettaset | 20 Comments |
Cloudera and Hortonworks
My clients at Cloudera have been around for a while, in effect positioned as “the Hadoop company.” Their business, in a nutshell, consists of:
- Packaging up a Cloudera distribution of Apache Hadoop. This distribution doesn’t have proprietary code; it’s just packaged by Cloudera from Apache projects (with a decent minority of the code happening to have been contributed by Cloudera engineers).
- Paid subscription support for Apache Hadoop and, in connection with that …
- … proprietary software that all support customers automatically get. There are two points to this proprietary software:
- It adds value for the customer.
- It makes Cloudera’s support job easier.
- Professional services around Hadoop.
- Training and conferences around Hadoop, which probably don’t generate all that much money, but are great marketing in terms of visibility, thought leadership, and lead generation.
Hortonworks spun out of Yahoo last week, with parts of the Cloudera business model, namely Hadoop support, training, and I guess conferences. Hortonworks emphatically rules out professional services, and says that it will contribute all code back to Apache Hadoop. Hortonworks does grudgingly admit that it might get into the proprietary software business at some point — but evidently hopes that day will never actually come.
Categories: Cloudera, Hadoop, Hortonworks, IBM and DB2, MapReduce, Open source, Yahoo | 9 Comments |
Hadapt update
I met with the Hadapt guys today. I think I can be a bit crisper than before in positioning Hadapt and its use cases, namely:
- Hadapt is additional software on a cluster that also runs fully functional Hadoop/HDFS. (Cloudera Hadoop more than straight-from-Apache Hadoop to date, but that’s not a requirement.)
- The cluster also runs a DBMS on every node, such as PostgreSQL or one of Infobright/Vectorwise.
- Hadapt’s software manages parallel SQL queries by distributing them to the DBMS living on each node. Hadapt says that the resulting query performance far outshines Hive’s.
- Hadapt further says that, by exploiting the partner DBMS, its SQL functionality outpaces Hive’s as well.
- Target Hadapt use cases are centered around keeping machine-generated or other poly-structured data in Hadoop, and extracting, enhancing, or otherwise deriving some of it to live in the relational store.
- In particular, Hadapt seems like an interesting choice when you want to use that relational data as you work on other data that’s still in HDFS, or if you want to keep using the relational data in other kinds of MapReduce jobs.
- That all fits well with my thoughts about the importance of derived data.
Other evolution from what I wrote about Hadapt a few months ago includes:
- Hadapt is in beta now.
- Hadapt has added adult supervision in the form of Philip Wickline, late of Endeca.
In other news, Hadapt is our newest client.