Notes, links and comments August 6, 2012
I haven’t done a notes/link/comments post for a while. Time for a little catch-up.
1. MySQL now has a memcached integration story. I haven’t checked the details. The MySQL team is pretty hard to talk with, due to the heavy-handedness of Oracle’s analyst relations.
2. The Large Hadron Collider offers some serious numbers, including:
- 1 petabyte/second.
- 6 x 109 collisions/second.
- Only 1 in 1013 collision records kept (which I guess knocks things down to a 100 byte/second average, from the standpoint of persistent storage).
- Real-time filtering by a cluster of several thousand machines, over a 25 nanosecond period.
3. One application area we don’t talk about much for analytic technologies is education. However: Read more
| Categories: Cache, memcached, Memory-centric data management, MySQL, Open source, Petabyte-scale data management, RDF and graphs, Scientific research | Leave a Comment |
People’s facility with statistics — extremely difficult to predict
My recent post on broadening the usefulness of statistics presupposed two things about the statistical sophistication of business intelligence tool users:
- It varies a lot.
- In many cases, it isn’t be very high.
Let me now say a little more on the subject. My basic message is — people’s facility with statistics is extremely difficult to predict.
If you DO have to make a point estimate, however, you could do worse than just putting quotation marks around the last four words of that sentence …
Suppose we measure people’s statistical understanding on a 5-point scale:
- People who haven’t clue what a p-value is.
- People who think a p-value of .05 signifies a 95% chance of truth.
- People who know better than that, but who still think that “statistically significant” is pretty close to the same as “true”.
- People who know better yet, but aren’t fluent in using statistical techniques correctly.
- People who are fluent in statistics.
Just knowing somebody’s job description, can you confidently predict their ranking to within, say, +/- 1 point? I suggest you can’t. People differ wildly in general numeracy and in specific statistical knowledge.
Even our guesses about average knowledge may be off, not least because education is changing things. Read more
Integrating statistical analysis into business intelligence
Business intelligence tools have been around for two decades.* In that time, many people have had the idea of integrating statistical analysis into classical BI. Yet I can’t think of a single example that was more than a small, niche success.
*Or four decades, if you count predecessor technologies.
The first challenge, I think, lies in the paradigm. Three choices that come to mind are:
- “I think I may have discovered a new trend or pattern. Tell me, Mr. BI — is the pattern real?”
- “I think I see a trend. Help me, Mr. BI, to launch a statistical analysis.”
- “If you want to do some statistical analysis, here’s a link to fire it up.”
But the first of those approaches requires too much intelligence from the software, while the third requires too much numeracy from the users. So only the second option has a reasonable chance to work, and even that one may be hard to pull off unless vendors focus on one vertical market at a time.
The challenges in full automation start: Read more
DBMS2.com is back up!
After several hours of DBMS 2 being down, I put out a “We’re broken” note from another blog. Naturally, the next fix I tried seems to have worked. My joy in that far outweighs my embarrassment. 🙂 This kind of thing just happens once in a while when one has business-critical software that isn’t good at having a test-to-production staging cycle.
In case anybody ever runs into the same problems, the short form of the story is:
1. DBMS2.com came down due to a corrupted automatic upgrade of WordPress.
2. The fix was to do an automatic install of WordPress to a dummy domain, then copy over the files in the domain’s root and wp-includes directories.
3. The one file that needed to be copied back from the old installation was wp-configure. (Once it occurred to me to start reading from index.php, it took me about 1 minute to figure that out …)
| Categories: About this blog | 1 Comment |
Some Vertica 6 features
Vertica 6 was recently announced, and so it seemed like a good time to catch up on Vertica features. The main topics I want to address are:
- External tables and the associated new Hadoop connector.
- Online schema evolution.
- Workload management.
Also:
- I have some tidbits to add to my June, 2011 coverage of Vertica’s analytic functionality.
- I’ll stand for now on my previous coverage of Vertica’s database organization.
In general, the main themes of Vertica 6 appear to be:
- Enterprise/SaaS-friendliness, high uptime, and so on.
- Improved analytic usefulness.
Let’s do the analytic functionality first. Notes on that include:
- Vertica has extended its user-defined function/analytic procedure/whatever functionality to include user-defined load. (Same SDK, different specific classes.)
- One of the languages Vertica supports is R. But for now, parallel R is limited to “Of course, you can run the same functions and procedures on many nodes at once.”
- Based on community activity around bugs and so on, it seems there are users for Vertica’s JSON-based Twitter sentiment analysis plug-in.
I’ll also take this opportunity to expand on something I wrote about a few vendors — including Vertica — at the end of my post on approximate query results. When I probed how customers of Vertica and other RDBMS-based analytic platform vendors used vendor-proprietary advanced analytic SQL and other analytic capabilities, answers included: Read more
SQL Server to MySQL migration — why?
Oracle wants you to help you migrate from Microsoft SQL Server to MySQL. I was asked for comment, and replied:
- There are many SQL Server/Windows uses for which MySQL/Linux would do just as well. (Edit: But see the comments below.)
- However, I’m not sure in how many cases it would be worth the trouble of migration.
- Many Microsoft users have adopted a thick Windows-based stack. MySQL migration doesn’t address them.
- At the other extreme, if your application is really trivial, why bother moving?
- A few Seattle-area internet companies may have adopted SQL Server and now be wondering why. For them, this offer could be appealing.
Am I missing anything?
| Categories: Microsoft and SQL*Server, Mid-range, MySQL | 12 Comments |
Thoughts on the next releases of Oracle and Exadata
A reporter asked me to speculate about the next releases of Oracle and Exadata. He and I agreed:
- It seems likely that they’ll be discussed at Oracle OpenWorld in a couple of months.
- Exadata in particular is due for a hardware refresh.
- Oracle12c is a good guess at a name, where “C” is for “Cloud”.
My answers mixed together thoughts on what Oracle should and will emphasize (which aren’t the same thing but hopefully bear some relationship to each other ;)). They were (lightly edited):
- The worst thing about Oracle is the ongoing DBA work for what should be automatic.
- Oracle RAC still makes scale-out too difficult. Presumably, Oracle is looking to build aggressively on recent steps in automating parallelism.
- For Exadata, assume that Oracle is always looking to improve how data gets allocated among disk, flash, and RAM. Look also for Exadata versions with different silicon-disk ratios than are available now.
- Tighter integration among the various appliances is surely a goal, …
- … but I don’t know whether Oracle will pick them apart and let you put various kinds of hardware in the same racks or not. I’d guess against that, because the current set-up gives them a pretext to sell you more capacity than you need.
- I wonder whether Oracle will finally introduce a true columnar storage option, a year behind Teradata. That would be the obvious enhancement on the data warehousing side, if they can pull it off. If they can’t, it’s a damning commentary on the core Oracle codebase.
- Probably Oracle will have something that it portrays as good multi-tenancy support. Some of that could be based on Label Security and so on.
- Anything that makes schema change easier could be a win on the DBA and multi-tenancy sides alike, which would be a nice two-fer.
| Categories: Clustering, Columnar database management, Data warehouse appliances, Data warehousing, Exadata, Oracle, Teradata | 7 Comments |
The eternal bogosity of performance marketing
Chris Kanaracus uncovered a case of Oracle actually pulling an ad after having been found “guilty” of false advertising. The essence seems to be that Oracle claimed 20X hardware performance vs. IBM, based on a comparison done against 6 year old hardware running an earlier version of the Oracle DBMS. My quotes in the article were:
- “Everybody’s guilty of that kind of exaggeration.”
- “Oracle tends to be even a little guiltier than others.”
- “If your new system can’t outperform somebody else’s old system by a huge factor on at least some queries, you’re doing something wrong.”
- “Use newer, better hardware; use newer, better software; have a top sales engineer do a great job of tuning it and of course you’ll see huge performance results.”
Another example of Oracle exaggeration was around the Exadata replacement of Teradata at Softbank. But the bogosity flows both ways. Netezza used to make a flat claim of 50X better performance than Oracle, while Vertica’s standard press release boilerplate long boasted
50x-1000x faster performance at 30% the cost of traditional solutions
Of course, reality is a lot more complicated. Even if you assume apples-to-apples comparisons in terms of hardware and software versions, performance comparisons can vary greatly depending upon queries, databases, or use cases. For example:
- Many queries are inherently much faster over columnar storage than over row-based.
- Different data sets respond very differently to various compression algorithms.
- Some analytic RDBMS can maintain strong performance at high levels of concurrent usage. Some can’t.
- Some queries that run very fast on one DBMS without tuning might require careful tuning in another system.
- Some DBMS scale out much better than others.
- Vendors optimize for different usage assumptions, which may or may not apply in your particular case.
And so, vendor marketing claims about across-the-board performance should be viewed with the utmost of suspicion.
Related links
| Categories: Columnar database management, Data warehouse appliances, Data warehousing, Database compression, Exadata, Netezza, Oracle, Vertica Systems | Leave a Comment |
Notes on Datameer
In a short October, 2011 post about Datameer, I wrote:
Datameer is designed to let you do simple stuff on large amounts of data, where “large amounts of data” typically means data in Hadoop, and “simple stuff” includes basic versions of a spreadsheet, of BI, and of EtL (Extract/Transform/Load, without much in the way of T).
That’s all still mainly true, although with the recent Datameer 2.0:
- You can run Datameer and the underlying Hadoop on a desktop or workgroup group.
- There are some infographics pretty-picture-drawing capabilities, which will surely delight those who like vector-based HTML 5 pictures of coffee cups, saucers and macaroons.
- No doubt Datameer has been generally enhanced on multiple fronts.
In essence, Datameer has two positionings.
- One is “OK, you’ve got Hadoop — now wouldn’t you like to do something useful with it?” That can include both business intelligence and ETL.
- Beyond that, Datameer founder/CEO Stefan Groschupf’s core argument is that schema-on-read is really, really useful, even at the cost of absorbing a potentially large performance hit. In other words, he’s making a case for a form of non-relational BI.
| Categories: Business intelligence, Data models and architecture, Datameer, EAI, EII, ETL, ELT, ETLT, Hadoop, Log analysis, Market share and customer counts, Web analytics | 8 Comments |
Hadoop YARN — beyond MapReduce
A lot of confusion seems to have built around the facts:
- Hadoop MapReduce is being opened up into something called MapReduce 2 (MRv2).
- Something called YARN (Yet Another Resource Negotiator) is involved.
- One purpose of the whole thing is to make MapReduce not be required for Hadoop.
- MPI (Message Passing Interface) was mentioned as a paradigmatic example of a MapReduce alternative, yet the MPI/YARN/Hadoop effort is somehow troubled.
- Cloudera shipped YARN in June, yet simultaneously warned people away from actually using it.
Here’s my best effort to make sense of all that, helped by a number of conversations with various Hadoop companies, but most importantly a chat Friday with Arun Murthy and other Hortonworks folks.
- YARN, as an aspect of Hadoop, has two major kinds of benefits:
- The ability to use programming frameworks other than MapReduce.
- Scalability, no matter what programming framework you use.
- The YARN availability story goes:
- YARN is in alpha.
- YARN is expected to be in production at year-end, give or take.
- Cloudera made the marketing decision to include YARN in its June Hadoop distribution release anyway, but advised that it was for experimentation rather than production.
- Hortonworks, in its own June release, only shipped code it advised putting into production.
- My take on the YARN/MPI story goes something like this:
- Numerous people have told me of YARN/MPI delays.
- One person suggested that Greenplum is taking the lead in YARN/MPI integration, but has gotten slow and reclusive, apparently due to some big company-itis.
- I find that credible because of the Greenplum/SAS/MPI connection.
- If I understood Arun correctly, the latency story on Hadoop MapReduce is approximately:
- Arun says that Hadoop’s reputation for taking 10s of seconds to start a Hadoop job is old news. It takes a low single-digit number of seconds.
- However, starting all that Java does take 100s of milliseconds at best — 200 milliseconds in an ideal case, 500 milliseconds more realistically, and that’s just on a single server.
- Thus, if you want human real-time interaction, Hadoop MapReduce is not and likely never will be the way to go. Getting Hadoop MapReduce latencies under a few seconds is likely to be more trouble than it’s worth — because of MapReduce, not because of Hadoop.
- In particular — instead of incurring the overhead of starting processes up, Arun thinks low-latency needs should be met in a different way, namely by serving them from already-running processes. The examples he kept mentioning were the event processing projects Storm (out of Twitter, via an acquisition) and S4 (out of Yahoo).
| Categories: Cloudera, Hadoop, Hortonworks, MapReduce, Workload management, Yahoo | 7 Comments |