Theory and architecture
Analysis of design choices in databases and database management systems. Related subjects include:
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Greenplum HAWQ
My former friends at Greenplum no longer talk to me, so in particular I wasn’t briefed on Pivotal HD and Greenplum HAWQ. Pivotal HD seems to be yet another Hadoop distribution, with the idea that you use Greenplum’s management tools. Greenplum HAWQ seems to be Greenplum tied to HDFS.
The basic idea seems to be much like what I mentioned a few days ago — the low-level file store for Greenplum can now be something else one has heard of before, namely HDFS (Hadoop Distributed File System, which is also an option for, say, NuoDB). Beyond that, two interesting quotes in a Greenplum blog post are:
When a query starts up, the data is loaded out of HDFS and into the HAWQ execution engine.
and
In addition, it has native support for HBase, supporting HBase predicate pushdown, hive[sic] connectivity, and offering a ton of intelligent features to retrieve HBase data.
The first sounds like the invisible loading that Daniel Abadi wrote about last September on Hadapt’s blog. (Edit: Actually, see Daniel’s comment below.) The second sounds like a good idea that, again, would also be a natural direction for vendors such as Hadapt.
| Categories: EMC, Greenplum, Hadapt, Hadoop, HBase, SQL/Hadoop integration | 14 Comments |
One database to rule them all?
Perhaps the single toughest question in all database technology is: Which different purposes can a single data store serve well? — or to phrase it more technically — Which different usage patterns can a single data store support efficiently? Ted Codd was on multiple sides of that issue, first suggesting that relational DBMS could do everything and then averring they could not. Mike Stonebraker too has been on multiple sides, first introducing universal DBMS attempts with Postgres and Illustra/Informix, then more recently suggesting the world needs 9 or so kinds of database technology. As for me — well, I agreed with Mike both times. 🙂
Since this is MUCH too big a subject for a single blog post, what I’ll do in this one is simply race through some background material. To a first approximation, this whole discussion is mainly about data layouts — but only if we interpret that concept broadly enough to comprise:
- Every level of storage (disk, RAM, etc.).
- Indexes, aggregates and raw data alike.
To date, nobody has ever discovered a data layout that is efficient for all usage patterns. As a general rule, simpler data layouts are often faster to write, while fancier ones can boost query performance. Specific tradeoffs include, but hardly are limited to: Read more
Notes and links, February 17, 2013
1. It boggles my mind that some database technology companies still don’t view compression as a major issue. Compression directly affects storage and bandwidth usage alike — for all kinds of storage (potentially including RAM) and for all kinds of bandwidth (network, I/O, and potentially on-server).
Trading off less-than-maximal compression so as to minimize CPU impact can make sense. Having no compression at all, however, is an admission of defeat.
2. People tend to misjudge Hadoop’s development pace in either of two directions. An overly expansive view is to note that some people working on Hadoop are trying to make it be all things for all people, and to somehow imagine those goals will soon be achieved. An overly narrow view is to note an important missing feature in Hadoop, and think there’s a big business to be made out of offering it alone.
At this point, I’d guess that Cloudera and Hortonworks have 500ish employees combined, many of whom are engineers. That allows for a low double-digit number of 5+ person engineering teams, along with a number of smaller projects. The most urgently needed features are indeed being built. On the other hand, a complete monument to computing will not soon emerge.
3. Schooner’s acquisition by SanDisk has led to the discontinuation of Schooner’s SQL DBMS SchoonerSQL. Schooner’s flash-optimized key-value store Membrain continues. I don’t have details, but the Membrain web page suggests both data store and cache use cases.
4. There’s considerable personnel movement at Boston-area database technology companies right now. Please ping me directly if you care.
It’s hard to make data easy to analyze
It’s hard to make data easy to analyze. While everybody seems to realize this — a few marketeers perhaps aside — some remarks might be useful even so.
Many different technologies purport to make data easy, or easier, to an analyze; so many, in fact, that cataloguing them all is forbiddingly hard. Major claims, and some technologies that make them, include:
- “We get data into a form in which it can be analyzed.” This is the story behind, among others:
- Most of the data integration and ETL (Extract/Transform/Load) industries, software vendors and consulting firms alike.
- Many things that purport to be “analytic applications” or data warehouse “quick starts”.
- “Data reduction” use cases in event processing.*
- Text analytics tools.
- Splunk.
- “Forget all that transformation foofarah — just load (or write) data into our thing and start analyzing it immediately.” This at various times has been much of the story behind:
- Relational DBMS, according to their inventor E. F. Codd.
- MOLAP (Multidimensional OnLine Analytic Processing), also according to RDBMS inventor E. F. Codd.
- Any kind of analytic DBMS, or general purpose DBMS used for data warehousing.
- Newer kinds of analytic DBMS that are faster than older kinds.
- The “data mart spin-out” feature of certain analytic DBMS.
- In-memory analytic data stores.
- Hadoop.
- NoSQL DBMS that have a few analytic features.
- TokuDB, similarly.
- Electronic spreadsheets, from VisiCalc to Datameer.
- Splunk.
- “Our tools help you with specific kinds of analyses or analytic displays.” This is the story underlying, among others:
- The business intelligence industry.
- The predictive analytics industry.
- Algorithmic trading use cases in complex event processing.*
- Some analytic applications.
- Splunk.
*Complex event/stream processing terminology is always problematic.
My thoughts on all this start: Read more
Key questions when selecting an analytic RDBMS
I recently complained that the Gartner Magic Quadrant for Data Warehouse DBMS conflates many use cases into one set of rankings. So perhaps now would be a good time to offer some thoughts on how to tell use cases apart. Assuming you know that you really want to manage your analytic database with a relational DBMS, the first questions you ask yourself could be:
- How big is your database? How big is your budget?
- How do you feel about appliances?
- How do you feel about the cloud?
- What are the size and shape of your workload?
- How fresh does the data need to be?
Let’s drill down. Read more
Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — evaluations
To my taste, the most glaring mis-rankings in the 2012/2013 Gartner Magic Quadrant for Data Warehouse Database Management are that it is too positive on Kognitio and too negative on Infobright. Secondarily, it is too negative on HP Vertica, and too positive on ParAccel and Actian/VectorWise. So let’s consider those vendors first.
Gartner seems confused about Kognitio’s products and history alike.
- Gartner calls Kognitio an “in-memory” DBMS, which is not accurate.
- Gartner doesn’t remark on Kognitio’s worst-in-class* compression.
- Gartner gives Kognitio oddly high marks for a late, me-too Hadoop integration strategy.
- Gartner writes as if Kognitio’s next attempt at the US market will be the first one, which is not the case.
- Gartner says that Kognitio pioneered data warehouse SaaS (Software as a Service), which actually has existed since the pre-relational 1970s.
Gartner is correct, however, to note that Kognitio doesn’t sell much stuff overall.
* non-existent
In the cases of HP Vertica, Infobright, ParAccel, and Actian/VectorWise, the 2012 Gartner Magic Quadrant for Data Warehouse Database Management’s facts are fairly accurate, but I dispute Gartner’s evaluation. When it comes to Vertica: Read more
Comments on Gartner’s 2012 Magic Quadrant for Data Warehouse Database Management Systems — concepts
The 2012 Gartner Magic Quadrant for Data Warehouse Database Management Systems is out. I’ll split my comments into two posts — this one on concepts, and a companion on specific vendor evaluations.
Links:
- Maintaining working links to Gartner Magic Quadrants is an adventure. But as of early February, 2013, this link seems live.
- I also commented on the 2011, 2010, 2009, 2008, 2007, and 2006 Gartner Magic Quadrants for Data Warehouse DBMS.
Let’s start by again noting that I regard Gartner Magic Quadrants as a bad use of good research. On the facts:
- Gartner collects a lot of input from traditional enterprises. I envy that resource.
- Gartner also does a good job of rounding up vendor claims about user base sizes and the like. If nothing else, you should skim the MQ report for that reason.
- Gartner observations about product feature sets are usually correct, although not so consistently that they should be relied on.
When it comes to evaluations, however, the Gartner Data Warehouse DBMS Magic Quadrant doesn’t do as well. My concerns (which overlap) start:
- The Gartner MQ conflates many different use cases into one ranking (inevitable in this kind of work, but still regrettable).
- A number of the MQ vendor evaluations seem hard to defend. So do some of Gartner’s specific comments.
- Some of Gartner’s criteria seemingly amount to “parrots back our opinions to us”.
- As do I, Gartner thinks a vendor’s business and financial strength are important. But Gartner overdoes the matter, drilling down into picky issues it can’t hope to judge, such as assessing a vendor’s “ability to generate and develop leads.” *
- The 2012 Gartner Data Warehouse DBMS Magic Quadrant is closer to being a 1-dimensional ranking than 2-dimensional, in that entries are clustered along the line x=y. This suggests strong correlation among the results on various specific evaluation criteria.
| Categories: Data integration and middleware, Data warehousing, Database compression, Emulation, transparency, portability, Hadoop, Market share and customer counts, Oracle, Text | 5 Comments |
Attack of the Frankenschemas
In typical debates, the extremists on both sides are wrong. “SQL vs. NoSQL” is an example of that rule. For many traditional categories of database or application, it is reasonable to say:
- Relational databases are usually still a good default assumption …
- … but increasingly often, the default should be overridden with a more useful alternative.
Reasons to abandon SQL in any given area usually start:
- Creating a traditional relational schema is possible …
- … but it’s tedious or difficult …
- … especially since schema design is supposed to be done before you start coding.
Some would further say that NoSQL is cheaper, scales better, is cooler or whatever, but given the range of NewSQL alternatives, those claims are often overstated.
Sectors where these reasons kick in include but are not limited to: Read more
| Categories: Health care, Investment research and trading, Log analysis, NewSQL, NoSQL, Web analytics | 8 Comments |
YCSB benchmark notes
Two different vendors recently tried to inflict benchmarks on me. Both were YCSBs, so I decided to look up what the YCSB (Yahoo! Cloud Serving Benchmark) actually is. It turns out that the YCSB:
- Was developed by — you guessed it! — Yahoo.
- Is meant to simulate workloads that fetch web pages, including the writing portions of those workloads.
- Was developed with NoSQL data managers in mind.
- Bakes in one kind of sensitivity analysis — latency vs. throughput.
- Is implemented in extensible open source code.
That actually sounds pretty good, especially the extensibility part;* it’s likely that the YCSB can be useful in a variety of product selection scenarios. Still, as recent examples show, benchmark marketing is an annoying blight upon the database industry.
*With extensibility you can test your own workloads and do your own sensitivity analyses.
A YCSB overview page features links both to the code and to the original explanatory paper. The clearest explanation of the YCSB I found there was: Read more
| Categories: Aerospike, Benchmarks and POCs, NewSQL, NoSQL, NuoDB, OLTP, Yahoo | 19 Comments |
NuoDB marketing mishegas
I must start by apologizing for giving a quote in a press release whose contents I deplore. Unlike occasions on which I’ve posted about inaccurate quotes, in this case the fault is mine. The quote is quite accurate. And NuoDB didn’t mislead me about the release’s contents; I just neglected to ask.
NuoDB evidently subscribes to the marketing fallacy:
- Big DBMS companies hit people repeatedly with marketing cudgels.
- We want to be a big DBMS company.
- Therefore we will hit people repeatedly with marketing cudgels too.
But to my taste, NuoDB’s worst travesty is not the deafening drumroll before launch (I asked off their mailing list months before), nor the claim that NuoDB’s launch would be a “big day” for the database industry (annoying but ordinary hype), nor the emergent flock of birds foofarah, nor even NuoDB’s overwrought benchmark marketing (distressingly many vendors do that).
Rather, I think NuoDB’s greatest marketing offense to date is its Codd-imitating “12 rules” for cloud database management. Read more
| Categories: Cloud computing, Clustering, NuoDB, Theory and architecture | 9 Comments |