Columnar database management
Analysis of products and issues in column-oriented database management systems. Related subjects include:
Some stuff I’m working on
1. I have some posts up on Strategic Messaging. The most recent are overviews of messaging, pricing, and positioning.
2. Numerous vendors are blending SQL and JSON management in their short-request DBMS. It will take some more work for me to have a strong opinion about the merits/demerits of various alternatives.
The default implementation — one example would be Clustrix’s — is to stick the JSON into something like a BLOB/CLOB field (Binary/Character Large Object), index on individual values, and treat those indexes just like any others for the purpose of SQL statements. Drawbacks include:
- You have to store or retrieve the JSON in whole documents at a time.
- If you are spectacularly careless, you could write JOINs with odd results.
IBM DB2 is one recent arrival to the JSON party. Unfortunately, I forgot to ask whether IBM’s JSON implementation was based on IBM DB2 pureXML when I had the chance, and IBM hasn’t gotten around to answering my followup query.
3. Nor has IBM gotten around to answering my followup queries on the subject of BLU, an interesting-sounding columnar option for DB2.
4. Numerous clients have asked me whether they should be active in DBaaS (DataBase as a Service). After all, Amazon, Google, Microsoft, Rackspace and salesforce.com are all in that business in some form, and other big companies have dipped toes in as well. Read more
More on Actian/ParAccel/VectorWise/Versant/etc.
My quick reaction to the Actian/ParAccel deal was negative. A few challenges to my views then emerged. They didn’t really change my mind.
Amazon Redshift
Amazon did a deal with ParAccel that amounted to:
- Amazon got a very cheap license to a limited subset of ParAccel’s product …
- … so that it could launch a service called Amazon Redshift.
- Amazon also invested in ParAccel.
Some argue that this is great for ParAccel’s future prospects. I’m not convinced.
No doubt there are and will be Redshift users, evidently including Infor. But so far as I can tell, Redshift uses very standard SQL, so it doesn’t seed a ParAccel market in terms of developer habits. The administration/operation story is similar. So outside of general validation/bragging rights, Redshift is not a big deal for ParAccel.
OEMs and bragging rights
It’s not just Amazon and Infor; there’s also a MicroStrategy deal to OEM ParAccel — I think it’s the real ParAccel software in that case — for a particular service, MicroStrategy Wisdom. But unless I’m terribly mistaken, HP Vertica, Sybase IQ and even Infobright each have a lot more OEMs than ParAccel, just as they have a lot more customers than ParAccel overall.
This OEM success is a great validation for the idea of columnar analytic RDBMS in general, but I don’t see where it’s an advantage for ParAccel vs. the columnar leaders. Read more
| Categories: Actian and Ingres, Amazon and its cloud, Columnar database management, HP and Neoview, Market share and customer counts, ParAccel, Sybase, VectorWise, Vertica Systems | 7 Comments |
Goodbye VectorWise, farewell ParAccel?
Actian, which already owns VectorWise, is also buying ParAccel. The argument for why this kills VectorWise is simple. ParAccel does most things VectorWise does, more or less as well. It also does a lot more:
- ParAccel scales out.
- ParAccel has added analytic platform capabilities.
- I don’t know for sure, but I’d guess ParAccel has more mature management/plumbing capabilities as well.
One might conjecture that ParAccel is bad at highly concurrent, single-node use cases, and VectorWise is better at them — but at the link above, ParAccel bragged of supporting 5,000 concurrent connections. Besides, if one is just looking for a high-use reporting server, why not get Sybase IQ?? Anyhow, Actian hasn’t been investing enough in VectorWise to make it a major market player, and they’re unlikely to start now that they own ParAccel as well.
But I expect ParAccel to fail too. Reasons include:
- ParAccel’s small market share and traction.
- The disruption of any acquisition like this one.
- My general view of Actian as a company.
| Categories: Actian and Ingres, Columnar database management, Data warehousing, HP and Neoview, ParAccel, Sybase, VectorWise, Vertica Systems | 10 Comments |
Introduction to Deep Information Sciences and DeepDB
I talked Friday with Deep Information Sciences, makers of DeepDB. Much like TokuDB — albeit with different technical strategies — DeepDB is a single-server DBMS in the form of a MySQL engine, whose technology is concentrated around writing indexes quickly. That said:
- DeepDB’s indexes can help you with analytic queries; hence, DeepDB is marketed as supporting OLTP (OnLine Transaction Processing) and analytics in the same system.
- DeepDB is marketed as “designed for big data and the cloud”, with reference to “Volume, Velocity, and Variety”. What I could discern in support of that is mainly:
- DeepDB has been tested at up to 3 terabytes at customer sites and up to 1 billion rows internally.
- Like most other NewSQL and NoSQL DBMS, DeepDB is append-only, and hence could be said to “stream” data to disk.
- DeepDB’s indexes could at some point in the future be made to work well with non-tabular data.*
- The Deep guys have plans and designs for scale-out — transparent sharding and so on.
*For reasons that do not seem closely related to product reality, DeepDB is marketed as if it supports “unstructured” data today.
Other NewSQL DBMS seem “designed for big data and the cloud” to at least the same extent DeepDB is. However, if we’re interpreting “big data” to include multi-structured data support — well, only half or so of the NewSQL products and companies I know of share Deep’s interest in branching out. In particular:
- Akiban definitely does. (Note: Stay tuned for some next-steps company news about Akiban.)
- Tokutek has planted a small stake there too.
- Key-value-store-backed NuoDB and GenieDB probably leans that way. (And SanDisk evidently shut down Schooner’s RDBMS while keeping its key-value store.)
- VoltDB, Clustrix, ScaleDB and MemSQL seem more strictly tabular, except insofar as text search is a requirement for everybody. (Edit: Oops; I forgot about Clustrix’s approach to JSON support.)
Edit: MySQL has some sort of an optional NoSQL interface, and hence so presumably do MySQL-compatible TokuDB, GenieDB, Clustrix, and MemSQL.
Also, some of those products do not today have the transparent scale-out that Deep plans to offer in the future.
Platfora at the time of first GA
Well-resourced Silicon Valley start-ups typically announce their existence multiple times. Company formation, angel funding, Series A funding, Series B funding, company launch, product beta, and product general availability may not be 7 different “news events”, but they’re apt to be at least 3-4. Platfora, no exception to this rule, is hitting general availability today, and in connection with that I learned a bit more about what they are up to.
In simplest terms, Platfora offers exploratory business intelligence against Hadoop-based data. As per last weekend’s post about exploratory BI, a key requirement is speed; and so far as I can tell, any technological innovation Platfora offers relates to the need for speed. Specifically, I drilled into Platfora’s performance architecture on the query processing side (and associated data movement); Platfora also brags of rendering 100s of 1000s of “marks” quickly in HTML5 visualizations, but I haven’t a clue as to whether that’s much of an accomplishment in itself.
Platfora’s marketing suggests it obviates the need for a data warehouse at all; for most enterprises, of course, that is a great exaggeration. But another dubious aspect of Platfora marketing actually serves to understate the product’s merits — Platfora claims to have an “in-memory” product, when what’s really the case is that Platfora’s memory-centric technology uses both RAM and disk to manage larger data marts than could reasonably be fit into RAM alone. Expanding on what I wrote about Platfora when it de-stealthed: Read more
DBMS development and other subjects
The cardinal rules of DBMS development
Rule 1: Developing a good DBMS requires 5-7 years and tens of millions of dollars.
That’s if things go extremely well.
Rule 2: You aren’t an exception to Rule 1.
In particular:
- Concurrent workloads benchmarked in the lab are poor predictors of concurrent performance in real life.
- Mixed workload management is harder than you’re assuming it is.
- Those minor edge cases in which your Version 1 product works poorly aren’t minor after all.
DBMS with Hadoop underpinnings …
… aren’t exceptions to the cardinal rules of DBMS development. That applies to Impala (Cloudera), Stinger (Hortonworks), and Hadapt, among others. Fortunately, the relevant vendors seem to be well aware of this fact. Read more
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
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
Are column stores really better at compression?
A consensus has evolved that:
- Columnar compression (i.e., value-based compression) compresses better than block-level compression (i.e., compression of bit strings).
- Columnar compression can be done pretty well in row stores.
Still somewhat controversial is the claim that:
- Columnar compression can be done even better in column stores than in row-based systems.
A strong plausibility argument for the latter point is that new in-memory analytic data stores tend to be columnar — think HANA or Platfora; compression is commonly cited as a big reason for the choice. (Another reason is that I/O bandwidth matters even when the I/O is from RAM, and there are further reasons yet.)
One group that made the in-memory columnar choice is the Spark/Shark guys at UC Berkeley’s AMP Lab. So when I talked with them Thursday (more on that another time, but it sounds like cool stuff), I took some time to ask why columnar stores are better at compression. In essence, they gave two reasons — simplicity, and speed of decompression.
In each case, the main supporting argument seemed to be that finding the values in a column is easier when they’re all together in a column store. Read more
| Categories: Columnar database management, Database compression, Databricks, Spark and BDAS, In-memory DBMS, Netezza | 10 Comments |
Notes on Microsoft SQL Server
I’ve been known to gripe that covering big companies such as Microsoft is hard. Still, Doug Leland of Microsoft’s SQL Server team checked in for phone calls in August and again today, and I think I got enough to be worth writing about, albeit at a survey level only,
Subjects I’ll mention include:
- Hadoop
- Parallel Data Warehouse
- PolyBase
- Columnar data management
- In-memory data management (Hekaton)
One topic I can’t yet comment about is MOLAP/ROLAP, which is a pity; if anybody can refute my claim that ROLAP trumps MOLAP, it’s either Microsoft or Oracle.
Microsoft’s slides mentioned Yahoo refining a 6 petabyte Hadoop cluster into a 24 terabyte SQL Server “cube”, which was surprising in light of Yahoo’s history as an Oracle reference.