Analysis of technology that compresses data within a database management system. Related subjects include:
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.
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.
- 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
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
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.
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.
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
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.
- 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|
Alternate title: TokuDB updates
Tokutek turns a performance argument into a functionality one. In particular, Tokutek claims that TokuDB does a much better job than alternatives of making it practical for you to update indexes at OLTP speeds. Hence, it claims to do a much better job than alternatives of making it practical for you to write and execute queries that only make sense when indexes (or other analytic performance boosts) are in place.
That’s all been true since I first wrote about Tokutek and TokuDB in 2009. However, TokuDB’s technical details have changed. In particular, Tokutek has deemphasized the ideas that:
- Vaguely justified the “fractal” metaphor, namely …
- … the stuff in that post about having one block each sized for each power of 2, …
- … which seem to be a form of what is more ordinarily called “cache-oblivious” technology.
Rather, Tokutek’s new focus for getting the same benefits is to provide a separate buffer for each node of a b-tree. In essence, Tokutek is taking the usual “big blocks are better” story and extending it to indexes. TokuDB also uses block-level compression. Notes on that include: Read more
|Categories: Akiban, Database compression, Market share and customer counts, NewSQL, Tokutek and TokuDB||7 Comments|
NuoDB has an interesting NewSQL story. NuoDB’s core design goals seem to be:
- Very flexible topology, including:
- Local replicas.
- Remote replicas.
- Easy deployment and management.
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|