DBMS product categories
Analysis of database management technology in specific product categories. Related subjects include:
Analytic platform — analytic glossary draft entry
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
In our usage, an “analytic platform” is an analytic DBMS with well-integrated in-database analytics, or a data warehouse appliance that includes one. The term is also sometimes used to refer to:
- Any analytic DBMS or data warehouse appliance.
- Other kinds of software, or software/hardware combination, that support broad analytic capabilities.
To varying extents, most major vendors of analytic DBMS or data warehouse appliances have extended their products into analytic platforms; see, for example, our original coverage of analytic platform versions of as Aster, Netezza, or Vertica.
Related posts
- Our original definition of “analytic platform” (February, 2011)
- Our original feature list for analytic platforms (January, 2011)
| Categories: Analytic glossary, Aster Data, Data warehouse appliances, Data warehousing, Netezza, Vertica Systems | 3 Comments |
Data warehouse appliance — analytic glossary draft entry
This is a draft entry for the DBMS2 analytic glossary. Please comment with any ideas you have for its improvement!
Note: Words and phrases in italics will be linked to other entries when the glossary is complete.
A data warehouse appliance is a combination of hardware and software that includes an analytic DBMS (DataBase Management System). However, some observers incorrectly apply the term “data warehouse appliance” to any analytic DBMS.
The paradigmatic vendors of data warehouse appliances are:
- Teradata, which embraced the term “data warehouse appliance” in 2008.
- Netezza — now an IBM company — which popularized the term “data warehouse appliance” in the 2000s.
Further, vendors of analytic DBMS commonly offer — directly or through partnerships — optional data warehouse appliance configurations; examples include:
- Greenplum, now part of EMC.
- Vertica, now an HP company.
- IBM DB2, under the brand “Smart Analytic System”.
- Microsoft (Parallel Data Warehouse).
Oracle Exadata is sometimes regarded as a data warehouse appliance as well, despite not being solely focused on analytic use cases.
Data warehouse appliances inherit marketing claims from the category of analytic DBMS, such as: Read more
| Categories: Analytic glossary, Data warehouse appliances, Data warehousing, EMC, Exadata, Greenplum, HP and Neoview, IBM and DB2, Microsoft and SQL*Server, Netezza, Oracle, Teradata | 4 Comments |
Notes on some basic database terminology
In a call Monday with a prominent company, I was told:
- Teradata, Netezza, Greenplum and Vertica aren’t relational.
- Teradata, Netezza, Greenplum and Vertica are all data warehouse appliances.
That, to put it mildly, is not accurate. So I shall try, yet again, to set the record straight.
In an industry where people often call a DBMS just a “database” — so that a database is something that manages a database! — one may wonder why I bother. Anyhow …
1. The products commonly known as Oracle, Exadata, DB2, Sybase, SQL Server, Teradata, Sybase IQ, Netezza, Vertica, Greenplum, Aster, Infobright, SAND, ParAccel, Exasol, Kognitio et al. all either are or incorporate relational database management systems, aka RDBMS or relational DBMS.
2. In principle, there can be difficulties in judging whether or not a DBMS is “relational”. In practice, those difficulties don’t arise — yet. Every significant DBMS still falls into one of two categories:
- Relational:
- Was designed to do relational stuff* from the get-go, even if it now does other things too.
- Supports a lot of SQL.
- Non-relational:
- Was designed primarily to do non-relational things.*
- Doesn’t support all that much SQL.
*I expect the distinction to get more confusing soon, at which point I’ll adopt terms more precise than “relational things” and “relational stuff”.
3. There are two chief kinds of relational DBMS: Read more
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 |
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 |
Clustrix 4.0 and other Clustrix stuff
It feels like time to write about Clustrix, which I last covered in detail in May, 2010, and which is releasing Clustrix 4.0 today. Clustrix and Clustrix 4.0 basics include:
- Clustrix makes a short-request processing appliance.
- As you might guess from the name, Clustrix is clustered — peer-to-peer, with no head node.
- The Clustrix appliance uses flash/solid-state storage.
- Traditionally, Clustrix has run a MySQL-compatible DBMS.
- Clustrix 4.0 introduces JSON support. More on that below.
- Clustrix 4.0 introduces a bunch of administrative features, and parallel backup.
- Also in today’s announcement is a Rackspace partnership to offer Clustrix remotely, at monthly pricing.
- Clustrix has been shipping product for about 4 years.
- Clustrix has 20 customers in production, running >125 Clustrix nodes total.
- Clustrix has 60 people.
- List price for a (smallest size) Clustrix system is $150K for 3 nodes. Highest-end maintenance costs 15%.
- There’s also a $100K version meant for high availability/disaster recovery. Over half of Clustrix’s customers use off-site disaster recovery.
- Clustrix is raising a C round. Part of it has already been raised from insiders, as a kind of bridge.
The biggest Clustrix installation seems to be 20 nodes or so. Others seem to have 10+. I presume those disaster recovery customers have 6 or more nodes each. I’m not quite sure how the arithmetic on that all works; perhaps the 125ish count of nodes is a bit low.
Clustrix technical notes include: Read more
| Categories: Cloud computing, Clustering, Clustrix, Database compression, Market share and customer counts, MySQL, OLTP, Pricing, Structured documents | 4 Comments |
Memory-centric data management when locality matters
Ron Pressler of Parallel Universe/SpaceBase pinged me about a data grid product he was open sourcing, called Galaxy. The idea is that a distributed RAM grid will allocate data, not randomly or via consistent hashing, but rather via a locality-sensitive approach. Notes include:
- The original technology was developed to track moving objects on behalf of the Israeli Air Force.
- The commercial product is focused on MMO (Massively MultiPlayer Online) games (or virtual worlds).
- The underpinnings are being open sourced.
- Ron suggests that, among other use cases, Galaxy might work well for graphs.
- Ron argues that one benefit is that when lots of things cluster together — e.g. characters in a game — there’s a natural way to split them elastically (shrink the radius for proximity).
- The design philosophy seems to be to adapt as many ideas as possible from the way CPUs manage (multiple levels of) RAM cache.
The whole thing is discussed in considerable detail in a blog post and a especially in a Hacker News comment thread. There’s also an error-riddled TechCrunch article. Read more
| Categories: Cache, Clustering, Games and virtual worlds, GIS and geospatial, Open source, RDF and graphs, Scientific research, Streaming and complex event processing (CEP) | 2 Comments |
Issues in regulatory compliance
From time to time, I hear of regulatory requirements to retain, analyze, and/or protect data in various ways. It’s hard to get a comprehensive picture of these, as they vary both by industry and jurisdiction; so I generally let such compliance issues slide. Still, perhaps I should use one post to pull together what is surely a very partial list.
Most such compliance requirements have one of two emphases: Either you need to keep your customers’ data safe against misuse, or else you’re supposed to supply information to government authorities. From a data management and analysis standpoint, the former area mainly boils down to:
- Information security. This can include access control, encryption, masking, auditing, and more.
- Keeping data in an approved geographical area. (E.g., its country of origin.) This seems to be one of the three big drivers for multi-data-center processing (along with latency and disaster recovery), and hence is an influence upon numerous users’ choices in areas such as clustering and replication.
The latter, however, has numerous aspects.
First, there are many purposes for the data retention and analysis, including but by no means limited to: Read more