Analysis of database management systems designed with a focus on OTLP (OnLine Transaction Processing) uses.
7-10 years ago, I repeatedly argued the viewpoints:
- Relational DBMS were the right choice in most cases.
- Multiple kinds of relational DBMS were needed, optimized for different kinds of use case.
- There were a variety of specialized use cases in which non-relational data models were best.
Since then, however:
- Hadoop has flourished.
- NoSQL has flourished.
- Graph DBMS have matured somewhat.
- Much of the action has shifted to machine-generated data, of which there are many kinds.
So it’s probably best to revisit all that in a somewhat organized way.
I’m taking a few weeks defocused from work, as a kind of grandpaternity leave. That said, the venue for my Dances of Infant Calming is a small-but-nice apartment in San Francisco, so a certain amount of thinking about tech industries is inevitable. I even found time last Tuesday to meet or speak with my clients at WibiData, MemSQL, Cloudera, Citus Data, and MongoDB. And thus:
1. I’ve been sloppy in my terminology around “geo-distribution”, in that I don’t always make it easy to distinguish between:
- Storing different parts of a database in different geographies, often for reasons of data privacy regulatory compliance.
- Replicating an entire database into different geographies, often for reasons of latency and/or availability/ disaster recovery,
The latter case can be subdivided further depending on whether multiple copies of the data can accept first writes (aka active-active, multi-master, or multi-active), or whether there’s a clear single master for each part of the database.
What made me think of this was a phone call with MongoDB in which I learned that the limit on number of replicas had been raised from 12 to 50, to support the full-replication/latency-reduction use case.
2. Three years ago I posted about agile (predictive) analytics. One of the points was:
… if you change your offers, prices, ad placement, ad text, ad appearance, call center scripts, or anything else, you immediately gain new information that isn’t well-reflected in your previous models.
Subsequently I’ve been hearing more about predictive experimentation such as bandit testing. WibiData, whose views are influenced by a couple of Very Famous Department Store clients (one of which is Macy’s), thinks experimentation is quite important. And it could be argued that experimentation is one of the simplest and most direct ways to increase the value of your data.
3. I’d further say that a number of developments, trends or possibilities I’m seeing are or could be connected. These include agile and experimental predictive analytics in general, as noted in the previous point, along with: Read more
As part of my series on the keys to and likelihood of success, I outlined some examples from the DBMS industry. The list turned out too long for a single post, so I split it up by millennia. The part on 20th Century DBMS success and failure went up Friday; in this one I’ll cover more recent events, organized in line with the original overview post. Categories addressed will include analytic RDBMS (including data warehouse appliances), NoSQL/non-SQL short-request DBMS, MySQL, PostgreSQL, NewSQL and Hadoop.
DBMS rarely have trouble with the criterion “Is there an identifiable buying process?” If an enterprise is doing application development projects, a DBMS is generally chosen for each one. And so the organization will generally have a process in place for buying DBMS, or accepting them for free. Central IT, departments, and — at least in the case of free open source stuff — developers all commonly have the capacity for DBMS acquisition.
In particular, at many enterprises either departments have the ability to buy their own analytic technology, or else IT will willingly buy and administer things for a single department. This dynamic fueled much of the early rise of analytic RDBMS.
Buyer inertia is a greater concern.
- A significant minority of enterprises are highly committed to their enterprise DBMS standards.
- Another significant minority aren’t quite as committed, but set pretty high bars for new DBMS products to cross nonetheless.
- FUD (Fear, Uncertainty and Doubt) about new DBMS is often justifiable, about stability and consistent performance alike.
A particularly complex version of this dynamic has played out in the market for analytic RDBMS/appliances.
- First the newer products (from Netezza onwards) were sold to organizations who knew they wanted great performance or price/performance.
- Then it became more about selling “business value” to organizations who needed more convincing about the benefits of great price/performance.
- Then the behemoth vendors became more competitive, as Teradata introduced lower-price models, Oracle introduced Exadata, Sybase got more aggressive with Sybase IQ, IBM bought Netezza, EMC bought Greenplum, HP bought Vertica and so on. It is now hard for a non-behemoth analytic RDBMS vendor to make headway at large enterprise accounts.
- Meanwhile, Hadoop has emerged as serious competitor for at least some analytic data management, especially but not only at internet companies.
Otherwise I’d say: Read more
I’m commonly asked to assess vendor claims of the kind:
- “Our system lets you do multiple kinds of processing against one database.”
- “Otherwise you’d need two or more data managers to get the job done, which would be a catastrophe of unthinkable proportion.”
So I thought it might be useful to quickly review some of the many ways organizations put multiple data stores to work. As usual, my bottom line is:
- The most extreme vendor marketing claims are false.
- There are many different choices that make sense in at least some use cases each.
Horses for courses
It’s now widely accepted that different data managers are better for different use cases, based on distinctions such as:
- Short-request vs. analytic.
- SQL vs. non-SQL (NoSQL or otherwise).
- Expensive/heavy-duty vs. cheap/easy-to-support.
Vendors are part of this consensus; already in 2005 I observed
For all practical purposes, there are no DBMS vendors left advocating single-server strategies.
Vendor agreement has become even stronger in the interim, as evidenced by Oracle/MySQL, IBM/Netezza, Oracle’s NoSQL dabblings, and various companies’ Hadoop offerings.
Multiple data stores for a single application
We commonly think of one data manager managing one or more databases, each in support of one or more applications. But the other way around works too; it’s normal for a single application to invoke multiple data stores. Indeed, all but the strictest relational bigots would likely agree: Read more
I frequently am asked questions that boil down to:
- When should one use NoSQL?
- When should one use a new SQL product (NewSQL or otherwise)?
- When should one use a traditional RDBMS (most likely Oracle, DB2, or SQL Server)?
The details vary with context — e.g. sometimes MySQL is a traditional RDBMS and sometimes it is a new kid — but the general class of questions keeps coming. And that’s just for short-request use cases; similar questions for analytic systems arise even more often.
My general answers start:
- Sometimes something isn’t broken, and doesn’t need fixing.
- Sometimes something is broken, and still doesn’t need fixing. Legacy decisions that you now regret may not be worth the trouble to change.
- Sometimes — especially but not only at smaller enterprises — choices are made for you. If you operate on SaaS, plus perhaps some generic web hosting technology, the whole DBMS discussion may be moot.
In particular, migration away from legacy DBMS raises many issues: Read more
|Categories: Columnar database management, Couchbase, HBase, In-memory DBMS, Microsoft and SQL*Server, NewSQL, NoSQL, OLTP, Oracle, Parallelization, SAP AG||17 Comments|
Relational DBMS used to be fairly straightforward product suites, which boiled down to:
- A big SQL interpreter.
- A bunch of administrative and operational tools.
- Some very optional add-ons, often including an application development tool.
Now, however, most RDBMS are sold as part of something bigger.
- Oracle has hugely thickened its stack, as part of an Innovator’s Solution strategy — hardware, middleware, applications, business intelligence, and more.
- IBM has moved aggressively to a bundled “appliance” strategy. Even before that, IBM DB2 long sold much better to committed IBM accounts than as a software-only offering.
- Microsoft SQL Server is part of a stack, starting with the Windows operating system.
- Sybase was an exception to this rule, with thin(ner) stacks for both Adaptive Server Enterprise and Sybase IQ. But Sybase is now owned by SAP, and increasingly integrated as a business with …
- … SAP HANA, which is closely associated with SAP’s applications.
- Teradata has always been a hardware/software vendor. The most successful of its analytic DBMS rivals, in some order, are:
- Netezza, a pure appliance vendor, now part of IBM.
- Greenplum, an appliance-mainly vendor for most (not all) of its existence, and in particular now as a part of EMC Pivotal.
- Vertica, more of a software-only vendor than the others, but now owned by and increasingly mainstreamed into hardware vendor HP.
- MySQL’s glory years were as part of the “LAMP” stack.
- Various thin-stack RDBMS that once were or could have been important market players … aren’t. Examples include Progress OpenEdge, IBM Informix, and the various strays adopted by Actian.
The 2013 Gartner Magic Quadrant for Operational Database Management Systems is out. “Operational” seems to be Gartner’s term for what I call short-request, in each case the point being that OLTP (OnLine Transaction Processing) is a dubious term when systems omit strict consistency, and when even strictly consistent systems may lack full transactional semantics. As is usually the case with Gartner Magic Quadrants:
- I admire the raw research.
- The opinions contained are generally reasonable (especially since Merv Adrian joined the Gartner team).
- Some of the details are questionable.
- There’s generally an excessive focus on Gartner’s perception of vendors’ business skills, and on vendors’ willingness to parrot all the buzzphrases Gartner wants to hear.
- The trends Gartner highlights are similar to those I see, although our emphasis may be different, and they may leave some important ones out. (Big omission — support for lightweight analytics integrated into operational applications, one of the more genuine forms of real-time analytics.)
Anyhow: Read more
Oracle announced its in-memory columnar option Sunday. As usual, I wasn’t briefed; still, I have some observations. For starters:
- Oracle, IBM (Edit: See the rebuttal comment below), and Microsoft are all doing something similar …
- … because it makes sense.
- The basic idea is to take the technology that manages indexes — which are basically columns+pointers — and massage it into an actual column store. However …
- … the devil is in the details. See, for example, my May post on IBM’s version, called BLU, outlining all the engineering IBM did around that feature.
- Notwithstanding certain merits of this approach, I don’t believe in complete alternatives to analytic RDBMS. The rise of analytic DBMS oriented toward multi-structured data just strengthens that point.
I’d also add that Larry Ellison’s pitch “build columns to avoid all that index messiness” sounds like 80% bunk. The physical overhead should be at least as bad, and the main saving in administrative overhead should be that, in effect, you’re indexing ALL columns rather than picking and choosing.
Anyhow, this technology should be viewed as applying to traditional business transaction data, much more than to — for example — web interaction logs, or other machine-generated data. My thoughts around that distinction start:
- I argued back in 2011 that traditional databases will wind up in RAM, basically because …
- … Moore’s Law will make it ever cheaper to store them there.
- Still, cheaper != cheap, so this is a technology only to use with your most valuable data — i.e., that transactional stuff.
- These are very tabular technologies, without much in the way of multi-structured data support.
|Categories: Columnar database management, Data warehousing, IBM and DB2, Memory-centric data management, Microsoft and SQL*Server, OLTP, Oracle, SAP AG, Workday||6 Comments|
Some subjects just keep coming up. And so I keep saying things like:
Most generalizations about “Big Data” are false. “Big Data” is a horrific catch-all term, with many different meanings.
Most generalizations about Hadoop are false. Reasons include:
- Hadoop is a collection of disparate things, most particularly data storage and application execution systems.
- The transition from Hadoop 1 to Hadoop 2 will be drastic.
- For key aspects of Hadoop — especially file format and execution engine — there are or will be widely varied options.
Hadoop won’t soon replace relational data warehouses, if indeed it ever does. SQL-on-Hadoop is still very immature. And you can’t replace data warehouses unless you have the power of SQL.
Note: SQL isn’t the only way to provide “the power of SQL”, but alternative approaches are just as immature.
Most generalizations about NoSQL are false. Different NoSQL products are … different. It’s not even accurate to say that all NoSQL systems lack SQL interfaces. (For example, SQL-on-Hadoop often includes SQL-on-HBase.)
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.