NoSQL
Discussion of NoSQL concepts, products, and vendors.
Couchbase business update
I decided I needed some Couchbase drilldown, on business and technology alike, so I had solid chats with both CEO Bob Wiederhold and Chief Architect Dustin Sallings. Pretty much everything I wrote at the time Membase and CouchOne merged to form Couchbase (the company) still holds up. But I have more detail now. 😉
Context for any comments on customer traction includes:
- Membase went into limited production release in October, and full release in January. Similar things are true of CouchDB.
- Hence, most sales of Couchbase’s products have been made over the past 6 months.
- Couchbase (the merged product) is at this point only in a pre-production developer’s release.
- Couchbase has both a direct sales force and a classic open-source “funnel”-based online selling model. Naturally, Couchbase’s understanding of what its customers are doing is more solid with respect to the direct sales base.
- Most of Couchbase’s revenue to date seems to have come from a limited number of big-ticket “lighthouse” accounts (as opposed to, say, the larger number of smaller deals that come in through the online funnel).
That said,
- Most Membase purchases are for new applications, as opposed to memcached migrations. However, customers are the kinds of companies that probably also are using memcached elsewhere.
- Most other Membase purchases are replacements for the Membase/MySQL combination. Bob says those are easy sales with short sales cycles.
- Pure memcached support is a small but non-zero business for Couchbase, and a fine source of upsell opportunities.
- In the pipeline but not so much yet in the customer base are SaaS vendors and the like who use and may want to replace traditional DBMS such as Oracle. Other than among those, Couchbase doesn’t compete much yet with Oracle et al.
- Pure CouchDB isn’t all that much of a business, at least relative to community size, as CouchDB is a single-server product commonly used by people who are content not to pay for support.
Membase sales are concentrated in five kinds of internet-centric companies, which in declining order are: Read more
Terminology: Dynamic- vs. fixed-schema databases
E. F. “Ted” Codd taught the computing world that databases should have fixed logical schemas (which protect the user from having to know about physical database organization). But he may not have been as universally correct as he thought. Cases I’ve noted in which fixed schemas may be problematic include:
- “A bunch of apps in one, similar but not the same” (in my recent post on MongoDB).
- Out-of-control product catalogs (ditto).
- Analytic use cases in which one keeps enhancing the database with derived data.
And if marketing profile analysis is ever done correctly, that will be a huge example for the list.
So what do we call those DBMS — for example NoSQL, object-oriented, or XML-based systems — that bake the schema into the applications or the records themselves? In the MongoDB post I went with “schemaless,” but I wasn’t really comfortable with that, so I took the discussion to Twitter. Comments from Vlad Didenko (in particular), Ryan Prociuk, Merv Adrian, and Roland Bouman favored the idea that schemas in such systems are changeable or late-bound, rather than entirely absent. I quickly agreed.
| Categories: Data models and architecture, NoSQL, Object, Structured documents | 49 Comments |
The Ted Codd guarantee
I write a lot about whether or not to use relational DBMS. For example:
- In May I surveyed relational vs. non-relational pros and cons at some length.
- Last November I mused about when it might be OK to do without joins.
- The question is implicit in a variety of posts about, say, document-oriented or object-oriented DBMS.
Before going further in that vein, I’d like to do a quick review of what E. F. “Ted” Codd was getting at with the relational model in the first place. Read more
| Categories: Data models and architecture, IBM and DB2, MOLAP, NoSQL | 3 Comments |
MongoDB users and use cases
I spoke with Eliot Horowitz and Max Schierson of 10gen last month about MongoDB users and use cases. The biggest clusters they came up with weren’t much over 100 nodes, but clusters an order of magnitude bigger were under development. The 100 node one we talked the most about had 33 replica sets, each with about 100 gigabytes of data, so that’s in the 3-4 terabyte range total. In general, the largest MongoDB databases are 20-30 TB; I’d guess those really do use the bulk of available disk space. Read more
| Categories: Data models and architecture, Games and virtual worlds, Log analysis, MongoDB, NoSQL, Solid-state memory, Specific users, Splunk, Telecommunications, Web analytics | 13 Comments |
Soundbites: the Facebook/MySQL/NoSQL/VoltDB/Stonebraker flap, continued
As a follow-up to the latest Stonebraker kerfuffle, Derrick Harris asked me a bunch of smart followup questions. My responses and afterthoughts include:
- Facebook et al. are in effect Software as a Service (SaaS) vendors, not enterprise technology users. In particular:
- They have the technical chops to rewrite their code as needed.
- Unlike packaged software vendors, they’re not answerable to anybody for keeping legacy code alive after a rewrite. That makes migration a lot easier.
- If they want to write different parts of their system on different technical underpinnings, nobody can stop them. For example …
- … Facebook innovated Cassandra, and is now heavily committed to HBase.
- It makes little sense to talk of Facebook’s use of “MySQL.” Better to talk of Facebook’s use of “MySQL + memcached + non-transparent sharding.” That said:
- It’s hard to see why somebody today would use MySQL + memcached + non-transparent sharding for a new project. At least one of Couchbase or transparently-sharded MySQL is very likely a superior alternative. Other alternatives might be better yet.
- As noted above in the example of Facebook, the many major web businesses that are using MySQL + memcached + non-transparent sharding for existing projects can be presumed able to migrate away from that stack as the need arises.
Continuing with that discussion of DBMS alternatives:
- If you just want to write to the memcached API anyway, why not go with Couchbase?
- If you want to go relational, why not go with MySQL? There are many alternatives for scaling or accelerating MySQL — dbShards, Schooner, Akiban, Tokutek, ScaleBase, ScaleDB, Clustrix, and Xeround come to mind quickly, so there’s a great chance that one or more will fit your use case. (And if you don’t get the choice of MySQL flavor right the first time, porting to another one shouldn’t be all THAT awful.)
- If you really, really want to go in-memory, and don’t mind writing Java stored procedures, and don’t need to do the kinds of joins it isn’t good at, but do need to do the kinds of joins it is, VoltDB could indeed be a good alternative.
And while we’re at it — going schema-free often makes a whole lot of sense. I need to write much more about the point, but for now let’s just say that I look favorably on the Big Four schema-free/NoSQL options of MongoDB, Couchbase, HBase, and Cassandra.
Eight kinds of analytic database (Part 2)
In Part 1 of this two-part series, I outlined four variants on the traditional enterprise data warehouse/data mart dichotomy, and suggested what kinds of DBMS products you might use for each. In Part 2 I’ll cover four more kinds of analytic database — even newer, for the most part, with a use case/product short list match that is even less clear. Read more
What colleges should teach in analytics
Based on a Teradata press release calling attention to the small amount of explicit university instruction in business intelligence, I was asked:
Does BI really need a dedicated undergrad track? What sort of BI and analytics-related skills should students look to obtain now in order to be viable in the job marketplace five years out?
My answers were (slightly edited):
- Most important is a basic, intuitive understanding of statistical significance. If you’re looking at an apparent trend, is it real or just random variation?
- Also crucial are general analytic and quantitative problem-solving skills.
- One also should have a comfort level learning how to use new software tools.
- Everybody in business should have those skillsets. So should people in science, medicine, teaching, journalism, government, and most other vocations.
- The more analytically oriented should add basic programming skills, and basic knowledge of SQL. While SQL’s utter dominance is ebbing a bit, it still will be with us for a very long time.
Of course, there are more specialized skills also worth teaching, in a number of areas, starting with statistics and other predictive modeling technologies. But it’s OK to go through life not knowing those.
| Categories: Analytic technologies, Business intelligence, Data warehousing, NoSQL, Predictive modeling and advanced analytics, Teradata | 1 Comment |
When it’s still best to use a relational DBMS
There are plenty of viable alternatives to relational database management systems. For short-request processing, both document stores and fully object-oriented DBMS can make sense. Text search engines have an important role to play. E. F. “Ted” Codd himself once suggested that relational DBMS weren’t best for analytics.* Analysis of machine-generated log data doesn’t always have a naturally relational aspect. And I could go on with more examples yet.
*Actually, he didn’t admit that what he was advocating was a different kind of DBMS, namely a MOLAP one — but he was. And he was wrong anyway about the necessity for MOLAP. But let’s overlook those details. 🙂
Nonetheless, relational DBMS dominate the market. As I see it, the reasons for relational dominance cluster into four areas (which of course overlap):
- Data re-use. Ted Codd’s famed original paper referred to shared data banks for a reason.
- The benefits of normalization, which include:
- You only have to do programming work of writing something once …
- … and you don’t have to do the programming work of keeping multiple versions of the information consistent.
- You only have to do processing work of writing something once.
- You only have to buy storage to hold each fact once.
- Separation of concerns.
- Different people can worry about programming and “database stuff.”
- Indeed, even performance optimization can sometimes be separated from programming (i.e., when all you have to do to get speed is implement the correct indexes).
- Maturity and momentum, as reflected in the availability of:
- People.
- A broad variety of mature relational DBMS.
- Vast amounts of packaged software that “talks” SQL.
Generally speaking, I find the reasons for sticking with relational technology compelling in cases such as: Read more
| Categories: Analytic technologies, Data models and architecture, Database diversity, MOLAP, NoSQL, Object, Theory and architecture | 21 Comments |
What to do about “unstructured data”
We hear much these days about unstructured or semi-structured (as opposed to) structured data. Those are misnomers, however, for at least two reasons. First, it’s not really the data that people think is un-, semi-, or fully structured; it’s databases.* Relational databases are highly structured, but the data within them is unstructured — just lists of numbers or character strings, whose only significance derives from the structure that the database imposes.
*Here I’m using the term “database” literally, rather than as a concise synonym for “database management system”. But see below.
Second, a more accurate distinction is not whether a database has one structure or none — it’s whether a database has one structure or many. The easiest way to see this is for databases that have clearly-defined schemas. A relational database has one schema (even if it is just the union of various unrelated sub-schemas); an XML database, however, can have as many schemas as it contains documents.
One small terminological problem is easily handled, namely that people don’t talk about true databases very often, at least when they’re discussing generalities; rather, they talk about data and DBMS.* So let’s talk of DBMS being “structured” singly or multiply or whatever, just as the databases they’re designed to manage are.
*And they refer to the DBMS as “databases,” because they don’t have much other use for the word.
All that said — I think that single vs. multiple database structures isn’t a bright-line binary distinction; rather, it’s a spectrum. For example: Read more
| Categories: Cassandra, Couchbase, Data models and architecture, HBase, IBM and DB2, MarkLogic, MongoDB, NoSQL, Splunk, Theory and architecture | 19 Comments |
The MongoDB story
Along with CouchDB/Couchbase, MongoDB was one of the top examples I had in mind when I wrote about document-oriented NoSQL. Invented by 10gen, MongoDB is an open source, no-schema DBMS, so it is suitable for very quick development cycles. Accordingly, there are a lot of MongoDB users who build small things quickly. But MongoDB has heftier uses as well, and naturally I’m focused more on those.
MongoDB’s data model is based on BSON, which seems to be JSON-on-steroids. In particular:
- You just bang things into single BSON objects managed by MongoDB; there is nothing like a foreign key to relate objects. However …
- … there are fields, datatypes, and so on within MongoDB BSON objects. The fields are indexed.
- There’s a multi-value/nested-data-structure flavor to MongoDB; for example, a BSON object might store multiple addresses in an array.
- You can’t do joins in MongoDB. Instead, you are encouraged to put what might be related records in a relational database into a single MongoDB object. If that doesn’t suffice, then use client-side logic to do the equivalent of joins. If that doesn’t suffice either, you’re not looking at a good MongoDB use case.
| Categories: Clustering, Data models and architecture, MapReduce, MongoDB, NoSQL, Parallelization | 10 Comments |