MongoDB and 10gen

Discussion of MongoDB and its sponsoring company 10gen.

October 31, 2012

Notes and comments — October 31, 2012

Time for another catch-all post. First and saddest — one of the earliest great commenters on this blog, and a beloved figure in the Boston-area database community, was Dan Weinreb, whom I had known since some Symbolics briefings in the early 1980s. He passed away recently, much much much too young. Looking back for a couple of examples — even if you’ve never heard of him before, I see that Dan ‘s 2009 comment on Tokutek is still interesting today, and so is a post on his own blog disagreeing with some of my choices in terminology.

Otherwise, in no particular order:

1. Chris Bird is learning MongoDB. As is common for Chris, his comments are both amusing and enlightening.

2. When I relayed Cloudera’s comments on Hadoop adoption, I left out a couple of categories. One Cloudera called “mobile”; when I probed, that was about HBase, with an example being messaging apps.

The other was “phone home” — i.e., the ingest of machine-generated data from a lot of different devices. This is something that’s obviously been coming for several years — but I’m increasingly getting the sense that it’s actually arrived.

Read more

April 7, 2012

Many kinds of memory-centric data management

I’m frequently asked to generalize in some way about in-memory or memory-centric data management. I can start:

Getting more specific than that is hard, however, because:

Consider, for example, some of the in-memory data management ideas kicking around. Read more

March 31, 2012

Our clients, and where they are located

From time to time, I disclose our vendor client lists. Another iteration is below, the first since a little over a year ago. To be clear:

For reasons explained below, I’ll group the clients geographically. Obviously, companies often have multiple locations, but this is approximately how it works from the standpoint of their interactions with me. Read more

February 1, 2012

Couchbase update

I checked in with James Phillips for a Couchbase update, and I understand better what’s going on. In particular:

Read more

October 23, 2011

NoSQL notes

Last week I visited with James Phillips of Couchbase, Max Schireson and Eliot Horowitz of 10gen, and Todd Lipcon, Eric Sammer, and Omer Trajman of Cloudera. I guess it’s time for a round-up NoSQL post. :)

Views of the NoSQL market horse race are reasonably consistent, with perhaps some elements of “Where you stand depends upon where you sit.”

Read more

July 27, 2011

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

July 15, 2011

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:

Continuing with that discussion of DBMS alternatives:

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.

May 15, 2011

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

May 14, 2011

Alternatives for Hadoop/MapReduce data storage and management

There’s been a flurry of announcements recently in the Hadoop world. Much of it has been concentrated on Hadoop data storage and management. This is understandable, since HDFS (Hadoop Distributed File System) is quite a young (i.e. immature) system, with much strengthening and Bottleneck Whack-A-Mole remaining in its future.

Known HDFS and Hadoop data storage and management issues include but are not limited to:

Different entities have different ideas about how such deficiencies should be addressed.  Read more

April 4, 2011

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:

Read more

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