Analysis of Mark Logic and its Marklogic Server search-friendly XML DBMS product. Related subjects include:

November 1, 2011

MarkLogic 5, and why you might care

MarkLogic is releasing MarkLogic 5. Key elements of the announcement are:

Also, MarkLogic is early with a feature that most serious DBMS vendors will soon have – support for tiered storage, with writes going first to solid-state storage, then being flushed to disk via a caching-style algorithm.* And as befits a sometime search-engine-substitute, MarkLogic has finally licensed a large set of document filters, from an Australian company called Isys. Apparently, the special virtue of the Isys filters is that they’re good at extracting not only text, but metadata as well.

*If there’s a caching algorithm that doesn’t contain a major element of LRU (Least Recently Used), I don’t recall ever hearing about it.

MarkLogic seems to have settled on a positioning that, although distressingly buzzword-heavy, is at least partly based upon reality. The real part includes:

Based on that reality, MarkLogic talks a lot about Volume, Velocity, Variety, Big Data, unstructured data, semi-structured data, and big data analytics.

Read more

October 10, 2011

Text data management, Part 2: General and short-request

This is Part 2 of a three post series. The posts cover:

  1. Confusion about text data management.
  2. Choices for text data management (general and short-request).
  3. Choices for text data management (analytic).

I’ve recently given widely varied advice about managing text (and similar files — images and so on), ranging from

Sure, just keep going with your old strategy of keeping .PDFs in the file system and pointing to them from the relational database. That’s an easy performance optimization vs. having the RDBMS manage them as BLOBs.


I suspect MongoDB isn’t heavyweight enough for your document management needs, let alone just dumping everything into Hadoop. Why don’t you take a look at MarkLogic?

Here are some reasons why.

There are three basic kinds of text management use case:

Read more

October 10, 2011

Text data management, Part 1: Confusion

This is Part 1 of a three post series. The posts cover:

  1. Confusion about text data management.
  2. Choices for text data management (general and short-request).
  3. Choices for text data management (analytic).

There’s much confusion about the management of text data, among technology users, vendors, and investors alike. Reasons seems to include:

Above all: The use cases for text data vary greatly, just as the use cases for simply-structured databases do.

There are probably fewer people now than there were six years ago who need to be told that text and relational database management are very different things. Other misconceptions, however, appear to be on the rise. Specific points that are commonly overlooked include: Read more

October 2, 2011

Defining NoSQL

A reporter tweeted:  “Is there a simple plain English definition for NoSQL?” After reminding him of my cynical yet accurate Third Law of Commercial Semantics, I gave it a serious try, and came up with the following. More precisely, I tweeted the bolded parts of what’s below; the rest is commentary added for this post.

NoSQL is most easily defined by what it excludes: SQL, joins, strong analytic alternatives to those, and some forms of database integrity. If you leave all four out, and you have a strong scale-out story, you’re in the NoSQL mainstream. Read more

September 6, 2011

Derived data, progressive enhancement, and schema evolution

The emphasis I’m putting on derived data is leading to a variety of questions, especially about how to tease apart several related concepts:

So let’s dive in.  Read more

May 30, 2011

Another category of derived data

Six months ago, I argued the importance of derived analytic data, saying

… there’s no escaping the importance of derived/augmented/enhanced/cooked/adjusted data for analytic data processing. The five areas I have in mind are, loosely speaking:

  • Aggregates, when they are maintained, generally for reasons of performance or response time.
  • Calculated scores, commonly based on data mining/predictive analytics.
  • Text analytics.
  • The kinds of ETL (Extract/Transform/Load) Hadoop and other forms of MapReduce are commonly used for.
  • Adjusted data, especially in scientific contexts.

Probably there are yet more examples that I am at the moment overlooking.

Well, I did overlook at least one category. 🙂

A surprisingly important kind of derived data is metadata, especially for large, poly-structured data sets. For example, CERN has vastly quantities of experiment sensor data, stored as files; just the metadata alone fills over 10 terabytes in an Oracle database. MarkLogic is big on storing derived metadata, both on the publishing/media and intelligence sides of the business.

Read more

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

April 5, 2011

Whither MarkLogic?

My clients at MarkLogic have a new CEO, Ken Bado, even though former CEO Dave Kellogg was quite successful. If you cut through all the happy talk and side issues, the reason for the change is surely that the board wants to see MarkLogic grow faster, and specifically to move beyond its traditional niches of publishing (especially technical publishing) and national intelligence.

So what other markets could MarkLogic pursue? Before Ken even started work, I sent over some thoughts. They included (but were not limited to):  Read more

February 28, 2011

Updating our vendor client disclosures

Edit: This disclosure has been superseded by a March, 2012 version.

From time to time, I disclose our vendor client lists. Another iteration is below. To be clear:

With that said, our vendor client disclosures at this time are:

Read more

February 7, 2011

Notes on document-oriented NoSQL

When people talk about document-oriented NoSQL or some similar term, they usually mean something like:

Database management that uses a JSON model and gives you reasonably robust access to individual field values inside a JSON (JavaScript Object Notation) object.

Or, if they really mean,

The essence of whatever it is that CouchDB and MongoDB have in common.

well, that’s pretty much the same thing as what I said in the first place. 🙂

Of the various questions that might arise, three of the more definitional ones are:

Let me take a crack at each.  Read more

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