April 16, 2015

Notes on indexes and index-like structures

Indexes are central to database management.

Perhaps it’s time for a round-up post on indexing. :)

1. First, let’s review some basics. Classically:

2. Further:  Read more

April 10, 2015

MariaDB and MaxScale

I chatted with the MariaDB folks on Tuesday. Let me start by noting:

The numbers around MariaDB are a little vague. I was given the figure that there were ~500 customers total, but I couldn’t figure out what they were customers for. Remote DBA services? MariaDB support subscriptions? Something else? I presume there are some customers in each category, but I don’t know the mix. Other notes on MariaDB the company are:

MariaDB, the company, also has an OEM business. Part of their pitch is licensing for connectors — specifically LGPL — that hopefully gets around some of the legal headaches for MySQL engine suppliers.

MaxScale is a proxy, which starts out by intercepting and parsing MariaDB queries. Read more

April 9, 2015

Which analytic technology problems are important to solve for whom?

I hear much discussion of shortfalls in analytic technology, especially from companies that want to fill in the gaps. But how much do these gaps actually matter? In many cases, that depends on what the analytic technology is being used for. So let’s think about some different kinds of analytic task, and where they each might most stress today’s available technology.

In separating out the task areas, I’ll focus first on the spectrum “To what extent is this supposed to produce novel insights?” and second on the dimension “To what extent is this supposed to be integrated into a production/operational system?” Issues of latency, algorithmic novelty, etc. can follow after those. In particular, let’s consider the tasks: Read more

March 23, 2015

A new logical data layer?

I’m skeptical of data federation. I’m skeptical of all-things-to-all-people claims about logical data layers, and in particular of Gartner’s years-premature “Logical Data Warehouse” buzzphrase. Still, a reasonable number of my clients are stealthily trying to do some kind of data layer middleware, as are other vendors more openly, and I don’t think they’re all crazy.

Here are some thoughts as to why, and also as to challenges that need to be overcome.

There are many things a logical data layer might be trying to facilitate — writing, querying, batch data integration, real-time data integration and more. That said:

Read more

March 17, 2015

More notes on HBase

1. Continuing from last week’s HBase post, the Cloudera folks were fairly proud of HBase’s features for performance and scalability. Indeed, they suggested that use cases which were a good technical match for HBase were those that required fast random reads and writes with high concurrency and strict consistency. Some of the HBase architecture for query performance seems to be:

Notwithstanding that a couple of those features sound like they might help with analytic queries, the base expectation is that you’ll periodically massage your HBase data into a more analytically-oriented form. For example — I was talking with Cloudera after all — you could put it into Parquet.

2. The discussion of which kinds of data are originally put into HBase was a bit confusing.

OpenTSDB, by the way, likes to store detailed data and aggregates side-by-side, which resembles a pattern I discussed in my recent BI for NoSQL post.

3. HBase supports caching, tiered storage, and so on. Cloudera is pretty sure that it is publicly known (I presume from blog posts or conference talks) that:  Read more

March 15, 2015

BI for NoSQL — some very early comments

Over the past couple years, there have been various quick comments and vague press releases about “BI for NoSQL”. I’ve had trouble, however, imagining what it could amount to that was particularly interesting, with my confusion boiling down to “Just what are you aggregating over what?” Recently I raised the subject with a few leading NoSQL companies. The result is that my confusion was expanded. :) Here’s the small amount that I have actually figured out.

As I noted in a recent post about data models, many databases — in particular SQL and NoSQL ones — can be viewed as collections of <name, value> pairs.

Consequently, a NoSQL database can often be viewed as a table or a collection of tables, except that:

That’s all straightforward to deal with if you’re willing to write scripts to extract the NoSQL data and transform or aggregate it as needed. But things get tricky when you try to insist on some kind of point-and-click. And by the way, that last comment pertains to BI and ETL (Extract/Transform/Load) alike. Indeed, multiple people I talked with on this subject conflated BI and ETL, and they were probably right to do so.

Read more

March 10, 2015

Notes on HBase

I talked with a couple of Cloudera folks about HBase last week. Let me frame things by saying:

Also:

Read more

March 5, 2015

Cask and CDAP

For starters:

Also:

So far as I can tell:

Read more

March 4, 2015

Quick update on Tachyon

I’m on record as believing that:

That said:

As a reminder of Tachyon basics:  Read more

February 28, 2015

Databricks and Spark update

I chatted last night with Ion Stoica, CEO of my client Databricks, for an update both on his company and Spark. Databricks’ actual business is Databricks Cloud, about which I can say:

I do not expect all of the above to remain true as Databricks Cloud matures.

Ion also said that Databricks is over 50 people, and has moved its office from Berkeley to San Francisco. He also offered some Spark numbers, such as: Read more

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