Databricks, Spark and BDAS

Discussion of BDAS (Berkeley Data Analytics Systems), especially Spark and related projects, and also of Databricks, the company commercializing Spark.

September 28, 2014

Some stuff on my mind, September 28, 2014

1. I wish I had some good, practical ideas about how to make a political difference around privacy and surveillance. Nothing else we discuss here is remotely as important. I presumably can contribute an opinion piece to, more or less, the technology publication(s) of my choice; that can have a small bit of impact. But I’d love to do better than that. Ideas, anybody?

2. A few thoughts on cloud, colocation, etc.:

3. As for the analytic DBMS industry: Read more

September 7, 2014

An idealized log management and analysis system — from whom?

I’ve talked with many companies recently that believe they are:

At best, I think such competitive claims are overwrought. Still, it’s a genuinely important subject and opportunity, so let’s consider what a great log management and analysis system might look like.

Much of this discussion could apply to machine-generated data in general. But right now I think more players are doing product management with an explicit conception either of log management or event-series analytics, so for this post I’ll share that focus too.

A short answer might be “Splunk, but with more analytic functionality and more scalable performance, at lower cost, plus numerous coupons for free pizza.” A more constructive and bottoms-up approach might start with:  Read more

June 8, 2014

Optimism, pessimism, and fatalism — fault-tolerance, Part 2

The pessimist thinks the glass is half-empty.
The optimist thinks the glass is half-full.
The engineer thinks the glass was poorly designed.

Most of what I wrote in Part 1 of this post was already true 15 years ago. But much gets added in the modern era, considering that:

And so there’s been innovation in numerous cluster-related subjects, two of which are:

Distributed database consistency

When a distributed database lives up to the same consistency standards as a single-node one, distributed query is straightforward. Performance may be an issue, however, which is why we have seen a lot of:

But in workloads with low-latency writes, living up to those standards is hard. The 1980s approach to distributed writing was two-phase commit (2PC), which may be summarized as:  Read more

May 6, 2014

Notes and comments, May 6, 2014

After visiting California recently, I made a flurry of posts, several of which generated considerable discussion.

Here is a catch-all post to complete the set.  Read more

April 30, 2014

Spark on fire

Spark is on the rise, to an even greater degree than I thought last month.

*Yes, my fingerprints are showing again.

The most official description of what Spark now contains is probably the “Spark ecosystem” diagram from Databricks. However, at the time of this writing it is slightly out of date, as per some email from Databricks CEO Ion Stoica (quoted with permission):

… but if I were to redraw it, SparkSQL will replace Shark, and Shark will eventually become a thin layer above SparkSQL and below BlinkDB.

With this change, all the modules on top of Spark (i.e., SparkStreaming, SparkSQL, GraphX, and MLlib) are part of the Spark distribution. You can think of these modules as libraries that come with Spark.

Read more

March 17, 2014

Notes and comments, March 17, 2014

I have ever more business-advice posts up on Strategic Messaging. Recent subjects include pricing and stealth-mode marketing. Other stuff I’ve been up to includes:

The Spark buzz keeps increasing; almost everybody I talk with expects Spark to win big, probably across several use cases.

Disclosure: I’ll soon be in a substantial client relationship with Databricks, hoping to improve their stealth-mode marketing. :D

The “real-time analytics” gold rush I called out last year continues. A large fraction of the vendors I talk with have some variant of “real-time analytics” as a central message.

Basho had a major change in leadership. A Twitter exchange ensued. :) Joab Jackson offered a more sober — figuratively and literally — take.

Hadapt laid off its sales and marketing folks, and perhaps some engineers as well. In a nutshell, Hadapt’s approach to SQL-on-Hadoop wasn’t selling vs. the many alternatives, and Hadapt is doubling down on poly-structured data*/schema-on-need.

*While Hadapt doesn’t to my knowledge use the term “poly-structured data”, some other vendors do. And so I may start using it more myself, at least when the poly-structured/multi-structured distinction actually seems significant.

WibiData is partnering with DataStax, WibiData is of course pleased to get access to Cassandra’s user base, which gave me the opportunity to ask why they thought Cassandra had beaten HBase in those accounts. The answer was performance and availability, while Cassandra’s traditional lead in geo-distribution wasn’t mentioned at all.

Disclosure: My fingerprints are all over that deal.

In other news, WibiData has had some executive departures as well, but seems to be staying the course on its strategy. I continue to think that WibiData has a really interesting vision about how to do large-data-volume interactive computing, and anybody in that space would do well to talk with them or at least look into the open source projects WibiData sponsors.

I encountered another apparently-popular machine-learning term — bandit model. It seems to be glorified A/B testing, and it seems to be popular. I think the point is that it tries to optimize for just how much you invest in testing unproven (for good or bad) alternatives.

I had an awkward set of interactions with Gooddata, including my longest conversations with them since 2009. Gooddata is in the early days of trying to offer an all-things-to-all-people analytic stack via SaaS (Software as a Service). I gather that Hadoop, Vertica, PostgreSQL (a cheaper Vertica alternative), Spark, Shark (as a faster version of Hive) and Cassandra (under the covers) are all in the mix — but please don’t hold me to those details.

I continue to think that computing is moving to a combination of appliances, clusters, and clouds. That said, I recently bought a new gaming-class computer, and spent many hours gaming on it just yesterday.* I.e., there’s room for general-purpose workstations as well. But otherwise, I’m not hearing anything that contradicts my core point.

*The last beta weekend for The Elder Scrolls Online; I loved Morrowind.

February 2, 2014

Some stuff I’m thinking about (early 2014)

From time to time I like to do “what I’m working on” posts. From my recent blogging, you probably already know that includes:

Other stuff on my mind includes but is not limited to:

1. Certain categories of buying organizations are inherently leading-edge.

Fine. But what really intrigues me is when more ordinary enterprises also put leading-edge technologies into production. I pester everybody for examples of that.

Read more

February 2, 2014

Spark and Databricks

I’ve heard a lot of buzz recently around Spark. So I caught up with Ion Stoica and Mike Franklin for a call. Let me start by acknowledging some sources of confusion.

The “What is Spark?” question may soon be just as difficult as the ever-popular “What is Hadoop?” That said — and referring back to my original technical post about Spark and also to a discussion of prominent Spark user ClearStory — my try at “What is Spark?” goes something like this:

Read more

January 3, 2014

Notes on memory-centric data management

I first wrote about in-memory data management a decade ago. But I long declined to use that term — because there’s almost always a persistence story outside of RAM — and coined “memory-centric” as an alternative. Then I relented 1 1/2 years ago, and defined in-memory DBMS as

DBMS designed under the assumption that substantially all database operations will be performed in RAM (Random Access Memory)

By way of contrast:

Hybrid memory-centric DBMS is our term for a DBMS that has two modes:

  • In-memory.
  • Querying and updating (or loading into) persistent storage.

These definitions, while a bit rough, seem to fit most cases. One awkward exception is Aerospike, which assumes semiconductor memory, but is happy to persist onto flash (just not spinning disk). Another is Kognitio, which is definitely lying when it claims its product was in-memory all along, but may or may not have redesigned its technology over the decades to have become more purely in-memory. (But if they have, what happened to all the previous disk-based users??)

Two other sources of confusion are:

With all that said, here’s a little update on in-memory data management and related subjects.

And finally,

October 30, 2013

Glassbeam instantiates a lot of trends

Glassbeam checked in recently, and they turn out to exemplify quite a few of the themes I’ve been writing about. For starters:

Glassbeam basics include:

All Glassbeam customers except one are SaaS/cloud (Software as a Service), and even that one was only offered a subscription (as oppose to perpetual license) price.

So what does Glassbeam’s technology do? Glassbeam says it is focused on “machine data analytics,” specifically for the “Internet of Things”, which it distinguishes from IT logs.* Specifically, Glassbeam sells to manufacturers of complex devices — IT (most of its sales so far ), medical, automotive (aspirational to date), etc. — and helps them analyze “phone home” data, for both support/customer service and marketing kinds of use cases. As of a recent release, the Glassbeam stack can: Read more

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