February 9, 2014

Distinctions in SQL/Hadoop integration

Ever more products try to integrate SQL with Hadoop, and discussions of them seem confused, in line with Monash’s First Law of Commercial Semantics. So let’s draw some distinctions, starting with (and these overlap):

In particular:

Let’s go to some examples. 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,

September 8, 2013

Layering of database technology & DBMS with multiple DMLs

Two subjects in one post, because they were too hard to separate from each other

Any sufficiently complex software is developed in modules and subsystems. DBMS are no exception; the core trinity of parser, optimizer/planner, and execution engine merely starts the discussion. But increasingly, database technology is layered in a more fundamental way as well, to the extent that different parts of what would seem to be an integrated DBMS can sometimes be developed by separate vendors.

Major examples of this trend — where by “major” I mean “spanning a lot of different vendors or projects” — include:

Other examples on my mind include:

And there are several others I hope to blog about soon, e.g. current-day PostgreSQL.

In an overlapping trend, DBMS increasingly have multiple data manipulation APIs. Examples include:  Read more

August 25, 2013

Cloudera Hadoop strategy and usage notes

When we scheduled a call to talk about Sentry, Cloudera’s Charles Zedlewski and I found time to discuss other stuff as well. One interesting part of our discussion was around the processing “frameworks” Cloudera sees as most important.

HBase was artificially omitted from this “frameworks” discussion because Cloudera sees it as a little bit more of a “storage” system than a processing one.

Another good subject was offloading work to Hadoop, in a couple different senses of “offload”: Read more

August 25, 2013

Cloudera Sentry and other security subjects

I chatted with Charles Zedlewski of Cloudera on Thursday about security — especially Cloudera’s new offering Sentry — and other Hadoop subjects.

Sentry is:

Apparently, Hadoop security options pre-Sentry boil down to:

Sentry adds role-based permissions for SQL access to Hadoop:

for a variety of actions — selections, transformations, schema changes, etc. Sentry does this by examining a query plan and checking whether each step in the plan is permissible.  Read more

August 24, 2013

Hortonworks business notes

Hortonworks did a business-oriented round of outreach, talking with at least Derrick Harris and me. Notes  from my call — for which Rob Bearden* didn’t bother showing up — include, in no particular order:

*Speaking of CEO Bearden, an interesting note from Derrick’s piece is that Bearden is quoted as saying “I started this company from day one …”, notwithstanding that the now-departed Eric Baldeschwieler was founding CEO.

In Hortonworks’ view, Hadoop adopters typically start with a specific use case around a new type of data, such as clickstream, sensor, server log, geolocation, or social.  Read more

August 6, 2013

Hortonworks, Hadoop, Stinger and Hive

I chatted yesterday with the Hortonworks gang. The main subject was Hortonworks’ approach to SQL-on-Hadoop — commonly called Stinger —  but at my request we cycled through a bunch of other topics as well. Company-specific notes include:

Our deployment and use case discussions were a little confused, because a key part of Hortonworks’ strategy is to support and encourage the idea of combining use cases and workloads on a single cluster. But I did hear:

*By the way — Teradata seems serious about pushing the UDA as a core message.

Ecosystem notes, in Hortonworks’ perception, included:

I also asked specifically about OpenStack. Hortonworks is a member of the OpenStack project, contributes nontrivially to Swift and other subprojects, and sees Rackspace as an important partner. But despite all that, I think strong Hadoop/OpenStack integration is something for the indefinite future.

Hortonworks’ views about Hadoop 2.0 start from the premise that its goal is to support running a multitude of workloads on a single cluster. (See, for example, what I previously posted about Tez and YARN.) Timing notes for Hadoop 2.0 include:

Frankly, I think Cloudera’s earlier and necessarily incremental Hadoop 2 rollout was a better choice than Hortonworks’ later big bang, even though the core-mission aspect of Hadoop 2.0 is what was least ready. HDFS (Hadoop Distributed File System) performance, NameNode failover and so on were well worth having, and it’s more than a year between Cloudera starting supporting them and when Hortonworks is offering Hadoop 2.0.

Hortonworks’ approach to doing SQL-on-Hadoop can be summarized simply as “Make Hive into as good an analytic RDBMS as possible, all in open source”. Key elements include:  Read more

June 23, 2013

Hadoop news and rumors, June 23, 2013

Cloudera

*Of course, there will always be exceptions. E.g., some formats can be updated on a short-request basis, while others can only be written to via batch conversions.

Everybody else

June 23, 2013

Impala and Parquet

I visited Cloudera Friday for, among other things, a chat about Impala with Marcel Kornacker and colleagues. Highlights included:

Data gets into Parquet via batch jobs only — one reason it’s important that Impala run against multiple file formats — but background format conversion is another roadmap item. A single table can be split across multiple formats — e.g., the freshest data could be in HBase, with the rest is in Parquet.

Read more

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