October 24, 2012

Quick notes on Impala

Edit: There is now a follow-up post on Cloudera Impala with substantially more detail.

In my world it’s possible to have a hasty 2-hour conversation, and that’s exactly what I had with Cloudera last week. We touched on hardware and general adoption, but much of the conversation was about Cloudera Impala, announced today. Like Hive, Impala turns Hadoop into a basic analytic RDBMS, with similar SQL/Hadoop integration benefits to those of Hadapt. In particular:

Beyond that:

On the whole, Impala seems less mature or capable than Hadapt. But Impala does have a few countervailing advantages:

Comments

6 Responses to “Quick notes on Impala”

  1. Notes on Hadoop hardware | DBMS 2 : DataBase Management System Services on October 31st, 2012 3:27 am

    [...] talked with Cloudera yesterday about an unannounced technology, and took the opportunity to ask some non-embargoed questions as well. In particular, I requested [...]

  2. Notes and comments — October 31, 2012 | DBMS 2 : DataBase Management System Services on October 31st, 2012 11:37 am

    [...] Stay tuned for more on Cloudera Impala. For one thing, I didn’t realize it would run over HBase as well as HDFS right out of the [...]

  3. More on Cloudera Impala | DBMS 2 : DataBase Management System Services on November 1st, 2012 7:12 am

    [...] What I wrote before about Cloudera Impala was woefully incomplete. After a followup call, I now feel I have a better handle on the whole thing. [...]

  4. Do you need an analytic RDBMS? | DBMS 2 : DataBase Management System Services on November 14th, 2012 9:21 pm

    [...] analytics. There are many examples in the Hadoop world — including the recent wave of SQL add-ons to Hadoop — and some in the graph area as well. But those choices will rarely suffice for the whole [...]

  5. Phillip W Young on May 28th, 2013 11:47 am

    Hive is not good for interactive ( < 20 second ) queries, but it has had columnar storage for quite some time. Also, people who are doing large joins in Hive should learn about the various flavors of map-side joins since they are quite fast as they eliminate the reduction (slow) phase

  6. Curt Monash on May 28th, 2013 4:11 pm

    I don’t think Hive has true columnar storage; I thought rcfile was more PAX-like.

Leave a Reply




Feed: DBMS (database management system), DW (data warehousing), BI (business intelligence), and analytics technology Subscribe to the Monash Research feed via RSS or email:

Login

Search our blogs and white papers

Monash Research blogs

User consulting

Building a short list? Refining your strategic plan? We can help.

Vendor advisory

We tell vendors what's happening -- and, more important, what they should do about it.

Monash Research highlights

Learn about white papers, webcasts, and blog highlights, by RSS or email.