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Impala: All content tagged as Impala in NoSQL databases and polyglot persistence

Big Data benchmark: Redshift, Hive, Impala, Shark, Stinger/Tez

Hosted on amplab, the origin of Spark this benchmark compares Redshift, Hive, Shark, Impala, Stinger/Tez:

Several analytic frameworks have been announced in the last year. Among them are inexpensive data-warehousing solutions based on traditional Massively Parallel Processor (MPP) architectures (Redshift), systems which impose MPP- like execution engines on top of Hadoop (Impala, HAWQ) and systems which optimize MapReduce to improve performance on analytical workloads (Shark, Stinger/Tez). This benchmark provides quantitative and qualitative comparisons of five systems. It is entirely hosted on EC2 and can be reproduced directly from your computer.

More important than the results:

  1. the clear methodology
  2. and its reproducibility

Original title and link: Big Data benchmark: Redshift, Hive, Impala, Shark, Stinger/Tez (NoSQL database©myNoSQL)

via: https://amplab.cs.berkeley.edu/benchmark/


SQL on Hadoop: An overview of frameworks and their applicability

An overview of the 3 SQL-on-Hadoop execution models — batch (10s of minutes and up), interactive (up to minutes), operational (sub-second), their applicability in the field of applications, and the main characteristics of the tools/frameworks in each of these categories:

Within the big data landscape there are multiple approaches to accessing, analyzing, and manipulating data in Hadoop. Each depends on key considerations such as latency, ANSI SQL completeness (and the ability to tolerate machine-generated SQL), developer and analyst skillsets, and architecture tradeoffs.

The usual suspects are included: Hive, Impala, Preso, Spark/Shark, Drill.

sql-on-hadoop-segments-diagram

Original title and link: SQL on Hadoop: An overview of frameworks and their applicability (NoSQL database©myNoSQL)

via: http://www.mapr.com/products/sql-on-hadoop-details


Everything is faster than Hive

Derrick Harris has brought together a series of benchmarks conducted by the different SQL-on-Hadoop implementors comparing their solution (Impala, Stinger/Tez, HAWQ, Shark) with

For what it’s worth, everyone is faster than Hive — that’s the whole point of all of these SQL-on-Hadoop technologies. How they compare with each other is harder to gauge, and a determination probably best left to individual companies to test on their own workloads as they’re making their own buying decisions. But for what it’s worth, here is a collection of more benchmark tests showing the performance of various Hadoop query engines against Hive, relational databases and, sometimes, themselves.

As Derrick Harris remarks, the only direct comparisons are between HAWQ and Impala (and this seems to be old as it mentions Impala being in beta) and the benchmark run by AMPlab (the guys behind Shark) comparing Redshift, Hive, Shark, and Impala.

The good part is that both the Hive Testbench and AMPlab benchmark are available on GitHub.

Original title and link: Everything is faster than Hive (NoSQL database©myNoSQL)

via: http://gigaom.com/2014/01/13/cloudera-says-impala-is-faster-than-hive-which-isnt-saying-much/


Cloudera shipped a mountain... what can you read between the lines

Cloudera Engineering (@ClouderaEng) shipped a mountain of new product (production-grade software, not just technical previews): Cloudera Impala, Cloudera Search, Cloudera Navigator, Cloudera Development Kit (now Kite SDK), new Apache Accumulo packages for CDH, and several iterative releases of CDH and Cloudera Manager. (And, the Cloudera Enterprise 5 Beta release was made available to the world.). Furthermore, as always, a ton of bug fixes and new features went upstream, with the features notably but not exclusively HiveServer2 and Apache Sentry (incubating).

How many things can you read in this paragraph?

  1. a not that subtle stab at Hortonwork’s series of technical previews.
  2. more and more projects brought under the CDH umbrella. Does more ever become too much? (I cannot explain why, but my first thought was “this feels so Oracle-style”)
  3. Cloudera’s current big bet is Impala. SQL and low latency querying. A big win for the project, but not necessarily a direct financial win for Cloudera, was its addition as a supported service on Amazon Elastic MapReduce.

Original title and link: Cloudera shipped a mountain… what can you read between the lines (NoSQL database©myNoSQL)

via: http://blog.cloudera.com/blog/2014/01/this-month-and-year-in-the-ecosystem-december-2013/


Integrating R with Cloudera Impala for Real-Time queries on Hadoop

A very long tutorial by Istvan Szegedi on how to integrate R with Cloudera Impala, through the ODBC driver:

Cloudera Impala is an exciting new technology to provide real-time, interactive queries in Hadoop environment. It supports ODBC connectors and this makes it possible to integrate it with many popular BI tools and statistical software such as R. Together R and Impala provide an excellent combination for data analyst to process massive data sets efficiently and they can also support graphical representation of the result sets.

Original title and link: Integrating R with Cloudera Impala for Real-Time queries on Hadoop (NoSQL database©myNoSQL)

via: http://bighadoop.wordpress.com/2013/11/25/integrating-r-with-cloudera-impala-for-real-time-queries-on-hadoop/


How Safari Books Online uses Google BigQuery for BI

Looking for alternative solutions to built our dashboards and enable interactive ad-hoc querying, we played with several technologies, including Hadoop. In the end, we decided to use Google BigQuery.

Compare the original processing flow:

BigQuery processing flow

with these 2 possible alternatives and tell me if you notice any significant differences.

Alternatives to BigQuery

Original title and link: How Safari Books Online uses Google BigQuery for BI (NoSQL database©myNoSQL)

via: http://googlecloudplatform.blogspot.com/2013/07/how-safari-books-online-uses-google.html


Cloudera Impala 1.0 Release Notes and A Couple of Questions

This is what I’ve been looking for since posting about Impala 1.0: the release notes. From the new features list:

  • support for ALTER TABLE
  • REFRESH for a single table
  • Hints for specifying particular join strategies
  • Dynamic resource management, allowing high concurrency for Impala queries

Question: if I remember correctly Impala uses a single process on each machine to execute queries.

  1. is it multi-threaded?
  2. does it do any memory/CPU management so one query is not completely exhausting any of these resources?
  3. what happens with the queries executing when this process fails?

Original title and link: Cloudera Impala 1.0 Release Notes and A Couple of Questions (NoSQL database©myNoSQL)


Cloudera Impala Brings SQL Querying To Hadoop

InformationWeek about today’s Impala 1.0 release:

Impala supports direct querying of data in the Hadoop Distributed File System (HDFS) and HBase (NoSQL database) indexes, and Cloudera claims it’s 3X to 30X faster than Hive. Beta customers report results that are falling into that range. Six3 Systems, for example, a systems integrator serving federal agencies, has seen at least 14X faster querying than Hive, according to analytics developer Wayne Wheeles.

Original title and link: Cloudera Impala Brings SQL Querying To Hadoop (NoSQL database©myNoSQL)

via: http://www.informationweek.com/big-data/news/software/information-management/cloudera-impala-brings-sql-querying-to-h/240153861


Impala 1.0 - That was fast

Cloudera announces Impala 1.0 GA release.

That was fast—I guess this is one of the (little) advantages of having Hortonworks working on Stinger, Pivotal on HAWQ, Qubole offering Hive, Pig and Sqoop as-a-Service

Original title and link: Impala 1.0 - That was fast (NoSQL database©myNoSQL)


Cloudera Pissed Off

Charles Zedlewki takes position for Cloudera to the recent attacks to Hadoop and Impala:

I’m reminded of our open source strategy this week not only because of the further validation of Hadoop’s popularity but also because of the entry of a new round of proprietary imitators. At one point there were six distinct vendors all promoting proprietary filesystems as alternatives to HDFS, many of which included breathless claims of how they could make Apache Hadoop faster and “more powerful.” This year we get to see history repeat itself, this time with SQL engines. The marketing is nearly identical to that of the proprietary filesystem era: damning open source with faint praise, pointing out its limitations and extolling the virtues of some feature(s) proprietary to that particular vendor.

Proprietary SQL vendors will pull a page from the proprietary storage playbook: damn open source Impala with faint praise and point out its limitations, both real and contrived. They will be equally ineffective. We will continue to bet on an open, integrated, and highly flexible big data platform. Saying you are “all in on Hadoop” while simultaneously promoting a proprietary platform means you are missing the point.

Neither Cloudera, nor other companies that invested a lot and everything in the Hadoop ecosystem are at the size not to care about large corporations attacking their bets. Every corporation is trying to emulate the Microsoft strategy: wait for a new technology to be confirmed, then jump at the opportunity with all your forces. But I really hope open source will prevail.

Original title and link: Cloudera Pissed Off (NoSQL database©myNoSQL)

via: http://blog.cloudera.com/blog/2013/02/open-source-flattery-and-the-platform-for-big-data/


Inside Cloudera Impala: Runtime Code Generation

Nong Li about Cloudera’s Impala implementation:

Cloudera Impala, the open-source real-time query engine for Apache Hadoop, uses many tools and techniques to get the best query performance. This blog post will discuss how we use runtime code generation to significantly improve our CPU efficiency and overall query execution time. We’ll explain the types of inefficiency that code-generation eliminates and go over in more detail one of the queries in the TPCH workload where code generation improves overall query speeds by close to 3x.

This reminded me of the days I was working on Java AOP frameworks whose implementation was based on bytecode generation for the same purpose of optimization. Everything worked perfectly well as long as the underlying assumptions remained the same.

Original title and link: Inside Cloudera Impala: Runtime Code Generation (NoSQL database©myNoSQL)

via: http://blog.cloudera.com/blog/2013/02/inside-cloudera-impala-runtime-code-generation/


Hadoop in 2013: What Hortonworks Will Focus On

Shaun Connolly summarizing a recent webinar about where Hortonwork’s work on Hadoop will focus in 2013:

[…] Interactive Query, Business Continuity (DR, Snapshots, etc.), Secure Access, as well as ongoing investments in Data Integration, Management (i.e. Ambari), and Online Data (i.e. HBase).
[…] Rather than abandon the Apache Hive community, Hortonworks is focused on working in the community to optimize Hive’s ability to serve big data exploration and interactive query in support of important BI use cases. Moreover, we are focused on enabling Hive to take advantage of YARN in Apache Hadoop 2.0, which will help ensure fast query workloads don’t compete for resources with the other jobs running in the cluster. Enabling Hadoop to predictably support enterprise workloads that span Batch, Interactive, and Online use cases is an important area of focus for us.

Basically this says that Hortonworks sees YARN and Hive as the answer to online or real-time interactive querying of Hadoop data. Cloudera’s take on this is different.

Original title and link: Hadoop in 2013: What Hortonworks Will Focus On (NoSQL database©myNoSQL)

via: http://hortonworks.com/blog/the-road-ahead-for-hortonworks-and-hadoop/