Cloudera Engineering Blog · Impala Posts

The Impala Cookbook

Bookmark this new living document to ensure use of current and proper configuration, sizing, management, and measurement practices.

Impala, the open source MPP analytic database for Apache Hadoop, is now firmly entrenched in the Big Data mainstream. How do we know this? For one, Impala is now the standard against which alternatives measure themselves, based on a proliferation of new benchmark testing. Furthermore, Impala has been adopted by multiple vendors as their solution for letting customers do exploratory analysis on Big Data, natively and in place (without the need for redundant architecture or ETL). Also significant, we’re seeing the emergence of best practices and patterns out of customer experiences.

NoSQL in a Hadoop World

The number of powerful data query tools in the Apache Hadoop ecosystem can be confusing, but understanding a few simple things about your needs usually makes the choice easy. 

Ah, the good old days. I recall vividly that in 2007, I was faced to store 1 billion XML documents and make them accessible as well as searchable. I had few choices on a given shoestring budget: build something one my own (it was the rage back then—and still is), use an existing open source database like PostgreSQL or MySQL, or try this thing that Google built successfully and that was now implemented in open source under the Apache umbrella: Hadoop.

New in CDH 5.2: Impala Authentication with LDAP and Kerberos

Impala authentication can now be handled by a combination of LDAP and Kerberos. Here’s why, and how.

Impala, the open source analytic database for Apache Hadoop, supports authentication—the act of proving you are who you say you are—using both Kerberos and LDAP. Kerberos has been supported since release 1.0, LDAP support was added more recently, and with CDH 5.2, you can use both at the same time.

New in CDH 5.2: Apache Sentry Delegated GRANT and REVOKE

This new feature, jointly developed by Cloudera and Intel engineers, makes management of role-based security much easier in Apache Hive, Impala, and Hue.

Apache Sentry (incubating) provides centralized authorization for services and applications in the Apache Hadoop ecosystem, allowing administrators to set up granular, role-based protection on resources, and to review them in one place. Previously, Sentry only designated administrators to GRANT and REVOKE privileges on an authorizable object. In Apache Sentry 1.5.0 (shipping inside CDH 5.2), we have implemented a new feature (SENTRY-327) that allows admin users to delegate the GRANT privilege to other users using WITH GRANT OPTION. If a user has the GRANT OPTION privilege on a specific resource, the user can now grant the GRANT privilege to other users on the same resource. Apache Hive, Impala, and Hue have all been updated to take advantage of this new Sentry functionality.

New in CDH 5.2: More SQL Functionality and Compatibility for Impala 2.0

Impala 2.0 is the most SQL-complete/SQL-compatible release yet.

As we reported in the most recent roadmap update (“What’s Next for Impala: Focus on Advanced SQL Functionality”), more complete SQL functionality (and better SQL compatibility with other vendor extensions) is a major theme in Impala 2.0.

Here’s Your Getting Started with Impala Book

Getting Started with Impala (now in early release)—another book in the Hadoop ecosystem books canon—is indispensable for people who want to get familiar with Impala, the open source MPP query engine for Apache Hadoop. We spoke with its author, Impala docs writer John Russell, about the book’s origin and mission.

Why did you decide to write this book?

New Benchmarks for SQL-on-Hadoop: Impala 1.4 Widens the Performance Gap

With 1.4, Impala’s performance lead over the SQL-on-Hadoop ecosystem gets wider, especially under multi-user load.

As noted in our recent post about the Impala 2.x roadmap (“What’s Next for Impala: Focus on Advanced SQL Functionality”), Impala’s ecosystem momentum continues to accelerate, with nearly 1 million downloads since the GA of 1.0, deployment by most of Cloudera’s enterprise data hub customers, and adoption by MapR, Amazon, and Oracle as a shipping product. Furthermore, in the past few months, independent sources such as IBM Research have confirmed that “Impala’s database-like architecture provides significant performance gains, compared to Hive’s MapReduce- or Tez-based runtime.”

How Impala Supports Mixed Workloads in Multi-User Environments

Our thanks to Melanie Imhof, Jonas Looser, Thierry Musy, and Kurt Stockinger of the Zurich University of Applied Science in Switzerland for the post below about their research into the query performance of Impala for mixed workloads.

Recently, we were approached by an industry partner to research and create a blueprint for a new Big Data, near real-time, query processing architecture that would replace its current architecture based on a popular open source database system.

What’s Next for Impala: Focus on Advanced SQL Functionality

Impala 2.0 will add much more complete SQL functionality to what is already the fastest SQL-on-Hadoop solution available.

In September 2013, we provided a roadmap for Impala — the open source MPP SQL query engine for Apache Hadoop, which was on release 1.1 at the time — that documented planned functionality through release 2.0 and beyond.

New in CDH 5.1: HDFS Read Caching

Applications using HDFS, such as Impala, will be able to read data up to 59x faster thanks to this new feature.

Server memory capacity and bandwidth have increased dramatically over the last few years. Beefier servers make in-memory computation quite attractive, since a lot of interesting data sets can fit into cluster memory, and memory is orders of magnitude faster than disk.

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