Cloudera Engineering Blog · Hive Posts
Our thanks to Rakesh Rao of Quaero, for allowing us to re-publish the post below about Quaero’s experiences using partitioning in Apache Hive.
In this post, we will talk about how we can use the partitioning features available in Hive to improve performance of Hive queries.
Two of the most vibrant communities in the Apache Hadoop ecosystem are now working together to bring users a Hive-on-Spark option that combines the best elements of both.
Apache Hive is a popular SQL interface for batch processing and ETL using Apache Hadoop. Until recently, MapReduce was the only execution engine in the Hadoop ecosystem, and Hive queries could only run on MapReduce. But today, alternative execution engines to MapReduce are available — such as Apache Spark and Apache Tez (incubating).
Learn how HiveServer, Apache Sentry, and Impala help make Hadoop play nicely with BI tools when Kerberos is involved.
In 2010, I wrote a simple pair of blog entries outlining the general considerations behind using Apache Hadoop with BI tools. The Cloudera partner ecosystem has positively exploded since then, and the technology has matured as well. Today, if JDBC is involved, all the pieces needed to expose Hadoop data through familiar BI tools are available:
Our thanks to Don Drake (@dondrake), an independent technology consultant who is currently working as a Principal Big Data Consultant at Allstate Insurance, for the guest post below about his experiences with Impala.
It started with a simple request from one of the managers in my group at Allstate to put together a demo of Tableau connecting to Cloudera Impala. I had previously worked on Impala with a large dataset about a year ago while it was still in beta, and was curious to see how Impala had improved since then in features and stability.
More than 300 bug fixes and stable features in Apache Hive 0.13 have already been backported into CDH 5.0.0.
Last week, the Hive community voted to release Hive 0.13. We’re excited about the continued efforts and progress in the project and the latest release — congratulations to all contributors involved!
The following post, by Sarah Cannon of Digital Reasoning, was originally published in that company’s blog. Digital Reasoning has graciously permitted us to re-publish here for your convenience.
At the beginning of each release cycle, engineers at Digital Reasoning are given time to explore the latest in Big Data technologies, examining how the frequently changing landscape might be best adapted to serve our mission. As we sat down in the early stages of planning for Synthesys 3.8 one of the biggest issues we faced involved reconciling the tradeoff between flexibility and performance. How can users quickly and easily retrieve knowledge from Synthesys without being tied to one strict data model?
The CDH software stack lets you use your tool of choice with the Parquet file format – - offering the benefits of columnar storage at each phase of data processing.
An open source project co-founded by Twitter and Cloudera, Parquet was designed from the ground up as a state-of-the-art, general-purpose, columnar file format for the Apache Hadoop ecosystem. In particular, Parquet has several features that make it highly suited to use with Cloudera Impala for data warehouse-style operations:
This quick demo illustrates how easy it is to implement role-based access and control in Impala using Sentry.
Apache Sentry (incubating) is the Apache Hadoop ecosystem tool for role-based access control (RBAC). In this how-to, I will demonstrate how to implement Sentry for RBAC in Impala. I feel this introduction is best motivated by a use case.
Bringing Parquet support to Hive was a community effort that deserves congratulations!
Previously, this blog introduced Parquet, an efficient ecosystem-wide columnar storage format for Apache Hadoop. As discussed in that blog post, Parquet encodes data extremely efficiently and as described in Google’s original Dremel paper. (For more technical details on the Parquet format read Dremel made simple with Parquet, or go directly to the open and community-driven Parquet Format specification.)
Migrating from the Hive CLI to Beeline isn’t as simple as changing the executable name, but this post makes it easy nonetheless.
In its original form, Apache Hive was a heavyweight command-line tool that accepted queries and executed them utilizing MapReduce. Later, the tool split into a client-server model, in which HiveServer1 is the server (responsible for compiling and monitoring MapReduce jobs) and Hive CLI is the command-line interface (sends SQL to the server).