|Apache Impala Adds ODBC Scalar Functions|
|Written by Kay Ewbank|
|Thursday, 17 May 2018|
Apache Impala has been updated to version 3, with new features including the addition of ODBC scalar functions that were missing from the previous release. Impala is an open source, native analytic database for Apache Hadoop that provides a high-performance distributed SQL engine.
Impala was originally developed by Cloudera, and donated to the Apache Software Foundation along with Apache Kudu. It can be used to run SQL queries on data stored in HDFS, HBase, Apache Kudu, Amazon S3, and Microsoft ADLS without requiring data movement or transformation. Impala is integrated with Hadoop to use the same ODBC driver, file and data formats, metadata, security and resource management frameworks used by MapReduce, Apache Hive, Apache Pig and other Hadoop software. Impala performs well in analytics, and can interchange data with other Hadoop components as both a consumer and a producer.
Impala keeps its table definitions in a standard MySQL or PostgreSQL database known as the metastore, in a similar way to how Apache Hive keeps this type of data. This means Impala can access tables defined or loaded by Hive. It also offers low latency and high concurrency for BI and analytic queries on Hadoop. Batch frameworks such as Apache Hive don't offer this option.
The new version has a number of improvements. The new features are the addition of a number of scalar functions to remove the need for ODBC driver translation for them, including Left, Right, Week, Quarter, and MonthName.
Support has also been added for Insert plan hints for for CREATE TABLE AS SELECT (CTAS). These were already supported for Insert statements. The improvements help tune ETL processes by making CTAS statements as efficient as when using Create + Insert + hints.
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|Last Updated ( Wednesday, 16 May 2018 )|