Hue, the Hadoop UIhas been supporting Impala closely since its first version and brings fast interactive queries within your browser.
Impala Parquet The new support for complex types in Impala makes running analytic workloads considerably simpler. This capability enables users to query against naturally nested data sets without having to perform ETL to flatten them.
This feature provides a few major benefits, including: It removes additional ETL and data modeling work to flatten data sets. It makes queries easier by maintaining the natural relationship between nested data elements.
It boosts performance by removing joins. This post is a gentle introduction to querying Impala tables with complex types; we will have follow-up posts that will go into more depth.
The Impala team is excited about the first Impala release with complex types support, and we hope to get you excited, too! The syntax should feel natural to the user, and should be a natural extension of SQL.
It should also allow the full expressiveness of SQL with complex types. Common querying patterns can be expressed concisely. Queries can be executed efficiently. Consequently, we came up with the following main ideas and extensions: This exposes the nested data as columns that can be referenced as usual.
Onward to the examples! Example Schema The following schema models a hypothetical customer data warehouse that contains data that might have been assembled from various data sources. There is routine customer data like name, address, orders, and so on, but also data about website and call-center interactions, all in a single table.
The schema presents a customer-centric view of the data with the intent of performing customer-centric analyses. Their meaning will become clear in the examples to follow.Service provided by Mani Impala.
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