Cloudera Impala

Coming to Impala from an RDBMS Background

When you come to Impala from a background with a traditional relational database product, you find the same familiar SQL query language and DDL statements. Data warehouse experts will already be familiar with the notion of partitioning. If you have only dealt with smaller OLTP-style databases, the emphasis on large data volumes will expand your horizons.

Standard SQL

The great thing about coming to Impala with relational database experience is that the query language is completely familiar: it’s just SQL! The SELECT syntax works like you are used to, with joins, views, relational operators, aggregate functions, ORDER BY and GROUP BY, casts, column aliases, built-in functions, and so on.

Because Impala is focused on analytic workloads, it currently doesn’t have OLTP-style operations such as DELETE, UPDATE, or COMMIT / ROLLBACK. It also does not have indexes, constraints, or foreign keys; data warehousing experts traditionally minimize their reliance on these relational features because they involve performance overhead that can be too much when dealing with large amounts of data.

The initial Impala release supports a set of core column data types: STRING instead of VARCHAR or VARCHAR2; INT and FLOAT instead of NUMBER; and no BLOB type.

The CREATE TABLE and INSERT statements incorporate some of the format clauses that you might expect to be part of a separate data-loading utility, because Impala is all about the shortest path to ingest and analyze ...