Pinot supports JOINs, including left, right, full, semi, anti, lateral, and equi JOINs. Use JOINs to connect two table to generate a unified view, based on a related column between the tables.
Important: To query using JOINs, you must use Pinot's multi-stage query engine (v2).
Pinot 1.0 introduces support for all JOIN types. JOINs in Pinot significantly reduce query latency and simplify architecture, achieving the best performance currently available for an OLAP database.
Use JOINs to combine two tables (a left and right table) together, based on a related column between the tables, and other join filters. JOINs let you gain more insights from your data.
The inner join selects rows that have matching values in both tables.
Syntax:
Joins a table containing user transactions with a table containing promotions shown to the users, to show the spending for every userID.
A left join returns all values from the left relation and the matched values from the right table, or appends NULL if there is no match. Also referred to as a left outer join.
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A right join returns all values from the right relation and the matched values from the left relation, or appends NULL if there is no match. It is also referred to as a right outer join.
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A full join returns all values from both relations, appending NULL values on the side that does not have a match. It is also referred to as a full outer join.
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A cross join returns the Cartesian product of two relations. If no WHERE clause is used along with CROSS JOIN, this produces a result set that is the number of rows in the first table multiplied by the number of rows in the second table. If a WHERE clause is included with CROSS JOIN, it functions like an INNER JOIN.
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Semi/anti-join returns rows from the first table where no matches are found in the second table. Returns one copy of each row in the first table for which no match is found.
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An equi join uses an equality operator to match a single or multiple column values of the relative tables.
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Pinot JOINs include the following optimizations:
Predicate push-down to individual tables
Indexing and pruning to reduce scanning and speeds up query processing
Smart data layout considerations to minimize data shuffling
Query hints for fine-tuning JOIN operations.