Major Description
Goals
- Add a new SQL grammar of Materialized column
- Implicitly rewrite SQL queries on the complex type of columns if there is a materialized columns for it
- If the data type of the materialized columns is atomic type, even though the origin column type is in complex type, enable vectorized read and filter pushdown to improve performance
Example
Create a normal table
CREATE TABLE x (
name STRING,
age INT,
params STRING,
event MAP<STRING, STRING>
) USING parquet;
Add materialized columns to an existing table
ALTER TABLE x ADD COLUMNS (
new_age INT MATERIALIZED age + 1,
city STRING MATERIALIZED get_json_object(params, '$.city'),
label STRING MATERIALIZED event['label']
);
When issue a query as below
SELECT name, age+1, get_json_object(params, '$.city'), event['label']
FROM x
WHER event['label'] = 'newuser';
It's equivalent to
SELECT name, new_age, city, label
FROM x
WHERE label = 'newuser';
The query performance improved dramatically because
- The new query (after rewritten) will read the new column city (in string type) instead of read the whole map of params(in map string). Much lesser data are need to read
- Vectorized read can be utilized in the new query and can not be used in the old one. Because vectorized read can only be enabled when all required columns are in atomic type
- Filter can be pushdown. Only filters on atomic column can be pushdown. The original filter event['label'] = 'newuser' is on complex column, so it can not be pushdown.
- The new query do not need to parse JSON any more. JSON parse is a CPU intensive operation which will impact performance dramatically