Hi, I'd like to propose a feature name materialized column. This feature will boost queries on complex type columns.
<goog_64495576> https://docs.google.com/document/d/186bzUv4CRwoYY_KliNWTexkNCysQo3VUTLQVrVijyl4/edit?usp=sharing *Background* In data warehouse domain, there is a common requirement to add new fields to existing tables. In practice, data engineers usually use complex type, such as Map (or they may use JSON), and put all subfields into it. However, it may impact the query performance dramatically because 1. It is a waste of IO. The whole column (in Map format) should be read and Spark extract the required keys from the map, even though the query requires only one or a few keys in the map 2. Vectorized read can not be exploit. Currently, vectorized read can be enabled only when all required columns are in atomic type. When a query read subfield in a complex type column, vectorized read can not be exploit 3. Filter pushdown can not be utilized. Only when all required fields are in atomic type can filter pushdown be enabled 4. CPU is wasted because of duplicated computation. When JSON is selected to store all keys, JSON happens each time we query a subfield in it. However, JSON parse is a CPU intensive operation, especially when the JSON string is very long *Goal* - 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 *Usage* *#1 Add materialized columns to an existing table* Step 1: Create a normal table > CREATE TABLE x ( > name STRING, > age INT, > params STRING, > event MAP<STRING, STRING> > ) USING parquet; Step 2: 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, '$.city'), > label STRING *MATERIALIZED* event['label'] > ); *#2 Create a new table with materialized table* > CREATE TABLE x ( > name STRING, > age INT, > params STRING, > event MAP<STRING, STRING>, > new_age INT MATERIALIZED age + 1, > city STRING MATERIALIZED get_json_object(params, '$.city'), > label STRING MATERIALIZED event['label'] > ) USING parquet; When issue a query on complex type column as below SELECT name, age+1, get_json_object(params, '$.city'), event['label'] FROM x WHERE event['label']='newuser'; It is equivalent to SELECT name, new_age, city, label FROM x WHERE label = 'newuser' The query performance improved dramatically because 1. 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 2. 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 3. 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. 4. The new query do not need to parse JSON any more. JSON parse is a CPU intensive operation which will impact performance dramatically -- Thanks & Best Regards, Jason Guo
