Nicholas Jiang created FLINK-29756:
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Summary: Support materialized column to improve query performance
for complex types
Key: FLINK-29756
URL: https://issues.apache.org/jira/browse/FLINK-29756
Project: Flink
Issue Type: New Feature
Components: Table Store
Affects Versions: table-store-0.3.0
Reporter: Nicholas Jiang
Fix For: table-store-0.3.0
In the world of data warehouse, it is very common to use one or more columns
from a complex type such as a map, or to put many subfields into it. These
operations can greatly affect query performance because:
# These operations are very wasteful IO. For example, if we have a field type
of Map, which contains dozens of subfields, we need to read the entire column
when reading this column. And Spark will traverse the entire map to get the
value of the target key.
# Cannot take advantage of vectorized reads when reading nested type columns.
# Filter pushdown cannot be used when reading nested columns.
It is necessary to introduce the materialized column feature in Flink Table
Store, which transparently solves the above problems of arbitrary columnar
storage (not just Parquet).
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