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https://issues.apache.org/jira/browse/FLINK-29756?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jingsong Lee updated FLINK-29756:
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Fix Version/s: table-store-0.4.0
(was: table-store-0.3.0)
> 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
> Priority: Minor
> Fix For: table-store-0.4.0
>
>
> 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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