ektravel commented on code in PR #14456: URL: https://github.com/apache/druid/pull/14456#discussion_r1261667605
########## docs/querying/nested-columns.md: ########## @@ -23,12 +23,14 @@ sidebar_label: Nested columns ~ under the License. --> -Apache Druid supports directly storing nested data structures in `COMPLEX<json>` columns. `COMPLEX<json>` columns store a copy of the structured data in JSON format and specialized internal columns and indexes for nested literal values—STRING, LONG, and DOUBLE types. An optimized [virtual column](./virtual-columns.md#nested-field-virtual-column) allows Druid to read and filter these values at speeds consistent with standard Druid LONG, DOUBLE, and STRING columns. +Apache Druid supports directly storing nested data structures in `COMPLEX<json>` columns. `COMPLEX<json>` columns store a copy of the structured data in JSON format and specialized internal columns and indexes for nested literal values—STRING, LONG, and DOUBLE types, as well as ARRAY of STRING, LONG, and DOUBLE values. An optimized [virtual column](./virtual-columns.md#nested-field-virtual-column) allows Druid to read and filter these values at speeds consistent with standard Druid LONG, DOUBLE, and STRING columns. Druid [SQL JSON functions](./sql-json-functions.md) allow you to extract, transform, and create `COMPLEX<json>` values in SQL queries, using the specialized virtual columns where appropriate. You can use the [JSON nested columns functions](math-expr.md#json-functions) in [native queries](./querying.md) using [expression virtual columns](./virtual-columns.md#expression-virtual-column), and in native ingestion with a [`transformSpec`](../ingestion/ingestion-spec.md#transformspec). You can use the JSON functions in INSERT and REPLACE statements in SQL-based ingestion, or in a `transformSpec` in native ingestion as an alternative to using a [`flattenSpec`](../ingestion/data-formats.md#flattenspec) object to "flatten" nested data for ingestion. +Columns ingested as `COMPLEX<json>` are automatically optimized to store the most appropriate physical column based on the data processed. For example, if only LONG values are processed, Druid will store a LONG column, ARRAY columns if the data consists of arrays, or `COMPLEX<json>` in the general case if the data is actually nested. This is the same functionality that powers ['type aware' schema discovery](../ingestion/schema-design.md#type-aware-schema-discovery). Review Comment: ```suggestion Columns ingested as `COMPLEX<json>` are automatically optimized to store the most appropriate physical column based on the data processed. For example, if only LONG values are processed, Druid stores a LONG column, ARRAY columns if the data consists of arrays, or `COMPLEX<json>` in the general case if the data is actually nested. This is the same functionality that powers ['type aware' schema discovery](../ingestion/schema-design.md#type-aware-schema-discovery). ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
