ektravel commented on code in PR #12350:
URL: https://github.com/apache/druid/pull/12350#discussion_r860073570
##########
docs/ingestion/native-batch.md:
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@@ -387,19 +400,44 @@ them to create the final segments. Finally, they push the
final segments to the
> the task may fail if the input changes in between the two passes.
#### Multi-dimension range partitioning
-> Multiple dimension (multi-dimension) range partitioning is an experimental
feature. Multi-dimension range partitioning is currently not supported in the
sequential mode of the Parallel task.
-When you use multi-dimension partitioning for your data, Druid is able to
distribute segment sizes more evenly than with single dimension partitioning.
+> Multiple dimension (multi-dimension) range partitioning is an experimental
feature.
+> Multi-dimension range partitioning is currently not supported in the
sequential mode of the
+> Parallel task.
-For segment pruning to be effective and translate into better query
performance, you must include the first of your `partitionDimensions` in the
`WHERE` clause at query time. For example, given the following
`partitionDimensions`:
-```
- "partitionsSpec": {
- "type": "range",
- "partitionDimensions":["coutryName","cityName"],
- "targetRowsPerSegment" : 5000
+When you use multi-dimension range partitioning for your data, Druid is able
to distribute segment
+sizes more evenly than with single dimension partitioning.
+
+Range partitioning has several benefits:
Review Comment:
Consider combining lines 348-364 and 411-440 and moving the text into a
separate section titled **Benefits of range partitioning**.
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