yaooqinn commented on a change in pull request #2057:
URL: https://github.com/apache/incubator-kyuubi/pull/2057#discussion_r821510811



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File path: docs/deployment/incremental_collection.md
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+
+<div align=center>
+
+![](../imgs/kyuubi_logo.png)
+
+</div>
+
+# Solution for Big Result Set
+
+Normally, when user sumbits a SELECT query to Spark SQL engine, the Driver 
calls `collect` to trigger calculation and
+retrieve all partitions from all Worker nodes, after all partitions data 
arrived, then Driver sends the data back to
+client through Kyuubi Server streamingly in small batch, the batch size 
decided by `TFetchResultsReq.maxRows`.
+
+Therefore, for query has big result set, the bottleneck is the Spark Driver, 
to avoid OOM, Spark has a configuration
+`spark.driver.maxResultSize` which default is `1g`, you should enlarge it as 
well as `spark.driver.memory` if your
+query has result set in several GB. But what if the result set size is dozens 
GB or event hundreds GB? You need 
+incremental collection.
+
+## Incremental collection
+
+Since v1.4.0-incubating, Kyuubi supports incremental collect mode, it is a 
solution for big results set. This feature
+is disabled in default, you can turn on it by setting the internal[1] 
configuration
+`kyuubi.operation.incremental.collect` to `true`.
+
+Incremental collection changes the gather method from `collect` to 
`toLocalIterator`. `toLocalIterator` is a Spark
+action which sequentially submit Jobs to retrieve partitions. As each 
partition is retrieved, the Driver sends it back
+to the client through Kyuubi Server streamingly. It reduces the amount of heap 
memory required on the Driver – from

Review comment:
       kindly reminder @pan3793




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