yaooqinn commented on code in PR #44290:
URL: https://github.com/apache/spark/pull/44290#discussion_r1423771440
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docs/sql-performance-tuning.md:
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@@ -31,7 +31,7 @@ Spark SQL can cache tables using an in-memory columnar format
by calling `spark.
Then Spark SQL will scan only required columns and will automatically tune
compression to minimize
memory usage and GC pressure. You can call
`spark.catalog.uncacheTable("tableName")` or `dataFrame.unpersist()` to remove
the table from memory.
-Configuration of in-memory caching can be done using the `setConf` method on
`SparkSession` or by running
+Configuration of in-memory caching can be done via `SparkSession.conf.set` or
by running
Review Comment:
nit: Like lines 30 and 32, use `spark.conf.set`?
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docs/sql-data-sources-parquet.md:
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@@ -431,7 +431,7 @@ Other generic options can be found in <a
href="https://spark.apache.org/docs/lat
### Configuration
-Configuration of Parquet can be done using the `setConf` method on
`SparkSession` or by running
+Configuration of Parquet can be done via `SparkSession.conf.set` or by running
Review Comment:
ditto
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