Github user zsxwing commented on the pull request:
https://github.com/apache/spark/pull/6747#issuecomment-110789261
A simple example of the `conf` command:
```Scala
scala> sqlContext.sql("conf").collect().foreach(v =>
println(v.toSeq.mkString("\t")))
spark.sql.inMemoryColumnarStorage.batchSize 10000 Controls the size of
batches for columnar caching. Larger batch sizes can improve memory
utilization and compression, but risk OOMs when caching data.
spark.sql.hive.verifyPartitionPath true <TODO>
spark.sql.retainGroupColumns true <TODO>
spark.sql.codegen false When true, code will be dynamically generated
at runtime for expression evaluation in a specific query. For some queries with
complicated expression this option can lead to significant speed-ups. However,
for simple queries this can actually slow down query execution.
spark.sql.planner.sortMergeJoin false <TODO>
spark.sql.shuffle.partitions 200 Configures the number of
partitions to use when shuffling data for joins or aggregations.
spark.sql.planner.externalSort false When true, performs sorts
spilling to disk as needed otherwise sort each partition in memory.
spark.sql.broadcastTimeout 300 <TODO>
spark.sql.parquet.filterPushdown false Turn on Parquet filter pushdown
optimization. This feature is turned off by default because of a known bug in
Paruet 1.6.0rc3 (<a
href="https://issues.apache.org/jira/browse/PARQUET-136";>PARQUET-136</a>).
However, if your table doesn't contain any nullable string or binary columns,
it's still safe to turn this feature on.
spark.sql.sources.outputCommitterClass <undefined>
spark.sql.parquet.cacheMetadata true Turns on caching of Parquet
schema metadata. Can speed up querying of static data.
spark.sql.unsafe.enabled false <TDDO>
spark.sql.useSerializer2 true <TODO>
...
```
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