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Diffstat (limited to 'docs/tuning.md')
| -rw-r--r-- | docs/tuning.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/tuning.md b/docs/tuning.md index 738c530458..32c7ab86e9 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -49,7 +49,7 @@ Finally, to register your classes with Kryo, create a public class that extends {% highlight scala %} import com.esotericsoftware.kryo.Kryo -class MyRegistrator extends KryoRegistrator { +class MyRegistrator extends spark.KryoRegistrator { override def registerClasses(kryo: Kryo) { kryo.register(classOf[MyClass1]) kryo.register(classOf[MyClass2]) @@ -213,10 +213,10 @@ but at a high level, managing how frequently full GC takes place can help in red Clusters will not be fully utilized unless you set the level of parallelism for each operation high enough. Spark automatically sets the number of "map" tasks to run on each file according to its size -(though you can control it through optional parameters to `SparkContext.textFile`, etc), but for -distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses a default value of 8. -You can pass the level of parallelism as a second argument (see the -[`spark.PairRDDFunctions`](api/core/index.html#spark.PairRDDFunctions) documentation), +(though you can control it through optional parameters to `SparkContext.textFile`, etc), and for +distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses the largest +parent RDD's number of partitions. You can pass the level of parallelism as a second argument +(see the [`spark.PairRDDFunctions`](api/core/index.html#spark.PairRDDFunctions) documentation), or set the system property `spark.default.parallelism` to change the default. In general, we recommend 2-3 tasks per CPU core in your cluster. |