Core Spark functionality.
Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.
In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs
of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions
contains operations available only on RDDs of Doubles; and
org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can
be saved as SequenceFiles. These operations are automatically available on any RDD of the right
type (e.g. RDD[(Int, Int)] through implicit conversions.
Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.
Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.
Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.
Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
Spark's broadcast variables, used to broadcast immutable datasets to all nodes.
ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.
ALPHA COMPONENT GraphX is a graph processing framework built on top of Spark.
IO codecs used for compression.
IO codecs used for compression. See org.apache.spark.io.CompressionCodec.
DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.
DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.
RDD-based machine learning APIs (in maintenance mode).
RDD-based machine learning APIs (in maintenance mode).
The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage
migration to the DataFrame-based APIs under the org.apache.spark.ml package.
While in maintenance mode,
spark.mllib package will be accepted, unless they block
implementing new features in the DataFrame-based spark.ml package;The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.
SPARK-4591 to track the progress of feature parity
Support for approximate results.
Support for approximate results. This provides convenient api and also implementation for approximate calculation.
Provides several RDD implementations.
Provides several RDD implementations. See org.apache.spark.rdd.RDD.
Spark's scheduling components.
Spark's scheduling components. This includes the org.apache.spark.scheduler.DAGScheduler and
lower level org.apache.spark.scheduler.TaskScheduler.
Pluggable serializers for RDD and shuffle data.
Pluggable serializers for RDD and shuffle data.
Allows the execution of relational queries, including those expressed in SQL using Spark.
Allows the execution of relational queries, including those expressed in SQL using Spark.
Spark Streaming functionality.
Spark Streaming functionality. org.apache.spark.streaming.StreamingContext serves as the main entry point to Spark Streaming, while org.apache.spark.streaming.dstream.DStream is the data type representing a continuous sequence of RDDs, representing a continuous stream of data.
In addition, org.apache.spark.streaming.dstream.PairDStreamFunctions contains operations
available only on DStreams
of key-value pairs, such as groupByKey and reduceByKey. These operations are automatically
available on any DStream of the right type (e.g. DStream[(Int, Int)] through implicit
conversions.
For the Java API of Spark Streaming, take a look at the org.apache.spark.streaming.api.java.JavaStreamingContext which serves as the entry point, and the org.apache.spark.streaming.api.java.JavaDStream and the org.apache.spark.streaming.api.java.JavaPairDStream which have the DStream functionality.
Spark utilities.
Spark utilities.
Support for approximate results. This provides convenient api and also implementation for approximate calculation.
A Double value with error bars and associated confidence.