Zipping Elements in a DataSet

In certain algorithms, one may need to assign unique identifiers to data set elements.This document shows how DataSetUtils can be used for that purpose.

• Zip with a Dense Index
• Zip with a Unique Identifier

Zip with a Dense Index

zipWithIndex assigns consecutive labels to the elements, receiving a data set as input and returning a new data set of (unique id, initial value) 2-tuples.This process requires two passes, first counting then labeling elements, and cannot be pipelined due to the synchronization of counts.The alternative zipWithUniqueId works in a pipelined fashion and is preferred when a unique labeling is sufficient.For example, the following code:

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataSet<String> in = env.fromElements("A", "B", "C", "D", "E", "F", "G", "H");
DataSet<Tuple2<Long, String>> result = DataSetUtils.zipWithIndex(in);
result.writeAsCsv(resultPath, "\n", ",");
env.execute();

import org.apache.flink.api.scala._
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
env.setParallelism(2)
val input: DataSet[String] = env.fromElements("A", "B", "C", "D", "E", "F", "G", "H")
val result: DataSet[(Long, String)] = input.zipWithIndex
result.writeAsCsv(resultPath, "\n", ",")
env.execute()

may yield the tuples: (0,G), (1,H), (2,A), (3,B), (4,C), (5,D), (6,E), (7,F)

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Zip with a Unique Identifier

In many cases one may not need to assign consecutive labels.zipWithUniqueId works in a pipelined fashion, speeding up the label assignment process. This method receives a data set as input and returns a new data set of (unique id, initial value) 2-tuples.For example, the following code:

ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(2);
DataSet<String> in = env.fromElements("A", "B", "C", "D", "E", "F", "G", "H");
DataSet<Tuple2<Long, String>> result = DataSetUtils.zipWithUniqueId(in);
result.writeAsCsv(resultPath, "\n", ",");
env.execute();

import org.apache.flink.api.scala._
val env: ExecutionEnvironment = ExecutionEnvironment.getExecutionEnvironment
env.setParallelism(2)
val input: DataSet[String] = env.fromElements("A", "B", "C", "D", "E", "F", "G", "H")
val result: DataSet[(Long, String)] = input.zipWithUniqueId
result.writeAsCsv(resultPath, "\n", ",")
env.execute()

may yield the tuples: (0,G), (1,A), (2,H), (3,B), (5,C), (7,D), (9,E), (11,F)

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