Zipping Elements
This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Starting with Flink 1.12 the DataSet API has been soft deprecated.

We recommend that you use the Table API and SQL to run efficient batch pipelines in a fully unified API. Table API is well integrated with common batch connectors and catalogs.

Alternatively, you can also use the DataStream API with BATCH execution mode. The linked section also outlines cases where it makes sense to use the DataSet API but those cases will become rarer as development progresses and the DataSet API will eventually be removed. Please also see FLIP-131 for background information on this decision.

给 DataSet 中的元素编号 #

在一些算法中，可能需要为数据集元素分配唯一标识符。 本文档阐述了如何将 DataSetUtils 用于此目的。

以密集索引编号 #

zipWithIndex 为元素分配连续的标签，接收数据集作为输入并返回一个新的 (unique id, initial value) 二元组的数据集。 这个过程需要分为两个（子）过程，首先是计数，然后是标记元素，由于计数操作的同步性，这个过程不能被 pipelined（流水线化）。

可供备选的 zipWithUniqueId 是以 pipelined 的方式进行工作的。当唯一标签足够时，首选 zipWithUniqueId 。 例如，下面的代码：

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()

from flink.plan.Environment import get_environment

env = get_environment()
env.set_parallelism(2)
input = env.from_elements("A", "B", "C", "D", "E", "F", "G", "H")

result = input.zip_with_index()

result.write_text(result_path)
env.execute()

可能会生成这些元组：(0,G)，(1,H)，(2,A)，(3,B)，(4,C)，(5,D)，(6,E)，(7,F)

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以唯一标识符编号 #

在许多情况下，可能不需要指定连续的标签。 zipWithUniqueId 以 pipelined 的方式工作，加快了标签分配的过程。该方法接收一个数据集作为输入，并返回一个新的 (unique id, initial value) 二元组的数据集。 例如，下面的代码：

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()

可能会产生这些元组：(0,G)，(1,A)，(2,H)，(3,B)，(5,C)，(7,D)，(9,E)，(11,F)

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