flink dataset api使用及原理

随着大数据技术在各行各业的广泛应用，要求能对海量数据进行实时处理的需求越来越多，同时数据处理的业务逻辑也越来越复杂，传统的批处理方式和早期的流式处理框架也越来越难以在延迟性、吞吐量、容错能力以及使用便捷性等方面满足业务日益苛刻的要求。

在这种形势下，新型流式处理框架Flink通过创造性地把现代大规模并行处理技术应用到流式处理中来，极大地改善了以前的流式处理框架所存在的问题。

1.概述：

flink提供DataSet Api用户处理批量数据。flink先将接入数据转换成DataSet数据集，并行分布在集群的每个节点上；然后将DataSet数据集进行各种转换操作(map，filter等)，最后通过DataSink操作将结果数据集输出到外部系统。

2.数据接入

输入InputFormat

/**
 * The base interface for data sources that produces records.
 * <p>
 * The input format handles the following:
 * <ul>
 *   <li>It describes how the input is split into splits that can be processed in parallel.</li>
 *   <li>It describes how to read records from the input split.</li>
 *   <li>It describes how to gather basic statistics from the input.</li> 
 * </ul>
 * <p>
 * The life cycle of an input format is the following:
 * <ol>
 *   <li>After being instantiated (parameterless), it is configured with a {@link Configuration} object. 
 *       Basic fields are read from the configuration, such as a file path, if the format describes
 *       files as input.</li>
 *   <li>Optionally: It is called by the compiler to produce basic statistics about the input.</li>
 *   <li>It is called to create the input splits.</li>
 *   <li>Each parallel input task creates an instance, configures it and opens it for a specific split.</li>
 *   <li>All records are read from the input</li>
 *   <li>The input format is closed</li>
 * </ol>
 * <p>
 * IMPORTANT NOTE: Input formats must be written such that an instance can be opened again after it was closed. That
 * is due to the fact that the input format is used for potentially multiple splits. After a split is done, the
 * format's close function is invoked and, if another split is available, the open function is invoked afterwards for
 * the next split.
 *  
 * @see InputSplit
 * @see BaseStatistics
 * 
 * @param <OT> The type of the produced records.
 * @param <T> The type of input split.
 */

3.数据转换

DataSet：一组相同类型的元素。DataSet可以通过transformation转换成其它的DataSet。示例如下：

DataSet#map(org.apache.flink.api.common.functions.MapFunction)
DataSet#reduce(org.apache.flink.api.common.functions.ReduceFunction)
DataSet#join(DataSet)
DataSet#coGroup(DataSet)

其中，Function：用户定义的业务逻辑，支持java 8 lambda表达式

 function的实现通过operator来做的，以map为例

    /**
     * Applies a Map transformation on this DataSet.
     *
     * <p>The transformation calls a {@link org.apache.flink.api.common.functions.MapFunction} for each element of the DataSet.
     * Each MapFunction call returns exactly one element.
     *
     * @param mapper The MapFunction that is called for each element of the DataSet.
     * @return A MapOperator that represents the transformed DataSet.
     *
     * @see org.apache.flink.api.common.functions.MapFunction
     * @see org.apache.flink.api.common.functions.RichMapFunction
     * @see MapOperator
     */
    public <R> MapOperator<T, R> map(MapFunction<T, R> mapper) {
        if (mapper == null) {
            throw new NullPointerException("Map function must not be null.");
        }

        String callLocation = Utils.getCallLocationName();
        TypeInformation<R> resultType = TypeExtractor.getMapReturnTypes(mapper, getType(), callLocation, true);
        return new MapOperator<>(this, resultType, clean(mapper), callLocation);
    }

其中，Operator

 4.数据输出

DataSink：一个用来存储数据结果的操作。

输出OutputFormat

 

例如，可以csv输出

    /**
     * Writes a {@link Tuple} DataSet as CSV file(s) to the specified location with the specified field and line delimiters.
     *
     * <p><b>Note: Only a Tuple DataSet can written as a CSV file.</b>
      * For each Tuple field the result of {@link Object#toString()} is written.
     *
     * @param filePath The path pointing to the location the CSV file is written to.
     * @param rowDelimiter The row delimiter to separate Tuples.
     * @param fieldDelimiter The field delimiter to separate Tuple fields.
     * @param writeMode The behavior regarding existing files. Options are NO_OVERWRITE and OVERWRITE.
     *
     * @see Tuple
     * @see CsvOutputFormat
     * @see DataSet#writeAsText(String) Output files and directories
     */
    public DataSink<T> writeAsCsv(String filePath, String rowDelimiter, String fieldDelimiter, WriteMode writeMode) {
        return internalWriteAsCsv(new Path(filePath), rowDelimiter, fieldDelimiter, writeMode);
    }

    @SuppressWarnings("unchecked")
    private <X extends Tuple> DataSink<T> internalWriteAsCsv(Path filePath, String rowDelimiter, String fieldDelimiter, WriteMode wm) {
        Preconditions.checkArgument(getType().isTupleType(), "The writeAsCsv() method can only be used on data sets of tuples.");
        CsvOutputFormat<X> of = new CsvOutputFormat<>(filePath, rowDelimiter, fieldDelimiter);
        if (wm != null) {
            of.setWriteMode(wm);
        }
        return output((OutputFormat<T>) of);
    }

 5.总结

　　1. flink通过InputFormat对各种数据源的数据进行读取转换成DataSet数据集

　　2. flink提供了丰富的转换操作，DataSet可以通过transformation转换成其它的DataSet，内部的实现是Function和Operator。

　　3. flink通过OutFormat将DataSet转换成DataSink，最终将数据写入到不同的存储介质。

参考资料：

【1】https://blog.51cto.com/13654660/2087705