HIVE自定義函數的擴展

時間  2019-11-24
標籤 hive 自定義 函數 擴展 欄目 Hadoop 简体版
原文   原文鏈接

做者簡介html

淳敏，物流架構師同時也是一位team leader，工做認真負責，曾在休假期間「面向大海編程」，不明覺厲java

在Hive中，用戶能夠自定義一些函數，用於擴展HiveQL的功能。Hive 自定義函數主要包含如下三種：git

  • UDF（user-defined function） 單獨處理一行，輸出也是以行輸出。許多Hive內置字符串，數學函數，時間函數都是這種類型。大多數狀況下編寫對應功能的處理函數就能知足需求。如：concat, split, length ,rand等。這種UDF主要有兩種寫法：繼承實現UDF類和繼承GenericUDF類（通用UDF）。
  • UDAF（user-defined aggregate function） 用於處理多行數據並造成累加結果。通常配合group by使用。主要用於累加操做，常見的函數有max， min， count， sum，collect_set等。這種UDF主要有兩種寫法：繼承實現 UDAF類和繼承實現AbstractGenericUDAFResolver類。
  • UDTF（user-defined table function） 處理一行數據產生多行數據或者將一列打成多列。 如explode, 一般配合Lateral View使用，實現列轉行的功能。parse_url_tuple將一列轉爲多列。

Hive的UDF機制是須要用戶實現: Resolver和Evaluator，其中Resolver就用來處理輸入，調用Evaluator，Evaluator就是具體功能的實現。sql

自定義UDF實現和調用機制

Hadoop提供了一個基礎類org.apache.hadoop.hive.ql.exec.UDF，在這個類中含有了一個UDFMethodResolver的接口實現類DefaultUDFMethodResolver的對象。apache

public class UDF {
  private UDFMethodResolver rslv;

  public UDF() {
    this.rslv = new DefaultUDFMethodResolver(this.getClass());
  }
	......
}
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在DefaultUDFMethodResolver中，提供了一個getEvalMethod的方法，從切面調用UDF的evaluate方法編程

public class DefaultUDFMethodResolver implements UDFMethodResolver {
  private final Class<? extends UDF> udfClass;

  public DefaultUDFMethodResolver(Class<? extends UDF> udfClass) {
    this.udfClass = udfClass;
  }

  public Method getEvalMethod(List<TypeInfo> argClasses) throws UDFArgumentException {
    return FunctionRegistry.getMethodInternal(this.udfClass, "evaluate", false, argClasses);
  }
}
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自定義UDF的實現上以繼承org.apache.hadoop.hive.ql.exec.UDF爲基礎，而後實現一個evaluate方法，該方法會被DefaultUDFMethodResolver對象執行。json

Case Study: 判斷座標點是否是在圖形中

public class DAIsContainPoint extends UDF {

  public Boolean evaluate(Double longitude, Double latitude, String geojson) {

    Boolean isContained = false;
    try {
      Polygon polygon = JTSHelper.parse(geojson);
      Coordinate center = new Coordinate(longitude, latitude);
      GeometryFactory factory = new GeometryFactory();
      Point point = factory.createPoint(center);
      isContained = polygon.contains(point);
    }catch (Throwable e){
      isContained = false;
    }finally {
      return isContained;
    }
  }
}
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完成了代碼定義以後須要對其進行打包，編譯成一個jar，注意: 最終的jar中須要包含全部依賴的jar，maven編譯上推薦使用maven-shade-plugin數組

<build>
  <plugins>
    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-shade-plugin</artifactId>
      <version>2.2</version>
      <executions>
        <execution>
          <phase>package</phase>
          <goals>
            <goal>shade</goal>
          </goals>
          <configuration>
            <filters>
              <filter>
                <artifact>*:*</artifact>
                <excludes>
                  <exclude>META-INF/*.SF</exclude>
                  <exclude>META-INF/*.DSA</exclude>
                  <exclude>META-INF/*.RSA</exclude>
                </excludes>
              </filter>
            </filters>
          </configuration>
        </execution>
      </executions>
    </plugin>
  </plugins>
</build>
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最後產生的jar文件須要在HIVE SQL中被引用架構

add jar hdfs://xxx/udf/ff8bd59f-d0a5-4b13-888b-5af239270869/udf.jar;
create temporary function is_in_polygon as 'me.ele.breat.hive.udf.DAIsContainPoint';

select lat, lng, geojson, is_in_polygon(lat, lng, geojson) as is_in from example;
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自定義UDAF和MapReduce

在Hive的聚合計算中，採用MapReduce的方式來加快聚合的速度，而UDAF就是用來撰寫聚合類自定義方法的擴展方式。關於MapReduce須要補充知識的請看這裏，爲了更好的說明白UDAF咱們須要知道一下MapReduce的流程app

回到Hive中來，在UDAF的實現中，首先須要繼承org.apache.hadoop.hive.ql.udf.generic.AbstractGenericUDAFResolver，並實現org.apache.hadoop.hive.ql.udf.generic.GenericUDAFResolver2接口。而後構造GenericUDAFEvaluator類，實現MapReduce的計算過程，其中有3個關鍵的方法

  • 方法iterate：獲取mapper，輸送去作merge
  • 方法merge：combiner合併mapper
  • 方法terminate：合併全部combiner返回結果

而後再實現一個繼承AbstractGenericUDAFResolver的類，重載其getEvaluator的方法，返回一個GenericUDAFEvaluator的實例

Case Study：合併地理圍欄

public class DAJoinV2 extends AbstractGenericUDAFResolver implements GenericUDAFResolver2 {

  @Override
  public GenericUDAFEvaluator getEvaluator(GenericUDAFParameterInfo genericUDAFParameterInfo) throws SemanticException {
    return new DAJoinStringEvaluator();
  }

  public GenericUDAFEvaluator getEvaluator(TypeInfo[] typeInfos) throws SemanticException {
    if (typeInfos.length != 1) {
      throw new UDFArgumentTypeException(typeInfos.length - 1,
          "Exactly one argument is expected.");
    }

    if (typeInfos[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
      throw new UDFArgumentTypeException(0,
          "Only primitive type arguments are accepted but "
              + typeInfos[0].getTypeName() + " is passed.");
    }

    switch (((PrimitiveTypeInfo) typeInfos[0]).getPrimitiveCategory()) {
      case STRING:
        return new DAJoinStringEvaluator();
      default:
        throw new UDFArgumentTypeException(0,
            "Only numeric or string type arguments are accepted but "
                + typeInfos[0].getTypeName() + " is passed.");
    }
  }

  public static class DAJoinStringEvaluator extends GenericUDAFEvaluator {

    private PrimitiveObjectInspector mInput;
    private Text mResult;

    // 存儲Geometry join的值的類
    static class PolygonAgg implements AggregationBuffer {
      Geometry geometry;
    }

    //定義：UDAF的返回類型，肯定了DAJoin自定義UDF的返回類型是Text類型
    @Override
    public ObjectInspector init(Mode m, ObjectInspector[] parameters) throws HiveException {
      assert (parameters.length == 1);
      super.init(m, parameters);
      mResult = new Text();
      mInput = (PrimitiveObjectInspector) parameters[0];
      return PrimitiveObjectInspectorFactory.writableStringObjectInspector;
    }

    //內存建立，用來存儲mapper,combiner,reducer運算過程當中的相加總和。
    public AggregationBuffer getNewAggregationBuffer() throws HiveException {
      PolygonAgg polygonAgg = new PolygonAgg();
      reset(polygonAgg);
      return polygonAgg;
    }

    public void reset(AggregationBuffer aggregationBuffer) throws HiveException {
      PolygonAgg polygonAgg = (PolygonAgg) aggregationBuffer;
      GeometryFactory factory = new GeometryFactory();
      polygonAgg.geometry = factory.createPolygon(new Coordinate[]{});
    }

    //map階段：獲取每一個mapper，去進行merge
    public void iterate(AggregationBuffer aggregationBuffer, Object[] objects) throws HiveException {
      assert (objects.length == 1);
      merge(aggregationBuffer, objects[0]);
    }

    //在一個子的partial中combiner合併map返回結果
    public Object terminatePartial(AggregationBuffer aggregationBuffer) throws HiveException {
      return terminate(aggregationBuffer);
    }

    //combiner合併map返回結果
    public void merge(AggregationBuffer aggregationBuffer, Object partial) throws HiveException {
      if (partial != null) {
        try {
          PolygonAgg polygonAgg = (PolygonAgg) aggregationBuffer;
          String geoJson = PrimitiveObjectInspectorUtils.getString(partial, mInput);
          Polygon polygon = JTSHelper.parse(geoJson);
          polygonAgg.geometry = polygonAgg.geometry.union(polygon);
        } catch (Exception e){

        }
      }
    }

    //reducer合併全部combiner返回結果
    public Object terminate(AggregationBuffer aggregationBuffer) throws HiveException {
      try {
        PolygonAgg polygonAgg = (PolygonAgg) aggregationBuffer;
        Geometry buffer = polygonAgg.geometry.buffer(0);
        mResult.set(JTSHelper.convert2String(buffer.convexHull()));
        return mResult;
      }catch (Exception e) {
        return "";
      }
    }

  }
}
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打包以後將其用在HIVE SQL中執行

add jar hdfs://xxx/udf/ff8bd59f-d0a5-4b13-888b-5af239270869/udf.jar;
create temporary function da_join as 'me.ele.breat.hive.udf.DAJoinV2';

create table udaf_example as
select id, da_join(da_range) as da_union_polygon
  from example
group by id
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自定義UDTF

在UDTF的實現中，首先須要繼承org.apache.hadoop.hive.ql.udf.generic.GenericUDTF，實現process,initialize和close方法

  • initialize返回StructObjectInspector對象，決定最後輸出的column的名稱和類型
  • process是對每個輸入record進行處理，產生出一個新數組，傳遞到forward方法中進行處理
  • close關閉整個調用的回調處，清理內存

Case Study: 輸入Polygon轉成一組S2Cell

public class S2SimpleRegionCoverV2 extends GenericUDTF {

  private final static  int LEVEL = 16;

  @Override
  public StructObjectInspector initialize(ObjectInspector[] argOIs) throws UDFArgumentException {
    List<String> structFieldNames = Lists.newArrayList("s2cellid");
    List<ObjectInspector> structFieldObjectInspectors = Lists.<ObjectInspector>newArrayList(
        PrimitiveObjectInspectorFactory.javaLongObjectInspector);

    return ObjectInspectorFactory
        .getStandardStructObjectInspector(structFieldNames, structFieldObjectInspectors);
  }

  @Override
  public void process(Object[] objects) throws HiveException {
    String json = String.valueOf(objects[0]);

    List<Long> s2cellids = toS2CellIds(json);

    for (Long s2cellid: s2cellids){
      forward(new Long[]{s2cellid});
    }
  }

  public static List<Long> toS2CellIds(String json) {
    GeometryFactory factory = new GeometryFactory();
    GeoJsonReader reader = new GeoJsonReader();

    Geometry geometry = null;
    try {
      geometry = reader.read(json);
    } catch (ParseException e) {
      geometry = factory.createPolygon(new Coordinate[]{});
    }

    List<S2Point> polygonS2Point = new ArrayList<S2Point>();
    for (Coordinate coordinate : geometry.getCoordinates()) {
      S2LatLng s2LatLng = S2LatLng.fromDegrees(coordinate.y, coordinate.x);
      polygonS2Point.add(s2LatLng.toPoint());
    }

    List<S2Point> points = polygonS2Point;

    if (points.size() == 0) {
      return Lists.newArrayList();
    }

    ArrayList<S2CellId> result = new ArrayList<S2CellId>();
    S2RegionCoverer
        .getSimpleCovering(new S2Polygon(new S2Loop(points)), points.get(0), LEVEL, result);

    List<Long> output = new ArrayList<Long>();
    for (S2CellId s2CellId : result) {
      output.add(s2CellId.id());
    }

    return output;
  }

  @Override
  public void close() throws HiveException {

  }
}
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在使用的時候和lateral view連在一塊兒用

add jar hdfs://bipcluster/data/upload/udf/ff8bd59f-d0a5-4b13-888b-5af239270869/google_s2_udf.jar;
create temporary function da_cover as 'me.ele.breat.hive.udf.S2SimpleRegionCoverV2';

drop table if exists temp.cm_s2_id_cover_list;

create table temp.cm_s2_id_cover_list as
select tb_s2cellid.s2cellid, source.shop_id
from (
select
  geometry,
  shop_id
from
  example) source
lateral view da_cover(geometry) tb_s2cellid as s2cellid;
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參考



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