HIVE UDAF和UDTF实现group by后获取top值

先自定义一个UDAF,由于udaf是多输入一条输出的聚合，所以结果拼成字符串输出，代码如下：

public class Top4GroupBy extends UDAF {

    //定义一个对象用于存储数据
    public static class State {
        private Map<Text, IntWritable> counts;
        private int limit;

    }

    /**
     * 累加数据，判断map的key中是否存在该字符串，如果存在累加，不存在放入map中
     * @param s
     * @param o
     * @param i
     */
    private static void increment(State s, Text o, int i) {
        if (s.counts == null) {
            s.counts = new HashMap<Text, IntWritable>();
        }
        IntWritable count = s.counts.get(o);
        if (count == null) {
            Text key = new Text();
            key.set(o);
            s.counts.put(key, new IntWritable(i));
        } else {
            count.set(count.get() + i);
        }

    }

    public static class Top4GroupByEvaluator implements UDAFEvaluator {

        private final State state;

        public Top4GroupByEvaluator() {
            state = new State();
        }

        @Override
        public void init() {
            if (state.counts != null) {
                state.counts.clear();
            }
            if (state.limit == 0) {
                state.limit = 100;
            }
        }

        public boolean iterate(Text value, IntWritable limits) {
            if (value == null || limits == null) {
                return false;
            } else {
                state.limit = limits.get();
                increment(state, value, 1);
            }
            return true;
        }

        public State terminatePartial() {
            return state;
        }

        public boolean merge(State other) {
            if (state == null || other == null) {
                return false;
            }
            state.limit = other.limit;
            for (Map.Entry<Text, IntWritable> e : other.counts.entrySet()) {
                increment(state, e.getKey(), e.getValue().get());
            }
            return true;
        }

        public Text terminate() {
            if (state == null || state.counts.size() == 0) {
                return null;
            }
            Map<Text, IntWritable> it = sortByValue(state.counts, true);
            StringBuffer str = new StringBuffer();
            int i = 0;
            for (Map.Entry<Text, IntWritable> e : it.entrySet()) {
                ++i;
                if (i > state.limit) {//只输出传入条数的结果，并拼成字符串
                    break;
                }
                str.append(e.getKey().toString()).append("$@").append(e.getValue().get()).append("$*");
            }
            return new Text(str.toString());
        }

        /*
         * 实现一个map按值的排序算法
         */
        @SuppressWarnings("unchecked")
        public static Map sortByValue(Map map, final boolean reverse) {
            List list = new LinkedList(map.entrySet());
            Collections.sort(list, new Comparator() {
                public int compare(Object o1, Object o2) {
                    if (reverse) {
                        return -((Comparable) ((Map.Entry) o1).getValue()).compareTo(((Map.Entry) o2).getValue());
                    }
                    return ((Comparable) ((Map.Entry) o1).getValue()).compareTo(((Map.Entry) o2).getValue());
                }
            });

            Map result = new LinkedHashMap();
            for (Iterator it = list.iterator(); it.hasNext();) {
                Map.Entry entry = (Map.Entry) it.next();
                result.put(entry.getKey(), entry.getValue());
            }
            return result;
        }

    }

}

还需要自定义一个UDTF，安装分隔符将字符串切分，将字符串转化为多行的列表输出：

public class ExplodeMap extends GenericUDTF {

    @Override
    public void close() throws HiveException {

    }

    @Override
    public StructObjectInspector initialize(ObjectInspector[] args) throws UDFArgumentException {
        if (args.length != 1) {
            throw new UDFArgumentLengthException("ExplodeMap takes only one argument");
        }
        if (args[0].getCategory() != ObjectInspector.Category.PRIMITIVE) {
            throw new UDFArgumentException("ExplodeMap takes string as a parameter");
        }
        ArrayList<String> fieldNames = new ArrayList<String>();
        ArrayList<ObjectInspector> fieldOIs = new ArrayList<ObjectInspector>();
        fieldNames.add("col1");
        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        fieldNames.add("col2");
        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);
        fieldNames.add("col3");
        fieldOIs.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector);

        return ObjectInspectorFactory.getStandardStructObjectInspector(fieldNames, fieldOIs);
    }

    @Override
    public void process(Object[] args) throws HiveException {
        String input = args[0].toString();
        String[] test = input.split("\\$\\*");
        for (int i = 0; i < test.length; i++) {
            try {
                String[] result  = new String[3];
                String[] sp= test[i].split("\\$\\@");
                result[0] =sp[0];
                result[1] =sp[1];
                result[2] = String.valueOf(i + 1);
                forward(result);
            } catch (Exception e) {
                continue;
            }
        }

    }

}

 两个函数分别以top_group和explode_map为函数名加入到hive函数库中，应用例子如下（获取前100个landingrefer的top url 100）

hive -e "select t.landingrefer, mytable.col1, mytable.col2,mytable.col3 from (select landingrefer, top_group(url，100) pro, count(sid) s from pvlog  where dt=20120719 and depth=1 group by landingrefer order by s desc limit 100) t lateral view explode_map(t.pro) mytable as col1, col2, col3;"> test