116.Spark大型电商项目-广告点击流量实时统计-基于动态黑名单进行点击行为过滤

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目录

代码

AdClickRealTimeStatSpark.java 

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本篇文章记录广告点击流量实时统计-基于动态黑名单进行点击行为过滤。

代码

spark.ad

AdClickRealTimeStatSpark.java 

    /**
     * 过滤广告黑名单用户日志
     * @param adRealTimeLogDStream
     * @return
     */

    private static JavaPairDStream<String,String> filterByBlacklist(JavaPairInputDStream<String,String> adRealTimeLogDStream){
        // 刚刚接受到原始的用户点击行为日志之后
        // 根据mysql中的动态黑名单，进行实时的黑名单过滤（黑名单用户的点击行为，直接过滤掉，不要了）
        // 使用transform算子（将dstream中的每个batch RDD进行处理，转换为任意的其他RDD，功能很强大）
        JavaPairDStream<String,String> filteredAdRealTimeLogDStream = adRealTimeLogDStream.transformToPair(new Function<JavaPairRDD<String, String>, JavaPairRDD<String, String>>() {
            private static final long serialVersionUID = 1L;
            @Override
            public JavaPairRDD<String, String> call(JavaPairRDD<String, String> rdd) throws Exception {

                // 首先，从mysql中查询所有黑名单用户，将其转换为一个rdd
                IAdBlacklistDAO adBlacklistDAO = DAOFactory.getAdBlacklistDAO();
                List<AdBlacklist> adBlacklists = adBlacklistDAO.findAll();

                List<Tuple2<Long,Boolean>> tuples = new ArrayList<Tuple2<Long, Boolean>>();

                for (AdBlacklist adBlacklist : adBlacklists){
                    tuples.add(new Tuple2<Long,Boolean>(adBlacklist.getUserid(),true));
                }

                JavaSparkContext sc = new JavaSparkContext(rdd.context());
                JavaPairRDD<Long,Boolean> blacklistRDD = sc.parallelizePairs(tuples);

                // 将原始数据rdd映射成<userid, tuple2<string, string>>

                JavaPairRDD<Long,Tuple2<String,String>> mappedRDD = rdd.mapToPair(new PairFunction<Tuple2<String, String>, Long, Tuple2<String, String>>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Tuple2<Long, Tuple2<String, String>> call(Tuple2<String, String> tuple) throws Exception {

                        String log = tuple._2;
                        String[] logSplited = log.split("_");
                        long userid = Long.valueOf(logSplited[3]);
                        return new Tuple2<Long,Tuple2<String, String>>(userid,tuple);
                    }
                });

                // 将原始日志数据rdd，与黑名单rdd，进行左外连接
                // 如果说原始日志的userid，没有在对应的黑名单中，join不到，左外连接
                // 用inner join，内连接，会导致数据丢失
                JavaPairRDD<Long, Tuple2<Tuple2<String, String>, Optional<Boolean>>> joinedRDD = mappedRDD.leftOuterJoin(blacklistRDD);

                JavaPairRDD<Long,Tuple2<Tuple2<String,String>,Optional<Boolean>>> filteredRDD = joinedRDD.filter(new Function<Tuple2<Long, Tuple2<Tuple2<String, String>, Optional<Boolean>>>, Boolean>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Boolean call(Tuple2<Long, Tuple2<Tuple2<String, String>, Optional<Boolean>>> tuple) throws Exception {
                        Optional<Boolean> optional = tuple._2._2;
                        // 如果这个值存在，那么说明原始日志中的userid，join到了某个黑名单用户
                        if (optional.isPresent() && optional.get()){
                            return false;
                        }
                        return true;
                    }
                });

                JavaPairRDD<String,String> resultRDD = filteredRDD.mapToPair(new PairFunction<Tuple2<Long, Tuple2<Tuple2<String, String>, Optional<Boolean>>>, String, String>() {
                    private static final long serialVersionUID = 1L;
                    @Override
                    public Tuple2<String, String> call(Tuple2<Long, Tuple2<Tuple2<String, String>, Optional<Boolean>>> tuple) throws Exception {
                        return tuple._2._1;
                    }
                });

                return resultRDD;
            }
        });
        return filteredAdRealTimeLogDStream;
    }