已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)
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val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,'/','-'),'yyyy-MM') AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)
以上就是关于问题已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)的答案
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qklbishe.com区块链毕设代做网专注|以太坊fabric-计算机|java|毕业设计|代做平台-javagopython毕设 » 已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)
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qklbishe.com区块链毕设代做网专注|以太坊fabric-计算机|java|毕业设计|代做平台-javagopython毕设 » 已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)
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qklbishe.com区块链毕设代做网专注|以太坊fabric-计算机|java|毕业设计|代做平台-javagopython毕设 » 已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)
qklbishe.com区块链毕设代做网专注|以太坊fabric-计算机|java|毕业设计|代做平台-javagopython毕设 » 已知 SPARK SQL 代码块如下: val spark = SparkSession.builder().config(conf).enableHiveSupport().getOrCreate() val sc = spark.sparkContext val SqlContext = spark.sqlContext val user_visit_log_rdd = sc.parallelize(List( "u1,2022/1/21,5", "u2,2022/1/23,6", "u3,2022/1/22,8", "u4,2022/1/20,3", "u1,2022/1/23,6", "u1,2022/2/21,8", "u2,2022/1/23,6", "u1,2022/2/22,4" )).map(v => { val temp = v.split(",") Row(temp(0), temp(1),temp(2).toInt) }) val user_visit_log_schema: StructType = StructType(Array( StructField("userId", StringType), StructField("visitDate", StringType), StructField("visitCount", IntegerType)) ) SqlContext.createDataFrame(user_visit_log_rdd, user_visit_log_schema).registerTempTable("user_visit_log") val sql ="select " + "s.userid ," + "s.month ," + "s.sum_per_month , " + "sum(s.sum_per_month) over(partition BY s.userid order by s.month) as total_visitcount" + "from ( select distinct t.userid as userid ,t.month as month " + " ,sum(t.visitCount) over(partition BY userid,month) as sum_per_month " + "from ( SELECT userid ,date_format(regexp_replace(visitdate,’/’,’-‘),’yyyy-MM’) AS month ,visitCount FROM user_visit_log ) t ) s" SqlContext.sql(sql).show(10)