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SparkSQL的代碼示例分析
這篇文章跟大家分析一下“Spark SQL的代碼示例分析”。內(nèi)容詳細(xì)易懂，對(duì)“Spark SQL的代碼示例分析”感興趣的朋友可以跟著小編的思路慢慢深入來(lái)閱讀一下，希望閱讀后能夠?qū)Υ蠹矣兴鶐椭Ｏ旅娓【幰黄鹕钊雽W(xué)習(xí)“Spark SQL的代碼示例分析”的知識(shí)吧。
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參考官網(wǎng)Spark SQL的例子，自己寫了一個(gè)腳本:
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.createSchemaRDD
case class UserLog(userid: String, time1: String, platform: String, ip: String, openplatform: String, appid: String)
// Create an RDD of Person objects and register it as a table.
val user = sc.textFile("/user/hive/warehouse/api_db_user_log/dt=20150517/*").map(_.split("\\^")).map(u => UserLog(u(0), u(1), u(2), u(3), u(4), u(5)))
user.registerTempTable("user_log")
// SQL statements can be run by using the sql methods provided by sqlContext.
val allusers = sqlContext.sql("SELECT * FROM user_log")
// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
allusers.map(t => "UserId:" + t(0)).collect().foreach(println)結(jié)果執(zhí)行出錯(cuò)：
org.apache.spark.SparkException: Job aborted due to stage failure: Task 1 in stage 50.0 failed 1 times, most recent failure: Lost task 1.0 in stage 50.0 (TID 73, localhost): java.lang.ArrayIndexOutOfBoundsException: 5 at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply(:30) at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$2.apply( :30) at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1319) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910) at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:910) at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1319) at org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:1319) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:196) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745)
從日志可以看出，是數(shù)組越界了。
用命令
sc.textFile("/user/hive/warehouse/api_db_user_log/dt=20150517/*").map(_.split("\\^")).foreach(x => println(x.size))發(fā)現(xiàn)有一行記錄split出來(lái)的大小是“5”
6 6 6 6 6 6 6 6 6 6 15/05/21 20:47:37 INFO Executor: Finished task 0.0 in stage 2.0 (TID 4). 1774 bytes result sent to driver 6 6 6 6 6 6 5 6 15/05/21 20:47:37 INFO Executor: Finished task 1.0 in stage 2.0 (TID 5). 1774 bytes result sent to driver
原因是這行記錄有空值“44671799^2015-03-27 20:56:05^2^117.93.193.238^0^^”
網(wǎng)上找到了解決辦法——使用split(str,int)函數(shù)。修改后代碼：
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext.createSchemaRDD
case class UserLog(userid: String, time1: String, platform: String, ip: String, openplatform: String, appid: String)
// Create an RDD of Person objects and register it as a table.
val user = sc.textFile("/user/hive/warehouse/api_db_user_log/dt=20150517/*").map(_.split("\\^", -1)).map(u => UserLog(u(0), u(1), u(2), u(3), u(4), u(5)))
user.registerTempTable("user_log")
// SQL statements can be run by using the sql methods provided by sqlContext.
val allusers = sqlContext.sql("SELECT * FROM user_log")
// The results of SQL queries are SchemaRDDs and support all the normal RDD operations.
// The columns of a row in the result can be accessed by ordinal.
allusers.map(t => "UserId:" + t(0)).collect().foreach(println)關(guān)于Spark SQL的代碼示例分析就分享到這里啦，希望上述內(nèi)容能夠讓大家有所提升。如果想要學(xué)習(xí)更多知識(shí)，請(qǐng)大家多多留意小編的更新。謝謝大家關(guān)注一下創(chuàng)新互聯(lián)網(wǎng)站！
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