007 Dataset

scala> // Define the case classes for using in conjunction with DataFrames and Dataset

scala> case class Trans(accNo: String, tranAmount: Double)
defined class Trans

scala> // Creation of the list from where the Dataset is going to be created using a case class.

scala> val acTransList = Seq(Trans("SB10001", 1000), Trans("SB10002",1200), Trans("SB10003", 8000), Trans("SB10004",400), Trans("SB10005",300),Trans("SB10006",10000), Trans("SB10007",500), Trans("SB10008",56),Trans("SB10009",30),Trans("SB10010",7000), Trans("CR10001",7000),Trans("SB10002",-10))
acTransList: Seq[Trans] = List(Trans(SB10001,1000.0), Trans(SB10002,1200.0), Trans(SB10003,8000.0), Trans(SB10004,400.0), Trans(SB10005,300.0), Trans(SB10006,10000.0), Trans(SB10007,500.0), Trans(SB10008,56.0), Trans(SB10009,30.0), Trans(SB10010,7000.0), Trans(CR10001,7000.0), Trans(SB10002,-10.0))

scala> // Create the Dataset

scala> val acTransDS = acTransList.toDS()
acTransDS: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> acTransDS.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10001|    1000.0|
|SB10002|    1200.0|
|SB10003|    8000.0|
|SB10004|     400.0|
|SB10005|     300.0|
|SB10006|   10000.0|
|SB10007|     500.0|
|SB10008|      56.0|
|SB10009|      30.0|
|SB10010|    7000.0|
|CR10001|    7000.0|
|SB10002|     -10.0|
+-------+----------+


scala> // Apply filter and create another Dataset of good transaction records

scala> val goodTransRecords = acTransDS.filter(_.tranAmount > 0).filter(_.accNo.startsWith("SB"))
goodTransRecords: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> goodTransRecords.show()
+-------+----------+                                                            
|  accNo|tranAmount|
+-------+----------+
|SB10001|    1000.0|
|SB10002|    1200.0|
|SB10003|    8000.0|
|SB10004|     400.0|
|SB10005|     300.0|
|SB10006|   10000.0|
|SB10007|     500.0|
|SB10008|      56.0|
|SB10009|      30.0|
|SB10010|    7000.0|
+-------+----------+


scala> // Apply filter and create another Dataset of high value transaction records

scala> val highValueTransRecords = goodTransRecords.filter(_.tranAmount > 1000)
highValueTransRecords: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> highValueTransRecords.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10002|    1200.0|
|SB10003|    8000.0|
|SB10006|   10000.0|
|SB10010|    7000.0|
+-------+----------+


scala> // The function that identifies the bad amounts

scala> val badAmountLambda = (trans: Trans) => trans.tranAmount <= 0
badAmountLambda: Trans => Boolean = <function1>

scala> // The function that identifies bad accounts

scala> val badAcNoLambda = (trans: Trans) => trans.accNo.startsWith("SB") == false
badAcNoLambda: Trans => Boolean = <function1>

scala> // The function that identifies bad accounts

scala> val badAcNoLambda = (trans: Trans) => trans.accNo.startsWith("SB") == false
badAcNoLambda: Trans => Boolean = <function1>

scala> // Apply filter and create another Dataset of bad amount records

scala> val badAmountRecords = acTransDS.filter(badAmountLambda)
badAmountRecords: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> badAmountRecords.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10002|     -10.0|
+-------+----------+


scala> // Apply filter and create another Dataset of bad account records

scala> val badAccountRecords = acTransDS.filter(badAcNoLambda)
badAccountRecords: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> badAccountRecords.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|CR10001|    7000.0|
+-------+----------+


scala> // Do the union of two Dataset and create another Dataset

scala> val badTransRecords = badAmountRecords.union(badAccountRecords)
badTransRecords: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> badTransRecords.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10002|     -10.0|
|CR10001|    7000.0|
+-------+----------+


scala> // Calculate the sum

scala> val sumAmount = goodTransRecords.map(trans => trans.tranAmount).reduce(_ + _)
sumAmount: Double = 28486.0

scala> // Calculate the maximum

scala> val maxAmount = goodTransRecords.map(trans => trans.tranAmount).reduce((a, b) => if (a > b) a else b)
maxAmount: Double = 10000.0

scala> // Calculate the minimum

scala> val minAmount = goodTransRecords.map(trans => trans.tranAmount).reduce((a, b) => if (a < b) a else b)
minAmount: Double = 30.0

scala> // Convert the Dataset to DataFrame

scala> val acTransDF = acTransDS.toDF()
acTransDF: org.apache.spark.sql.DataFrame = [accNo: string, tranAmount: double]

scala> acTransDF.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10001|    1000.0|
|SB10002|    1200.0|
|SB10003|    8000.0|
|SB10004|     400.0|
|SB10005|     300.0|
|SB10006|   10000.0|
|SB10007|     500.0|
|SB10008|      56.0|
|SB10009|      30.0|
|SB10010|    7000.0|
|CR10001|    7000.0|
|SB10002|     -10.0|
+-------+----------+


scala> // Use Spark SQL to find out invalid transaction records

scala> acTransDF.createOrReplaceTempView("trans")

scala> val invalidTransactions = spark.sql("SELECT accNo, tranAmount FROM trans WHERE (accNo NOT LIKE 'SB%') OR tranAmount <= 0")
19/12/02 22:29:36 WARN ObjectStore: Failed to get database global_temp, returning NoSuchObjectException
invalidTransactions: org.apache.spark.sql.DataFrame = [accNo: string, tranAmount: double]

scala> invalidTransactions.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|CR10001|    7000.0|
|SB10002|     -10.0|
+-------+----------+


scala> // Interoperability of RDD, DataFrame and Dataset

scala> // Create RDD

scala> val acTransRDD = sc.parallelize(acTransList)
acTransRDD: org.apache.spark.rdd.RDD[Trans] = ParallelCollectionRDD[41] at parallelize at <console>:26

scala> // Convert RDD to DataFrame

scala> val acTransRDDtoDF = acTransRDD.toDF()
acTransRDDtoDF: org.apache.spark.sql.DataFrame = [accNo: string, tranAmount: double]

scala> // Convert the DataFrame to Dataset with the type checking

scala> val acTransDFtoDS = acTransRDDtoDF.as[Trans]
acTransDFtoDS: org.apache.spark.sql.Dataset[Trans] = [accNo: string, tranAmount: double]

scala> acTransDFtoDS.show()
+-------+----------+
|  accNo|tranAmount|
+-------+----------+
|SB10001|    1000.0|
|SB10002|    1200.0|
|SB10003|    8000.0|
|SB10004|     400.0|
|SB10005|     300.0|
|SB10006|   10000.0|
|SB10007|     500.0|
|SB10008|      56.0|
|SB10009|      30.0|
|SB10010|    7000.0|
|CR10001|    7000.0|
|SB10002|     -10.0|
+-------+----------+

人面猴
序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
沈念sama阅读 201,681评论 5赞 474
死咒
序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
沈念sama阅读 84,710评论 2赞 377
救了他两次的神仙让他今天三更去死
文/潘晓璐我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
开封第一讲书人阅读 148,623评论 0赞 334
道士缉凶录：失踪的卖姜人
文/不坏的土叔我叫张陵，是天一观的道长。经常有香客问我，道长，这世上最难降的妖魔是什么？我笑而不...
开封第一讲书人阅读 54,202评论 1赞 272
港岛之恋（遗憾婚礼）
正文为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
茶点故事阅读 63,232评论 5赞 363
恶毒庶女顶嫁案：这布局不是一般人想出来的
文/花漫我一把揭开白布。她就那样静静地躺着，像睡着了一般。火红的嫁衣衬着肌肤如雪。梳的纹丝不乱的头发上，一...
开封第一讲书人阅读 48,368评论 1赞 281
城市分裂传说
那天，我揣着相机与录音，去河边找鬼。笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。我是一名探鬼主播，决...
沈念sama阅读 37,795评论 3赞 393
双鸳鸯连环套：你想象不到人心有多黑
文/苍兰香墨我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
开封第一讲书人阅读 36,461评论 0赞 256
万荣杀人案实录
序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
沈念sama阅读 40,647评论 1赞 295
护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
茶点故事阅读 35,476评论 2赞 317
白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
茶点故事阅读 37,525评论 1赞 329
活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
沈念sama阅读 33,226评论 3赞 318
日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
茶点故事阅读 38,785评论 3赞 303
男人毒药：我在死后第九天来索命
文/蒙蒙一、第九天我趴在偏房一处隐蔽的房顶上张望。院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
开封第一讲书人阅读 29,857评论 0赞 19
一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
开封第一讲书人阅读 31,090评论 1赞 258
情欲美人皮
我被黑心中介骗来泰国打工，没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。一个月前我还...
沈念sama阅读 42,647评论 2赞 348
代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
茶点故事阅读 42,215评论 2赞 341

007 Dataset

推荐阅读更多精彩内容