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Introduce to
Spark SQL 1.3.0
Introduce to Spark sql 1.3.0
Introduce to Spark sql 1.3.0
Introduce to Spark sql 1.3.0
Optimization
Introduce to Spark sql 1.3.0
Introduce to Spark sql 1.3.0
效率提升
主要的物件
● https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sq
l.package
spark-shell
● 除了sc之外,還會起SQL Context
Spark context available as sc.
15/03/22 02:09:11 INFO SparkILoop: Created sql context (with Hive support)..
SQL context available as sqlContext.
JAR
val sc: SparkContext // An existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
// this is used to implicitly convert an RDD to a DataFrame.
import sqlContext.implicits._
DF from RDD
● 先轉成RDD
scala> val data = sc.textFile("hdfs://localhost:54310/user/hadoop/ml-100k/u.data")
● 建立case class
case class Rattings(userId: Int, itemID: Int, rating: Int, timestmap:String)
● 轉成Data Frame
scala> val ratting = data.map(_.split("t")).map(p => Rattings(p(0).trim.toInt,
p(1).trim.toInt, p(2).trim.toInt, p(3))).toDF()
ratting: org.apache.spark.sql.DataFrame = [userId: int, itemID: int, rating: int, timestmap:
string]
DF from json
● 格式
{"movieID":242,"name":"test1"}
{"movieID":307,"name":"test2"}
● 可以直接呼叫
scala> val movie = sqlContext.jsonFile("hdfs://localhost:54310/user/hadoop/ml-
100k/movies.json")
Dataframe Operations
● Show()
userId itemID rating timestmap
196 242 3 881250949
186 302 3 891717742
22 377 1 878887116
244 51 2 880606923
253 465 5 891628467
● head(5)
res11: Array[org.apache.spark.sql.Row] = Array([196,242,3,881250949],
[186,302,3,891717742], [22,377,1,878887116], [244,51,2,880606923],
[166,346,1,886397596])
● printSchema() ←根本神技
scala> ratting.printSchema()
root
|-- userId: integer (nullable = false)
|-- itemID: integer (nullable = false)
|-- rating: integer (nullable = false)
|-- timestmap: string (nullable = true)
Select
● Select Column
scala> ratting.select("userId").show()
● Condition Select
scala> ratting.select(ratting("itemID")>100).show()
(itemID > 100)
true
true
true
filter
● 篩選條件
scala> ratting.filter(ratting("rating")>3).show()
userId itemID rating timestmap
298 474 4 884182806
253 465 5 891628467
286 1014 5 879781125
200 222 5 876042340
122 387 5 879270459
291 1042 4 874834944
119 392 4
● 偷懶寫法
ratting.filter('rating>3).show()
● 合併使用
scala> ratting.filter(ratting("rating")>3).select("userID","itemID")show()
userID itemID
298 474
286 1014
● 也可以
ratting.filter('userID>500).select(avg('rating),max('rating),sum('rating))show()
GROUP BY
● count()
scala> ratting.groupBy("userId").count().show()
userId count
831 73
631 20
● agg()
scala> ratting.groupBy("userId").agg("rating"->"avg","userID" -> "count").show()
● 可以連用
scala> ratting.groupBy("userId").count().sort("count","userID").show()
GROUP BY
● 其他
o avg
o max
o min
o mean
o sum
● 更多Function
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.functions$
● 大雜燴
ratting.groupBy('userID).agg(('userID),avg('rating),max('rating),sum('rating),count('
userID AVG('rating) MAX('rating) SUM('rating) COUNT('rating)
831 3.5205479452054793 5 257 73
631 3.1 4 62 20
31 3.9166666666666665 5 141 36
431 3.380952380952381 5 71 21
231 3.6666666666666665 5 77 21
832 2.96 5 74 25
632 3.6610169491525424 5 432 118
UnionAll
● 合併相同欄位表格
scala> val ratting1_3 = ratting.filter(ratting("rating")<=3)
scala> ratting1_3.count() //res79: Long = 44625
scala> val ratting4_5 = ratting.filter(ratting("rating")>3)
scala> ratting4_5.count() //res80: Long = 55375
ratting1_3.unionAll(ratting4_5).count() //res81: Long = 100000
● 欄位不同無法UNION
scala> ratting1_3.unionAll(test).count()
java.lang.AssertionError: assertion failed
JOIN
● 基本語法
scala> ratting.join(movie, $"itemID" === $"movieID", "inner").show()
userId itemID rating timestmap movieID name
196 242 3 881250949 242 test1
63 242 3 875747190 242 test1
● 可支援的join型態:inner, outer, left_outer,
right_outer, semijoin.
也可以把表格註冊成TABLE
● 註冊
scala> ratting.registerTempTable("ratting_table")
● 寫SQL
sqlContext.sql("SELECT us
scala> sqlContext.sql("SELECT userID FROM ratting_table").show()
DF支援RDD操作
● MAP
scala> result.map(t => "user:" + t(0)).collect().foreach(println)
● 取出來的物件型態是Any
scala> ratting.map(t => t(2)).take(5)
● 先轉string再轉int
scala> ratting.map(t => Array(t(0),t(2).toString.toInt * 10)).take(5)
res130: Array[Array[Any]] = Array(Array(196, 30), Array(186, 30), Array(22, 10),
Array(244, 20), Array(166, 10))
SAVE DATA
● Save()
ratting.select("itemID").save("hdfs://localhost:54310/test2.json","json")
● saveAsParquetFile
● saveAsTable(Hive Table)
DataType
● Numeric types
● String type
● Binary type
● Boolean type
● Datetime type
o TimestampType: Represents values comprising values of fields year, month, day, hour, minute,
and second.
o DateType: Represents values comprising values of fields year, month, day.
● Complex types
發展方向
Introduce to Spark sql 1.3.0
Reference
1. https://databricks.com/blog/2015/02/17/introducing-
dataframes-in-spark-for-large-scale-data-science.html
1. https://www.youtube.com/watch?v=vxeLcoELaP4
1. http://www.slideshare.net/databricks/introducing-
dataframes-in-spark-for-large-scale-data-science

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