[英]Scala Spark, compare two DataFrames and select the value of another column
[英]spark scala compare dataframes having timestamp column
我正在尝试比较两组数据。 一个是 dataframe 一组 static 数据并以 Avro 格式写入。现在这个比较从 Avro 读回并检查其中有一个时间戳列并且比较失败,因为 Avro 将数据存储为 Long 并且 ZAC5C74B164B4B835AZEF2 的转换值不同
**--CREATE THE DATAFRAME**
val data = Seq(Row("1",java.sql.Timestamp.valueOf("2019-03-15 18:20:06.456")))
val schemaOrig = List( StructField("rowkey",StringType,true)
,StructField("txn_ts",TimestampType,true))
val sourceDf = spark.createDataFrame(spark.sparkContext.parallelize(data),StructType(schemaOrig))
sourceDf.write.avro("test")
sourceDf.printSchema
root
|-- rowkey: string (nullable = true)
|-- txn_ts: timestamp (nullable = true)
sourceDf.show(false)
+----------------+-----------------------+
|rowkey |txn_ts |
+----------------+-----------------------+
|1 |2019-03-15 18:20:06.456|
+----------------+-----------------------+
--As shown above the avro file has the expected schema specified ie String and Timestamp
--Now Read the data back from Avro
val avroDf=spark.read.avro("test")
avroDf.printSchema
root
|-- rowkey: string (nullable = true)
|-- txn_ts: long (nullable = true)
avroDf.show(false)
--Avro Df schema is printing the timestamp field as long and data showing epoch time
+----------------+-------------+
|rowkey |txn_ts |
+----------------+-------------+
|1 |1552688406456|
+----------------+-------------+
compare the 2 Df
sourceDf.except(avroDf).show(false)
--Gives error due to datatype mismatch
org.apache.spark.sql.AnalysisException: Except can only be performed on tables with the compatible column types. bigint <> timestamp at the second column of the second table;;
'Except
:- AnalysisBarrier
CAST the avro data long field back to time
stamp
val modifiedAvroDf=avroDf.withColumn("txn_ts", col("txn_ts").cast(TimestampType))
modifiedAvroDf.printSchema
|-- rowkey: string (nullable = true)
|-- txn_ts: timestamp (nullable = true)
modifiedAvroDf.show(false)
--Showing wrong timestamp value
+----------------+-----------------------+
|rowkey |txn_ts |
+----------------+-----------------------+
|1 |51172-09-26 11:07:366.0|
+----------------+-----------------------+
--Now Try to cast the source column to long
val sourceModDf=sourceDf.withColumn("txn_ts",col("txn_ts").cast(LongType))
sourceModDf.printSchema
|-- rowkey: string (nullable = true)
|-- txn_ts: long (nullable = true)
sourceModDf.show(false)
sourceModDf.except(modifiedAvroDf).show(false)
创建 UDF 以将 long 转换为时间戳字符串。 请检查以下代码。
scala> val df = Seq(1552688406456L).toDF
df: org.apache.spark.sql.DataFrame = [value: bigint]
scala> import org.joda.time.DateTime
import org.joda.time.DateTime
scala> import org.joda.time.DateTimeZone
import org.joda.time.DateTimeZone
scala> val datetime = udf((date: Long) => new DateTime(date, DateTimeZone.UTC).toString.replace("Z","").replace("T"," "))
datetime: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,StringType,Some(List(LongType)))
scala> df.select(datetime($"value").as("dt")).show(false)
+------------------------+
|dt |
+------------------------+
|2019-03-15 22:20:06.456 |
+------------------------+
scala> df.select(datetime($"value").as("dt").cast("timestamp")).show(false)
+-----------------------+
|dt |
+-----------------------+
|2019-03-15 22:20:06.456|
+-----------------------+
scala> df.select(datetime($"value").as("dt").cast("timestamp")).printSchema
root
|-- dt: timestamp (nullable = true)
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