Spark中Json列的哈希值

Question

我有Cassandra表，在最后一個名為“ fullJson”的列中是JSON日志文件。 我需要使用MD5在該JSON行中哈希userID值。 這是我的方法，但由於某些原因，我總是會在某些時候陷入困境。 加載的Cassandra表：

scala> val rawCass = sc.cassandraTable[cassFormat]("keyspace", "logs").repartition(200)
rawCass: org.apache.spark.rdd.RDD[cassFormat] = MapPartitionsRDD[73] at coalesce at CassandraTableScanRDD.scala:256

我得到：

scala> val cassDF2 = spark.createDataFrame(rawCass).select("fullJson")
cassDF2: org.apache.spark.sql.DataFrame = [fullJson: string]

scala> cassDF2.printSchema
root
 |-- fullJson: string (nullable = true)

我的JSON文件由“ header”和“ body”組成，我猜最好的方法是獲取Data Frame ，然后選擇列userID並使用MD5對其進行哈希處理。

scala> val nestedJson = spark.read.json(cassDF2.select("fullJson").rdd.map(_.getString(0))).select("header","body")
nestedJson: org.apache.spark.sql.DataFrame = [header: struct<KPI: string, action: string ... 16 more fields>, body: struct<1MYield: double, 1YYield: double ... 147 more fields>]

scala> nestedJson.printSchema
root
 |-- header: struct (nullable = true)
 |    |-- KPI: string (nullable = true)
 |    |-- action: string (nullable = true)
 |    |-- appID: string (nullable = true)
 |    |-- appVersion: string (nullable = true)
 |    |-- context: string (nullable = true)
 |    |-- eventID: string (nullable = true)
 |    |-- interestArea: string (nullable = true)
 |    |-- location: struct (nullable = true)
 |    |    |-- lat: string (nullable = true)
 |    |    |-- lon: string (nullable = true)
 |    |-- navigationGroup: string (nullable = true)
 |    |-- sessionID: string (nullable = true)
 |    |-- timestamp: string (nullable = true)
 |    |-- userAge: string (nullable = true)
 |    |-- userAgent: struct (nullable = true)
 |    |    |-- browser: string (nullable = true)
 |    |    |-- browserVersion: string (nullable = true)
 |    |    |-- deviceName: string (nullable = true)
 |    |    |-- deviceResolution: string (nullable = true)
 |    |    |-- deviceType: string (nullable = true)
 |    |    |-- deviceVendor: string (nullable = true)
 |    |    |-- os: string (nullable = true)
 |    |    |-- osVersion: string (nullable = true)
 |    |-- userID: string (nullable = true)
 |    |-- userSegment: string (nullable = true)
 |-- body: struct (nullable = true)
 |    |-- OS: string (nullable = true)
 |    |-- active: boolean (nullable = true)
 |    |-- amount: double (nullable = true)
 |    |-- amountCritical: string (nullable = true)
 |    |-- beneficiary: struct (nullable = true)
 |    |    |-- beneficiaryAccounts: array (nullable = true)
 |    |    |    |-- element: struct (containsNull = true)
 |    |    |    |    |-- beneficiaryAccountBank: string (nullable = true)
...

現在哈希header.userID ：

val newDF = nestedJson.withColumn("header.userID", md5($"header.userID"))

我想將其保存在CSV文件中，但是由於它是結構，所以無法完成。

newDF.write.format("com.databricks.spark.csv").option("header", "true").option("delimiter", "|").save("cass_full.csv")

嘗試避免使用struct類型，但由於其他嵌套而無法使用（例如， location由lat, lon ）

scala> val tempT = newDF.select($"header.*",$"body.*")
tempT: org.apache.spark.sql.DataFrame = [KPI: string, action: string ... 165 more fields]

scala> tempT.printSchema
root
 |-- KPI: string (nullable = true)
 |-- action: string (nullable = true)
 |-- appID: string (nullable = true)
 |-- appVersion: string (nullable = true)
 |-- context: string (nullable = true)
 |-- eventID: string (nullable = true)
 |-- interestArea: string (nullable = true)
 |-- location: struct (nullable = true)
 |    |-- lat: string (nullable = true)
 |    |-- lon: string (nullable = true)
 |-- navigationGroup: string (nullable = true)
...

基本問題什么是最簡單，最可取的方法。 我應該只更改JSON每一行的userID值，還是可以使用Data Frames進行某種方式的更改？ 這樣做的原因是因為我有另一個數據庫中的另一個CSV文件，該文件也需要使用相同的算法進行哈希處理，然后再加入。

Answer 1

請嘗試將其保存在parquet然后繼續邏輯的第二部分加入。

希望這可以幫助！