如何使用 Spark DataFrames 查询 JSON 数据列?

问题描述

限时删除！！

我有一个 Cassandra 表，为了简单起见，它看起来像:

I have a Cassandra table that for simplicity looks something like:

key: text
jsonData: text
blobData: blob

我可以使用 spark 和 spark-cassandra-connector 为此创建一个基本数据框:

I can create a basic data frame for this using spark and the spark-cassandra-connector using:

val df = sqlContext.read
  .format("org.apache.spark.sql.cassandra")
  .options(Map("table" -> "mytable", "keyspace" -> "ks1"))
  .load()

我正在努力将 JSON 数据扩展到其底层结构.我最终希望能够根据 json 字符串中的属性进行过滤并返回 blob 数据.类似于 jsonData.foo = "bar" 并返回 blobData.这目前可能吗?

I'm struggling though to expand the JSON data into its underlying structure. I ultimately want to be able to filter based on the attributes within the json string and return the blob data. Something like jsonData.foo = "bar" and return blobData. Is this currently possible?

推荐答案

Spark >= 2.4

如果需要，可以使用 schema_of_json 函数(请注意，这假定任意行是模式的有效代表).

If needed, schema can be determined using schema_of_json function (please note that this assumes that an arbitrary row is a valid representative of the schema).

import org.apache.spark.sql.functions.{lit, schema_of_json, from_json}
import collection.JavaConverters._

val schema = schema_of_json(lit(df.select($"jsonData").as[String].first))
df.withColumn("jsonData", from_json($"jsonData", schema, Map[String, String]().asJava))

Spark >= 2.1

您可以使用 from_json 功能:

You can use from_json function:

import org.apache.spark.sql.functions.from_json
import org.apache.spark.sql.types._

val schema = StructType(Seq(
  StructField("k", StringType, true), StructField("v", DoubleType, true)
))

df.withColumn("jsonData", from_json($"jsonData", schema))

Spark >= 1.6

你可以使用 get_json_object 它接受一列和一个路径:

You can use get_json_object which takes a column and a path:

import org.apache.spark.sql.functions.get_json_object

val exprs = Seq("k", "v").map(
  c => get_json_object($"jsonData", s"$$.$c").alias(c))

df.select($"*" +: exprs: _*)

并将字段提取到可以进一步转换为预期类型的​​单个字符串.

and extracts fields to individual strings which can be further casted to expected types.

path 参数使用点语法表示，前导 $. 表示文档根(因为上面的代码使用字符串插值 $被转义，因此 $$.).

The path argument is expressed using dot syntax, with leading $. denoting document root (since the code above uses string interpolation $ has to be escaped, hence $$.).

火花 :

目前有可能吗?

据我所知，这不是直接可能的.您可以尝试类似的操作:

As far as I know it is not directly possible. You can try something similar to this:

val df = sc.parallelize(Seq(
  ("1", """{"k": "foo", "v": 1.0}""", "some_other_field_1"),
  ("2", """{"k": "bar", "v": 3.0}""", "some_other_field_2")
)).toDF("key", "jsonData", "blobData")

我假设 blob 字段不能用 JSON 表示.否则，您将省略拆分和加入:

I assume that blob field cannot be represented in JSON. Otherwise you cab omit splitting and joining:

import org.apache.spark.sql.Row

val blobs = df.drop("jsonData").withColumnRenamed("key", "bkey")
val jsons = sqlContext.read.json(df.drop("blobData").map{
  case Row(key: String, json: String) =>
    s"""{"key": "$key", "jsonData": $json}"""
})

val parsed = jsons.join(blobs, $"key" === $"bkey").drop("bkey")
parsed.printSchema

// root
//  |-- jsonData: struct (nullable = true)
//  |    |-- k: string (nullable = true)
//  |    |-- v: double (nullable = true)
//  |-- key: long (nullable = true)
//  |-- blobData: string (nullable = true)

另一种(更便宜但更复杂)的方法是使用 UDF 来解析 JSON 并输出 struct 或 map 列.例如这样的事情:

An alternative (cheaper, although more complex) approach is to use an UDF to parse JSON and output a struct or map column. For example something like this:

import net.liftweb.json.parse

case class KV(k: String, v: Int)

val parseJson = udf((s: String) => {
  implicit val formats = net.liftweb.json.DefaultFormats
  parse(s).extract[KV]
})

val parsed = df.withColumn("parsedJSON", parseJson($"jsonData"))
parsed.show

// +---+--------------------+------------------+----------+
// |key|            jsonData|          blobData|parsedJSON|
// +---+--------------------+------------------+----------+
// |  1|{"k": "foo", "v":...|some_other_field_1|   [foo,1]|
// |  2|{"k": "bar", "v":...|some_other_field_2|   [bar,3]|
// +---+--------------------+------------------+----------+

parsed.printSchema

// root
//  |-- key: string (nullable = true)
//  |-- jsonData: string (nullable = true)
//  |-- blobData: string (nullable = true)
//  |-- parsedJSON: struct (nullable = true)
//  |    |-- k: string (nullable = true)
//  |    |-- v: integer (nullable = false)

这篇关于如何使用 Spark DataFrames 查询 JSON 数据列?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！

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