dataframe column json parser spark scala

To parse a JSON string in a DataFrame column using Spark and Scala, you can use the from_json function provided by the Spark SQL library. This function allows you to parse JSON strings into a structured format.

Here's an example of how you can use from_json to parse a JSON column in a DataFrame:

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

// Define the JSON schema
val schema = StructType(Seq(
  StructField("name", StringType),
  StructField("age", IntegerType),
  StructField("city", StringType)

// Create a DataFrame with a JSON column
val data = Seq(
  """{"name":"John","age":30,"city":"New York"}""",
  """{"name":"Jane","age":25,"city":"San Francisco"}"""
val df = spark.createDataFrame(data.zipWithIndex)
  .toDF("json", "id")

// Parse the JSON column
val parsedDf = df.withColumn("parsed_json", from_json(col("json"), schema))

// Extract values from the parsed column
val resultDf =

// Show the result

In this example, we first import the necessary classes and define the JSON schema that matches the structure of the JSON strings in the column. Then, we create a DataFrame with a column named "json" that contains the JSON strings.

Next, we use the from_json function to parse the "json" column into a new column named "parsed_json". We can then extract the values from the parsed column using the select function.

Finally, we show the result by calling show() on the resulting DataFrame.

This code snippet demonstrates how to parse a JSON column in a DataFrame using Spark and Scala. Remember to adjust the schema and column names based on your specific JSON structure.