CODEWITHSUNDEEP
javajsonapache-sparkapache-spark-sql
Seffy
Seffy

Reputation: 1085

Spark - read JSON array from column

Using Spark 2.11, I've the following Dataset (read from Cassandra table):

+------------+----------------------------------------------------------+
|id         |attributes                                                 |
+------------+----------------------------------------------------------+
|YH8B135U123|[{"id":1,"name":"function","score":10.0,"snippets":1}]     |
+------------+----------------------------------------------------------+

This is the printSchema():

root
 |-- id: string (nullable = true)
 |-- attributes: string (nullable = true)

The attributes column is an array of JSON objects. I'm trying to explode it into Dataset but keep failing. I was trying to define schema as follow:

StructType type = new StructType()
                .add("id", new IntegerType(), false)
                .add("name", new StringType(), false)
                .add("score", new FloatType(), false)
                .add("snippets", new IntegerType(), false );
        
ArrayType schema = new ArrayType(type, false);

And provide it to from_json as follow:

df = df.withColumn("val", functions.from_json(df.col("attributes"), schema));

This fails with MatchError:

Exception in thread "main" scala.MatchError: org.apache.spark.sql.types.IntegerType@43756cb (of class org.apache.spark.sql.types.IntegerType)

What's the correct way to do that?

Upvotes: 0

Views: 1382

Answers (2)

blackbishop
blackbishop

Reputation: 32640

You can specify the schema this way :

val schema = ArrayType(
  StructType(Array(
    StructField("id", IntegerType, false),
    StructField("name", StringType, false),
    StructField("score", FloatType, false),
    StructField("snippets", IntegerType, false)
  )),
  false
)

val df1 = df.withColumn("val", from_json(col("attributes"), schema))

df1.show(false)

//+-----------+------------------------------------------------------+------------------------+
//|id         |attributes                                            |val                     |
//+-----------+------------------------------------------------------+------------------------+
//|YH8B135U123|[{"id":1,"name":"function","score":10.0,"snippets":1}]|[[1, function, 10.0, 1]]|
//+-----------+------------------------------------------------------+------------------------+

Or for Java:

import static org.apache.spark.sql.types.DataTypes.*;


StructType schema = createArrayType(createStructType(Arrays.asList(
    createStructField("id", IntegerType, false),
    createStructField("name", StringType, false),
    createStructField("score", FloatType, false),
    createStructField("snippets", StringType, false)
)), false);

Upvotes: 2

mck
mck

Reputation: 42332

You can define the schema as a literal string instead:

val df2 = df.withColumn(
    "val",
    from_json(
        df.col("attributes"),
        lit("array<struct<id: int, name: string, score: float, snippets: int>>")
    )
)

df2.show(false)
+-----------+------------------------------------------------------+------------------------+
|id         |attributes                                            |val                     |
+-----------+------------------------------------------------------+------------------------+
|YH8B135U123|[{"id":1,"name":"function","score":10.0,"snippets":1}]|[[1, function, 10.0, 1]]|
+-----------+------------------------------------------------------+------------------------+

If you prefer to use a schema:

val spark_struct = new StructType()
                .add("id", IntegerType, false)
                .add("name", StringType, false)
                .add("score", FloatType, false)
                .add("snippets", IntegerType, false)

val schema = new ArrayType(spark_struct, false)

val df2 = df.withColumn(
    "val",
    from_json(
        df.col("attributes"),
        schema
    )
)

Two problems with your original code were: (1) you used the reserved keyword type as a variable name, and (2) you don't need to use new in add.

Upvotes: 1

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