[jira] [Created] (SPARK-43201) Inconsistency between from_avro and from_json function

["Philip Adetiloye (Jira)" <ji...@apache.org>]

Philip Adetiloye created SPARK-43201:
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             Summary: Inconsistency between from_avro and from_json function
                 Key: SPARK-43201
                 URL: https://issues.apache.org/jira/browse/SPARK-43201
             Project: Spark
          Issue Type: Improvement
          Components: Spark Core
    Affects Versions: 3.4.0
            Reporter: Philip Adetiloye


Spark from_avro function does not allow schema to use dataframe column but takes a String schema:
{code:java}
def from_avro(col: Column, jsonFormatSchema: String): Column {code}
This makes it impossible to deserialize rows of Avro records with different schema since only one schema string could be pass externally. 

 

Here is what I would expect:
{code:java}
def from_avro(col: Column, jsonFormatSchema: Column): Column  {code}
code example:
{code:java}
import org.apache.spark.sql.functions.from_avro

val avroSchema1 = """{"type":"record","name":"myrecord","fields":[{"name":"str1","type":"string"},{"name":"str2","type":"string"}]}"""val 

val avroSchema2 = """{"type":"record","name":"myrecord","fields":[{"name":"str1","type":"string"},{"name":"str2","type":"string"}]}"""


val df = Seq(
  (Array[Byte](10, 97, 112, 112, 108, 101, 49, 0), avroSchema1),
  (Array[Byte](10, 97, 112, 112, 108, 101, 50, 0), avroSchema2)
).toDF("binaryData", "schema")


val parsed = df.select(from_avro($"binaryData", $"schema").as("parsedData"))parsed.show()


// Output:
// +------------+
// |  parsedData|
// +------------+
// |[apple1, 1.0]|
// |[apple2, 2.0]|
// +------------+
 {code}
 



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