Hi @zakaria belamri, You can add custom metadata to an Avro file in PySpark by creating an Avro schema with the custom metadata fields and passing it to the DataFrameWriter as an option. Here's an example code snippet that demonstrates how to do this:
from pyspark.sql import SparkSession
import pyspark.sql.functions as F
# create SparkSession
spark = SparkSession.builder.appName("AvroCustomMetadata").getOrCreate()
# create example DataFrame
df = spark.range(10).withColumn("value", F.lit("hello"))
# define custom metadata fields as a dictionary
custom_metadata = {
"key1": "value1",
"key2": "value2"
}
# create Avro schema with custom metadata fields
avro_schema = """
{
"type": "record",
"name": "ExampleRecord",
"fields": [
{"name": "id", "type": "long"},
{"name": "value", "type": "string"}
],
"metadata": {
"custom": %s
}
}
""" % (json.dumps(custom_metadata),)
# write DataFrame to Avro file with custom metadata
df.write.format("avro").option("avroSchema", avro_schema).save("example.avro")
# read Avro file and display custom metadata
read_df = spark.read.format("avro").load("example.avro")
print(read_df.schema.metadata["custom"])
- In this example, we create an example DataFrame with ten rows and a single value column.
- We then define a dictionary of custom metadata fields we want to add to the Avro schema.
- We create an Avro schema with these custom metadata fields by embedding the glossary in the metadata field of the schema definition.
- We then use the DataFrameWriter to write the DataFrame to an Avro file with the custom metadata included in the schema.
- Finally, we use the DataFrameReader to read the Avro file and display the custom metadata.
Note that in this example, we use the JSON.dumps method to convert the dictionary of custom metadata to a JSON string that can be embedded in the Avro schema definition.
