02-26-2024 07:14 PM
03-14-2024 04:25 AM
Hi @vishwanath_1, Let’s address the issue with null values in your DataFrame when pushing it to a MongoDB collection.
The ignoreNullValues
option you’ve used is intended for Databricks’s integration with MongoDB, but it might not work as expected in your case. This option is specific to Databricks and may not be supported by the standard Spark MongoDB connector.
Here are a couple of alternative approaches you can consider:
Convert DataFrame to a Dictionary of Records and Insert Many:
to_dict('records')
method).insert_many
method from PyMongo to insert the records into your MongoDB collection.import json
records = json.loads(df.to_json(orient='records'))
db.collection.insert_many(records)
Handle Null Values Explicitly:
"N/A"
or None
).inputproddata.fillna("N/A").write.format("mongo").option("spark.mongodb.output.uri", connectionString).option("database", database).option("collection", collection).mode("append").save()
"N/A"
) based on your requirements.Remember to adapt these solutions to your specific use case and adjust any additional settings as needed. If you encounter any issues or need further assistance, feel free to ask! 😊
03-14-2024 04:25 AM
Hi @vishwanath_1, Let’s address the issue with null values in your DataFrame when pushing it to a MongoDB collection.
The ignoreNullValues
option you’ve used is intended for Databricks’s integration with MongoDB, but it might not work as expected in your case. This option is specific to Databricks and may not be supported by the standard Spark MongoDB connector.
Here are a couple of alternative approaches you can consider:
Convert DataFrame to a Dictionary of Records and Insert Many:
to_dict('records')
method).insert_many
method from PyMongo to insert the records into your MongoDB collection.import json
records = json.loads(df.to_json(orient='records'))
db.collection.insert_many(records)
Handle Null Values Explicitly:
"N/A"
or None
).inputproddata.fillna("N/A").write.format("mongo").option("spark.mongodb.output.uri", connectionString).option("database", database).option("collection", collection).mode("append").save()
"N/A"
) based on your requirements.Remember to adapt these solutions to your specific use case and adjust any additional settings as needed. If you encounter any issues or need further assistance, feel free to ask! 😊
03-14-2024 05:11 AM
First approach works.. Thanks
03-14-2024 04:33 AM
Hi @vishwanath_1, Let’s address the issue with null values in your DataFrame when pushing it to a MongoDB collection.
The ignoreNullValues
option in Spark is designed to control whether null values should be ignored during write operations. However, it seems that even after setting ignoreNullValues
to false
, the null values are still being omitted.
Here are a few potential reasons why this might be happening and some alternative solutions:
Schema Mismatch:
Data Transformation:
na
functions in Spark to handle nulls (e.g., df.na.fill()
or df.na.drop()
).Custom Logic:
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType, DoubleType
# Define UDFs to handle null values
def handle_null_string(value):
return value if value is not None else ""
def handle_null_double(value):
return value if value is not None else float("nan")
# Apply UDFs to relevant columns
df = df.withColumn("string_column", udf(handle_null_string, StringType())("string_column"))
df = df.withColumn("double_column", udf(handle_null_double, DoubleType())("double_column"))
# Write to MongoDB
df.write.format("mongo").option("spark.mongodb.output.uri", connectionString).option("database", database).option("collection", collection).mode("append").save()
MongoDB Document Validation:
Remember to thoroughly test any changes you make to ensure that null values are correctly handled during the writing process. If you encounter any issues, review the MongoDB logs for additional insights.
Feel free to explore these alternatives and choose the one that best fits your use case! 🚀
Join our fast-growing data practitioner and expert community of 80K+ members, ready to discover, help and collaborate together while making meaningful connections.
Click here to register and join today!
Engage in exciting technical discussions, join a group with your peers and meet our Featured Members.