Loading spark dataframe to Mongo collection isn't allowing nulls

[vishwanath_1]

I am using below command to push DataFrame to Mongo Collection.

There are few null values in String and Double datatype columns , we see these are getting missed when pushed to mongo even after using the option("ignoreNullValues", false)

inputproddata.write.format("mongo").option("spark.mongodb.output.uri", connectionString).option("database",database).option("collection",collection).option("ignoreNullValues", false).mode("append").save()

Please let me know why this option does not work and suggest for any alternative solution 

[Kaniz · Accepted solution]

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:

1. Convert DataFrame to a Dictionary of Records and Insert Many:

   • Convert your DataFrame to a dictionary of records (using the to_dict('records') method).
   • Then use the insert_many method from PyMongo to insert the records into your MongoDB collection.
   • This approach allows you to handle null values more flexibly.
   • Example code snippet:

     import json
     records = json.loads(df.to_json(orient='records'))
     db.collection.insert_many(records)
     

   • Note that this method preserves data types and handles null values appropriately ¹.

2. Handle Null Values Explicitly:

   • Before writing to MongoDB, explicitly replace null values in your DataFrame with a placeholder (e.g., "N/A" or None).
   • Then write the modified DataFrame to MongoDB.
   • This way, you ensure that null values are included in the documents.
   • Example code snippet:

     inputproddata.fillna("N/A").write.format("mongo").option("spark.mongodb.output.uri", connectionString).option("database", database).option("collection", collection).mode("append").save()
     

   • Adjust the placeholder value ("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! 😊

[vishwanath_1]

First approach works.. Thanks

[Kaniz]

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:

1. Schema Mismatch:

   • Ensure that the schema of your DataFrame matches the schema of the target MongoDB collection. If there’s a mismatch (e.g., missing fields or different data types), Spark may drop null values during the write process.
   • Verify that the column names and data types in your DataFrame match the corresponding fields in the MongoDB collection.

2. Data Transformation:

   • Before writing to MongoDB, consider transforming your DataFrame to handle null values explicitly. For example:
     • Replace null values with default values (e.g., empty strings for String columns, 0 for numeric columns).
     • Convert null Double values to NaN or a specific sentinel value.
     • Use the na functions in Spark to handle nulls (e.g., df.na.fill() or df.na.drop()).

3. Custom Logic:

   • Implement custom logic to handle null values during the write process. You can create a UDF (User-Defined Function) that replaces null values with appropriate defaults before saving to MongoDB.
   • Example (Python):

     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()
     

4. MongoDB Document Validation:

   • MongoDB allows you to define document validation rules using JSON Schema. You can specify validation rules for each field, including handling null values.
   • Consider defining a validation rule that allows null values for specific fields. For example, allow nulls for String fields but not for Double fields.

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! 🚀