Hello Guys,

I have a table mongo which size is 67GB, I use streaming to ingest but is very slow to copying all data to Delta table. 
Someone have an answer to this?  I use connector mongodb V10.5 

this is my code 

pipeline_mongo_sec = [
    {
        "$unwind": "$data"
    },
    {
        "$project": {
            "_id":0,
            "point": { 
                "$toUpper": "$point" 
            },
            "since": { 
                "$dateFromString": {
                    "dateString": "$since",
                    "timezone": "UTC"
                }
            },
            "date": {
                "$dateFromString": {
                    "dateString": "$data.date",
                    "timezone": "UTC"
                }
            },
            "label": "$data.label",
            "measure_type": "$data.measure",
            "tariff": "$data.cost",
            "unit": "$data.unit",
            "value": {
                "$toDecimal": "$data.value"
            }
        }
            
    }
]

self.spark.read.format("mongo")
            .option("spark.mongodb.input.uri", self.mongo_uri)
            .option("database", self.mongo_database)
            .option("collection", self.collection)
            .option("partitioner", "MongoSamplePartitioner")

def _write_to_output(self, df) -> bool:

        try:
            if(len(self.partition_columns) > 0😞
                print(f"Avec partitionning : {','.join(self.partition_columns)}")
                df.write.partitionBy(*self.partition_columns).mode("overwrite").parquet(self.output_path)
            else:
                df.write.mode(self.write_mode).parquet(self.output_path)
            
            return True
        except Exception as e:
            print(f"❌ Erreur d'ecriture pour {self.mongo_database}.{self.collection} : {str(e)}")
            return False