2/16/2023 0 Comments Parquet to redshift data typesInserted into the database columns col1 and col3. Based on the schema we provide in a schema file, the code will format the data accordingly before writing it to the Parquet file. This is because when a Parquet binary file is created, the data type of each column is retained as well. If the DataFrame has two columns col1 and col3 and use_column_names is True, data will only be We need to specify the schema of the data we’re going to write in the Parquet file. Use_column_names ( bool) – If set to True, will use the column names of the DataFrame for generating the INSERT SQL Query.Į.g. Precombine_key ( str, optional) – When there is a primary_key match during upsert, this column will change the upsert method,Ĭomparing the values of the specified column from source and target, and keeping the Max_rows_by_file ( int) – Max number of rows in each file.ĭefault is None i.e. Useful when you have columns with undetermined or mixed data types. Index ( bool) – True to store the DataFrame index in file, otherwise False to ignore it.ĭtype ( Dict, optional) – Dictionary of columns names and Athena/Glue types to be casted. This is only needed when you are using temporary credentials. Iam_role ( str, optional) – AWS IAM role with the related permissions.Īws_access_key_id ( str, optional) – The access key for your AWS account.Īws_secret_access_key ( str, optional) – The secret key for your AWS account.Īws_session_token ( str, optional) – The session key for your AWS account. “credentials directly or wr.nnect() to fetch it from the Glue Catalog. s3://bucket_name/any_name/).Ĭon ( redshift_connector.Connection) – Use redshift_nnect() to use ” Path ( str) – S3 path to write stage files (e.g. Parametersĭf ( pandas.DataFrame) – Pandas DataFrame. That will be spawned will be gotten from os.cpu_count(). In case of use_threads=True the number of threads Than the regular wr.redshift.to_sql() function, so it is only recommended This strategy has more overhead and requires more IAM privileges This is a HIGH latency and HIGH throughput alternative to wr.redshift.to_sql() to load largeĭataFrames into Amazon Redshift through the ** SQL COPY command**. Load Pandas DataFrame as a Table on Amazon Redshift using parquet files on S3 as stage. copy ( df : DataFrame, path : str, con : Connection, table : str, schema : str, iam_role : Optional = None, aws_access_key_id : Optional = None, aws_secret_access_key : Optional = None, aws_session_token : Optional = None, index : bool = False, dtype : Optional ] = None, mode : str = 'append', overwrite_method : str = 'drop', diststyle : str = 'AUTO', distkey : Optional = None, sortstyle : str = 'COMPOUND', sortkey : Optional ] = None, primary_keys : Optional ] = None, varchar_lengths_default : int = 256, varchar_lengths : Optional ] = None, serialize_to_json : bool = False, keep_files : bool = False, use_threads : Union = True, lock : bool = False, sql_copy_extra_params : Optional ] = None, boto3_session : Optional = None, s3_additional_kwargs : Optional ] = None, max_rows_by_file : Optional = 10000000, precombine_key : Optional = None, use_column_names : bool = False ) → None ¶
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