Slow relative to the other methods.ĭiststyle ( str) – Redshift distribution styles. Transaction & starts a new one, hence the overwrite happens in two transactions and is not atomic. truncates the table, but immediately commits current CASCADE - drops the table, and all views that depend on it. Fails if there are any views that depend on it. Mode ( str) – Append, overwrite or upsert.ĭrop, cascade, truncate, or delete. Parquet_infer_sampling ( float) – Random sample ratio of files that will have the metadata inspected. 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/prefix/)Ĭon ( redshift_connector.Connection) – Use redshift_nnect() to use ” That will be spawned will be gotten from os.cpu_count(). In case of use_threads=True the number of threads Load Parquet files from S3 to a Table on Amazon Redshift (Through COPY command). copy_from_files ( 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, parquet_infer_sampling : float = 1.0, 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, path_suffix : Optional = None, path_ignore_suffix : Optional = None, use_threads : Union = True, lock : bool = False, commit_transaction : bool = True, manifest : Optional = False, sql_copy_extra_params : Optional ] = None, boto3_session : Optional = None, s3_additional_kwargs : Optional ] = None, precombine_key : Optional = None, column_names : Optional ] = None ) → None ¶ To set the date column as the index df = df._from_files ¶ awswrangler.redshift. Let take a look at an example dataset city_sales.csv, which has 1,795,144 rows data df = pd.read_csv('data/city_sales.csv',parse_dates=) df.info() RangeIndex: 1795144 entries, 0 to 1795143 Data columns (total 3 columns): # Column Dtype - 0 date datetime64 1 num int64 2 city object dtypes: datetime64(1), int64(1), object(1) memory usage: 41.1+ MB Then, you can select data by date using df.loc. If you are going to do a lot of selections by date, it would be faster to set date column as the index first so you take advantage of the Pandas built-in optimization. And in fact, this solution is slow when you are doing a lot of selections by date in a large dataset. This solution normally requires start_date, end_date and date column to be datetime format. For example condition = (df > start_date) & (df <= end_date) df.loc Improve performance by setting date column as the indexĪ common solution to select data by date is using a boolean maks.
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