awswrangler.redshift.to_sql

awswrangler.redshift.to_sql(df: pandas.core.frame.DataFrame, con: redshift_connector.core.Connection, table: str, schema: str, mode: str = 'append', overwrite_method: str = 'drop', index: bool = False, dtype: Optional[Dict[str, str]] = None, diststyle: str = 'AUTO', distkey: Optional[str] = None, sortstyle: str = 'COMPOUND', sortkey: Optional[List[str]] = None, primary_keys: Optional[List[str]] = None, varchar_lengths_default: int = 256, varchar_lengths: Optional[Dict[str, int]] = None, use_column_names: bool = False, lock: bool = False, chunksize: int = 200)Any

Write records stored in a DataFrame into Redshift.

Note

For large DataFrames (1K+ rows) consider the function wr.redshift.copy().

Note

This function has arguments which can be configured globally through wr.config or environment variables:

  • chunksize

Check out the Global Configurations Tutorial for details.

Parameters
  • df (pandas.DataFrame) – Pandas DataFrame https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

  • con (redshift_connector.Connection) – Use redshift_connector.connect() to use ” “credentials directly or wr.redshift.connect() to fetch it from the Glue Catalog.

  • table (str) – Table name

  • schema (str) – Schema name

  • mode (str) – Append, overwrite or upsert.

  • overwrite_method (str) –

    Drop, cascade, truncate, or delete. Only applicable in overwrite mode.

    ”drop” - DROP ... RESTRICT - drops the table. Fails if there are any views that depend on it. “cascade” - DROP ... CASCADE - drops the table, and all views that depend on it. “truncate” - TRUNCATE ... - truncates the table, but immediatly commits current transaction & starts a new one, hence the overwrite happens in two transactions and is not atomic. “delete” - DELETE FROM ... - deletes all rows from the table. Slow relative to the other methods.

  • index (bool) – True to store the DataFrame index as a column in the table, otherwise False to ignore it.

  • dtype (Dict[str, str], optional) – Dictionary of columns names and Redshift types to be casted. Useful when you have columns with undetermined or mixed data types. (e.g. {‘col name’: ‘VARCHAR(10)’, ‘col2 name’: ‘FLOAT’}) diststyle : str Redshift distribution styles. Must be in [“AUTO”, “EVEN”, “ALL”, “KEY”]. https://docs.aws.amazon.com/redshift/latest/dg/t_Distributing_data.html

  • distkey (str, optional) – Specifies a column name or positional number for the distribution key.

  • sortstyle (str) – Sorting can be “COMPOUND” or “INTERLEAVED”. https://docs.aws.amazon.com/redshift/latest/dg/t_Sorting_data.html

  • sortkey (List[str], optional) – List of columns to be sorted.

  • primary_keys (List[str], optional) – Primary keys.

  • varchar_lengths_default (int) – The size that will be set for all VARCHAR columns not specified with varchar_lengths.

  • varchar_lengths (Dict[str, int], optional) – Dict of VARCHAR length by columns. (e.g. {“col1”: 10, “col5”: 200}).

  • use_column_names (bool) – If set to True, will use the column names of the DataFrame for generating the INSERT SQL Query. E.g. If the DataFrame has two columns col1 and col3 and use_column_names is True, data will only be inserted into the database columns col1 and col3.

  • lock (bool) – True to execute LOCK command inside the transaction to force serializable isolation.

  • chunksize (int) – Number of rows which are inserted with each SQL query. Defaults to inserting 200 rows per query.

Returns

None.

Return type

None

Examples

Writing to Redshift using a Glue Catalog Connections

>>> import awswrangler as wr
>>> con = wr.redshift.connect("MY_GLUE_CONNECTION")
>>> wr.redshift.to_sql(
...     df=df,
...     table="my_table",
...     schema="public",
...     con=con
... )
>>> con.close()