def db_table(
request,
con: redshift_connector.Connection) -> redshift_connector.Connection:
filterwarnings("ignore", "DB-API extension cursor.next()")
filterwarnings("ignore", "DB-API extension cursor.__iter__()")
con.paramstyle = "format" # type: ignore
with con.cursor() as cursor:
cursor.execute("drop table if exists book")
cursor.execute(
"create Temp table book(bookname varchar,author varchar)")
def fin() -> None:
try:
with con.cursor() as cursor:
cursor.execute("drop table if exists book")
except redshift_connector.ProgrammingError:
pass
request.addfinalizer(fin)
return con
def db_table(
request,
con: redshift_connector.Connection) -> redshift_connector.Connection:
filterwarnings("ignore", "DB-API extension cursor.next()")
filterwarnings("ignore", "DB-API extension cursor.__iter__()")
con.paramstyle = "format" # type: ignore
with con.cursor() as cursor:
cursor.execute("DROP TABLE IF EXISTS t1")
cursor.execute(
"CREATE TEMPORARY TABLE t1 (f1 int primary key, f2 bigint not null, f3 varchar(50) null) "
)
def fin() -> None:
try:
with con.cursor() as cursor:
cursor.execute("drop table if exists t1")
except redshift_connector.ProgrammingError:
pass
request.addfinalizer(fin)
return con
def copy_from_files( # pylint: disable=too-many-locals,too-many-arguments
path: str,
con: redshift_connector.Connection,
table: str,
schema: str,
iam_role: str,
parquet_infer_sampling: float = 1.0,
mode: str = "append",
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_threads: bool = True,
boto3_session: Optional[boto3.Session] = None,
s3_additional_kwargs: Optional[Dict[str, str]] = None,
) -> None:
"""Load Parquet files from S3 to a Table on Amazon Redshift (Through COPY command).
https://docs.aws.amazon.com/redshift/latest/dg/r_COPY.html
Note
----
If the table does not exist yet,
it will be automatically created for you
using the Parquet metadata to
infer the columns data types.
Note
----
In case of `use_threads=True` the number of threads
that will be spawned will be gotten from os.cpu_count().
Parameters
----------
path : str
S3 prefix (e.g. s3://bucket/prefix/)
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
iam_role : str
AWS IAM role with the related permissions.
parquet_infer_sampling : float
Random sample ratio of files that will have the metadata inspected.
Must be `0.0 < sampling <= 1.0`.
The higher, the more accurate.
The lower, the faster.
mode : str
Append, overwrite or upsert.
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_threads : bool
True to enable concurrent requests, False to disable multiple threads.
If enabled os.cpu_count() will be used as the max number of threads.
boto3_session : boto3.Session(), optional
Boto3 Session. The default boto3 session will be used if boto3_session receive None.
s3_additional_kwargs:
Forward to botocore requests. Valid parameters: "ACL", "Metadata", "ServerSideEncryption", "StorageClass",
"SSECustomerAlgorithm", "SSECustomerKey", "SSEKMSKeyId", "SSEKMSEncryptionContext", "Tagging".
e.g. s3_additional_kwargs={'ServerSideEncryption': 'aws:kms', 'SSEKMSKeyId': 'YOUR_KMS_KEY_ARN'}
Returns
-------
None
None.
Examples
--------
>>> import awswrangler as wr
>>> con = wr.redshift.connect("MY_GLUE_CONNECTION")
>>> wr.db.copy_from_files(
... path="s3://bucket/my_parquet_files/",
... con=con,
... table="my_table",
... schema="public"
... iam_role="arn:aws:iam::XXX:role/XXX"
... )
>>> con.close()
"""
con.autocommit = False
try:
with con.cursor() as cursor:
created_table, created_schema = _create_table(
df=None,
path=path,
parquet_infer_sampling=parquet_infer_sampling,
cursor=cursor,
table=table,
schema=schema,
mode=mode,
diststyle=diststyle,
sortstyle=sortstyle,
distkey=distkey,
sortkey=sortkey,
primary_keys=primary_keys,
varchar_lengths_default=varchar_lengths_default,
varchar_lengths=varchar_lengths,
index=False,
dtype=None,
use_threads=use_threads,
boto3_session=boto3_session,
s3_additional_kwargs=s3_additional_kwargs,
)
_copy(
cursor=cursor,
path=path,
table=created_table,
schema=created_schema,
iam_role=iam_role,
)
if table != created_table: # upsert
_upsert(cursor=cursor,
schema=schema,
table=table,
temp_table=created_table,
primary_keys=primary_keys)
con.commit()
except Exception as ex:
con.rollback()
_logger.error(ex)
raise
def unload_to_files(
sql: str,
path: str,
con: redshift_connector.Connection,
iam_role: str,
region: Optional[str] = None,
max_file_size: Optional[float] = None,
kms_key_id: Optional[str] = None,
manifest: bool = False,
use_threads: bool = True,
partition_cols: Optional[List[str]] = None,
boto3_session: Optional[boto3.Session] = None,
) -> None:
"""Unload Parquet files on s3 from a Redshift query result (Through the UNLOAD command).
https://docs.aws.amazon.com/redshift/latest/dg/r_UNLOAD.html
Note
----
In case of `use_threads=True` the number of threads
that will be spawned will be gotten from os.cpu_count().
Parameters
----------
sql: str
SQL query.
path : Union[str, List[str]]
S3 path to write stage files (e.g. s3://bucket_name/any_name/)
con : redshift_connector.Connection
Use redshift_connector.connect() to use "
"credentials directly or wr.redshift.connect() to fetch it from the Glue Catalog.
iam_role : str
AWS IAM role with the related permissions.
region : str, optional
Specifies the AWS Region where the target Amazon S3 bucket is located.
REGION is required for UNLOAD to an Amazon S3 bucket that isn't in the
same AWS Region as the Amazon Redshift cluster. By default, UNLOAD
assumes that the target Amazon S3 bucket is located in the same AWS
Region as the Amazon Redshift cluster.
max_file_size : float, optional
Specifies the maximum size (MB) of files that UNLOAD creates in Amazon S3.
Specify a decimal value between 5.0 MB and 6200.0 MB. If None, the default
maximum file size is 6200.0 MB.
kms_key_id : str, optional
Specifies the key ID for an AWS Key Management Service (AWS KMS) key to be
used to encrypt data files on Amazon S3.
use_threads : bool
True to enable concurrent requests, False to disable multiple threads.
If enabled os.cpu_count() will be used as the max number of threads.
manifest : bool
Unload a manifest file on S3.
partition_cols: List[str], optional
Specifies the partition keys for the unload operation.
boto3_session : boto3.Session(), optional
Boto3 Session. The default boto3 session will be used if boto3_session receive None.
Returns
-------
None
Examples
--------
>>> import awswrangler as wr
>>> con = wr.redshift.connect("MY_GLUE_CONNECTION")
>>> wr.redshift.unload_to_files(
... sql="SELECT * FROM public.mytable",
... path="s3://bucket/extracted_parquet_files/",
... con=con,
... iam_role="arn:aws:iam::XXX:role/XXX"
... )
>>> con.close()
"""
path = path if path.endswith("/") else f"{path}/"
session: boto3.Session = _utils.ensure_session(session=boto3_session)
s3.delete_objects(path=path,
use_threads=use_threads,
boto3_session=session)
with con.cursor() as cursor:
partition_str: str = f"\nPARTITION BY ({','.join(partition_cols)})" if partition_cols else ""
manifest_str: str = "\nmanifest" if manifest is True else ""
region_str: str = f"\nREGION AS '{region}'" if region is not None else ""
max_file_size_str: str = f"\nMAXFILESIZE AS {max_file_size} MB" if max_file_size is not None else ""
kms_key_id_str: str = f"\nKMS_KEY_ID '{kms_key_id}'" if kms_key_id is not None else ""
sql = (f"UNLOAD ('{sql}')\n"
f"TO '{path}'\n"
f"IAM_ROLE '{iam_role}'\n"
"ALLOWOVERWRITE\n"
"PARALLEL ON\n"
"FORMAT PARQUET\n"
"ENCRYPTED"
f"{kms_key_id_str}"
f"{partition_str}"
f"{region_str}"
f"{max_file_size_str}"
f"{manifest_str};")
_logger.debug("sql: \n%s", sql)
cursor.execute(sql)
def to_sql(
df: pd.DataFrame,
con: redshift_connector.Connection,
table: str,
schema: str,
mode: str = "append",
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,
) -> None:
"""Write records stored in a DataFrame into Redshift.
Note
----
For large DataFrames (1K+ rows) consider the function **wr.redshift.copy()**.
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.
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}).
Returns
-------
None
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()
"""
if df.empty is True:
raise exceptions.EmptyDataFrame()
_validate_connection(con=con)
con.autocommit = False
try:
with con.cursor() as cursor:
created_table, created_schema = _create_table(
df=df,
path=None,
cursor=cursor,
table=table,
schema=schema,
mode=mode,
index=index,
dtype=dtype,
diststyle=diststyle,
sortstyle=sortstyle,
distkey=distkey,
sortkey=sortkey,
primary_keys=primary_keys,
varchar_lengths_default=varchar_lengths_default,
varchar_lengths=varchar_lengths,
)
if index:
df.reset_index(level=df.index.names, inplace=True)
placeholders: str = ", ".join(["%s"] * len(df.columns))
schema_str = f"{created_schema}." if created_schema else ""
sql: str = f"INSERT INTO {schema_str}{created_table} VALUES ({placeholders})"
_logger.debug("sql: %s", sql)
parameters: List[List[Any]] = _db_utils.extract_parameters(df=df)
cursor.executemany(sql, parameters)
if table != created_table: # upsert
_upsert(cursor=cursor,
schema=schema,
table=table,
temp_table=created_table,
primary_keys=primary_keys)
con.commit()
except Exception as ex:
con.rollback()
_logger.error(ex)
raise