def test_iam_user(self):
'''Verify that iam_user will assume the value of ec2_user when run on an ec2 server'''
sh = SessionHelper(region='cheeseburger',
cluster_id='cheeseburger_id',
host='cheeseburger_host',
port='cheeseburger_port',
db_name='cheeseburger_params',
ec2_user='******')
# Fake is_ec2_flag to be True
sh.is_ec2_flag = True
sh.set_iam_user()
assert sh.iam_user is 'cheeseburger_user'
def test_no_ec2(self):
'''Verify that False is returned if not run on an ec2 server'''
is_ec2 = SessionHelper._is_ec2(self)
assert is_ec2 is False
'''Verify that ec2 flag evaluates to false if not run on an ec2 server'''
sh = SessionHelper(region='cheeseburger',
cluster_id='cheeseburger_id',
host='cheeseburger_host',
port='cheeseburger_port',
db_name='cheeseburger_params',
ec2_user='******')
assert sh.is_ec2_flag is False
def create_table(table_name: str, schema_name: str, columns: dict,
partitions: dict, path: str, session_helper: SessionHelper):
"""
Creates a table in AWS redshift. The table will be named schema_name.table_name and belong to the (existing) Redshift db db_name.
Args:
table_name: name of created table. NOTE: THIS WILL ERROR IF table_name ALREADY EXISTS IN REDSHIFT
schema_name: name of schema in redshift. Schema must be external and already exist!
columns: Dictionary with keys corresponding to column names and values corresponding to pandas dtypes, excluding partition columns.
partitions: Dict similar to columns, except ONLY with partition columns
path: Path to published contract in s3 (excluding partitions)
session_helper: Instance of Redshift s3parq.session_helper
"""
_redshift_name_validator(table_name)
redshift_columns = _datatype_mapper(columns)
redshift_partitions = _datatype_mapper(partitions)
with session_helper.db_session_scope() as scope:
if_exists_query = f'SELECT EXISTS(SELECT schemaname, tablename FROM SVV_EXTERNAL_TABLES WHERE tablename=\'{table_name}\' AND schemaname=\'{schema_name}\');'
table_exists = scope.execute(if_exists_query).first()[0]
if table_exists: return
if not partitions:
new_schema_query = (
f'CREATE EXTERNAL TABLE {schema_name}.{table_name} {redshift_columns} \
STORED AS PARQUET \
LOCATION \'{path}\';')
else:
new_schema_query = (
f'CREATE EXTERNAL TABLE {schema_name}.{table_name} {redshift_columns} \
PARTITIONED BY {redshift_partitions} STORED AS PARQUET \
LOCATION \'{path}\';')
logger.info(f'Running query to create table: {new_schema_query}')
scope.execute(new_schema_query)
def create_partitions(bucket: str, schema: str, table: str, filepath: str,
session_helper: SessionHelper) -> str:
'''
Generates partitioning SQL
Args:
bucket (str): S3 bucket where data is stored
schema (str): name of redshift schema (must already exist)
table (str): name of table in schema. Must have partitions scoped out in `CREATE TABLE ...`
filepath (str): path to data in S3 that will be queryable by it's partitions
----
Returns:
query (str): a raw SQL string that adds the partitioned data to the table
--------
Example:
Args:
bucket = 'MyBucket'
schema = 'MySchema'
table = 'MyTable'
filepath = 'path/to/data/apple=abcd/banana=1234/abcd1234.parquet'
Returns:
"ALTER TABLE MySchema.MyTable ADD PARTITION (apple='abcd' ,banana='1234') LOCATION 's3://MyBucket/path/to/data/apple=abcd';"
'''
partitions = _get_partitions_for_spectrum(filepath)
formatted_partitions = _format_partition_strings_for_sql(partitions)
path_to_data = _get_partition_location(filepath)
with session_helper.db_session_scope() as scope:
partitions_query = f"ALTER TABLE {schema}.{table} \
ADD IF NOT EXISTS PARTITION ({', '.join(formatted_partitions)}) \
LOCATION 's3://{bucket}/{path_to_data}';"
logger.info(f'Running query to create: {partitions_query}')
scope.execute(partitions_query)
def create_partitions(bucket: str, schema: str, table: str, filepath: str, session_helper: SessionHelper) -> None:
''' Executes the SQL that creates partitions on the given table for an
individual file
Args:
bucket (str): S3 bucket where data is stored
schema (str): name of redshift schema (must already exist)
table (str): name of table in schema. Must have partitions scoped out in `CREATE TABLE ...`
filepath (str): path to data in S3 that will be queryable by it's partitions
NOTE: This is to the single parquet file, including the partitions
session_helper (SessionHelper): a configured s3parq.session_helper.SessionHelper session
Returns:
None
Example:
Args:
bucket = 'MyBucket'
schema = 'MySchema'
table = 'MyTable'
filepath = 'path/to/data/apple=abcd/banana=1234/abcd1234.parquet'
session_helper = some_configured_session
'''
partitions = _get_partitions_for_spectrum(filepath)
formatted_partitions = _format_partition_strings_for_sql(partitions)
path_to_data = _get_partition_location(filepath)
with session_helper.db_session_scope() as scope:
partitions_query = f"ALTER TABLE {schema}.{table} \
ADD IF NOT EXISTS PARTITION ({', '.join(formatted_partitions)}) \
LOCATION 's3://{bucket}/{path_to_data}';"
logger.info(f'Running query to create: {partitions_query}')
scope.execute(partitions_query)
def test_ec2_user(self):
'''Verify that SessionHelper can accept a string as an argument for ec2 user'''
sh = SessionHelper(region='cheeseburger',
cluster_id='cheeseburger_id',
host='cheeseburger_host',
port='cheeseburger_port',
db_name='cheeseburger_params',
ec2_user='******')
assert (isinstance(sh.ec2_user, str)) is True
def test_no_ec2_user(self):
'''Verify that SessionHelper can accept None as an argument for ec2 user'''
sh = SessionHelper(region='cheeseburger',
cluster_id='cheeseburger_id',
host='cheeseburger_host',
port='cheeseburger_port',
db_name='cheeseburger_params',
ec2_user=None)
assert (type(sh.ec2_user)) is type(None)
def create_custom_table(table_name: str, schema_name: str, partitions: dict, path: str, custom_redshift_columns: dict, session_helper: SessionHelper) -> None:
""" Creates a table in AWS redshift. The table will be named
schema_name.table_name and belong to the (existing) Redshift db db_name.
The created table will use the CUSTOM redshift column data types defined
in custom_redshift_columns.
Args:
table_name (str): name of created table
NOTE: THIS WILL ERROR IF table_name ALREADY EXISTS IN REDSHIFT
schema_name (str): name of schema in redshift; Schema must be external
and already exist!
partitions (dict): Dict similar to columns, except ONLY with partition columns
path (str): Path to published dataset in s3 (excluding partitions)
custom_redshift_columns (dict):
This dictionary contains custom column data type definitions for redshift.
The params should be formatted as follows:
- column name (str)
- data type (str)
session_helper (SessionHelper): Instance of Redshift s3parq.session_helper
"""
logger.info("Running create_custom_table...")
_redshift_name_validator(table_name)
logger.info("Generating create columns sql statement with custom redshift columns...")
logger.info("Generating create partitions sql statement with custom redshift columns...")
redshift_columns_sql = ""
redshift_partitions_sql = ""
for k, v in custom_redshift_columns.items():
if k in partitions:
redshift_partitions_sql += f'{k} {v}, '
else:
redshift_columns_sql += f'{k} {v}, '
redshift_columns = f"({redshift_columns_sql[:-2]})" # Slice off the last space and comma
redshift_partitions = f"({redshift_partitions_sql[:-2]})" # Slice off the last space and comma
with session_helper.db_session_scope() as scope:
if_exists_query = f'SELECT EXISTS(SELECT schemaname, tablename FROM SVV_EXTERNAL_TABLES WHERE tablename=\'{table_name}\' AND schemaname=\'{schema_name}\');'
table_exists = scope.execute(if_exists_query).first()[0]
if table_exists:
return
if not partitions:
new_schema_query = (
f'CREATE EXTERNAL TABLE {schema_name}.{table_name} {redshift_columns} \
STORED AS PARQUET \
LOCATION \'{path}\';'
)
else:
new_schema_query = (
f'CREATE EXTERNAL TABLE {schema_name}.{table_name} {redshift_columns} \
PARTITIONED BY {redshift_partitions} STORED AS PARQUET \
LOCATION \'{path}\';'
)
logger.info(f'Running query to create table: {new_schema_query}')
scope.execute(new_schema_query)
def create_schema(schema_name: str, db_name: str, iam_role: str,
session_helper: SessionHelper):
"""Creates a schema in AWS redshift using a given iam_role. The schema is named schema_name and belongs to the (existing) Redshift db db_name.
iam_role is a link to an existing AWS IAM Role with Redshift Spectrum write permissions."""
_redshift_name_validator(schema_name, db_name)
with session_helper.db_session_scope() as scope:
new_schema_query = f"CREATE EXTERNAL SCHEMA IF NOT EXISTS {schema_name} \
FROM DATA CATALOG \
database '{db_name}' \
iam_role '{iam_role}';"
logger.info(f'Running query to create schema: {new_schema_query}')
scope.execute(new_schema_query)
def create_schema(schema_name: str, db_name: str, iam_role: str, session_helper: SessionHelper) -> None:
""" Creates a schema in AWS redshift using a given iam_role
Args:
schema_name (str): Name of the schema to create in Redshift Spectrum
db_name (str): (Existing) database that the schema should belong to
iam_role (str): link to an existing AWS IAM Role with Redshift Spectrum
write permissions
session_helper (str): Active and configured session_helper session to use
"""
_redshift_name_validator(schema_name, db_name)
with session_helper.db_session_scope() as scope:
new_schema_query = f"CREATE EXTERNAL SCHEMA IF NOT EXISTS {schema_name} \
FROM DATA CATALOG \
database '{db_name}' \
iam_role '{iam_role}';"
logger.info(f'Running query to create schema: {new_schema_query}')
scope.execute(new_schema_query)
def custom_publish(bucket: str,
key: str,
partitions: List[str],
dataframe: pd.DataFrame,
custom_redshift_columns: dict,
redshift_params: dict = None) -> List[str]:
""" Dataframe to S3 Parquet Publisher with a CUSTOM redshift column definition.
Custom publish allows custom defined redshift column definitions to be used and
enables support for Redshift's decimal data type.
This function handles the portion of work that will see a dataframe converted
to parquet and then published to the given S3 location.
It supports partitions and will use the custom redshift columns defined in the
custom_redshift_columns dictionary when creating the table schema for the parquet file.
View the Custom Publishes section of s3parq's readme file for more guidance on formatting
the custom_redshift_columns dictionary. It also has the option to automatically publish up
to Redshift Spectrum for the newly published parquet files.
Args:
bucket (str): S3 Bucket name
key (str): S3 key to lead to the desired dataset
partitions (List[str]): List of columns that should be partitioned on
dataframe (pd.DataFrame): Dataframe to be published
custom_redshift_columns (dict):
This dictionary contains custom column data type definitions for redshift.
The params should be formatted as follows:
- column name (str)
- data type (str)
redshift_params (dict, Optional):
This dictionary should be provided in the following format in order
for data to be published to Spectrum. Leave out entirely to avoid
publishing to Spectrum.
The params should be formatted as follows:
- schema_name (str): Name of the Spectrum schema to publish to
- table_name (str): Name of the table to write the dataset as
- iam_role (str): Role to take while writing data to Spectrum
- region (str): AWS region for Spectrum
- cluster_id (str): Spectrum cluster id
- host (str): Redshift Spectrum host name
- port (str): Redshift Spectrum port to use
- db_name (str): Redshift Spectrum database name to use
- ec2_user (str): If on ec2, the user that should be used
Returns:
A str list of all the newly published object keys
"""
logger.debug("Running custom publish...")
session_helper = None
if redshift_params:
if "index" in dataframe.columns:
raise ValueError(
"'index' is a reserved keyword in Redshift. Please remove or rename your DataFrame's 'index' column."
)
logger.debug(
"Found redshift parameters. Checking validity of params...")
redshift_params = validate_redshift_params(redshift_params)
logger.debug("Redshift parameters valid. Opening Session helper.")
session_helper = SessionHelper(
region=redshift_params['region'],
cluster_id=redshift_params['cluster_id'],
host=redshift_params['host'],
port=redshift_params['port'],
db_name=redshift_params['db_name'],
ec2_user=redshift_params['ec2_user'])
session_helper.configure_session_helper()
publish_redshift.create_schema(redshift_params['schema_name'],
redshift_params['db_name'],
redshift_params['iam_role'],
session_helper)
logger.debug(
f"Schema {redshift_params['schema_name']} created. Creating table {redshift_params['table_name']}..."
)
publish_redshift.create_custom_table(redshift_params['table_name'],
redshift_params['schema_name'],
partitions, s3_url(bucket, key),
custom_redshift_columns,
session_helper)
logger.debug(f"Custom table {redshift_params['table_name']} created.")
logger.debug("Checking publish params...")
check_empty_dataframe(dataframe)
check_dataframe_for_timedelta(dataframe)
check_partitions(partitions, dataframe)
logger.debug("Publish params valid.")
logger.debug("Begin writing to S3..")
files = []
for frame_params in _sized_dataframes(dataframe):
logger.info(
f"Publishing dataframe chunk : {frame_params['lower']} to {frame_params['upper']}"
)
frame = pd.DataFrame(
dataframe[frame_params['lower']:frame_params['upper']])
_gen_parquet_to_s3(bucket=bucket,
key=key,
dataframe=frame,
partitions=partitions,
custom_redshift_columns=custom_redshift_columns)
published_files = _assign_partition_meta(
bucket=bucket,
key=key,
dataframe=frame,
partitions=partitions,
session_helper=session_helper,
redshift_params=redshift_params,
custom_redshift_columns=custom_redshift_columns)
files = files + published_files
logger.info("Done writing to S3.")
return files
def publish(bucket: str,
key: str,
partitions: List['str'],
dataframe: pd.DataFrame,
redshift_params=None) -> None:
"""Redshift Params:
ARGS:
schema_name: str
table_name: str
iam_role: str
region: str
cluster_id: str
host: str
port: str
db_name: str
"""
session_helper = None
if redshift_params:
if "index" in dataframe.columns:
raise ValueError(
"'index' is a reserved keyword in Redshift. Please remove or rename your DataFrame's 'index' column."
)
logger.debug(
"Found redshift parameters. Checking validity of params...")
check_redshift_params(redshift_params)
logger.debug("Redshift parameters valid. Opening Session helper.")
session_helper = SessionHelper(
region=redshift_params['region'],
cluster_id=redshift_params['cluster_id'],
host=redshift_params['host'],
port=redshift_params['port'],
db_name=redshift_params['db_name'])
session_helper.configure_session_helper()
publish_redshift.create_schema(redshift_params['schema_name'],
redshift_params['db_name'],
redshift_params['iam_role'],
session_helper)
logger.debug(
f"Schema {redshift_params['schema_name']} created. Creating table {redshift_params['table_name']}..."
)
df_types = _get_dataframe_datatypes(dataframe, partitions)
partition_types = _get_dataframe_datatypes(dataframe, partitions, True)
publish_redshift.create_table(redshift_params['table_name'],
redshift_params['schema_name'],
df_types, partition_types,
s3_url(bucket, key), session_helper)
logger.debug(f"Table {redshift_params['table_name']} created.")
logger.info("Checking params...")
check_empty_dataframe(dataframe)
check_dataframe_for_timedelta(dataframe)
check_partitions(partitions, dataframe)
logger.info("Params valid.")
logger.debug("Begin writing to S3..")
files = []
for frame in _sized_dataframes(dataframe):
_gen_parquet_to_s3(bucket=bucket,
key=key,
dataframe=frame,
partitions=partitions)
published_files = _assign_partition_meta(
bucket=bucket,
key=key,
dataframe=frame,
partitions=partitions,
session_helper=session_helper,
redshift_params=redshift_params)
files = files + published_files
logger.debug("Done writing to S3.")
return files
def create_schema(schema_name: str, session_helper: SessionHelper):
schema_name_validator(schema_name)
with session_helper.db_session_scope() as scope:
scope.execute(f'CREATE SCHEMA IF NOT EXISTS {schema_name}')
