def create_temporary_table(self, df, temporary_table_name, key, engine):
"""
Snippet to create a temporary table in the SQL database
Inpired from https://stackoverflow.com/questions/30867390/python-pandas-to-sql-how-to-create-a-table-with-a-primary-key
"""
# Local variables
eng = self.engine
with eng.connect() as conn, conn.begin():
pandas_engine = pandasSQL_builder(conn)
# creating a table
table = TemporaryTable(temporary_table_name,
pandas_engine,
frame=df,
if_exists="replace")
table.create()
# dumping to the existing table
df.to_sql(temporary_table_name,
conn,
index=False,
if_exists="replace")
# Simply add the primary key after uploading the table with pandas.
with eng.connect() as con:
con.execute('ALTER TABLE ' + temporary_table_name +
' ADD PRIMARY KEY (' + key + ');')
def get_temp_table(conn, data, create=False, **kwargs):
"""Reuse Pandas logic for creating a temp table. The definition will be
formed based on the first row of data passed"""
table_name = get_temp_table_name()
pandas_engine = pandasSQL_builder(conn, schema=kwargs.get("schema", None))
if isinstance(conn, sqlite3.Connection):
cls = SQLiteTemporaryTable
elif is_sqlalchemy_conn(conn):
cls = TemporaryTable
else:
raise AssertionError(
"Only sqlite3 and SQLAlchemy conns are currently supported"
)
df = data
if not isinstance(data, pd.DataFrame):
# This reuses pandas logic for creating tables, but doesn't fully
# convert rows to a dataframe since that may be expensive and
# unwanted. Assumes its an iterable of dicts and keys() returns column
# names in proper order.
first_row = data[0]
df = pd.DataFrame.from_records([first_row], columns=first_row.keys())
table = cls(table_name, pandas_engine, frame=df, if_exists="fail")
if create:
table.create()
return table
def _get_sqa_table(self,
table_name: str,
table_type: str,
schema: str = None,
database: str = None,
dtype=None
) -> None:
"""
Method that extracts sqa table object
"""
self._load_df_if_empty()
pandas_sql = pandasSQL_builder(con=self._con)
sqllite = False
if not isinstance(pandas_sql, SQLDatabase):
sqllite = True
pd_db = SQLDatabase(engine=self._con)
table_klass = SQLTable if not sqllite else SQLiteTable
target_frame = (self._data if table_type == "staging"
else self._his_data)
target_name = self._build_name(table=table_name, table_type=table_type)
table = table_klass(
name=target_name,
pandas_sql_engine=pd_db,
index=False,
frame=target_frame
)
return table._create_table_setup()
def load_table(filename, tablename, engine=None, infer_size=100, chunk_size=1000):
engine = engine or sa.create_engine(config.SQLA_URI)
file = ensure_csv(filename)
# Pass data types to iterator to ensure consistent types across chunks
dtypes = pd.read_csv(file.name, nrows=infer_size).dtypes
chunks = pd.read_csv(file.name, chunksize=chunk_size, iterator=True, dtype=dtypes)
for idx, chunk in enumerate(chunks):
chunk.index += chunk_size * idx
sql_engine = pandasSQL_builder(engine)
to_sql(
tablename, sql_engine, chunk,
chunksize=chunk_size, keys='index', if_exists='append',
)
def execute_from_model(self, tt: task_models.TaskTemplate,
**kwargs) -> typing.Any:
if tt.custom["secret_connect_args"] is not None:
for key, secret_dict in tt.custom["secret_connect_args"].items():
value = current_context().secrets.get(
group=secret_dict["group"], key=secret_dict["key"])
tt.custom["connect_args"][key] = value
engine = create_engine(tt.custom["uri"],
connect_args=tt.custom["connect_args"],
echo=False)
print(f"Connecting to db {tt.custom['uri']}")
interpolated_query = SQLAlchemyTask.interpolate_query(
tt.custom["query_template"], **kwargs)
print(f"Interpolated query {interpolated_query}")
with engine.begin() as connection:
df = None
if tt.interface.outputs:
df = pd.read_sql_query(interpolated_query, connection)
else:
pandasSQL_builder(connection).execute(interpolated_query)
return df
def get_sa_table_for_dataframe(dataframe, tablename, schemaname):
sa_engine = get_engine()
# get max lengths for strings and use it to set dtypes
dtypes = {}
object_types = get_dataframe_column_object_types(dataframe)
for c in object_types:
if dataframe[c].dtype == np.dtype('O'):
n = dataframe[c].map(lambda c: len(str(c)) if c else None).max()
# we use 10 times the max length or varchar(max)
dtypes[c] = VARCHAR(min([n * 10, 65535]))
table = SQLTable(tablename,
pandasSQL_builder(sa_engine, schema=schemaname),
dataframe,
if_exists=True,
index=False,
dtype=dtypes)
return table
def load_table(filename,
tablename,
engine=None,
infer_size=100,
chunk_size=1000):
engine = engine or sa.create_engine(config.SQLA_URI)
file = ensure_csv(filename)
# Pass data types to iterator to ensure consistent types across chunks
dtypes = pd.read_csv(file.name, nrows=infer_size).dtypes
chunks = pd.read_csv(file.name,
chunksize=chunk_size,
iterator=True,
dtype=dtypes)
for idx, chunk in enumerate(chunks):
chunk.index += chunk_size * idx
sql_engine = pandasSQL_builder(engine)
to_sql(
tablename,
sql_engine,
chunk,
chunksize=chunk_size,
keys='index',
if_exists='append',
)
def __init__(self,
connection,
table_name,
primary_key,
insert_timestamp_field=None,
update_timestamp_field=None,
use_on_duplicate=False):
self.use_on_duplicate = use_on_duplicate
self.connection = get_connection(connection)
tps = table_name.split(".")
self.table_name = tps[-1]
self.schema = len(tps) > 1 and tps[0] or None
self.full_table_name = self.schema and "%s.%s" % (
self.connection.ops.quote_name(
self.schema), self.connection.ops.quote_name(
self.table_name)) or self.connection.ops.quote_name(
self.table_name)
self.primary_key = primary_key
self.fields = {}
self.insert_timestamp_field = insert_timestamp_field
self.update_timestamp_field = update_timestamp_field
self.pd_sql = pandasSQL_builder(db_sqlalchemy_str(self.connection),
schema=self.schema)
self.detect_fields()
def to_redshift(self,
table_name,
s3_bucket,
s3_key,
engine=None,
schema=None,
if_exists="fail",
index=False,
compress=True,
primary_key=None,
aws_access_key_id=None,
aws_secret_access_key=None,
**kwargs):
if not engine:
engine = generate_redshift_engine_string()
if not aws_access_key_id:
aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID")
if not aws_secret_access_key:
aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY")
# Get Pandas SQLTable object
table = SQLTable(
table_name,
pandasSQL_builder(engine, schema=schema),
self,
if_exists=if_exists,
schema=schema,
index=index,
)
def quote(s):
return '"' + str(s) + '"'
# Full table name with schema
if schema:
full_table_name = quote(schema) + "." + quote(table_name)
else:
full_table_name = quote(table_name)
# Check table
if table.exists():
if if_exists == "fail":
raise ValueError("Table {} already exists.".format(table_name))
elif if_exists == "append":
queue = [
CopyCommand(
to=table,
data_location="s3://{}/{}".format(s3_bucket, s3_key),
access_key_id=aws_access_key_id,
secret_access_key=aws_secret_access_key,
format="CSV",
compression="GZIP" if compress else None,
)
]
elif if_exists == "replace":
queue = [
"drop table {};".format(full_table_name),
table.sql_schema() + ";",
CopyCommand(
to=table,
data_location="s3://{}/{}".format(s3_bucket, s3_key),
access_key_id=aws_access_key_id,
secret_access_key=aws_secret_access_key,
format="CSV",
compression="GZIP" if compress else None,
),
]
elif if_exists == "update":
staging_table = "{}_staging".format(table_name)
if not primary_key:
raise ValueError(
"Expected a primary key to update existing table")
queue = [
"begin;",
"drop table if exists {};".format(staging_table),
"create temporary table {} (like {});".format(
staging_table, full_table_name),
CopyCommand(
to=table,
data_location="s3://{}/{}".format(s3_bucket, s3_key),
access_key_id=aws_access_key_id,
secret_access_key=aws_secret_access_key,
format="CSV",
compression="GZIP" if compress else None,
),
"delete from {full_table_name} where {primary_key} in (select {primary_key} from {staging_table});"
.format(
full_table_name=full_table_name,
primary_key=primary_key,
staging_table=staging_table,
),
"insert into {} (select * from {});".format(
full_table_name, staging_table),
"end;",
]
else:
raise ValueError("{} is not valid for if_exists".format(if_exists))
else:
queue = [
table.sql_schema() + ";",
CopyCommand(
to=table,
data_location="s3://{}/{}".format(s3_bucket, s3_key),
access_key_id=aws_access_key_id,
secret_access_key=aws_secret_access_key,
format="CSV",
compression="GZIP" if compress else None,
),
]
# Save DataFrame to S3
self.to_s3(bucket=s3_bucket, key=s3_key, index=index, compress=compress)
# Execute queued statements
engine = _engine_builder(engine)
with engine.begin() as con:
for stmt in queue:
con.execute(stmt)
def to_redshift(self, table_name, engine, bucket, keypath=None,
schema=None, if_exists='fail', index=True, index_label=None,
aws_access_key_id=None, aws_secret_access_key=None,
columns=None, null_as=None, emptyasnull=True):
"""
Write a DataFrame to redshift via S3
Parameters
=========
table_name : str. (unqualified) name in redshift
engine : SQLA engine
bucket : str; s3 bucket
keypath : str; keypath in s3 (without bucket name)
schema : redshift schema
if_exits : str; {'fail', 'append', 'replace'}
index : bool; include DataFrames index
index_label : bool; label for the index
aws_access_key_id / aws_secret_access_key : from ~/.boto by default
columns : subset of columns to include
null_as : treat these as null
emptyasnull bool; whether '' is null
"""
url = self.to_s3(keypath, engine, bucket=bucket, index=index,
index_label=index_label)
qualname = resolve_qualname(table_name, schema)
table = SQLTable(table_name, pandasSQL_builder(engine, schema=schema),
self, if_exists=if_exists, index=index)
if columns is None:
columns = ''
else:
columns = '()'.format(','.join(columns))
print("Creating table {}".format(qualname))
if table.exists():
if if_exists == 'fail':
raise ValueError("Table Exists")
elif if_exists == 'append':
queue = []
elif if_exists == 'replace':
queue = ['drop table {}'.format(qualname), table.sql_schema()]
else:
raise ValueError("Bad option for `if_exists`")
else:
queue = [table.sql_schema()]
with engine.begin() as con:
for stmt in queue:
con.execute(stmt)
s3conn = boto.connect_s3(aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key)
conn = psycopg2.connect(database=engine.url.database,
user=engine.url.username,
password=engine.url.password,
host=engine.url.host,
port=engine.url.port,
sslmode='require')
cur = conn.cursor()
if null_as is not None:
null_as = "NULL AS '{}'".format(null_as)
else:
null_as = ''
if emptyasnull:
emptyasnull = "EMPTYASNULL"
else:
emptyasnull = ''
full_keypath = 's3://' + url
print("COPYing")
stmt = ("copy {qualname} {columns} from '{keypath}' "
"credentials 'aws_access_key_id={key};aws_secret_access_key={secret}' "
"GZIP "
"{null_as} "
"{emptyasnull}"
"CSV;".format(qualname=qualname,
columns=columns,
keypath=full_keypath,
key=s3conn.aws_access_key_id,
secret=s3conn.aws_secret_access_key,
null_as=null_as,
emptyasnull=emptyasnull))
cur.execute(stmt)
conn.commit()
conn.close()
def to_sql(name, engine, frame, chunksize=None, **kwargs):
pandas_sql_engine = pandasSQL_builder(engine)
table = SQLTable(name, pandas_sql_engine, frame=frame, **kwargs)
table.create()
table.insert(chunksize)
def write(self,
data_frame,
routine_name,
table_name,
bucketname=None,
if_exists='replace',
sub_routine=None):
"""Write data table
:param data_frame: dataframe
:param routine_name: routine name
:param table_name: table name
:param bucketname: bucket name
:param if_exists: method if exists
:param sub_routine: sub routine
:return: None
"""
# todo this function is pretty verbose as it is, please use logger instead of print
# todo make sure log statement is understandable for outside observer
# todo bucketname should always be project_name, redshift should know its own project_name
# todo when table is new, write metadata, but give an option to skip metadata
self.bucket = bucketname
if (table_name != 'meta_database') & (sub_routine is None):
table_name = routine_name + '/' + table_name
elif (table_name == 'meta_database') & (sub_routine is None):
table_name = table_name
else:
table_name = routine_name + '/' + sub_routine + '/' + table_name
print(table_name)
logging.info('Writing table {} :'.format(table_name))
s3 = boto3.resource('s3')
bucket = s3.Bucket(bucketname)
con = psycopg2.connect(self.redshift_path)
con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT)
cur = con.cursor()
# write DF to string stream
csv_buffer = StringIO()
data_frame.to_csv(csv_buffer, index=None, header=None, sep='|')
# reset stream position
csv_buffer.seek(0)
# create binary stream
gz_buffer = BytesIO()
# compress string stream using gzip
with gzip.GzipFile(mode='w', fileobj=gz_buffer) as gz_file:
gz_file.write(bytes(csv_buffer.getvalue(), 'utf-8'))
# write stream to S3
timestamp = datetime.datetime.strftime(datetime.datetime.now(),
'%Y%m%d%H%M%S')
bucket.put_object(Key='tmp_' + timestamp + '.gz',
Body=gz_buffer.getvalue())
print('saved file ')
# CREATE THE COPY STATEMENT TO SEND FROM S3 TO THE TABLE IN REDSHIFT
s3_path_tmp_file = 's3://{0}/{1}'.format(bucketname,
'tmp_' + timestamp + '.gz')
print('create table')
table = SQLTable(table_name,
pandasSQL_builder(self.engine, schema=None),
data_frame,
if_exists=if_exists,
index=None)
statements = []
if table.exists():
if if_exists == 'fail':
raise ValueError("Table Exists")
elif if_exists == 'append':
statements = []
elif if_exists == 'replace':
statements = [
""" truncate "{}"; rollback; drop table "{}";""".format(
table_name, table_name)
]
else:
raise ValueError("Bad option for `if_exists`")
statements.append(table.sql_schema() + ';')
statement = """
copy "{0}"
from '{1}'
delimiter '{2}'
region 'us-east-1'
CREDENTIALS 'aws_access_key_id={3};aws_secret_access_key={4}'
FORMAT AS CSV NULL AS '@NULL@'
GZIP
TRUNCATECOLUMNS
""".format(table_name, s3_path_tmp_file, '|',
'AKIAIVCDQREXD2TPPRAQ',
'SCemMCgkq1rUruSrIDbFdjorHthnvY6E4j8/UEfg')
statements.append(statement)
try:
logging.info('excucte statement')
for stmt in statements:
print(stmt)
cur.execute(stmt)
# con.commit()
logging.info('finish execute')
except Exception as e:
print(e)
traceback.print_exc(file=sys.stdout)
con.rollback()
raise
s3.Object(bucketname, 'tmp_' + timestamp + '.gz').delete()
logging.info('FILLING THE TABLE IN REDSHIFT')
logging.info('\n--------------- write already -----------------')
def to_sql(name, engine, frame, chunksize=None, **kwargs):
pandas_sql_engine = pandasSQL_builder(engine)
table = SQLTable(name, pandas_sql_engine, frame=frame, **kwargs)
table.create()
table.insert(chunksize)
def to_redshift(self, table_name, engine, bucket, keypath=None,
schema=None, if_exists='fail', index=True, index_label=None,
aws_access_key_id=None, aws_secret_access_key=None,
columns=None, null_as=None, emptyasnull=True):
"""
Write a DataFrame to redshift via S3
Parameters
=========
table_name : str. (unqualified) name in redshift
engine : SQLA engine
bucket : str; s3 bucket
keypath : str; keypath in s3 (without bucket name)
schema : redshift schema
if_exits : str; {'fail', 'append', 'replace'}
index : bool; include DataFrames index
index_label : bool; label for the index
aws_access_key_id / aws_secret_access_key : from ~/.boto by default
columns : subset of columns to include
null_as : treat these as null
emptyasnull bool; whether '' is null
"""
url = self.to_s3(keypath, engine, bucket=bucket, index=index,
index_label=index_label)
qualname = resolve_qualname(table_name, schema)
table = SQLTable(table_name, pandasSQL_builder(engine, schema=schema),
self, if_exists=if_exists, index=index)
if columns is None:
columns = ''
else:
columns = '()'.format(','.join(columns))
print("Creating table {}".format(qualname))
if table.exists():
if if_exists == 'fail':
raise ValueError("Table Exists")
elif if_exists == 'append':
queue = []
elif if_exists == 'replace':
queue = ['drop table {}'.format(qualname), table.sql_schema()]
else:
raise ValueError("Bad option for `if_exists`")
else:
queue = [table.sql_schema()]
with engine.begin() as con:
for stmt in queue:
con.execute(stmt)
s3conn = boto.connect_s3(aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key)
conn = psycopg2.connect(database=engine.url.database,
user=engine.url.username,
password=engine.url.password,
host=engine.url.host,
port=engine.url.port,
sslmode='require')
cur = conn.cursor()
if null_as is not None:
null_as = "NULL AS '{}'".format(null_as)
else:
null_as = ''
if emptyasnull:
emptyasnull = "EMPTYASNULL"
else:
emptyasnull = ''
full_keypath = 's3://' + url
print("COPYing")
stmt = ("copy {qualname} {columns} from '{keypath}' "
"credentials 'aws_access_key_id={key};aws_secret_access_key={secret}' "
"GZIP "
"{null_as} "
"{emptyasnull}"
"CSV;".format(qualname=qualname,
columns=columns,
keypath=full_keypath,
key=s3conn.aws_access_key_id,
secret=s3conn.aws_secret_access_key,
null_as=null_as,
emptyasnull=emptyasnull))
cur.execute(stmt)
conn.commit()
conn.close()
