PySnappyData is a collection of Python DB-API and SQLAlchemy interfaces for SnappyData
from pysnappydata import snappydata
cursor = snappydata.connect('localhost').cursor()
cursor.execute('SELECT * FROM test')
print cursor.fetchall()First install SQLAlchemy, then install this package to register it with SQLAlchemy:
from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
engine = create_engine('snappydata://localhost')
metadata = MetaData()
user = Table('user_c', metadata,
Column('id', INTEGER, primary_key=True),
Column('name', VARCHAR(20)),
)
metadata.create_all(engine)
conn = engine.connect()
i = user.insert()
u = dict(id=0, name='yy')
r = conn.execute(i, **u)
print r.rowcount
s = select([user.c.name]).where(user.c.id==0)
r = conn.execute(s)
print r.rowcount
print r.fetchall()from sqlalchemy import *
from sqlalchemy.engine import create_engine
from sqlalchemy.schema import *
engine = create_engine('snappydata://localhost:1527', connect_args = {'locator':True})
metadata = MetaData()
user = Table('user_c', metadata,
Column('id', INTEGER, primary_key=True),
Column('name', VARCHAR(20)),
)
metadata.create_all(engine)
conn = engine.connect()
user.drop(engine);
conn.close()With SQLAlchemy supports, we can benefit from Superset. However, time series Chart is not compatible as snappydata always return the columns' name in upper case, while Superset's key "DTTM_ALIAS" is lower case "__datetime".
A few lines need patch to the file superset/viz.py
def get_df(self, query_obj=None):
"""Returns a pandas dataframe based on the query object"""
...
if df is None or df.empty:
self.status = utils.QueryStatus.FAILED
if not self.error_message:
self.error_message = 'No data.'
return pd.DataFrame()
else:
_tmp = []
for _c in df.columns:
if _c.lower() == DTTM_ALIAS:
_tmp.append(DTTM_ALIAS)
else:
_tmp.append(_c)
df.columns = _tmp