def load_cdr(self):
call_dimension = Dimension(
name='"DimCall"',
key='uuid',
attributes=[
"SipFromUser", 'SipToUser', 'HangupCause', 'Direction',
'SwitchName', 'CallerContext', 'IsAnswered', 'Duration',
'ObjClass', 'ObjType', 'ObjCategory', 'CompanyId', 'TenantId',
'AppId', 'AgentSkill', 'OriginatedLegs', 'DVPCallDirection',
'HangupDisposition', 'AgentAnswered', 'IsQueued',
'SipResource', 'BusinessUnit', 'CampaignId', 'CampaignName',
'ExtraData'
])
call_fact_table = FactTable(
name='"FactCall"',
keyrefs=[
'uuid', 'CallUuid', 'BridgeUuid', 'CreatedDateDimId',
'AnsweredDateDimId', 'BridgedDateDimId', 'HangupDateDimId',
'CreatedTimeDimId', 'AnsweredTimeDimId', 'BridgedTimeDimId',
'HangupTimeDimId'
],
measures=[
'CreatedTime', 'AnsweredTime', 'BridgedTime', 'HangupTime',
'BillSec', 'HoldSec', 'ProgressSec', 'QueueSec', 'AnswerSec',
'WaitSec', 'ProgressMediaSec', 'FlowBillSec'
])
self.row['uuid'] = call_dimension.ensure(self.row)
call_fact_table.insert(self.row)
def load_tickets(self):
ticket_dimension = Dimension(
name='"DimTicket"',
key='ticket_id',
attributes=[
'active', 'tid', 'is_sub_ticket', 'subject', 'reference',
'description', 'priority', 'requester', 'submitter',
'collaborators', 'watchers', 'attachments', 'sub_tickets',
'related_tickets', 'merged_tickets', 'isolated_tags', 'sla',
'comments', 'tags', 'ticket_type', 'status_type', 'channel',
'company', 'tenant', 'assignee', 'assignee_group',
'security_level', 'event_author'
])
ticket_fact_table = FactTable(
name='"FactTicket"',
keyrefs=[
'engagement_session_id', 'ticket_id', 'created_at_dim_id',
'updated_at_dim_id', 'due_at_dim_id', 'created_time_dim_id',
'updated_time_dim_id', 'due_time_dim_id'
],
measures=[
'created_at', 'updated_at', 'due_at', 'time_estimation',
're_opens', 'waited_time', 'worked_time', 'resolution_time'
])
# Updates the row with the primary keys of each dimension while at the same time inserting new data into
# each dimension
self.row['ticket_id'] = ticket_dimension.ensure(self.row)
ticket_fact_table.insert(self.row)
def load_engagement(self):
engagement_dimension = Dimension(
name='"DimEngagement"',
key='engagement_id',
attributes=['profile', 'channel_from', 'channel_to', 'company', 'has_profile', 'tenant', 'notes', 'channel'])
engagement_fact_table = FactTable(
name='"FactEngagement"',
keyrefs=['engagement_id', 'created_at_dim_id', 'updated_at_dim_id', 'created_time_dim_id', 'updated_time_dim_id'],
measures=['updated_at', 'created_at'])
self.row['eng_dim_id'] = engagement_dimension.ensure(self.row)
engagement_fact_table.insert(self.row)
return True
def load_ticket_events(self):
ticket_event_dimension = Dimension(
name='"DimTicketEvent"',
key='ticket_event_id',
attributes=['event_type', 'event_author', 'event_body'])
ticket_event_fact_table = FactTable(
name='"FactTicketEvent"',
keyrefs=['ticket_id', 'ticket_event_id'],
measures=['event_created_at_dim_id', 'event_created_at'])
# Update the row with the primary keys of each dimension while at the same time inserting new data into
# each dimension
ticket_event_dimension.ensure(self.row)
ticket_event_fact_table.insert(self.row)
def load_external_users(self):
external_users_dimension = Dimension(
name='"DimExternalUsers"',
key='external_user_id',
attributes=['title', 'name', 'avatar', 'birthday', 'gender', 'first_name', 'last_name',
'locale', 'ssn', 'password', 'phone', 'email', 'landnumber', 'facebook', 'twitter', 'linkedin',
'googleplus', 'skype', 'thirdpartyreference', 'company', 'tenant', 'custom_fields', 'tags',
'contacts', 'zipcode', 'address_number', 'city', 'province', 'country'])
external_users_fact_table = FactTable(
name='"FactExternalUsers"',
keyrefs=['external_user_id', 'created_at_dim_id', 'updated_at_dim_id', 'created_time_dim_id', 'updated_time_dim_id'],
measures=['created_at', 'updated_at'])
self.row['external_user_id'] = external_users_dimension.ensure(self.row)
external_users_fact_table.insert(self.row)
# We do not use ensure() for the location dimension, as city
# and region information of all stores has already been loaded into
# the table, and having a sale from a store we do not know about is
# probably either an error in the sales or region data. We use lookup
# instead which does not insert data and returns None, if no row with
# the requested data is available, allowing for simple implementation
# of error handling in ETL flow, which is shown here as an exception
row['locationid'] = location_dimension.lookup(row)
# A simple example of how to check if a lookup was successful, so
# errors can be handled in some way. Here we just give up, and throw
# an error.
if not row['locationid']:
raise ValueError("City was not present in the location dimension")
# As the number of sales was already conveniently aggregated in the
# sales table, the row can now be inserted into the data warehouse as
# we have all the IDs we need. If aggregations, or other more advanced
# manipulation is required, the full power Python is available as shown
# with the call to the split_timestamp(row) function.
fact_table.insert(row)
# After all the data is inserted, we close the connection in order to
# ensure that all data is committed to the database and that the
# connection is correctly released
dw_conn_wrapper.commit()
dw_conn_wrapper.close()
# Finally, the connection to the sales database is closed
sales_pgconn.close()
def setup(dbname, number):
open(os.path.expanduser(dbname), 'w')
output_conn = sqlite3.connect(dbname)
output_cur = output_conn.cursor()
output_cur.execute("CREATE TABLE dim1 "
"(key1 INTEGER PRIMARY KEY, attr1 INTEGER, "
"attr2 INTEGER)")
output_cur.execute("CREATE TABLE dim2 "
"(key2 INTEGER PRIMARY KEY, attr3 INTEGER, "
"attr4 INTEGER)")
output_cur.execute("CREATE TABLE ft1 "
"(measure INTEGER, key1 INTEGER, key2 INTEGER, "
"PRIMARY KEY(key1, key2))")
output_conn.commit()
output_wrapper = pygrametl.ConnectionWrapper(connection=output_conn)
dim1 = Dimension(
name='dim1',
key='key1',
attributes=['attr1', 'attr2']
)
dim2 = Dimension(
name='dim2',
key='key2',
attributes=['attr3', 'attr4'],
)
ft1 = FactTable(
name='ft1',
keyrefs=['key1', 'key2'],
measures=['measure']
)
facts = []
dim1_data = []
dim2_data = []
total_elapsed = 0.
print('Generating ft1 data')
start = time.monotonic()
counter = 0
for i in range(1, number + 1):
for j in range(1, number + 1):
counter += 10
facts.append({'key1': i, 'key2': j, 'measure': counter})
end = time.monotonic()
elapsed = end - start
print('Generated: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('Generating dim1 data')
start = time.monotonic()
for i in range(1, number + 1):
dim1_data.append({'attr1': i, 'attr2': number + 1 - i})
end = time.monotonic()
elapsed = end - start
print('Generated: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('Generating dim2 data')
start = time.monotonic()
for i in range(1, number + 1):
dim2_data.append({'attr3': i, 'attr4': number + 1 - i})
end = time.monotonic()
elapsed = end - start
print('Generated: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('Inserting data into ft1')
start = time.monotonic()
for row in facts:
ft1.insert(row)
end = time.monotonic()
elapsed = end - start
print('Inserted: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('Inserting data into dim1')
start = time.monotonic()
for row in dim1_data:
dim1.insert(row)
end = time.monotonic()
elapsed = end - start
print('Inserted: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('Inserting data into dim2')
start = time.monotonic()
for row in dim2_data:
dim2.insert(row)
end = time.monotonic()
elapsed = end - start
print('Inserted: {}{}'.format(round(elapsed, 3), 's'))
total_elapsed += elapsed
print('DW populated')
print('Total time: {}{}\n'.format(round(total_elapsed, 3), 's'))
output_conn.commit()
# We put the regions into our location_dimension
[location_dimension.insert(row) for row in region_source]
csv_file_handle.close()
# Each row in the sales database is iterated through and inserted
for row in sales_source:
# Each row is passed to the timestamp split function for splitting
split_timestamp(row)
# Lookups are performed to find the key in each dimension for the fact
# and if the data is not there, it is inserted from the sales row
row['bookid'] = book_dimension.ensure(row)
row['timeid'] = time_dimension.ensure(row)
# For the location dimension, all the data is already present, so a
# missing row must be an error
row['locationid'] = location_dimension.lookup(row)
if not row['locationid']:
raise ValueError("city was not present in the location dimension")
# The row can then be inserted into the fact table
fact_table.insert(row)
# The data warehouse connection is then ordered to commit and close
dw_conn_wrapper.commit()
dw_conn_wrapper.close()
sales_conn.close()
