def __init__(self):
try:
# pass in environment variables
self.project_id = os.environ["GCLOUD_PROJECT_NAME"]
self.instance_id = os.environ["BIGTABLE_CLUSTER"]
self.table_id = os.environ["TABLE_NAME"]
self.cloud_region = os.environ["CLOUD_REGION"]
self.iot_registry = os.environ["IOT_REGISTRY"]
self.row_filter_count = int(os.environ["ROW_FILTER"])
self.key_ring_id = os.environ["KEY_RING_ID"]
self.crypto_key_id = os.environ["CRYPTO_KEY_ID"]
self.__service_account_ciphertext = base64.b64decode(
os.environ["GOOGLE_APP_CREDENTIALS"])
# setup bigtable variables
self.row_filter = row_filters.CellsColumnLimitFilter(
(self.row_filter_count))
self.bigtable_client = bigtable.Client(project=self.project_id,
admin=True)
self.column = "device-temp".encode()
self.column_family_id = "device-family"
self.instance = self.bigtable_client.instance(self.instance_id)
self.table = self.instance.table(self.table_id)
# error handling messages
self.index_error_message = "Refresh browser until live data starts flowing through and infrastructure is deployed! Exiting application now."
self.type_error_message = (
"Ensure GOOGLE_APP_CREDENTIALS env var is base64 decoded ciphertext"
)
except KeyError as e:
print(
f"Make sure this variable is defined in the application env vars: {str(e)}"
)
sys.exit()
def Resolve(self, subject, attribute, token=None):
"""Retrieve the latest value set for a subject's attribute.
Args:
subject: The subject URN.
attribute: The attribute.
token: The security token used in this call.
Returns:
A (string, timestamp in microseconds) stored in the bigtable
cell, or (None, 0).
Raises:
AccessError: if anything goes wrong.
"""
subject = utils.SmartStr(subject)
attribute = utils.SmartStr(attribute)
family, column = self.GetFamilyColumn(attribute)
col_filter = row_filters.ColumnRangeFilter(
family, start_column=column, end_column=column)
# Most recent
latest_filter = row_filters.CellsColumnLimitFilter(1)
row_filter = row_filters.RowFilterChain(filters=[col_filter, latest_filter])
row_data = self.table.read_row(subject, filter_=row_filter)
if row_data:
for cell in row_data.cells[family][column]:
return self.Decode(
attribute, cell.value), self.DatetimeToMicroseconds(cell.timestamp)
return None, 0
def _Acquire(self, lease_time):
now = int(time.time() * 1e6)
expires = int((time.time() + lease_time) * 1e6)
# Only latest value
latest_value = row_filters.CellsColumnLimitFilter(1)
# Match any lease time value > now which means someone else holds a lock
# We can't store these as ints, encode to str.
current_lease = row_filters.ValueRangeFilter(
start_value=utils.SmartStr(now), inclusive_start=False)
# aff4:lease
family, column = self.store.GetFamilyColumn(self.store.LEASE_ATTRIBUTE)
col_filter = row_filters.ColumnRangeFilter(
family, start_column=column, end_column=column)
# Note filter chains are evaluated in order so there are performance
# considerations with which filter to apply first
filter_chain = row_filters.RowFilterChain(
[col_filter, current_lease, latest_value])
mutate_row = self.store.table.row(self.subject, filter_=filter_chain)
# state=False means no lease or it's expired, in this case take the lock.
mutate_row.set_cell(family, column, utils.SmartStr(expires), state=False)
# Check in review: I think we want to retry the RPC here? Or should we just
# raise like we can't get the lock?
existing_lock = self.store.CallWithRetry(mutate_row.commit, "write")
if existing_lock:
raise ExistingLock("Row %s locked." % self.subject)
# We have the lock
self.expires = expires
self.locked = True
def __init__(self, device_data):
# pass through environment vars
self.project_id = os.environ["GCLOUD_PROJECT_NAME"]
self.instance_id = os.environ["BIGTABLE_CLUSTER"]
self.table_id = os.environ["TABLE_NAME"]
self.row_filter_count = int(os.environ["ROW_FILTER"])
# setup table config
self.client = bigtable.Client(project=self.project_id, admin=True)
self.instance = self.client.instance(self.instance_id)
self.column = "device-temp".encode()
self.column_family_id = "device-family"
self.row_filter = row_filters.CellsColumnLimitFilter(
(self.row_filter_count))
# setup row value config
self.device_data = literal_eval(device_data)
# reverse the timestamp as that is the most common query
# https://cloud.google.com/bigtable/docs/schema-design-time-series#reverse_timestamps_only_when_necessary
self.row_key = "device#{}#{}".format(
self.device_data["device"],
(sys.maxsize - (self.device_data["timestamp"]))).encode()
# convert to string as bigtable can't accept float types
# https://streamsets.com/documentation/datacollector/latest/help/datacollector/UserGuide/Destinations/Bigtable.html
self.value = str(self.device_data["temperature"])
def cbt_get_global_trajectory_buffer(cbt_table):
row_filter = row_filters.CellsColumnLimitFilter(1)
row = cbt_table.read_row('global_traj_buff'.encode())
if row is not None:
return np.flip(np.frombuffer(row.cells['global']['traj_buff'.encode()][0].value, dtype=np.int32), axis=0)
else:
print("Table is empty.")
exit()
def get_random_poem(self):
random_n = random.randint(0, 499)
column = 'poem'.encode()
key = ('poem' + str(random_n)).encode()
row_filter = row_filters.CellsColumnLimitFilter(1)
row = self.table.read_row(key, row_filter)
cell = row.cells[self.column_family_id][column][0]
return cell.value.decode('utf-8')
def filter_limit_cells_per_col(project_id, instance_id, table_id):
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)
table = instance.table(table_id)
rows = table.read_rows(filter_=row_filters.CellsColumnLimitFilter(2))
for row in rows:
print_row(row)
def filter_composing_chain(project_id, instance_id, table_id):
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)
table = instance.table(table_id)
rows = table.read_rows(filter_=row_filters.RowFilterChain(
filters=[row_filters.CellsColumnLimitFilter(1),
row_filters.FamilyNameRegexFilter("cell_plan")]))
for row in rows:
print_row(row)
def query(self, host, dc, region, t, limit=1, window=60):
t0 = int(t) - window
t1 = int(t)
start_key = rowkey(host, dc, region, t0)
end_key = rowkey(host, dc, region, t1)
row_set = RowSet()
row_set.add_row_range(RowRange(start_key, end_key))
return self.table.read_rows(
limit=limit,
filter_=row_filters.CellsColumnLimitFilter(1),
row_set=row_set)
def _TimestampToFilter(self, timestamp):
if timestamp == data_store.DataStore.ALL_TIMESTAMPS:
return None
if timestamp is None or timestamp == data_store.DataStore.NEWEST_TIMESTAMP:
# Latest value only
return row_filters.CellsColumnLimitFilter(1)
if isinstance(timestamp, tuple):
return row_filters.TimestampRangeFilter(
self._TimestampRangeFromTuple(timestamp))
raise data_store.Error("Invalid timestamp specification: %s." % timestamp)
def get(self):
bt_array = []
try:
table = instance.table(bt_table_name)
row_set = RowSet()
for row_key in row_keys:
row_set.add_row_key(row_key)
colFilters = []
for name, bt_name in bt_mapping_dict.items():
colFilters.append(
row_filters.ColumnQualifierRegexFilter(bt_name))
print("before read_rows...")
rows = table.read_rows(
row_set=row_set,
filter_=row_filters.RowFilterChain(filters=[
row_filters.CellsColumnLimitFilter(1),
row_filters.RowFilterUnion(filters=colFilters)
]),
retry=bigtable.table.DEFAULT_RETRY_READ_ROWS.with_deadline(
60.0))
print("after read_rows...")
for row in rows:
print("Reading data for {}:".format(
row.row_key.decode('utf-8')))
for cf, cols in sorted(row.cells.items()):
bt_dict = {}
bt_dict['id'] = row.row_key.decode('utf-8')
key = None
# using BT mapping to return data
for col, cells in sorted(cols.items()):
for cell in cells:
for name, bt_name in bt_mapping_dict.items():
if col.decode('utf-8') == bt_name:
key = name
break
if key is not None:
bt_dict[key] = cell.value.decode('utf-8')
bt_array.append(bt_dict)
except BaseException as error:
logging.error(
'An exception occurred - DemoBigTableGet::get(): {}'.format(
error))
print(bt_array)
return json.dumps(bt_array), 200, {'ContentType': 'application/json'}
def get_client_profile(self, client_id):
"""This fetches a single client record out of GCP BigTable
"""
try:
row_key = client_id.encode()
table = self._instance.table(self._table_id)
row_filter = row_filters.CellsColumnLimitFilter(1)
row = table.read_row(row_key, row_filter)
cell = row.cells[self._column_family_id][self._column_name][0]
jdata = json.loads(zlib.decompress(cell.value).decode("utf-8"))
return jdata
except Exception:
logger = self._ctx[IMozLogging].get_logger("taar")
logger.warning(f"Error loading client profile for {client_id}")
return None
def read_from_bt(request):
bt_client = bigtable.Client(project=BT_PROJECT, admin=True)
instance = bt_client.instance(BT_INSTANCE)
table = instance.table(BT_TABLE)
max_versions_rule = column_family.MaxVersionsGCRule(2)
column_families = {COLUMN_FAMILY_ID: max_versions_rule}
if not table.exists():
table.create(column_families=column_families)
bt_row_filter = row_filters.CellsColumnLimitFilter(1)
bt_row_key = request['receipt_id']
bt_row = table.read_row(bt_row_key.encode('utf-8'), bt_row_filter)
return bt_row
def cbt_global_iterator(cbt_table):
row_filter = row_filters.CellsColumnLimitFilter(1)
gi_row = cbt_table.read_row('global_iterator'.encode())
if gi_row is not None:
global_i = gi_row.cells['global']['i'.encode()][0].value
global_i = struct.unpack('i', global_i)[0]
else:
gi_row = cbt_table.row('global_iterator'.encode())
gi_row.set_cell(column_family_id='global',
column='i'.encode(),
value=struct.pack('i', 0),
timestamp=datetime.datetime.utcnow())
cbt_table.mutate_rows([gi_row])
global_i = 0
return global_i
def main(project_id, instance_id, table_id):
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)
print('Creating the {} table.'.format(table_id))
table = instance.table(table_id)
print('Creating column family cf1 with Max Version GC rule...')
max_versions_rule = column_family.MaxVersionsGCRule(2)
column_family_id = 'cf1'
column_families = {column_family_id: max_versions_rule}
if not table.exists():
table.create(column_families=column_families)
else:
print("Table {} already exists.".format(table_id))
print('Writing some greetings to the table.')
greetings = ['Hello World!', 'Hello Cloud Bigtable!', 'Hello Python!']
rows = []
column = 'greeting'.encode()
for i, value in enumerate(greetings):
row_key = 'greeting{}'.format(i).encode()
row = table.row(row_key)
row.set_cell(column_family_id,
column,
value,
timestamp=datetime.datetime.utcnow())
rows.append(row)
table.mutate_rows(rows)
row_filter = row_filters.CellsColumnLimitFilter(1)
print('Getting a single greeting by row key.')
key = 'greeting0'.encode()
row = table.read_row(key, row_filter)
cell = row.cells[column_family_id][column][0]
print(cell.value.decode('utf-8'))
print('Scanning for all greetings:')
partial_rows = table.read_rows(filter_=row_filter)
for row in partial_rows:
cell = row.cells[column_family_id][column][0]
print(cell.value.decode('utf-8'))
print('Deleting the {} table.'.format(table_id))
table.delete()
def cbt_global_trajectory_buffer(cbt_table, local_traj_buff, global_traj_buff_size):
row_filter = row_filters.CellsColumnLimitFilter(1)
old_row = cbt_table.read_row('global_traj_buff'.encode())
if old_row is not None:
global_traj_buff = np.frombuffer(old_row.cells['global']['traj_buff'.encode()][0].value, dtype=np.int32)
global_traj_buff = np.append(global_traj_buff, local_traj_buff)
update_size = local_traj_buff.shape[0] - (global_traj_buff_size - global_traj_buff.shape[0])
if update_size > 0: global_traj_buff = global_traj_buff[update_size:]
else:
global_traj_buff = local_traj_buff
new_row = cbt_table.row('global_traj_buff'.encode())
new_row.set_cell(column_family_id='global',
column='traj_buff'.encode(),
value=global_traj_buff.tobytes(),
timestamp=datetime.datetime.utcnow())
cbt_table.mutate_rows([new_row])
def get_data(sensortype):
client = bigtable.Client(project='sensorray', admin=True)
instance = client.instance('instance')
table = instance.table('table')
row_filter = row_filters.CellsColumnLimitFilter(1)
print("Getting 250 most recent records for " + sensortype)
slist = []
for i in range(0, 10):
for j in range(0, 25):
pod = str(i).zfill(2) + str(j).zfill(2)
key = pod.encode()
row = table.read_row(key, row_filter)
cell = row.cells['sensor'][sensortype.encode()][0]
slist.append(int.from_bytes(cell.value, 'big'))
slist = np.array(slist).reshape(10, 25)
return slist
def setUp(self):
self.project_id = 'aerospike2bt'
self.instance_id = 'bookshelf'
self.table_id = 'books'
self.client = bigtable.Client(project=self.project_id, admin=True)
self.instance = self.client.instance(self.instance_id)
self.table = self.instance.table(self.table_id)
self.cf = 'info'
self.title = 'title'.encode()
self.author = 'author'.encode()
self.published_date = 'published_date'.encode()
self.image_url = 'image_url'.encode()
self.description = 'description'.encode()
self.created_by = 'created_by'.encode()
self.created_by_id = 'created_by_id'.encode()
self.row_filter = row_filters.CellsColumnLimitFilter(1)
self.test_data = {
'id': '123456789',
'title': 'test_title',
'author': 'test_author',
'publishedDate': 'test_published_date',
'imageUrl': 'test_image_url',
'description': 'test_description',
'createdBy': 'test_created_by',
'createdById': 'test_created_by_id'
}
self.test_data_updated = {
'id': '123456789',
'title': 'updated_title',
'author': 'test_author',
'publishedDate': 'test_published_date',
'imageUrl': 'test_image_url',
'description': 'test_description',
'createdBy': 'test_created_by',
'createdById': 'test_created_by_id'
}
self.row_key = (self.test_data['createdById'] + '_' +
self.test_data['id']).encode()
def ScanAttributes(self,
subject_prefix,
attributes,
after_urn=None,
max_records=None,
token=None,
relaxed_order=False):
subject_prefix = self._CleanSubjectPrefix(subject_prefix)
after_urn = self._CleanAfterURN(after_urn, subject_prefix)
# Turn subject prefix into an actual regex
subject_prefix += ".*"
self.security_manager.CheckDataStoreAccess(token, [subject_prefix], "rq")
subject_filter = row_filters.RowKeyRegexFilter(
utils.SmartStr(subject_prefix))
latest_value = row_filters.CellsColumnLimitFilter(1)
attribute_filters = self._GetAttributeFilterUnion(attributes)
# Subject AND (attr1 OR attr2) AND latest_value
query_filter = row_filters.RowFilterChain(
[subject_filter, attribute_filters, latest_value])
# The API results include the start row, we want to exclude it, append a
# null to do so.
if after_urn is not None:
after_urn += "\x00"
rows_data = self.CallWithRetry(
self.table.read_rows,
"read",
start_key=after_urn,
limit=max_records,
filter_=query_filter)
# Ideally we should be able to stream and yield, but it seems we can't:
# https://github.com/GoogleCloudPlatform/google-cloud-python/issues/1812
self.CallWithRetry(rows_data.consume_all, "read")
results = []
if rows_data.rows:
for subject, row_data in rows_data.rows.iteritems():
subject_results = self._ReOrderRowResults(row_data)
results.append((subject, subject_results))
return sorted(results, key=lambda x: x[0])
def cbt_global_iterator(cbt_table):
""" Fetches and sets global iterator from bigtable.
cbt_table -- bigtable object (default none)
"""
row_filter = row_filters.CellsColumnLimitFilter(1)
gi_row = cbt_table.read_row('collection_global_iterator'.encode())
if gi_row is not None:
global_i = gi_row.cells['global']['i'.encode()][0].value
global_i = struct.unpack('i', global_i)[0] + 1
else:
global_i = 0
gi_row = cbt_table.row('collection_global_iterator'.encode())
gi_row.set_cell(column_family_id='global',
column='i'.encode(),
value=struct.pack('i',global_i),
timestamp=datetime.datetime.utcnow())
cbt_table.mutate_rows([gi_row])
return global_i
scoped_credentials = credentials.with_scopes(
['https://www.googleapis.com/auth/cloud-platform'])
# In[87]:
project_id = ''
instance_id = 'accidentdb'
table_id = 'accident'
# In[88]:
client = bigtable.Client(project=project_id,
admin=True,
credentials=credentials)
instance = client.instance(instance_id)
row_filter = row_filters.CellsColumnLimitFilter(1)
table = instance.table(table_id)
column_family_id = 'location'
partial_rows = table.read_rows(filter_=row_filter)
lc = []
# In[89]:
print('Scanning for all rows:')
for row in partial_rows:
loc = row.cells[column_family_id]
lng = loc[b'Longitude'][0]
lt = loc[b'Latitude'][0]
valy = struct.unpack('>d', lng.value)[0]
valx = struct.unpack('>d', lt.value)[0]
lc.append([valx, valy])
def main(project_id, instance_id, table_id):
# [START bigtable_hw_connect]
# The client must be created with admin=True because it will create a
# table.
client = bigtable.Client(project=project_id, admin=True)
instance = client.instance(instance_id)
# [END bigtable_hw_connect]
# [START bigtable_hw_create_table]
print('Creating the {} table.'.format(table_id))
table = instance.table(table_id)
print('Creating column family cf1 with Max Version GC rule...')
# Create a column family with GC policy : most recent N versions
# Define the GC policy to retain only the most recent 2 versions
max_versions_rule = column_family.MaxVersionsGCRule(2)
column_family_id = 'cf1'
column_families = {column_family_id: max_versions_rule}
if not table.exists():
table.create(column_families=column_families)
else:
print("Table {} already exists.".format(table_id))
# [END bigtable_hw_create_table]
# [START bigtable_hw_write_rows]
print('Writing some greetings to the table.')
greetings = ['Hello World!', 'Hello Cloud Bigtable!', 'Hello Python!']
rows = []
column = 'greeting'.encode()
for i, value in enumerate(greetings):
# Note: This example uses sequential numeric IDs for simplicity,
# but this can result in poor performance in a production
# application. Since rows are stored in sorted order by key,
# sequential keys can result in poor distribution of operations
# across nodes.
#
# For more information about how to design a Bigtable schema for
# the best performance, see the documentation:
#
# https://cloud.google.com/bigtable/docs/schema-design
row_key = 'greeting{}'.format(i).encode()
row = table.direct_row(row_key)
row.set_cell(column_family_id,
column,
value,
timestamp=datetime.datetime.utcnow())
rows.append(row)
table.mutate_rows(rows)
# [END bigtable_hw_write_rows]
# [START bigtable_hw_create_filter]
# Create a filter to only retrieve the most recent version of the cell
# for each column accross entire row.
row_filter = row_filters.CellsColumnLimitFilter(1)
# [END bigtable_hw_create_filter]
# [START bigtable_hw_get_with_filter]
print('Getting a single greeting by row key.')
key = 'greeting0'.encode()
row = table.read_row(key, row_filter)
cell = row.cells[column_family_id][column][0]
print(cell.value.decode('utf-8'))
# [END bigtable_hw_get_with_filter]
# [START bigtable_hw_scan_with_filter]
print('Scanning for all greetings:')
partial_rows = table.read_rows(filter_=row_filter)
for row in partial_rows:
cell = row.cells[column_family_id][column][0]
print(cell.value.decode('utf-8'))
# [END bigtable_hw_scan_with_filter]
# [START bigtable_hw_delete_table]
print('Deleting the {} table.'.format(table_id))
table.delete()
def bigtable_func(project_id, instance_id, table_id):
from google.cloud import bigtable
from google.cloud.bigtable import column_family
from google.cloud.bigtable import row_filters
from google.auth.credentials import AnonymousCredentials
os.environ["BIGTABLE_EMULATOR_HOST"] = "localhost:8086"
# [START bigtable_hw_connect]
# The client must be created with admin=True because it will create a
# table.
client = bigtable.Client(project=project_id,
admin=True,
credentials=AnonymousCredentials())
instance = client.instance(instance_id)
# [END bigtable_hw_connect]
# [START bigtable_hw_create_table]
print(f"Creating the {table_id} table.")
table = instance.table(table_id)
print("Creating column family cf1 with Max Version GC rule...")
# Create a column family with GC policy : most recent N versions
# Define the GC policy to retain only the most recent 2 versions
max_versions_rule = column_family.MaxVersionsGCRule(2)
column_family_id = "cf1"
column_families = {column_family_id: max_versions_rule}
if not table.exists():
table.create(column_families=column_families)
else:
print(f"Table {table_id} already exists.")
# [END bigtable_hw_create_table]
# [START bigtable_hw_write_rows]
print("Writing some greetings to the table.")
greetings = ["Hello World!", "Hello Cloud Bigtable!", "Hello Python!"]
rows = []
column = b"greeting"
for i, value in enumerate(greetings):
# Note: This example uses sequential numeric IDs for simplicity,
# but this can result in poor performance in a production
# application. Since rows are stored in sorted order by key,
# sequential keys can result in poor distribution of operations
# across nodes.
#
# For more information about how to design a Bigtable schema for
# the best performance, see the documentation:
#
# https://cloud.google.com/bigtable/docs/schema-design
row_key = f"greeting{i}".encode()
row = table.direct_row(row_key)
row.set_cell(column_family_id,
column,
value,
timestamp=datetime.datetime.utcnow())
rows.append(row)
table.mutate_rows(rows)
# [END bigtable_hw_write_rows]
# [START bigtable_hw_create_filter]
# Create a filter to only retrieve the most recent version of the cell
# for each column accross entire row.
row_filter = row_filters.CellsColumnLimitFilter(1)
# [END bigtable_hw_create_filter]
# [START bigtable_hw_get_with_filter]
print("Getting a single greeting by row key.")
key = b"greeting0"
row = table.read_row(key, row_filter)
cell = row.cells[column_family_id][column][0]
print(cell.value.decode("utf-8"))
# [END bigtable_hw_get_with_filter]
# [START bigtable_hw_scan_with_filter]
print("Scanning for all greetings:")
partial_rows = table.read_rows(filter_=row_filter)
for row in partial_rows:
cell = row.cells[column_family_id][column][0]
print(cell.value.decode("utf-8"))
# [END bigtable_hw_scan_with_filter]
# [START bigtable_hw_delete_table]
print(f"Deleting the {table_id} table.")
table.delete()