def setup_databricks(
cls,
host,
project,
port=443,
region_name="default",
secrets_store="parameterstore",
cert_folder="hops",
hostname_verification=True,
trust_store_path=None,
api_key_file=None,
):
connection = cls(
host,
port,
project,
region_name,
secrets_store,
hostname_verification,
trust_store_path,
cert_folder,
api_key_file,
)
dbfs_folder = client.get_instance()._cert_folder_base
os.makedirs(os.path.join(dbfs_folder, "scripts"), exist_ok=True)
connection._get_clients(dbfs_folder)
hive_host = connection._get_hivemetastore_hostname()
connection._write_init_script(dbfs_folder)
connection._print_instructions(
cert_folder, client.get_instance()._cert_folder, hive_host
)
return connection
def _get_kafka_config(self, write_options: dict = {}) -> dict:
# producer configuration properties
# https://docs.confluent.io/platform/current/clients/librdkafka/html/md_CONFIGURATION.html
config = {
"security.protocol": "SSL",
"ssl.ca.location": client.get_instance()._get_ca_chain_path(),
"ssl.certificate.location": client.get_instance()._get_client_cert_path(),
"ssl.key.location": client.get_instance()._get_client_key_path(),
"client.id": socket.gethostname(),
**write_options.get("kafka_producer_config", {}),
}
if isinstance(client.get_instance(), hopsworks.Client) or write_options.get(
"internal_kafka", False
):
config["bootstrap.servers"] = ",".join(
[
endpoint.replace("INTERNAL://", "")
for endpoint in self._kafka_api.get_broker_endpoints(
externalListeners=False
)
]
)
elif isinstance(client.get_instance(), external.Client):
config["bootstrap.servers"] = ",".join(
[
endpoint.replace("EXTERNAL://", "")
for endpoint in self._kafka_api.get_broker_endpoints(
externalListeners=True
)
]
)
return config
def get_hostname_replaced_url(sub_path: str):
"""
construct and return an url with public hopsworks hostname and sub path
:param self:
:param sub_path: url sub-path after base url
:return: href url
"""
href = urljoin(client.get_instance()._base_url, sub_path)
url_parsed = client.get_instance().replace_public_host(urlparse(href))
return url_parsed.geturl()
def _get_conn_str(self):
credentials = {
"sslTrustStore": client.get_instance()._get_jks_trust_store_path(),
"trustStorePassword": client.get_instance()._cert_key,
"sslKeyStore": client.get_instance()._get_jks_key_store_path(),
"keyStorePassword": client.get_instance()._cert_key,
}
return self._connstr + ";".join(
["{}={}".format(option[0], option[1]) for option in credentials.items()]
)
def _create_hive_connection(self, feature_store):
return hive.Connection(
host=client.get_instance()._host,
port=9085,
# database needs to be set every time, 'default' doesn't work in pyhive
database=feature_store,
auth="CERTIFICATES",
truststore=client.get_instance()._get_jks_trust_store_path(),
keystore=client.get_instance()._get_jks_key_store_path(),
keystore_password=client.get_instance()._cert_key,
)
def get(self, metadata_instance, name: str = None):
"""Get the tags of a training dataset or feature group.
Gets all tags if no tag name is specified.
:param metadata_instance: metadata object of training dataset
to get the tags for
:type metadata_instance: TrainingDataset, FeatureGroup
:param name: tag name
:type name: str
:return: dict of tag name/values
:rtype: dict
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
self._entity_type,
metadata_instance.id,
"tags",
]
if name is not None:
path_params.append(name)
return {
tag._name: json.loads(tag._value)
for tag in tag.Tag.from_response_json(
_client._send_request("GET", path_params)
)
}
def get(self, name, connector_type):
"""Get storage connector with name and type.
:param name: name of the storage connector
:type name: str
:param connector_type: connector type
:type connector_type: str
:return: the storage connector
:rtype: StorageConnector
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"storageconnectors",
connector_type,
]
result = [
conn for conn in _client._send_request("GET", path_params)
if conn["name"] == name
]
if len(result) == 1:
return storage_connector.StorageConnector.from_response_json(
result[0])
else:
raise Exception(
"Could not find the storage connector `{}` with type `{}`.".
format(name, connector_type))
def save_stream_dataframe(
self,
feature_group,
dataframe,
query_name,
output_mode,
await_termination,
timeout,
checkpoint_dir,
write_options,
):
serialized_df = self._online_fg_to_avro(
feature_group,
self._encode_complex_features(feature_group, dataframe))
if query_name is None:
query_name = "insert_stream_" + feature_group._online_topic_name
query = (serialized_df.writeStream.outputMode(output_mode).format(
self.KAFKA_FORMAT).option(
"checkpointLocation",
"/Projects/" + client.get_instance()._project_name +
"/Resources/" + query_name +
"-checkpoint" if checkpoint_dir is None else checkpoint_dir,
).options(**write_options).option(
"topic", feature_group._online_topic_name).queryName(
query_name).start())
if await_termination:
query.awaitTermination(timeout)
return query
def update_statistics_config(self, feature_group_instance):
"""Update the statistics configuration of a feature group.
:param feature_group_instance: metadata object of feature group
:type feature_group_instance: FeatureGroup
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_instance.id,
]
headers = {"content-type": "application/json"}
query_params = {"updateStatsSettings": True}
return feature_group_instance.update_from_response_json(
_client._send_request(
"PUT",
path_params,
query_params,
headers=headers,
data=feature_group_instance.json(),
), )
def get_all(self, feature_group_id):
"""Get the validation report attached to a featuregroup.
:return: validation report
:rtype: list[dict]
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_id,
"validationreport",
]
headers = {"content-type": "application/json"}
query_params = {
"sort_by": "validation_time:desc",
"offset": 0,
"fields": "content",
}
return ValidationReport.from_response_json(
_client._send_request("GET",
path_params,
query_params,
headers=headers))
def post(
self,
metadata_instance,
code,
entity_id,
code_type,
databricks_cluster_id=None,
):
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
self._entity_type,
metadata_instance.id,
"code",
]
headers = {"content-type": "application/json"}
query_params = {
"entityId": entity_id,
"type": code_type,
"databricksClusterId": databricks_cluster_id,
}
_client._send_request(
"POST", path_params, query_params, headers=headers, data=code.json()
)
def create_mysql_engine(online_conn, external):
online_options = online_conn.spark_options()
# Here we are replacing the first part of the string returned by Hopsworks,
# jdbc:mysql:// with the sqlalchemy one + username and password
# useSSL and allowPublicKeyRetrieval are not valid properties for the pymysql driver
# to use SSL we'll have to something like this:
# ssl_args = {'ssl_ca': ca_path}
# engine = create_engine("mysql+pymysql://<user>:<pass>@<addr>/<schema>", connect_args=ssl_args)
if external:
# This only works with external clients.
# Hopsworks clients should use the storage connector
online_options["url"] = re.sub(
"/[0-9.]+:",
"/{}:".format(client.get_instance().host),
online_options["url"],
)
sql_alchemy_conn_str = (online_options["url"].replace(
"jdbc:mysql://",
"mysql+pymysql://" + online_options["user"] + ":" +
online_options["password"] + "@",
).replace("useSSL=false&", "").replace("?allowPublicKeyRetrieval=true",
""))
# default connection pool size kept by engine is 5
sql_alchemy_engine = create_engine(sql_alchemy_conn_str, pool_recycle=3600)
return sql_alchemy_engine
def compute(self, training_dataset_instance, td_app_conf):
"""
Setup a Hopsworks job to compute the query and write the training dataset
Args:
training_dataset_instance (training_dataset): the metadata instance of the training dataset
app_options ([type]): the configuration for the training dataset job application
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"trainingdatasets",
training_dataset_instance.id,
"compute",
]
headers = {"content-type": "application/json"}
return job.Job.from_response_json(
_client._send_request("POST",
path_params,
headers=headers,
data=td_app_conf.json()))
def get_service(self, service):
_client = client.get_instance()
path_params = [
"services",
service,
]
return _client._send_request("GET", path_params)["items"][0]
def get(self, metadata_instance, validation_time=None, commit_time=None):
"""Gets the statistics for a specific commit time for an instance."""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
self._entity_type,
metadata_instance.id,
"validations",
]
headers = {"content-type": "application/json"}
if validation_time is not None:
query_params = {
"filter_by": "validation_time_eq:" + str(validation_time),
}
elif commit_time:
query_params = {
"filter_by": "commit_time_eq:" + str(commit_time),
}
else:
query_params = None
return fgv.FeatureGroupValidation.from_response_json(
_client._send_request("GET", path_params, query_params, headers=headers)
)
def get(self, name, version, fg_type):
"""Get the metadata of a feature group with a certain name and version.
:param name: name of the feature group
:type name: str
:param version: version of the feature group
:type version: int
:param fg_type: type of the feature group to return
:type version: string
:return: feature group metadata object
:rtype: FeatureGroup
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
name,
]
query_params = {"version": version}
fg_json = _client._send_request("GET", path_params, query_params)[0]
if fg_type == self.CACHED:
return feature_group.FeatureGroup.from_response_json(fg_json)
else:
return feature_group.OnDemandFeatureGroup.from_response_json(fg_json)
def get_last(self, feature_group_id):
"""Gets the latest Validation Report of a featuregroup."""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_id,
"validationreport",
]
headers = {"content-type": "application/json"}
query_params = {
"sort_by": "validation_time:desc",
"offset": 0,
"limit": 1,
"fields": "content",
}
return ValidationReport.from_response_json(
_client._send_request("GET",
path_params,
query_params,
headers=headers))[0]
def create(self, expectation):
"""Create and Feature Store expectation or Attach it by name to a Feature Group.
:param expectation: expectation object to be created for a feature store
:type expectation: `Expectation`
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"expectations",
]
headers = {"content-type": "application/json"}
print("ExpectationsApi.expectation.to_dict()" +
str(expectation.to_dict()))
print("ExpectationsApi.expectation.rules[0].to_dict()" +
str(expectation.rules[0].to_dict()))
payload = expectation.json() if expectation else None
print("ExpectationsApi.expectation.payload" + str(payload))
_client._send_request("PUT",
path_params,
headers=headers,
data=payload)
def get_transformation_fn(self, name, version):
"""
Retrieve transformation function from backend
Args:
name: TransformationFunction name, required
name of transformation function.
version: TransformationFunction version, required
version of transformation function.
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"transformationfunctions",
]
if name:
query_params = {"name": name}
if version:
query_params["version"] = version
return transformation_function.TransformationFunction.from_response_json(
_client._send_request("GET", path_params, query_params))
else:
return transformation_function.TransformationFunction.from_response_json(
_client._send_request("GET", path_params))
def get(self, name=None, feature_group=None):
"""Get the expectations of a feature store or feature group.
Gets all feature store expectations if no feature group is specified.
Gets all feature store or feature group expectations if no name is specified.
:param name: expectation name
:type name: str
:param feature_group: feature group to get the expectations of
:type feature_group: FeatureGroup
:return: list of expectations
:rtype: list of dict
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
]
if feature_group is not None:
path_params.extend([self._entity_type, feature_group.id, "expectations"])
else:
path_params.append("expectations")
if name:
path_params.append(name)
return expectation.Expectation.from_response_json(
_client._send_request("GET", path_params)
)
def save(self, feature_group_instance):
"""Save feature group metadata to the feature store.
:param feature_group_instance: metadata object of feature group to be
saved
:type feature_group_instance: FeatureGroup
:return: updated metadata object of the feature group
:rtype: FeatureGroup
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
]
headers = {"content-type": "application/json"}
return feature_group_instance.update_from_response_json(
_client._send_request(
"POST",
path_params,
headers=headers,
data=feature_group_instance.json(),
), )
def get_path(self, metadata_instance, training_dataset_version=None):
_client = client.get_instance()
if isinstance(metadata_instance, feature_view.FeatureView):
return [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featureview",
metadata_instance.name,
"version",
metadata_instance.version,
"trainingdatasets",
"version",
training_dataset_version,
"statistics",
]
else:
return [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
self._entity_type,
metadata_instance.id,
"statistics",
]
def init_serving(self, entity, batch, external):
if external is None:
external = isinstance(client.get_instance(), client.external.Client)
# `init_prepared_statement` should be the last because other initialisations
# has to be done successfully before it is able to fetch feature vectors.
self.init_transformation(entity)
self.init_prepared_statement(entity, batch, external)
def launch(self, name):
_client = client.get_instance()
path_params = [
"project", _client._project_id, "jobs", name, "executions"
]
_client._send_request("POST", path_params)
def get(self, name, connector_type):
"""Get storage connector with name and type.
:param name: name of the storage connector
:type name: str
:param connector_type: connector type
:type connector_type: str
:return: the storage connector
:rtype: StorageConnector
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"storageconnectors",
connector_type,
name,
]
query_params = {"temporaryCredentials": True}
return storage_connector.StorageConnector.from_response_json(
_client._send_request("GET",
path_params,
query_params=query_params))
def commit(self, feature_group_instance, feature_group_commit_instance):
"""
Save feature group commit metadata.
# Arguments
feature_group_instance: FeatureGroup, required
metadata object of feature group.
feature_group_commit_instance: FeatureGroupCommit, required
metadata object of feature group commit.
# Returns
`FeatureGroupCommit`.
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_instance.id,
"commits",
]
headers = {"content-type": "application/json"}
return feature_group_commit_instance.update_from_response_json(
_client._send_request(
"POST",
path_params,
headers=headers,
data=feature_group_commit_instance.json(),
),
)
def add(self, metadata_instance, name, value):
"""Attach a name/value tag to a training dataset or feature group.
A tag consists of a name/value pair. Tag names are unique identifiers.
The value of a tag can be any valid json - primitives, arrays or json objects.
:param metadata_instance: metadata object of the instance to add the
tag for
:type metadata_instance: TrainingDataset, FeatureGroup
:param name: name of the tag to be added
:type name: str
:param value: value of the tag to be added
:type value: str
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
self._entity_type,
metadata_instance.id,
"tags",
name,
]
headers = {"content-type": "application/json"}
json_value = json.dumps(value)
_client._send_request("PUT", path_params, headers=headers, data=json_value)
def get_commit_details(self, feature_group_instance, wallclock_timestamp, limit):
"""
Get feature group commit metadata.
# Arguments
feature_group_instance: FeatureGroup, required
metadata object of feature group.
limit: number of commits to retrieve
wallclock_timestamp: specific point in time.
# Returns
`FeatureGroupCommit`.
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_instance.id,
"commits",
]
headers = {"content-type": "application/json"}
query_params = {"sort_by": "committed_on:desc", "offset": 0, "limit": limit}
if wallclock_timestamp is not None:
query_params["filter_by"] = "commited_on_ltoeq:" + str(wallclock_timestamp)
return feature_group_commit.FeatureGroupCommit.from_response_json(
_client._send_request("GET", path_params, query_params, headers=headers),
)
def ingestion(self, feature_group_instance, ingestion_conf):
"""
Setup a Hopsworks job for dataframe ingestion
Args:
feature_group_instance: FeatureGroup, required
metadata object of feature group.
ingestion_conf: the configuration for the ingestion job application
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_instance.id,
"ingestion",
]
headers = {"content-type": "application/json"}
return ingestion_job.IngestionJob.from_response_json(
_client._send_request(
"POST", path_params, headers=headers, data=ingestion_conf.json()
),
)
def commit_details(self, feature_group_instance, limit):
"""
Get feature group commit metadata.
# Arguments
feature_group_instance: FeatureGroup, required
metadata object of feature group.
limit: number of commits to retrieve
# Returns
`FeatureGroupCommit`.
"""
_client = client.get_instance()
path_params = [
"project",
_client._project_id,
"featurestores",
self._feature_store_id,
"featuregroups",
feature_group_instance.id,
"commits",
]
headers = {"content-type": "application/json"}
query_params = {
"sort_by": "committed_on:desc",
"offset": 0,
"limit": limit
}
return feature_group_commit.FeatureGroupCommit.from_response_json(
_client._send_request("GET",
path_params,
query_params,
headers=headers), )
