def __init__(self, linking_service: LinkingService,
metric_provider: Type[MetricProvider]):
self.applications: Dict[str, K8sApp] = {}
self.kafka_connectors: List[KafkaConnector] = []
self.data_flow = DataFlowGraph(metric_provider=metric_provider)
self.linking_service = linking_service
self.kafka = Kafka()
async def test_get_positioned_json_graph(self, df: DataFlowGraph):
df.add_streaming_app(K8sApp.factory(get_streaming_app_deployment()))
await df.get_positioned_graph()
nodes = df.graph.nodes(data=True)
for _, data in iter(nodes):
assert data.get("x") is not None
assert data.get("y") is not None
def test_get_positioned_json_graph(self, df: DataFlowGraph):
df.add_streaming_app(self.get_k8s_app())
df.get_positioned_graph()
nodes = df.graph.nodes(data=True)
for _, data in nodes:
assert data.get("x") is not None
assert data.get("y") is not None
def test_pipeline_cronjob(self, df: DataFlowGraph):
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_cronjob(
error_topic=None,
pipeline="pipeline1",
)
)
)
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
input_topics="output-topic",
error_topic=None,
output_topic="output-topic2",
pipeline="pipeline1",
)
)
)
assert len(df.pipelines) == 1
assert "pipeline1" in df.pipelines
pipeline1 = df.pipelines["pipeline1"]
assert set(pipeline1.nodes) == {
"test-cronjob",
"output-topic",
"test-app",
"output-topic2",
}
def test_add_streaming_app(self, df: DataFlowGraph):
df.add_streaming_app(self.get_k8s_app())
assert len(df.graph.nodes) == 4
assert df.graph.has_edge("input-topic", "test-app")
assert df.graph.has_edge("test-app", "output-topic")
assert df.graph.has_edge("test-app", "error-topic")
# should have multiple input topic
df.reset()
df.add_streaming_app(self.get_k8s_app(input_topics="input-topic1,input-topic2"))
assert len(df.graph.nodes) == 5
assert df.graph.has_edge("input-topic1", "test-app")
assert df.graph.has_edge("input-topic2", "test-app")
assert df.graph.has_edge("test-app", "output-topic")
assert df.graph.has_edge("test-app", "error-topic")
df.reset()
df.add_streaming_app(
self.get_k8s_app(multiple_outputs="1=extra-output1,2=extra-output2")
)
assert len(df.graph.nodes) == 6
assert df.graph.has_edge("input-topic", "test-app")
assert df.graph.has_edge("test-app", "output-topic")
assert df.graph.has_edge("test-app", "error-topic")
assert df.graph.has_edge("test-app", "extra-output1")
assert df.graph.has_edge("test-app", "extra-output2")
def test_add_source(self, df: DataFlowGraph):
source = Source(
name="test-source",
node_type="test-type",
target="test-app",
)
df.add_streaming_app(self.get_k8s_app())
df.add_source(source)
assert len(df.graph.nodes) == 5
assert df.graph.has_edge("test-source", "test-app")
def test_add_sink(self, df: DataFlowGraph):
sink = Sink(
name="test-sink",
node_type="test-type",
source="test-app",
)
df.add_streaming_app(self.get_k8s_app())
df.add_sink(sink)
assert len(df.graph.nodes) == 5
assert df.graph.has_edge("test-app", "test-sink")
def test_add_source(self, df: DataFlowGraph):
source = Source(
name="test-source",
node_type="test-type",
target="test-app",
)
df.add_streaming_app(K8sApp.factory(get_streaming_app_deployment()))
df.add_source(source)
assert len(df.graph.nodes) == 5
assert df.graph.has_edge("test-source", "test-app")
assert len(df.pipelines) == 0
def test_node_attributes(self, df: DataFlowGraph):
df.add_streaming_app(
self.get_k8s_app(
name="test-app1",
pipeline="pipeline1",
)
)
assert df.graph.nodes["test-app1"].get("pipeline") == "pipeline1"
df.add_streaming_app(
self.get_k8s_app(
name="test-app2",
pipeline=None,
)
)
assert df.graph.nodes["test-app2"].get("pipeline") is None
def test_node_attributes(self, df: DataFlowGraph):
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app1",
pipeline="pipeline1",
)
)
)
assert df.graph.nodes["test-app1"].get(ATTR_PIPELINE) == "pipeline1"
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app2",
pipeline=None,
)
)
)
assert df.graph.nodes["test-app2"].get(ATTR_PIPELINE) is None
def test_pipeline_graph(self, df: DataFlowGraph):
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
pipeline="pipeline1",
)
)
)
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app2",
input_topics="input-topic2",
error_topic="error-topic2",
output_topic="output-topic2",
pipeline="pipeline2",
multiple_inputs="0=output-topic",
)
)
)
assert len(df.pipelines) == 2
assert "pipeline1" in df.pipelines
assert "pipeline2" in df.pipelines
pipeline1 = df.pipelines["pipeline1"]
pipeline2 = df.pipelines["pipeline2"]
assert set(pipeline1.nodes) == {
"input-topic",
"output-topic",
"test-app",
"error-topic",
}
assert set(pipeline2.nodes) == {
"output-topic",
"input-topic2",
"output-topic2",
"test-app2",
"error-topic2",
}
df.add_sink(Sink("test-sink", "output-topic"))
assert "test-sink" in pipeline1.nodes
source_connector = KafkaConnector(
name="test-source-connector",
type=KafkaConnectorTypesEnum.SOURCE,
topics=["input-topic2", "source-topic"],
config={},
)
df.add_connector(source_connector)
assert "test-source-connector" in pipeline2.nodes
assert "source-topic" in pipeline2.nodes
def test_multiple_pipelines_apps(self, df: DataFlowGraph):
"""Ensures apps have separate pipelines despite them being connected."""
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app1",
input_topics="input-topic1",
error_topic=None,
output_topic="output-topic1",
pipeline="pipeline1",
)
)
)
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app2",
input_topics="output-topic1",
error_topic=None,
output_topic="output-topic2",
pipeline="pipeline2",
)
)
)
assert len(df.pipelines) == 2
assert "pipeline1" in df.pipelines
assert "pipeline2" in df.pipelines
pipeline1 = df.pipelines["pipeline1"]
pipeline2 = df.pipelines["pipeline2"]
assert set(pipeline1.nodes) == {
"test-app1",
"input-topic1",
"output-topic1",
}
assert set(pipeline2.nodes) == {
"test-app2",
"output-topic1",
"output-topic2",
}
def test_extract_independent_pipelines(self, df: DataFlowGraph):
settings.k8s.independent_graph.label = "pipeline"
df.add_streaming_app(self.get_k8s_app())
df.add_streaming_app(
self.get_k8s_app(
name="type2-app2",
input_topics="input-topic2",
error_topic="error-topic2",
output_topic="output-topic2",
pipeline="pipeline2",
)
)
df.extract_independent_pipelines()
assert "test-app" in df.independent_graphs
assert "pipeline2" in df.independent_graphs
df.graph.add_node("test-node")
df.extract_independent_pipelines()
assert "test-node" in df.independent_graphs
def test_add_connector(self, df: DataFlowGraph):
sink_connector = KafkaConnector(
name="test-sink-connector",
type=KafkaConnectorTypesEnum.SINK,
topics=["output-topic"],
config={},
error_topic="dead-letter-topic",
)
source_connector = KafkaConnector(
name="test-source-connector",
type=KafkaConnectorTypesEnum.SOURCE,
topics=["input-topic", "input-topic2"],
config={},
)
df.add_streaming_app(self.get_k8s_app())
df.add_connector(sink_connector)
df.add_connector(source_connector)
assert len(df.graph.nodes) == 8
assert df.graph.has_edge("output-topic", "test-sink-connector")
assert df.graph.has_edge("test-sink-connector", "dead-letter-topic")
assert df.graph.has_edge("test-source-connector", "input-topic")
assert df.graph.has_edge("test-source-connector", "input-topic2")
class StreamsExplorer:
context = settings.k8s.deployment.context
namespaces = settings.k8s.deployment.namespaces
def __init__(self, linking_service: LinkingService,
metric_provider: Type[MetricProvider]):
self.applications: Dict[str, K8sApp] = {}
self.kafka_connectors: List[KafkaConnector] = []
self.data_flow = DataFlowGraph(metric_provider=metric_provider)
self.linking_service = linking_service
self.kafka = Kafka()
def setup(self):
self.__setup_k8s_environment()
async def update(self):
self.applications = {}
self.kafka_connectors = []
extractor_container.reset()
self.data_flow.reset()
self.__retrieve_deployments()
self.__retrieve_cron_jobs()
self.__get_connectors()
self.__create_graph()
self.data_flow.setup_metric_provider()
await self.data_flow.store_json_graph()
def get_positioned_json_graph(self) -> dict:
return self.data_flow.json_graph
async def get_positioned_pipeline_json_graph(
self, pipeline_name: str) -> Optional[dict]:
return await self.data_flow.get_positioned_pipeline_graph(pipeline_name
)
def get_pipeline_names(self) -> List[str]:
return list(self.data_flow.pipelines.keys())
async def get_metrics(self) -> List[Metric]:
return await self.data_flow.get_metrics()
@ttl_cache(ttl=settings.node_info.cache_ttl)
def get_node_information(self, node_id: str):
node_type = self.data_flow.get_node_type(node_id)
if node_type == NodeTypesEnum.CONNECTOR:
config = KafkaConnect.get_connector_config(node_id)
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=self.linking_service.connector_info +
get_displayed_information_connector(config),
)
elif node_type == NodeTypesEnum.TOPIC or node_type == NodeTypesEnum.ERROR_TOPIC:
info = self.linking_service.topic_info
if self.kafka.enabled:
partitions = self.kafka.get_topic_partitions(node_id)
if partitions is not None:
info.append(
NodeInfoListItem(
name="Partitions",
value=len(partitions),
type=NodeInfoType.BASIC,
))
config = self.kafka.get_topic_config(node_id)
info += get_displayed_information_topic(config)
info.append(
NodeInfoListItem(
name="Schema",
value={},
type=NodeInfoType.JSON,
))
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=info,
)
elif node_type == NodeTypesEnum.STREAMING_APP:
info = get_displayed_information_deployment(
self.applications[node_id])
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=self.linking_service.streaming_app_info + info,
)
elif node_type in self.linking_service.sink_source_info:
return NodeInformation(
node_id=node_id,
node_type=NodeTypesEnum.SINK_SOURCE,
info=self.linking_service.sink_source_info[node_type],
)
def get_link(self, node_id: str, link_type: Optional[str]):
node_type = self.data_flow.get_node_type(node_id)
if node_type == NodeTypesEnum.CONNECTOR:
config = KafkaConnect.get_connector_config(node_id)
return self.linking_service.get_redirect_connector(
config, link_type)
if node_type == NodeTypesEnum.TOPIC or node_type == NodeTypesEnum.ERROR_TOPIC:
return self.linking_service.get_redirect_topic(node_id, link_type)
if node_type == NodeTypesEnum.STREAMING_APP:
return self.linking_service.get_redirect_streaming_app(
self.applications[node_id], link_type)
if node_type in self.linking_service.sink_source_redirects:
return self.linking_service.get_sink_source_redirects(
node_type, node_id)
def __setup_k8s_environment(self):
try:
if settings.k8s.deployment.cluster:
logger.info("Setup K8s environment in cluster")
kubernetes.config.load_incluster_config()
else:
logger.info("Setup K8s environment")
kubernetes.config.load_kube_config(context=self.context)
except kubernetes.config.ConfigException:
raise Exception("Could not load K8s environment configuration")
self.k8s_app_client = kubernetes.client.AppsV1Api()
self.k8s_batch_client = kubernetes.client.BatchV1beta1Api()
def __retrieve_deployments(self):
items: List[K8sObject] = []
items += self.get_deployments()
items += self.get_stateful_sets()
for item in items:
try:
app = K8sApp.factory(item)
self.__add_app(app)
except Exception as e:
logger.debug(e)
def get_deployments(self) -> List[V1Deployment]:
deployments: List[V1Deployment] = []
for namespace in self.namespaces:
logger.info(f"List deployments in namespace {namespace}")
deployments += self.k8s_app_client.list_namespaced_deployment(
namespace=namespace, watch=False).items
return deployments
def get_stateful_sets(self) -> List[V1StatefulSet]:
stateful_sets: List[V1StatefulSet] = []
for namespace in self.namespaces:
logger.info(f"List statefulsets in namespace {namespace}")
stateful_sets += self.k8s_app_client.list_namespaced_stateful_set(
namespace=namespace, watch=False).items
return stateful_sets
def __retrieve_cron_jobs(self):
logger.info("Retrieve cronjob descriptions")
cron_jobs = self.get_cron_jobs()
for cron_job in cron_jobs:
if app := extractor_container.on_cron_job(cron_job):
self.__add_app(app)
class StreamsExplorer:
context = settings.k8s.deployment.context
namespaces = settings.k8s.deployment.namespaces
def __init__(self, linking_service: LinkingService,
metric_provider: Type[MetricProvider]):
self.applications: Dict[str, K8sApp] = {}
self.kafka_connectors: List[KafkaConnector] = []
self.data_flow = DataFlowGraph(metric_provider=metric_provider)
self.linking_service = linking_service
def setup(self):
self.__setup_k8s_environment()
def update(self):
self.applications = {}
self.kafka_connectors = []
extractor_container.reset()
self.data_flow.reset()
self.__retrieve_deployments()
self.__retrieve_cron_jobs()
self.__get_connectors()
self.__create_graph()
def get_positioned_json_graph(self) -> dict:
return self.data_flow.get_positioned_graph()
def get_positioned_pipeline_json_graph(self, pipeline_name) -> dict:
return self.data_flow.get_positioned_pipeline_graph(pipeline_name)
def get_pipeline_names(self) -> List[str]:
return list(self.data_flow.independent_graphs.keys())
def get_metrics(self) -> List:
return self.data_flow.get_metrics()
def get_node_information(self, node_id: str):
node_type = self.data_flow.get_node_type(node_id)
if node_type == NodeTypesEnum.CONNECTOR:
config = KafkaConnect.get_connector_config(node_id)
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=self.linking_service.connector_info +
get_displayed_information_connector(config),
)
if node_type == NodeTypesEnum.TOPIC or node_type == NodeTypesEnum.ERROR_TOPIC:
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=self.linking_service.topic_info + [
NodeInfoListItem(
name="Schema",
value=SchemaRegistry.get_newest_topic_value_schema(
node_id),
type=NodeInfoType.JSON,
)
],
)
if node_type == NodeTypesEnum.STREAMING_APP:
info = get_displayed_information_deployment(
self.applications[node_id])
return NodeInformation(
node_id=node_id,
node_type=node_type,
info=self.linking_service.streaming_app_info + info,
)
if node_type in self.linking_service.sink_source_info:
return NodeInformation(
node_id=node_id,
node_type=NodeTypesEnum.SINK_SOURCE,
info=self.linking_service.sink_source_info[node_type],
)
def get_link(self, node_id: str, link_type: Optional[str]):
node_type = self.data_flow.get_node_type(node_id)
if node_type == NodeTypesEnum.CONNECTOR:
config = KafkaConnect.get_connector_config(node_id)
return self.linking_service.get_redirect_connector(
config, link_type)
if node_type == NodeTypesEnum.TOPIC or node_type == NodeTypesEnum.ERROR_TOPIC:
return self.linking_service.get_redirect_topic(node_id, link_type)
if node_type == NodeTypesEnum.STREAMING_APP:
return self.linking_service.get_redirect_streaming_app(
self.applications[node_id], link_type)
if node_type in self.linking_service.sink_source_redirects:
return self.linking_service.get_sink_source_redirects(
node_type, node_id)
def __setup_k8s_environment(self):
try:
if settings.k8s.deployment.cluster:
logger.info("Setup K8s environment in cluster")
kubernetes.config.load_incluster_config()
else:
logger.info("Setup K8s environment")
kubernetes.config.load_kube_config(context=self.context)
except kubernetes.config.ConfigException:
raise Exception("Could not load K8s environment configuration")
self.k8s_app_client = kubernetes.client.AppsV1Api()
self.k8s_batch_client = kubernetes.client.BatchV1beta1Api()
def __retrieve_deployments(self):
items = self.get_deployments() + self.get_stateful_sets()
for item in items:
try:
app = K8sApp.factory(item)
if app.is_streams_bootstrap_app():
self.applications[app.name] = app
except Exception as e:
logger.debug(e)
def get_deployments(self) -> List[V1Deployment]:
deployments: List[V1Deployment] = []
for namespace in self.namespaces:
logger.info(f"List deployments in namespace {namespace}")
deployments += self.k8s_app_client.list_namespaced_deployment(
namespace=namespace, watch=False).items
return deployments
def get_stateful_sets(self) -> List[V1StatefulSet]:
stateful_sets: List[V1StatefulSet] = []
for namespace in self.namespaces:
logger.info(f"List statefulsets in namespace {namespace}")
stateful_sets += self.k8s_app_client.list_namespaced_stateful_set(
namespace=namespace, watch=False).items
return stateful_sets
def __retrieve_cron_jobs(self):
logger.info("Retrieve cronjob descriptions")
cron_jobs = self.get_cron_jobs()
for cron_job in cron_jobs:
app: Optional[K8sApp] = extractor_container.on_cron_job(cron_job)
if app:
self.applications[app.name] = app
def get_cron_jobs(self) -> List[V1beta1CronJob]:
cron_jobs: List[V1beta1CronJob] = []
for namespace in self.namespaces:
logger.info(f"List cronjobs in namespace {namespace}")
cron_jobs += self.k8s_batch_client.list_namespaced_cron_job(
namespace=namespace, watch=False).items
return cron_jobs
def __get_connectors(self):
logger.info("Retrieve Kafka connectors")
self.kafka_connectors = KafkaConnect.connectors()
def __create_graph(self):
logger.info("Setup pipeline graph")
for _, app in self.applications.items():
self.data_flow.add_streaming_app(app)
for connector in self.kafka_connectors:
self.data_flow.add_connector(connector)
sources, sinks = extractor_container.get_sources_sinks()
for source in sources:
self.data_flow.add_source(source)
for sink in sinks:
self.data_flow.add_sink(sink)
# extract subgraphs
logger.info("Extract independent pipelines")
self.data_flow.extract_independent_pipelines()
def test_multiple_pipelines_sink_source(self, df: DataFlowGraph):
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app1",
input_topics="input-topic1",
error_topic="error-topic1",
output_topic="output-topic1",
pipeline="pipeline1",
)
)
)
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app2",
input_topics="input-topic2",
error_topic="error-topic2",
output_topic="output-topic2",
pipeline="pipeline2",
)
)
)
assert len(df.pipelines) == 2
assert "pipeline1" in df.pipelines
assert "pipeline2" in df.pipelines
pipeline1 = df.pipelines["pipeline1"]
pipeline2 = df.pipelines["pipeline2"]
assert set(pipeline1.nodes) == {
"test-app1",
"input-topic1",
"output-topic1",
"error-topic1",
}
assert set(pipeline2.nodes) == {
"test-app2",
"input-topic2",
"output-topic2",
"error-topic2",
}
sink_connector = KafkaConnector(
name="test-sink-connector",
type=KafkaConnectorTypesEnum.SINK,
topics=["output-topic1", "output-topic2"],
config={},
)
df.add_connector(sink_connector)
assert "test-sink-connector" in df.graph.nodes
assert "test-sink-connector" in pipeline1.nodes
assert "test-sink-connector" in pipeline2.nodes
df.add_sink(Sink("test-sink", "test-sink-connector"))
assert "test-sink" in df.graph.nodes
assert "test-sink" in pipeline1.nodes
assert "test-sink" in pipeline2.nodes
source_connector = KafkaConnector(
name="test-source-connector",
type=KafkaConnectorTypesEnum.SOURCE,
topics=["input-topic1", "input-topic2"],
config={},
)
df.add_connector(source_connector)
assert "test-source-connector" in df.graph.nodes
assert "test-source-connector" in pipeline1.nodes
assert "test-source-connector" in pipeline2.nodes
df.add_source(Source("test-source", "test-source-connector"))
assert "test-source" in df.graph.nodes
assert "test-source" in pipeline1.nodes
assert "test-source" in pipeline2.nodes
unrelated_sink_connector = KafkaConnector(
name="unrelated-sink-connector",
type=KafkaConnectorTypesEnum.SINK,
topics=["input-topic1"],
config={},
)
df.add_connector(unrelated_sink_connector)
assert "unrelated-sink-connector" in df.graph.nodes
assert "unrelated-sink-connector" not in pipeline1.nodes
assert "unrelated-sink-connector" not in pipeline2.nodes
unrelated_source_connector = KafkaConnector(
name="unrelated-source-connector",
type=KafkaConnectorTypesEnum.SOURCE,
topics=["output-topic1"],
config={},
)
df.add_connector(unrelated_source_connector)
assert "unrelated-source-connector" in df.graph.nodes
assert "unrelated-source-connector" not in pipeline1.nodes
assert "unrelated-source-connector" not in pipeline2.nodes
def test_verify_connector_exists_in_pipeline(self, df: DataFlowGraph):
"""Verify that connector exists in specific pipeline before adding it from a sink or source."""
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app1",
input_topics="input-topic1",
error_topic=None,
output_topic="output-topic1",
pipeline="pipeline1",
)
)
)
df.add_streaming_app(
K8sApp.factory(
get_streaming_app_deployment(
name="test-app2",
input_topics="input-topic2",
error_topic=None,
output_topic="output-topic2",
pipeline="pipeline2",
)
)
)
sink_connector1 = KafkaConnector(
name="sink-connector1",
type=KafkaConnectorTypesEnum.SINK,
topics=["output-topic1"],
config={},
)
df.add_connector(sink_connector1)
sink_connector2 = KafkaConnector(
name="sink-connector2",
type=KafkaConnectorTypesEnum.SINK,
topics=["output-topic2"],
config={},
)
df.add_connector(sink_connector2)
sink = Sink(
name="test-sink",
node_type="test-type",
source="sink-connector1",
)
df.add_sink(sink)
sink.source = "sink-connector2"
df.add_sink(sink)
assert len(df.pipelines) == 2
assert "pipeline1" in df.pipelines
assert "pipeline2" in df.pipelines
pipeline1 = df.pipelines["pipeline1"]
pipeline2 = df.pipelines["pipeline2"]
assert "sink-connector1" in pipeline1.nodes
assert "sink-connector1" not in pipeline2.nodes
assert "sink-connector2" in pipeline2.nodes
assert "sink-connector2" not in pipeline1.nodes
assert "test-sink" in pipeline1.nodes
assert "test-sink" in pipeline2.nodes
assert set(pipeline1.nodes) == {
"test-app1",
"input-topic1",
"output-topic1",
"sink-connector1",
"test-sink",
}
assert set(pipeline2.nodes) == {
"test-app2",
"input-topic2",
"output-topic2",
"sink-connector2",
"test-sink",
}
def test_get_node_type(self, df: DataFlowGraph):
df.add_streaming_app(self.get_k8s_app())
assert df.get_node_type("test-app") == "streaming-app"
def df(self) -> DataFlowGraph:
return DataFlowGraph(metric_provider=MetricProvider)
def test_get_node_type(self, df: DataFlowGraph):
df.add_streaming_app(K8sApp.factory(get_streaming_app_deployment()))
assert df.get_node_type("test-app") == "streaming-app"
