feat(ingest/kafka-connect): support MongoSourceConnector

metadata-ingestion/src/datahub/ingestion/source/kafka_connect.py

@@ -75,7 +75,7 @@ class KafkaConnectSourceConfig(DatasetLineageProviderConfigBase):
     )
     generic_connectors: List[GenericConnectorConfig] = Field(
         default=[],
-        description="Provide lineage graph for sources connectors other than Confluent JDBC Source Connector or Debezium Source Connector",
+        description="Provide lineage graph for sources connectors other than Confluent JDBC Source Connector, Debezium Source Connector, and Mongo Source Connector",
     )
 
 
@@ -556,6 +556,68 @@ def _extract_lineages(self):
             return
 
 
+@dataclass
+class MongoSourceConnector:
+    # https://www.mongodb.com/docs/kafka-connector/current/source-connector/
+
+    connector_manifest: ConnectorManifest
+
+    def __init__(
+        self, connector_manifest: ConnectorManifest, config: KafkaConnectSourceConfig
+    ) -> None:
+        self.connector_manifest = connector_manifest
+        self.config = config
+        self._extract_lineages()
+
+    @dataclass
+    class MongoSourceParser:
+        db_connection_url: Optional[str]
+        source_platform: str
+        database_name: Optional[str]
+        topic_prefix: Optional[str]
+        transforms: List[str]
+
+    def get_parser(
+        self,
+        connector_manifest: ConnectorManifest,
+    ) -> MongoSourceParser:
+        parser = self.MongoSourceParser(
+            db_connection_url=connector_manifest.config.get("connection.uri"),
+            source_platform="mongodb",
+            database_name=connector_manifest.config.get("database"),
+            topic_prefix=connector_manifest.config.get("topic_prefix"),
+            transforms=connector_manifest.config["transforms"].split(",")
+            if "transforms" in connector_manifest.config
+            else [],
+        )
+
+        return parser
+
+    def _extract_lineages(self):
+        lineages: List[KafkaConnectLineage] = list()
+        parser = self.get_parser(self.connector_manifest)
+        source_platform = parser.source_platform
+        topic_naming_pattern = r"mongodb\.(\w+)\.(\w+)"
+
+        if not self.connector_manifest.topic_names:
+            return lineages
+
+        for topic in self.connector_manifest.topic_names:
+            found = re.search(re.compile(topic_naming_pattern), topic)
+
+            if found:
+                table_name = get_dataset_name(found.group(1), None, found.group(2))
+
+                lineage = KafkaConnectLineage(
+                    source_dataset=table_name,
+                    source_platform=source_platform,
+                    target_dataset=topic,
+                    target_platform="kafka",
+                )
+                lineages.append(lineage)
+        self.connector_manifest.lineages = lineages
+
+
 @dataclass
 class DebeziumSourceConnector:
     connector_manifest: ConnectorManifest
@@ -846,7 +908,7 @@ class KafkaConnectSource(Source):
     Current limitations:
 
     - works only for
-        - JDBC and Debezium source connectors
+        - JDBC, Debezium, and Mongo source connectors
         - Generic connectors with user-defined lineage graph
         - BigQuery sink connector
     """
@@ -941,6 +1003,13 @@ def get_connectors_manifest(self) -> List[ConnectorManifest]:
                     connector_manifest = DebeziumSourceConnector(
                         connector_manifest=connector_manifest, config=self.config
                     ).connector_manifest
+                elif (
+                    connector_manifest.config.get("connector.class", "")
+                    == "com.mongodb.kafka.connect.MongoSourceConnector"
+                ):
+                    connector_manifest = MongoSourceConnector(
+                        connector_manifest=connector_manifest, config=self.config
+                    ).connector_manifest
                 else:
                     # Find the target connector object in the list, or log an error if unknown.
                     target_connector = None

metadata-ingestion/tests/integration/kafka-connect/docker-compose.override.yml

@@ -11,6 +11,7 @@ services:
       - zookeeper
       - broker
       - mysqldb
+      - mongo
     ports:
       - "58083:58083"    
 #    volumes:        
@@ -34,6 +35,8 @@ services:
         #
         #confluent-hub install --no-prompt wepay/kafka-connect-bigquery:1.6.8
         #
+        confluent-hub install --no-prompt mongodb/kafka-connect-mongodb:1.8.0
+        #
         curl -k -SL "https://dev.mysql.com/get/Downloads/Connector-J/mysql-connector-java-8.0.27.tar.gz" \
              | tar -xzf - -C /usr/share/confluent-hub-components/confluentinc-kafka-connect-jdbc/lib \
              --strip-components=1 mysql-connector-java-8.0.27/mysql-connector-java-8.0.27.jar
@@ -71,5 +74,21 @@ services:
     ports:
       - "5432:5432"
 
+  mongo:
+    hostname: mongo
+    image: mongo:4.2.9
+    container_name: "test_mongo"
+    ports:
+      - "27017:27017"
+    command: --replSet rs0
+    environment:
+      - MONGO_INITDB_ROOT_USERNAME=admin
+      - MONGO_INITDB_ROOT_PASSWORD=admin
+      - MONGO_INITDB_DATABASE=test_db
+      - MONGO_INITDB_USERNAME=kafka-connector
+      - MONGO_INITDB_PASSWORD=password
+    volumes:
+      - ./../kafka-connect/setup/conf/mongo-init.sh:/docker-entrypoint-initdb.d/mongo-init.sh:ro
+
 volumes:
     test_zkdata:

metadata-ingestion/tests/integration/kafka-connect/kafka_connect_mongo_mces_golden.json

@@ -0,0 +1,72 @@
+[
+  {
+    "entityType": "dataFlow",
+    "entityUrn": "urn:li:dataFlow:(kafka-connect,source_mongodb_connector,PROD)",
+    "changeType": "UPSERT",
+    "aspectName": "dataFlowInfo",
+    "aspect": {
+      "value": "{\"customProperties\": {}, \"name\": \"source_mongodb_connector\", \"description\": \"Source connector using `com.mongodb.kafka.connect.MongoSourceConnector` plugin.\"}",
+      "contentType": "application/json"
+    },
+    "systemMetadata": {
+      "lastObserved": 1635166800000,
+      "runId": "kafka-connect-run"
+    }
+  },
+  {
+    "entityType": "dataJob",
+    "entityUrn": "urn:li:dataJob:(urn:li:dataFlow:(kafka-connect,source_mongodb_connector,PROD),test_db.purchases)",
+    "changeType": "UPSERT",
+    "aspectName": "dataJobInfo",
+    "aspect": {
+      "value": "{\"customProperties\": {}, \"name\": \"source_mongodb_connector:test_db.purchases\", \"type\": {\"string\": \"COMMAND\"}}",
+      "contentType": "application/json"
+    },
+    "systemMetadata": {
+      "lastObserved": 1635166800000,
+      "runId": "kafka-connect-run"
+    }
+  },
+  {
+    "entityType": "dataJob",
+    "entityUrn": "urn:li:dataJob:(urn:li:dataFlow:(kafka-connect,source_mongodb_connector,PROD),test_db.purchases)",
+    "changeType": "UPSERT",
+    "aspectName": "dataJobInputOutput",
+    "aspect": {
+      "value": "{\"inputDatasets\": [\"urn:li:dataset:(urn:li:dataPlatform:mongodb,test_db.purchases,PROD)\"], \"outputDatasets\": [\"urn:li:dataset:(urn:li:dataPlatform:kafka,mongodb.test_db.purchases,PROD)\"]}",
+      "contentType": "application/json"
+    },
+    "systemMetadata": {
+      "lastObserved": 1635166800000,
+      "runId": "kafka-connect-run"
+    }
+  },
+  {
+    "entityType": "dataset",
+    "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:kafka,mongodb.test_db.purchases,PROD)",
+    "changeType": "UPSERT",
+    "aspectName": "dataPlatformInstance",
+    "aspect": {
+      "value": "{\"platform\": \"urn:li:dataPlatform:kafka\"}",
+      "contentType": "application/json"
+    },
+    "systemMetadata": {
+      "lastObserved": 1635166800000,
+      "runId": "kafka-connect-run"
+    }
+  },
+  {
+    "entityType": "dataset",
+    "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:mongodb,test_db.purchases,PROD)",
+    "changeType": "UPSERT",
+    "aspectName": "dataPlatformInstance",
+    "aspect": {
+      "value": "{\"platform\": \"urn:li:dataPlatform:mongodb\"}",
+      "contentType": "application/json"
+    },
+    "systemMetadata": {
+      "lastObserved": 1635166800000,
+      "runId": "kafka-connect-run"
+    }
+  }
+]

metadata-ingestion/tests/integration/kafka-connect/setup/conf/mongo-init.sh

@@ -0,0 +1,23 @@
+#!/bin/bash
+
+mongo -- "$MONGO_INITDB_DATABASE" <<-EOJS
+    conn = new Mongo();
+    db = conn.getDB("test_db");
+    db.purchases.insertOne({ _id: 3, item: "lamp post", price: 12 });
+    db.purchases.insertOne({ _id: 4, item: "lamp post", price: 13 });
+EOJS
+
+
+{
+sleep 3 &&
+mongo -- "$MONGO_INITDB_DATABASE" <<-EOJS
+    var rootUser = '$MONGO_INITDB_ROOT_USERNAME';
+    var rootPassword = '$MONGO_INITDB_ROOT_PASSWORD';
+    var admin = db.getSiblingDB('admin');
+    admin.auth(rootUser, rootPassword);
+    rs.initiate();
+EOJS
+} &
+
+
+

metadata-ingestion/tests/integration/kafka-connect/test_kafka_connect.py

@@ -53,6 +53,7 @@ def test_kafka_connect_ingest(docker_compose_runner, pytestconfig, tmp_path, moc
             ).status_code
             == 200,
         )
+
         # Creating MySQL source with no transformations , only topic prefix
         r = requests.post(
             "http://localhost:58083/connectors",
@@ -252,3 +253,76 @@ def test_kafka_connect_ingest(docker_compose_runner, pytestconfig, tmp_path, moc
             golden_path=test_resources_dir / "kafka_connect_mces_golden.json",
             ignore_paths=[],
         )
+
+
+@freeze_time(FROZEN_TIME)
+@pytest.mark.integration_batch_1
+def test_kafka_connect_mongosourceconnect_ingest(
+    docker_compose_runner, pytestconfig, tmp_path, mock_time
+):
+    test_resources_dir = pytestconfig.rootpath / "tests/integration/kafka-connect"
+    test_resources_dir_kafka = pytestconfig.rootpath / "tests/integration/kafka"
+
+    # Share Compose configurations between files and projects
+    # https://docs.docker.com/compose/extends/
+    docker_compose_file = [
+        str(test_resources_dir_kafka / "docker-compose.yml"),
+        str(test_resources_dir / "docker-compose.override.yml"),
+    ]
+    with docker_compose_runner(docker_compose_file, "kafka-connect") as docker_services:
+        wait_for_port(docker_services, "test_broker", 59092, timeout=120)
+        wait_for_port(docker_services, "test_connect", 58083, timeout=120)
+        docker_services.wait_until_responsive(
+            timeout=30,
+            pause=1,
+            check=lambda: requests.get(
+                "http://localhost:58083/connectors",
+            ).status_code
+            == 200,
+        )
+        # Creating MongoDB source
+        r = requests.post(
+            "http://localhost:58083/connectors",
+            headers={"Content-Type": "application/json"},
+            data=r"""{
+                    "name": "source_mongodb_connector",
+                    "config": {
+                        "tasks.max": "1",
+                        "connector.class": "com.mongodb.kafka.connect.MongoSourceConnector",
+                        "connection.uri": "mongodb://admin:admin@test_mongo:27017",
+                        "topic.prefix": "mongodb",
+                        "database": "test_db",
+                        "collection": "purchases",
+                        "copy.existing": true,
+                        "copy.existing.namespace.regex": "test_db.purchases",
+                        "change.stream.full.document": "updateLookup",
+                        "topic.creation.enable": "true",
+                        "topic.creation.default.replication.factor": "-1",
+                        "topic.creation.default.partitions": "-1",
+                        "output.json.formatter": "com.mongodb.kafka.connect.source.json.formatter.SimplifiedJson",
+                        "key.converter": "org.apache.kafka.connect.storage.StringConverter",
+                        "value.converter": "org.apache.kafka.connect.storage.StringConverter",
+                        "key.converter.schemas.enable": false,
+                        "value.converter.schemas.enable": false,
+                        "output.format.key": "schema",
+                        "output.format.value": "json",
+                        "output.schema.infer.value": false,
+                        "publish.full.document.only":true
+                    }
+                }""",
+        )
+
+        # Give time for connectors to process the table data
+        time.sleep(60)
+
+        # Run the metadata ingestion pipeline.
+        config_file = (test_resources_dir / "kafka_connect_to_file.yml").resolve()
+        run_datahub_cmd(["ingest", "-c", f"{config_file}"], tmp_path=tmp_path)
+
+        # Verify the output.
+        mce_helpers.check_golden_file(
+            pytestconfig,
+            output_path=tmp_path / "kafka_connect_mces.json",
+            golden_path=test_resources_dir / "kafka_connect_mongo_mces_golden.json",
+            ignore_paths=[],
+        )