7.9 KiB
:::note Stateful Ingestion is available only when a Platform Instance is assigned to this source. :::
Connecting to Confluent Cloud
If using Confluent Cloud you can use a recipe like this. In this consumer_config.sasl.username and consumer_config.sasl.password are the API credentials that you get (in the Confluent UI) from your cluster -> Data Integration -> API Keys. schema_registry_config.basic.auth.user.info has API credentials for Confluent schema registry which you get (in Confluent UI) from Schema Registry -> API credentials.
When creating API Key for the cluster ensure that the ACLs associated with the key are set like below. This is required for DataHub to read topic metadata from topics in Confluent Cloud.
Topic Name = *
Permission = ALLOW
Operation = DESCRIBE
Pattern Type = LITERAL
source:
type: "kafka"
config:
platform_instance: "YOUR_CLUSTER_ID"
connection:
bootstrap: "abc-defg.eu-west-1.aws.confluent.cloud:9092"
consumer_config:
security.protocol: "SASL_SSL"
sasl.mechanism: "PLAIN"
sasl.username: "${CLUSTER_API_KEY_ID}"
sasl.password: "${CLUSTER_API_KEY_SECRET}"
schema_registry_url: "https://abc-defgh.us-east-2.aws.confluent.cloud"
schema_registry_config:
basic.auth.user.info: "${REGISTRY_API_KEY_ID}:${REGISTRY_API_KEY_SECRET}"
sink:
# sink configs
If you are trying to add domains to your topics you can use a configuration like below.
source:
type: "kafka"
config:
# ...connection block
domain:
"urn:li:domain:13ae4d85-d955-49fc-8474-9004c663a810":
allow:
- ".*"
"urn:li:domain:d6ec9868-6736-4b1f-8aa6-fee4c5948f17":
deny:
- ".*"
Note that the domain in config above can be either an urn or a domain id (i.e. urn:li:domain:13ae4d85-d955-49fc-8474-9004c663a810 or simply 13ae4d85-d955-49fc-8474-9004c663a810). The Domain should exist in your DataHub instance before ingesting data into the Domain. To create a Domain on DataHub, check out the Domains User Guide.
If you are using a non-default subject naming strategy in the schema registry, such as RecordNameStrategy, the mapping for the topic's key and value schemas to the schema registry subject names should be provided via topic_subject_map as shown in the configuration below.
source:
type: "kafka"
config:
# ...connection block
# Defines the mapping for the key & value schemas associated with a topic & the subject name registered with the
# kafka schema registry.
topic_subject_map:
# Defines both key & value schema for topic 'my_topic_1'
"my_topic_1-key": "io.acryl.Schema1"
"my_topic_1-value": "io.acryl.Schema2"
# Defines only the value schema for topic 'my_topic_2' (the topic doesn't have a key schema).
"my_topic_2-value": "io.acryl.Schema3"
Custom Schema Registry
The Kafka Source uses the schema registry to figure out the schema associated with both key and value for the topic.
By default it uses the Confluent's Kafka Schema registry
and supports the AVRO and PROTOBUF schema types.
If you're using a custom schema registry, or you are using schema type other than AVRO or PROTOBUF, then you can provide your own
custom implementation of the KafkaSchemaRegistryBase class, and implement the get_schema_metadata(topic, platform_urn) method that
given a topic name would return object of SchemaMetadata containing schema for that topic. Please refer
datahub.ingestion.source.confluent_schema_registry::ConfluentSchemaRegistry for sample implementation of this class.
class KafkaSchemaRegistryBase(ABC):
@abstractmethod
def get_schema_metadata(
self, topic: str, platform_urn: str
) -> Optional[SchemaMetadata]:
pass
The custom schema registry class can be configured using the schema_registry_class config param of the kafka source as shown below.
source:
type: "kafka"
config:
# Set the custom schema registry implementation class
schema_registry_class: "datahub.ingestion.source.confluent_schema_registry.ConfluentSchemaRegistry"
# Coordinates
connection:
bootstrap: "broker:9092"
schema_registry_url: http://localhost:8081
OAuth Callback
The OAuth callback function can be set up using config.connection.consumer_config.oauth_cb.
You need to specify a Python function reference in the format <python-module>:<function-name>.
For example, in the configuration oauth:create_token, create_token is a function defined in oauth.py, and oauth.py must be accessible in the PYTHONPATH.
source:
type: "kafka"
config:
# Set the custom schema registry implementation class
schema_registry_class: "datahub.ingestion.source.confluent_schema_registry.ConfluentSchemaRegistry"
# Coordinates
connection:
bootstrap: "broker:9092"
schema_registry_url: http://localhost:8081
consumer_config:
security.protocol: "SASL_PLAINTEXT"
sasl.mechanism: "OAUTHBEARER"
oauth_cb: "oauth:create_token"
# sink configs
Limitations of PROTOBUF schema types implementation
The current implementation of the support for PROTOBUF schema type has the following limitations:
- Recursive types are not supported.
- If the schemas of different topics define a type in the same package, the source would raise an exception.
In addition to this, maps are represented as arrays of messages. The following message,
message MessageWithMap {
map<int, string> map_1 = 1;
}
becomes:
message Map1Entry {
int key = 1;
string value = 2/
}
message MessageWithMap {
repeated Map1Entry map_1 = 1;
}
Enriching DataHub metadata with automated meta mapping
:::note Meta mapping is currently only available for Avro schemas, and requires that those Avro schemas are pushed to the schema registry. :::
Avro schemas are permitted to have additional attributes not defined by the specification as arbitrary metadata. A common pattern is to utilize this for business metadata. The Kafka source has the ability to transform this directly into DataHub Owners, Tags and Terms.
Simple tags
If you simply have a list of tags embedded into an Avro schema (either at the top-level or for an individual field), you can use the schema_tags_field config.
Example Avro schema:
{
"name": "sampleRecord",
"type": "record",
"tags": ["tag1", "tag2"],
"fields": [
{
"name": "field_1",
"type": "string",
"tags": ["tag3", "tag4"]
}
]
}
The name of the field containing a list of tags can be configured with the schema_tags_field property:
config:
schema_tags_field: tags
meta mapping
You can also map specific Avro fields into Owners, Tags and Terms using meta mapping.
Example Avro schema:
{
"name": "sampleRecord",
"type": "record",
"owning_team": "@Data-Science",
"data_tier": "Bronze",
"fields": [
{
"name": "field_1",
"type": "string",
"gdpr": {
"pii": true
}
}
]
}
This can be mapped to DataHub metadata with meta_mapping config:
config:
meta_mapping:
owning_team:
match: "^@(.*)"
operation: "add_owner"
config:
owner_type: group
data_tier:
match: "Bronze|Silver|Gold"
operation: "add_term"
config:
term: "{{ $match }}"
field_meta_mapping:
gdpr.pii:
match: true
operation: "add_tag"
config:
tag: "pii"
The underlying implementation is similar to dbt meta mapping, which has more detailed examples that can be used for reference.