Deprecation Warning
Please note that this repo is not maintained in the open source community. The code and examples contained in this repository are for demonstration purposes only.
You can read the latest from Yelp Engineering on our tech blog.
Data Pipeline Clientlib provides an interface to tail and publish to data pipeline topics.
git clone [email protected]:Yelp/data_pipeline.git
Running unit tests
make -f Makefile-opensource test
Include the data_pipeline namespace in your module_env_config of config.yaml
and configure following values for kafka_ip, zk_ip and schematizer_ip
module_env_config:
...
- namespace: data_pipeline
config:
kafka_broker_list:
- <kafka_ip>:9092
kafka_zookeeper: <zk_ip>:2181
schematizer_host_and_port: <schematizer_ip>:8888
...
Registering a simple schema with the Schematizer service.
from data_pipeline.schematizer_clientlib.schematizer import get_schematizer
test_avro_schema_json = {
"type": "record",
"namespace": "test_namespace",
"source": "test_source",
"name": "test_name",
"doc": "test_doc",
"fields": [
{"type": "string", "doc": "test_doc1", "name": "key1"},
{"type": "string", "doc": "test_doc2", "name": "key2"}
]
}
schema_info = get_schematizer().register_schema_from_schema_json(
namespace="test_namespace",
source="test_source",
schema_json=test_avro_schema_json,
source_owner_email="[email protected]",
contains_pii=False
)
Creating a simple Data Pipeline Message from payload data.
from data_pipeline.message import Message
message = Message(
schema_id = schema_info.schema_id,
payload_data = {
'key1': 'value1',
'key2': 'value2'
}
)
Starting a Producer and publishing messages with it::
from data_pipeline.producer import Producer
with Producer() as producer:
producer.publish(message)
Starting a Consumer with name my_consumer that listens for
messages in all topics within the test_namespace and test_source.
In this example, the consumer consumes a single message, processes it, and
commits the offset.
from data_pipeline.consumer import Consumer
from data_pipeline.consumer_source import TopicInSource
consumer_source = TopicInSource("test_namespace", "test_source")
with Consumer(
consumer_name='my_consumer',
team_name='bam',
expected_frequency_seconds=12345,
consumer_source=consumer_source
) as consumer:
while True:
message = consumer.get_message()
if message is not None:
... do stuff with message ...
consumer.commit_message(message)
We're still in the process of setting up this package as a stand-alone. There may be additional work required to run Producers/Consumers and integrate with other applications.
Data Pipeline Clientlib is licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0
Everyone is encouraged to contribute to Data Pipeline Clientlib by forking the Github repository and making a pull request or opening an issue.