Sources API
quixstreams.sources.base.source
BaseSource
This is the base class for all sources.
Sources are executed in a sub-process of the main application.
To create your own source you need to implement:
startstopdefault_topic
BaseSource is the most basic interface, and the framework expects every
source to implement it.
Use Source to benefit from a base implementation.
You can connect a source to a StreamingDataframe using the Application.
Example snippet:
class RandomNumbersSource(BaseSource):
def __init__(self):
super().__init__()
self._running = False
def start(self):
self._running = True
while self._running:
number = random.randint(0, 100)
serialized = self._producer_topic.serialize(value=number)
self._producer.produce(
topic=self._producer_topic.name,
key=serialized.key,
value=serialized.value,
)
def stop(self):
self._running = False
def default_topic(self) -> Topic:
return Topic(
name="topic-name",
value_deserializer="json",
value_serializer="json",
)
def main():
app = Application(broker_address="localhost:9092")
source = RandomNumbersSource()
sdf = app.dataframe(source=source)
sdf.print(metadata=True)
app.run()
if __name__ == "__main__":
main()
BaseSource.configure
This method is triggered when the source is registered to the Application.
It configures the source's Kafka producer and the topic it will produce to.
BaseSource.start
This method is triggered in the subprocess when the source is started.
The subprocess will run as long as the start method executes. Use it to fetch data and produce it to Kafka.
BaseSource.stop
This method is triggered when the application is shutting down.
The source must ensure that the run method is completed soon.
BaseSource.default_topic
This method is triggered when the topic is not provided to the source.
The source must return a default topic configuration.
Source
A base class for custom Sources that provides a basic implementation of BaseSource
interface.
It is recommended to interface to create custom sources.
Subclass it and implement the run method to fetch data and produce it to Kafka.
Example:
from quixstreams import Application
import random
from quixstreams.sources import Source
class RandomNumbersSource(Source):
def run(self):
while self.running:
number = random.randint(0, 100)
serialized = self._producer_topic.serialize(value=number)
self.produce(key=str(number), value=serialized.value)
def main():
app = Application(broker_address="localhost:9092")
source = RandomNumbersSource(name="random-source")
sdf = app.dataframe(source=source)
sdf.print(metadata=True)
app.run()
if __name__ == "__main__":
main()
Helper methods and properties:
serialize()produce()flush()running
Source.__init__
Arguments:
name: The source unique name. It is used to generate the topic configuration.shutdown_timeout: Time in second the application waits for the source to gracefully shutdown.
Source.running
Property indicating if the source is running.
The stop method will set it to False. Use it to stop the source gracefully.
Source.cleanup
This method is triggered once the run method completes.
Use it to clean up the resources and shut down the source gracefully.
It flushes the producer when _run completes successfully.
Source.stop
This method is triggered when the application is shutting down.
It sets the running property to False.
Source.start
This method is triggered in the subprocess when the source is started.
It marks the source as running, execute it's run method and ensure cleanup happens.
Source.run
This method is triggered in the subprocess when the source is started.
The subprocess will run as long as the run method executes. Use it to fetch data and produce it to Kafka.
Source.serialize
def serialize(key: Optional[object] = None,
value: Optional[object] = None,
headers: Optional[Headers] = None,
timestamp_ms: Optional[int] = None) -> KafkaMessage
Serialize data to bytes using the producer topic serializers and return a quixstreams.models.messages.KafkaMessage.
Returns:
quixstreams.models.messages.KafkaMessage
Source.produce
def produce(value: Optional[Union[str, bytes]] = None,
key: Optional[Union[str, bytes]] = None,
headers: Optional[Headers] = None,
partition: Optional[int] = None,
timestamp: Optional[int] = None,
poll_timeout: float = 5.0,
buffer_error_max_tries: int = 3) -> None
Produce a message to the configured source topic in Kafka.
Source.flush
This method flush the producer.
It ensures all messages are successfully delivered to Kafka.
Arguments:
timeout(float): time to attempt flushing (seconds). None use producer default or -1 is infinite. Default: None
Raises:
CheckpointProducerTimeout: if any message fails to produce before the timeout
Source.default_topic
Return a default topic matching the source name.
The default topic will not be used if the topic has already been provided to the source.
Returns:
quixstreams.models.topics.Topic
quixstreams.sources.core.csv
CSVSource
CSVSource.__init__
def __init__(path: Union[str, Path],
name: str,
key_extractor: Optional[Callable[[dict], AnyStr]] = None,
timestamp_extractor: Optional[Callable[[dict], int]] = None,
delay: float = 0,
shutdown_timeout: float = 10,
dialect: str = "excel") -> None
A base CSV source that reads data from a CSV file and produces rows
to the Kafka topic in JSON format.
Arguments:
path: a path to the CSV file.name: a unique name for the Source. It is used as a part of the default topic name.key_extractor: an optional callable to extract the message key from the row. It must return eitherstrorbytes. If empty, the Kafka messages will be produced without keys. Default -None.timestamp_extractor: an optional callable to extract the message timestamp from the row. It must return time in milliseconds asint. If empty, the current epoch will be used. Default -Nonedelay: an optional delay after producing each row for stream simulation. Default -0.shutdown_timeout: Time in second the application waits for the source to gracefully shut down.dialect: a CSV dialect to use. It affects quoting and delimiters. See the "csv" module docs for more info. Default -"excel".
quixstreams.sources.core.kafka.kafka
KafkaReplicatorSource
Source implementation that replicates a topic from a Kafka broker to your application broker.
Running multiple instances of this source is supported.
Example Snippet:
from quixstreams import Application
from quixstreams.sources.kafka import KafkaReplicatorSource
app = Application(
consumer_group="group",
)
source = KafkaReplicatorSource(
name="source-second-kafka",
app_config=app.config,
topic="second-kafka-topic",
broker_address="localhost:9092",
)
sdf = app.dataframe(source=source)
sdf = sdf.print()
app.run()
KafkaReplicatorSource.__init__
def __init__(name: str,
app_config: "ApplicationConfig",
topic: str,
broker_address: Union[str, ConnectionConfig],
auto_offset_reset: AutoOffsetReset = "latest",
consumer_extra_config: Optional[dict] = None,
consumer_poll_timeout: Optional[float] = None,
shutdown_timeout: float = 10,
on_consumer_error: Optional[
ConsumerErrorCallback] = default_on_consumer_error,
value_deserializer: DeserializerType = "json",
key_deserializer: DeserializerType = "bytes") -> None
Arguments:
name: The source unique name. It is used to generate the default topic name and consumer group name on the source broker. Running multiple instances ofKafkaReplicatorSourcewith the same name connected to the same broker will make them share the same consumer group.app_config: The configuration of the application. Used by the source to connect to the application kafka broker.topic: The topic to replicate.broker_address: The connection settings for the source Kafka.auto_offset_reset: Consumerauto.offset.resetsetting. Default - Use the Applicationauto_offset_resetsetting.consumer_extra_config: A dictionary with additional options that will be passed toconfluent_kafka.Consumeras is. Default -Noneconsumer_poll_timeout: timeout forRowConsumer.poll()Default - Use the Applicationconsumer_poll_timeoutsetting.shutdown_timeout: Time in second the application waits for the source to gracefully shutdown.on_consumer_error: Triggered when the sourceConsumerfails to poll Kafka.value_deserializer: The default topic value deserializer, used by StreamingDataframe connected to the source. Default -jsonkey_deserializer: The default topic key deserializer, used by StreamingDataframe connected to the source. Default -json
quixstreams.sources.core.kafka.quix
QuixEnvironmentSource
Source implementation that replicates a topic from a Quix Cloud environment to your application broker. It can copy messages for development and testing without risking producing them back or affecting the consumer groups.
Running multiple instances of this source is supported.
Example Snippet:
from quixstreams import Application
from quixstreams.sources.kafka import QuixEnvironmentSource
app = Application(
consumer_group="group",
)
source = QuixEnvironmentSource(
name="source-quix",
app_config=app.config,
quix_workspace_id="WORKSPACE_ID",
quix_sdk_token="WORKSPACE_SDK_TOKEN",
topic="quix-source-topic",
)
sdf = app.dataframe(source=source)
sdf = sdf.print()
app.run()
QuixEnvironmentSource.__init__
def __init__(name: str,
app_config: "ApplicationConfig",
topic: str,
quix_sdk_token: str,
quix_workspace_id: str,
quix_portal_api: Optional[str] = None,
auto_offset_reset: Optional[AutoOffsetReset] = None,
consumer_extra_config: Optional[dict] = None,
consumer_poll_timeout: Optional[float] = None,
shutdown_timeout: float = 10,
on_consumer_error: Optional[
ConsumerErrorCallback] = default_on_consumer_error,
value_deserializer: DeserializerType = "json",
key_deserializer: DeserializerType = "bytes") -> None
Arguments:
quix_workspace_id: The Quix workspace ID of the source environment.quix_sdk_token: Quix cloud sdk token used to connect to the source environment.quix_portal_api: The Quix portal API URL of the source environment. Default -Quix__Portal__Apienvironment variable or Quix cloud production URL
For other parameters See quixstreams.sources.kafka.KafkaReplicatorSource
quixstreams.sources.community.file.file
FileSource
Ingest a set of local files into kafka by iterating through the provided folder and processing all nested files within it.
Expects folder and file structures as generated by the related FileSink connector:
my_topics/ ├── topic_a/ │ ├── 0/ │ │ ├── 0000.ext │ │ └── 0011.ext │ └── 1/ │ ├── 0003.ext │ └── 0016.ext └── topic_b/ └── etc...
Intended to be used with a single topic (ex: topic_a), but will recursively read from whatever entrypoint is passed to it.
File format structure depends on the file format.
See the .formats and .compressions modules to see what is supported.
Example:
from quixstreams import Application from quixstreams.sources.community.file import FileSource
app = Application(broker_address="localhost:9092", auto_offset_reset="earliest") source = FileSource( filepath="/path/to/my/topic_folder", file_format="json", file_compression="gzip", ) sdf = app.dataframe(source=source).print(metadata=True)
if name == "main": app.run()
FileSource.__init__
def __init__(filepath: Union[str, Path],
file_format: Union[Format, FormatName],
file_compression: Optional[CompressionName] = None,
as_replay: bool = True,
name: Optional[str] = None,
shutdown_timeout: float = 10)
Arguments:
filepath: a filepath to recursively read through; it is recommended to provide the path to a given topic folder (ex:/path/to/topic_a).file_format: what format the message files are in (ex: json, parquet). Optionally, can provide aFormatinstance if more than file_compression is necessary to define (file_compression will then be ignored).file_compression: what compression is used on the given files, if any.as_replay: Produce the messages with the original time delay between them. Otherwise, produce the messages as fast as possible. NOTE: Time delay will only be accurate per partition, NOT overall.name: The name of the Source application (Default: last folder name).shutdown_timeout: Time in seconds the application waits for the source to gracefully shutdown
FileSource.default_topic
Uses the file structure to generate the desired partition count for the
internal topic.
Returns:
the original default topic, with updated partition count
quixstreams.sources.community.file.compressions.gzip
quixstreams.sources.community.file.formats.json
JSONFormat
JSONFormat.__init__
Read a JSON-formatted file (along with decompressing it).
Arguments:
compression: the compression type used on the fileloads: A custom function to deserialize objects to the expected dict with {_key: str, _value: dict, _timestamp: int}.