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Sources API

quixstreams.sources.base.source

BaseSource

class BaseSource(ABC)

[VIEW SOURCE]

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:

  • start
  • stop
  • default_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

def configure(topic: Topic, producer: RowProducer, **kwargs) -> None

[VIEW SOURCE]

This method is triggered before the source is started.

It configures the source's Kafka producer, the topic it will produce to and optional dependencies.



BaseSource.start

@abstractmethod
def start() -> None

[VIEW SOURCE]

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

@abstractmethod
def stop() -> None

[VIEW SOURCE]

This method is triggered when the application is shutting down.

The source must ensure that the run method is completed soon.



BaseSource.default_topic

@abstractmethod
def default_topic() -> Topic

[VIEW SOURCE]

This method is triggered when the topic is not provided to the source.

The source must return a default topic configuration.

Note: if the default topic is used, the Application will prefix its name with "source__".

Source

class Source(BaseSource)

[VIEW 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:

import random
import time

from quixstreams import Application
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)
            time.sleep(0.5)


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__

def __init__(name: str, shutdown_timeout: float = 10) -> None

[VIEW SOURCE]


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
def running() -> bool

[VIEW SOURCE]

Property indicating if the source is running.

The stop method will set it to False. Use it to stop the source gracefully.



Source.cleanup

def cleanup(failed: bool) -> None

[VIEW SOURCE]

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

def stop() -> None

[VIEW SOURCE]

This method is triggered when the application is shutting down.

It sets the running property to False.



Source.start

def start() -> None

[VIEW SOURCE]

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

@abstractmethod
def run()

[VIEW SOURCE]

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

[VIEW SOURCE]

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

[VIEW SOURCE]

Produce a message to the configured source topic in Kafka.



Source.flush

def flush(timeout: Optional[float] = None) -> None

[VIEW SOURCE]

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

def default_topic() -> Topic

[VIEW SOURCE]

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.

Note: if the default topic is used, the Application will prefix its name with "source__".


Returns:

quixstreams.models.topics.Topic

StatefulSource

class StatefulSource(Source)

[VIEW SOURCE]

A Source class for custom Sources that need a state.

Subclasses are responsible for flushing, by calling flush, at reasonable intervals.

Example:

import random
import time

from quixstreams import Application
from quixstreams.sources import StatefulSource


class RandomNumbersSource(StatefulSource):
    def run(self):

        i = 0
        while self.running:
            previous = self.state.get("number", 0)
            current = random.randint(0, 100)
            self.state.set("number", current)

            serialized = self._producer_topic.serialize(value=current + previous)
            self.produce(key=str(current), value=serialized.value)
            time.sleep(0.5)

            # flush the state every 10 messages
            i += 1
            if i % 10 == 0:
                self.flush()


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()



StatefulSource.__init__

def __init__(name: str, shutdown_timeout: float = 10) -> None

[VIEW SOURCE]


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.



StatefulSource.configure

def configure(topic: Topic,
              producer: RowProducer,
              *,
              store_partition: Optional[StorePartition] = None,
              **kwargs) -> None

[VIEW SOURCE]

This method is triggered before the source is started.

It configures the source's Kafka producer, the topic it will produce to and the store partition.



StatefulSource.store_partitions_count

@property
def store_partitions_count() -> int

[VIEW SOURCE]

Count of store partitions.

Used to configure the number of partition in the changelog topic.



StatefulSource.assigned_store_partition

@property
def assigned_store_partition() -> int

[VIEW SOURCE]

The store partition assigned to this instance



StatefulSource.store_name

@property
def store_name() -> str

[VIEW SOURCE]

The source store name



StatefulSource.state

@property
def state() -> State

[VIEW SOURCE]

Access the State of the source.

The State lifecycle is tied to the store transaction. A transaction is only valid until the next .flush() call. If no valid transaction exist, a new transaction is created.

Important: after each .flush() call, a previously returned instance is invalidated and cannot be used. The property must be called again.



StatefulSource.flush

def flush(timeout: Optional[float] = None) -> None

[VIEW SOURCE]

This method commit the state and flush the producer.

It ensures the state is published to the changelog topic and 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

quixstreams.sources.core.csv

CSVSource

class CSVSource(Source)

[VIEW SOURCE]



CSVSource.__init__

def __init__(path: Union[str, Path],
             name: str,
             key_extractor: Optional[Callable[[dict], Union[str,
                                                            bytes]]] = None,
             timestamp_extractor: Optional[Callable[[dict], int]] = None,
             delay: float = 0,
             shutdown_timeout: float = 10,
             dialect: str = "excel") -> None

[VIEW SOURCE]

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 either str or bytes. 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 as int. If empty, the current epoch will be used. Default - None
  • delay: 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

class KafkaReplicatorSource(Source)

[VIEW SOURCE]

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: Optional[AutoOffsetReset] = "latest",
        consumer_extra_config: Optional[dict] = None,
        consumer_poll_timeout: Optional[float] = None,
        shutdown_timeout: float = 10,
        on_consumer_error: ConsumerErrorCallback = default_on_consumer_error,
        value_deserializer: DeserializerType = "json",
        key_deserializer: DeserializerType = "bytes") -> None

[VIEW SOURCE]


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 of KafkaReplicatorSource with 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: Consumer auto.offset.reset setting. Default - Use the Application auto_offset_reset setting.
  • consumer_extra_config: A dictionary with additional options that will be passed to confluent_kafka.Consumer as is. Default - None
  • consumer_poll_timeout: timeout for RowConsumer.poll() Default - Use the Application consumer_poll_timeout setting.
  • shutdown_timeout: Time in second the application waits for the source to gracefully shutdown.
  • on_consumer_error: Triggered when the source Consumer fails to poll Kafka.
  • value_deserializer: The default topic value deserializer, used by StreamingDataframe connected to the source. Default - json
  • key_deserializer: The default topic key deserializer, used by StreamingDataframe connected to the source. Default - json

quixstreams.sources.core.kafka.quix

QuixEnvironmentSource

class QuixEnvironmentSource(KafkaReplicatorSource)

[VIEW SOURCE]

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: ConsumerErrorCallback = default_on_consumer_error,
        value_deserializer: DeserializerType = "json",
        key_deserializer: DeserializerType = "bytes") -> None

[VIEW SOURCE]


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__Api environment variable or Quix cloud production URL

For other parameters See quixstreams.sources.kafka.KafkaReplicatorSource

quixstreams.sources.community.file.file

FileSource

class FileSource(Source)

[VIEW SOURCE]

Ingest a set of files from a desired origin into Kafka by iterating through the provided folder and processing all nested files within it.

Origins include a local filestore, AWS S3, or Microsoft Azure.

FileSource defaults to a local filestore (LocalOrigin) + JSON format.

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 Usage:

from quixstreams import Application
from quixstreams.sources.community.file import FileSource
from quixstreams.sources.community.file.origins import S3Origin

app = Application(broker_address="localhost:9092", auto_offset_reset="earliest")

origin = S3Origin(
    bucket="<YOUR BUCKET>",
    aws_access_key_id="<YOUR KEY ID>",
    aws_secret_access_key="<YOUR SECRET KEY>",
    aws_region="<YOUR REGION>",
)
source = FileSource(
    directory="path/to/your/topic_folder/",
    origin=origin,
    format="json",
    compression="gzip",
)
sdf = app.dataframe(source=source).print(metadata=True)
# YOUR LOGIC HERE!

if __name__ == "__main__":
    app.run()



FileSource.__init__

def __init__(directory: Union[str, Path],
             format: Union[Format, FormatName] = "json",
             origin: Origin = LocalOrigin(),
             compression: Optional[CompressionName] = None,
             replay_speed: float = 1.0,
             name: Optional[str] = None,
             shutdown_timeout: float = 10)

[VIEW SOURCE]


Arguments:

  • directory: a directory to recursively read through; it is recommended to provide the path to a given topic folder (ex: /path/to/topic_a).
  • format: what format the message files are in (ex: json, parquet). Optionally, can provide a Format instance if more than compression is necessary to define (compression will then be ignored).
  • origin: an Origin type (defaults to reading local files).
  • compression: what compression is used on the given files, if any.
  • replay_speed: Produce the messages with this speed multiplier, which roughly reflects the time "delay" between the original message producing. Use any float >= 0, where 0 is no delay, and 1 is the original speed. 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

def default_topic() -> Topic

[VIEW SOURCE]

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.origins.local

quixstreams.sources.community.file.origins.s3

S3Origin

class S3Origin(Origin)

[VIEW SOURCE]



S3Origin.__init__

def __init__(
    bucket: str,
    region_name: Optional[str] = getenv("AWS_REGION"),
    aws_access_key_id: Optional[str] = getenv("AWS_ACCESS_KEY_ID"),
    aws_secret_access_key: Optional[str] = getenv("AWS_SECRET_ACCESS_KEY"),
    endpoint_url: Optional[str] = getenv("AWS_ENDPOINT_URL_S3"))

[VIEW SOURCE]

Configure IcebergSink to work with AWS Glue.


Arguments:

  • bucket: The S3 bucket name only (ex: 'your-bucket').
  • region_name: The AWS region. NOTE: can alternatively set the AWS_REGION environment variable
  • aws_access_key_id: the AWS access key ID. NOTE: can alternatively set the AWS_ACCESS_KEY_ID environment variable
  • aws_secret_access_key: the AWS secret access key. NOTE: can alternatively set the AWS_SECRET_ACCESS_KEY environment variable
  • endpoint_url: the endpoint URL to use; only required for connecting to a locally hosted S3. NOTE: can alternatively set the AWS_ENDPOINT_URL_S3 environment variable

quixstreams.sources.community.file.formats.json

JSONFormat

class JSONFormat(Format)

[VIEW SOURCE]



JSONFormat.__init__

def __init__(compression: Optional[CompressionName],
             loads: Optional[Callable[[str], dict]] = None)

[VIEW SOURCE]

Read a JSON-formatted file (along with decompressing it).


Arguments:

  • compression: the compression type used on the file
  • loads: A custom function to deserialize objects to the expected dict with {_key: str, _value: dict, _timestamp: int}.

quixstreams.sources.community.file.formats.parquet

quixstreams.sources.community.kinesis.kinesis

KinesisSource

class KinesisSource(StatefulSource)

[VIEW SOURCE]

NOTE: Requires pip install quixstreams[kinesis] to work.

This source reads data from an Amazon Kinesis stream, dumping it to a kafka topic using desired StreamingDataFrame-based transformations.

Provides "at-least-once" guarantees.

The incoming message value will be in bytes, so transform in your SDF accordingly.

Example Usage:

from quixstreams import Application
from quixstreams.sources.community.kinesis import KinesisSource


kinesis = KinesisSource(
    stream_name="<YOUR STREAM>",
    aws_access_key_id="<YOUR KEY ID>",
    aws_secret_access_key="<YOUR SECRET KEY>",
    aws_region="<YOUR REGION>",
    auto_offset_reset="earliest",  # start from the beginning of the stream (vs end)
)

app = Application(
    broker_address="<YOUR BROKER INFO>",
    consumer_group="<YOUR GROUP>",
)

sdf = app.dataframe(source=kinesis).print(metadata=True)
# YOUR LOGIC HERE!

if __name__ == "__main__":
    app.run()



KinesisSource.__init__

def __init__(
        stream_name: str,
        aws_region: Optional[str] = getenv("AWS_REGION"),
        aws_access_key_id: Optional[str] = getenv("AWS_ACCESS_KEY_ID"),
        aws_secret_access_key: Optional[str] = getenv("AWS_SECRET_ACCESS_KEY"),
        aws_endpoint_url: Optional[str] = getenv("AWS_ENDPOINT_URL_KINESIS"),
        shutdown_timeout: float = 10,
        auto_offset_reset: AutoOffsetResetType = "latest",
        max_records_per_shard: int = 1000,
        commit_interval: float = 5.0,
        retry_backoff_secs: float = 5.0)

[VIEW SOURCE]


Arguments:

  • stream_name: name of the desired Kinesis stream to consume.
  • aws_region: The AWS region. NOTE: can alternatively set the AWS_REGION environment variable
  • aws_access_key_id: the AWS access key ID. NOTE: can alternatively set the AWS_ACCESS_KEY_ID environment variable
  • aws_secret_access_key: the AWS secret access key. NOTE: can alternatively set the AWS_SECRET_ACCESS_KEY environment variable
  • aws_endpoint_url: the endpoint URL to use; only required for connecting to a locally hosted Kinesis. NOTE: can alternatively set the AWS_ENDPOINT_URL_KINESIS environment variable
  • shutdown_timeout:
  • auto_offset_reset: When no previous offset has been recorded, whether to start from the beginning ("earliest") or end ("latest") of the stream.
  • max_records_per_shard: During round-robin consumption, how many records to consume per shard (partition) per consume (NOT per-commit).
  • commit_interval: the time between commits
  • retry_backoff_secs: how long to back off from doing HTTP calls for a shard when Kinesis consumer encounters handled/expected errors.

quixstreams.sources.community.pubsub.pubsub

PubSubSource

class PubSubSource(Source)

[VIEW SOURCE]

This source enables reading from a Google Cloud Pub/Sub topic, dumping it to a kafka topic using desired SDF-based transformations.

Provides "at-least-once" guarantees.

Currently, forwarding message keys ("ordered messages" in Pub/Sub) is unsupported.

The incoming message value will be in bytes, so transform in your SDF accordingly.

Example Usage:

from quixstreams import Application
from quixstreams.sources.community.pubsub import PubSubSource
from os import environ

source = PubSubSource(
    # Suggested: pass JSON-formatted credentials from an environment variable.
    service_account_json = environ["PUBSUB_SERVICE_ACCOUNT_JSON"],
    project_id="<project ID>",
    topic_id="<topic ID>",  # NOTE: NOT the full /x/y/z path!
    subscription_id="<subscription ID>",  # NOTE: NOT the full /x/y/z path!
    create_subscription=True,
)
app = Application(
    broker_address="localhost:9092",
    auto_offset_reset="earliest",
    consumer_group="gcp",
    loglevel="INFO"
)
sdf = app.dataframe(source=source).print(metadata=True)

if __name__ == "__main__":
    app.run()



PubSubSource.__init__

def __init__(project_id: str,
             topic_id: str,
             subscription_id: str,
             service_account_json: Optional[str] = None,
             commit_every: int = 100,
             commit_interval: float = 5.0,
             create_subscription: bool = False,
             enable_message_ordering: bool = False,
             shutdown_timeout: float = 10.0)

[VIEW SOURCE]


Arguments:

  • project_id: a Google Cloud project ID.
  • topic_id: a Pub/Sub topic ID (NOT the full path).
  • subscription_id: a Pub/Sub subscription ID (NOT the full path).
  • service_account_json: a Google Cloud Credentials JSON as a string Can instead use environment variables (which have different behavior):
  • "GOOGLE_APPLICATION_CREDENTIALS" set to a JSON filepath i.e. /x/y/z.json
  • "PUBSUB_EMULATOR_HOST" set to a URL if using an emulated Pub/Sub
  • commit_every: max records allowed to be processed before committing.
  • commit_interval: max allowed elapsed time between commits.
  • create_subscription: whether to attempt to create a subscription at startup; if it already exists, it instead logs its details (DEBUG level).
  • enable_message_ordering: When creating a Pub/Sub subscription, whether to allow message ordering. NOTE: does NOT affect existing subscriptions!
  • shutdown_timeout: How long to wait for a graceful shutdown of the source.
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