bytewax.inputs

Low-level input interfaces and input helpers.

If you want pre-built connectors for various external systems, see bytewax.connectors.

Data

S: TypeVar

X: TypeVar

Classes

class DynamicSource

Bases:

• Source[X]

An input source where all workers can read distinct items.

Does not support storing any resume state. Thus these kind of sources only naively can support at-most-once processing.

The source must somehow support supplying disjoint data for each worker. If you re-read the same items on multiple workers, the dataflow will process these as duplicate items.

abstract build(

now: datetime,

worker_index: int,

worker_count: int,

) → StatelessSourcePartition[X]

Build an input source for a worker.

Will be called once on each worker.

Args: now: The current time.

worker_index: Index of this worker. Workers are zero-indexed.

worker_count: Total number of workers.

Returns: The built partition.

class FixedPartitionedSource

Bases:

• Source[X]

• Generic[X, S]

An input source with a fixed number of independent partitions.

Will maintain the state of each source and re-build using it during resume. If the source supports seeking, this input can support exactly-once processing.

Each partition must contain unique data. If you re-read the same data in multiple partitions, the dataflow will process these duplicate items.

abstract list_parts() → List[str]

List all local partitions this worker has access to.

You do not need to list all partitions globally.

Returns: Local partition keys.

abstract build_part(

now: datetime,

for_part: str,

resume_state: Optional[S],

) → StatefulSourcePartition[X, S]

Build anew or resume an input partition.

Will be called once per execution for each partition key on a worker that reported that partition was local in list_parts.

Do not pre-build state about a partition in the constructor. All state must be derived from resume_state for recovery to work properly.

Args: now: The current time.

for_part: Which partition to build. Will always be one of
    the keys returned by `list_parts` on this worker.

resume_state: State data containing where in the input
    stream this partition should be begin reading during
    this execution.

Returns: The built partition.

class SimplePollingSource(

interval: timedelta,

align_to: Optional[datetime] = None,

)

Bases:

• FixedPartitionedSource[X, None]

Calls a user defined function at a regular interval.

class URLSource(SimplePollingSource): … def init(self): … super(interval=timedelta(seconds=10)) … … def next_item(self): … res = requests.get(“https://example.com”) … if not res.ok: … raise SimplePollingSource.Retry(timedelta(seconds=1)) … return res.text

There is no parallelism; only one worker will poll this source.

Does not support storing any resume state. Thus these kind of sources only naively can support at-most-once processing.

This is best for low-throughput polling on the order of seconds to hours.

If you need a high-throughput source, or custom retry or timing, avoid this. Instead create a source using one of the other Source subclasses where you can have increased paralellism, batching, and finer control over timing.

Initialization

Init.

Args: interval: The interval between calling next_item. align_to: Align awake times to the given datetime. Defaults to now.

exception Retry

Bases:

• Exception

Raise this to try to get items before the usual interval.

Args: timeout: How long to wait before calling SimplePollingSource.next_item again.

Initialization

Initialize self. See help(type(self)) for accurate signature.

timeout: timedelta

list_parts() → List[str]

Assumes the source has a single partition.

build_part(

now: datetime,

for_part: str,

resume_state: Optional[None],

) → bytewax.inputs._SimplePollingPartition[X]

See ABC docstring.

abstract next_item() → X

Override with custom logic to poll your source.

Raises: Retry: Raise if you can’t fetch items and would like to call this function sooner than the usual interval.

Returns: Next item to emit into the dataflow. If None, no item is emitted.

class Source

Bases:

• ABC

• Generic[X]

A location to read input items from.

Base class for all input sources. Do not subclass this.

If you want to implement a custom connector, instead subclass one of the specific source sub-types below in this module.

class StatefulSourcePartition

Bases:

• ABC

• Generic[X, S]

Input partition that maintains state of its position.

abstract next_batch(

sched: Optional[datetime],

) → Iterable[X]

Attempt to get the next batch of input items.

This must participate in a kind of cooperative multi-tasking, never blocking but returning an empty list if there are no items to emit yet.

Args: sched: The scheduled awake time, if one was returned by next_awake.

Returns: Items immediately ready. May be empty if no new items.

Raises: StopIteration: When the source is complete.

next_awake() → Optional[datetime]

When to next attempt to get input items.

next_batch() will not be called until the most recently returned time has past.

This will be called upon initialization of the source and after next_batch(), but also possibly at other times. Multiple times are not stored; you must return the next awake time on every call, if any.

If this returns None, next_batch() will be called immediately unless the previous batch had no items, in which case there is a 1 millisecond delay.

Use this instead of time.sleep in next_batch().

Returns: Next awake time or None to indicate automatic behavior.

abstract snapshot() → S

Snapshot the position of the next read of this partition.

This will be returned to you via the resume_state parameter of your input builder.

Be careful of “off by one” errors in resume state. This should return a state that, when built into a partition, resumes reading after the last read item item, not the same item that next() last returned.

This is guaranteed to never be called after close().

Returns: Resume state.

close() → None

Cleanup this partition when the dataflow completes.

This is not guaranteed to be called. It will only be called when the dataflow finishes on finite input. It will not be called during an abrupt or abort shutdown.

class StatelessSourcePartition

Bases:

• ABC

• Generic[X]

Input partition that is stateless.

abstract next_batch(sched: datetime) → Iterable[X]

Attempt to get the next batch of input items.

This must participate in a kind of cooperative multi-tasking, never blocking but yielding an empty list if there are no new items yet.

Args: sched: The scheduled awake time.

Returns: Items immediately ready. May be empty if no new items.

Raises: StopIteration: When the source is complete.

next_awake() → Optional[datetime]

When to next attempt to get input items.

next_batch() will not be called until the most recently returned time has past.

This will be called upon initialization of the source and after next_batch(), but also possibly at other times. Multiple times are not stored; you must return the next awake time on every call, if any.

If this returns None, next_batch() will be called immediately unless the previous batch had no items, in which case there is a 1 millisecond delay.

Use this instead of time.sleep in next_batch().

Returns: Next awake time or None to indicate automatic behavior.

close() → None

Cleanup this partition when the dataflow completes.

This is not guaranteed to be called. It will only be called when the dataflow finishes on finite input. It will not be called during an abrupt or abort shutdown.

Functions

batch(

ib: Iterable[X],

batch_size: int,

) → Iterator[List[X]]

Batch an iterable.

Use this to easily generate batches of items for a source’s next_batch method.

Args: ib: The underlying source iterable of items. batch_size: Maximum number of items to yield in a batch.

Yields: The next gathered batch of items.

batch_async(

aib: AsyncIterable[X],

timeout: timedelta,

batch_size: int,

loop=None,

) → Iterator[List[X]]

Batch an async iterable synchronously up to a timeout.

This allows using an async iterator as an input source. The next_batch method on an input source must never block, this allows running an async iterator up to a timeout so that you correctly cooperatively multitask with the rest of the dataflow.

Args: aib: The underlying source async iterable of items. timeout: Duration of time to repeatedly poll the source async iterator for items. batch_size: Maximum number of items to yield in a batch, even if the timeout has not been hit. loop: Custom asyncio run loop to use, if any.

Yields: The next gathered batch of items.

This function will take up to `timeout` time to yield, or
will return a list with length up to `max_len`.

batch_getter(

getter: Callable[[], X],

batch_size: int,

yield_on: Optional[X] = None,

) → Iterator[List[X]]

Batch from a getter function that might not return an item.

Use this to easily generate batches of items for a source’s next_batch method.

Args: getter: Function to call to get the next item. Should raise StopIteration on EOF.

batch_size:
    Maximum number of items to yield in a batch.

yield_on:
    Sentinel value that indicates that there are no more items
    yet, and to return the current batch. Defaults to `None`.

Yields: The next gathered batch of items.

batch_getter_ex(

getter: Callable[[], X],

batch_size: int,

yield_ex: Type[Exception] = queue.Empty,

) → Iterator[List[X]]

Batch from a getter function that raises on no items yet.

Use this to easily generate batches of items for a source’s next_batch method.

Args: getter: Function to call to get the next item. Should raise StopIteration on EOF.

batch_size:
    Maximum number of items to return in a batch.

yield_ex:
    Exception raised by `getter` that indicates that there are
    no more items yet, and to return the current
    batch. Defaults to `queue.Empty`.

Yields: The next gathered batch of items.