apache_beam.transforms.trigger module

Support for Dataflow triggers.

Triggers control when in processing time windows get emitted.

class apache_beam.transforms.trigger.AccumulationMode

Bases: object

Controls what to do with data when a trigger fires multiple times.

DISCARDING = 1

ACCUMULATING = 2

class apache_beam.transforms.trigger.TriggerFn

Bases: object

A TriggerFn determines when window (panes) are emitted.

See https://beam.apache.org/documentation/programming-guide/#triggers

on_element(element, window, context)

Called when a new element arrives in a window.

Parameters:

• element – the element being added
• window – the window to which the element is being added
• context – a context (e.g. a TriggerContext instance) for managing state and setting timers

on_merge(to_be_merged, merge_result, context)

Called when multiple windows are merged.

Parameters:

• to_be_merged – the set of windows to be merged
• merge_result – the window into which the windows are being merged
• context – a context (e.g. a TriggerContext instance) for managing state and setting timers

should_fire(watermark, window, context)

Whether this trigger should cause the window to fire.

Parameters:

• watermark – (a lower bound on) the watermark of the system
• window – the window whose trigger is being considered
• context – a context (e.g. a TriggerContext instance) for managing state and setting timers

Returns:

whether this trigger should cause a firing

on_fire(watermark, window, context)

Called when a trigger actually fires.

Parameters:

• watermark – (a lower bound on) the watermark of the system
• window – the window whose trigger is being fired
• context – a context (e.g. a TriggerContext instance) for managing state and setting timers

Returns:

whether this trigger is finished

reset(window, context)

Clear any state and timers used by this TriggerFn.

static from_runner_api(proto, context)

to_runner_api(unused_context)

class apache_beam.transforms.trigger.DefaultTrigger

Bases: apache_beam.transforms.trigger.TriggerFn

Semantically Repeatedly(AfterWatermark()), but more optimized.

on_element(element, window, context)

on_merge(to_be_merged, merge_result, context)

should_fire(watermark, window, context)

on_fire(watermark, window, context)

reset(window, context)

static from_runner_api(proto, context)

to_runner_api(unused_context)

class apache_beam.transforms.trigger.AfterWatermark(early=None, late=None)

Bases: apache_beam.transforms.trigger.TriggerFn

Fire exactly once when the watermark passes the end of the window.

Parameters:

• early – if not None, a speculative trigger to repeatedly evaluate before the watermark passes the end of the window
• late – if not None, a speculative trigger to repeatedly evaluate after the watermark passes the end of the window

LATE_TAG = CombiningValueStateTag(is_late, CallableWrapperCombineFn(<built-in function any>))

is_late(context)

on_element(element, window, context)

on_merge(to_be_merged, merge_result, context)

should_fire(watermark, window, context)

on_fire(watermark, window, context)

reset(window, context)

static from_runner_api(proto, context)

to_runner_api(context)

class apache_beam.transforms.trigger.AfterCount(count)

Bases: apache_beam.transforms.trigger.TriggerFn

Fire when there are at least count elements in this window pane.

AfterCount is experimental. No backwards compatibility guarantees.

COUNT_TAG = CombiningValueStateTag(count, <apache_beam.transforms.combiners.CountCombineFn object>)

on_element(element, window, context)

on_merge(to_be_merged, merge_result, context)

should_fire(watermark, window, context)

on_fire(watermark, window, context)

reset(window, context)

static from_runner_api(proto, unused_context)

to_runner_api(unused_context)

class apache_beam.transforms.trigger.Repeatedly(underlying)

Bases: apache_beam.transforms.trigger.TriggerFn

Repeatedly invoke the given trigger, never finishing.

on_element(element, window, context)

on_merge(to_be_merged, merge_result, context)

should_fire(watermark, window, context)

on_fire(watermark, window, context)

reset(window, context)

static from_runner_api(proto, context)

to_runner_api(context)

class apache_beam.transforms.trigger.AfterAny(*triggers)

Bases: apache_beam.transforms.trigger._ParallelTriggerFn

Fires when any subtrigger fires.

Also finishes when any subtrigger finishes.

combine_op()

any(iterable) -> bool

Return True if bool(x) is True for any x in the iterable. If the iterable is empty, return False.

class apache_beam.transforms.trigger.AfterAll(*triggers)

Bases: apache_beam.transforms.trigger._ParallelTriggerFn

Fires when all subtriggers have fired.

Also finishes when all subtriggers have finished.

combine_op()

all(iterable) -> bool

Return True if bool(x) is True for all values x in the iterable. If the iterable is empty, return True.

class apache_beam.transforms.trigger.AfterEach(*triggers)

Bases: apache_beam.transforms.trigger.TriggerFn

INDEX_TAG = CombiningValueStateTag(index, CallableWrapperCombineFn(<function <lambda>>))

on_element(element, window, context)

on_merge(to_be_merged, merge_result, context)

should_fire(watermark, window, context)

on_fire(watermark, window, context)

reset(window, context)

static from_runner_api(proto, context)

to_runner_api(context)

class apache_beam.transforms.trigger.OrFinally(*triggers)

Bases: apache_beam.transforms.trigger.AfterAny

static from_runner_api(proto, context)

to_runner_api(context)