WithTimestamps
Assigns timestamps to all the elements of a collection.
Examples
In the following examples, we create a pipeline with a PCollection
and attach a timestamp value to each of its elements.
When windowing and late data play an important role in streaming pipelines, timestamps are especially useful.
Example 1: Timestamp by event time
The elements themselves often already contain a timestamp field.
beam.window.TimestampedValue
takes a value and a
Unix timestamp
in the form of seconds.
import apache_beam as beam
class GetTimestamp(beam.DoFn):
def process(self, plant, timestamp=beam.DoFn.TimestampParam):
yield '{} - {}'.format(timestamp.to_utc_datetime(), plant['name'])
with beam.Pipeline() as pipeline:
plant_timestamps = (
pipeline
| 'Garden plants' >> beam.Create([
{'name': 'Strawberry', 'season': 1585699200}, # April, 2020
{'name': 'Carrot', 'season': 1590969600}, # June, 2020
{'name': 'Artichoke', 'season': 1583020800}, # March, 2020
{'name': 'Tomato', 'season': 1588291200}, # May, 2020
{'name': 'Potato', 'season': 1598918400}, # September, 2020
])
| 'With timestamps' >> beam.Map(
lambda plant: beam.window.TimestampedValue(plant, plant['season']))
| 'Get timestamp' >> beam.ParDo(GetTimestamp())
| beam.Map(print)
)
Output:
2020-04-01 00:00:00 - Strawberry
2020-06-01 00:00:00 - Carrot
2020-03-01 00:00:00 - Artichoke
2020-05-01 00:00:00 - Tomato
2020-09-01 00:00:00 - Potato
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To convert from a
time.struct_time
to unix_time
you can use
time.mktime
.
For more information on time formatting options, see
time.strftime
.
import time
time_tuple = time.strptime('2020-03-19 20:50:00', '%Y-%m-%d %H:%M:%S')
unix_time = time.mktime(time_tuple)
To convert from a
datetime.datetime
to unix_time
you can use convert it to a time.struct_time
first with
datetime.timetuple
.
import time
import datetime
now = datetime.datetime.now()
time_tuple = now.timetuple()
unix_time = time.mktime(time_tuple)
Example 2: Timestamp by logical clock
If each element has a chronological number, these numbers can be used as a logical clock. These numbers have to be converted to a “seconds” equivalent, which can be especially important depending on your windowing and late data rules.
import apache_beam as beam
class GetTimestamp(beam.DoFn):
def process(self, plant, timestamp=beam.DoFn.TimestampParam):
event_id = int(timestamp.micros / 1e6) # equivalent to seconds
yield '{} - {}'.format(event_id, plant['name'])
with beam.Pipeline() as pipeline:
plant_events = (
pipeline
| 'Garden plants' >> beam.Create([
{'name': 'Strawberry', 'event_id': 1},
{'name': 'Carrot', 'event_id': 4},
{'name': 'Artichoke', 'event_id': 2},
{'name': 'Tomato', 'event_id': 3},
{'name': 'Potato', 'event_id': 5},
])
| 'With timestamps' >> beam.Map(lambda plant: \
beam.window.TimestampedValue(plant, plant['event_id']))
| 'Get timestamp' >> beam.ParDo(GetTimestamp())
| beam.Map(print)
)
Output:
1 - Strawberry
4 - Carrot
2 - Artichoke
3 - Tomato
5 - Potato
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Example 3: Timestamp by processing time
If the elements do not have any time data available, you can also use the current processing time for each element. Note that this grabs the local time of the worker that is processing each element. Workers might have time deltas, so using this method is not a reliable way to do precise ordering.
By using processing time, there is no way of knowing if data is arriving late because the timestamp is attached when the element enters into the pipeline.
import apache_beam as beam
import time
class GetTimestamp(beam.DoFn):
def process(self, plant, timestamp=beam.DoFn.TimestampParam):
yield '{} - {}'.format(timestamp.to_utc_datetime(), plant['name'])
with beam.Pipeline() as pipeline:
plant_processing_times = (
pipeline
| 'Garden plants' >> beam.Create([
{'name': 'Strawberry'},
{'name': 'Carrot'},
{'name': 'Artichoke'},
{'name': 'Tomato'},
{'name': 'Potato'},
])
| 'With timestamps' >> beam.Map(lambda plant: \
beam.window.TimestampedValue(plant, time.time()))
| 'Get timestamp' >> beam.ParDo(GetTimestamp())
| beam.Map(print)
)
Output:
2020-03-20 20:12:42.145594 - Strawberry
2020-03-20 20:12:42.145827 - Carrot
2020-03-20 20:12:42.145962 - Artichoke
2020-03-20 20:12:42.146093 - Tomato
2020-03-20 20:12:42.146216 - Potato
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Related transforms
- Reify converts between explicit and implicit forms of Beam values.