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To run a code cell, you can click the Run cell button at the top left of the cell,
or select it and press Shift+Enter
.
Try modifying a code cell and re-running it to see what happens.
To learn more about Colab, see Welcome to Colaboratory!.
First, let's install the apache-beam
module.
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!pip install --quiet -U apache-beam
The elements themselves often already contain a timestamp field.
beam.window.TimestampedValue
takes a value and a
Unix timestamp
in the form of seconds.
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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)
)
|
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
.
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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
.
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import time
import datetime
now = datetime.datetime.now()
time_tuple = now.timetuple()
unix_time = time.mktime(time_tuple)
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.
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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)
)
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.
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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)
)