#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.

"""
Explanation: <a href="https://colab.research.google.com/github/apache/beam/blob/master//Users/dcavazos/src/beam/examples/notebooks/documentation/transforms/python/elementwise/withtimestamps-py.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab"/></a>
<table align="left"><td><a target="_blank" href="https://beam.apache.org/documentation/transforms/python/elementwise/withtimestamps"><img src="https://beam.apache.org/images/logos/full-color/name-bottom/beam-logo-full-color-name-bottom-100.png" width="32" height="32" />View the docs</a></td></table>
End of explanation
"""


!pip install --quiet -U apache-beam

"""
Explanation: WithTimestamps
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>

Assigns timestamps to all the elements of a collection.
Setup
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.
End of explanation
"""


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)
  )

"""
Explanation: 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.
End of explanation
"""


import time

time_tuple = time.strptime('2020-03-19 20:50:00', '%Y-%m-%d %H:%M:%S')
unix_time = time.mktime(time_tuple)

"""
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/withtimestamps.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code"/> View source code</a>
  </td>
</table>

<br/><br/><br/>
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.
End of explanation
"""


import time
import datetime

now = datetime.datetime.now()
time_tuple = now.timetuple()
unix_time = time.mktime(time_tuple)

"""
Explanation: To convert from a
datetime.datetime
to unix_time you can use convert it to a time.struct_time first with
datetime.timetuple.
End of explanation
"""


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)
  )

"""
Explanation: 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.
End of explanation
"""


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)
  )

"""
Explanation: <table align="left" style="margin-right:1em">
  <td>
    <a class="button" target="_blank" href="https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/snippets/transforms/elementwise/withtimestamps.py"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" width="32px" height="32px" alt="View source code"/> View source code</a>
  </td>
</table>

<br/><br/><br/>
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.
End of explanation
"""


