[BEAM-4005] Futurize tools subpackage

sdks/python/apache_beam/tools/__init__.py

@@ -19,3 +19,4 @@
 
 For internal use only; no backwards-compatibility guarantees.
 """
+from __future__ import absolute_import

sdks/python/apache_beam/tools/distribution_counter_microbenchmark.py

@@ -23,11 +23,14 @@
   python -m apache_beam.tools.distribution_counter_microbenchmark
 """
 
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import random
 import sys
 import time
+from builtins import range
 
 from apache_beam.tools import utils
 
@@ -44,7 +47,7 @@ def run_benchmark(num_runs=100, num_input=10000, seed=time.time()):
   total_time = 0
   random.seed(seed)
   lower_bound = 0
-  upper_bound = sys.maxint
+  upper_bound = sys.maxsize
   inputs = generate_input_values(num_input, lower_bound, upper_bound)
   from apache_beam.transforms import DataflowDistributionCounter
   print("Number of runs:", num_runs)
@@ -57,8 +60,8 @@ def run_benchmark(num_runs=100, num_input=10000, seed=time.time()):
     counter.add_inputs_for_test(inputs)
     time_cost = time.time() - start
     print("Run %d: Total time cost %g sec" % (i+1, time_cost))
-    total_time += time_cost/num_input
-  print("Per element update time cost:", total_time/num_runs)
+    total_time += time_cost // num_input
+  print("Per element update time cost:", total_time // num_runs)
 
 
 if __name__ == '__main__':

sdks/python/apache_beam/tools/map_fn_microbenchmark.py

@@ -30,9 +30,13 @@
    python -m apache_beam.tools.map_fn_microbenchmark
 """
 
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import time
+from builtins import range
+from builtins import zip
 
 import apache_beam as beam
 from apache_beam.tools import utils
@@ -45,7 +49,7 @@ def run_benchmark(num_maps=100, num_runs=10, num_elements_step=1000):
     num_elements = num_elements_step * run + 1
     start = time.time()
     with beam.Pipeline() as p:
-      pc = p | beam.Create(range(num_elements))
+      pc = p | beam.Create(list(range(num_elements)))
       for ix in range(num_maps):
         pc = pc | 'Map%d' % ix >> beam.FlatMap(lambda x: (None,))
     timings[num_elements] = time.time() - start
@@ -55,9 +59,9 @@ def run_benchmark(num_maps=100, num_runs=10, num_elements_step=1000):
   print
   # pylint: disable=unused-variable
   gradient, intercept, r_value, p_value, std_err = stats.linregress(
-      *zip(*timings.items()))
+      *list(zip(*list(timings.items()))))
   print("Fixed cost  ", intercept)
-  print("Per-element ", gradient / num_maps)
+  print("Per-element ", gradient // num_maps)
   print("R^2         ", r_value**2)
 
 

sdks/python/apache_beam/tools/sideinput_microbenchmark.py

@@ -22,9 +22,12 @@
   python -m apache_beam.tools.sideinput_microbenchmark
 """
 
+from __future__ import absolute_import
+from __future__ import division
 from __future__ import print_function
 
 import time
+from builtins import range
 
 from apache_beam.runners.worker import opcounters
 from apache_beam.runners.worker import sideinputs
@@ -64,9 +67,10 @@ def run_benchmark(num_runs=50, input_per_source=4000, num_sources=4):
 
   print("Runtimes:", times)
 
-  avg_runtime = sum(times)/len(times)
+  avg_runtime = sum(times) // len(times)
   print("Average runtime:", avg_runtime)
-  print("Time per element:", avg_runtime/(input_per_source * num_sources))
+  print("Time per element:", avg_runtime // (input_per_source *
+                                             num_sources))
 
 
 if __name__ == '__main__':

sdks/python/apache_beam/tools/utils.py

@@ -17,6 +17,8 @@
 
 """Utility functions for all microbenchmarks."""
 
+from __future__ import absolute_import
+
 import os
 
 

sdks/python/tox.ini

@@ -99,6 +99,7 @@ deps =
   flake8==3.5.0
 modules =
   apache_beam/coders
+  apache_beam/tools
 commands =
   python --version
   pip --version