text: #!/usr/bin/env python
  # Copyright 2017 gRPC authors.
  #
  # Licensed 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.
  
  import cgi
  import multiprocessing
  import os
  import subprocess
  import sys
  import argparse
  
  import python_utils.jobset as jobset
  import python_utils.start_port_server as start_port_server
  
  sys.path.append(
      os.path.join(
          os.path.dirname(sys.argv[0]), '..', 'profiling', 'microbenchmarks',
          'bm_diff'))
  import bm_constants
  
  flamegraph_dir = os.path.join(os.path.expanduser('~'), 'FlameGraph')
  
  os.chdir(os.path.join(os.path.dirname(sys.argv[0]), '../..'))
  if not os.path.exists('reports'):
      os.makedirs('reports')
  
  start_port_server.start_port_server()
  
  
  def fnize(s):
      out = ''
      for c in s:
          if c in '<>, /':
              if len(out) and out[-1] == '_': continue
              out += '_'
          else:
              out += c
      return out
  
  
  # index html
  index_html = """
  <html>
  <head>
  <title>Microbenchmark Results</title>
  </head>
  <body>
  """
  
  
  def heading(name):
      global index_html
      index_html += "<h1>%s</h1>\n" % name
  
  
  def link(txt, tgt):
      global index_html
      index_html += "<p><a href=\"%s\">%s</a></p>\n" % (
          cgi.escape(tgt, quote=True), cgi.escape(txt))
  
  
  def text(txt):
      global index_html
      index_html += "<p><pre>%s</pre></p>\n" % cgi.escape(txt)
  
  
  def collect_latency(bm_name, args):
      """generate latency profiles"""
      benchmarks = []
      profile_analysis = []
      cleanup = []
  
      heading('Latency Profiles: %s' % bm_name)
      subprocess.check_call([
          'make', bm_name, 'CONFIG=basicprof', '-j',
          '%d' % multiprocessing.cpu_count()
      ])
      for line in subprocess.check_output(
          ['bins/basicprof/%s' % bm_name, '--benchmark_list_tests']).splitlines():
          link(line, '%s.txt' % fnize(line))
          benchmarks.append(
              jobset.JobSpec(
                  [
                      'bins/basicprof/%s' % bm_name, '--benchmark_filter=^%s$' %
                      line, '--benchmark_min_time=0.05'
                  ],
                  environ={'LATENCY_TRACE': '%s.trace' % fnize(line)},
                  shortname='profile-%s' % fnize(line)))
          profile_analysis.append(
              jobset.JobSpec(
                  [
                      sys.executable,
                      'tools/profiling/latency_profile/profile_analyzer.py',
                      '--source', '%s.trace' % fnize(line), '--fmt', 'simple',
                      '--out', 'reports/%s.txt' % fnize(line)
                  ],
                  timeout_seconds=20 * 60,
                  shortname='analyze-%s' % fnize(line)))
          cleanup.append(jobset.JobSpec(['rm', '%s.trace' % fnize(line)]))
          # periodically flush out the list of jobs: profile_analysis jobs at least
          # consume upwards of five gigabytes of ram in some cases, and so analysing
          # hundreds of them at once is impractical -- but we want at least some
          # concurrency or the work takes too long
          if len(benchmarks) >= min(16, multiprocessing.cpu_count()):
              # run up to half the cpu count: each benchmark can use up to two cores
              # (one for the microbenchmark, one for the data flush)
              jobset.run(
                  benchmarks, maxjobs=max(1, multiprocessing.cpu_count() / 2))
              jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
              jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
              benchmarks = []
              profile_analysis = []
              cleanup = []
      # run the remaining benchmarks that weren't flushed
      if len(benchmarks):
          jobset.run(benchmarks, maxjobs=max(1, multiprocessing.cpu_count() / 2))
          jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
          jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
  
  
  def collect_perf(bm_name, args):
      """generate flamegraphs"""
      heading('Flamegraphs: %s' % bm_name)
      subprocess.check_call([
          'make', bm_name, 'CONFIG=mutrace', '-j',
          '%d' % multiprocessing.cpu_count()
      ])
      benchmarks = []
      profile_analysis = []
      cleanup = []
      for line in subprocess.check_output(
          ['bins/mutrace/%s' % bm_name, '--benchmark_list_tests']).splitlines():
          link(line, '%s.svg' % fnize(line))
          benchmarks.append(
              jobset.JobSpec(
                  [
                      'perf', 'record', '-o', '%s-perf.data' % fnize(
                          line), '-g', '-F', '997', 'bins/mutrace/%s' % bm_name,
                      '--benchmark_filter=^%s$' % line, '--benchmark_min_time=10'
                  ],
                  shortname='perf-%s' % fnize(line)))
          profile_analysis.append(
              jobset.JobSpec(
                  [
                      'tools/run_tests/performance/process_local_perf_flamegraphs.sh'
                  ],
                  environ={
                      'PERF_BASE_NAME': fnize(line),
                      'OUTPUT_DIR': 'reports',
                      'OUTPUT_FILENAME': fnize(line),
                  },
                  shortname='flame-%s' % fnize(line)))
          cleanup.append(jobset.JobSpec(['rm', '%s-perf.data' % fnize(line)]))
          cleanup.append(jobset.JobSpec(['rm', '%s-out.perf' % fnize(line)]))
          # periodically flush out the list of jobs: temporary space required for this
          # processing is large
          if len(benchmarks) >= 20:
              # run up to half the cpu count: each benchmark can use up to two cores
              # (one for the microbenchmark, one for the data flush)
              jobset.run(benchmarks, maxjobs=1)
              jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
              jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
              benchmarks = []
              profile_analysis = []
              cleanup = []
      # run the remaining benchmarks that weren't flushed
      if len(benchmarks):
          jobset.run(benchmarks, maxjobs=1)
          jobset.run(profile_analysis, maxjobs=multiprocessing.cpu_count())
          jobset.run(cleanup, maxjobs=multiprocessing.cpu_count())
  
  
  def run_summary(bm_name, cfg, base_json_name):
      subprocess.check_call([
          'make', bm_name, 'CONFIG=%s' % cfg, '-j',
          '%d' % multiprocessing.cpu_count()
      ])
      cmd = [
          'bins/%s/%s' % (cfg, bm_name), '--benchmark_out=%s.%s.json' %
          (base_json_name, cfg), '--benchmark_out_format=json'
      ]
      if args.summary_time is not None:
          cmd += ['--benchmark_min_time=%d' % args.summary_time]
      return subprocess.check_output(cmd)
  
  
  def collect_summary(bm_name, args):
      heading('Summary: %s [no counters]' % bm_name)
      text(run_summary(bm_name, 'opt', bm_name))
      heading('Summary: %s [with counters]' % bm_name)
      text(run_summary(bm_name, 'counters', bm_name))
      if args.bigquery_upload:
          with open('%s.csv' % bm_name, 'w') as f:
              f.write(
                  subprocess.check_output([
                      'tools/profiling/microbenchmarks/bm2bq.py',
                      '%s.counters.json' % bm_name, '%s.opt.json' % bm_name
                  ]))
          subprocess.check_call([
              'bq', 'load', 'microbenchmarks.microbenchmarks', '%s.csv' % bm_name
          ])
  
  
  collectors = {
      'latency': collect_latency,
      'perf': collect_perf,
      'summary': collect_summary,
  }
  
  argp = argparse.ArgumentParser(description='Collect data from microbenchmarks')
  argp.add_argument(
      '-c',
      '--collect',
      choices=sorted(collectors.keys()),
      nargs='*',
      default=sorted(collectors.keys()),
      help='Which collectors should be run against each benchmark')
  argp.add_argument(
      '-b',
      '--benchmarks',
      choices=bm_constants._AVAILABLE_BENCHMARK_TESTS,
      default=bm_constants._AVAILABLE_BENCHMARK_TESTS,
      nargs='+',
      type=str,
      help='Which microbenchmarks should be run')
  argp.add_argument(
      '--bigquery_upload',
      default=False,
      action='store_const',
      const=True,
      help='Upload results from summary collection to bigquery')
  argp.add_argument(
      '--summary_time',
      default=None,
      type=int,
      help='Minimum time to run benchmarks for the summary collection')
  args = argp.parse_args()
  
  try:
      for collect in args.collect:
          for bm_name in args.benchmarks:
              collectors[collect](bm_name, args)
  finally:
      if not os.path.exists('reports'):
          os.makedirs('reports')
      index_html += "</body>\n</html>\n"
      with open('reports/index.html', 'w') as f:
          f.write(index_html)
  