xref: /aosp_15_r20/external/grpc-grpc/tools/profiling/microbenchmarks/bm2bq.py (revision cc02d7e222339f7a4f6ba5f422e6413f4bd931f2)

#!/usr/bin/env python3
#
# 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.

# Convert google-benchmark json output to something that can be uploaded to
# BigQuery

import csv
import json
import subprocess
import sys

import bm_json

columns = []

for row in json.loads(
    # TODO(jtattermusch): make sure the dataset name is not hardcoded
    subprocess.check_output(
        ["bq", "--format=json", "show", "microbenchmarks.microbenchmarks"]
    )
)["schema"]["fields"]:
    columns.append((row["name"], row["type"].lower()))

SANITIZE = {
    "integer": int,
    "float": float,
    "boolean": bool,
    "string": str,
    "timestamp": str,
}

# TODO(jtattermusch): add proper argparse argument, rather than trying
# to emulate with manual argv inspection.
if sys.argv[1] == "--schema":
    print(",\n".join("%s:%s" % (k, t.upper()) for k, t in columns))
    sys.exit(0)

with open(sys.argv[1]) as f:
    js = json.loads(f.read())

if len(sys.argv) > 2:
    with open(sys.argv[2]) as f:
        js2 = json.loads(f.read())
else:
    js2 = None

# TODO(jtattermusch): write directly to a file instead of stdout
writer = csv.DictWriter(sys.stdout, [c for c, t in columns])

for row in bm_json.expand_json(js, js2):
    sane_row = {}
    for name, sql_type in columns:
        if name in row:
            if row[name] == "":
                continue
            sane_row[name] = SANITIZE[sql_type](row[name])
    writer.writerow(sane_row)