grpc/tools/run_tests/performance/bq_upload_result.py

#!/usr/bin/env python3
# Copyright 2016 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.

# Uploads performance benchmark result file to bigquery.

import argparse
import calendar
import json
import os
import sys
import time
import uuid

gcp_utils_dir = os.path.abspath(
    os.path.join(os.path.dirname(__file__), "../../gcp/utils")
)
sys.path.append(gcp_utils_dir)
import big_query_utils

_PROJECT_ID = "grpc-testing"


def _upload_netperf_latency_csv_to_bigquery(dataset_id, table_id, result_file):
    with open(result_file, "r") as f:
        (col1, col2, col3) = f.read().split(",")
        latency50 = float(col1.strip()) * 1000
        latency90 = float(col2.strip()) * 1000
        latency99 = float(col3.strip()) * 1000

        scenario_result = {
            "scenario": {"name": "netperf_tcp_rr"},
            "summary": {
                "latency50": latency50,
                "latency90": latency90,
                "latency99": latency99,
            },
        }

    bq = big_query_utils.create_big_query()
    _create_results_table(bq, dataset_id, table_id)

    if not _insert_result(
        bq, dataset_id, table_id, scenario_result, flatten=False
    ):
        print("Error uploading result to bigquery.")
        sys.exit(1)


def _upload_scenario_result_to_bigquery(
    dataset_id,
    table_id,
    result_file,
    metadata_file,
    node_info_file,
    prometheus_query_results_file,
):
    with open(result_file, "r") as f:
        scenario_result = json.loads(f.read())

    bq = big_query_utils.create_big_query()
    _create_results_table(bq, dataset_id, table_id)

    if not _insert_scenario_result(
        bq,
        dataset_id,
        table_id,
        scenario_result,
        metadata_file,
        node_info_file,
        prometheus_query_results_file,
    ):
        print("Error uploading result to bigquery.")
        sys.exit(1)


def _insert_result(bq, dataset_id, table_id, scenario_result, flatten=True):
    if flatten:
        _flatten_result_inplace(scenario_result)
    _populate_metadata_inplace(scenario_result)
    row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result)
    return big_query_utils.insert_rows(
        bq, _PROJECT_ID, dataset_id, table_id, [row]
    )


def _insert_scenario_result(
    bq,
    dataset_id,
    table_id,
    scenario_result,
    test_metadata_file,
    node_info_file,
    prometheus_query_results_file,
    flatten=True,
):
    if flatten:
        _flatten_result_inplace(scenario_result)
    _populate_metadata_from_file(scenario_result, test_metadata_file)
    _populate_node_metadata_from_file(scenario_result, node_info_file)
    _populate_prometheus_query_results_from_file(
        scenario_result, prometheus_query_results_file
    )
    row = big_query_utils.make_row(str(uuid.uuid4()), scenario_result)
    return big_query_utils.insert_rows(
        bq, _PROJECT_ID, dataset_id, table_id, [row]
    )


def _create_results_table(bq, dataset_id, table_id):
    with open(
        os.path.dirname(__file__) + "/scenario_result_schema.json", "r"
    ) as f:
        table_schema = json.loads(f.read())
    desc = "Results of performance benchmarks."
    return big_query_utils.create_table2(
        bq, _PROJECT_ID, dataset_id, table_id, table_schema, desc
    )


def _flatten_result_inplace(scenario_result):
    """Bigquery is not really great for handling deeply nested data
    and repeated fields. To maintain values of some fields while keeping
    the schema relatively simple, we artificially leave some of the fields
    as JSON strings.
    """
    scenario_result["scenario"]["clientConfig"] = json.dumps(
        scenario_result["scenario"]["clientConfig"]
    )
    scenario_result["scenario"]["serverConfig"] = json.dumps(
        scenario_result["scenario"]["serverConfig"]
    )
    scenario_result["latencies"] = json.dumps(scenario_result["latencies"])
    scenario_result["serverCpuStats"] = []
    for stats in scenario_result["serverStats"]:
        scenario_result["serverCpuStats"].append(dict())
        scenario_result["serverCpuStats"][-1]["totalCpuTime"] = stats.pop(
            "totalCpuTime", None
        )
        scenario_result["serverCpuStats"][-1]["idleCpuTime"] = stats.pop(
            "idleCpuTime", None
        )
    for stats in scenario_result["clientStats"]:
        stats["latencies"] = json.dumps(stats["latencies"])
        stats.pop("requestResults", None)
    scenario_result["serverCores"] = json.dumps(scenario_result["serverCores"])
    scenario_result["clientSuccess"] = json.dumps(
        scenario_result["clientSuccess"]
    )
    scenario_result["serverSuccess"] = json.dumps(
        scenario_result["serverSuccess"]
    )
    scenario_result["requestResults"] = json.dumps(
        scenario_result.get("requestResults", [])
    )
    scenario_result["serverCpuUsage"] = scenario_result["summary"].pop(
        "serverCpuUsage", None
    )
    scenario_result["summary"].pop("successfulRequestsPerSecond", None)
    scenario_result["summary"].pop("failedRequestsPerSecond", None)


def _populate_metadata_inplace(scenario_result):
    """Populates metadata based on environment variables set by Jenkins."""
    # NOTE: Grabbing the Kokoro environment variables will only work if the
    # driver is running locally on the same machine where Kokoro has started
    # the job. For our setup, this is currently the case, so just assume that.
    build_number = os.getenv("KOKORO_BUILD_NUMBER")
    build_url = (
        "https://source.cloud.google.com/results/invocations/%s"
        % os.getenv("KOKORO_BUILD_ID")
    )
    job_name = os.getenv("KOKORO_JOB_NAME")
    git_commit = os.getenv("KOKORO_GIT_COMMIT")
    # actual commit is the actual head of PR that is getting tested
    # TODO(jtattermusch): unclear how to obtain on Kokoro
    git_actual_commit = os.getenv("ghprbActualCommit")

    utc_timestamp = str(calendar.timegm(time.gmtime()))
    metadata = {"created": utc_timestamp}

    if build_number:
        metadata["buildNumber"] = build_number
    if build_url:
        metadata["buildUrl"] = build_url
    if job_name:
        metadata["jobName"] = job_name
    if git_commit:
        metadata["gitCommit"] = git_commit
    if git_actual_commit:
        metadata["gitActualCommit"] = git_actual_commit

    scenario_result["metadata"] = metadata


def _populate_metadata_from_file(scenario_result, test_metadata_file):
    utc_timestamp = str(calendar.timegm(time.gmtime()))
    metadata = {"created": utc_timestamp}

    _annotation_to_bq_metadata_key_map = {
        "ci_" + key: key
        for key in (
            "buildNumber",
            "buildUrl",
            "jobName",
            "gitCommit",
            "gitActualCommit",
        )
    }

    if os.access(test_metadata_file, os.R_OK):
        with open(test_metadata_file, "r") as f:
            test_metadata = json.loads(f.read())

        # eliminate managedFields from metadata set
        if "managedFields" in test_metadata:
            del test_metadata["managedFields"]

        annotations = test_metadata.get("annotations", {})

        # if use kubectl apply ..., kubectl will append current configuration to
        # annotation, the field is deleted since it includes a lot of irrelevant
        # information
        if "kubectl.kubernetes.io/last-applied-configuration" in annotations:
            del annotations["kubectl.kubernetes.io/last-applied-configuration"]

        # dump all metadata as JSON to testMetadata field
        scenario_result["testMetadata"] = json.dumps(test_metadata)
        for key, value in _annotation_to_bq_metadata_key_map.items():
            if key in annotations:
                metadata[value] = annotations[key]

    scenario_result["metadata"] = metadata


def _populate_node_metadata_from_file(scenario_result, node_info_file):
    node_metadata = {"driver": {}, "servers": [], "clients": []}
    _node_info_to_bq_node_metadata_key_map = {
        "Name": "name",
        "PodIP": "podIP",
        "NodeName": "nodeName",
    }

    if os.access(node_info_file, os.R_OK):
        with open(node_info_file, "r") as f:
            file_metadata = json.loads(f.read())
        for key, value in _node_info_to_bq_node_metadata_key_map.items():
            node_metadata["driver"][value] = file_metadata["Driver"][key]
        for clientNodeInfo in file_metadata["Clients"]:
            node_metadata["clients"].append(
                {
                    value: clientNodeInfo[key]
                    for key, value in _node_info_to_bq_node_metadata_key_map.items()
                }
            )
        for serverNodeInfo in file_metadata["Servers"]:
            node_metadata["servers"].append(
                {
                    value: serverNodeInfo[key]
                    for key, value in _node_info_to_bq_node_metadata_key_map.items()
                }
            )

    scenario_result["nodeMetadata"] = node_metadata


def _populate_prometheus_query_results_from_file(
    scenario_result, prometheus_query_result_file
):
    """Populate the results from Prometheus query to Bigquery table"""
    if os.access(prometheus_query_result_file, os.R_OK):
        with open(prometheus_query_result_file, "r", encoding="utf8") as f:
            file_query_results = json.loads(f.read())

            scenario_result["testDurationSeconds"] = file_query_results[
                "testDurationSeconds"
            ]
            clientsPrometheusData = []
            if "clients" in file_query_results:
                for client_name, client_data in file_query_results[
                    "clients"
                ].items():
                    clientPrometheusData = {"name": client_name}
                    containersPrometheusData = []
                    for container_name, container_data in client_data.items():
                        containerPrometheusData = {
                            "name": container_name,
                            "cpuSeconds": container_data["cpuSeconds"],
                            "memoryMean": container_data["memoryMean"],
                        }
                        containersPrometheusData.append(containerPrometheusData)
                    clientPrometheusData[
                        "containers"
                    ] = containersPrometheusData
                    clientsPrometheusData.append(clientPrometheusData)
                scenario_result["clientsPrometheusData"] = clientsPrometheusData

            serversPrometheusData = []
            if "servers" in file_query_results:
                for server_name, server_data in file_query_results[
                    "servers"
                ].items():
                    serverPrometheusData = {"name": server_name}
                    containersPrometheusData = []
                    for container_name, container_data in server_data.items():
                        containerPrometheusData = {
                            "name": container_name,
                            "cpuSeconds": container_data["cpuSeconds"],
                            "memoryMean": container_data["memoryMean"],
                        }
                        containersPrometheusData.append(containerPrometheusData)
                    serverPrometheusData[
                        "containers"
                    ] = containersPrometheusData
                    serversPrometheusData.append(serverPrometheusData)
            scenario_result["serversPrometheusData"] = serversPrometheusData


argp = argparse.ArgumentParser(description="Upload result to big query.")
argp.add_argument(
    "--bq_result_table",
    required=True,
    default=None,
    type=str,
    help='Bigquery "dataset.table" to upload results to.',
)
argp.add_argument(
    "--file_to_upload",
    default="scenario_result.json",
    type=str,
    help="Report file to upload.",
)
argp.add_argument(
    "--metadata_file_to_upload",
    default="metadata.json",
    type=str,
    help="Metadata file to upload.",
)
argp.add_argument(
    "--node_info_file_to_upload",
    default="node_info.json",
    type=str,
    help="Node information file to upload.",
)
argp.add_argument(
    "--prometheus_query_results_to_upload",
    default="prometheus_query_result.json",
    type=str,
    help="Prometheus query result file to upload.",
)
argp.add_argument(
    "--file_format",
    choices=["scenario_result", "netperf_latency_csv"],
    default="scenario_result",
    help="Format of the file to upload.",
)

args = argp.parse_args()

dataset_id, table_id = args.bq_result_table.split(".", 2)

if args.file_format == "netperf_latency_csv":
    _upload_netperf_latency_csv_to_bigquery(
        dataset_id, table_id, args.file_to_upload
    )
else:
    _upload_scenario_result_to_bigquery(
        dataset_id,
        table_id,
        args.file_to_upload,
        args.metadata_file_to_upload,
        args.node_info_file_to_upload,
        args.prometheus_query_results_to_upload,
    )
print(
    "Successfully uploaded %s, %s, %s and %s to BigQuery.\n"
    % (
        args.file_to_upload,
        args.metadata_file_to_upload,
        args.node_info_file_to_upload,
        args.prometheus_query_results_to_upload,
    )
)