Help us understand the problem. What is going on with this article?

[JAWS-UG CLI] Lambda:#21 Lambda関数の作成 (datadog-process-rds-metrics: Python版)

More than 3 years have passed since last update.

Lambdaのブループリントのうちdatadog-process-rds-metricsを利用して、Lambda関数を作成してみます。

今回は、KMSを使わないこととします。

前提条件

Lambdaへの権限

Lambdaに対してフル権限があること。

AWS CLI

以下のバージョンで動作確認済

  • AWS CLI 1.11.28
コマンド
aws --version

結果(例):

  aws-cli/1.11.28 Python/2.7.10 Darwin/15.6.0 botocore/1.4.85

バージョンが古い場合は最新版に更新しましょう。

コマンド
sudo -H pip install -U awscli

IAM Role

'lambdaBasicExecution'ロールが存在すること。

変数の設定
IAM_ROLE_NAME='lambdaBasicExecution'
コマンド
aws iam get-role \
         --role-name ${IAM_ROLE_NAME}

結果(例):

  {
      "Role": {
        "AssumeRolePolicyDocument": {
            "Version": "2012-10-17",
            "Statement": [
                {
                    "Action": "sts:AssumeRole",
                    "Principal": {
                        "Service": "lambda.amazonaws.com"
                    },
                    "Effect": "Allow",
                    "Sid": ""
                }
            ]
        },
        "RoleId": "AROAXXXXXXXXXXXXXXXXX",
        "CreateDate": "2016-12-18T01:23:45Z",
        "RoleName": "lambdaBasicExecution",
        "Path": "/",
        "Arn": "arn:aws:iam::XXXXXXXXXXXX:role/lambdaBasicExecution"
      }
  }

IAMロールが存在しない場合、
http://qiita.com/tcsh/items/6353876a5c4fef63b4d8 の手順に従って作成し
てください。

0. 準備

0.1. リージョンの決定

変数の設定
export AWS_DEFAULT_REGION='ap-northeast-1'

0.2. 変数の確認

プロファイルが想定のものになっていることを確認します。

変数の確認
aws configure list

結果(例):

        Name                    Value             Type    Location
        ----                    -----             ----    --------
     profile       lambdaFull-prjz-mbp13        env    AWS_DEFAULT_PROFILE
  access_key     ****************XXXX shared-credentials-file
  secret_key     ****************XXXX shared-credentials-file
      region        ap-northeast-1        env    AWS_DEFAULT_REGION

1. 事前作業

1.1. IAM RoleのARN取得

コマンド
IAM_ROLE_ARN=$( \
        aws iam get-role \
          --role-name ${IAM_ROLE_NAME} \
          --query 'Role.Arn' \
          --output text \
) \
        && echo ${IAM_ROLE_ARN}

結果(例):

  arn:aws:iam::XXXXXXXXXXXX:role/lambdaBasicExecution

1.2. DatadogのAPIキーの設定

https://app.datadoghq.com/account/settings#api にアクセスします。

変数の設定
DD_API_KEY='<API KeysのKeyの値>'

New application keyに'lambda'と入力し、Create Application Keyボタンをクリックします。

Hashの値を変数に取り込みます。

変数の設定
DD_APP_KEY='<Application KeyのHashの値>'

1.3. Lambda関数名の決定

変数の設定
LAMBDA_FUNC_NAME="datadog_process_rds_metrics-$( date '+%Y%m%d' )" \
        && echo ${LAMBDA_FUNC_NAME}

同名のLambda関数の不存在確認

コマンド
aws lambda get-function \
        --function-name ${LAMBDA_FUNC_NAME}

結果(例):

  A client error (ResourceNotFoundException) occurred when calling the GetFunction operation: Function not found: arn:aws:lambda:ap-northeast-1:XXXXXXXXXXXX:function:datadog_process_rds_metrics-20161219

1.4. Lambda関数

変数の設定
FILE_LAMBDA_FUNC="${LAMBDA_FUNC_NAME}.py"
PY_FUNC_NAME='lambda_handler'
変数の確認
cat << ETX

          FILE_LAMBDA_FUNC: ${FILE_LAMBDA_FUNC}
          PY_FUNC_NAME:     ${PY_FUNC_NAME}
          DD_API_KEY:       ${DD_API_KEY}
          DD_APP_KEY:       ${DD_APP_KEY}

ETX
コマンド
cat << EOF > ${FILE_LAMBDA_FUNC}
from __future__ import print_function

import os
import gzip
import json
import re
import time
import urllib
import urllib2
from base64 import b64decode
from StringIO import StringIO

import boto3

# retrieve datadog options from KMS
#KMS_ENCRYPTED_KEYS = os.environ['kmsEncryptedKeys']
#kms = boto3.client('kms')
datadog_keys = json.loads('{"api_key":"${DD_API_KEY}", "app_key":"${DD_APP_KEY}"}')

print('INFO Lambda function initialized, ready to send metrics')


def _process_rds_enhanced_monitoring_message(ts, message, account, region):
    instance_id = message['instanceID']
    host_id = message['instanceResourceID']
    tags = [
        'dbinstanceidentifier:%s' % instance_id,
        'aws_account:%s' % account,
        'engine:%s' % message["engine"],
    ]

    # metrics generation

    uptime = 0
    uptime_msg = re.split(' days?, ', message['uptime'])
    if len(uptime_msg) == 2:
        uptime += 24 * 3600 * int(uptime_msg[0])
    uptime_day = uptime_msg[-1].split(':')
    uptime += 3600 * int(uptime_day[0])
    uptime += 60 * int(uptime_day[1])
    uptime += int(uptime_day[2])
    stats.gauge('aws.rds.uptime', uptime, timestamp=ts, tags=tags, host=host_id)

    stats.gauge('aws.rds.virtual_cpus', message['numVCPUs'], timestamp=ts, tags=tags, host=host_id)

    stats.gauge('aws.rds.load.1', message['loadAverageMinute']['one'], timestamp=ts, tags=tags, host=host_id)
    stats.gauge('aws.rds.load.5', message['loadAverageMinute']['five'], timestamp=ts, tags=tags, host=host_id)
    stats.gauge('aws.rds.load.15', message['loadAverageMinute']['fifteen'], timestamp=ts, tags=tags, host=host_id)

    for namespace in ['cpuUtilization', 'memory', 'tasks', 'swap']:
        for key, value in message[namespace].iteritems():
            stats.gauge('aws.rds.%s.%s' % (namespace.lower(), key), value, timestamp=ts, tags=tags, host=host_id)

    for network_stats in message['network']:
        network_tag = ['interface:%s' % network_stats.pop('interface')]
        for key, value in network_stats.iteritems():
            stats.gauge('aws.rds.network.%s' % key, value, timestamp=ts, tags=tags + network_tag, host=host_id)

    disk_stats = message['diskIO'][0]  # we never expect to have more than one disk
    for key, value in disk_stats.iteritems():
        stats.gauge('aws.rds.diskio.%s' % key, value, timestamp=ts, tags=tags, host=host_id)

    for fs_stats in message['fileSys']:
        fs_tag = [
            'name:%s' % fs_stats.pop('name'),
            'mountPoint:%s' % fs_stats.pop('mountPoint')
        ]
        for key, value in fs_stats.iteritems():
            stats.gauge('aws.rds.filesystem.%s' % key, value, timestamp=ts, tags=tags + fs_tag, host=host_id)

    for process_stats in message['processList']:
        process_tag = [
            'name:%s' % process_stats.pop('name'),
            'id:%s' % process_stats.pop('id')
        ]
        for key, value in process_stats.iteritems():
            stats.gauge('aws.rds.process.%s' % key, value, timestamp=ts, tags=tags + process_tag, host=host_id)


def ${PY_FUNC_NAME}(event, context):
    ''' Process a RDS enhenced monitoring DATA_MESSAGE,
        coming from CLOUDWATCH LOGS
    '''
    # event is a dict containing a base64 string gzipped
    event = json.loads(gzip.GzipFile(fileobj=StringIO(event['awslogs']['data'].decode('base64'))).read())

    account = event['owner']
    region = context.invoked_function_arn.split(':', 4)[3]

    log_events = event['logEvents']

    for log_event in log_events:
        message = json.loads(log_event['message'])
        ts = log_event['timestamp'] / 1000
        _process_rds_enhanced_monitoring_message(ts, message, account, region)

    stats.flush()
    return {'Status': 'OK'}


# Helpers to send data to Datadog, inspired from https://github.com/DataDog/datadogpy

class Stats(object):

    def __init__(self):
        self.series = []

    def gauge(self, metric, value, timestamp=None, tags=None, host=None):
        base_dict = {
            'metric': metric,
            'points': [(int(timestamp or time.time()), value)],
            'type': 'gauge',
            'tags': tags,
        }
        if host:
            base_dict.update({'host': host})
        self.series.append(base_dict)

    def flush(self):
        metrics_dict = {
            'series': self.series,
        }
        self.series = []

        creds = urllib.urlencode(datadog_keys)
        data = json.dumps(metrics_dict)
        url = '%s?%s' % (datadog_keys.get('api_host', 'https://app.datadoghq.com/api/v1/series'), creds)
        req = urllib2.Request(url, data, {'Content-Type': 'application/json'})
        response = urllib2.urlopen(req)
        print('INFO Submitted data with status {}'.format(response.getcode()))

stats = Stats()
EOF

cat ${FILE_LAMBDA_FUNC}
コマンド
zip ${LAMBDA_FUNC_NAME}.zip ${FILE_LAMBDA_FUNC}

結果(例):

  adding: datadog_process_rds_metrics-20161219.py (deflated 43%)

2. Lambda関数の作成

2.1. Lambda関数の作成

変数の設定
LAMBDA_FUNC_DESC='Pushes RDS Enhanced metrics to Datadog.'
LAMBDA_RUNTIME='python2.7'
LAMBDA_HANDLER="${LAMBDA_FUNC_NAME}.${PY_FUNC_NAME}"
FILE_LAMBDA_ZIP="${LAMBDA_FUNC_NAME}.zip"
変数の確認
cat << ETX

        LAMBDA_FUNC_NAME:  ${LAMBDA_FUNC_NAME}
        LAMBDA_FUNC_DESC: "${LAMBDA_FUNC_DESC}"
        LAMBDA_RUNTIME:    ${LAMBDA_RUNTIME}
        FILE_LAMBDA_ZIP    ${FILE_LAMBDA_ZIP}
        IAM_ROLE_ARN:      ${IAM_ROLE_ARN}
        LAMBDA_HANDLER:    ${LAMBDA_HANDLER}

ETX
コマンド
aws lambda create-function \
        --function-name ${LAMBDA_FUNC_NAME} \
        --description "${LAMBDA_FUNC_DESC}" \
        --zip-file fileb://${FILE_LAMBDA_ZIP} \
        --runtime ${LAMBDA_RUNTIME} \
        --role ${IAM_ROLE_ARN} \
        --handler ${LAMBDA_HANDLER}

結果(例):

  {
    "CodeSha256": "lKbgNPMuV0D2blwwCSWwKLwlTrzoPAsFAdB6/FxJ+Q4=",
    "FunctionName": "datadog_process_rds_metrics-20161219",
    "CodeSize": 1962,
    "MemorySize": 128,
    "FunctionArn": "arn:aws:lambda:ap-northeast-1:XXXXXXXXXXXX:function:datadog_process_rds_metrics-20161219",
    "Version": "$LATEST",
    "Role": "arn:aws:iam::XXXXXXXXXXXX:role/lambdaBasicExecution",
    "Timeout": 3,
    "LastModified": "2016-12-18T01:23:45.678+0000",
    "Handler": "datadog_process_rds_metrics-20161219.lambda_handler",
    "Runtime": "python2.7",
    "Description": "Pushes RDS Enhanced metrics to Datadog."
  }
コマンド
aws lambda get-function \
        --function-name ${LAMBDA_FUNC_NAME}

結果(例):

  {
    "Code": {
      "RepositoryType": "S3",
      "Location": "https://awslambda-ap-ne-1-tasks.s3-ap-northeast-1.amazonaws.com/snapshots/XXXXXXXXXXXX/HelloWorld-2979ba79-b08f-495d-9ee6-46397c95ba13?x-amz-security-token=AQoDYXdzEDoa8AMR6t8h66eOXhN3%2Fx7XpuRxvf7pVn7IuWV4cEmwx0CtZT6yxCJ1%2BWmigYXqGoyQHuBYOWnxbhmwEcTg839qMuhSu1fk0fXpXf0oJOLkhKMudNqhdElyFQpzyT6Q8GDfhAsfbX9wvwCDTty4imxz7MczF%2FQl6tgvTYdip08ap5fAyrknZGV1%2B1Ggnp5w6JOjydYxuUsWwhoxoEWzi7SoVTmpRQQA91c4VW9lNotOAHACFxo6klzDPM8mxR9RJl66WxFugL0wQJyLUpmtjS9XoArD86sEWWiIccMpV2BQipTPQlzL%2F1Hoy%2BDF6QUxyPUihlDjPBoJTISTP8W1wxmzW%2BLbilAfFQRPY7CFjzR0k%2FA%2FIX5x9iyz52Pu1Q0ASTw1l%2Fq%2Fo3pRbvzWR79QS%2BpxXrwbYzoQHKiK62DSTsQo5tqKPsiDCYzrPxbq8lm7pNBPG%2FsxjePRWBVJeRl08WxEjSjoRRwBOPX5mz1BCUoUBPGG5tEENp87A%2FCdDgibFWM5DdYhwtaYPY7FTmi8DvqjQHL9jOmP8YuVteBTBcv8nFW6UbErPjwwn79FKG1u5M9HoTWUqUMBByz6D4tTRSEw6iJU7XdCujFnhnHe5V8imZ1KGI7fDWpciJhrhml0wnKPCK%2Fe9lK1P2kO7ldSWc7zn5hcIOD2tbEF&AWSAccessKeyId=ASIAJFVALOKV5SJVYPPA&Expires=1445825978&Signature=bvwu1Ny34LgTmZeOO3q4sn7x3Fg%3D"
    },
    "Configuration": {
      "Version": "$LATEST",
      "CodeSha256": "lKbgNPMuV0D2blwwCSWwKLwlTrzoPAsFAdB6/FxJ+Q4=",
      "FunctionName": "datadog_process_rds_metrics-20161219",
      "MemorySize": 128,
      "CodeSize": 350,
      "FunctionArn": "arn:aws:lambda:ap-northeast-1:XXXXXXXXXXXX:function:datadog_process_rds_metrics-20161219",
      "Handler": "datadog_process_rds_metrics-20161219.lambda_handler",
      "Role": "arn:aws:iam::XXXXXXXXXXXX:role/lambdaBasicExecution",
      "Timeout": 3,
      "LastModified": "2016-12-18T01:23:45.678+0000",
      "Runtime": "python2.7",
      "Description": "Pushes RDS Enhanced metrics to Datadog."
    }
  }

2.2. Lambda関数の更新

デフォルトの3秒ではタイムアウトする可能性が高いので、ここでは30秒に変更します。

変数の設定
LAMBDA_TIMEOUT='30'
変数の確認
cat << ETX

        LAMBDA_FUNC_NAME: ${LAMBDA_FUNC_NAME}
        LAMBDA_TIMEOUT:   ${LAMBDA_TIMEOUT}

ETX
コマンド
aws lambda update-function-configuration \
        --function-name ${LAMBDA_FUNC_NAME} \
        --timeout "${LAMBDA_TIMEOUT}"

結果(例):

  {
    "CodeSha256": "lKbgNPMuV0D2blwwCSWwKLwlTrzoPAsFAdB6/FxJ+Q4=",
    "FunctionName": "datadog_process_rds_metrics-20161219",
    "VpcConfig": {
        "SubnetIds": [],
        "SecurityGroupIds": []
    },
    "CodeSize": 350,
    "MemorySize": 128,
    "FunctionArn": "arn:aws:lambda:ap-northeast-1:XXXXXXXXXXXX:function:datadog_process_rds_metrics-20161219",
    "Version": "$LATEST",
    "Role": "arn:aws:iam::XXXXXXXXXXXX:role/lambdaBasicExecution",
    "Timeout": 30,
    "LastModified": "2016-12-18T01:23:45.678+0000",
    "Handler": "datadog_process_rds_metrics-20161219.handler",
    "Runtime": "python2.7",
    "Description": "Pushes RDS Enhanced metrics to Datadog."
  }

3. Lambda関数の動作確認

3.1. サンプルデータの作成

変数の設定
FILE_INPUT="${LAMBDA_FUNC_NAME}-log-data.json" \
          && echo ${FILE_INPUT}
サンプルデータ
cat << EOF > ${FILE_INPUT}
{
        "messageType":"DATA_MESSAGE",
        "owner":"123456789123",
        "logGroup":"testLogGroup",
        "logStream":"testLogStream",
        "subscriptionFilters":[
          "testFilter"
        ],
        "logEvents":[
          {
            "id":"eventId1",
            "timestamp":1440442987000,
            "message": "{\"engine\":\"Postgres\",\"instanceID\":\"postgresql-redmine-20161211\",\"instanceResourceID\":\"db-7ZOMGTEKHCZNLIFRXB3TOTR2XQ\",\"timestamp\":\"2016-12-13T06:11:44Z\",\"version\":1.00,\"uptime\":\"2 days, 0:40:25\",\"numVCPUs\":1,\"cpuUtilization\":{\"guest\":0.00,\"irq\":0.00,\"system\":0.27,\"wait\":0.20,\"idle\":98.80,\"user\":0.67,\"total\":1.21,\"steal\":0.00,\"nice\":0.07},\"loadAverageMinute\":{\"fifteen\":0.05,\"five\":0.01,\"one\":0.00},\"memory\":{\"writeback\":12,\"hugePagesFree\":0,\"hugePagesRsvd\":0,\"hugePagesSurp\":0,\"cached\":591812,\"hugePagesSize\":2048,\"free\":103168,\"hugePagesTotal\":0,\"inactive\":388232,\"pageTables\":4740,\"dirty\":164,\"mapped\":33312,\"active\":428844,\"total\":1020188,\"slab\":44440,\"buffers\":56164},\"tasks\":{\"sleeping\":146,\"zombie\":0,\"running\":4,\"stopped\":0,\"total\":150,\"blocked\":0},\"swap\":{\"cached\":0,\"total\":4095996,\"free\":4095928},\"network\":[{\"interface\":\"eth0\",\"rx\":451.53,\"tx\":3785.40}],\"diskIO\":[{\"writeKbPS\":16.80,\"readIOsPS\":0.00,\"await\":3.87,\"readKbPS\":0.00,\"rrqmPS\":0.00,\"util\":0.08,\"avgQueueLen\":0.24,\"tps\":4.20,\"readKb\":0,\"device\":\"rdsdev\",\"writeKb\":252,\"avgReqSz\":4.00,\"wrqmPS\":0.00,\"writeIOsPS\":4.20}],\"fileSys\":[{\"used\":625804,\"name\":\"rdsfilesys\",\"usedFiles\":1910,\"usedFilePercent\":0.58,\"maxFiles\":327040,\"mountPoint\":\"/rdsdbdata\",\"total\":5017092,\"usedPercent\":12.47}],\"processList\":[{\"vss\":407876,\"name\":\"postgres: pgadmin redmine 172.18.16.8(35898) idle\",\"tgid\":3097,\"parentID\":3320,\"memoryUsedPc\":1.44,\"cpuUsedPc\":0.00,\"id\":3097,\"rss\":14740},{\"vss\":68748,\"name\":\"postgres: logger process   \",\"tgid\":3321,\"parentID\":3320,\"memoryUsedPc\":0.16,\"cpuUsedPc\":0.00,\"id\":3321,\"rss\":1660},{\"vss\":289936,\"name\":\"postgres: checkpointer process   \",\"tgid\":3323,\"parentID\":3320,\"memoryUsedPc\":1.43,\"cpuUsedPc\":0.00,\"id\":3323,\"rss\":14636},{\"vss\":289936,\"name\":\"postgres: writer process   \",\"tgid\":3324,\"parentID\":3320,\"memoryUsedPc\":0.51,\"cpuUsedPc\":0.00,\"id\":3324,\"rss\":5216},{\"vss\":289936,\"name\":\"postgres: wal writer process   \",\"tgid\":3325,\"parentID\":3320,\"memoryUsedPc\":0.79,\"cpuUsedPc\":0.00,\"id\":3325,\"rss\":8100},{\"vss\":289936,\"name\":\"postgres: autovacuum launcher process   \",\"tgid\":3326,\"parentID\":3320,\"memoryUsedPc\":0.27,\"cpuUsedPc\":0.00,\"id\":3326,\"rss\":2784},{\"vss\":68744,\"name\":\"postgres: archiver process   last was 00000001000000020000002B\",\"tgid\":3327,\"parentID\":3320,\"memoryUsedPc\":0.16,\"cpuUsedPc\":0.00,\"id\":3327,\"rss\":1672},{\"vss\":68744,\"name\":\"postgres: stats collector process   \",\"tgid\":3328,\"parentID\":3320,\"memoryUsedPc\":0.19,\"cpuUsedPc\":0.00,\"id\":3328,\"rss\":1968},{\"vss\":399712,\"name\":\"postgres: pgadmin redmine 172.18.16.8(36634) idle\",\"tgid\":6552,\"parentID\":3320,\"memoryUsedPc\":0.89,\"cpuUsedPc\":0.00,\"id\":6552,\"rss\":9128},{\"vss\":393516,\"name\":\"postgres: rdsadmin rdsadmin localhost(63217) idle\",\"tgid\":27304,\"parentID\":3320,\"memoryUsedPc\":0.77,\"cpuUsedPc\":0.00,\"id\":27304,\"rss\":7832},{\"vss\":289936,\"name\":\"postgres\",\"tgid\":3320,\"parentID\":1,\"memoryUsedPc\":1.78,\"cpuUsedPc\":0.00,\"id\":3320,\"rss\":18140},{\"vss\":657332,\"name\":\"OS processes\",\"tgid\":0,\"parentID\":0,\"memoryUsedPc\":2.22,\"cpuUsedPc\":0.00,\"id\":0,\"rss\":22472},{\"vss\":887200,\"name\":\"RDS processes\",\"tgid\":0,\"parentID\":0,\"memoryUsedPc\":15.71,\"cpuUsedPc\":0.07,\"id\":0,\"rss\":160176}]}"
          }
        ]
}
EOF

cat ${FILE_INPUT}

JSONファイルを作成したら、フォーマットが壊れてないか必ず確認します。

コマンド
jsonlint -q ${FILE_INPUT}

エラーが出力されなければOKです。

コマンド
gzip ${FILE_INPUT}
コマンド
STR_DATA=$( cat ${FILE_INPUT}.gz | base64 ) \
        && echo ${STR_DATA}
変数の設定
FILE_INPUT="${LAMBDA_FUNC_NAME}-data.json" \
          && echo ${FILE_INPUT}
サンプルデータ
cat << EOF > ${FILE_INPUT}
{
        "awslogs": {
          "data": "${STR_DATA}"
        }
}
EOF

cat ${FILE_INPUT}

JSONファイルを作成したら、フォーマットが壊れてないか必ず確認します。

コマンド
jsonlint -q ${FILE_INPUT}

エラーが出力されなければOKです。

3.2. lambda関数の手動実行

変数の設定
FILE_OUTPUT_LAMBDA="${LAMBDA_FUNC_NAME}-out.txt"
FILE_LOG_LAMBDA="${LAMBDA_FUNC_NAME}-$(date +%Y%m%d%H%M%S).log"
変数の確認
cat << ETX

        LAMBDA_FUNC_NAME:   ${LAMBDA_FUNC_NAME}
        FILE_INPUT:         ${FILE_INPUT}
        FILE_OUTPUT_LAMBDA: ${FILE_OUTPUT_LAMBDA}
        FILE_LOG_LAMBDA:    ${FILE_LOG_LAMBDA}

ETX
コマンド
aws lambda invoke \
        --function-name ${LAMBDA_FUNC_NAME} \
        --log-type Tail \
        --payload file://${FILE_INPUT} \
        ${FILE_OUTPUT_LAMBDA} \
        > ${FILE_LOG_LAMBDA}
コマンド
cat ${FILE_LOG_LAMBDA} \
        | jp.py 'StatusCode'

結果(例):

  200

3.3. lambda関数の実行結果の確認

コマンド
cat ${FILE_OUTPUT_LAMBDA}

結果(例):

  {"Status": "OK"}

3.4. lambda関数のログの確認

コマンド
cat ${FILE_LOG_LAMBDA} \
        | jp.py 'LogResult' \
        | sed 's/"//g' \
        | base64 --decode

結果(例):

  START RequestId: 4620fd3f-c0fb-11e6-be7f-5d539d6c06cd Version: $LATEST
  INFO Submitted data with status 202
  END RequestId: 4620fd3f-c0fb-11e6-be7f-5d539d6c06cd
  REPORT RequestId: 4620fd3f-c0fb-11e6-be7f-5d539d6c06cd      Duration: 1019.04 ms    Billed Duration: 1100 ms        Memory Size: 128 MB     Max Memory Used: 31 MB

完了

Why not register and get more from Qiita?
  1. We will deliver articles that match you
    By following users and tags, you can catch up information on technical fields that you are interested in as a whole
  2. you can read useful information later efficiently
    By "stocking" the articles you like, you can search right away