Kiro のアクティビティレポートを OpenTelemetry でエクスポートして PromQL で集計してみた

# 1. CMK を作成する（既定の aws/secretsmanager では Kiro から復号できない）
aws kms create-key --description "Kiro OTel export secret encryption" --region us-east-1
aws kms create-alias --alias-name alias/kiro-otel-export --target-key-id <key-id> --region us-east-1
aws kms put-key-policy --key-id <key-id> --policy-name default \
  --policy file://kms-key-policy.json --region us-east-1

# 2. CloudWatch へ送信するための IAM ユーザーと API キーを作る
aws iam create-user --user-name kiro-otel-metrics-user
aws iam attach-user-policy --user-name kiro-otel-metrics-user \
  --policy-arn arn:aws:iam::aws:policy/CloudWatchAPIKeyAccess
aws iam create-service-specific-credential --user-name kiro-otel-metrics-user \
  --service-name cloudwatch.amazonaws.com

# 3. エンドポイントと認証情報をシークレットに入れる（Kiro プロファイルと同一リージョン）
# API キーをシェル履歴に残したくない場合は --secret-string file://secret.json でも渡せる
aws secretsmanager create-secret --name kiro-otel-export --kms-key-id <key-id> \
  --secret-string '{"OTEL_EXPORTER_OTLP_ENDPOINT":"https://monitoring.us-east-1.amazonaws.com/v1/metrics","OTEL_EXPORTER_OTLP_HEADERS":"Authorization=Bearer <SERVICE_CREDENTIAL_SECRET>"}' \
  --region us-east-1
aws secretsmanager put-resource-policy --secret-id kiro-otel-export \
  --resource-policy file://secret-resource-policy.json --region us-east-1

REGION = "us-east-1"
HOST = f"https://monitoring.{REGION}.amazonaws.com"
SERVICE = "monitoring"

def call(path, params):
    creds = botocore.session.get_session().get_credentials().get_frozen_credentials()
    if METHOD == "GET":
        qs = urllib.parse.urlencode(params, doseq=True, quote_via=urllib.parse.quote)
        url = f"{HOST}{path}" + (f"?{qs}" if qs else "")
        req = AWSRequest(method="GET", url=url)
        SigV4Auth(creds, SERVICE, REGION).add_auth(req)
        return _send(urllib.request.Request(url, headers=dict(req.headers), method="GET"))

# メトリクス名の一覧（start / end に epoch 秒を渡す。付けないと空配列が返る）
python3 promql.py names 1788825600 1789344000

# ラベル名の一覧
python3 promql.py labels 1788825600 1789344000

# インスタントクエリ（レポート日 2026-09-13 のデータポイントは 2026-09-14T00:00Z に立つ）
python3 promql.py query 'sum by ("kiro.client.type") ({"kiro.daily.credits", date="2026-09-13"})' 1789344000

{
  "status": "success",
  "data": [
    "kiro.daily.conversations",
    "kiro.daily.credits",
    "kiro.daily.messages",
    "kiro.daily.model_messages",
    "kiro.daily.overage_credits",
    "otel.sdk.metric_reader.collection.duration"
  ]
}

Date,UserId,Client_Type,Chat_Conversations,Credits_Used,Overage_Cap,Overage_Credits_Used,Overage_Enabled,ProfileId,Subscription_Tier,Total_Messages,New_User,User_Email,Usage_Limit,auto_messages,claude_opus_5_messages,claude_sonnet_4.6_messages,gpt_5.6_luna_messages,gpt_5.6_sol_messages,gpt_5.6_terra_messages
2026-09-13,xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxx0a,KIRO_CLI,323,752.1328506192372,5000.0,0.0,true,arn:aws:codewhisperer:us-east-1:123456789012:profile/xxxxxxxxxxxxx,POWER,1401,false,xxxxxxxx-a@example.com,10000,1401,0,0,0,0,0

for path in sys.argv[1:]:
    for row in csv.DictReader(open(path)):
        key = (row["Date"][:10], row["Client_Type"])
        credits[key] += float(row["Credits_Used"] or 0)
        messages[row["Date"][:10]] += int(row["Total_Messages"] or 0)

# 同じ日の CLI / IDE / WEB の 3 ファイルをまとめて集計する
python3 csv_aggregate.py s3-2026-09-12-*.csv s3-2026-09-13-*.csv

2026-09-12	KIRO_CLI	929.981886
2026-09-12	KIRO_IDE	30.041061
2026-09-12	KIRO_WEB	34.783152
2026-09-13	KIRO_CLI	1795.626213
2026-09-13	KIRO_IDE	70.219843
2026-09-13	KIRO_WEB	29.099002
2026-09-12	Total_Messages	2200
2026-09-13	Total_Messages	3551

{
  "query": "sum by (\"kiro.client.type\") ({\"kiro.daily.credits\", date=\"2026-09-13\"})",
  "response": {
    "status": "success",
    "data": {
      "resultType": "vector",
      "result": [
        {
          "metric": {
            "kiro.client.type": "KIRO_IDE"
          },
          "value": [
            1789344000.0,
            "70.21984331824211"
          ]
        },
        {
          "metric": {
            "kiro.client.type": "KIRO_CLI"
          },
          "value": [
            1789344000.0,
            "1795.6262133304313"
          ]
        },
        {
          "metric": {
            "kiro.client.type": "KIRO_WEB"
          },
          "value": [
            1789344000.0,
            "29.099002319237147"
          ]
        }
      ]
    }
  }
}

{
  "query": "sum by (\"kiro.model.name\") ({\"kiro.daily.model_messages\", date=\"2026-09-13\"})",
  "response": {
    "status": "success",
    "data": {
      "resultType": "vector",
      "result": [
        {
          "metric": {
            "kiro.model.name": "gpt-5.6-sol"
          },
          "value": [
            1789344000.0,
            "37"
          ]
        },
        {
          "metric": {
            "kiro.model.name": "gpt-5.6-luna"
          },
          "value": [
            1789344000.0,
            "8"
          ]
        },
        {
          "metric": {
            "kiro.model.name": "claude-opus-5"
          },
          "value": [
            1789344000.0,
            "714"
          ]
        },
        {
          "metric": {
            "kiro.model.name": "auto"
          },
          "value": [
            1789344000.0,
            "2443"
          ]
        },
        {
          "metric": {
            "kiro.model.name": "gpt-5.6-terra"
          },
          "value": [
            1789344000.0,
            "345"
          ]
        },
        {
          "metric": {
            "kiro.model.name": "claude-sonnet-4.6"
          },
          "value": [
            1789344000.0,
            "4"
          ]
        }
      ]
    }
  }
}

{
  "type": "chart",
  "x": 0,
  "y": 0,
  "width": 12,
  "height": 6,
  "properties": {
    "view": "line",
    "title": "日次クレジット（クライアント種別）",
    "region": "us-east-1",
    "data": {
      "queries": [
        {
          "id": "credits_by_client",
          "type": "cloudwatch-metrics",
          "language": "PromQL",
          "query": "sum by (\"kiro.client.type\") ({\"kiro.daily.credits\"})",
          "label": "__verbose__",
          "step": 86400
        }
      ]
    },
    "plotOptions": {
      "legend": {
        "position": "bottom",
        "show": true
      },
      "style": {
        "lineWidth": 2
      }
    }
  }
}

aws cloudwatch put-dashboard \
  --dashboard-name kiro-otel-promql \
  --dashboard-body file://dashboard-kiro-otel.json \
  --region us-east-1

{
    "DashboardValidationMessages": [
        {
            "DataPath": "/widgets/0",
            "Message": "The \"x\" property is not expected to be part of a widget definition, will be ignored"
        }
    ]
}

{
  "query": "sum({\"kiro.daily.credits\"})",
  "http": 400,
  "response": {
    "error": "Query time range 1209600000ms exceeds 691200000ms limit",
    "errorType": "bad_data",
    "status": "error"
  }
}

{
  "query": "sum(sum_over_time({\"kiro.daily.credits\"}[7d]))",
  "http": 400,
  "response": {
    "error": "Range selector 604800000ms exceeds 86400000ms limit",
    "errorType": "bad_data",
    "status": "error"
  }
}

MAX_WINDOW_DAYS = 8  # 実測の上限（ドキュメントの記載は 7 日）

def chunks(first, last):
    """レポート日 first..last を 8 日以下の窓に分割する。

    データポイントのタイムスタンプはレポート日 +1 日 00:00 UTC に立つため、
    問い合わせ窓は 1 日後ろへずらす。
    """
    out = []
    cur = first
    while cur <= last:
        end = min(cur + datetime.timedelta(days=MAX_WINDOW_DAYS - 1), last)
        out.append(
            (
                cur,
                end,
                int(
                    datetime.datetime.combine(
                        cur + datetime.timedelta(days=1),
                        datetime.time(),
                        datetime.UTC,
                    ).timestamp()
                ),
                int(
                    datetime.datetime.combine(
                        end + datetime.timedelta(days=1),
                        datetime.time(),
                        datetime.UTC,
                    ).timestamp()
                ),
            )
        )
        cur = end + datetime.timedelta(days=1)
    return out

# 前月（2026-08）
python3 monthly_credits.py 2026-08 <kiro.user.id> --raw-out monthly-2026-08.json

# 当月（2026-09）
python3 monthly_credits.py 2026-09 <kiro.user.id> --raw-out monthly-2026-09.json

{
  "chunk": 1,
  "report_date_from": "2026-08-01",
  "report_date_to": "2026-08-08",
  "start_epoch": 1785628800,
  "end_epoch": 1786233600,
  "http_status": 200,
  "response": {
    "status": "success",
    "data": {
      "resultType": "matrix",
      "result": []
    }
  }
}

METRICS = [
    "kiro.daily.credits",
    "kiro.daily.overage_credits",
    "kiro.daily.messages",
    "kiro.daily.conversations",
    "kiro.daily.model_messages",
]

# 複数メトリクスを `or` で連結すると、ラベルセットが同一の系列は左辺に畳まれて消える。
# メトリクス名を保持したままフラット化するため、ここではメトリクスごとに 1 リクエストずつ発行する
# （`label_replace` で名前を通常ラベルへ写せば 1 リクエストにまとめられる）

# 2 日分の生シリーズを全メトリクス分ダンプしてフラット CSV にする
python3 dump_raw.py 2026-09-12 2026-09-13 --outdir raw

# SQLite に投入して SQL で集計する
python3 load_sqlite.py raw/promql-flat-2026-09-12_2026-09-13.csv kiro_otel.db

metric,date,kiro.user.id,kiro.user.email,kiro.client.type,kiro.model.name,kiro.subscription.tier,kiro.usage.limit,kiro.overage.enabled,kiro.overage.cap,kiro.user.new,__unit__,__type__,__temporality__,timestamp,value
kiro.daily.credits,2026-09-12,xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxa0,xxxxxxxx-b@example.com,KIRO_CLI,,PRO,1000,true,5000,false,credits,Sum,delta,2026-09-13T00:00:00+00:00,17.16783699751244

CREATE TABLE kiro_otel (
    metric TEXT, report_date TEXT, user_id TEXT, email TEXT,
    client_type TEXT, model_name TEXT, tier TEXT, usage_limit INTEGER,
    overage_enabled TEXT, overage_cap INTEGER, is_new TEXT,
    unit TEXT, type TEXT, temporality TEXT, ts TEXT, value REAL
);

SELECT report_date,
       ROUND(SUM(CASE WHEN metric='kiro.daily.credits' THEN value END), 3) AS credits,
       CAST(SUM(CASE WHEN metric='kiro.daily.messages' THEN value END) AS INT) AS messages,
       CAST(SUM(CASE WHEN metric='kiro.daily.conversations' THEN value END) AS INT) AS conversations,
       ROUND(SUM(CASE WHEN metric='kiro.daily.overage_credits' THEN value END), 3) AS overage
FROM kiro_otel GROUP BY report_date ORDER BY report_date;

--- 期間合計（PromQL では 8 日窓の分割が必要な集計）
from_date | to_date | total_credits
2026-09-12 | 2026-09-13 | 2889.751158

--- クレジットとメッセージを1クエリで突き合わせ（PromQL の or では畳まれる組み合わせ）
report_date | credits | messages | conversations | overage
2026-09-12 | 994.806000 | 2200 | 277 | 0.000000
2026-09-13 | 1894.945000 | 3551 | 502 | 381.488000