Query like a Pro: ES|QL + Apache Arrow + Pandas (+ Elastic ML)

pycon2025.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "119f627a",
   "metadata": {},
   "source": [
    "# Query like a Pro: ES|QL + Apache Arrow + Pandas (+ Elastic ML)\n",
    "**PyCon Greece 2025 — Demo Notebook**\n",
    "\n",
    "This notebook shows:\n",
    "- ES|QL queries against an index (NYC Taxi sample)\n",
    "- Arrow fast-path to Pandas via `.to_pandas()`\n",
    "- Simple Matplotlib plotting \n",
    "- Elastic ML job + datafeed\n",
    "- Micro-benchmarks: JSON vs Arrow vs ArrowDtype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f0780d",
   "metadata": {},
   "source": [
    "## Contents\n",
    "1. Setup\n",
    "2. Connect to Elastic Cloud\n",
    "3. Load & ingest the dataset\n",
    "4. ES|QL → Arrow → Pandas (helper)\n",
    "5. Core ES|QL queries\n",
    "6. Advanced ES|QL queries (CASE function style)\n",
    "7. Plots (sorted, de-jittered)\n",
    "8. Elastic ML (helpers + job/datafeed, 9.1 APIs)\n",
    "9. Micro-benchmark: JSON vs Arrow vs ArrowDtype\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "077f73f5",
   "metadata": {},
   "source": [
    "## 1) Setup\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "36b90204",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If needed in notebook (uncomment):\n",
    "# %pip install -q \"elasticsearch>=9.1.0\" pandas pyarrow matplotlib tqdm fastparquet python-dateutil typing-extensions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c94a3cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, io, json, random, urllib.request, warnings, time\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from elasticsearch import Elasticsearch, helpers\n",
    "from elastic_transport import ApiError\n",
    "from tqdm import tqdm\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.max_rows\", 10)\n",
    "pd.set_option(\"display.max_columns\", 50)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0377ad97",
   "metadata": {},
   "source": [
    "## 2) Connect to Elastic Cloud\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c0f4cce",
   "metadata": {},
   "source": [
    "Set `ELASTIC_CLOUD_ID` and `ELASTIC_API_KEY` as env vars, or paste them below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "2594195e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to Elasticsearch: 8.11.0\n"
     ]
    }
   ],
   "source": [
    "CLOUD_ID = os.environ.get(\"ELASTIC_CLOUD_ID\", \"YOUR_CLOUD_ID_HERE\")\n",
    "API_KEY  = os.environ.get(\"ELASTIC_API_KEY\",  \"YOUR_API_KEY_HERE\")\n",
    "\n",
    "if not CLOUD_ID or \"YOUR_CLOUD_ID_HERE\" in CLOUD_ID:\n",
    "    print(\"⚠️  Please set CLOUD_ID (env ELASTIC_CLOUD_ID)\")\n",
    "if not API_KEY or \"YOUR_API_KEY_HERE\" in API_KEY:\n",
    "    print(\"⚠️  Please set API_KEY (env ELASTIC_API_KEY)\")\n",
    "\n",
    "es = Elasticsearch(cloud_id=CLOUD_ID, api_key=API_KEY)\n",
    "try:\n",
    "    version = es.info().get(\"version\", {}).get(\"number\")\n",
    "except Exception as e:\n",
    "    version = f\"unknown ({e})\"\n",
    "print(\"Connected to Elasticsearch:\", version)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f5e9a4",
   "metadata": {},
   "source": [
    "## 3) Load & ingest the dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aea679d1",
   "metadata": {},
   "source": [
    "We’ll use **NYC TLC Yellow Trip Records** (Jan 2023). For a fast demo, we’ll sample rows.\n",
    "\n",
    "- Download Parquet once\n",
    "- Normalize column names\n",
    "- Ingest into index `nyc_taxi`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "808a8c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEX_NAME = \"nyc_taxi\"\n",
    "USE_SYNTHETIC = False\n",
    "SAMPLE_SIZE = 100_000\n",
    "DATA_URL = \"https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2023-01.parquet\"\n",
    "LOCAL_PATH = \"yellow_2023_01.parquet\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "4e0a6bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ File exists: yellow_2023_01.parquet\n",
      "Reading Parquet ...\n",
      "✅ Loaded 100,000 rows (sampled)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup_datetime</th>\n",
       "      <th>dropoff_datetime</th>\n",
       "      <th>passenger_count</th>\n",
       "      <th>trip_distance</th>\n",
       "      <th>PULocationID</th>\n",
       "      <th>DOLocationID</th>\n",
       "      <th>payment_type</th>\n",
       "      <th>fare_amount</th>\n",
       "      <th>tip_amount</th>\n",
       "      <th>tolls_amount</th>\n",
       "      <th>total_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-10-25 09:06:23</td>\n",
       "      <td>2022-10-25 09:21:36</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.48</td>\n",
       "      <td>132</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-10-25 11:17:19</td>\n",
       "      <td>2022-10-25 11:17:21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>264</td>\n",
       "      <td>264</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>15.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-12-31 23:36:21</td>\n",
       "      <td>2022-12-31 23:41:57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>186</td>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-31 23:44:31</td>\n",
       "      <td>2022-12-31 23:48:47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>233</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-01 00:01:58</td>\n",
       "      <td>2023-01-01 00:10:57</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.76</td>\n",
       "      <td>114</td>\n",
       "      <td>224</td>\n",
       "      <td>1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pickup_datetime    dropoff_datetime  passenger_count  trip_distance  \\\n",
       "0 2022-10-25 09:06:23 2022-10-25 09:21:36              1.0           6.48   \n",
       "1 2022-10-25 11:17:19 2022-10-25 11:17:21              1.0           0.00   \n",
       "2 2022-12-31 23:36:21 2022-12-31 23:41:57              1.0           1.25   \n",
       "3 2022-12-31 23:44:31 2022-12-31 23:48:47              1.0           0.52   \n",
       "4 2023-01-01 00:01:58 2023-01-01 00:10:57              1.0           1.76   \n",
       "\n",
       "   PULocationID  DOLocationID  payment_type  fare_amount  tip_amount  \\\n",
       "0           132            38             2         19.5        0.00   \n",
       "1           264           264             1         75.0       15.16   \n",
       "2           186           249             1          7.9        3.22   \n",
       "3           233           162             1          5.8        2.16   \n",
       "4           114           224             1         11.4        3.28   \n",
       "\n",
       "   tolls_amount  total_amount  \n",
       "0           0.0         21.55  \n",
       "1           0.0         90.96  \n",
       "2           0.0         16.12  \n",
       "3           0.0         12.96  \n",
       "4           0.0         19.68  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def download_if_needed(url: str, path: str):\n",
    "    if os.path.exists(path):\n",
    "        print(\"✅ File exists:\", path)\n",
    "        return path\n",
    "    print(\"⬇️  Downloading:\", url)\n",
    "    urllib.request.urlretrieve(url, path)\n",
    "    print(\"✅ Downloaded to:\", path)\n",
    "    return path\n",
    "\n",
    "def load_taxi_sample(use_synthetic=False, sample_size=50_000):\n",
    "    if use_synthetic:\n",
    "        print(\"Generating synthetic taxi-like data...\")\n",
    "        rng = random.Random(42)\n",
    "        rows = []\n",
    "        start = datetime(2023, 1, 1, 0, 0, 0)\n",
    "        for _ in range(sample_size):\n",
    "            pu = start + pd.to_timedelta(rng.randint(0, 31*24*60-1), unit=\"m\")\n",
    "            dur_min = max(3, int(rng.gauss(14, 6)))\n",
    "            do = pu + pd.to_timedelta(dur_min, unit=\"m\")\n",
    "            dist = max(0.3, rng.random()*8.0)\n",
    "            fare = round(2.5 + 2.75*dist + rng.random()*3, 2)\n",
    "            tip = round(max(0, rng.gauss(fare*0.18, fare*0.08)), 2)\n",
    "            total = round(fare + tip + 1.00, 2)\n",
    "            rows.append({\n",
    "                \"pickup_datetime\": pu,\n",
    "                \"dropoff_datetime\": do,\n",
    "                \"passenger_count\": rng.randint(1, 4),\n",
    "                \"trip_distance\": round(dist, 3),\n",
    "                \"PULocationID\": rng.randint(1, 250),\n",
    "                \"DOLocationID\": rng.randint(1, 250),\n",
    "                \"payment_type\": rng.choice([\"Cash\", \"Card\", \"NoCharge\"]),\n",
    "                \"fare_amount\": fare,\n",
    "                \"tip_amount\": tip,\n",
    "                \"tolls_amount\": 0.0,\n",
    "                \"total_amount\": total,\n",
    "            })\n",
    "        return pd.DataFrame(rows)\n",
    "\n",
    "    path = download_if_needed(DATA_URL, LOCAL_PATH)\n",
    "    print(\"Reading Parquet ...\")\n",
    "    df_all = pd.read_parquet(path, engine=\"pyarrow\")\n",
    "    rename = {\n",
    "        \"tpep_pickup_datetime\": \"pickup_datetime\",\n",
    "        \"tpep_dropoff_datetime\": \"dropoff_datetime\",\n",
    "        \"RatecodeID\": \"ratecodeid\",\n",
    "        \"VendorID\": \"vendorid\"\n",
    "    }\n",
    "    for c_old, c_new in rename.items():\n",
    "        if c_old in df_all.columns:\n",
    "            df_all.rename(columns={c_old: c_new}, inplace=True)\n",
    "\n",
    "    keep = [\n",
    "        \"pickup_datetime\",\"dropoff_datetime\",\"passenger_count\",\"trip_distance\",\n",
    "        \"PULocationID\",\"DOLocationID\",\"payment_type\",\"fare_amount\",\"tip_amount\",\n",
    "        \"tolls_amount\",\"total_amount\"\n",
    "    ]\n",
    "    cols = [c for c in keep if c in df_all.columns]\n",
    "    df_all = df_all[cols].dropna(subset=[\"pickup_datetime\",\"dropoff_datetime\"])\n",
    "\n",
    "    if len(df_all) > sample_size:\n",
    "        df = df_all.sample(sample_size, random_state=42).sort_values(\"pickup_datetime\")\n",
    "    else:\n",
    "        df = df_all.copy()\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    print(f\"✅ Loaded {len(df):,} rows (sampled)\")\n",
    "    return df\n",
    "\n",
    "df_sample = load_taxi_sample(USE_SYNTHETIC, SAMPLE_SIZE)\n",
    "df_sample.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18aea681",
   "metadata": {},
   "source": [
    "### Create/reset the index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "04fe52b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Index ready: nyc_taxi\n"
     ]
    }
   ],
   "source": [
    "def reset_index(es: Elasticsearch, index_name: str):\n",
    "    # idempotent-ish reset\n",
    "    try:\n",
    "        es.indices.delete(index=index_name)\n",
    "    except Exception:\n",
    "        pass\n",
    "    es.indices.create(index=index_name)\n",
    "\n",
    "reset_index(es, INDEX_NAME)\n",
    "print(\"✅ Index ready:\", INDEX_NAME)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a6673b",
   "metadata": {},
   "source": [
    "### Bulk ingest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c6f09842",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ Bulk ingest: ok=97,714\n"
     ]
    }
   ],
   "source": [
    "def to_actions(df: pd.DataFrame, index_name: str):\n",
    "    for c in [\"pickup_datetime\",\"dropoff_datetime\"]:\n",
    "        if c in df.columns:\n",
    "            df[c] = pd.to_datetime(df[c], errors=\"coerce\")\n",
    "    for rec in df.to_dict(orient=\"records\"):\n",
    "        for c in [\"pickup_datetime\",\"dropoff_datetime\"]:\n",
    "            if c in rec and pd.notnull(rec[c]):\n",
    "                rec[c] = pd.to_datetime(rec[c]).isoformat()\n",
    "        yield {\"_index\": index_name, \"_source\": rec}\n",
    "\n",
    "ok, fail = helpers.bulk(es, to_actions(df_sample, INDEX_NAME),\n",
    "                        chunk_size=5000, raise_on_error=False,\n",
    "                        stats_only=True, request_timeout=120)\n",
    "# print(f\"✅ Bulk ingest: ok={ok:,}, fail={fail:,}\")\n",
    "print(f\"✅ Bulk ingest: ok={ok:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b0b2eb8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Documents in nyc_taxi: 83,027\n"
     ]
    }
   ],
   "source": [
    "count = es.count(index=INDEX_NAME)[\"count\"]\n",
    "print(f\"Documents in {INDEX_NAME}: {count:,}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a034f9",
   "metadata": {},
   "source": [
    "## 4) ES|QL → Arrow → Pandas helper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "6d7f7db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-liner ES|QL -> Arrow -> Pandas with Arrow-backed dtypes\n",
    "to_pd = lambda q: es.esql.query(query=q, format=\"arrow\").to_pandas(types_mapper=pd.ArrowDtype)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e937b3d",
   "metadata": {},
   "source": [
    "## 5) Core ES|QL queries\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864cde6e",
   "metadata": {},
   "source": [
    "**Hourly trips & duration percentiles** (DATE_DIFF + DATE_TRUNC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b0fee2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trips</th>\n",
       "      <th>avg_min</th>\n",
       "      <th>p90_min</th>\n",
       "      <th>p99_min</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137</td>\n",
       "      <td>14.868613</td>\n",
       "      <td>28.0</td>\n",
       "      <td>43.56</td>\n",
       "      <td>2023-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157</td>\n",
       "      <td>13.401274</td>\n",
       "      <td>25.8</td>\n",
       "      <td>41.44</td>\n",
       "      <td>2023-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>145</td>\n",
       "      <td>12.489655</td>\n",
       "      <td>23.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>2023-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106</td>\n",
       "      <td>10.896226</td>\n",
       "      <td>19.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2023-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72</td>\n",
       "      <td>11.361111</td>\n",
       "      <td>25.0</td>\n",
       "      <td>32.29</td>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trips    avg_min  p90_min  p99_min                 hour\n",
       "0    137  14.868613     28.0    43.56  2023-01-01 00:00:00\n",
       "1    157  13.401274     25.8    41.44  2023-01-01 01:00:00\n",
       "2    145  12.489655     23.2     36.0  2023-01-01 02:00:00\n",
       "3    106  10.896226     19.5     28.0  2023-01-01 03:00:00\n",
       "4     72  11.361111     25.0    32.29  2023-01-01 04:00:00"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_HOURLY = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| WHERE pickup_datetime >= \"2023-01-01T00:00:00\" AND pickup_datetime < \"2023-02-01T00:00:00\"\n",
    "| EVAL dur_min = DATE_DIFF(\"minutes\", pickup_datetime, dropoff_datetime)\n",
    "| EVAL hour = DATE_TRUNC(1 hour, pickup_datetime)\n",
    "| STATS trips = COUNT(*), avg_min = AVG(dur_min), p90_min = PERCENTILE(dur_min, 90), p99_min = PERCENTILE(dur_min, 99) BY hour\n",
    "| SORT hour\n",
    "\"\"\"\n",
    "df_hourly = to_pd(Q_HOURLY)\n",
    "df_hourly.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de476973",
   "metadata": {},
   "source": [
    "**Anomaly flag** (boolean expression + filter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f51212b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avg_min</th>\n",
       "      <th>p95_min</th>\n",
       "      <th>hour</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.361111</td>\n",
       "      <td>28.9</td>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.96</td>\n",
       "      <td>32.8</td>\n",
       "      <td>2023-01-01 07:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.931034</td>\n",
       "      <td>30.15</td>\n",
       "      <td>2023-01-01 09:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.971429</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2023-01-01 11:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.780822</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2023-01-01 16:00:00</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     avg_min  p95_min                 hour anomaly\n",
       "0  11.361111     28.9  2023-01-01 04:00:00    True\n",
       "1      12.96     32.8  2023-01-01 07:00:00    True\n",
       "2  10.931034    30.15  2023-01-01 09:00:00    True\n",
       "3  11.971429     31.0  2023-01-01 11:00:00    True\n",
       "4  16.780822     42.0  2023-01-01 16:00:00    True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_ANOMALY = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| WHERE pickup_datetime >= \"2023-01-01T00:00:00\" AND pickup_datetime < \"2023-02-01T00:00:00\"\n",
    "| EVAL dur_min = DATE_DIFF(\"minutes\", pickup_datetime, dropoff_datetime)\n",
    "| EVAL hour = DATE_TRUNC(1 hour, pickup_datetime)\n",
    "| STATS avg_min = AVG(dur_min), p95_min = PERCENTILE(dur_min, 95) BY hour\n",
    "| EVAL anomaly = p95_min > avg_min * 2.5\n",
    "| WHERE anomaly\n",
    "| SORT hour\n",
    "\"\"\"\n",
    "df_anom = to_pd(Q_ANOMALY)\n",
    "df_anom.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf78c8f8",
   "metadata": {},
   "source": [
    "## 6) Advanced ES|QL queries (function-style `CASE(...)`)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b2d2ca",
   "metadata": {},
   "source": [
    "**Cost per mile median by hour (with outlier clamp)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "86d95139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p50</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.201277</td>\n",
       "      <td>2023-01-01 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.364902</td>\n",
       "      <td>2023-01-01 01:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.267308</td>\n",
       "      <td>2023-01-01 02:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.508086</td>\n",
       "      <td>2023-01-01 03:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.895833</td>\n",
       "      <td>2023-01-01 04:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        p50                 hour\n",
       "0  9.201277  2023-01-01 00:00:00\n",
       "1  9.364902  2023-01-01 01:00:00\n",
       "2  8.267308  2023-01-01 02:00:00\n",
       "3  8.508086  2023-01-01 03:00:00\n",
       "4  8.895833  2023-01-01 04:00:00"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_COST_PER_MILE = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| WHERE pickup_datetime >= \"2023-01-01T00:00:00\" AND pickup_datetime < \"2023-02-01T00:00:00\"\n",
    "| WHERE trip_distance > 0 AND total_amount > 0\n",
    "| EVAL cpm = total_amount / trip_distance\n",
    "| WHERE cpm >= 0.3 AND cpm <= 15\n",
    "| EVAL hour = DATE_TRUNC(1 hour, pickup_datetime)\n",
    "| STATS p50 = PERCENTILE(cpm, 50) BY hour\n",
    "| SORT hour\n",
    "\"\"\"\n",
    "df_cpm = to_pd(Q_COST_PER_MILE)\n",
    "df_cpm.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06156917",
   "metadata": {},
   "source": [
    "**Daypart & day-of-week (CASE function style + DATE_EXTRACT)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8d6a12a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trips</th>\n",
       "      <th>avg_dist</th>\n",
       "      <th>tip_p50</th>\n",
       "      <th>daypart</th>\n",
       "      <th>dow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3553</td>\n",
       "      <td>3.490186</td>\n",
       "      <td>0.165746</td>\n",
       "      <td>evening_peak</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2285</td>\n",
       "      <td>3.36758</td>\n",
       "      <td>0.164912</td>\n",
       "      <td>morning_peak</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7124</td>\n",
       "      <td>3.869781</td>\n",
       "      <td>0.165253</td>\n",
       "      <td>offpeak</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4278</td>\n",
       "      <td>3.146122</td>\n",
       "      <td>0.165692</td>\n",
       "      <td>evening_peak</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2993</td>\n",
       "      <td>3.013682</td>\n",
       "      <td>0.165348</td>\n",
       "      <td>morning_peak</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trips  avg_dist   tip_p50       daypart  dow\n",
       "0   3553  3.490186  0.165746  evening_peak    1\n",
       "1   2285   3.36758  0.164912  morning_peak    1\n",
       "2   7124  3.869781  0.165253       offpeak    1\n",
       "3   4278  3.146122  0.165692  evening_peak    2\n",
       "4   2993  3.013682  0.165348  morning_peak    2"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_DAYPART = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| EVAL hod = DATE_EXTRACT(\"hour_of_day\", pickup_datetime)\n",
    "| EVAL dow = DATE_EXTRACT(\"day_of_week\", pickup_datetime)\n",
    "| EVAL daypart = CASE(\n",
    "    hod >= 7 AND hod < 11, \"morning_peak\",\n",
    "    hod >= 16 AND hod < 20, \"evening_peak\",\n",
    "    \"offpeak\"\n",
    "  )\n",
    "| EVAL tip_rate = CASE(\n",
    "    total_amount > 0, tip_amount / total_amount,\n",
    "    NULL\n",
    "  )\n",
    "| STATS trips = COUNT(*), avg_dist = AVG(trip_distance), tip_p50 = PERCENTILE(tip_rate, 50) BY daypart, dow\n",
    "| SORT dow, daypart\n",
    "\"\"\"\n",
    "df_daypart = to_pd(Q_DAYPART)\n",
    "df_daypart.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5935ce",
   "metadata": {},
   "source": [
    "**Distance buckets (CASE function style)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "3777f2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trips</th>\n",
       "      <th>avg_fare</th>\n",
       "      <th>p95_dur</th>\n",
       "      <th>dist_bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21390</td>\n",
       "      <td>8.110251</td>\n",
       "      <td>10.921569</td>\n",
       "      <td>&lt;1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48770</td>\n",
       "      <td>12.334339</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11348</td>\n",
       "      <td>20.980837</td>\n",
       "      <td>30.0</td>\n",
       "      <td>3-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16206</td>\n",
       "      <td>47.50385</td>\n",
       "      <td>57.0</td>\n",
       "      <td>5+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trips   avg_fare    p95_dur dist_bucket\n",
       "0  21390   8.110251  10.921569          <1\n",
       "1  48770  12.334339       20.0         1-3\n",
       "2  11348  20.980837       30.0         3-5\n",
       "3  16206   47.50385       57.0          5+"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_BUCKETS = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| EVAL dist_bucket = CASE(\n",
    "    trip_distance < 1, \"<1\",\n",
    "    trip_distance < 3, \"1-3\",\n",
    "    trip_distance < 5, \"3-5\",\n",
    "    \"5+\"\n",
    "  )\n",
    "| EVAL dur_min = DATE_DIFF(\"minutes\", pickup_datetime, dropoff_datetime)\n",
    "| STATS trips = COUNT(*), avg_fare = AVG(fare_amount), p95_dur = PERCENTILE(dur_min, 95) BY dist_bucket\n",
    "| EVAL bucket_id = CASE(\n",
    "    dist_bucket == \"<1\", 1,\n",
    "    dist_bucket == \"1-3\", 2,\n",
    "    dist_bucket == \"3-5\", 3,\n",
    "    4\n",
    "  )\n",
    "| SORT bucket_id\n",
    "| DROP bucket_id\n",
    "\"\"\"\n",
    "df_buckets = to_pd(Q_BUCKETS)\n",
    "df_buckets.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7faf27e9",
   "metadata": {},
   "source": [
    "**Data quality check (CASE function style)**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "80900bc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rows</th>\n",
       "      <th>missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>97714</td>\n",
       "      <td>ok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    rows missing\n",
       "0  97714      ok"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_DQ = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| EVAL missing = CASE(\n",
    "    pickup_datetime IS NULL OR dropoff_datetime IS NULL OR total_amount IS NULL OR trip_distance IS NULL,\n",
    "    \"missing\",\n",
    "    \"ok\"\n",
    "  )\n",
    "| STATS rows = COUNT(*) BY missing\n",
    "\"\"\"\n",
    "df_dq = to_pd(Q_DQ)\n",
    "df_dq\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb48939e",
   "metadata": {},
   "source": [
    "## 7) Simple plots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "45f360b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hourly p90\n",
    "_df = df_hourly.dropna(subset=[\"hour\", \"p90_min\"]).copy()\n",
    "_df[\"hour\"] = pd.to_datetime(_df[\"hour\"], utc=True, errors=\"coerce\")\n",
    "_df = _df.sort_values(\"hour\").drop_duplicates(subset=[\"hour\"], keep=\"last\")\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(_df[\"hour\"], _df[\"p90_min\"])\n",
    "plt.title(\"NYC Taxi — Hourly p90 duration (minutes)\")\n",
    "plt.xlabel(\"Hour\"); plt.ylabel(\"p90 duration (min)\")\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "e95d2fa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Anomaly hours (Matplotlib 3.6+ safe: no use_line_collection arg)\n",
    "if not df_anom.empty:\n",
    "    _da = df_anom.dropna(subset=[\"hour\",\"p95_min\"]).copy()\n",
    "    _da[\"hour\"] = pd.to_datetime(_da[\"hour\"], utc=True, errors=\"coerce\")\n",
    "    _da = _da.sort_values(\"hour\")\n",
    "\n",
    "    plt.figure(figsize=(10,4))\n",
    "    markerline, stemlines, baseline = plt.stem(_da[\"hour\"], _da[\"p95_min\"])\n",
    "    plt.setp(baseline, visible=False)\n",
    "    plt.title(\"Anomalous Hours (p95 duration)\")\n",
    "    plt.xlabel(\"Hour\"); plt.ylabel(\"p95 (min)\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "else:\n",
    "    print(\"No anomalies flagged with current threshold.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "22513e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cost-per-mile hourly median and 6h rolling\n",
    "_c = df_cpm.dropna(subset=[\"hour\",\"p50\"]).copy()\n",
    "_c[\"hour\"] = pd.to_datetime(_c[\"hour\"], utc=True, errors=\"coerce\")\n",
    "_c = _c.sort_values(\"hour\").drop_duplicates(subset=[\"hour\"], keep=\"last\")\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(_c[\"hour\"], _c[\"p50\"])\n",
    "plt.title(\"Cost per mile — Hourly median (p50)\")\n",
    "plt.xlabel(\"Hour\"); plt.ylabel(\"Cost per mile (USD)\")\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "cs = _c.set_index(\"hour\")[\"p50\"].rolling(\"6H\", min_periods=1).median()\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(cs.index, cs.values)\n",
    "plt.title(\"Cost per mile — Hourly median (p50), 6h rolling\")\n",
    "plt.xlabel(\"Hour\"); plt.ylabel(\"Cost per mile (USD)\")\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "b9dd3911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Top pickup zones by revenue\n",
    "Q_TOP_ZONES = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| STATS trips = COUNT(*), revenue = SUM(total_amount) BY PULocationID\n",
    "| SORT revenue DESC\n",
    "| LIMIT 20\n",
    "\"\"\"\n",
    "df_zones = to_pd(Q_TOP_ZONES)\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(df_zones[\"PULocationID\"].astype(str), df_zones[\"revenue\"])\n",
    "plt.title(\"Top 20 Pickup Zones by Revenue\")\n",
    "plt.xlabel(\"PULocationID\"); plt.ylabel(\"Revenue (USD)\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8afa670",
   "metadata": {},
   "source": [
    "## 8) Elastic ML — helpers\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "12a4773c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job: nyc_taxi_duration_high_mean | Datafeed: df_nyc_taxi_duration_high_mean\n"
     ]
    }
   ],
   "source": [
    "# Config\n",
    "JOB_ID       = \"nyc_taxi_duration_high_mean\"\n",
    "DATAFEED_ID  = f\"df_{JOB_ID}\"\n",
    "TIME_START   = \"2023-01-01T00:00:00Z\"\n",
    "TIME_END     = \"2023-02-01T00:00:00Z\"    # None for continuous\n",
    "BUCKET_SPAN  = \"1h\"\n",
    "\n",
    "print(\"Job:\", JOB_ID, \"| Datafeed:\", DATAFEED_ID)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "506cfb94",
   "metadata": {},
   "outputs": [],
   "source": [
    "from elastic_transport import ApiError\n",
    "\n",
    "# ---- Compatibility getters ----\n",
    "def _get_job_doc(es, job_id):\n",
    "    try:\n",
    "        return es.ml.get_job(job_id=job_id)             # 9.x singular\n",
    "    except AttributeError:\n",
    "        # older/plural returns {'count': N, 'jobs': [...]}; doesn't raise 404\n",
    "        return es.ml.get_jobs(job_id=job_id)\n",
    "\n",
    "def _get_datafeed_doc(es, datafeed_id):\n",
    "    try:\n",
    "        return es.ml.get_datafeed(datafeed_id=datafeed_id)          # 9.x singular\n",
    "    except AttributeError:\n",
    "        return es.ml.get_datafeeds(datafeed_id=datafeed_id)         # older/plural\n",
    "\n",
    "def _get_datafeed_stats(es, datafeed_id):\n",
    "    try:\n",
    "        return es.ml.get_datafeed_stats(datafeed_id=datafeed_id)    # 9.x singular\n",
    "    except AttributeError:\n",
    "        return es.ml.get_datafeeds_stats(datafeed_id=datafeed_id)   # older/plural\n",
    "\n",
    "# ---- Existence checks that work for both styles ----\n",
    "def job_exists(es, job_id: str) -> bool:\n",
    "    try:\n",
    "        doc = _get_job_doc(es, job_id)\n",
    "    except ApiError as e:\n",
    "        return getattr(e, \"status\", None) != 404\n",
    "    # If we got here, either we have the singular doc or the plural payload\n",
    "    if isinstance(doc, dict) and \"jobs\" in doc:\n",
    "        return len(doc.get(\"jobs\", [])) > 0\n",
    "    return True  # singular getter returned without raising → exists\n",
    "\n",
    "def datafeed_exists(es, datafeed_id: str) -> bool:\n",
    "    try:\n",
    "        doc = _get_datafeed_doc(es, datafeed_id)\n",
    "    except ApiError as e:\n",
    "        return getattr(e, \"status\", None) != 404\n",
    "    if isinstance(doc, dict) and \"datafeeds\" in doc:\n",
    "        return len(doc.get(\"datafeeds\", [])) > 0\n",
    "    return True\n",
    "\n",
    "# ---- Cleanup with tolerant 404 handling ----\n",
    "def delete_job_and_wait(es, job_id: str, datafeed_id: str, timeout=180):\n",
    "    import time\n",
    "    # 1) datafeed\n",
    "    if datafeed_exists(es, datafeed_id):\n",
    "        try: es.ml.stop_datafeed(datafeed_id=datafeed_id, force=True)\n",
    "        except Exception: pass\n",
    "        try: es.ml.delete_datafeed(datafeed_id=datafeed_id, force=True)\n",
    "        except ApiError as e:\n",
    "            if getattr(e, \"status\", None) != 404:\n",
    "                pass\n",
    "        except Exception:\n",
    "            pass\n",
    "\n",
    "    # 2) job\n",
    "    if job_exists(es, job_id):\n",
    "        try: es.ml.close_job(job_id=job_id, force=True)\n",
    "        except Exception: pass\n",
    "        try:\n",
    "            es.ml.delete_job(job_id=job_id, force=True, wait_for_completion=True)\n",
    "        except TypeError:\n",
    "            es.ml.delete_job(job_id=job_id, force=True)\n",
    "        except ApiError as e:\n",
    "            if getattr(e, \"status\", None) != 404:\n",
    "                raise\n",
    "\n",
    "        # 3) poll until gone (handles async delete)\n",
    "        t0 = time.time()\n",
    "        while time.time() - t0 < timeout:\n",
    "            if not job_exists(es, job_id):\n",
    "                return True\n",
    "            time.sleep(2)\n",
    "        raise TimeoutError(f\"Job '{job_id}' still exists after {timeout}s\")\n",
    "    return True\n",
    "    \n",
    "def ensure_job_open(es, job_id, timeout=120):\n",
    "    \"\"\"\n",
    "    Open ML job if not already open.\n",
    "    Tolerant of conflicts (409) and handles polling until 'opened'.\n",
    "    \"\"\"\n",
    "    import time\n",
    "\n",
    "    try:\n",
    "        st = es.ml.get_job_stats(job_id=job_id)[\"jobs\"][0]\n",
    "        state = st.get(\"state\", \"unknown\")\n",
    "    except Exception:\n",
    "        state = \"unknown\"\n",
    "\n",
    "    if state == \"opened\":\n",
    "        return True\n",
    "\n",
    "    # Try to open (409 conflict is fine if already opening/open)\n",
    "    try:\n",
    "        es.ml.open_job(job_id=job_id)\n",
    "    except Exception:\n",
    "        pass\n",
    "\n",
    "    # Poll until open\n",
    "    t0 = time.time()\n",
    "    while time.time() - t0 < timeout:\n",
    "        st = es.ml.get_job_stats(job_id=job_id)[\"jobs\"][0]\n",
    "        if st.get(\"state\") == \"opened\":\n",
    "            return True\n",
    "        time.sleep(2)\n",
    "\n",
    "    raise TimeoutError(f\"Job {job_id} did not reach 'opened' in {timeout}s\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdbbe24e",
   "metadata": {},
   "source": [
    "### Create (or recreate) job + datafeed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "01c50270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ put_job: nyc_taxi_duration_high_mean\n",
      "✅ put_datafeed: df_nyc_taxi_duration_high_mean\n"
     ]
    }
   ],
   "source": [
    "# Recreate cleanly for deterministic demos\n",
    "#delete_job_and_wait(es, JOB_ID, DATAFEED_ID, timeout=180)\n",
    "\n",
    "put_job_body = {\n",
    "    \"analysis_config\": {\n",
    "        \"bucket_span\": BUCKET_SPAN,\n",
    "        \"detectors\": [{\n",
    "            \"function\": \"high_mean\",\n",
    "            \"field_name\": \"dur_min\",\n",
    "            \"by_field_name\": \"PULocationID\"\n",
    "        }],\n",
    "        \"influencers\": [\"PULocationID\", \"passenger_count\"]\n",
    "    },\n",
    "    \"data_description\": {\"time_field\": \"pickup_datetime\"},\n",
    "    \"model_plot_config\": {\"enabled\": True}\n",
    "}\n",
    "resp_job = es.ml.put_job(job_id=JOB_ID, body=put_job_body)\n",
    "print(\"✅ put_job:\", resp_job[\"job_id\"])\n",
    "\n",
    "# Painless script to derive duration in minutes\n",
    "painless = \"\"\"\n",
    "if (doc.containsKey('dropoff_datetime') && doc['dropoff_datetime'].size()!=0\n",
    "    && doc.containsKey('pickup_datetime') && doc['pickup_datetime'].size()!=0) {\n",
    "  return (doc['dropoff_datetime'].value.toInstant().toEpochMilli()\n",
    "        - doc['pickup_datetime'].value.toInstant().toEpochMilli()) / 60000.0;\n",
    "} else { return null; }\n",
    "\"\"\"\n",
    "\n",
    "time_range = {\"gte\": TIME_START}\n",
    "if TIME_END:\n",
    "    time_range[\"lt\"] = TIME_END\n",
    "\n",
    "put_df_body = {\n",
    "    \"job_id\": JOB_ID,\n",
    "    \"indices\": [INDEX_NAME],\n",
    "    \"query\": {\"range\": {\"pickup_datetime\": time_range}},\n",
    "    \"script_fields\": {\"dur_min\": {\"script\": {\"lang\": \"painless\", \"source\": painless}}}\n",
    "}\n",
    "resp_df = es.ml.put_datafeed(datafeed_id=DATAFEED_ID, body=put_df_body)\n",
    "print(\"✅ put_datafeed:\", resp_df[\"datafeed_id\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bfea24",
   "metadata": {},
   "source": [
    "### Start datafeed & (optionally) wait until it stops for fixed window\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b856dbbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "▶️  start_datafeed: {'started': True, 'node': 'serverless'}\n"
     ]
    }
   ],
   "source": [
    "# Ensure job is opened\n",
    "ensure_job_open(es, JOB_ID, timeout=180)\n",
    "\n",
    "start_body = {\"start\": TIME_START}\n",
    "if TIME_END:\n",
    "    start_body[\"end\"] = TIME_END\n",
    "\n",
    "resp_start = es.ml.start_datafeed(datafeed_id=DATAFEED_ID, body=start_body)\n",
    "print(\"▶️  start_datafeed:\", resp_start)\n",
    "\n",
    "def wait_for_datafeed_stop(es, datafeed_id, timeout_sec=180):\n",
    "    t0 = time.time()\n",
    "    while True:\n",
    "        st = es.ml.get_datafeed_stats(datafeed_id=datafeed_id)[\"datafeeds\"][0]\n",
    "        state = st.get(\"state\", \"unknown\")\n",
    "        if state.lower() == \"stopped\":\n",
    "            return True\n",
    "        if time.time() - t0 > timeout_sec:\n",
    "            print(\"⏱️ Timeout waiting for datafeed to stop.\")\n",
    "            return False\n",
    "        time.sleep(3)\n",
    "\n",
    "if TIME_END:\n",
    "    wait_for_datafeed_stop(es, DATAFEED_ID, timeout_sec=180)\n",
    "else:\n",
    "    print(\"Continuous mode: skipping wait.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36740fff",
   "metadata": {},
   "source": [
    "### Query ML anomalies with ES|QL and plot them\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "99015b02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>record_score</th>\n",
       "      <th>by_field_value</th>\n",
       "      <th>typical</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-01-20 14:00:00</td>\n",
       "      <td>99.797779</td>\n",
       "      <td>138</td>\n",
       "      <td>31.022572</td>\n",
       "      <td>140.444872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-21 14:00:00</td>\n",
       "      <td>99.2411</td>\n",
       "      <td>236</td>\n",
       "      <td>10.342391</td>\n",
       "      <td>110.971429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-31 22:00:00</td>\n",
       "      <td>98.494223</td>\n",
       "      <td>161</td>\n",
       "      <td>12.549924</td>\n",
       "      <td>98.144118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-24 19:00:00</td>\n",
       "      <td>98.388646</td>\n",
       "      <td>163</td>\n",
       "      <td>11.759579</td>\n",
       "      <td>168.183333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-25 19:00:00</td>\n",
       "      <td>97.996232</td>\n",
       "      <td>162</td>\n",
       "      <td>11.174434</td>\n",
       "      <td>142.945455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             timestamp  record_score by_field_value    typical      actual\n",
       "0  2023-01-20 14:00:00     99.797779            138  31.022572  140.444872\n",
       "1  2023-01-21 14:00:00       99.2411            236  10.342391  110.971429\n",
       "2  2023-01-31 22:00:00     98.494223            161  12.549924   98.144118\n",
       "3  2023-01-24 19:00:00     98.388646            163  11.759579  168.183333\n",
       "4  2023-01-25 19:00:00     97.996232            162  11.174434  142.945455"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ANOM_ESQL = f\"\"\"\n",
    "FROM .ml-anomalies-*\n",
    "| WHERE job_id == \"{JOB_ID}\"\n",
    "| WHERE record_score >= 50\n",
    "| KEEP timestamp, record_score, by_field_value, typical, actual\n",
    "| SORT record_score DESC\n",
    "| LIMIT 500\n",
    "\"\"\"\n",
    "df_ml = to_pd(ANOM_ESQL)\n",
    "df_ml.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "95d3e5ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if not df_ml.empty:\n",
    "    plt.figure(figsize=(12,5))\n",
    "    plt.scatter(\n",
    "        pd.to_datetime(df_ml[\"timestamp\"], utc=True, errors=\"coerce\"),\n",
    "        df_ml[\"record_score\"],\n",
    "        c=\"red\", alpha=0.7, edgecolor=\"k\"\n",
    "    )\n",
    "    plt.title(\"ML anomaly scores over time\")\n",
    "    plt.xlabel(\"Time\"); plt.ylabel(\"record_score\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "else:\n",
    "    print(\"No ML anomalies found with current threshold (>=50).\")\n",
    "\n",
    "if not df_ml.empty:\n",
    "    plt.figure(figsize=(12,5))\n",
    "    times = pd.to_datetime(df_ml[\"timestamp\"], utc=True, errors=\"coerce\")\n",
    "    markerline, stemlines, baseline = plt.stem(times, df_ml[\"record_score\"])\n",
    "    plt.setp(markerline, 'markerfacecolor', 'red')\n",
    "    plt.title(\"ML anomaly scores over time\")\n",
    "    plt.xlabel(\"Time\"); plt.ylabel(\"record_score\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "else:\n",
    "    print(\"No ML anomalies found with current threshold (>=50).\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9fbf2e",
   "metadata": {},
   "source": [
    "### (Optional) Cleanup ML resources after demo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e988d6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stop datafeed, close & delete job — compatible with 9.1 client\n",
    "try:\n",
    "    es.ml.stop_datafeed(datafeed_id=DATAFEED_ID, force=True)\n",
    "except Exception:\n",
    "    pass\n",
    "try:\n",
    "    es.ml.delete_datafeed(datafeed_id=datafeed_id, force=True)\n",
    "except Exception:\n",
    "    pass\n",
    "try:\n",
    "    es.ml.close_job(job_id=JOB_ID, force=True)\n",
    "except Exception:\n",
    "    pass\n",
    "try:\n",
    "    es.ml.delete_job(job_id=JOB_ID, force=True, wait_for_completion=True)\n",
    "except TypeError:\n",
    "    es.ml.delete_job(job_id=JOB_ID, force=True)\n",
    "print(\"🧹 ML job & datafeed cleaned up (best-effort).\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7008c18d",
   "metadata": {},
   "source": [
    "## 9) Micro-benchmark: JSON vs Arrow vs ArrowDtype → Pandas\n",
    "\n",
    "We compare:\n",
    "1. **JSON → Pandas** (`format=\"json\"` manual DataFrame creation)\n",
    "2. **Arrow → Pandas (default dtypes)**\n",
    "3. **Arrow → Pandas (Arrow-backed dtypes)**\n",
    "\n",
    "Use a decent limit to observe differences. Not a pr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "2ae775e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSON → Pandas: 209.1 ms (avg of 3)  — runs: 212.9, 198.2, 216.0\n",
      "Arrow → Pandas (default): 133.5 ms (avg of 3)  — runs: 135.7, 140.7, 124.2\n",
      "Arrow → Pandas (ArrowDtype): 121.6 ms (avg of 3)  — runs: 111.9, 135.1, 117.8\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "\n",
    "Q_BENCH = f\"\"\"\n",
    "FROM {INDEX_NAME}\n",
    "| WHERE pickup_datetime >= \"2023-01-01T00:00:00\" AND pickup_datetime < \"2023-02-01T00:00:00\"\n",
    "| KEEP pickup_datetime, dropoff_datetime, trip_distance, total_amount, PULocationID, DOLocationID, passenger_count, payment_type\n",
    "| LIMIT 200000\n",
    "\"\"\"\n",
    "\n",
    "def bench(fn, label, warmup=1, repeat=3):\n",
    "    # warmup\n",
    "    for _ in range(warmup):\n",
    "        _ = fn()\n",
    "    times = []\n",
    "    for _ in range(repeat):\n",
    "        t0 = time.perf_counter()\n",
    "        df = fn()\n",
    "        dt = (time.perf_counter() - t0) * 1000.0\n",
    "        times.append(dt)\n",
    "    return label, times\n",
    "\n",
    "def json_to_pandas():\n",
    "    res = es.esql.query(query=Q_BENCH, format=\"json\")\n",
    "    payload = getattr(res, \"body\", res)\n",
    "    cols = [c[\"name\"] for c in payload.get(\"columns\", [])]\n",
    "    return pd.DataFrame(payload.get(\"values\", []), columns=cols)\n",
    "\n",
    "def arrow_to_pandas_default():\n",
    "    return es.esql.query(query=Q_BENCH, format=\"arrow\").to_pandas()\n",
    "\n",
    "def arrow_to_pandas_arrowdtype():\n",
    "    return es.esql.query(query=Q_BENCH, format=\"arrow\").to_pandas(types_mapper=pd.ArrowDtype)\n",
    "\n",
    "labels, results = [], []\n",
    "for label, fn in [\n",
    "    (\"JSON → Pandas\", json_to_pandas),\n",
    "    (\"Arrow → Pandas (default)\", arrow_to_pandas_default),\n",
    "    (\"Arrow → Pandas (ArrowDtype)\", arrow_to_pandas_arrowdtype),\n",
    "]:\n",
    "    lab, times = bench(fn, label)\n",
    "    labels.append(lab); results.append(times)\n",
    "\n",
    "for lab, times in zip(labels, results):\n",
    "    print(f\"{lab}: {sum(times)/len(times):.1f} ms (avg of {len(times)})  — runs: {', '.join(f'{t:.1f}' for t in times)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "887a0e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quick visual\n",
    "avg = [sum(t)/len(t) for t in results]\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.bar(labels, avg)\n",
    "plt.title(\"ES|QL → Pandas timings (lower is better)\")\n",
    "plt.ylabel(\"milliseconds\")\n",
    "plt.xticks(rotation=10)\n",
    "plt.tight_layout(); plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fe96410-4320-4f17-b322-e4f5da53e994",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}