первый commit

notebooks/03_partitioning_and_schema_evolution.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Лаба 3: партиционирование и эволюция схемы",
+    "",
+    "В этом ноутбуке мы посмотрим, как Iceberg работает с партиционированными таблицами и эволюцией схемы поверх общего каталога `lakehouse`.",
+    "",
+    "Перед началом убедись, что стенд запущен (`docker compose up -d`) и Spark-кластер доступен."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession",
+    "",
+    "spark = (",
+    "    SparkSession.builder",
+    "    .appName(\"lab3-partitioning-schema-evolution\")",
+    "    .master(\"spark://spark-master:7077\")",
+    "    .getOrCreate()",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Часть 1. Партиционированная таблица",
+    "",
+    "Для начала создадим партиционированную Iceberg-таблицу `lakehouse.default.partition_demo` с помощью готового SQL-скрипта из `src/spark/partitioned_table_demo.sql`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path",
+    "",
+    "sql_path = Path(\"/opt/src/spark/partitioned_table_demo.sql\")",
+    "sql_text = sql_path.read_text(encoding=\"utf-8\")",
+    "",
+    "for statement in sql_text.split(\";\"):",
+    "    stmt = statement.strip()",
+    "    if stmt:",
+    "        spark.sql(stmt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Посмотрим, какие данные записаны по датам, и обсудим партиционирование по `event_date`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.sql(\"\"\"",
+    "SELECT",
+    "  event_date,",
+    "  COUNT(*) AS cnt,",
+    "  SUM(amount) AS total_amount",
+    "FROM lakehouse.default.partition_demo",
+    "GROUP BY event_date",
+    "ORDER BY event_date",
+    "\"\"\").show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.sql(\"\"\"",
+    "SELECT *",
+    "FROM lakehouse.default.partition_demo",
+    "WHERE event_date = DATE '2024-01-01'",
+    "ORDER BY user_id",
+    "\"\"\").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Часть 2. Эволюция схемы",
+    "",
+    "Теперь посмотрим на эволюцию схемы: создадим таблицу, добавим колонку и вставим новые строки с дополнительными данными.",
+    "",
+    "Для подготовки таблицы используем скрипт `src/spark/schema_evolution_demo.sql`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sql_path = Path(\"/opt/src/spark/schema_evolution_demo.sql\")",
+    "sql_text = sql_path.read_text(encoding=\"utf-8\")",
+    "",
+    "for statement in sql_text.split(\";\"):",
+    "    stmt = statement.strip()",
+    "    if stmt:",
+    "        spark.sql(stmt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.sql(\"DESCRIBE TABLE lakehouse.default.schema_evolution_demo\").show(truncate=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.sql(\"\"\"",
+    "SELECT *",
+    "FROM lakehouse.default.schema_evolution_demo",
+    "ORDER BY id",
+    "\"\"\").show(truncate=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Обрати внимание, что старые строки имеют `NULL` в колонке `metadata`, а новые — заполненное значение.",
+    "Iceberg хранит историю снапшотов и позволяет эволюцию схемы без сложных миграций."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/spark-basic-test.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ca5e5822-18cb-405e-90f3-a0f2f8ad2260",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting default log level to \"WARN\".\n",
+      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
+      "25/12/03 09:08:48 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
+      "25/12/03 09:08:52 WARN MetricsConfig: Cannot locate configuration: tried hadoop-metrics2-s3a-file-system.properties,hadoop-metrics2.properties\n",
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+---+---+\n",
+      "| id|txt|\n",
+      "+---+---+\n",
+      "|  1| ok|\n",
+      "+---+---+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "\n",
+    "spark = (\n",
+    "    SparkSession.builder\n",
+    "    .appName(\"check\")\n",
+    "    .master(\"spark://spark-master:7077\")\n",
+    "    .getOrCreate()\n",
+    ")\n",
+    "\n",
+    "spark.sql(\"\"\"\n",
+    "    CREATE TABLE IF NOT EXISTS lakehouse.default.demo_fix (\n",
+    "        id BIGINT,\n",
+    "        txt STRING\n",
+    "    )\n",
+    "    USING iceberg\n",
+    "\"\"\")\n",
+    "\n",
+    "spark.sql(\"INSERT INTO lakehouse.default.demo_fix VALUES (1, 'ok')\")\n",
+    "\n",
+    "spark.sql(\"SELECT * FROM lakehouse.default.demo_fix\").show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e125075-feab-4974-baa2-f787e41b46fd",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}