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bronze -> silver postgres

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CC
2026-10-18 18:03:05 +01:00
parent 22930da424
commit bb36c54f96
1 changed files with 22 additions and 47 deletions
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69
spotify.ipynb
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@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"id": "e9bf1d26",
"metadata": {},
"outputs": [],
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 9,
"id": "11bb5dc2",
"metadata": {},
"outputs": [],
@@ -29,7 +29,7 @@
"def start_spark(app_name):\n",
" jar_path = str(Path.cwd() / \"postgresql.jar\")\n",
" spark = SparkSession.builder.appName(app_name).config(\"spark.jars\", jar_path).config(\"spark.driver.extraClassPath\", jar_path).getOrCreate()\n",
" spark.sparkContext.setLogLevel(\"WARN\")\n",
" spark.sparkContext.setLogLevel(\"ERROR\")\n",
" return spark\n",
"\n",
"def music_source_paths():\n",
@@ -56,7 +56,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"id": "a898326e",
"metadata": {},
"outputs": [],
@@ -103,18 +103,18 @@
"def add_time_parts(df: DataFrame, ts_col) -> DataFrame:\n",
" return (df\n",
" .withColumn(\"ts\", F.to_timestamp(\"ts\"))\n",
" .withColumn(\"offline_timestamp\", F.to_timestamp(\"offline_timestamp\"))\n",
" .withColumn(\"offline_timestamped\", F.to_timestamp(\"offline_timestamp\"))\n",
" .withColumn('time', F.date_format('ts', 'HH:mm:ss'))\n",
" .withColumn('date', F.date_format('ts', 'yyyy-MM-dd'))\n",
" .withColumn('offline_time', F.date_format('offline_timestamp', 'HH:mm:ss'))\n",
" .withColumn('offline_date', F.date_format('offline_timestamp', 'yyyy-MM-dd')) \n",
" .withColumn('offline_time', F.date_format('offline_timestamped', 'HH:mm:ss'))\n",
" .withColumn('offline_date', F.date_format('offline_timestamped', 'yyyy-MM-dd')) \n",
" )\n",
"\n",
"def clean_track_df(df: DataFrame) -> DataFrame:\n",
" return(df\n",
" .transform(rename_columns, SPOTIFY_RENAMES, MUSIC_DROP_COLUMNS)\n",
" .transform(track_df)\n",
" .transform(parse_timestamps, [\"ts\", \"offline_timestamp\"])\n",
" # .transform(parse_timestamps, [\"ts\", \"offline_timestamp\"])\n",
" # .transform(parse_booleans, [\"offline\", \"shuffle\", \"skipped\"]) ##returns only the selected columns\n",
" .transform(add_minutes_played, \"ms_played\", \"ms_played\")\n",
" .transform(add_time_parts, \"ts\")\n",
@@ -123,7 +123,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 11,
"id": "817475cf",
"metadata": {},
"outputs": [],
@@ -163,7 +163,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 12,
"id": "c609291c",
"metadata": {},
"outputs": [],
@@ -185,7 +185,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 13,
"id": "4cc14bd0",
"metadata": {},
"outputs": [
@@ -193,58 +193,33 @@
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Using incubator modules: jdk.incubator.vector\n",
"Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties\n",
"26/10/18 17:36:31 WARN Utils: Your hostname, MacBook-Neo-232.local, resolves to a loopback address: 127.0.0.1; using 192.168.10.130 instead (on interface en0)\n",
"26/10/18 17:36:31 WARN Utils: Set SPARK_LOCAL_IP if you need to bind to another address\n",
"26/10/18 17:36:32 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
"Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties\n",
"Setting default log level to \"WARN\".\n",
"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n"
" \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/carlacastillo/Documents/Gitea/media_ml/postgresql.jar\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"26/10/18 17:36:36 WARN SparkStringUtils: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n",
" \r"
" Raw DF Rows: 105477\n",
" Transformed DF Rows: 102626\n",
" Row difference is 2851\n"
]
}
],
"source": [
" ## MAIN ## \n",
"\n",
"# app_name = \"ml_media\"\n",
"# spark = start_spark(app_name)\n",
"\n",
"# raw_df = read_json(spark, music_source_paths())\n",
"# raw_df.show(1)\n",
"\n",
"# transformed_df = clean_track_df(raw_df)\n",
"# # print(transformed_df.tail(5))\n",
"# # transformed_df.printSchema()\n",
"\n",
"# print(f\" Raw DF Rows: {raw_df.count()}\")\n",
"# print(f\" Transformed DF Rows: {transformed_df.count()}\")\n",
"# print(f\" Row difference is {raw_df.count() - transformed_df.count()}\") \n",
"\n",
"\n",
"def main():\n",
" app_name = \"ml_media\"\n",
" spark = start_spark(app_name)\n",
" print(spark.conf.get(\"spark.jars\", \"NOT SET\"))\n",
"\n",
" table_name = \"spotify_data\"\n",
" raw_df = read_json(spark, music_source_paths())\n",
" transformed_df = clean_track_df(raw_df)\n",
" load_data(transformed_df, table_name)\n",
"\n",
" print(f\" Raw DF Rows: {raw_df.count()}\")\n",
" print(f\" Transformed DF Rows: {transformed_df.count()}\")\n",
" print(f\" Row difference is {raw_df.count() - transformed_df.count()}\") \n",
" spark.stop\n",
"if __name__ == \"__main__\":\n",
" main()\n",
@@ -255,7 +230,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 14,
"id": "faa6a8cb",
"metadata": {},
"outputs": [
@@ -266,7 +241,7 @@
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m## Potential Test Help\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m print(metadata_df(music_df).rdd.getNumPartitions()) \u001b[38;5;66;03m# Partitions for Pyspark\u001b[39;00m\n\u001b[32m 3\u001b[39m print(len(metadata_df(music_df).columns)) \u001b[38;5;66;03m# # of columns in JSON\u001b[39;00m\n\u001b[32m 4\u001b[39m null_counts = null_counts_by_column(metadata_df(music_df)) \u001b[38;5;66;03m# Null counts by column\u001b[39;00m\n\u001b[32m 5\u001b[39m null_counts.show(vertical=\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# Print Null counts by column_df\u001b[39;00m\n",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m## Potential Test Help\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m print(metadata_df(music_df).rdd.getNumPartitions()) \u001b[38;5;66;03m# Partitions for Pyspark\u001b[39;00m\n\u001b[32m 3\u001b[39m print(len(metadata_df(music_df).columns)) \u001b[38;5;66;03m# # of columns in JSON\u001b[39;00m\n\u001b[32m 4\u001b[39m null_counts = null_counts_by_column(metadata_df(music_df)) \u001b[38;5;66;03m# Null counts by column\u001b[39;00m\n\u001b[32m 5\u001b[39m null_counts.show(vertical=\u001b[38;5;28;01mTrue\u001b[39;00m) \u001b[38;5;66;03m# Print Null counts by column_df\u001b[39;00m\n",
"\u001b[31mNameError\u001b[39m: name 'music_df' is not defined"
]
}