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log2ml/3-3-security-and-performance-log-analysis-sw/Elasticsearch-Pandas-vs-Polars-May-15-2024.ipynb
marius af9146ba78 sorting of files
2024-06-25 16:48:23 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "fa00684b-2e50-4cf5-b8f3-bd28f583391b",
"metadata": {},
"source": [
"# Elasticsearch and tabular integration\n",
"\n",
"Elasticsearch is a NoSQL database, which indexes JSON records.\n",
"In the following the Winlog Beat index gets queried, which holds Windows EventLog data.\n",
"The Elasticsearch SQL endpoint is used to define a query, and the resulting data is retrieved as a JSON stream.\n",
"The data gets read into in-memory dataframe objects which allow data-manipulation tasks.\n",
"\n",
"In-memory processing can be difficult if the datasets grow large.\n",
"Therefore a comparison is made between two polular in-memory dataframe libraries:\n",
"\n",
"1.) Pandas\n",
"2.) Polars\n",
"\n",
"The memory footprint is assessed, because runtime memory is the limiting factor for the implementations."
]
},
{
"cell_type": "markdown",
"id": "0bd2a29a-68e0-42a6-b46d-13aa95eca26d",
"metadata": {},
"source": [
"## Versions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "9ea2636c-b07e-44c8-ba5f-aace81dba245",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: pandas\n",
"Version: 2.1.4\n"
]
}
],
"source": [
"!pip show pandas | grep -E 'Name:|Version:'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "590ac008-153d-4603-83bc-02f87a7a8d60",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Name: polars\n",
"Version: 0.20.26\n"
]
}
],
"source": [
"!pip show polars | grep -E 'Name:|Version:'"
]
},
{
"cell_type": "markdown",
"id": "b7760198-a975-4810-b3d4-25554e4fe3c4",
"metadata": {},
"source": [
"## Elasticsearch API\n",
"\n",
"The Elasticsearch API uses HTTP and is available on port 9200.\n",
"\n",
"The index \"winlogbeat-\" contains data from the period. It's a periodically rotating index.\n",
"\n",
"Here the Elasticsearch DSL is used, and an event timeline is being retrieved, in time-descending order.\n",
"\n",
"The resulting JSON data is piped to the `jq` utility, which is prettier on a command-line.\n",
"Only the first JSON record is analyzed. \n",
"\n",
"The output shows the index and the timestamp."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "cce35135-52d7-484b-bbae-d1c876836433",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"index\": \"winlogbeat-7.10.0-2024.05.15-000008\",\n",
" \"timestamp\": \"2024-05-15T15:57:22.877Z\"\n",
"}\n"
]
}
],
"source": [
"%%bash\n",
"curl -s -X GET \"http://192.168.20.106:9200/winlogbeat-*/_search\" -H 'Content-Type: application/json' -d '{\n",
" \"size\": 1,\n",
" \"sort\": [\n",
" {\n",
" \"@timestamp\": {\n",
" \"order\": \"desc\"\n",
" }\n",
" }\n",
" ]\n",
"}' | jq '.hits.hits[0] | {index: ._index, timestamp: ._source[\"@timestamp\"]}'\n"
]
},
{
"cell_type": "markdown",
"id": "4e6efd1c-2f22-4f5b-9ad7-b569065f182d",
"metadata": {},
"source": [
"The following Bash command shows a SQL query.\n",
"\n",
"The `Limit 1` is a common SQL statement.\n",
"The output is further limited with the `head` command. Only the first fields of the first record are shown.\n",
"\n",
"By default the order of records doesn't represent a timeline, but the order of records in the index."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "2d3f97cb-cc71-4d81-ad9c-df11125cd109",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"size\": 1,\n",
" \"_source\": {\n"
]
}
],
"source": [
"%%bash\n",
"curl -s -X POST \"http://192.168.20.106:9200/_sql/translate\" -H 'Content-Type: application/json' -d '{\n",
" \"query\": \"SELECT * FROM \\\"winlogbeat-7.10.0-2024.05.15-*\\\" LIMIT 1\"\n",
"}' | jq | head -n 3\n"
]
},
{
"cell_type": "markdown",
"id": "9e42a51f-e5a0-480d-9e2e-9744a288aef7",
"metadata": {},
"source": [
"## Elasticsearch tabular-integration and Pandas\n",
"\n",
"Pandas is the de-facto standard for data-manipulation of small to medium datasets in Data Science.\n",
"It offers robust functions for in-memory data transactions and tabular feature integration.\n",
"\n",
"In the following the expansion of JSON data is used to allow a simple feature selection for further processing.\n",
"The data is returned from Elasticsearch, from an SQL query.\n",
"\n",
"The data is provided via a Scrolling API, which delivers a portion of the data each time.\n",
"This simplifies batch processing of large datasets."
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "f8747542-a2d1-4814-8dc2-acf172db2d0c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieved 1000 documents.\n",
"Retrieved 2000 documents.\n",
"Retrieved 3000 documents.\n",
"Retrieved 4000 documents.\n",
"Retrieved 5000 documents.\n",
"Files have been written.\n"
]
}
],
"source": [
"import requests\n",
"import pandas as pd\n",
"import json\n",
"\n",
"# Function to recursively normalize nested columns in a DataFrame\n",
"def recursively_normalize(data):\n",
" df = pd.json_normalize(data)\n",
" while True:\n",
" nested_cols = [col for col in df.columns if isinstance(df[col].iloc[0], (dict, list))]\n",
" if not nested_cols:\n",
" break\n",
" for col in nested_cols:\n",
" if isinstance(df[col].iloc[0], dict):\n",
" normalized = pd.json_normalize(df[col])\n",
" df = df.drop(columns=[col]).join(normalized)\n",
" elif isinstance(df[col].iloc[0], list):\n",
" df = df.explode(col)\n",
" normalized = pd.json_normalize(df[col])\n",
" df = df.drop(columns=[col]).join(normalized)\n",
" return df\n",
"\n",
"# Function to fetch the next batch using the cursor\n",
"def fetch_next_batch(cursor):\n",
" response = requests.post(\n",
" f\"{base_url}/_sql?format=json\",\n",
" headers={\"Content-Type\": \"application/json\"},\n",
" json={\"cursor\": cursor}\n",
" ).json()\n",
" return response\n",
"\n",
"# Elasticsearch base URL\n",
"base_url = \"http://192.168.20.106:9200\"\n",
"# Index name\n",
"index = \"winlogbeat-*\"\n",
"\n",
"# SQL query for initial search\n",
"sql_query = \"\"\"\n",
"SELECT \"@timestamp\", host.hostname, host.ip, log.level, winlog.event_id, winlog.task, message FROM \"winlogbeat-7.10.0-2024.05.15-*\"\n",
"LIMIT 5000\n",
"\"\"\"\n",
"\n",
"# Initial search request to start scrolling\n",
"initial_response = requests.post(\n",
" f\"{base_url}/_sql?format=json\",\n",
" headers={\"Content-Type\": \"application/json\"},\n",
" json={\n",
" \"query\": sql_query,\n",
" \"field_multi_value_leniency\": True\n",
" }\n",
").json()\n",
"\n",
"# Extract the cursor for scrolling\n",
"cursor = initial_response.get('cursor')\n",
"rows = initial_response.get('rows')\n",
"columns = [col['name'] for col in initial_response['columns']]\n",
"\n",
"# Initialize CSV file (assumes the first batch is not empty)\n",
"if rows:\n",
" df = pd.DataFrame(rows, columns=columns)\n",
" df = recursively_normalize(df.to_dict(orient='records'))\n",
" df.to_csv(\"lab_logs_normal_activity.csv\", mode='w', index=False, header=True)\n",
"\n",
"# Track total documents retrieved\n",
"total_documents_retrieved = len(rows)\n",
"print(f\"Retrieved {total_documents_retrieved} documents.\")\n",
"\n",
"# Loop to fetch subsequent batches of documents until no more documents are left\n",
"while cursor:\n",
" # Fetch next batch of documents using cursor\n",
" response = fetch_next_batch(cursor)\n",
" \n",
" # Update cursor for the next batch\n",
" cursor = response.get('cursor')\n",
" rows = response.get('rows')\n",
" \n",
" # If no rows, break out of the loop\n",
" if not rows:\n",
" break\n",
" \n",
" # Normalize data and append to CSV\n",
" df = pd.DataFrame(rows, columns=columns)\n",
" df = recursively_normalize(df.to_dict(orient='records'))\n",
" \n",
" # Append to CSV file without headers\n",
" df.to_csv(\"lab_logs_normal_activity.csv\", mode='a', index=False, header=False)\n",
" \n",
" # Convert DataFrame to JSON, line by line\n",
" json_lines = df.to_json(orient='records', lines=True).splitlines()\n",
" # Append each line to an existing JSON file\n",
" with open(\"lab_logs_normal_activity.json\", 'a') as file:\n",
" for line in json_lines:\n",
" file.write(line + '\\n') # Append each line and add a newline\n",
" \n",
" # Update total documents retrieved\n",
" total_documents_retrieved += len(rows)\n",
" \n",
" print(f\"Retrieved {total_documents_retrieved} documents.\")\n",
"\n",
"print(\"Files have been written.\")\n"
]
},
{
"cell_type": "markdown",
"id": "1b236f1c-7060-43a0-b4e7-2b9697114a3e",
"metadata": {},
"source": [
"## Alternative approach with polars\n",
"\n",
"Polars is a newer tabular-integration library, which challenges Pandas. \n",
"It's supposed to me more memory efficient, because it's backend is written in Rust."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78e37d61-4554-4bbb-99d9-ecbb2e892557",
"metadata": {},
"outputs": [],
"source": [
"%pip install polars"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "328b8d13-3cc0-4239-b3e5-d98da9bb51ec",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Retrieved 1000 documents.\n",
"Retrieved 2000 documents.\n",
"Retrieved 3000 documents.\n",
"Retrieved 4000 documents.\n",
"Retrieved 5000 documents.\n",
"Files have been written.\n"
]
}
],
"source": [
"import requests\n",
"import polars as pl\n",
"import json\n",
"\n",
"# Function to recursively unnest nested columns in a DataFrame\n",
"def recursively_unnest(df):\n",
" nested = True\n",
" while nested:\n",
" nested = False\n",
" for col in df.columns:\n",
" if df[col].dtype == pl.List:\n",
" df = df.explode(col)\n",
" nested = True\n",
" elif df[col].dtype == pl.Struct:\n",
" df = df.unnest(col)\n",
" nested = True\n",
" return df\n",
"\n",
"# Function to fetch the next batch using the cursor\n",
"def fetch_next_batch(cursor):\n",
" response = requests.post(\n",
" f\"{base_url}/_sql?format=json\",\n",
" headers={\"Content-Type\": \"application/json\"},\n",
" json={\"cursor\": cursor}\n",
" ).json()\n",
" return response\n",
"\n",
"# Elasticsearch base URL\n",
"base_url = \"http://192.168.20.106:9200\"\n",
"# Index name\n",
"index = \"winlogbeat-*\"\n",
"\n",
"# SQL query for initial search\n",
"sql_query = \"\"\"\n",
"SELECT \"@timestamp\", host.hostname, host.ip, log.level, winlog.event_id, winlog.task, message FROM \"winlogbeat-7.10.0-2024.05.15-*\"\n",
"LIMIT 5000\n",
"\"\"\"\n",
"\n",
"# Initial search request to start scrolling\n",
"initial_response = requests.post(\n",
" f\"{base_url}/_sql?format=json\",\n",
" headers={\"Content-Type\": \"application/json\"},\n",
" json={\n",
" \"query\": sql_query,\n",
" \"field_multi_value_leniency\": True\n",
" }\n",
").json()\n",
"\n",
"# Extract the cursor for scrolling\n",
"cursor = initial_response.get('cursor')\n",
"rows = initial_response.get('rows')\n",
"columns = [col['name'] for col in initial_response['columns']]\n",
"\n",
"# Initialize CSV file (assumes the first batch is not empty)\n",
"if rows:\n",
" df = pl.DataFrame(rows, schema=columns)\n",
" df = recursively_unnest(df)\n",
" df.write_csv(\"lab_logs_normal_activity.csv\", include_header=True)\n",
"\n",
"# Track total documents retrieved\n",
"total_documents_retrieved = len(rows)\n",
"print(f\"Retrieved {total_documents_retrieved} documents.\")\n",
"\n",
"# Loop to fetch subsequent batches of documents until no more documents are left\n",
"while cursor:\n",
" # Fetch next batch of documents using cursor\n",
" response = fetch_next_batch(cursor)\n",
" \n",
" # Update cursor for the next batch\n",
" cursor = response.get('cursor')\n",
" rows = response.get('rows')\n",
" \n",
" # If no rows, break out of the loop\n",
" if not rows:\n",
" break\n",
" \n",
" # Normalize data and append to CSV\n",
" df = pl.DataFrame(rows, schema=columns)\n",
" df = recursively_unnest(df)\n",
" \n",
" # Manually write the CSV to avoid headers\n",
" with open(\"lab_logs_normal_activity.csv\", 'a') as f:\n",
" df.write_csv(f, include_header=False)\n",
" \n",
" # Convert DataFrame to JSON, line by line\n",
" json_lines = [json.dumps(record) for record in df.to_dicts()]\n",
" # Append each line to an existing JSON file\n",
" with open(\"lab_logs_normal_activity.json\", 'a') as file:\n",
" for line in json_lines:\n",
" file.write(line + '\\n') # Append each line and add a newline\n",
" \n",
" # Update total documents retrieved\n",
" total_documents_retrieved += len(rows)\n",
" \n",
" print(f\"Retrieved {total_documents_retrieved} documents.\")\n",
"\n",
"print(\"Files have been written.\")\n"
]
},
{
"cell_type": "markdown",
"id": "3dd720a7-c716-4d41-9ab4-37652acca137",
"metadata": {
"tags": []
},
"source": [
"## Memory footprint and profile comparison"
]
},
{
"cell_type": "markdown",
"id": "0e433322-5120-4451-9aa4-cfd5795aaa24",
"metadata": {},
"source": [
"A JSON schema is provided in both cases to improve the comparison."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "93a2116d-1fdd-432b-a48c-8be77c67e0e7",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
]
}
],
"source": [
"%reset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bdf4020a-3b23-47e0-b7c4-3335bf3d5d8c",
"metadata": {},
"outputs": [],
"source": [
"!pip install git+https://github.com/H4dr1en/jupyterflame.git"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "427b969f-0e68-44da-b74d-5cada875f74f",
"metadata": {},
"outputs": [],
"source": [
"# directly on the shell within the conda env: conda install -y perl"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "08db61e8-d70f-4434-bfcc-6225405b81f2",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The jupyterflame extension is already loaded. To reload it, use:\n",
" %reload_ext jupyterflame\n"
]
}
],
"source": [
"%load_ext jupyterflame"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "eefffe2a-f61c-47c8-90e3-d0de0ab932d6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"@timestamp object\n",
"host.hostname object\n",
"host.ip object\n",
"log.level object\n",
"winlog.event_id int64\n",
"winlog.task object\n",
"message object\n",
"dtype: object\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"# Read a small chunk of the JSON file\n",
"file_path = \"lab_logs_normal_activity.json\"\n",
"pd_df = pd.read_json(file_path, lines=True, nrows=10)\n",
"\n",
"print(pd_df.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "0b2be27e-a56c-411b-bbff-dc42e533ca80",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Polars Schema: {'@timestamp': String, 'host.hostname': String, 'host.ip': String, 'log.level': String, 'winlog.event_id': Int64, 'winlog.task': String, 'message': String}\n",
"Pandas Schema: {'@timestamp': 'str', 'host.hostname': 'str', 'host.ip': 'str', 'log.level': 'str', 'winlog.event_id': 'int64', 'winlog.task': 'str', 'message': 'str'}\n"
]
}
],
"source": [
"import polars as pl\n",
"\n",
"# Define the mapping from Pandas dtype to Polars dtype\n",
"dtype_mapping = {\n",
" \"object\": pl.Utf8,\n",
" \"int64\": pl.Int64,\n",
" \"float64\": pl.Float64,\n",
" # Add more mappings if needed\n",
"}\n",
"\n",
"pandas_dtype_mapping = {\n",
" \"object\": \"str\",\n",
" \"int64\": \"int64\",\n",
" \"float64\": \"float64\",\n",
" # Add more mappings if needed\n",
"}\n",
"\n",
"\n",
"# Generate the schema for Polars from Pandas dtype\n",
"polars_schema = {col: dtype_mapping[str(dtype)] for col, dtype in pd_df.dtypes.items()}\n",
"print(\"Polars Schema:\", polars_schema)\n",
"\n",
"pandas_schema = {col: pandas_dtype_mapping[str(dtype)] for col, dtype in pd_df.dtypes.items()}\n",
"print(\"Pandas Schema:\", pandas_schema)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5ccc9d58-8e27-43d0-bf69-7f2ff44c9874",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def test_polars():\n",
" # Read the JSON file using the defined schema\n",
" lazy_df = pl.scan_ndjson(file_path)\n",
"\n",
" # Collect the LazyFrame to a DataFrame\n",
" pl_df = lazy_df.collect()\n",
"\n",
" # Convert columns to the correct data types according to the schema\n",
" pl_df = pl_df.with_columns([pl.col(col).cast(dtype) for col, dtype in polars_schema.items()])\n",
"\n",
" # Print the DataFrame and its memory usage\n",
" print(pl_df)\n",
"\n",
" num_rows_polars = pl_df.shape[0]\n",
"\n",
" print(f\"Polars DataFarme number of rows: {num_rows_polars}\")\n",
" print(f\"Polars DataFrame memory usage: {pl_df.estimated_size() / (1024 ** 2):.2f} MB\")"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "6e1ca70b-9aae-43af-b1c0-cc8d6f19a7ce",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shape: (8_000, 7)\n",
"┌──────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┬─────────────┐\n",
"│ @timestamp ┆ host.hostna ┆ host.ip ┆ log.level ┆ winlog.even ┆ winlog.task ┆ message │\n",
"│ --- ┆ me ┆ --- ┆ --- ┆ t_id ┆ --- ┆ --- │\n",
"│ str ┆ --- ┆ str ┆ str ┆ --- ┆ str ┆ str │\n",
"│ ┆ str ┆ ┆ ┆ i64 ┆ ┆ │\n",
"╞══════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╪═════════════╡\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
"│ 5:57:18.471Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ value set ┆ value set: │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ (rule: ┆ RuleName: … │\n",
"│ ┆ ┆ ┆ ┆ ┆ Regi… ┆ │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
"│ 5:57:18.471Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ value set ┆ value set: │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ (rule: ┆ RuleName: … │\n",
"│ ┆ ┆ ┆ ┆ ┆ Regi… ┆ │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
"│ 5:57:18.471Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ value set ┆ value set: │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ (rule: ┆ RuleName: … │\n",
"│ ┆ ┆ ┆ ┆ ┆ Regi… ┆ │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
"│ 5:57:18.471Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ value set ┆ value set: │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ (rule: ┆ RuleName: … │\n",
"│ ┆ ┆ ┆ ┆ ┆ Regi… ┆ │\n",
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"└──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\n",
"Pandas DataFarme number of rows: 8000\n",
"Polars DataFrame memory usage: 4.76 MB\n",
" "
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],
"source": [
"%%flame -q --inverted\n",
"test_polars()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "87f043b9-6cfa-4c3b-b550-25818e29bd45",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"shape: (8_000, 7)\n",
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"│ ┆ str ┆ ┆ ┆ i64 ┆ ┆ │\n",
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"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
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"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
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"│ ┆ ┆ ┆ ┆ ┆ Regi… ┆ │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 13 ┆ Registry ┆ Registry │\n",
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"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 4663 ┆ Removable ┆ An attempt │\n",
"│ 6:10:07.136Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ Storage ┆ was made to │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ ┆ access … │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 4663 ┆ Removable ┆ An attempt │\n",
"│ 6:10:07.136Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ Storage ┆ was made to │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ ┆ access … │\n",
"│ 2024-05-15T1 ┆ win10 ┆ fe80::24b4: ┆ information ┆ 4663 ┆ Removable ┆ An attempt │\n",
"│ 6:10:07.149Z ┆ ┆ 3691:44a6:3 ┆ ┆ ┆ Storage ┆ was made to │\n",
"│ ┆ ┆ 8a1 ┆ ┆ ┆ ┆ access … │\n",
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"└──────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┴─────────────┘\n",
"Pandas DataFarme number of rows: 8000\n",
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" "
]
},
{
"data": {
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" 256 function calls (253 primitive calls) in 0.020 seconds\n",
"\n",
" Ordered by: internal time\n",
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},
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}
],
"source": [
"%%prun\n",
"test_polars()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "547f7253-cd62-44c6-8d7a-840dab2dbbbd",
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"source": [
"def test_pandas():\n",
" # Load the JSON file into a Pandas DataFrame\n",
" pd_df = pd.read_json(file_path, lines=True, dtype=pandas_schema)\n",
" pd_memory_usage = pd_df.memory_usage(deep=True).sum()\n",
"\n",
" # Get the number of rows in the Pandas DataFrame\n",
" num_rows_pandas = pd_df.shape[0]\n",
"\n",
" print(pd_df)\n",
"\n",
" print(f\"Pandas DataFarme number of rows: {num_rows_pandas}\")\n",
" print(f\"Pandas DataFrame memory usage: {pd_memory_usage / (1024 ** 2):.2f} MB\") \n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "50230892-0a0e-4144-a17e-27d2714de1e8",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" @timestamp host.hostname host.ip \\\n",
"0 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"1 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"2 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"3 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"4 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"... ... ... ... \n",
"7995 2024-05-15T16:10:07.128Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7996 2024-05-15T16:10:07.136Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7997 2024-05-15T16:10:07.136Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7998 2024-05-15T16:10:07.149Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7999 2024-05-15T16:10:07.149Z win10 fe80::24b4:3691:44a6:38a1 \n",
"\n",
" log.level winlog.event_id winlog.task \\\n",
"0 information 13 Registry value set (rule: RegistryEvent) \n",
"1 information 13 Registry value set (rule: RegistryEvent) \n",
"2 information 13 Registry value set (rule: RegistryEvent) \n",
"3 information 13 Registry value set (rule: RegistryEvent) \n",
"4 information 13 Registry value set (rule: RegistryEvent) \n",
"... ... ... ... \n",
"7995 information 4663 Removable Storage \n",
"7996 information 4663 Removable Storage \n",
"7997 information 4663 Removable Storage \n",
"7998 information 4663 Removable Storage \n",
"7999 information 4663 Removable Storage \n",
"\n",
" message \n",
"0 Registry value set:\\nRuleName: InvDB-Ver\\nEven... \n",
"1 Registry value set:\\nRuleName: InvDB-Path\\nEve... \n",
"2 Registry value set:\\nRuleName: InvDB-Pub\\nEven... \n",
"3 Registry value set:\\nRuleName: InvDB-CompileTi... \n",
"4 Registry value set:\\nRuleName: InvDB-Ver\\nEven... \n",
"... ... \n",
"7995 An attempt was made to access an object.\\n\\nSu... \n",
"7996 An attempt was made to access an object.\\n\\nSu... \n",
"7997 An attempt was made to access an object.\\n\\nSu... \n",
"7998 An attempt was made to access an object.\\n\\nSu... \n",
"7999 An attempt was made to access an object.\\n\\nSu... \n",
"\n",
"[8000 rows x 7 columns]\n",
"Pandas DataFarme number of rows: 8000\n",
"Pandas DataFrame memory usage: 7.56 MB\n",
" "
]
},
{
"data": {
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"\t</linearGradient>\n",
"</defs>\n",
"<style type=\"text/css\">\n",
"\ttext { font-family:Verdana; font-size:12px; fill:rgb(0,0,0); }\n",
"\t#search { opacity:0.1; cursor:pointer; }\n",
"\t#search:hover, #search.show { opacity:1; }\n",
"\t#subtitle { text-anchor:middle; font-color:rgb(160,160,160); }\n",
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"</style>\n",
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"\t\t\t\te.preventDefault();\n",
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"\t\t\tif (target.classList.contains(\"parent\")) unzoom();\n",
"\t\t\tzoom(target);\n",
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"\t\telse if (e.target.id == \"unzoom\") unzoom();\n",
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"\t\treturn;\n",
"\t}\n",
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"\t\tvar parent = node.parentElement;\n",
"\t\tif (!parent) return;\n",
"\t\tif (parent.id == \"frames\") return node;\n",
"\t\treturn find_group(parent);\n",
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"\t\tif (e.attributes[\"_orig_\" + attr] != undefined) return;\n",
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"\t}\n",
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"\t\te.removeAttribute(\"_orig_\"+attr);\n",
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"\t\treturn (text)\n",
"\t}\n",
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"\t\tvar func = g_to_text(e);\n",
"\t\t// if there's any manipulation we want to do to the function\n",
"\t\t// name before it's searched, do it here before returning.\n",
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"\t\tvar r = find_child(e, \"rect\");\n",
"\t\tvar t = find_child(e, \"text\");\n",
"\t\tvar w = parseFloat(r.attributes.width.value) -3;\n",
"\t\tvar txt = find_child(e, \"title\").textContent.replace(/\\([^(]*\\)$/,\"\");\n",
"\t\tt.attributes.x.value = parseFloat(r.attributes.x.value) + 3;\n",
"\n",
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"\t\t\tif (e.attributes.x != undefined) {\n",
"\t\t\t\torig_save(e, \"x\");\n",
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"\t\t\t\torig_save(e, \"width\");\n",
"\t\t\t\te.attributes.width.value = parseInt(svg.width.baseVal.value) - (10 * 2);\n",
"\t\t\t}\n",
"\t\t}\n",
"\t\tif (e.childNodes == undefined) return;\n",
"\t\tfor (var i = 0, c = e.childNodes; i < c.length; i++) {\n",
"\t\t\tzoom_parent(c[i]);\n",
"\t\t}\n",
"\t}\n",
"\tfunction zoom(node) {\n",
"\t\tvar attr = find_child(node, \"rect\").attributes;\n",
"\t\tvar width = parseFloat(attr.width.value);\n",
"\t\tvar xmin = parseFloat(attr.x.value);\n",
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"\t\tvar ymin = parseFloat(attr.y.value);\n",
"\t\tvar ratio = (svg.width.baseVal.value - 2 * 10) / width;\n",
"\n",
"\t\t// XXX: Workaround for JavaScript float issues (fix me)\n",
"\t\tvar fudge = 0.0001;\n",
"\n",
"\t\tunzoombtn.classList.remove(\"hide\");\n",
"\n",
"\t\tvar el = document.getElementById(\"frames\").children;\n",
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"\t\t\tvar ex = parseFloat(a.x.value);\n",
"\t\t\tvar ew = parseFloat(a.width.value);\n",
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"\t\t\t// Is it an ancestor\n",
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"\t\t\t\tupstack = parseFloat(a.y.value) > ymin;\n",
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"\t\tvar el = document.getElementById(\"frames\").children;\n",
"\t\tfor(var i = 0; i < el.length; i++) {\n",
"\t\t\tel[i].classList.remove(\"parent\");\n",
"\t\t\tel[i].classList.remove(\"hide\");\n",
"\t\t\tzoom_reset(el[i]);\n",
"\t\t\tupdate_text(el[i]);\n",
"\t\t}\n",
"\t}\n",
"\n",
"\t// search\n",
"\tfunction reset_search() {\n",
"\t\tvar el = document.querySelectorAll(\"#frames rect\");\n",
"\t\tfor (var i = 0; i < el.length; i++) {\n",
"\t\t\torig_load(el[i], \"fill\")\n",
"\t\t}\n",
"\t}\n",
"\tfunction search_prompt() {\n",
"\t\tif (!searching) {\n",
"\t\t\tvar term = prompt(\"Enter a search term (regexp \" +\n",
"\t\t\t \"allowed, eg: ^ext4_)\", \"\");\n",
"\t\t\tif (term != null) {\n",
"\t\t\t\tsearch(term)\n",
"\t\t\t}\n",
"\t\t} else {\n",
"\t\t\treset_search();\n",
"\t\t\tsearching = 0;\n",
"\t\t\tsearchbtn.classList.remove(\"show\");\n",
"\t\t\tsearchbtn.firstChild.nodeValue = \"Search\"\n",
"\t\t\tmatchedtxt.classList.add(\"hide\");\n",
"\t\t\tmatchedtxt.firstChild.nodeValue = \"\"\n",
"\t\t}\n",
"\t}\n",
"\tfunction search(term) {\n",
"\t\tvar re = new RegExp(term);\n",
"\t\tvar el = document.getElementById(\"frames\").children;\n",
"\t\tvar matches = new Object();\n",
"\t\tvar maxwidth = 0;\n",
"\t\tfor (var i = 0; i < el.length; i++) {\n",
"\t\t\tvar e = el[i];\n",
"\t\t\tvar func = g_to_func(e);\n",
"\t\t\tvar rect = find_child(e, \"rect\");\n",
"\t\t\tif (func == null || rect == null)\n",
"\t\t\t\tcontinue;\n",
"\n",
"\t\t\t// Save max width. Only works as we have a root frame\n",
"\t\t\tvar w = parseFloat(rect.attributes.width.value);\n",
"\t\t\tif (w > maxwidth)\n",
"\t\t\t\tmaxwidth = w;\n",
"\n",
"\t\t\tif (func.match(re)) {\n",
"\t\t\t\t// highlight\n",
"\t\t\t\tvar x = parseFloat(rect.attributes.x.value);\n",
"\t\t\t\torig_save(rect, \"fill\");\n",
"\t\t\t\trect.attributes.fill.value = \"rgb(230,0,230)\";\n",
"\n",
"\t\t\t\t// remember matches\n",
"\t\t\t\tif (matches[x] == undefined) {\n",
"\t\t\t\t\tmatches[x] = w;\n",
"\t\t\t\t} else {\n",
"\t\t\t\t\tif (w > matches[x]) {\n",
"\t\t\t\t\t\t// overwrite with parent\n",
"\t\t\t\t\t\tmatches[x] = w;\n",
"\t\t\t\t\t}\n",
"\t\t\t\t}\n",
"\t\t\t\tsearching = 1;\n",
"\t\t\t}\n",
"\t\t}\n",
"\t\tif (!searching)\n",
"\t\t\treturn;\n",
"\n",
"\t\tsearchbtn.classList.add(\"show\");\n",
"\t\tsearchbtn.firstChild.nodeValue = \"Reset Search\";\n",
"\n",
"\t\t// calculate percent matched, excluding vertical overlap\n",
"\t\tvar count = 0;\n",
"\t\tvar lastx = -1;\n",
"\t\tvar lastw = 0;\n",
"\t\tvar keys = Array();\n",
"\t\tfor (k in matches) {\n",
"\t\t\tif (matches.hasOwnProperty(k))\n",
"\t\t\t\tkeys.push(k);\n",
"\t\t}\n",
"\t\t// sort the matched frames by their x location\n",
"\t\t// ascending, then width descending\n",
"\t\tkeys.sort(function(a, b){\n",
"\t\t\treturn a - b;\n",
"\t\t});\n",
"\t\t// Step through frames saving only the biggest bottom-up frames\n",
"\t\t// thanks to the sort order. This relies on the tree property\n",
"\t\t// where children are always smaller than their parents.\n",
"\t\tvar fudge = 0.0001;\t// JavaScript floating point\n",
"\t\tfor (var k in keys) {\n",
"\t\t\tvar x = parseFloat(keys[k]);\n",
"\t\t\tvar w = matches[keys[k]];\n",
"\t\t\tif (x >= lastx + lastw - fudge) {\n",
"\t\t\t\tcount += w;\n",
"\t\t\t\tlastx = x;\n",
"\t\t\t\tlastw = w;\n",
"\t\t\t}\n",
"\t\t}\n",
"\t\t// display matched percent\n",
"\t\tmatchedtxt.classList.remove(\"hide\");\n",
"\t\tvar pct = 100 * count / maxwidth;\n",
"\t\tif (pct != 100) pct = pct.toFixed(1)\n",
"\t\tmatchedtxt.firstChild.nodeValue = \"Matched: \" + pct + \"%\";\n",
"\t}\n",
"]]>\n",
"</script>\n",
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"<text id=\"details\" x=\"10.00\" y=\"389\" > </text>\n",
"<text id=\"unzoom\" x=\"10.00\" y=\"24\" class=\"hide\">Reset Zoom</text>\n",
"<text id=\"search\" x=\"1090.00\" y=\"24\" >Search</text>\n",
"<text id=\"matched\" x=\"1090.00\" y=\"389\" > </text>\n",
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"</g>\n",
"<g >\n",
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"</g>\n",
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"execution_count": 51,
"metadata": {},
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}
],
"source": [
"%%flame -q --inverted\n",
"test_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "4d47d7ec-d3f1-4fac-9933-ec330651a6f4",
"metadata": {
"tags": []
},
"outputs": [
{
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"text": [
" @timestamp host.hostname host.ip \\\n",
"0 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"1 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"2 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
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"4 2024-05-15T15:57:18.471Z win10 fe80::24b4:3691:44a6:38a1 \n",
"... ... ... ... \n",
"7995 2024-05-15T16:10:07.128Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7996 2024-05-15T16:10:07.136Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7997 2024-05-15T16:10:07.136Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7998 2024-05-15T16:10:07.149Z win10 fe80::24b4:3691:44a6:38a1 \n",
"7999 2024-05-15T16:10:07.149Z win10 fe80::24b4:3691:44a6:38a1 \n",
"\n",
" log.level winlog.event_id winlog.task \\\n",
"0 information 13 Registry value set (rule: RegistryEvent) \n",
"1 information 13 Registry value set (rule: RegistryEvent) \n",
"2 information 13 Registry value set (rule: RegistryEvent) \n",
"3 information 13 Registry value set (rule: RegistryEvent) \n",
"4 information 13 Registry value set (rule: RegistryEvent) \n",
"... ... ... ... \n",
"7995 information 4663 Removable Storage \n",
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"7997 information 4663 Removable Storage \n",
"7998 information 4663 Removable Storage \n",
"7999 information 4663 Removable Storage \n",
"\n",
" message \n",
"0 Registry value set:\\nRuleName: InvDB-Ver\\nEven... \n",
"1 Registry value set:\\nRuleName: InvDB-Path\\nEve... \n",
"2 Registry value set:\\nRuleName: InvDB-Pub\\nEven... \n",
"3 Registry value set:\\nRuleName: InvDB-CompileTi... \n",
"4 Registry value set:\\nRuleName: InvDB-Ver\\nEven... \n",
"... ... \n",
"7995 An attempt was made to access an object.\\n\\nSu... \n",
"7996 An attempt was made to access an object.\\n\\nSu... \n",
"7997 An attempt was made to access an object.\\n\\nSu... \n",
"7998 An attempt was made to access an object.\\n\\nSu... \n",
"7999 An attempt was made to access an object.\\n\\nSu... \n",
"\n",
"[8000 rows x 7 columns]\n",
"Pandas DataFarme number of rows: 8000\n",
"Pandas DataFrame memory usage: 7.56 MB\n",
" "
]
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{
"data": {
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" 1 0.000 0.000 0.000 0.000 common.py:503(get_compression_method)\n",
" 1 0.000 0.000 0.000 0.000 {built-in method builtins.min}\n",
" 1 0.000 0.000 0.000 0.000 _methods.py:47(_sum)\n",
" 1 0.000 0.000 0.000 0.000 dispatch.py:17(should_extension_dispatch)\n",
" 1 0.000 0.000 0.000 0.000 {method 'getvalue' of '_io.StringIO' objects}\n",
" 1 0.000 0.000 0.000 0.000 format.py:665(_initialize_sparsify)\n",
" 1 0.000 0.000 0.000 0.000 range.py:922(<listcomp>)\n",
" 1 0.000 0.000 0.000 0.000 threading.py:568(is_set)\n",
" 1 0.000 0.000 0.000 0.000 managers.py:536(nblocks)\n",
" 1 0.000 0.000 0.000 0.000 shape_base.py:207(_arrays_for_stack_dispatcher)\n",
" 1 0.000 0.000 0.000 0.000 inference.py:306(is_named_tuple)\n",
" 1 0.000 0.000 0.000 0.000 string.py:63(_need_to_wrap_around)\n",
" 1 0.000 0.000 0.000 0.000 _validators.py:226(validate_bool_kwarg)\n",
" 2 0.000 0.000 0.000 0.000 base.py:974(dtype)\n",
" 3 0.000 0.000 0.000 0.000 range.py:299(stop)\n",
" 2 0.000 0.000 0.000 0.000 managers.py:235(items)\n",
" 1 0.000 0.000 0.000 0.000 {built-in method _codecs.lookup_error}\n",
" 2 0.000 0.000 0.000 0.000 indexing.py:2665(need_slice)\n",
" 3 0.000 0.000 0.000 0.000 {built-in method builtins.id}\n",
" 2 0.000 0.000 0.000 0.000 format.py:959(<dictcomp>)\n",
" 1 0.000 0.000 0.000 0.000 managers.py:2242(<listcomp>)\n",
" 2 0.000 0.000 0.000 0.000 concat.py:766(_maybe_check_integrity)\n",
" 1 0.000 0.000 0.000 0.000 <frozen codecs>:260(__init__)\n",
" 1 0.000 0.000 0.000 0.000 nanops.py:209(_maybe_get_mask)\n",
" 1 0.000 0.000 0.000 0.000 {method 'pop' of 'list' objects}\n",
" 1 0.000 0.000 0.000 0.000 base.py:2756(_is_multi)\n",
" 1 0.000 0.000 0.000 0.000 function.py:64(__call__)\n",
" 1 0.000 0.000 0.000 0.000 format.py:697(_initialize_colspace)\n",
" 1 0.000 0.000 0.000 0.000 {method 'append' of 'collections.deque' objects}\n",
" 1 0.000 0.000 0.000 0.000 {method 'isdigit' of 'str' objects}\n",
" 2 0.000 0.000 0.000 0.000 {built-in method numpy.asanyarray}\n",
" 1 0.000 0.000 0.000 0.000 {method 'isspace' of 'str' objects}\n",
" 1 0.000 0.000 0.000 0.000 nanops.py:1491(_maybe_null_out)\n",
" 1 0.000 0.000 0.000 0.000 fromnumeric.py:1034(_argsort_dispatcher)\n",
" 1 0.000 0.000 0.000 0.000 _parser.py:1056(_could_be_tzname)\n",
" 1 0.000 0.000 0.000 0.000 numeric.py:2403(_array_equal_dispatcher)\n",
" 1 0.000 0.000 0.000 0.000 {method 'values' of 'dict' objects}\n",
" 2 0.000 0.000 0.000 0.000 {function FrozenList.__getitem__ at 0x7f0665c34860}\n",
" 1 0.000 0.000 0.000 0.000 _parser.py:186(__iter__)\n",
" 2 0.000 0.000 0.000 0.000 parse.py:108(_noop)\n",
" 1 0.000 0.000 0.000 0.000 {method 'reverse' of 'list' objects}\n",
" 2 0.000 0.000 0.000 0.000 _json.py:1102(__enter__)\n",
" 2 0.000 0.000 0.000 0.000 base.py:1954(nlevels)\n",
" 1 0.000 0.000 0.000 0.000 interactiveshell.py:637(get_ipython)\n",
" 1 0.000 0.000 0.000 0.000 range.py:228(_constructor)\n",
" 1 0.000 0.000 0.000 0.000 format.py:670(_initialize_formatters)\n",
" 1 0.000 0.000 0.000 0.000 {pandas._libs.lib.is_period}\n",
" 1 0.000 0.000 0.000 0.000 shape_base.py:77(_atleast_2d_dispatcher)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%prun\n",
"test_pandas()"
]
},
{
"cell_type": "markdown",
"id": "18519e3f-6d5e-477a-a3e5-1fd0e0b30fcc",
"metadata": {},
"source": [
"# Results\n",
"\n",
"Polars and Pandas both processed the same data (8000 rows, categorical data represented as strings).\n",
"\n",
"\n",
"## Versions\n",
"\n",
"\n",
"* Pandas: 2.1.4\n",
"* Polars: 0.20.26\n",
"\n",
"## Memory usage comparison\n",
"\n",
"File on disk: 6,0 MB (du -sh), 8000 rows, 7 columns. \n",
"\n",
"* Polars: 4,76 MB\n",
"* Pandas: 7,56 MB\n",
"\n",
"-> Polars was more memory efficient: ~ 1,6 times less memory\n",
"\n",
"\n",
"## Profile comparison\n",
"\n",
"* Polars: 256 function calls (253 primitive calls) in 0.020 seconds\n",
"* Pandas: 46681 function calls (46472 primitive calls) in 0.113 seconds\n",
"\n",
"-> Polars was ~ 5,6 times faster and needed ~ 180x less function and primitive calls. \n",
"\n",
"\n",
"## Conclusion\n",
"\n",
"Polars should be used whenever possible."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bdbf106d-4117-491b-9773-85dcd9d5914c",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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