WITH parsed AS (
 SELECT
 regexp_extract(log_line, '^([^ ]+)') AS ip,
 regexp_extract(log_line, '\[([^\]]+)\]') AS timestamp_raw,
 regexp_extract(log_line, '"([^"]+)"') AS request,
 regexp_extract(log_line, ' (\d{3}) ')::INT AS status_code,
 regexp_extract(log_line, ' (\d+) "')::INT AS body_bytes,
 regexp_extract(log_line, '"([^"]*)"$') AS user_agent
 FROM read_text('access.log')
)
SELECT status_code, count(*) AS cnt
FROM parsed
GROUP BY status_code
ORDER BY cnt DESC;

WITH parsed AS (
 SELECT
 regexp_extract(log_line, '"([^"]+)"') AS request,
 regexp_extract(log_line, ' (\d{3}) ')::INT AS status_code
 FROM read_text('access.log')
)
SELECT
 regexp_extract(request, '^([^ ]+)') AS http_method,
 regexp_extract(request, ' ([^ ]+) ') AS path,
 status_code,
 count(*) AS cnt
FROM parsed
GROUP BY http_method, path, status_code
ORDER BY cnt DESC
LIMIT 10;

pip install duckdb streamlit pandas openpyxl

#!/usr/bin/env python3
"""
DuckDB + Streamlit Log Anomaly Detection Dashboard
Generates mock Nginx logs → DuckDB analysis → Streamlit dashboard → Excel export
"""

import duckdb
import pandas as pd
import random
import datetime
import os

# ============================================================
# Step 1: Generate Mock Nginx Access Logs
# ============================================================

def generate_nginx_logs(num_lines=10000, output_file="nginx_access.log"):
 """Generate mock Nginx access logs with some anomalies"""
 
 ips = [f"192.168.1.{i}" for i in range(1, 21)]
 paths = [
 "/api/users", "/api/products", "/api/orders",
 "/api/payments", "/api/auth/login", "/api/auth/logout",
 "/api/search", "/api/recommend", "/api/cart", "/api/checkout"
 ]
 methods = ["GET", "POST", "PUT", "DELETE"]
 user_agents = [
 "Mozilla/5.0 (Windows NT 10.0; Win64; x64)",
 "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)",
 "curl/7.68.0",
 "python-requests/2.25.1",
 "PostmanRuntime/7.28.4"
 ]
 
 base_time = datetime.datetime(2026, 5, 13, 0, 0, 0)
 
 with open(output_file, "w") as f:
 for i in range(num_lines):
 base_time += datetime.timedelta(seconds=random.uniform(0.1, 5))
 timestamp = base_time.strftime("%d/%b/%Y:%H:%M:%S +0800")
 
 ip = random.choice(ips)
 method = random.choice(methods)
 path = random.choice(paths)
 
 # Inject ~5% server errors
 if random.random() < 0.05:
 status = random.choice([500, 502, 503, 504])
 bytes_sent = random.randint(0, 200)
 response_time = random.uniform(3, 15)
 elif random.random() < 0.10:
 status = random.choice([400, 401, 403, 404, 429])
 bytes_sent = random.randint(50, 500)
 response_time = random.uniform(0.1, 2)
 else:
 status = random.choice([200, 201, 204, 301, 302])
 bytes_sent = random.randint(200, 15000)
 response_time = random.uniform(0.01, 1.5)
 
 ua = random.choice(user_agents)
 
 log_line = (
 f'{ip} - - [{timestamp}] '
 f'"{method} {path} HTTP/1.1" {status} {bytes_sent} '
 f'"{random.choice(["-", "https://example.com"])}" '
 f'"{ua}" {response_time:.3f}\n'
 )
 f.write(log_line)
 
 print(f"✅ Generated {num_lines} mock log lines → {output_file}")
 return output_file


# ============================================================
# Step 2: DuckDB Log Analysis Engine
# ============================================================

class LogAnalyzer:
 """DuckDB-powered log analysis engine"""
 
 def __init__(self, log_file="nginx_access.log"):
 self.con = duckdb.connect()
 self.log_file = log_file
 self._load_and_parse()
 
 def _load_and_parse(self):
 """Load log file and parse into structured fields"""
 self.con.execute(f"""
 CREATE TABLE logs AS
 SELECT
 regexp_extract(line, '^([^ ]+)') AS ip,
 regexp_extract(line, '\\[([^\\]]+)\\]') AS timestamp_raw,
 regexp_extract(line, '"([^"]+)"') AS request,
 regexp_extract(line, ' (\\d{{3}}) ')::INT AS status_code,
 regexp_extract(line, ' (\\d+) "')::INT AS body_bytes,
 regexp_extract(line, '"([^"]*)"$') AS user_agent,
 regexp_extract(line, ' (\\d+\\.\\d+)$')::DOUBLE AS response_time
 FROM read_text('{self.log_file}')
 """)
 
 # Parse HTTP method and path
 self.con.execute("""
 ALTER TABLE logs ADD COLUMN http_method VARCHAR;
 ALTER TABLE logs ADD COLUMN path VARCHAR;
 """)
 self.con.execute("""
 UPDATE logs SET
 http_method = regexp_extract(request, '^([^ ]+)'),
 path = regexp_extract(request, ' ([^ ]+) ')
 """)
 
 # Parse timestamp
 self.con.execute("""
 ALTER TABLE logs ADD COLUMN request_time TIMESTAMP;
 """)
 self.con.execute("""
 UPDATE logs SET
 request_time = strptime(
 regexp_replace(timestamp_raw, ':', ' ', 1, 1),
 '%d/%b/%Y %H:%M:%S'
 )
 """)
 
 row_count = self.con.execute("SELECT count(*) FROM logs").fetchone()[0]
 print(f"✅ DuckDB parsed {row_count} log entries")
 
 def status_distribution(self):
 """Analysis 1: Status code distribution"""
 return self.con.execute("""
 SELECT status_code, count(*) AS cnt,
 round(count(*) * 100.0 / sum(count(*) OVER (), 2) AS pct
 FROM logs
 GROUP BY status_code
 ORDER BY cnt DESC
 """).fetchdf()
 
 def error_paths(self, top_n=10):
 """Analysis 2: Most error-prone API paths"""
 return self.con.execute(f"""
 SELECT path, http_method,
 count(*) AS total_requests,
 sum(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS server_errors,
 sum(CASE WHEN status_code >= 400 AND status_code < 500 THEN 1 ELSE 0 END) AS client_errors,
 round(AVG(response_time), 3) AS avg_response_time,
 round(MAX(response_time), 3) AS max_response_time
 FROM logs
 GROUP BY path, http_method
 HAVING server_errors > 0 OR client_errors > 0
 ORDER BY server_errors DESC
 LIMIT {top_n}
 """).fetchdf()
 
 def slowest_apis(self, top_n=10):
 """Analysis 3: Slowest API endpoints by P95 latency"""
 return self.con.execute(f"""
 SELECT path, http_method,
 count(*) AS cnt,
 round(AVG(response_time), 3) AS avg_ms,
 round(PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY response_time), 3) AS p95_ms,
 round(MAX(response_time), 3) AS max_ms
 FROM logs
 GROUP BY path, http_method
 HAVING cnt > 5
 ORDER BY avg_ms DESC
 LIMIT {top_n}
 """).fetchdf()
 
 def time_series(self, interval='5 minutes'):
 """Analysis 4: Time series trend"""
 return self.con.execute(f"""
 SELECT date_trunc('{interval}', request_time) AS bucket,
 count(*) AS total_requests,
 sum(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS errors,
 round(AVG(response_time), 3) AS avg_response_time
 FROM logs
 GROUP BY bucket
 ORDER BY bucket
 """).fetchdf()
 
 def top_error_users(self, top_n=5):
 """Analysis 5: Top error-causing IPs"""
 return self.con.execute(f"""
 SELECT ip,
 count(*) AS total_requests,
 sum(CASE WHEN status_code >= 500 THEN 1 ELSE 0 END) AS server_errors,
 round(AVG(response_time), 3) AS avg_response_time
 FROM logs
 GROUP BY ip
 HAVING server_errors > 0
 ORDER BY server_errors DESC
 LIMIT {top_n}
 """).fetchdf()
 
 def export_excel(self, output_file="log_analysis_report.xlsx"):
 """Export complete report to Excel"""
 with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
 self.status_distribution().to_excel(writer, sheet_name='Status Codes', index=False)
 self.error_paths().to_excel(writer, sheet_name='Error Paths', index=False)
 self.slowest_apis().to_excel(writer, sheet_name='Slow APIs', index=False)
 self.time_series().to_excel(writer, sheet_name='Time Trends', index=False)
 self.top_error_users().to_excel(writer, sheet_name='Problem Users', index=False)
 print(f"✅ Report exported → {output_file}")
 return output_file


# ============================================================
# Step 3: Streamlit Dashboard
# ============================================================

def run_dashboard():
 """Launch Streamlit interactive dashboard"""
 import streamlit as st
 
 st.set_page_config(
 page_title="Log Anomaly Detection Dashboard",
 page_icon="📊",
 layout="wide"
 )
 
 st.title("📊 Log Anomaly Detection Dashboard")
 st.markdown("DuckDB-powered Nginx access log analysis engine")
 
 # Initialize
 log_file = "nginx_access.log"
 if not os.path.exists(log_file):
 st.info("Generating mock log data...")
 generate_nginx_logs(10000, log_file)
 
 analyzer = LogAnalyzer(log_file)
 
 # ---- Key Metrics ----
 col1, col2, col3, col4 = st.columns(4)
 with col1:
 total = analyzer.con.execute("SELECT count(*) FROM logs").fetchone()[0]
 st.metric("Total Requests", f"{total:,}")
 with col2:
 errors = analyzer.con.execute(
 "SELECT count(*) FROM logs WHERE status_code >= 500"
 ).fetchone()[0]
 st.metric("Server Errors", errors)
 with col3:
 client_errors = analyzer.con.execute(
 "SELECT count(*) FROM logs WHERE status_code >= 400 AND status_code < 500"
 ).fetchone()[0]
 st.metric("Client Errors", client_errors)
 with col4:
 avg_resp = analyzer.con.execute(
 "SELECT round(AVG(response_time), 3) FROM logs"
 ).fetchone()[0]
 st.metric("Avg Response Time", f"{avg_resp:.2f}s")
 
 # ---- Tabs ----
 tab1, tab2, tab3, tab4, tab5 = st.tabs([
 "🔴 Error Analysis", "🐢 Slow APIs", "📈 Trends", "👤 Users", "📋 Status Codes"
 ])
 
 with tab1:
 st.subheader("Most Error-Prone API Paths")
 df_errors = analyzer.error_paths(15)
 st.dataframe(df_errors, use_container_width=True)
 st.bar_chart(df_errors.set_index("path")["server_errors"])
 
 with tab2:
 st.subheader("Slowest APIs (P95 Latency)")
 df_slow = analyzer.slowest_apis(15)
 st.dataframe(df_slow, use_container_width=True)
 st.bar_chart(df_slow.set_index("path")["p95_ms"])
 
 with tab3:
 st.subheader("Request Volume & Error Trend")
 df_ts = analyzer.time_interval()
 st.line_chart(df_ts.set_index("bucket")[["total_requests", "errors"]])
 
 with tab4:
 st.subheader("Top Error-Causing Client IPs")
 df_users = analyzer.top_error_users(10)
 st.dataframe(df_users, use_container_width=True)
 
 with tab5:
 st.subheader("HTTP Status Code Distribution")
 df_status = analyzer.status_distribution()
 st.dataframe(df_status, use_container_width=True)
 st.bar_chart(df_status.set_index("status_code")["cnt"])
 
 # ---- Export ----
 if st.button("📥 Export Full Report (Excel)"):
 filepath = analyzer.export_excel()
 with open(filepath, "rb") as f:
 st.download_button(
 "Download Excel Report",
 f,
 file_name="log_analysis_report.xlsx",
 mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
 )
 
 st.markdown("---")
 st.caption("Powered by DuckDB 🦆 + Streamlit")


# ============================================================
# Entry Point
# ============================================================

if __name__ == "__main__":
 import sys
 
 if "streamlit" in sys.argv[0] or "STREAMLIT_SCRIPT" in os.environ:
 run_dashboard()
 else:
 # CLI mode
 print("=" * 50)
 print("🦆 DuckDB Log Analyzer (CLI Mode)")
 print("=" * 50)
 
 log_file = generate_nginx_logs(10000)
 analyzer = LogAnalyzer(log_file)
 
 print("\n📊 Status Code Distribution:")
 print(analyzer.status_distribution().to_string(index=False))
 
 print("\n🔴 Most Error-Prone APIs:")
 print(analyzer.error_paths().to_string(index=False))
 
 print("\n🐢 Slowest APIs:")
 print(analyzer.slowest_apis().to_string(index=False))
 
 print("\n👤 Problem Users:")
 print(analyzer.top_error_users().to_string(index=False))
 
 analyzer.export_excel()
 
 print("\n✅ Analysis complete!")
 print("💡 Tip: Run `streamlit run this_script.py` for interactive dashboard")

python3 log_analyzer.py

streamlit run log_analyzer.py