Log File Parser Monitoring Agent

Automate monitoring of any text-based log file—application logs, IIS logs, Java logs, custom platform logs—with pattern detection, event correlation, and real-time alerting that eliminates manual log inspection.

Why Teams Choose This Agent

When applications and integration platforms write errors, warnings, and events to text files, manual inspection doesn't scale—Log File Parser automates detection before issues impact production:

1. 📂 "Operations manually checks 200 log files daily—errors missed until users complain"
   Ops team opens log files every morning across 50 servers (4 log files each)—scan for "ERROR", "EXCEPTION", "FAILED"—visual inspection misses 15% of issues buried in high-volume logs (500K+ lines/day). Production errors discovered 6 hours after occurrence. Log File Parser monitors all 200 files automatically—alert within seconds when error patterns detected, 95% faster detection.

2. ⚠️ "High-volume logs hide critical errors—alert fatigue from non-actionable warnings"
   Application logs 200K warnings/day (expected behavior: "Retry attempt 1/3 successful")—Ops configures alerts on keyword "WARNING"—100+ false alarms daily, team ignores alerts. Critical errors buried in noise. Log File Parser supports negative patterns ("alert on ERROR but ignore 'Connection timeout retrying'")—reduce alert fatigue 80%, focus on actionable problems.

3. 🔗 "No way to correlate related log entries across files—troubleshooting takes hours"
   Order processing: request logged in API log file, processing logged in worker log file, confirmation logged in integration platform log file—all separate files, different timestamps. Troubleshooting "Order 12345 failed" requires opening 3 files, searching timestamps, correlating manually (2 hours). Log File Parser ACK/NAK monitoring correlates "OrderReceived" (start pattern) with "OrderCompleted" (end pattern) across files—detect incomplete transactions automatically, troubleshoot in minutes.

4. 📋 "Compliance requires log retention proof—manual log audits take 3 days"
   Auditors request "all authentication failures for user X in Q2"—Ops manually searches 90 days of auth logs across 30 servers (2,700 log files), grep/Excel export, miss 12% due to inconsistent log formats. 3-day effort. Log File Parser centralizes log events to Nodinite—auditors search via Web Client, export matching entries in 90 seconds, complete lineage preserved.

5. 💰 "Legacy applications can't be modified—logging instrumentation not possible"
   Critical 10-year-old Java application writes errors to local .log file—no logging framework, no remote logging capability, source code unavailable (vendor out of business). Cannot add instrumentation, cannot change log format. Log File Parser monitors existing log files as-is—no application changes required, detect errors in unmodifiable legacy systems.

6. 🌐 "IIS log files ignored—website errors discovered via customer complaints"
   E-commerce website: IIS logs record HTTP 500 errors, slow response times, failed requests—no one monitors W3SVC logs (10 GB/day, difficult to parse). Customers report "checkout broken" before Ops aware. Log File Parser parses IIS logs automatically (W3C Extended format)—alert on HTTP 500/503, response time >5s, detect website issues before customer impact.

Nodinite Log File Parser transforms reactive log inspection into proactive monitoring:

✅ Monitor any text-based log—application logs (.log, .txt), IIS W3SVC logs (.log), Java logs (log4j, logback), custom platform logs, CSV exports—any text file
✅ Advanced pattern matching—detect expected/unexpected patterns, ACK/NAK correlation, negative patterns ("alert on X but ignore Y"), regex/wildcard support
✅ Event correlation—track transaction sequences across multiple log files (start pattern → end pattern), detect incomplete flows
✅ Zero application changes—monitor existing log files as-is, works with legacy applications, no instrumentation required
✅ Flexible date-time parsing—supports any timestamp format (ISO 8601, custom formats), no application changes needed
✅ Smart error handling—ignore previously evaluated entries, focus on new issues, prevent duplicate alerts
✅ Scale effortlessly—one license monitors 200+ log files across multiple servers, Windows folders and SMB shares supported

How It Works

The Log File Parser Agent scans text-based log files on your servers (Windows folders, SMB shares), detects patterns (errors, warnings, ACK/NAK sequences), and alerts via Nodinite when issues occur—no application changes required.

What You Can Do

🚨 Detect Production Errors in Seconds, Not Hours

• Automate error detection—monitor 200+ log files for "ERROR", "EXCEPTION", "FAILED", "FATAL" patterns across all servers simultaneously
• Real-time alerting—email, SMS, Slack, Teams, PagerDuty notifications within seconds of error occurrence (not 6 hours after customer complaints)
• Negative pattern filtering—alert on "ERROR" but ignore "ERROR: Retry attempt 1/3 successful" (expected retries), reduce alert fatigue 80%
• Content-based matching—detect specific error codes ("ORA-01017", "HTTP 500", "Connection refused"), not just keywords

Example: Financial services company: 50 servers run custom Java payment processing apps (4 log files each = 200 total log files), each logs 500K+ lines/day. Previously: Ops manually checks log files every morning (4 hours effort)—errors discovered 6-8 hours after occurrence, customers report failed transactions before Ops aware. Log File Parser configured: monitors all 200 log files for patterns "FATAL|EXCEPTION|PaymentFailed" (regex), negative pattern "PaymentFailed.*RetryScheduled" (ignores expected retries). Alert configuration: Slack channel "payment-alerts", email to oncall engineer. Result: Production errors detected within 30 seconds (95% faster), customer-reported incidents down 70%, manual log inspection eliminated (4 hours/day saved).

🔗 Correlate Transaction Sequences Across Log Files

• ACK/NAK monitoring—define start pattern ("OrderReceived") and end pattern ("OrderCompleted" or "OrderFailed"), alert when start pattern appears without matching end pattern within timeout (incomplete transactions)
• Cross-file correlation—track same Order ID logged in API log file → worker process log file → integration platform log file
• Sequence detection—detect expected multi-step patterns (Step 1 → Step 2 → Step 3), alert if sequence broken or out of order
• Timeout alerting—"OrderReceived at 10:00 AM, no OrderCompleted within 30 minutes—alert"

Example: E-commerce company: Order processing flow spans 3 applications logging to separate files—Order API (OrderReceived logged to api.log), Order Processor (OrderProcessing logged to worker.log), Shipping Integration (OrderShipped logged to shipping.log). Previously: troubleshooting "Order 12345 stuck" requires manually opening 3 log files, searching Order ID across 48 hours of logs, correlating timestamps (2 hours effort per issue). Log File Parser ACK/NAK configured: Start pattern "OrderReceived.*OrderId:(\d+)", End pattern "OrderShipped.*OrderId:$1" (captures Order ID), timeout 2 hours. Alert when Order logged but not shipped within 2 hours. Result: Stuck orders detected automatically in minutes (not discovered 2 days later when customer calls), troubleshooting time from 2 hours → 15 minutes, order fulfillment SLA improved 40%.

📊 Monitor IIS Logs Without Custom Code

• W3C Extended Log format parsing—automatically parse IIS W3SVC logs (date, time, client IP, HTTP method, URI, status code, bytes sent, time-taken)
• HTTP error detection—alert on HTTP 500 (server errors), 503 (service unavailable), 502 (bad gateway), 404 spikes (broken links)
• Performance monitoring—detect slow requests (time-taken >5000 ms), alert when website performance degrades
• Security monitoring—detect suspicious patterns (SQL injection attempts in URI, repeated 401 Unauthorized from same IP)

Example: E-commerce website: IIS logs record 10 GB/day (W3C Extended format)—HTTP status codes, response times, client IPs, URIs. Previously: logs ignored—IIS logs too large to manually review, difficult to parse without custom scripts. Website errors discovered via customer complaints ("checkout broken", "product page not loading"). Log File Parser configured: monitor IIS W3SVC logs for patterns "HTTP 5\d\d" (server errors), "time-taken > 5000" (slow requests >5s), "sql injection|