Structured Log Analyzer

Detect common log formats, extract core fields, infer field types, and export parsed logs as JSON, CSV, or SQL inserts

Example Results

1 examples

Turn mixed application logs into a CSV-ready table

Normalize JSON lines, Apache access logs, and syslog snippets into rows with inferred field types.

Structured log report
View input parameters
{ "logInput": "{\"level\":\"error\",\"service\":\"billing\",\"message\":\"Charge failed\"}\nMar 10 14:03:02 host app[123]: INFO Worker started", "exportFormat": "csv", "aggregateMultiline": true }

Click to upload file or drag and drop file here

Maximum file size: 20MB Supported formats: text/plain, application/json, application/x-ndjson, text/*

Key Facts

Category
Data & Tables
Input Types
textarea, file, select, checkbox, text
Output Type
html
Sample Coverage
4
API Ready
Yes

Overview

The Structured Log Analyzer automatically detects and parses common log formats like JSONL, Apache, Nginx, and Syslog. It extracts core fields, infers data types, and lets you export the cleaned data as JSON, CSV, or SQL inserts for easier querying, spreadsheet analysis, and database ingestion.

When to Use

  • •When you need to convert raw, unstructured server logs into a tabular CSV format for spreadsheet analysis.
  • •When migrating legacy log files into a relational database using generated SQL insert statements.
  • •When troubleshooting application errors that span multiple lines, such as Java stack traces, and need them grouped as single entries.

How It Works

  • •Paste your raw log entries into the text area or upload a log file up to 20MB.
  • •Select your desired export format: JSON, CSV, or SQL.
  • •Optionally, enable multiline aggregation for stack traces or provide a custom regex with named capture groups for proprietary formats.
  • •The tool parses the logs, extracts the fields, and generates the structured output.

Use Cases

Converting mixed application logs into a CSV file to build charts and pivot tables in Excel.
Extracting specific error messages and timestamps from custom application logs using named regex groups.
Preparing Nginx access logs for database ingestion by generating SQL insert statements.

Examples

1. Convert Mixed Logs to CSV

System Administrator
Background
A sysadmin needs to review a mix of JSON application logs and standard syslog entries to identify a recurring service failure.
Problem
Reading raw, mixed-format logs in a terminal is difficult and prevents easy sorting by error level or timestamp.
How to Use
Paste the mixed log entries into the input area, select 'CSV' as the export format, and enable multiline aggregation.
Example Config
Export Format: CSV, Aggregate Multiline: True
Outcome
The tool normalizes the JSON and syslog entries into a single CSV structure, allowing the sysadmin to sort by timestamp and filter by error level in a spreadsheet.

2. Parse Custom Logs with Regex

Backend Developer
Background
A developer is debugging an older legacy application that writes logs in a proprietary, non-standard text format.
Problem
Standard parsers fail to extract the timestamp, severity, and message from the legacy log strings.
How to Use
Upload the legacy log file and input a custom regex with named capture groups to define the exact field boundaries.
Example Config
Custom Regex: ^(?<timestamp>\S+) (?<level>\w+) (?<source>\w+) (?<message>.+)$, Export Format: JSON
Outcome
The tool uses the custom regex to accurately extract the fields and outputs a clean JSON array of structured log objects.

Try with Samples

json, csv, sql
ELK Stack Log Analysis Samples
Comprehensive ELK Stack (Elasticsearch, Logstash, Kibana) samples for log aggregation, processing, and visualization in distributed systems
title token log
text, json
Apache Arrow Samples
Apache Arrow in-memory columnar format examples for high-performance data processing and analytics
preferred input family text,json
text, json
WebRTC Real-Time Communication Samples
Comprehensive WebRTC samples for peer-to-peer audio/video communication, data channels, screen sharing, and signaling server implementation
preferred input family text,json
text, json
Web Data Serialization Rust Samples
Web Rust data serialization examples including JSON serialization, deserialization, and XML parsing
preferred input family text,json
text, json

Related Hubs

Json Format Tools
Explore 9 json tools for format workflows and compare closely related utilities quickly.
Text Format Tools
Explore 9 text tools for format workflows and compare closely related utilities quickly.
Json Convert Tools
Explore 100 json tools for convert workflows and compare closely related utilities quickly.
Text Convert Tools
Explore 80 text tools for convert workflows and compare closely related utilities quickly.

FAQ

Which log formats are supported automatically?

The tool automatically detects and parses common formats including JSON Lines (JSONL), Apache access logs, Nginx logs, and standard Syslog entries.

Can I parse a custom log format?

Yes, you can provide a custom regular expression with named capture groups (e.g., `(?<level>\w+)`) to extract specific fields from proprietary log formats.

How does the multiline aggregation work?

When enabled, the tool groups subsequent indented or unformatted lines, such as stack traces, with the preceding log entry instead of treating them as separate, broken records.

What is the maximum file size I can upload?

You can upload log files up to 20MB in size. For larger datasets, consider splitting your files before uploading.

Can I export the parsed logs to a database?

Yes, by selecting the SQL export format, the tool generates standard SQL INSERT statements based on the extracted fields and inferred data types.

API Documentation

Request Endpoint

POST /en/api/tools/structured-log-analyzer

Request Parameters

Parameter Name Type Required Description
logInput textarea No -
logFile file (Upload required) No -
exportFormat select No -
aggregateMultiline checkbox No -
customRegex text No -

File type parameters need to be uploaded first via POST /upload/structured-log-analyzer to get filePath, then pass filePath to the corresponding file field.

Response Format

{
  "result": "
Processed HTML content
",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}
HTML: HTML

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-structured-log-analyzer": {
      "name": "structured-log-analyzer",
      "description": "Detect common log formats, extract core fields, infer field types, and export parsed logs as JSON, CSV, or SQL inserts",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=structured-log-analyzer",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

Supports URL file links or Base64 encoding for file parameters.

If you encounter any issues, please contact us at [email protected]