Documentation | Augment AI Platform

Installation

Deploy AI LogAnalyzer to start intelligent log monitoring and anomaly detection across your infrastructure.

System Requirements

Python 3.9 or higher
Elasticsearch 7.0+ (for log storage and indexing)
Redis 6.0+ (for real-time processing)
Minimum 16GB RAM (32GB recommended for high volume)
Network access to log sources and notification systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-log-analyzer

# Install via Docker Compose
curl -O https://raw.githubusercontent.com/augment-ai/log-analyzer/main/docker-compose.yml
docker-compose up -d

# Install from source
git clone https://github.com/augment-ai/log-analyzer
cd log-analyzer
pip install -e .

# Install dependencies
pip install elasticsearch redis numpy pandas scikit-learn

# Verify installation
log-analyzer --version

Service Configuration

Configure required services and API access:

Service Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure Elasticsearch connection
export ELASTICSEARCH_URL=http://localhost:9200
export ELASTICSEARCH_USERNAME=elastic
export ELASTICSEARCH_PASSWORD=your_password

# Configure Redis connection
export REDIS_URL=redis://localhost:6379

# Initialize log analyzer
log-analyzer init --create-indices

# Verify services
log-analyzer health-check

Quick Start

Start analyzing logs and detecting anomalies in minutes with intelligent pattern recognition.

1. Configure Log Sources

Log Source Setupbash

# Add application log source
log-analyzer add-source --type application \
  --path "/var/log/app/*.log" \
  --format json \
  --name "web-application"

# Add system log source
log-analyzer add-source --type syslog \
  --host syslog-server \
  --port 514 \
  --name "system-logs"

# Add cloud service logs
log-analyzer add-source --type aws-cloudwatch \
  --log-group "/aws/lambda/my-function" \
  --region us-east-1 \
  --name "lambda-logs"

# This creates .log-analyzer.yaml config file

2. Start Log Processing

Log Processingbash

# Start real-time log analysis
log-analyzer start --daemon

# Process historical logs
log-analyzer process --historical --days 7

# Train anomaly detection models
log-analyzer train --source web-application --baseline-days 30

# Monitor processing status
log-analyzer status --verbose

3. Generate Analysis Reports

Analysis Reportsbash

# Generate anomaly report
log-analyzer report --type anomalies --output anomalies.html

# Export insights as JSON
log-analyzer export --format json --output insights.json

# Create dashboard summary
log-analyzer dashboard --timeframe 24h --output dashboard.html

# Generate security analysis
log-analyzer security-report --output security-analysis.pdf

Configuration

Configure AI LogAnalyzer to match your logging infrastructure and analysis requirements.

Basic Configuration

.log-analyzer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

data_sources:
  - name: "web-application"
    type: "file"
    path: "/var/log/app/*.log"
    format: "json"
    fields:
      timestamp: "timestamp"
      level: "level"
      message: "message"
      user_id: "user_id"
  
  - name: "system-logs"
    type: "syslog"
    host: "syslog-server"
    port: 514
    facility: ["kern", "mail", "auth"]

elasticsearch:
  url: "http://localhost:9200"
  index_prefix: "logs-"
  retention_days: 90
  shards: 3
  replicas: 1

redis:
  url: "redis://localhost:6379"
  db: 0
  key_prefix: "log-analyzer:"

anomaly_detection:
  algorithms: ["isolation_forest", "one_class_svm", "lstm"]
  sensitivity: "medium"
  baseline_days: 30
  update_frequency: "daily"
  confidence_threshold: 0.8

alerts:
  enabled: true
  channels:
    - type: "slack"
      webhook: "{SLACK_WEBHOOK}"
      channel: "#alerts"
    - type: "email"
      smtp_server: "smtp.company.com"
      recipients: ["ops@company.com"]

processing:
  batch_size: 1000
  workers: 4
  buffer_size: 10000
  real_time: true

Anomaly Detection

AI LogAnalyzer uses multiple machine learning algorithms to detect anomalies and unusual patterns in log data.

Statistical Anomalies

• Unusual volume spikes or drops
• Response time outliers
• Error rate deviations
• Traffic pattern changes

Behavioral Anomalies

• Unusual user access patterns
• Abnormal system behavior
• Unexpected application flows
• Security-related activities

Temporal Anomalies

• Off-hours activity detection
• Seasonal pattern deviations
• Burst activity detection
• Time-series outliers

Content Anomalies

• New error message patterns
• Unusual log message content
• Malformed log entries
• Suspicious text patterns

Anomaly Configuration

Anomaly Detection Setupbash

# Configure anomaly detection models
log-analyzer anomaly config --algorithm isolation_forest \
  --sensitivity high \
  --features timestamp,level,response_time

# Set custom thresholds
log-analyzer anomaly threshold --metric error_rate --threshold 0.05
log-analyzer anomaly threshold --metric response_time --threshold 2000

# Train models on historical data
log-analyzer anomaly train --source web-application \
  --start-date 2025-08-01 \
  --end-date 2025-09-01

# Enable real-time anomaly detection
log-analyzer anomaly enable --real-time --alert-on-detection

Environment Variables

Configure AI LogAnalyzer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
LOG_ANALYZER_CONFIG	Path to configuration file	.log-analyzer.yaml
ELASTICSEARCH_URL	Elasticsearch connection URL	http://localhost:9200
REDIS_URL	Redis connection URL	redis://localhost:6379
LOG_ANALYZER_WORKERS	Number of processing workers	4

Basic Usage

Learn the fundamental log analysis patterns and anomaly detection workflows.

Analysis Commands

Log Analysisbash

# Search logs with intelligent filtering
log-analyzer search --query "error OR exception" --timeframe 1h

# Analyze log patterns
log-analyzer patterns --source web-application --timeframe 24h

# Detect anomalies in real-time
log-analyzer detect --live --sensitivity medium

# Generate insights from log data
log-analyzer insights --source system-logs --focus security

CLI Commands Reference

Complete reference for all log analysis and anomaly detection commands.

analyze

Run comprehensive log analysis with AI-powered insights

log-analyzer analyze [options]

Options:
  --source <source>        Log source to analyze
  --timeframe <period>     Analysis timeframe (1h|24h|7d|30d)
  --start-time <datetime>  Start time for analysis
  --end-time <datetime>    End time for analysis
  --focus <area>          Focus area (security|performance|errors|all)
  --anomalies             Include anomaly detection
  --patterns              Extract log patterns
  --output <file>         Output file path
  --format <format>       Output format (json|html|csv)
  --real-time             Enable real-time analysis
  --alert-threshold <num> Alert threshold for anomalies

search

Intelligent log search with natural language queries

log-analyzer search [options]

Options:
  --query <query>         Search query (supports natural language)
  --source <source>       Specific log source to search
  --timeframe <period>    Time period for search
  --fields <fields>       Fields to include in results
  --limit <number>        Maximum number of results
  --sort <field>          Sort by field (timestamp|relevance)
  --export <file>         Export results to file
  --correlate             Enable log correlation

Best Practices

Log analysis best practices to maximize detection accuracy and operational insights.

Log Analysis Strategy

Standardize log formats across applications and systems
Include sufficient context in log messages for analysis
Train anomaly detection models on representative baseline data
Set appropriate sensitivity levels to balance detection and noise
Regularly update models with new normal behavior patterns
Implement proper log retention and archival policies

Log Sources

AI LogAnalyzer supports a wide variety of log sources and formats for comprehensive analysis.

Supported Log Sources

System Logs

Syslog, EventLog

Application

JSON, Plain Text

Web Servers

Apache, Nginx

Cloud Services

AWS, Azure, GCP

Custom Log Source Configuration

Custom Log Sourcesbash

# Add custom application log source
log-analyzer add-source --type custom \
  --name "my-app" \
  --path "/var/log/myapp/*.log" \
  --format "custom" \
  --pattern "%{TIMESTAMP:timestamp} %{LOGLEVEL:level} %{GREEDYDATA:message}"

# Add database log source
log-analyzer add-source --type database \
  --name "postgres-logs" \
  --host "db-server" \
  --port 5432 \
  --query "SELECT * FROM pg_log"

# Add cloud service logs
log-analyzer add-source --type azure-logs \
  --name "app-service" \
  --subscription-id "sub-123" \
  --resource-group "production"

Alerting

Intelligent alerting system that reduces noise and provides actionable notifications.

Alert Configuration

Alert Setupbash

# Configure Slack alerts
log-analyzer alert config --channel slack \
  --webhook "https://hooks.slack.com/..." \
  --channel "#alerts" \
  --severity critical,high

# Configure email alerts
log-analyzer alert config --channel email \
  --smtp-server "smtp.company.com" \
  --recipients "ops@company.com,security@company.com" \
  --template "security-alert"

# Configure PagerDuty integration
log-analyzer alert config --channel pagerduty \
  --service-key "your-service-key" \
  --escalation-policy "critical-issues"

# Set alert thresholds
log-analyzer alert threshold --metric anomaly_score --threshold 0.8
log-analyzer alert threshold --metric error_rate --threshold 0.05

API Integration

Integrate AI LogAnalyzer into your monitoring and observability stack.

REST API

API Usagebash

# Submit logs for analysis via API
curl -X POST https://api.augment.cfd/v1/logs/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source": "web-application",
    "logs": [
      {
        "timestamp": "2025-09-18T10:30:00Z",
        "level": "ERROR",
        "message": "Database connection failed",
        "user_id": "user123"
      }
    ],
    "detect_anomalies": true
  }'

Python SDK

Python Integrationpython

from augment_log_analyzer import LogAnalyzer

# Initialize log analyzer
analyzer = LogAnalyzer(api_key=os.environ['AUGMENT_API_KEY'])

# Submit logs for analysis
analysis_result = await analyzer.analyze_logs(
    source='web-application',
    logs=log_entries,
    detect_anomalies=True,
    timeframe='1h'
)

# Get detected anomalies
anomalies = analysis_result.get_anomalies()
print(f"Detected {len(anomalies)} anomalies")

# Set up real-time monitoring
async def on_anomaly(anomaly):
    print(f"Anomaly detected: {anomaly.description}")
    await send_alert(anomaly)

monitor = analyzer.start_monitoring(
    source='web-application',
    on_anomaly=on_anomaly,
    sensitivity='medium'
)

# Search logs with natural language
search_results = await analyzer.search(
    query="database errors in the last hour",
    source='web-application'
)

API Reference

Complete API documentation for integrating log analysis into your applications.

Log Analysis Endpoint

POST /v1/logs/analyze

Analyze log entries and detect anomalies using AI.

Request Body:

{
  "source": "web-application",
  "logs": [
    {
      "timestamp": "2025-09-18T10:30:00Z",
      "level": "ERROR",
      "message": "Database connection failed",
      "user_id": "user123",
      "request_id": "req-456",
      "response_time": 5000
    }
  ],
  "options": {
    "detect_anomalies": true,
    "extract_patterns": true,
    "correlate_events": true,
    "generate_insights": true
  },
  "analysis_type": "real_time|batch|historical",
  "timeframe": "1h|24h|7d",
  "sensitivity": "low|medium|high"
}

Response:

{
  "analysis_id": "analysis-789",
  "status": "completed",
  "summary": {
    "total_logs": 1000,
    "anomalies_detected": 5,
    "patterns_found": 12,
    "severity_distribution": {
      "critical": 1,
      "high": 2,
      "medium": 2
    }
  },
  "anomalies": [
    {
      "id": "anomaly-001",
      "timestamp": "2025-09-18T10:30:00Z",
      "type": "statistical",
      "severity": "high",
      "confidence": 0.92,
      "description": "Unusual spike in database connection errors",
      "affected_logs": 15,
      "baseline_value": 2.1,
      "detected_value": 47.3,
      "context": {
        "time_window": "5m",
        "related_events": ["deployment-start", "config-change"]
      },
      "suggested_actions": [
        "Check database server health",
        "Review recent configuration changes",
        "Scale database connections"
      ]
    }
  ],
  "patterns": [
    {
      "id": "pattern-001",
      "type": "temporal",
      "description": "High error rate during deployment windows",
      "frequency": "weekly",
      "confidence": 0.87,
      "examples": [
        "2025-09-18T10:30:00Z: Deploy started, errors increased 300%",
        "2025-09-11T10:30:00Z: Deploy started, errors increased 250%"
      ]
    }
  ],
  "insights": [
    {
      "type": "recommendation",
      "title": "Implement circuit breaker pattern",
      "description": "High database error rates suggest need for resilience patterns",
      "priority": "high",
      "estimated_impact": "Reduce error propagation by 60%"
    }
  ]
}

Troubleshooting

Common issues and solutions when running log analysis and anomaly detection.

Common Issues

High Memory Usage

Log analyzer consuming excessive memory during processing

Solutions:

Reduce batch size for log processing
Increase number of workers for parallel processing
Implement log sampling for high-volume sources
Optimize Elasticsearch index settings

False Positive Anomalies

Too many false positive anomaly alerts

Solutions:

Adjust anomaly detection sensitivity settings
Extend baseline training period for models
Add known patterns to whitelist
Fine-tune confidence thresholds

Log Processing Lag

Delay between log generation and analysis

Solutions:

Optimize log shipper configuration
Increase Redis buffer size
Scale Elasticsearch cluster horizontally
Implement log preprocessing pipelines

Log Analysis Documentation Complete!

You now have comprehensive knowledge to implement AI LogAnalyzer in your observability stack. From basic log processing to advanced anomaly detection, you're equipped to gain intelligent insights from your log data with AI-powered analysis.

Ready to revolutionize your log analysis? Start your free trial today and discover how AI can transform your log data into actionable insights and proactive monitoring.

Installation

Deploy AI LogAnalyzer to start intelligent log monitoring and anomaly detection across your infrastructure.

System Requirements

Python 3.9 or higher
Elasticsearch 7.0+ (for log storage and indexing)
Redis 6.0+ (for real-time processing)
Minimum 16GB RAM (32GB recommended for high volume)
Network access to log sources and notification systems

Install via Package Manager

Installationbash

# Install via pip
pip install augment-log-analyzer

# Install via Docker Compose
curl -O https://raw.githubusercontent.com/augment-ai/log-analyzer/main/docker-compose.yml
docker-compose up -d

# Install from source
git clone https://github.com/augment-ai/log-analyzer
cd log-analyzer
pip install -e .

# Install dependencies
pip install elasticsearch redis numpy pandas scikit-learn

# Verify installation
log-analyzer --version

Service Configuration

Configure required services and API access:

Service Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure Elasticsearch connection
export ELASTICSEARCH_URL=http://localhost:9200
export ELASTICSEARCH_USERNAME=elastic
export ELASTICSEARCH_PASSWORD=your_password

# Configure Redis connection
export REDIS_URL=redis://localhost:6379

# Initialize log analyzer
log-analyzer init --create-indices

# Verify services
log-analyzer health-check

Quick Start

Start analyzing logs and detecting anomalies in minutes with intelligent pattern recognition.

1. Configure Log Sources

Log Source Setupbash

# Add application log source
log-analyzer add-source --type application \
  --path "/var/log/app/*.log" \
  --format json \
  --name "web-application"

# Add system log source
log-analyzer add-source --type syslog \
  --host syslog-server \
  --port 514 \
  --name "system-logs"

# Add cloud service logs
log-analyzer add-source --type aws-cloudwatch \
  --log-group "/aws/lambda/my-function" \
  --region us-east-1 \
  --name "lambda-logs"

# This creates .log-analyzer.yaml config file

2. Start Log Processing

Log Processingbash

# Start real-time log analysis
log-analyzer start --daemon

# Process historical logs
log-analyzer process --historical --days 7

# Train anomaly detection models
log-analyzer train --source web-application --baseline-days 30

# Monitor processing status
log-analyzer status --verbose

3. Generate Analysis Reports

Analysis Reportsbash

# Generate anomaly report
log-analyzer report --type anomalies --output anomalies.html

# Export insights as JSON
log-analyzer export --format json --output insights.json

# Create dashboard summary
log-analyzer dashboard --timeframe 24h --output dashboard.html

# Generate security analysis
log-analyzer security-report --output security-analysis.pdf

Configuration

Configure AI LogAnalyzer to match your logging infrastructure and analysis requirements.

Basic Configuration

.log-analyzer.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

data_sources:
  - name: "web-application"
    type: "file"
    path: "/var/log/app/*.log"
    format: "json"
    fields:
      timestamp: "timestamp"
      level: "level"
      message: "message"
      user_id: "user_id"
  
  - name: "system-logs"
    type: "syslog"
    host: "syslog-server"
    port: 514
    facility: ["kern", "mail", "auth"]

elasticsearch:
  url: "http://localhost:9200"
  index_prefix: "logs-"
  retention_days: 90
  shards: 3
  replicas: 1

redis:
  url: "redis://localhost:6379"
  db: 0
  key_prefix: "log-analyzer:"

anomaly_detection:
  algorithms: ["isolation_forest", "one_class_svm", "lstm"]
  sensitivity: "medium"
  baseline_days: 30
  update_frequency: "daily"
  confidence_threshold: 0.8

alerts:
  enabled: true
  channels:
    - type: "slack"
      webhook: "{SLACK_WEBHOOK}"
      channel: "#alerts"
    - type: "email"
      smtp_server: "smtp.company.com"
      recipients: ["ops@company.com"]

processing:
  batch_size: 1000
  workers: 4
  buffer_size: 10000
  real_time: true

Anomaly Detection

AI LogAnalyzer uses multiple machine learning algorithms to detect anomalies and unusual patterns in log data.

Statistical Anomalies

• Unusual volume spikes or drops
• Response time outliers
• Error rate deviations
• Traffic pattern changes

Behavioral Anomalies

• Unusual user access patterns
• Abnormal system behavior
• Unexpected application flows
• Security-related activities

Temporal Anomalies

• Off-hours activity detection
• Seasonal pattern deviations
• Burst activity detection
• Time-series outliers

Content Anomalies

• New error message patterns
• Unusual log message content
• Malformed log entries
• Suspicious text patterns

Anomaly Configuration

Anomaly Detection Setupbash

# Configure anomaly detection models
log-analyzer anomaly config --algorithm isolation_forest \
  --sensitivity high \
  --features timestamp,level,response_time

# Set custom thresholds
log-analyzer anomaly threshold --metric error_rate --threshold 0.05
log-analyzer anomaly threshold --metric response_time --threshold 2000

# Train models on historical data
log-analyzer anomaly train --source web-application \
  --start-date 2025-08-01 \
  --end-date 2025-09-01

# Enable real-time anomaly detection
log-analyzer anomaly enable --real-time --alert-on-detection

Environment Variables

Configure AI LogAnalyzer behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
LOG_ANALYZER_CONFIG	Path to configuration file	.log-analyzer.yaml
ELASTICSEARCH_URL	Elasticsearch connection URL	http://localhost:9200
REDIS_URL	Redis connection URL	redis://localhost:6379
LOG_ANALYZER_WORKERS	Number of processing workers	4

Basic Usage

Learn the fundamental log analysis patterns and anomaly detection workflows.

Analysis Commands

Log Analysisbash

# Search logs with intelligent filtering
log-analyzer search --query "error OR exception" --timeframe 1h

# Analyze log patterns
log-analyzer patterns --source web-application --timeframe 24h

# Detect anomalies in real-time
log-analyzer detect --live --sensitivity medium

# Generate insights from log data
log-analyzer insights --source system-logs --focus security

CLI Commands Reference

Complete reference for all log analysis and anomaly detection commands.

analyze

Run comprehensive log analysis with AI-powered insights

log-analyzer analyze [options]

Options:
  --source <source>        Log source to analyze
  --timeframe <period>     Analysis timeframe (1h|24h|7d|30d)
  --start-time <datetime>  Start time for analysis
  --end-time <datetime>    End time for analysis
  --focus <area>          Focus area (security|performance|errors|all)
  --anomalies             Include anomaly detection
  --patterns              Extract log patterns
  --output <file>         Output file path
  --format <format>       Output format (json|html|csv)
  --real-time             Enable real-time analysis
  --alert-threshold <num> Alert threshold for anomalies

search

Intelligent log search with natural language queries

log-analyzer search [options]

Options:
  --query <query>         Search query (supports natural language)
  --source <source>       Specific log source to search
  --timeframe <period>    Time period for search
  --fields <fields>       Fields to include in results
  --limit <number>        Maximum number of results
  --sort <field>          Sort by field (timestamp|relevance)
  --export <file>         Export results to file
  --correlate             Enable log correlation

Best Practices

Log analysis best practices to maximize detection accuracy and operational insights.

Log Analysis Strategy

Standardize log formats across applications and systems
Include sufficient context in log messages for analysis
Train anomaly detection models on representative baseline data
Set appropriate sensitivity levels to balance detection and noise
Regularly update models with new normal behavior patterns
Implement proper log retention and archival policies

Log Sources

AI LogAnalyzer supports a wide variety of log sources and formats for comprehensive analysis.

Supported Log Sources

System Logs

Syslog, EventLog

Application

JSON, Plain Text

Web Servers

Apache, Nginx

Cloud Services

AWS, Azure, GCP

Custom Log Source Configuration

Custom Log Sourcesbash

# Add custom application log source
log-analyzer add-source --type custom \
  --name "my-app" \
  --path "/var/log/myapp/*.log" \
  --format "custom" \
  --pattern "%{TIMESTAMP:timestamp} %{LOGLEVEL:level} %{GREEDYDATA:message}"

# Add database log source
log-analyzer add-source --type database \
  --name "postgres-logs" \
  --host "db-server" \
  --port 5432 \
  --query "SELECT * FROM pg_log"

# Add cloud service logs
log-analyzer add-source --type azure-logs \
  --name "app-service" \
  --subscription-id "sub-123" \
  --resource-group "production"

Alerting

Intelligent alerting system that reduces noise and provides actionable notifications.

Alert Configuration

Alert Setupbash

# Configure Slack alerts
log-analyzer alert config --channel slack \
  --webhook "https://hooks.slack.com/..." \
  --channel "#alerts" \
  --severity critical,high

# Configure email alerts
log-analyzer alert config --channel email \
  --smtp-server "smtp.company.com" \
  --recipients "ops@company.com,security@company.com" \
  --template "security-alert"

# Configure PagerDuty integration
log-analyzer alert config --channel pagerduty \
  --service-key "your-service-key" \
  --escalation-policy "critical-issues"

# Set alert thresholds
log-analyzer alert threshold --metric anomaly_score --threshold 0.8
log-analyzer alert threshold --metric error_rate --threshold 0.05

API Integration

Integrate AI LogAnalyzer into your monitoring and observability stack.

REST API

API Usagebash

# Submit logs for analysis via API
curl -X POST https://api.augment.cfd/v1/logs/analyze \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "source": "web-application",
    "logs": [
      {
        "timestamp": "2025-09-18T10:30:00Z",
        "level": "ERROR",
        "message": "Database connection failed",
        "user_id": "user123"
      }
    ],
    "detect_anomalies": true
  }'

Python SDK

Python Integrationpython

from augment_log_analyzer import LogAnalyzer

# Initialize log analyzer
analyzer = LogAnalyzer(api_key=os.environ['AUGMENT_API_KEY'])

# Submit logs for analysis
analysis_result = await analyzer.analyze_logs(
    source='web-application',
    logs=log_entries,
    detect_anomalies=True,
    timeframe='1h'
)

# Get detected anomalies
anomalies = analysis_result.get_anomalies()
print(f"Detected {len(anomalies)} anomalies")

# Set up real-time monitoring
async def on_anomaly(anomaly):
    print(f"Anomaly detected: {anomaly.description}")
    await send_alert(anomaly)

monitor = analyzer.start_monitoring(
    source='web-application',
    on_anomaly=on_anomaly,
    sensitivity='medium'
)

# Search logs with natural language
search_results = await analyzer.search(
    query="database errors in the last hour",
    source='web-application'
)

API Reference

Complete API documentation for integrating log analysis into your applications.

Log Analysis Endpoint

POST /v1/logs/analyze

Analyze log entries and detect anomalies using AI.

Request Body:

{
  "source": "web-application",
  "logs": [
    {
      "timestamp": "2025-09-18T10:30:00Z",
      "level": "ERROR",
      "message": "Database connection failed",
      "user_id": "user123",
      "request_id": "req-456",
      "response_time": 5000
    }
  ],
  "options": {
    "detect_anomalies": true,
    "extract_patterns": true,
    "correlate_events": true,
    "generate_insights": true
  },
  "analysis_type": "real_time|batch|historical",
  "timeframe": "1h|24h|7d",
  "sensitivity": "low|medium|high"
}

Response:

{
  "analysis_id": "analysis-789",
  "status": "completed",
  "summary": {
    "total_logs": 1000,
    "anomalies_detected": 5,
    "patterns_found": 12,
    "severity_distribution": {
      "critical": 1,
      "high": 2,
      "medium": 2
    }
  },
  "anomalies": [
    {
      "id": "anomaly-001",
      "timestamp": "2025-09-18T10:30:00Z",
      "type": "statistical",
      "severity": "high",
      "confidence": 0.92,
      "description": "Unusual spike in database connection errors",
      "affected_logs": 15,
      "baseline_value": 2.1,
      "detected_value": 47.3,
      "context": {
        "time_window": "5m",
        "related_events": ["deployment-start", "config-change"]
      },
      "suggested_actions": [
        "Check database server health",
        "Review recent configuration changes",
        "Scale database connections"
      ]
    }
  ],
  "patterns": [
    {
      "id": "pattern-001",
      "type": "temporal",
      "description": "High error rate during deployment windows",
      "frequency": "weekly",
      "confidence": 0.87,
      "examples": [
        "2025-09-18T10:30:00Z: Deploy started, errors increased 300%",
        "2025-09-11T10:30:00Z: Deploy started, errors increased 250%"
      ]
    }
  ],
  "insights": [
    {
      "type": "recommendation",
      "title": "Implement circuit breaker pattern",
      "description": "High database error rates suggest need for resilience patterns",
      "priority": "high",
      "estimated_impact": "Reduce error propagation by 60%"
    }
  ]
}

Troubleshooting

Common issues and solutions when running log analysis and anomaly detection.

Common Issues

High Memory Usage

Log analyzer consuming excessive memory during processing

Solutions:

Reduce batch size for log processing
Increase number of workers for parallel processing
Implement log sampling for high-volume sources
Optimize Elasticsearch index settings

False Positive Anomalies

Too many false positive anomaly alerts

Solutions:

Adjust anomaly detection sensitivity settings
Extend baseline training period for models
Add known patterns to whitelist
Fine-tune confidence thresholds

Log Processing Lag

Delay between log generation and analysis

Solutions:

Optimize log shipper configuration
Increase Redis buffer size
Scale Elasticsearch cluster horizontally
Implement log preprocessing pipelines

Log Analysis Documentation Complete!

Ready to revolutionize your log analysis? Start your free trial today and discover how AI can transform your log data into actionable insights and proactive monitoring.

AI LogAnalyzer

AI-Powered Analysis

Real-time Processing

Behavioral Learning

Smart Alerting

Installation

System Requirements

Install via Package Manager

Service Configuration

Quick Start

1. Configure Log Sources

2. Start Log Processing

3. Generate Analysis Reports

Configuration

Basic Configuration

Anomaly Detection

Statistical Anomalies

Behavioral Anomalies

Temporal Anomalies

Content Anomalies

Anomaly Configuration

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

search

Best Practices

Log Analysis Strategy

Log Sources

Supported Log Sources

Custom Log Source Configuration

Alerting

Alert Configuration

API Integration

REST API

Python SDK

API Reference

Log Analysis Endpoint

POST /v1/logs/analyze

Request Body:

Response:

Troubleshooting

Common Issues

High Memory Usage

False Positive Anomalies

Log Processing Lag

Log Analysis Documentation Complete!

AI LogAnalyzer

AI-Powered Analysis

Real-time Processing

Behavioral Learning

Smart Alerting

Installation

System Requirements

Install via Package Manager

Service Configuration

Quick Start

1. Configure Log Sources

2. Start Log Processing

3. Generate Analysis Reports

Configuration

Basic Configuration

Anomaly Detection

Statistical Anomalies

Behavioral Anomalies

Temporal Anomalies

Content Anomalies

Anomaly Configuration

Environment Variables

Basic Usage

Analysis Commands

CLI Commands Reference

analyze

search

Best Practices

Log Analysis Strategy

Log Sources

Supported Log Sources

Custom Log Source Configuration