rust-task-queue 0.1.5

Production-ready Redis task queue with intelligent auto-scaling, Actix Web integration, and enterprise-grade observability for high-performance async Rust applications.
Documentation 
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# Production Task Queue Configuration
# This configuration is optimized for production environments with
# security, performance, and monitoring best practices
#
# ENHANCED AUTO-SCALING: 
# Features multidimensional scaling metrics, adaptive threshold learning,
# and stability controls for intelligent worker management

[redis]
# Redis connection with SSL/TLS support for production
url = "rediss://redis-cluster.production.com:6380"
pool_size = 20
connection_timeout = 10  # seconds
command_timeout = 5      # seconds

[workers]
# Optimized for production workloads
initial_count = 10
max_concurrent_tasks = 25
heartbeat_interval = 30      # seconds
shutdown_grace_period = 120  # seconds

[autoscaler]
enabled = true
min_workers = 5
max_workers = 50
scale_up_count = 2           # workers to add when scaling up
scale_down_count = 1         # workers to remove when scaling down

# Multidimensional scaling triggers (Enhanced Auto-scaling)
# The system now considers 5 different metrics simultaneously for scaling decisions:
# - Queue pressure: weighted queue depth accounting for priority queues
# - Worker utilization: actual busy/idle ratio of workers
# - Task complexity: analysis of task execution patterns
# - Error rate: system health indicator
# - Memory pressure: resource utilization per worker
[autoscaler.scaling_triggers]
queue_pressure_threshold = 1.2        # weighted queue depth per worker
worker_utilization_threshold = 0.85   # target worker utilization (85%)
task_complexity_threshold = 2.0       # complex task overload factor
error_rate_threshold = 0.03           # maximum 3% error rate
memory_pressure_threshold = 1024.0    # memory usage per worker (MB)

# Adaptive threshold learning (SLA-driven optimization)
enable_adaptive_thresholds = true
learning_rate = 0.05                  # conservative learning for production
adaptation_window_minutes = 60        # longer window for stability

# Hysteresis and stability controls
scale_up_cooldown_seconds = 180       # 3 minutes between scale-ups
scale_down_cooldown_seconds = 900     # 15 minutes between scale-downs
consecutive_signals_required = 3      # require 3 consecutive signals

# SLA performance targets for adaptive learning
# When adaptive thresholds are enabled, the system automatically adjusts
# scaling triggers based on actual performance vs these SLA targets
[autoscaler.target_sla]
max_p95_latency_ms = 3000.0           # 3 second P95 latency target
min_success_rate = 0.99               # 99% success rate target
max_queue_wait_time_ms = 5000.0       # 5 second max queue wait
target_worker_utilization = 0.75     # optimal 75% worker utilization

[scheduler]
enabled = true
tick_interval = 30           # seconds
max_tasks_per_tick = 100

[auto_register]
enabled = true

[metrics]
# Enhanced production monitoring
enabled = true
export_interval = 60         # seconds
retention_days = 30
enable_alerts = true

# Enhanced auto-scaling metrics collection
collect_scaling_metrics = true
collect_sla_metrics = true
collect_adaptive_threshold_metrics = true
scaling_metrics_resolution = 30      # seconds between scaling metric snapshots

[alerts]
# Production alert thresholds
max_queue_size = 10000
max_error_rate = 0.05        # 5%
max_task_duration_ms = 300000 # 5 minutes
max_memory_usage_mb = 2048   # 2GB
max_worker_idle_time_sec = 600 # 10 minutes

# Enhanced auto-scaling monitoring thresholds
max_queue_pressure_score = 2.0      # alert if queue pressure exceeds 2.0
min_worker_utilization = 0.10       # alert if workers severely underutilized
max_worker_utilization = 0.95       # alert if workers overloaded
max_scaling_frequency_per_hour = 10 # alert if scaling too frequently
min_sla_success_rate = 0.98         # alert if SLA success rate drops
max_p95_latency_breach_ms = 5000    # alert if P95 latency exceeds 5s

[security]
# Security configurations
max_payload_size_mb = 16
enable_input_validation = true
allowed_queue_patterns = ["^[a-zA-Z0-9_-]+$"]
rate_limiting = true
max_requests_per_minute = 1000

[logging]
# Structured logging for production
level = "info"
format = "json"
enable_metrics_logging = true
log_sensitive_data = false

[health_checks]
# Health check endpoints configuration
enabled = true
port = 8080
path = "/health"
include_detailed_metrics = false
timeout_ms = 5000

[actix]
# Production web server settings
auto_configure_routes = true
route_prefix = "/api/v1/tasks"
enable_metrics = true
enable_health_check = true

[axum]
# Production web server settings
auto_configure_routes = true
route_prefix = "/api/v1/tasks"
enable_metrics = true
enable_health_check = true