Cache

Tacnode's Cache system provides intelligent data acceleration capabilities, dramatically improving query performance against cold storage and data lake sources while reducing operational costs.

Why Use Cache?

Cache is a critical component for enhancing data query performance and reducing storage costs. In big data scenarios, direct access to cold storage or lake storage can result in high latency and increased costs. By implementing cache, you can achieve:

🚀 Performance Benefits

Enhanced Query Performance: Store frequently accessed data in high-speed cache, significantly reducing query latency
Improved Response Times: Faster query execution delivers better user experience
Optimized Resource Utilization: Intelligent caching strategies improve overall system efficiency

💰 Cost Optimization

Reduced Storage Access Costs: Minimize direct access to underlying storage systems
Lower Operational Expenses: Efficient caching reduces compute and I/O overhead
Better Resource Allocation: Smart cache management optimizes infrastructure usage

Distributed Cache Service

Cache is a distributed caching resource designed to accelerate query access to cold storage and data lake systems. The cache implements an LRU (Least Recently Used) eviction strategy for optimal memory management.

Key Features

Intelligent Cache Management

Automatic cache optimization by Tacnode
Configurable maximum cache space allocation
Usage never exceeds configured limits

Flexible Sharing Models

Shared Cache: Multiple Nodegroups can share a single cache instance
Dedicated Cache: Exclusive cache allocation for specific Nodegroups
I/O Isolation: Recommended for scenarios requiring strict performance isolation

High Availability

Cache service remains stable during cluster scaling operations
Persistent service availability ensures consistent performance
Automatic failover capabilities

Cache Metrics and Billing

Cache billing comprises two main components:

1. Actual Cache Usage

Measured by the actual data stored in cache
Billed based on storage consumption over time
Scales with your data access patterns

2. Cache Miss Requests

GET requests to underlying storage when cache cannot fulfill requests
Indicates cache efficiency and sizing adequacy
Higher miss rates suggest need for cache expansion

Performance Optimization Guidelines

Monitoring Cache Efficiency

High cache usage with low hit rates indicates insufficient cache space
Increased direct storage GET requests signal need for cache expansion
Regular monitoring helps optimize cache configuration

Best Practices

Set cache size based on data access patterns
Monitor cache hit rates and adjust configuration accordingly
Use dedicated caches for scenarios requiring I/O isolation
Plan cache capacity based on workload characteristics

Navigate to the Nodegroup creation page
Select "Create Cache" option during setup
Configure initial cache parameters
Complete Nodegroup and cache creation together

Option 2: Standalone Cache Creation

Create independent cache instances in the Cache management section:

Go to Data → Cache in the Tacnode console
Click "Create Cache"
Configure cache parameters:
- Minimum Size: 100GB
- Increment: 10GB steps
- Recommended Initial Size: 1/10 of cold storage/data lake size

Cache Creation Best Practices

✅ Capacity Planning

Estimate initial cache size based on data volume and access patterns
Start with smaller cache and scale based on actual usage
Consider business peak periods when sizing cache

✅ Growth Strategy

Begin with conservative sizing to understand usage patterns
Monitor performance metrics before expanding
Scale gradually based on hit rate and performance data

Cache Binding

Binding During Nodegroup Creation

When creating a new Nodegroup:

Select "Bind Existing Cache" option
Choose from available cache instances
Configure binding parameters
Complete Nodegroup creation with cache attached

Managing Cache Bindings

In the Cache management interface:

Navigate to the cache details page
Click "Bind" to attach cache to Nodegroups
Select target Nodegroups from the available list
Confirm binding configuration

Unbinding Caches

Select cache instance in management console
Choose "Unbind" option
Select Nodegroups to disconnect
Confirm unbinding operation

Cache Binding Strategies

✅ Shared vs. Dedicated

Shared Cache: Cost-effective for similar workloads
Dedicated Cache: Better performance isolation and predictability
Hybrid Approach: Mix of shared and dedicated based on requirements

✅ Binding Management

Regularly review binding relationships
Ensure bindings align with current performance requirements
Document binding strategies for team reference

Cache Configuration

Editing Cache Properties

Modify cache settings through the management interface:

Configurable Parameters

Cache Name: Update descriptive names for better organization
Cache Size: Adjust storage allocation based on usage patterns
Description: Maintain documentation for cache purpose and configuration

Configuration Best Practices

⚠️ Important Considerations

Cache size adjustments may require brief reallocation periods
Name changes do not affect cached data or performance
Schedule size adjustments during low-traffic periods
Test configuration changes in development environments first

Cache Monitoring

Key Metrics to Track

Cache hit rate percentage
Storage utilization levels
Request volume and patterns
Cost per query performance ratio

Optimization Signals

Low hit rates indicate potential undersizing
High storage costs suggest over-provisioning
Increased latency may signal cache configuration issues

Cache Release

Pre-Release Checklist

Before releasing a cache instance:

✅ Validation Steps

Confirm cache is no longer needed for operations
Unbind all associated Nodegroups
Assess impact on query performance
Notify relevant business teams of the change

✅ Release Process

Unbind Dependencies: Remove all Nodegroup associations
Performance Assessment: Evaluate impact on query performance
Team Notification: Inform stakeholders of cache removal
Execute Release: Complete cache deletion through console

Cache Management Best Practices

1. Capacity Planning

Align cache capacity with data access patterns and business requirements
Regular capacity reviews based on growth projections
Balance performance needs with cost considerations

2. Performance Monitoring

Continuous monitoring of cache hit rates and performance metrics
Automated alerting for performance degradation
Regular performance reviews and optimization

3. Configuration Optimization

Periodic review and adjustment of cache configurations
A/B testing of different cache strategies
Documentation of optimal configurations for different workloads

4. Cost Management

Balance cache performance benefits with operational costs
Avoid over-provisioning that leads to unnecessary expenses
Regular cost-benefit analysis of cache utilization

5. Disaster Recovery

Establish cache failure response procedures
Define emergency protocols for cache unavailability
Maintain backup strategies for critical cached data

Advanced Cache Strategies

Workload-Specific Optimization

Analytics Workloads

Larger cache sizes for complex analytical queries
Longer retention periods for frequently accessed datasets
Dedicated caches for mission-critical analytics

Operational Workloads

Smaller, faster caches for real-time operations
Shared caches for similar operational patterns
Quick eviction for transactional data

Mixed Workloads

Hybrid cache strategies combining shared and dedicated approaches
Workload-aware cache partitioning
Dynamic cache allocation based on demand patterns

Cache Performance Tuning

Optimization Techniques

Monitor access patterns to identify optimization opportunities
Adjust cache sizes based on actual vs. expected usage
Implement cache warming strategies for predictable workloads
Use cache prefetching for known access patterns

Troubleshooting Common Issues

Low Hit Rates: Increase cache size or review data access patterns
High Costs: Optimize cache size or review binding strategies
Performance Degradation: Check cache health and resource allocation
Capacity Issues: Scale cache resources or implement better eviction policies

By implementing these comprehensive cache management strategies, you can maximize the performance benefits of Tacnode's caching system while maintaining cost-effective operations.