Summarize Performance Analysis Article

Problem/Feature Description

A DevOps team is evaluating different caching strategies for their high-traffic API. They've found a detailed performance analysis article that compares Redis, Memcached, and in-memory caching across various metrics. The team needs a summary that preserves the quantitative data so they can make data-driven decisions in their next architecture review meeting.

The article contains specific performance numbers, latency measurements, and resource utilization statistics that are critical for making the right technology choice.

Input Files

The following files are provided as inputs. Extract them before beginning.

=============== FILE: inputs/cache-performance.txt ===============

Caching Strategies: A Performance Comparison

We benchmarked three caching approaches for a typical web API serving 10,000 requests per second.

Test Setup

• Load: 10,000 concurrent requests
• Cache hit rate: 80%
• Test duration: 1 hour
• Infrastructure: AWS c5.xlarge instances

Redis

Redis provided the best feature set with persistence and atomic operations. However, performance comes with overhead:

• Latency: p50 of 2.1ms, p99 of 12.3ms
• Throughput: Maxed out at 45,000 requests/second per instance
• Memory efficiency: 1.3x overhead (130MB for 100MB of data)
• CPU usage: 65% under sustained load
• Cost: $0.15 per million requests

The persistence features require approximately 15% additional CPU for AOF logging.

Memcached

Memcached's simplicity translated to raw performance:

• Latency: p50 of 0.8ms, p99 of 3.2ms
• Throughput: Achieved 78,000 requests/second per instance
• Memory efficiency: 1.05x overhead (105MB for 100MB of data)
• CPU usage: 42% under sustained load
• Cost: $0.08 per million requests

The lack of persistence means restart = empty cache, which caused a 15-minute performance degradation in our production testing.

In-Memory (Application Cache)

Keeping cache in the application process eliminated network calls:

• Latency: p50 of 0.09ms, p99 of 0.3ms
• Throughput: Limited only by application, not cache (200,000+ requests/second)
• Memory efficiency: 1.0x overhead (100MB for 100MB of data)
• CPU usage: 28% under sustained load
• Cost: No additional infrastructure

The downside: no cache sharing between application instances, leading to 60% higher database load during cache misses compared to shared cache solutions.

Connection Overhead Analysis

Network latency accounted for significant performance differences:

• Redis TCP connection: 1.5ms average
• Memcached TCP connection: 1.2ms average
• In-memory (no network): 0ms

For our use case with 80% cache hit rate, eliminating network calls saved approximately 320 CPU-seconds per hour across our application fleet.

Recommendation

For APIs with:

• High read, low write → Memcached (2.6x better latency than Redis)
• Need persistence → Redis (only option with durability)
• Single instance or stateless → In-memory (23x better latency than Redis, 9x better than Memcached)
• Distributed with coordination → Redis (atomic operations required)

Our team chose in-memory caching with a 5-minute TTL, accepting the higher database load for the dramatic latency improvements. Our API response times dropped from p99 of 45ms to p99 of 18ms. =============== END FILE ===============

Output Specification

Create a file named cache-analysis-summary.md that captures the essential information from the performance analysis. The summary should help the DevOps team quickly compare the options and understand the trade-offs during their decision-making meeting.