Time-series database performance under ecommerce load: real benchmark results

Your monitoring stack becomes your worst enemy during traffic spikes if you pick the wrong time-series database. I've seen checkout systems lose visibility during Black Friday precisely when teams needed it most.

A typical ecommerce platform handling 50K daily orders generates 2.4M metric points hourly. That's 665 metrics per second at baseline, spiking to 4,200+ during flash sales. Your database choice determines whether you maintain observability or go blind when it matters.

The setup

I benchmarked InfluxDB 2.7, Prometheus 2.45, and TimescaleDB 2.11 on identical hardware: 8 cores, 32GB RAM, NVMe storage. No resource contention, no excuses.

The test simulated realistic ecommerce metrics:

• Application: response times, error rates, queue depths
• Infrastructure: CPU, memory, disk I/O, network stats
• Business: orders/minute, cart abandonment, payment times
• UX: page loads, JS errors, third-party service latency

72-hour test with three load patterns:

• Baseline: 665 metrics/sec
• Traffic spike: 2,100 metrics/sec (2 hours)
• Flash sale: 4,200 metrics/sec (30 minutes)

Write performance: who keeps up?

InfluxDB wins for consistency. During flash sale simulation, it held sub-10ms p95 latency while Prometheus started queueing writes. That's the difference between seeing your metrics and flying blind.

Prometheus handles steady loads well but chokes on bursts. Its pull-based model creates scraping bottlenecks when targets can't keep up.

TimescaleDB showed higher baseline latency but predictable scaling. PostgreSQL's stability showed through.

Query performance: dashboard responsiveness

Tested common ecommerce queries:

Different winners for different needs:

• InfluxDB crushes real-time queries (conversion rates, immediate alerts)
• Prometheus excels at medium-term trends (1-hour operational views)
• TimescaleDB dominates complex analytics (capacity planning, root cause analysis)

Configuration insights

Here's what worked for each:

InfluxDB config tweaks:

[storage-engine]
 wal-fsync-delay = "100ms"
 cache-max-memory-size = "2g"

[data]
 cache-snapshot-memory-size = "512m"
 cache-snapshot-write-cold-duration = "5m"

Prometheus optimization:

global:
 scrape_interval: 15s
 evaluation_interval: 15s

storage:
 tsdb:
 retention: 30d
 min-block-duration: 2h
 max-block-duration: 36h

TimescaleDB tuning:

ALTER SYSTEM SET shared_buffers = '8GB';
ALTER SYSTEM SET effective_cache_size = '24GB';
ALTER SYSTEM SET work_mem = '256MB';
SELECT add_compression_policy('metrics', INTERVAL '7 days');

Production reality check

Numbers are meaningless without context:

• Flash sales: InfluxDB's write performance keeps you online when traffic spikes 6x
• Incident response: That 45ms vs 123ms difference in conversion rate queries matters when checkout drops from 3.2% to 1.8%
• Cost optimization: TimescaleDB's complex query speed pays off for capacity planning and historical analysis

Storage efficiency surprised me. InfluxDB used 35% less disk space than Prometheus for identical datasets, but consumed 40% more RAM during write bursts.

The verdict

Pick InfluxDB for real-time dashboards and instant incident response. Best write throughput, fastest recent data queries.

Pick Prometheus for cloud-native stacks. Kubernetes integration, extensive ecosystem, solid medium-term query performance.

Pick TimescaleDB for analytical workloads. Complex queries, familiar SQL interface, best for teams already running PostgreSQL.

Testing limitations

• Single datacenter setup (network latency not tested)
• 72-hour window (long-term degradation unknown)
• Optimized configs (production tuning varies)
• No clustering/federation tested

Your mileage will vary based on metric cardinality, retention needs, and team expertise.

The wrong choice doesn't just slow dashboards; it creates blind spots when you need visibility most. Choose based on your primary use case, not just raw performance numbers.

Originally published on binadit.com