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?
| Database |
p50 Latency |
p95 Latency |
p99 Latency |
Max Throughput |
| InfluxDB |
2.3ms |
8.7ms |
24.1ms |
8,500 pts/sec |
| Prometheus |
1.8ms |
12.4ms |
45.2ms |
6,200 pts/sec |
| TimescaleDB |
4.1ms |
15.6ms |
38.9ms |
7,800 pts/sec |
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:
| Query Type |
InfluxDB |
Prometheus |
TimescaleDB |
| 5-min conversion rate |
45ms |
123ms |
78ms |
| 1-hour page loads |
234ms |
89ms |
156ms |
| 24-hour error trends |
1.2s |
2.8s |
890ms |
| Multi-series analysis |
890ms |
1.1s |
445ms |
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