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Short answer: a time-series database (TSDB) stores timestamped measurements such as request duration, error rate, uptime checks, and CPU use so your monitoring system can retain history, answer time-window questions, render dashboards, and evaluate alerts. The right TSDB depends on how metrics arrive, how many labeled series you create, your query and alert workload, retention and recovery requirements, and the operational cost your team can carry. Prometheus is a clear open-source reference implementation, but its local storage is a single-node, non-replicated database rather than a universal answer.
What is a time-series database doing in website monitoring?
Website monitoring turns behavior into numbers recorded over time. A probe might record an HTTP status, DNS or TLS timing, page-load duration, or a Boolean availability result. An instrumented application can expose request counts, latency histograms, queue depth, and resource metrics. A TSDB associates each sample with a timestamp and usually with dimensions such as service, route, region, or status code.
That history enables questions that a single current value cannot answer:
- How did p95 checkout latency change during the last 30 minutes?
- Did errors begin after a deployment, and only in one region?
- Has memory use grown steadily for seven days?
- Was the site unavailable for five minutes, or did one probe fail?
The database is one part of a monitoring system. Keep these responsibilities separate when designing it:
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- Collection: agents, exporters, browser checks, or application instrumentation produce samples.
- Storage: the TSDB writes samples, indexes their labels, and enforces retention.
- Query and analysis: a query language calculates rates, percentiles, joins, and time-window aggregates.
- Visualization: Grafana or another client turns query results into dashboards.
- Alert delivery: a rule evaluator detects a condition and sends notifications through an alerting component.
Prometheus documents this end-to-end pattern: it scrapes targets over HTTP, stores samples locally, evaluates recording and alerting rules, and exposes an API for Grafana and other clients. Its documentation describes web-server request times as a useful metric because request counts and latency help investigate a slow application (Prometheus overview).
How Prometheus models and processes website metrics
Series, labels, and samples
In Prometheus, a time series is identified by a metric name plus optional key-value labels. A counter such as http_requests_total might have labels for method, route, status, and service. Every distinct label combination is a separate series. This is powerful for filtering and grouping, but uncontrolled labels (for example, a user ID or full URL) can create excessive cardinality.
PromQL can select, correlate, and transform those series. Typical website queries calculate a request rate, aggregate by service, or derive a latency percentile from histogram buckets. The same expressions can feed a dashboard panel and an alert rule, which keeps visual diagnosis and automated detection consistent. Prometheus positions itself as a reliability-oriented system for diagnosing outages; its authors write that it is designed to be the system you use during an outage to quickly diagnose problems (Prometheus Authors).
Scraping and push integration
Prometheus normally pulls an HTTP metrics endpoint at intervals. Exporters expose metrics for systems that cannot instrument themselves, and a Pushgateway is available for short-lived jobs that cannot be scraped reliably. Pulling gives the server a regular view of target health, while pushing can be useful for batch work; choose deliberately rather than mixing models without ownership of timestamps and failure semantics.
Rules, dashboards, and alerts
Recording rules precompute expensive expressions for faster dashboards or long windows. Alerting rules evaluate expressions and hand firing alerts to an alerting component for routing and notification. Grafana and other API consumers can query the same retained samples. A TSDB therefore does more than archive values: it is the time-window calculation engine behind operational decisions.
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Prometheus storage limits you must design around
Prometheus’s local TSDB is neither clustered nor replicated. A single Prometheus process writes to local disk; a disk or node failure can make that local history unavailable. Prometheus provides remote-write and remote-read interfaces so a deployment can integrate a separate storage system when it needs longer retention, cross-node resilience, or centralized querying (Prometheus storage documentation).
Do not turn this limitation into a fixed maximum retention period. Years of retention can be possible with suitable architecture, sizing, and remote storage. Plan for the sample rate, active series, compaction behavior, backups, and the recovery point your team actually requires.
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For size-based retention, Prometheus recommends setting the retention size to no more than 80–85% of allocated Prometheus disk space, leaving 15–20% for temporary compaction space. This is the Prometheus Authors’ current storage guidance (accessed 2026), not a universal capacity guarantee. Local storage also requires a supported POSIX-compliant filesystem; the documentation specifically warns that NFS implementations are unsupported because corruption can occur.
Compare systems by workload, not by a universal ranking
A useful comparison starts with the shape of your monitoring traffic. Record these dimensions before selecting a backend:
- Ingestion model: pull scraping, push gateways, OTLP or other exposition protocols, batch size, and sample interval.
- Series and cardinality: number of active series, label count, label churn, and whether dimensions are regular or highly variable.
- Query mix: dashboard refreshes, ad-hoc investigations, long-range rollups, joins, and alert evaluations running concurrently with writes.
- Retention: hours of raw data, months of downsampled data, and whether data must survive a node or disk loss.
- Operations: topology, upgrades, backups, filesystem requirements, capacity monitoring, and the expertise available to run it.
- Cost: compute, storage, egress, replicas, and the staff time required for self-hosting or a managed service.
| System | Documented fit | Important qualification |
|---|---|---|
| Prometheus | Open-source scrape-oriented monitoring; local collection, PromQL, rules, and API clients | Local storage is single-node and not replicated; use remote storage for additional resilience or retention. |
| VictoriaMetrics | Vendor documentation describes Prometheus compatibility, Grafana compatibility, multiple ingestion protocols, a single-instance option, and a horizontally scalable cluster option; long-term Prometheus storage is a stated use case. | Capacity, speed, and savings language on the product page is vendor material, not an independent benchmark. See VictoriaMetrics product documentation. |
| InfluxDB 1.x | InfluxData’s version-specific platform page discusses ingestion and querying, downsampling, retention policies, and the TICK stack (Telegraf, InfluxDB, Chronograf, Kapacitor). | Those details are explicitly about InfluxDB 1.x. Do not apply them to InfluxDB 2.x or 3.x without checking current version-specific documentation: InfluxData platform page. |
VictoriaMetrics also documents open-source, enterprise, cloud, and OpenTelemetry product areas (documentation home). That establishes available deployment categories and compatibility, not a neutral cost or performance result. No controlled, comparable performance or adoption statistic establishes a universal winner.
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Benchmark the monitoring workload you actually have
If you test candidates, make the test reproducible and publish the workload assumptions internally. A recent SciTSv2 preprint frames six useful dimensions: connection parallelism, batch ingestion, regular versus irregular time series, multivariate series, mixed read/write workloads, and system metrics (preprint record). It is a workload framework, not a performance ranking, and its opened record has inconsistent future copyright metadata.
- Export a representative metric set with realistic label distributions and cardinality.
- Replay your actual scrape or push interval, including bursts and failed targets.
- Run the dashboard queries, alert expressions, and long-range investigations concurrently with ingestion.
- Measure ingestion delay, query latency, disk growth, compaction impact, restart recovery, and alert evaluation delay.
- Repeat with the retention period and replication or remote-write topology you intend to operate.
Keep hardware, software versions, configuration, and query definitions identical where possible. A result from one axis—such as batch ingestion—cannot rank systems for an interactive dashboard workload.
A practical Prometheus deployment checklist
Control cardinality before adding labels
Prefer bounded dimensions such as service, method, and normalized route. Avoid raw user identifiers, request IDs, and unbounded URL parameters. Estimate active series before production and alert on unexpected growth.
Size disk and retention together
Estimate samples per second from scrape interval and series count, then add compaction and failure headroom. Apply the 80–85% retention-size guidance when using size-based retention. Monitor disk usage and compaction health as first-class metrics.
Define recovery expectations
Decide whether losing local history is acceptable. If not, configure remote write to a suitable backend, test restore procedures, and document what happens during a Prometheus or storage outage. Do not place the local TSDB on unsupported NFS storage.
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Alert on scrape failures, rule-evaluation lag, remote-write backlog, disk pressure, and process restarts. A dashboard that is unavailable during an incident is itself an incident signal.
Common failure modes and fixes
- Queries time out: narrow the time range, reduce high-cardinality grouping, use recording rules, or scale the query/storage tier. A timeout is not proof that the database is inherently slow.
- Disk fills unexpectedly: inspect active-series growth, label churn, scrape intervals, and compaction; enforce a tested retention policy and leave the documented headroom.
- Alerts miss data: check target reachability, scrape timestamps, rule evaluation logs, and remote-write lag. A push-only design can hide target failure if no independent health signal exists.
- History disappears after a node loss: this is expected for an unreplicated local Prometheus store. Add remote storage or a topology designed for the required recovery objective.
- InfluxDB guidance conflicts with your deployment: verify the product version. The cited platform page is for InfluxDB 1.x, not a generic statement about later releases.
- Benchmark conclusions do not hold in production: compare the test’s cardinality, concurrency, batch shape, retention, hardware, and version with your real workload before acting on it.
Add visual evidence to a monitoring workflow
Metrics explain that a page is slow or failing; a screenshot can show what a visitor actually saw. For a do-it-yourself capture, run a browser in a controlled environment, wait for the page’s critical selector, dismiss consent UI, hide chat and newsletter elements, and save the resulting PNG or WebP alongside the alert timestamp. Browser automation gives control, but it adds a browser runtime, timing races, cookie state, and maintenance.
Or skip the browser setup
ScreenshotNeo provides a website screenshot API and MCP server. One request can return PNG, JPEG, WebP, or PDF; it can accept a consent banner before capture and remove more than 60 known consent platforms, newsletter popups, and chat widgets. Each step can be disabled. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits are not billed, and response headers identify the page verdict and billing status.
Use the API with the options your monitoring job needs:
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Decision framework
- Describe the monitoring job: metrics, probes, dashboards, alerts, and required evidence.
- Measure active series, scrape or push rate, query concurrency, retention, and cardinality limits.
- Choose a collection and query model that matches existing instrumentation and operator skills.
- Specify resilience: local recovery, remote storage, replication, backups, and acceptable data loss.
- Price the complete system—compute, storage, network, replicas, managed fees, and maintenance—not only ingestion.
- Run a workload-specific pilot and document the assumptions before committing.
Frequently Asked Questions
Is a time-series database the same thing as a monitoring platform?
No. A TSDB stores and queries timestamped measurements. Collection agents, dashboards, rule evaluators, and notification services complete the monitoring platform.
Can Prometheus store every request for billing?
Prometheus’s overview says it is not the right choice when 100% accuracy is required for uses such as per-request billing. Use a system designed for that accounting requirement.
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When should I add remote storage to Prometheus?
Consider it when local-node loss, longer retention, centralized querying, or cross-site resilience exceeds what a single local disk can provide; validate the remote system’s compatibility and recovery behavior.
What should I record when comparing TSDBs?
Record ingestion model, active series and labels, query concurrency, retention, resilience topology, hardware, software versions, and operating cost so results remain comparable.
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