Build Your Own LogViewer: A Quick Guide for DevelopersLogs are the lifeblood of modern software systems. They record application behavior, surface errors, chronicle security events, and provide the raw material for observability. While many teams rely on hosted or open-source log management platforms, building a custom LogViewer can be a valuable exercise: it gives you control over UX, lets you tailor features to your workflow, and can be lightweight and cost-effective for small to mid-sized projects. This guide walks through core concepts, architecture, implementation tips, and practical features to build a production-ready LogViewer.
Why build a LogViewer?
- Custom UX and workflow: Tailor search, filtering, and alerting to your team’s needs.
- Cost control: Avoid ingest/storage bills from third-party providers for predictable log volumes.
- Privacy and compliance: Keep logs in-house for sensitive data or regulatory requirements.
- Learning and flexibility: Implement only the features you need and extend them as requirements evolve.
Key requirements and core features
Before coding, define the scope. At minimum, a useful LogViewer should provide:
- Log ingestion and storage (or access to existing log files)
- Efficient searching and filtering (time range, severity, text, fields)
- Live tailing for real-time debugging
- Structured log parsing (JSON, key-value) and unstructured support
- Highlighting and context (show surrounding lines)
- Basic analytics: counts, trends, grouping
- Role-based access controls and secure access (TLS, authentication)
- Exporting, bookmarking, and sharing links to specific log views
Architecture overview
A typical LogViewer can be split into three layers:
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Ingestion and storage
- Options: direct file access, centralized collector (Fluentd/Fluent Bit, Logstash), or push API from apps.
- Storage choices: append-only files, time-series DBs, search indexes (Elasticsearch/OpenSearch), or columnar stores (ClickHouse).
- Consider retention policies and compression for cost control.
-
Indexing and query layer
- Indexing structured fields enables fast filtering.
- Use inverted indexes or columnar storage for text-heavy queries.
- Provide both full-text search and structured queries (e.g., severity:error AND user_id:123).
-
Frontend / UX
- Real-time tail via WebSockets or Server-Sent Events (SSE).
- Rich filtering UI (multi-selects, regex, saved queries).
- Highlighting, line expansion, and context controls.
Data model and parsing
Design a simple unified schema that supports structured and unstructured logs. Example fields:
- timestamp (ISO 8601 with timezone)
- level (ERROR, WARN, INFO, DEBUG, TRACE)
- service / source
- host / instance
- message (raw or parsed)
- json / map for structured fields (user_id, request_id, latency, status)
- file path, line number (if tailing files)
- tags / labels
Parsing strategy:
- Accept raw text and attempt JSON parse; if valid, treat as structured.
- Support custom parsers (grok, regex) for common formats (NGINX, Apache, JVM).
- Normalize timestamps to UTC and store original timezone if present.
Choosing storage and search tech
Pick based on scale and query patterns:
-
Small scale / file-centric
- Store compressed log files on disk or object storage (S3). Use a lightweight index (SQLite or simple inverted index).
- Use tailing for recent logs and direct file reads for archival fetches.
-
Medium to large scale
- Use Elasticsearch/OpenSearch for full-text search and aggregations.
- ClickHouse works well for analytical queries and high ingestion rates, combined with a text index for messages.
- Loki (Grafana Loki) is optimized for log streams and works with Promtail for collection; pairs well with Grafana for visualization.
Hybrid approach: keep recent logs indexed for fast queries and archive older logs in object storage with delayed re-indexing on demand.
Ingestion pipeline
-
Collect
- Agents (Fluentd, Fluent Bit, Promtail) or sidecar log shippers.
- Direct application SDKs that push logs via HTTPS to an ingest endpoint.
-
Process
- Parse and enrich logs (add environment, service, host, Kubernetes metadata).
- Redact sensitive fields (PII, secrets) before storage if required.
-
Index/Store
- Write to chosen storage and update search indexes.
- Ensure idempotency where duplicate events may occur (use event ID or hash).
Scaling tips:
- Use batching and backpressure to prevent overload.
- Implement retry/backoff for transient failures.
- Partition by time or service to improve read/write locality.
Frontend design and features
User experience is critical. Key UI elements:
- Timeline and time-range picker with quick ranges (last 5m, 1h, 24h) and custom range.
- Search bar supporting:
- Plain-text, case-sensitive toggle
- Structured queries (field:value)
- Regex support
- Lucene-like syntax if using Elasticsearch
- Filters panel with facets (service, host, level)
- Live tail view with pause/auto-scroll and line-wrapping controls
- Log detail pane showing parsed fields, raw line, and nearby context
- Bookmarks and shareable permalink for a query + time range
- Alerts/notifications for matching queries (via webhook, email, or chat)
UX performance:
- Load only visible lines (virtualized list rendering).
- Use incremental loading for context lines.
- Rate-limit live updates to avoid overwhelming the client.
Security and privacy
- Require authentication (OIDC, SAML, corporate OAuth) and role-based access (who can view, query, export).
- Transport encryption (TLS) for both ingestion and UI.
- Sanitize and redact sensitive fields early in the pipeline.
- Audit logs of who queried or exported data.
- Apply retention and deletion policies to comply with data protection rules.
Example stack (small-to-medium teams)
- Ingestion: Fluent Bit (agent) -> Fluentd for processing
- Storage: Elasticsearch (or OpenSearch)
- Query/API: Node.js or Go service that proxies queries and adds auth
- Frontend: React + TypeScript, use virtualized list (react-virtualized)
- Real-time: WebSocket server for tailing
- Deployment: Kubernetes, CI/CD with Helm charts
Implementation sketch (backend API examples)
- POST /ingest — accept log lines (JSON or text) and return ingestion status.
- GET /logs — query with params: q (query), start, end, limit, cursor
- GET /logs/tail — websocket endpoint for real-time tailing
- GET /logs/{id} — fetch single log entry and surrounding context
- POST /alerts — create alert rules that evaluate queries on schedule
Sample query parameters:
- q=service:auth AND level:ERROR
- start=2025-09-01T12:00:00Z
- end=2025-09-01T12:10:00Z
- limit=500
- cursor=eyJvZmZzIjoxMDAwfQ==
Performance and operational concerns
- Monitor ingestion lag, index size, and query latency.
- Implement retention and index rotation (rollover indices in ES).
- Backup indexes and archived logs regularly.
- Provide health endpoints for collectors and alert on failures.
- Use circuit breakers to avoid cascading failures from heavy queries.
Testing and validation
- Load-test ingestion and query endpoints with realistic payloads.
- Validate parsing rules with sample logs from all services.
- Test redaction rules to ensure no sensitive fields leak.
- Simulate node failures and verify recovery and no data loss.
Advanced features to consider
- Structured log query language with auto-completion.
- Correlation across traces and metrics (link to tracing IDs).
- Anomaly detection using ML for unusual error spikes or new log signatures.
- Rate-limiting and sampling for noisy services.
- Multi-tenant isolation for SaaS or multiple teams.
Example development roadmap (12 weeks)
Weeks 1–2: Requirements, schema design, simple ingestion endpoint, file-based storage for POC.
Weeks 3–4: Frontend skeleton, basic search and tailing.
Weeks 5–6: Structured parsing, indexing into search backend, filters/facets.
Weeks 7–8: Auth, RBAC, TLS, redaction.
Weeks 9–10: Performance tuning, retention policies, backups.
Weeks 11–12: Alerts, saved queries, documentation, user testing.
Conclusion
Building your own LogViewer is a pragmatic way to gain control over observability tailored to your team’s needs. Start small with core features (ingest, search, tail), iterate on parsing and UX, and add security and analytics as you scale. With careful choices about indexing, storage, and UI performance, a custom LogViewer can be an efficient and privacy-friendly alternative to off-the-shelf systems.
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