Datalust provides Seq, a centralized telemetry server designed for the ingestion, storage, and analysis of structured log data. Seq operates as a self‑hosted system that applications send events to over an HTTP interface. The platform stores these events in an optimized, disk‑backed data store and exposes them through an interactive web interface for searching, filtering, and correlation. Seq focuses on structured logging rather than plain text logs, enabling precise queries over event properties, timestamps, and relationships. It is intended for development and operations teams that require reliable insight into application behavior, failures, and performance across distributed systems.

Key Features

Centralized Log Ingestion Collects telemetry from multiple applications and services.

• HTTP‑based event ingestion
• Support for structured event payloads

Structured Event Storage Stores logs as structured data rather than text.

• Property‑based indexing
• Efficient disk‑backed storage engine

Powerful Query Engine Enables detailed analysis of telemetry data.

• SQL‑like query language
• Filtering, aggregation, and correlation of events

Web‑Based User Interface Provides interactive access to telemetry data.

• Search and exploration of logs
• Visual inspection of event timelines

Retention and Data Management Controls storage usage and data lifecycle.

• Configurable retention policies
• Automatic cleanup of expired data

Alerting and Signals Detects conditions of interest in log streams.

• Rule‑based event detection
• Triggered notifications on matching patterns

Extensible Input Support Accepts data from various sources.

• Logging frameworks and agents
• Syslog and forwarding components

Benefits

Improved Application Visibility Provides clear insight into runtime behavior.

• Easier diagnosis of failures
• Better understanding of system activity

Faster Troubleshooting Reduces time to identify root causes.

• Structured queries instead of text search
• Correlation across multiple services

Scalable Log Handling Supports high‑volume telemetry workloads.

• Efficient ingestion and indexing
• Designed for continuous data growth

Operational Control Keeps data under organizational ownership.

• Self‑hosted deployment model
• Configurable storage and access policies

Consistent Observability Across Systems Unifies telemetry from diverse platforms.

• Common data model for logs
• Central point of analysis