FAST ACCESS TO COMPRESSED LOG ARCHIVES
QSLE Vault — archival service
A drop-in archival service for log and event data.
Fast access to years of compressed logs — without full rehydration.
Designed for efficient, time-slice queries on long-retained data, with minimal compute and no pipeline changes.
WHAT IT IS
QSLE Vault is a managed archival backend service for long-term log and event data.
Spiral Phase Labs operates alongside existing logging and observability stacks, handling long-term retention and historical access without requiring changes to existing ingestion pipelines, schemas, or query tools in typical deployments.
Teams activate the service incrementally—only where long-term access becomes expensive or slow..
WHY IT MATTERS
The challenge isn’t storing data. It’s accessing it efficiently.
Modern systems generate vast volumes of log and event data that must be retained for compliance, security, or reliability, but are rarely queried. When historical access is required, many systems rehydrate entire datasets, driving unnecessary cost and latency.
Spiral Phase Labs enables targeted access to historical data while minimizing unnecessary reads.
HOW IT WORKS
Historical data is stored in a compressed, time-ordered format optimized for bounded queries.
Queries retrieve only the archive segments required for the requested time window, avoiding full dataset rehydration and reducing compute overhead.
Evaluated using production-scale datasets.
Using public GitHub event streams:
~46× effective compression
1–2% of archive segments accessed per bounded query
Sub-second to low-hundreds-millisecond query latency
No full dataset rehydration
Benchmarks were conducted on time-ordered NDJSON event data under realistic access patterns.
Results observed under benchmark conditions; performance may vary by dataset and access pattern.
A detailed benchmark report is available upon request.
HOW WE KNOW
Infrastructure and platform teams
Reliability and security engineering teams
Organizations with long log-retention requirements
Companies spending $50k–$500k+ annually on log storage
The service complements existing observability tools rather than replacing them.
WHO IT’S FOR
HOW TO START
Evaluation first. No required migration. No disruption to existing systems.
Engagements begin with a limited benchmark on historical data, allowing teams to assess performance and cost impact before any production use.
Activation is incremental and scoped.