Grepr Blog

Grepr helps companies lower observability costs while keeping engineers' workflows unchanged. A key feature is its ability to ensure full logs for a sampled set of traces, even with log aggregation and sampling in place. Users can specify how much of their data to sample and provide trace ID paths to identify relevant logs. Grepr's system uses intelligent sampling, backfilling, and a rule engine to collect and process trace logs efficiently. To optimize performance and memory, it evolved from a basic set-based approach to a scalable design using Bloom filters and a custom resizing Bloom filter. This allows Grepr to maintain high throughput while managing memory use effectively, ensuring reliable trace log visibility.

Discover how Grepr's intelligent log management solution can reduce your logging costs by 90% without sacrificing visibility. Our two-tier storage system uses machine learning to identify patterns and store less critical logs in low-cost storage, while maintaining immediate access to important data. When incidents occur, Grepr's dynamic backfill feature automatically retrieves relevant logs to your existing tools. Implement smarter logging today without changing your workflows or compromising on troubleshooting capabilities.

Storing logs long-term doesn't have to be super expensive. Using a data lake can reduce storage costs by more than 90% while still keeping the logs queryable and immediately accessible.

Three advanced techniques for reducing log volumes by 90% or more, including automatic pattern sampling, logarithmic sampling, and sampling with automatic backfilling, each designed to scale for enterprise environments without sacrificing critical troubleshooting data.

Grepr's Monitored Objects approach groups metrics by entity, aggregates normal behavior, and only sends detailed data to Datadog when anomalies occur, dramatically reducing billable custom metrics and hosts without sacrificing troubleshooting capability.

This blog discusses 6 ways that the Grepr Data Lake is optimized for logs.

Four foundational techniques for reducing log volumes, including severity thresholds, log-to-metrics conversion, uniform sampling, and drop rules, along with the tradeoffs engineering teams should weigh before implementing each one.

Announcing our raise from Andreessen Horowitz and boldstart ventures to tackle the exponentially growing cost of observability without forcing migrations. Grepr combines machine learning with an observability data lake to reduce costs by 90% with minimal effort.