Time Travel With Dynamic Backfill

Steve Waterworth
May 2, 2025

Application logs provide vital information to assist with finding the root cause of an issue when an incident occurs. Under these circumstances there is no such thing as too much logging; the more information the better. However, the greater the volume of log information the greater the cost to process and store it whether using your own servers or those from one of the many SaaS providers.

Some application frameworks support changing the log level without restarting, allowing for increased detail when an issue is detected. While this is better than nothing in most circumstances the vital piece of information was logged before the issue was detected and has already been lost.

Dynamic Detail When You Need It

Grepr slips in like a shim between the log shippers and the log processing and storage servers. All logs messages are retained in low cost storage then using machine learning and a rules engine only less frequent unique messages and summaries of the more frequent noisy messages are sent through. Using these advanced techniques results in a typical reduction of 90% to the volume of messages being processed and stored by the existing logging backend.

During normal operation, this level of information provides just the right level of detail for users: low-frequency messages that are usually important like errors or misbehaviors are passed straight through, so they’re searchable and easy to find while reading through a log stream. Noisy messages that repeat often are summarized so they don’t clog the log stream and make it harder to read.

Remember no log messages are dropped, all messages are retained in low cost storage. When an incident occurs any log messages pertinent to the incident can be selectively and quickly backfilled into the logging backend. Thus providing the engineers all the detailed information they need in the tools they are familiar with.

Grepr dynamic backfill is just like going back in time to increase log level detail before the issue happened, ensuring that all the information is captured for diagnosis.

Reduce Cost Without Reducing Logging

Utilising Grepr the majority of log messages are retained in low cost storage, significantly reducing the cost of processing and storing them. The compromise between the level of detail captured in log messages and the cost now swings firmly in favour of capturing more detailed information all the time. More detail available in log messages enables engineers to diagnose issues and resolve incidents quicker, freeing them to work on new features and fixes.

Give Time Travel A Try

Give Grepr a spin and see how easy it is to start saving 90% on your logging services cost with zero interruption to your existing workflows. Stop worrying about achieving that fine balance between logging visibility and cost. With Grepr dynamic backfill you can use detailed logging at low cost and debug with your current tools.

Share this post

More blog posts

All blog posts
Product

Utilize Cloudflare Logs For Cost Optimization

Cloudflare generates numerous logs of different types, including HTTP request logs, firewall events, access logs, DNS query logs, etc. These logs contain plenty of helpful information that can provide insight into the health and performance of web applications. However, the profusion of data presents a challenge in extracting the useful signals from all the noise. The Grepr Intelligent Observability Data Engine can suppress the noise and provide a clear signal.
September 15, 2025
Product

Monitoring Kubernetes Audit Logs

Kubernetes audit logs are extremely useful for tracking interactions with the API Server for debugging and providing insight into workloads. By default the audit logs are retained in etcd for only one hour. With the low cost storage of Grepr, much longer retention periods are possible for minimal cost and greater insight.
September 5, 2025
Product

Use Grepr With Splunk

This blog post provides a comprehensive, step-by-step guide on how to seamlessly integrate the Grepr Intelligent Observability Data Engine with Splunk. It explains that with a few simple configuration changes, you can reroute your logs to Grepr, which uses machine learning to automatically detect and summarize frequent log patterns. This process can reduce your Splunk log volume and associated cloud costs by up to 90%, all without discarding any data. The post walks you through the entire setup, from configuring integrations for Splunk S2S or HEC to creating pipelines and datasets, ultimately demonstrating how to achieve significant cost savings while maintaining full diagnostic visibility.
August 29, 2025

Get started free and see Grepr in action in 20 minutes.