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Grepr Intelligent Observability Data Engine reduces log volume by 90% by analyzing log message semantics and using machine learning to maintain pattern matches. Frequent messages are summarized, while unique ones pass through automatically without configuration. Integrations with Datadog and New Relic allow metrics and alerts from logs, but Grepr's summarization can skew these. To address this, users can either refactor alerts to use Grepr's repeat count attribute or enable exception rules by scanning integrations to exclude certain logs from summarization. This reduces log volume efficiency but preserves existing workflows with minimal effort. Configuration involves selecting exceptions per pipeline via a simple interface. Grepr's automation simplifies initial setup and ongoing maintenance, as its semantic machine learning adapts to new application components automatically. More information is available at Grepr.ai.


What is the main function of Grepr Intelligent Observability Data Engine?
Grepr Intelligent Observability Data Engine reduces log volume by 90% by continuously analyzing the semantics of log messages and maintaining an active set of pattern matches using machine learning.

How does Grepr handle frequently occurring and less frequent log messages?
Frequently occurring messages are sent through as summaries, while less frequent unique messages get passed straight through.

Does Grepr require configuration to operate?
No, all this happens entirely automatically with zero configuration.

What potential issue arises when Grepr reduces log volume and sends summaries in relation to Datadog and New Relic?
Alerts and metrics generated from log messages in Datadog and New Relic may become skewed because Grepr reduces log volume and sends through summaries for these entries.

What are the two approaches to mitigate the impact of Grepr's log summarization on alerts and metrics?
One approach is to refactor alert and metric definitions to use the additional attribute repeat count that Grepr inserts into all messages. The other is for Grepr to scan Datadog and New Relic integrations, automatically discover alert and metric definitions, and enable them as exception rules so that matching log entries are not summarized.

What is the trade-off when enabling exception rules in Grappa for existing alerts and metrics?
Enabling exception rules reduces the efficiency of log volume reduction but allows quick startup with no impact on existing workflows and minimal effort.

How can users view and manage automatically detected log-based alerts and metrics in Grepr?
Users can click on the cloud icon to see detected alerts and metrics, then enable them for each pipeline by selecting Exceptions from the side menu, adding exceptions, and choosing which entries to exclude from summary processing.

What ongoing maintenance is required after integrating Grepr?
Ongoing maintenance is minimal because the semantic machine learning continually tunes the active set of pattern matches and automatically adapts to new application components without additional configuration.

Where can one find more information about Grepr Intelligent Observability Data Engine?
More information can be found on the website at Grepr.ai or by scanning the QR code provided.