Open-Source Observability · google

mtail

mtail is a Google-backed tool that extracts metrics from application logs and exports them to monitoring systems like Prometheus. It lets you instrument legacy applications that don't natively export metrics by writing simple pattern-matching rules instead of modifying the application code.

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Source: GitHub — github.com/google/mtail

GitHub stars

392

Forks

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	google/mtail
Owner	google
Primary language	Go
License	Apache-2.0 — OSI-approved
Stars	4k
Forks	392
Open issues	59
Latest release	v3.0.8 (2024-08-08)
Last updated	2026-03-19
Source	https://github.com/google/mtail

What mtail is

mtail is a Go-based log metric extractor that compiles custom DSL programs into bytecode, parses logs in real-time, and exports metrics via HTTP (JSON/Prometheus format) or push to collectd/StatsD/Graphite. It sits between log sources and time-series databases to bridge the instrumentation gap for applications that only log output.

Quickstart

Get the mtail source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/google/mtail.git2cd mtail3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Legacy Application Monitoring

Extract structured metrics from applications that lack native instrumentation or SDKs. Write mtail programs to parse log patterns and generate counters, gauges, or histograms without modifying the application.

Multi-Format Log Aggregation

Normalize metrics from diverse log formats (syslog, JSON, text) into a unified time-series schema. Deploy a single mtail instance to standardize metrics from multiple applications with different logging patterns.

Prometheus-Based Monitoring Pipelines

Integrate with existing Prometheus infrastructure as a scrape target. mtail handles the extraction logic, letting Prometheus focus on storage and alerting without custom collector scripts.

Implementation considerations

Learn the mtail DSL for writing extraction programs; simple patterns are straightforward, but complex state management and regex performance tuning can require expertise.
Plan log path watching and rotation handling; mtail needs reliable access to log files or stdin and must cope with log rotation, truncation, and compression.
Test programs incrementally against sample logs before production deployment; incorrect patterns can miss metrics or generate false data.
Monitor mtail's own resource usage (CPU, memory) when parsing high-volume logs; tune buffer sizes and regex complexity if performance degrades.
Design metric names and labels to avoid cardinality explosion; unbounded label values can overwhelm downstream time-series databases.

When to avoid it — and what to weigh

Real-Time, Sub-Second Latency Requirements — mtail processes logs sequentially and compiles programs at startup. Not suitable for use cases requiring microsecond-level metric generation or extremely high-frequency log parsing.
Applications with Native Metrics Export — If the application already exports metrics via StatsD, OpenTelemetry, or Prometheus endpoints, using mtail adds unnecessary complexity. Deploy a direct exporter or collector instead.
Unstructured, High-Variance Logs — mtail relies on consistent log patterns. If logs vary wildly or have no predictable format, writing and maintaining mtail programs becomes impractical; centralized log analysis tools may be better.
Proprietary/Embedded Monitoring Requirements — mtail is a standalone tool; if you need metrics deeply embedded in application logic or require closed-source licensing, this OSS project is not a fit.

License & commercial use

mtail is licensed under Apache License 2.0, a permissive OSI-approved license. You may use, modify, and distribute mtail in commercial and proprietary projects, provided you include the original license and copyright notice.

Apache 2.0 permits commercial use without royalties. Ensure you comply with the license terms (attribution, license inclusion in distributions). No implied warranty; Google provides the project as-is. For production SLA or support, check Google's official channels or community forums.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Low
DEV.co fit	Good
Assessment confidence	High

Security considerations

mtail reads from application logs and exports metrics over HTTP; consider network isolation and access controls. No explicit security audit data provided. Runs with file-system read access to logs; limit permissions to necessary log paths. Be cautious with regex complexity to avoid ReDoS (regular expression denial of service) in maliciously crafted logs. Use HTTPS and authentication if metrics contain sensitive data. No known CVEs documented in the excerpt, but always review latest security advisories.

Alternatives to consider

Filebeat + Logstash

Full-featured log shipping and processing pipeline with richer transformation capabilities, but heavier and requires more infrastructure than mtail for simple metric extraction.

Fluentd / Fluent Bit

Log aggregation and routing with metric extraction plugins; more flexible for complex pipelines, but steeper learning curve and higher resource overhead than mtail.

Vector

Modern, Rust-based log processor with native Prometheus integration; faster and lower-latency than mtail, but smaller community and less mature ecosystem for log parsing patterns.

Software development agency

Build on mtail with DEV.co software developers

Explore mtail's programming guide, review our implementation checklist, and evaluate fit for your monitoring pipeline. Contact us if you need help designing a log-to-metrics strategy.

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mtail FAQ

Can mtail handle rotated log files?

Yes, mtail is designed to follow log files and handle rotation. See the Deploying documentation for configuration options around log tailing and rotation strategies.

What is the performance overhead of mtail?

Performance depends on log volume, regex complexity, and program sophistication. Typical deployments consume modest CPU and memory, but no benchmark data is provided. Test with your log volume to confirm suitability.

Does mtail require code changes to my application?

No. mtail works entirely by parsing existing logs. Your application needs only to output logs in a consistent format; no instrumentation SDK or code modification is required.

Is commercial support available?

mtail is a Google-backed open-source project with community support via GitHub Discussions and a mailing list. For commercial SLA or dedicated support, you would need to check directly with Google or hire an integrator; none is explicitly mentioned in the project.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If mtail is part of your open-source observability roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Instrument Legacy Applications?

Explore mtail's programming guide, review our implementation checklist, and evaluate fit for your monitoring pipeline. Contact us if you need help designing a log-to-metrics strategy.

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