As adoption of high-performance observability stacks grows, tools like VictoriaLogs and its query language LogsQL are increasingly used for log analytics and storage at scale. While the ecosystem is powerful, there are key nuances and limitations that can trip up even experienced users.

The Zumidian Logs Service (included in our incident management service) is based on VictoriaLogs, and this article distills a few practical insights and lessons we’ve learned from working extensively with VictoriaLogs and LogsQL within Grafana.

Getting Started: What is VictoriaLogs and LogsQL?

VictoriaLogs is a fast, cost-efficient, and scalable log database designed for high-ingestion and low-latency querying. It supports LogsQL, a PromQL-like syntax tailored for logs, making it a natural companion for Grafana-based observability platforms.

LogsQL enables powerful operations like regex matching, field filtering, grouping, and time-based aggregations. However, understanding the engine behind the language—namely RE2 for regex and the limitations of log-based, non-relational querying—is crucial.

Regex Matching: RE2-Compatible Regex Only

Challenge:

Trying to use advanced regex features such as backreferences (e.g., \1, \2) causes 400 errors due to RE2’s limitations.

Solution:

RE2 does not support backreferences or lookaheads/lookbehinds. Instead, workarounds involve explicitly listing patterns. For example, to find logs where the step is either the first (0/x) or last (x/x) (e.g. Running: [] 0/11 complete or Running: [] 11/11 complete), you must manually expand those matches:

|~ "Running: \[\] (0/\d+|1/1|2/2|3/3|...|40/40) complete"

This matches both the start and completion of multi-step jobs, crucial for workflow monitoring.

Real-World Example:

In a CI/CD pipeline, to measure the time from the initial job run to full completion:

job:*
AND user:in ("$User")
AND environment:in ("$Environment")
|~ "Running: \[\] (0/\d+|1/1|...|40/40) complete"
| by (jobid, size, user) count()
| sort by (size)

Time Difference and Duration Calculation in Grafana

Challenge:

Users often want to measure how long jobs take—from the first step to the last. However, LogsQL lacks native timestamp arithmetic or multi-line joins.

Solution:

Use Grafana’s transformation pipeline:

1. Query logs grouped by job identifier (e.g., jobid).
2. Use an Extract Field to extract the Grafana’s built in log timestamp.
3. Apply a Reduce transformation to calculate the time difference between the first and last timestamps per series.

This two-step Grafana-native approach enables job duration monitoring without exporting logs to an external processor.

Query Structure Pitfalls

Challenge:

Incorrect logical grouping of filters can lead to unintended results. For example:

sequence:>70 OR sequence:<6 AND user:in ("$User")

This mixes AND and OR without parentheses, leading to faulty logic.

Solution:

Always wrap mixed logical operators in parentheses:

AND (sequence:>70 OR sequence:<6)

This ensures correct evaluation order and accurate filtering.

Variable Substitution with Regex

Challenge:

Using variables in regex queries can lead to confusing results, especially when trying to generalize queries without specific context:

|~ "(?i)Deployment to $Environment Failed"

This attempts to inject the variable into the regex pattern, which can break the match if $Environment is unset or malformed.

Solution:

Remove the dependency on the variable when possible:

|~ "(?i)Deployment to .* Failed"

This broadens the search to all failed deployments without relying on Grafana variable state.

Real-World Example:

Track any failed deployment regardless of environment:

job:* |~ "(?i)Deployment to .* Failed"

LogsQL and SQL-Like Syntax: Bridging Familiarity with Power

VictoriaLogs provides SQL-to-LogsQL mapping that eases the learning curve for those transitioning from traditional SQL-based log analytics. While LogsQL isn’t relational, it uses similar constructs:

Real-World Query Mappings

SQL-style:

SELECT COUNT(*)
FROM logs
WHERE job = 'build-runner' AND status LIKE 'Fail%'
GROUP BY user
ORDER BY timestamp DESC;

LogsQL Equivalent:

job:build-runner AND status:~ "Fail.*"
| by (user) count()
| sort by (_time)

Understanding this mapping helps SQL-savvy users translate intuition into LogsQL quickly. See VictoriaLogs docs for more examples.

Migrating Queries from Loki to LogsQL: Challenges and Best Practices

Challenge:

Loki’s query language looks similar to LogsQL, but includes specific features like line_format, pipeline stages, and label selectors that don’t always map 1:1.

Key Differences & Tips:

• Label Filters: In Loki: {job="myjob"} → In LogsQL: job:myjob
• Regex Matching: Loki uses |~ but allows more regex flexibility; LogsQL uses RE2, so rewrite complex expressions.
• Line Parsing: Loki’s | json is not available in LogsQL—log lines should be pre-structured or parsed upstream.
• Transformations: Loki’s unwrap or rate() functions are metric-focused. In LogsQL, use count(), avg(), and by() to mimic aggregation.

Best Practices:

• Start with simple filters to validate field availability.
• Use Grafana’s Explore mode to prototype and adjust syntax.
• If migrating dashboards, refactor panel-by-panel to ensure correctness.

Real-World Example:

From Loki:

{job="ci-runner"} |~ "Failed step:.*"

To LogsQL:

job:ci-runner |~ "Failed step:.*"

See Loki to LogsQL Migration Guide for a full comparison.

Key Takeaways

• RE2 limits mean you must avoid backreferences and lookarounds—plan patterns accordingly.
• Use Grafana transformations for time-based operations LogsQL doesn’t support.
• Be explicit and defensive with logic grouping in filters.
• Avoid unnecessary variable interpolation in regex—prefer general patterns when possible.
• Leverage SQL-like patterns to quickly adopt LogsQL.
• When migrating from Loki, understand language and capability differences to adjust expectations and logic.

Final Thoughts

VictoriaLogs and LogsQL offer exceptional speed and flexibility for log analysis—but they require a clear understanding of their limitations and strengths. With the right patterns, transformations, and awareness of regex capabilities, you can extract meaningful insights from logs with high precision and efficiency.

This guide distills real-world problems and solutions encountered in daily use—offering a practical blueprint for power users looking to unlock the full potential of VictoriaLogs in Grafana.