Service Log Viewer

svclog viewer helps developers and SREs inspect service logs, filter by time and service, and extract key metrics quickly.

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About This Tool

svclog viewer is a domain-focused analysis tool that helps engineering teams quickly inspect service logs, extract metrics, and identify health issues across distributed systems. It targets SREs, software engineers, and DevOps practitioners who work with multi-source log streams and need fast, query-free access to trends, anomalies, and incident context. Typical scenarios include post-incident reviews, real-time health monitoring, and capacity planning where a concise view of activity by service, time window, and severity is essential. The tool emphasizes accuracy, repeatability, and exportable outputs for audits and postmortems.
Conceptually, the tool ingests logs from files, streams, or endpoints, applies time-range and service-level filters, and computes aggregations such as total event counts, error counts, and throughput. It supports severity grouping and optional latency extraction from fields like latency_ms. The core logic partitions data by service, computes rates, and derives latency percentiles (P95/P99) when latency data is present. Results are returned as structured summaries plus optional per-service or per-severity breakdowns, enabling quick drill-down without writing queries.
Unique value arises from an integrated, tool-chain-friendly design: consistent schema for inputs/outputs, deterministic aggregation rules, and export formats suitable for dashboards or incident reports. Users gain faster incident triage, reproducible analysis, and the ability to compare windows or services side-by-side. This tool is purpose-built for web-scale log volumes and can be paired with alerting pipelines. It favors offline processing for repeatable results and supports localization-friendly time handling for UTC and user time zones.
Core features include multi-source log ingestion, time-range filtering, service and severity filters, total and per-level counts, error rate calculation, latency metrics (P95, P99), and export in JSON or CSV. Use cases span incident retrospectives, health dashboards, capacity planning, and compliance reporting. The tool excludes live streaming analysis and encryption-aware parsing beyond standard text logs, focusing on reproducible batch-style analysis and straightforward integration with existing log pipelines.

How to Use

  1. Provide inputs: log_source, time_range_start, time_range_end
  2. Optionally set service_filter, severity_levels, and max_results
  3. Run analysis to compute counts, error rate, and latency metrics
  4. Review results in summary and per-level breakdown
  5. Export results in JSON or CSV
How to use svclog viewer

Frequently Asked Questions

Find Quick Answers

What data sources are supported?
The tool accepts logs from local files, network endpoints, or message streams via standard connectors. Typical inputs include file paths, Kafka topics, or HTTP endpoints. If a source is unreachable, the tool returns partial results with a warning and continues processing available data.
What metrics are computed?
Core metrics include total events, per-service counts, error rate, and throughput, plus latency distribution when latency data is available. Outputs include a summary, a per-service breakdown, and optional latency percentiles (P95, P99). The format is JSON with optional CSV export.
What are typical use cases?
Use cases cover incident response, postmortems, and ongoing health monitoring. Teams compare time windows, identify services with high error rates, and quantify latency trends. The tool also supports exporting data for dashboards and reports to improve accountability.
How does the tool handle missing data?
If data is missing or delayed, the tool flags gaps, uses available data to compute partial metrics, and, if a required input is missing, returns a documented error. It does not infer values from absent fields; users should supply or adjust data sources.

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