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* Add Unstructured engagement report as uv workspace member Three-tier Plotly Dash app (Executive Brief, Engineering Team, Full Detail) with data in JSON, theme constants in theme.py, and Dash production improvements (Google Fonts, clientside callbacks, meta tags). Also: add .playwright-mcp/ to .gitignore, add reports/* ruff overrides, remove tracked .codeflash/observability/read-tracker. * Rewrite statusline to derive context from git state Detects active area from changed files (reports, packages, plugin, .codeflash, case-studies, evals), falls back to branch name convention (perf/*, feat/*, fix/*), shows dirty indicator. Uses whoami for cross-platform user detection. * Add pre-push lint rule to commit guidelines * Exclude .codeflash/ from ruff linting Benchmark and profiling scripts in .codeflash/ are scratch work, not package source. Excluding them prevents CI failures from ad-hoc scripts. * Run ruff format across packages, scripts, evals, and plugin refs * Fix github-app async test failures in CI Add asyncio_mode = "auto" to root pytest config so async tests are detected when running from the repo root via uv run pytest packages/.
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
from log_analyzer.analyzer import analyze_logs
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def test_analyze_logs_basic():
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entries = [
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{"level": "ERROR", "source": "auth", "message": "login failed"},
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{"level": "INFO", "source": "auth", "message": "login succeeded"},
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{"level": "ERROR", "source": "db", "message": "connection timeout"},
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]
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result = analyze_logs(entries)
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assert "frequencies" in result
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assert "anomalies" in result
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assert len(result["anomalies"]) > 0
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def test_analyze_large_batch():
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"""Realistic batch — this is the one that uses too much memory."""
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sources = [f"service-{i}" for i in range(50)]
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levels = ["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"]
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entries = []
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for i in range(10_000):
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entries.append(
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{
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"level": levels[(i // len(sources)) % len(levels)],
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"source": sources[i % len(sources)],
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"message": f"Event {i}: something happened in the system",
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}
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)
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result = analyze_logs(entries)
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assert len(result["frequencies"]) == len(sources) * len(levels)
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assert len(result["anomalies"]) > 0
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