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33faedf427
* 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/.
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
from log_analyzer.streamer import aggregate_results, stream_results
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def test_stream_basic():
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template = {"version": "1.0", "source": "default"}
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records = [
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{"id": 1, "value": "a"},
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{"id": 2, "value": "b", "source": "custom"},
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]
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result = stream_results(records, template)
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assert len(result) == 2
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def test_stream_large_batch():
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"""Large batch — this function is too slow."""
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template = {
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"version": "1.0",
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"metadata": {
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"region": "us-east",
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"env": "prod",
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"settings": {
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"retry": {"count": 3, "backoff": [1, 2, 4, 8]},
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"timeouts": {"connect": 5, "read": 30, "write": 10},
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},
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},
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"tags": ["log", "processed", "v2", "analytics"],
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"schema": {
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"fields": [
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{"name": "id", "type": "int", "required": True},
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{"name": "value", "type": "string", "required": True},
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{"name": "timestamp", "type": "datetime", "required": True},
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{"name": "source", "type": "string", "required": False},
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{"name": "priority", "type": "int", "required": False},
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],
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},
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}
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records = []
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for i in range(50_000):
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records.append(
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{
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"id": i,
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"value": f"data-{i}",
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"timestamp": f"2024-01-{(i % 28) + 1:02d}",
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}
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)
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result = stream_results(records, template)
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assert len(result) == 50_000
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summary = aggregate_results(result)
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assert summary["id"]["count"] == 50_000
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