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1b367e2b62
The tree was a debranded snapshot of upstream taken around 2026-02-20, and upstream has moved about five months since. This replaces it with upstream master, mechanically re-derived: bin/debrand does the rename, and the pipeline and squashed migrations are restored from the shipped tree. Not a merge. The previous import replaced the tree wholesale in a single commit, so there is no shared content history to merge against. Reproducing the rename on the new tree is what actually converges. What survives: .hanzo/workflows, the Dockerfiles and bin/docker* entrypoints, compose files, LLM.md, and insights/migrations. The squashed initial migration is load-bearing — the live database records it by name, so upstream's ~918 individual migrations cannot come back. Nine products are dropped as unreferenced. Twenty-one more that we do not run are kept anyway because the core app still imports them; unpicking those is a refactor and does not belong in a rebase. This base does not build yet, and the reasons are not in the rename: upstream now wants Python 3.13.13 against our 3.12.12 and Django 5.2 against our 4.2, and the dependency manifests still name upstream packages that we republish under our own names. Those are decisions, not renames.
1.6 KiB
1.6 KiB
Get a time-bucketed sparkline of log volume, broken down by severity or service. Use this to understand log volume patterns before querying individual log entries — it is much cheaper than a full log query.
All parameters must be nested inside a query object.
Parameters
query.dateRange
Date range for the sparkline. Defaults to the last hour (-1h).
date_from: Start of the range. Accepts ISO 8601 timestamps or relative formats:-1h,-6h,-1d,-7d.date_to: End of the range. Same format. Omit or null for "now".
query.serviceNames
Filter by service names.
query.severityLevels
Filter by log severity: trace, debug, info, warn, error, fatal. Omit to include all levels.
query.searchTerm
Full-text search across log bodies.
query.filterGroup
Property filters to narrow results. Same format as query-logs filters.
query.sparklineBreakdownBy
Break down the sparkline by "severity" (default) or "service". Use "service" to see which services are producing the most logs.
Examples
Error volume over the last day
{
"query": {
"serviceNames": ["api-gateway"],
"severityLevels": ["error", "fatal"],
"dateRange": { "date_from": "-1d" }
}
}
Log volume by service
{
"query": {
"serviceNames": ["api-gateway"],
"sparklineBreakdownBy": "service",
"dateRange": { "date_from": "-6h" }
}
}
Log volume by severity
{
"query": {
"serviceNames": ["api-gateway"],
"sparklineBreakdownBy": "severity",
"dateRange": { "date_from": "-1d" }
}
}