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Merges the Astro migration and pipeline refactor from main, adapting the client-versions notebook to the new workflow: - Convert 04-client-versions.qmd to .ipynb format - Add client_versions query to pipeline.yaml - Add client-versions notebook entry to pipeline.yaml - Update README with client-versions notebook 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Preview Deployment🚀 Preview is ready! URL: https://observatory-staging.ethp2p.dev/pr-11/ Updated: 2025-12-23T23:51:27.779Z |
Split single raw-data query into three server-side aggregated queries: - client_hourly: hourly connection counts by client (for time series) - client_version_dist: top 15 versions per client (for version charts) - client_summary: unique peers, version count, top version per client Benefits: - Reduces data transfer from millions of rows to ~300 aggregated rows - Moves client categorization (CASE statement) to SQL - Uses ClickHouse's uniqExact() and argMax() for accurate stats - Simplifies notebook code by removing in-memory aggregation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
raulk
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Dec 23, 2025
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The orignal code was exporting all connections in a Parquet file and then performing aggregations locally in Pandas dataframes. This was inefficient. I adjusted the queries to offload the aggregations to Clickhouse. We now only generate aggregated Parquet files.
I created an "Unknown" category and labelled all empty or unidentified clients as "Unknown", excluding them from plots as they were too noisy.
The plots can benefit from more tweaks @bharath-123:
- Plot unknown against known peers.
- Plot connections per client label over time (aggregated, e.g. in 5min windows).
- Plot unique peers over time x client labels.
- Figure out a denser way of visualizing client/version clusters in a single diagram.
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This notebook shows the client diversity and versions of different clients connected to xatu nodes.
Resolves: #10