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Claude
dc43b6ab67
Crawled 743 of 5,846 reachable relays with amy for events signed 2026-10-07..08: 2.36M events, 573 kinds, 386 without a typed quartz class. Adds the crawl/harvest/profile scripts, the kind histogram, the per-kind profiles and researched findings (what each kind is, spec/source, relevance to Amethyst). Raw samples stay local: they hold third-party personal data. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FrQx1ZvQka64aFAnZM1Hjd
52 lines
2.3 KiB
Python
52 lines
2.3 KiB
Python
"""Fold one amy store (events.db) into the compact census DB, then the caller can delete it.
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census.db keeps:
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ev(id, kind, pubkey, created_at) every distinct event seen (all kinds)
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raw(id, kind, json) full signed JSON, only for kinds quartz has no typed class for
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relay_kind(relay, kind, events) how many events of each kind each relay served
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"""
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import json, sqlite3
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SCHEMA = """
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create table if not exists ev(id text primary key, kind int, pubkey text, created_at int) without rowid;
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create table if not exists raw(id text primary key, kind int, json text) without rowid;
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create table if not exists relay_kind(relay text, kind int, events int, primary key(relay, kind)) without rowid;
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"""
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def open_census(path):
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# check_same_thread=False: crawl.py shares one connection across workers behind a lock.
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c = sqlite3.connect(path, timeout=120, check_same_thread=False)
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c.executescript(SCHEMA)
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c.execute("pragma journal_mode=wal")
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c.execute("pragma synchronous=normal")
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return c
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def harvest(census, store_path, relay, since, known):
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src = sqlite3.connect(f"file:{store_path}?mode=ro", uri=True, timeout=60)
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rows = src.execute(
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"select id,pubkey,created_at,kind,tags,content,sig from event_headers where created_at >= ?", (since,)
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).fetchall()
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src.close()
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per_kind = {}
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ev_rows, raw_rows = [], []
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for id_, pk, ca, kind, tags, content, sig in rows:
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per_kind[kind] = per_kind.get(kind, 0) + 1
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ev_rows.append((id_, kind, pk, ca))
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if kind not in known:
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raw_rows.append((id_, kind, json.dumps(
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{"id": id_, "pubkey": pk, "created_at": ca, "kind": kind,
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"tags": json.loads(tags), "content": content, "sig": sig}, ensure_ascii=False)))
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with census:
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census.executemany("insert or ignore into ev values (?,?,?,?)", ev_rows)
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census.executemany("insert or ignore into raw values (?,?,?)", raw_rows)
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census.executemany(
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"insert into relay_kind values (?,?,?) on conflict(relay,kind) do update set events=max(events, excluded.events)",
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[(relay, k, n) for k, n in per_kind.items()])
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return len(rows)
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def load_known(tsv):
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return {int(l.split("\t")[0]) for l in open(tsv) if l.strip()}
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