987317b4ac
- Tag bloom filter as primary peer discovery (2048 bits x 3 hashes) - Filter table gossip for transitive peer discovery - Scoped queries for on-demand tag lookup - Liveness tracking + peer eviction (5 failures, 7-day TTL) - Topic subscriptions filter content sync at both ends - Two-theme CSS system (default minimal + kodama2) - Status bar on all pages (topics, peers, filters) - Bracketless tags, grouped moderation page, cleaner forms - Match main site heading level (h1 -> h2)
48 lines
1.3 KiB
Python
48 lines
1.3 KiB
Python
import hashlib
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class BloomFilter:
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def __init__(self, size=2048, num_hashes=3):
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self.size = size
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self.num_hashes = num_hashes
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self.bits = bytearray(size // 8 + 1)
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def _hash_positions(self, item):
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h = hashlib.sha256(item.encode("utf-8")).digest()
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for i in range(self.num_hashes):
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val = int.from_bytes(h[i*4:(i+1)*4], "big") % self.size
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yield val
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def add(self, item):
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for pos in self._hash_positions(item):
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self.bits[pos // 8] |= 1 << (pos % 8)
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def might_contain(self, item):
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return all(
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bool(self.bits[pos // 8] & (1 << (pos % 8)))
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for pos in self._hash_positions(item)
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)
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@property
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def bytes(self):
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return bytes(self.bits)
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@classmethod
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def from_bytes(cls, data, size=2048, num_hashes=3):
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bf = cls(size=size, num_hashes=num_hashes)
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bf.bits = bytearray(data)
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return bf
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@staticmethod
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def from_items(items, size=2048, num_hashes=3):
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bf = BloomFilter(size=size, num_hashes=num_hashes)
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for item in items:
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bf.add(item)
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return bf
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@staticmethod
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def from_tags(tags, size=2048, num_hashes=3):
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bf = BloomFilter(size=size, num_hashes=num_hashes)
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for tag in tags:
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bf.add(tag.strip().lower())
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return bf
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