ethereum.forks.dao_fork.bloomethereum.forks.tangerine_whistle.bloom
Ethereum Logs Bloom.
.. contents:: Table of Contents :backlinks: none :local:
Introduction
This module defines functions for calculating bloom filters of logs. For the
general theory of bloom filters see e.g. Wikipedia <https://en.wikipedia.org/wiki/Bloom_filter>_. Bloom filters are used to allow
for efficient searching of logs by address and/or topic, by rapidly
eliminating blocks and receipts from their search.
add_to_bloom ¶
Add a bloom entry to the bloom filter (bloom).
The number of hash functions used is 3. They are calculated by taking the
least significant 11 bits from the first 3 16-bit words of the
keccak_256() hash of bloom_entry.
Parameters
bloom : The bloom filter. bloom_entry : An entry which is to be added to bloom filter.
def add_to_bloom(bloom: bytearray, bloom_entry: Bytes) -> None:
| 30 | <snip> |
|---|---|
| 45 | hashed = keccak256(bloom_entry) |
| 46 | |
| 47 | for idx in (0, 2, 4): |
| 48 | # Obtain the least significant 11 bits from the pair of bytes |
| 49 | # (16 bits), and set this bit in bloom bytearray. |
| 50 | # The obtained bit is 0-indexed in the bloom filter from the least |
| 51 | # significant bit to the most significant bit. |
| 52 | bit_to_set = Uint.from_be_bytes(hashed[idx : idx + 2]) & Uint(0x07FF) |
| 53 | # Below is the index of the bit in the bytearray (where 0-indexed |
| 54 | # byte is the most significant byte) |
| 55 | bit_index = 0x07FF - int(bit_to_set) |
| 56 | |
| 57 | byte_index = bit_index // 8 |
| 58 | bit_value = 1 << (7 - (bit_index % 8)) |
| 59 | bloom[byte_index] = bloom[byte_index] | bit_value |
logs_bloom ¶
Obtain the logs bloom from a list of log entries.
The address and each topic of a log are added to the bloom filter.
Parameters
logs : List of logs for which the logs bloom is to be obtained.
Returns
logs_bloom : Bloom
The logs bloom obtained which is 256 bytes with some bits set as per
the caller address and the log topics.