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d5efd34010
It's a PR based on #33303 and introduces an approach for trienode history indexing. --- In the current archive node design, resolving a historical trie node at a specific block involves the following steps: - Look up the corresponding trie node index and locate the first entry whose state ID is greater than the target state ID. - Resolve the trie node from the associated trienode history object. A naive approach would be to store mutation records for every trie node, similar to how flat state mutations are recorded. However, the total number of trie nodes is extremely large (approximately 2.4 billion), and the vast majority of them are rarely modified. Creating an index entry for each individual trie node would be very wasteful in both storage and indexing overhead. To address this, we aggregate multiple trie nodes into chunks and index mutations at the chunk level instead. --- For a storage trie, the trie is vertically partitioned into multiple sub tries, each spanning three consecutive levels. The top three levels (1 + 16 + 256 nodes) form the first chunk, and every subsequent three-level segment forms another chunk. ``` Original trie structure Level 0 [ ROOT ] 1 node Level 1 [0] [1] [2] ... [f] 16 nodes Level 2 [00] [01] ... [0f] [10] ... [ff] 256 nodes Level 3 [000] [001] ... [00f] [010] ... [fff] 4096 nodes Level 4 [0000] ... [000f] [0010] ... [001f] ... [ffff] 65536 nodes Vertical split into chunks (3 levels per chunk) Level0 [ ROOT ] 1 chunk Level3 [000] ... [fff] 4096 chunks Level6 [000000] ... [fffffff] 16777216 chunks ``` Within each chunk, there are 273 nodes in total, regardless of the chunk's depth in the trie. ``` Level 0 [ 0 ] 1 node Level 1 [ 1 ] … [ 16 ] 16 nodes Level 2 [ 17 ] … … [ 272 ] 256 nodes ``` Each chunk is uniquely identified by the path prefix of the root node of its corresponding sub-trie. Within a chunk, nodes are identified by a numeric index ranging from 0 to 272. For example, suppose that at block 100, the nodes with paths `[]`, `[0]`, `[f]`, `[00]`, and `[ff]` are modified. The mutation record for chunk 0 is then appended with the following entry: `[100 → [0, 1, 16, 17, 272]]`, `272` is the numeric ID of path `[ff]`. Furthermore, due to the structural properties of the Merkle Patricia Trie, if a child node is modified, all of its ancestors along the same path must also be updated. As a result, in the above example, recording mutations for nodes `00` and `ff` alone is sufficient, as this implicitly indicates that their ancestor nodes `[]`, `[0]` and `[f]` were also modified at block 100. --- Query processing is slightly more complicated. Since trie nodes are indexed at the chunk level, each individual trie node lookup requires an additional filtering step to ensure that a given mutation record actually corresponds to the target trie node. As mentioned earlier, mutation records store only the numeric identifiers of leaf nodes, while ancestor nodes are omitted for storage efficiency. Consequently, when querying an ancestor node, additional checks are required to determine whether the mutation record implicitly represents a modification to that ancestor. Moreover, since trie nodes are indexed at the chunk level, some trie nodes may be updated frequently, causing their mutation records to dominate the index. Queries targeting rarely modified trie nodes would then scan a large amount of irrelevant index data, significantly degrading performance. To address this issue, a bitmap is introduced for each index block and stored in the chunk's metadata. Before loading a specific index block, the bitmap is checked to determine whether the block contains mutation records relevant to the target trie node. If the bitmap indicates that the block does not contain such records, the block is skipped entirely. |
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| .. | ||
| database | ||
| hashdb | ||
| pathdb | ||
| database.go | ||
| history.go | ||
| preimages.go | ||
| preimages_test.go | ||
| states.go | ||