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# Merkle Mountain Ranges

Read this in other languages: Korean, 简体中文.

## Structure

Merkle Mountain Ranges  are an alternative to Merkle trees . While the latter relies on perfectly balanced binary trees, the former can be seen either as list of perfectly balance binary trees or a single binary tree that would have been truncated from the top right. A Merkle Mountain Range (MMR) is strictly append-only: elements are added from the left to the right, adding a parent as soon as 2 children exist, filling up the range accordingly.

This illustrates a range with 11 inserted leaves and total size 19, where each node is annotated with its order of insertion.

``````Height

3              14
/    \
/      \
/        \
/          \
2        6            13
/   \        /    \
1     2     5      9     12     17
/ \   / \    / \   /  \   /  \
0   0   1 3   4  7   8 10  11 15  16 18
``````

This can be represented as a flat list, here storing the height of each node at their position of insertion:

``````0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
0  0  1  0  0  1  2  0  0  1  0  0  1  2  3  0  0  1  0
``````

This structure can be fully described simply from its size (19). It's also fairly simple, using fast binary operations, to navigate within a MMR. Given a node's position `n`, we can compute its height, the position of its parent, its siblings, etc.

## Hashing and Bagging

Just like with Merkle trees, parent nodes in a MMR have for value the hash of their 2 children. Grin uses the Blake2b hash function throughout, and always prepends the node's position in the MMR before hashing to avoid collisions. So for a leaf `l` at index `n` storing data `D` (in the case of an output, the data is its Pedersen commitment, for example), we have:

``````Node(l) = Blake2b(n | D)
``````

And for any parent `p` at index `m`:

``````Node(p) = Blake2b(m | Node(left_child(p)) | Node(right_child(p)))
``````

Contrarily to a Merkle tree, a MMR generally has no single root by construction so we need a method to compute one (otherwise it would defeat the purpose of using a hash tree). This process is called "bagging the peaks" for reasons described in .

First, we identify the peaks of the MMR; we will define one method of doing so here. We first write another small example MMR but with the indexes written as binary (instead of decimal), starting from 1:

``````Height

2        111
/     \
1     11     110       1010
/  \    / \      /    \
0   1   10 100 101  1000  1001  1011
``````

This MMR has 11 nodes and its peaks are at position 111 (7), 1010 (10) and 1011 (11). We first notice how the first leftmost peak is always going to be the highest and always "all ones" when expressed in binary. Therefore that peak will have a position of the form `2^n - 1` and will always be the largest such position that is inside the MMR (its position is lesser than the total size). We process iteratively for a MMR of size 11:

``````2^0 - 1 = 0, and 0 < 11
2^1 - 1 = 1, and 1 < 11
2^2 - 1 = 3, and 3 < 11
2^3 - 1 = 7, and 7 < 11
2^4 - 1 = 15, and 15 is not < 11
``````

(This can also be calculated non-iteratively as `2^(binary logarithm of size + 1) - 1`

Therefore the first peak is 7. To find the next peak, we then need to "jump" to its right sibling. If that node is not in the MMR (and it won't), take its left child. If that child is not in the MMR either, keep taking its left child until we have a node that exists in our MMR. Once we find that next peak, keep repeating the process until we're at the last node.

All these operations are very simple. Jumping to the right sibling of a node at height `h` is adding `2^(h+1) - 1` to its position. Taking its left child is subtracting `2^h`.

Finally, once all the positions of the peaks are known, "bagging" the peaks consists of hashing them iteratively from the right, using the total size of the MMR as prefix. For a MMR of size N with 3 peaks p1, p2 and p3 we get the final top peak:

``````P = Blake2b(N | Blake2b(N | Node(p3) | Node(p2)) | Node(p1))
``````

## Pruning

In Grin, a lot of the data that gets hashed and stored in MMRs can eventually be removed. As this happens, the presence of some leaf hashes in the corresponding MMRs become unnecessary and their hash can be removed. When enough leaves are removed, the presence of their parents may become unnecessary as well. We can therefore prune a significant part of a MMR from the removal of its leaves.

Pruning a MMR relies on a simple iterative process. `X` is first initialized as the leaf we wish to prune.

1. Prune `X`.
2. If `X` has a sibling, stop here.
3. If 'X' has no sibling, assign the parent of `X` as `X`.

To visualize the result, starting from our first MMR example and removing leaves [0, 3, 4, 8, 16] leads to the following pruned MMR:

``````Height

3             14
/    \
/      \
/        \
/          \
2       6            13
/            /   \
1     2            9     12     17
\          /     /  \   /
0       1        7     10  11 15     18
``````

 Peter Todd, merkle-mountain-range