070b06f809
details: * Tested features + Successively store filters with increasing filter ID (think block number) + Cascading through fifos, deeper fifos merge groups of filters + Fetch squash merged N top fifos + Delete N top fifos, push back merged fifo, continue storing + Fifo chain is verified by hashes and filter ID * Not tested yet + Real live scenario (using data dumps) + Real filter data (only shallow filters used so far) |
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|---|---|---|
| .. | ||
| aristo_check | ||
| aristo_desc | ||
| aristo_filter | ||
| aristo_init | ||
| aristo_walk | ||
| .gitignore | ||
| README.md | ||
| aristo_check.nim | ||
| aristo_constants.nim | ||
| aristo_debug.nim | ||
| aristo_delete.nim | ||
| aristo_desc.nim | ||
| aristo_fetch.nim | ||
| aristo_filter.nim | ||
| aristo_get.nim | ||
| aristo_hashify.nim | ||
| aristo_hike.nim | ||
| aristo_init.nim | ||
| aristo_merge.nim | ||
| aristo_nearby.nim | ||
| aristo_path.nim | ||
| aristo_persistent.nim | ||
| aristo_transcode.nim | ||
| aristo_tx.nim | ||
| aristo_utils.nim | ||
| aristo_vid.nim | ||
| aristo_walk.nim | ||
README.md
Aristo Trie -- a Patricia Trie with Merkle hash labeled edges
These data structures allows to overlay the Patricia Trie with Merkel Trie hashes. With a particular layout, the structure is called and Aristo Trie (Patricia = Roman Aristocrat, Patrician.)
This description does assume familiarity with the abstract notion of a hexary Merkle Patricia Trie. Suffice it to say the state of a valid Merkle Patricia Tree is uniquely verified by its top level vertex.
- Deleting entries in a compact Merkle Patricia Tree
The main feature of the Aristo Trie representation is that there are no double used nodes any sub-trie as it happens with the representation as a compact Merkle Patricia Tree. For example, consider the following state data for the latter.
leaf = (0xf,0x12345678) (1)
branch = (a,a,a,,, ..) with a = hash(leaf)
root = hash(branch)
These two nodes, called leaf and branch, and the root hash are a state (aka key-value pairs) representation as a compact Merkle Patricia Tree. The actual state is
0x0f ==> 0x12345678
0x1f ==> 0x12345678
0x2f ==> 0x12345678
The elements from (1) can be organised in a key-value table with the Merkle hashes as lookup keys
a -> leaf
root -> branch
This is a space efficient way of keeping data as there is no duplication of the sub-trees made up by the Leaf node with the same payload 0x12345678 and path snippet 0xf. One can imagine how this property applies to more general sub-trees in a similar fashion.
Now delete some key-value pair of the state, e.g. for the key 0x0f. This amounts to removing the first of the three a hashes from the branch record. The new state of the Merkle Patricia Tree will look like
leaf = (0xf,0x12345678) (2)
branch1 = (,a,a,,, ..)
root1 = hash(branch1)
a -> leaf
root1 -> branch1
A problem arises when all keys are deleted and there is no reference to the leaf data record, anymore. One should find out in general when it can be deleted, too. It might be unknown whether the previous states leading to here had only a single Branch record referencing to this leaf data record.
Finding a stale data record can be achieved by a mark and sweep algorithm, but it becomes too clumsy to be useful on a large state (i.e. database). Reference counts come to mind but maintaining these is generally error prone when actors concurrently manipulate the state (i.e. database).
- Patricia Trie example with Merkle hash labelled edges
Continuing with the example from chapter 1, the branch node is extended by an additional set of structural identifiers x, w, z. It allows to handle the deletion of entries in a more benign way while keeping the Merkle hashes for validating sub-trees.
A solution for the deletion problem is to represent the situation (1) as
leaf-a = (0xf,0x12345678) copy of leaf from (1) (3)
leaf-b = (0xf,0x12345678) copy of leaf from (1)
leaf-c = (0xf,0x12345678) copy of leaf from (1)
branch2 = ((x,y,z,,, ..)(a,b,c,,, ..))
root2 = (w,root) with root from (1)
where
a = hash(leaf-a) same as a from (1)
b = hash(leaf-b) same as a from (1)
c = hash(leaf-c) same as a from (1)
w,x,y,z numbers, mutually different
The records above are stored in a key-value database as
w -> branch2
x -> leaf-a
y -> leaf-b
z -> leaf-c
Then this structure encodes the key-value pairs as before
0x0f ==> 0x12345678
0x1f ==> 0x12345678
0x2f ==> 0x12345678
Deleting the data for key 0x0f now results in the new state
leaf-b = (0xf,0x12345678) (4)
leaf-c = (0xf,0x12345678)
branch3 = ((,y,z,,, ..)(,b,c,,, ..))
w -> branch3
y -> leaf-b
z -> leaf-c
Due to duplication of the leaf node in (3), no reference count is needed in order to detect stale records cleanly when deleting key 0x0f. Removing this key allows to remove hash a from branch2 as well as also structural key x which will consequently be deleted from the lookup table.
A minor observation is that manipulating a state entry, e.g. changing the payload associated with key 0x0f to
0x0f ==> 0x987654321
the structural layout of the above trie will not change, that is the indexes w, x, y, z of the table that holds the data records as values. All that changes are values.
leaf-d = (0xf,0x987654321) (5)
leaf-b = (0xf,0x12345678)
leaf-c = (0xf,0x12345678)
branch3 = ((x,y,z,,, ..)(d,b,c,,, ..))
root3 = (w,hash(d,b,c,,, ..))
- Discussion of the examples (1) and (3)
Examples (1) and (3) differ in that the structural Patricia Trie information from (1) has been removed from the Merkle hash instances and implemented as separate table lookup IDs (called vertexIDs later on.) The values of these lookup IDs are arbitrary as long as they are all different.
In fact, the Erigon project discusses a similar situation in Separation of keys and the structure, albeit aiming for a another scenario with the goal of using mostly flat data lookup structures.
A graph for the example (1) would look like
|
root
|
+-------------+
| branch |
+-------------+
| | |
a a a
| | |
leaf
while example (2) has
(root) (6)
|
w
|
+-------------+
| branch2 |
| (a) (b) (c) |
+-------------+
/ | \
x y z
/ | \
leaf-a leaf-b leaf-c
The labels on the edges indicate the downward target of an edge while the round brackets enclose separated Merkle hash information.
This last example (6) can be completely split into structural tree and Merkel hash mapping.
structural trie hash map (7)
--------------- --------
| (root) -> w
w (a) -> x
| (b) -> y
+-------------+ (c) -> z
| branch2 |
+-------------+
/ | \
x y z
/ | \
leaf-a leaf-b leaf-c
- Patricia Trie node serialisation with Merkle hash labelled edges
The data structure for the Aristo Trie forllows example (7) by keeping structural information separate from the Merkle hash labels. As for teminology,
- an Aristo Trie is a pair (structural trie, hash map) where
- the structural trie realises a haxary Patricia Trie containing the payload values in the leaf records
- the hash map contains the hash information so that this trie operates as a Merkle Patricia Tree.
In order to accommodate for the additional structural elements, a non RLP-based data layout is used for the Branch, Extension, and Leaf containers used in the key-value table that implements the Patricia Trie. It is now called Aristo Trie for this particular data layout.
The structural keys w, x, y, z from the example (3) are called vertexID and implemented as 64 bit values, stored Big Endian in the serialisation.
4.1 Branch record serialisation
0 +--+--+--+--+--+--+--+--+--+
| | -- first vertexID
8 +--+--+--+--+--+--+--+--+--+
... -- more vertexIDs
+--+--+
| | -- access(16) bitmap
+--+--+
| | -- marker(8), 0x08
+--+
where
marker(8) is the eight bit array *0000-1000*
For a given index n between 0..15, if the bit at position n of the bit vector access(16) is reset to zero, then there is no n-th structural vertexID. Otherwise one calculates
the n-th vertexID is at position Vn * 8
for Vn the number of non-zero bits in the range 0..(n-1) of access(16)
Note that data are stored Big Endian, so the bits 0..7 of access are stored in the right byte of the serialised bitmap.
4.2 Extension record serialisation
0 +--+--+--+--+--+--+--+--+--+
| | -- vertex ID
8 +--+--+--+--+--+--+--+--+--+
| | ... -- path segment
+--+
|| | -- marker(2) + pathSegmentLen(6)
+--+
where
marker(2) is the double bit array 10
The path segment of the Extension record is compact encoded. So it has at least one byte. The first byte P0 has bit 5 reset, i.e. P0 and 0x20 is zero (bit 4 is set if the right nibble is the first part of the path.)
Note that the pathSegmentLen(6) is redunant as it is determined by the length of the extension record (as recordLen - 9.)
4.3 Leaf record serialisation
0 +-- ..
... -- payload (may be empty)
+--+
| | ... -- path segment
+--+
|| | -- marker(2) + pathSegmentLen(6)
+--+
where
marker(2) is the double bit array 11
A Leaf record path segment is compact encoded. So it has at least one byte. The first byte P0 has bit 5 set, i.e. P0 and 0x20 is non-zero (bit 4 is also set if the right nibble is the first part of the path.)
If present, the serialisation of the payload field can be either for account data, for RLP encoded or for unstructured data as defined below.
4.4 Leaf record payload serialisation for account data
0 +-- .. --+
| | -- nonce, 0 or 8 bytes
+-- .. --+--+
| | -- balance, 0, 8, or 32 bytes
+-- .. --+--+
| | -- storage ID, 0 or 8 bytes
+-- .. --+--+
| | -- code hash, 0, 8 or 32 bytes
+--+ .. --+--+
| | -- 4 x bitmask(2), word array
+--+
where each bitmask(2)-word array entry defines the length of
the preceeding data fields:
00 -- field is missing
01 -- field lengthh is 8 bytes
10 -- field lengthh is 32 bytes
Apparently, entries 0 and and 2 of the 4 x bitmask(2) word array cannot have the two bit value 10 as they refer to the nonce and the storage ID data fields. So, joining the 4 x bitmask(2) word array to a single byte, the maximum value of that byte is 0x99.
4.5 Leaf record payload serialisation for RLP encoded data
0 +--+ .. --+
| | | -- data, at least one byte
+--+ .. --+
| | -- marker(8), 0x6a
+--+
where
marker(8) is the eight bit array *0110-1010*
4.6 Leaf record payload serialisation for unstructured data
0 +--+ .. --+
| | | -- data, at least one byte
+--+ .. --+
| | -- marker(8), 0x6b
+--+
where
marker(8) is the eight bit array *0110-1011*
4.7 Serialisation of the list of unused vertex IDs
0 +-- ..
... -- recycled vertexIDs
+--+--+--+--+--+--+--+--+
| | -- last unused vertex IDs
+--+--+--+--+--+--+--+--+
| | -- marker(8), 0x7c
+--+
where
marker(8) is the eight bit array *0111-1100*
The vertex IDs in this record must all be non-zero. The last entry in the list indicates that all ID values greater or equal than this value are free and can be used as vertex IDs. If this record is missing, the value (1u64,0x01) is assumed, i.e. the list with the single vertex ID 1.
4.8 Backend filter record serialisation
0 +--+--+--+--+--+ .. --+
| | -- filter ID
8 +--+--+--+--+--+ .. --+--+ .. --+
| | -- 32 bytes filter source hash
40 +--+--+--+--+--+ .. --+--+ .. --+
| | -- 32 bytes filter target hash
72 +--+--+--+--+--+ .. --+--+ .. --+
| | -- number of unused vertex IDs
76 +--+--+--+--+
| | -- number of structural triplets
80 +--+--+--+--+--+ .. --+
| | -- first unused vertex ID
88 +--+--+--+--+--+ .. --+
... -- more unused vertex ID
N1 +--+--+--+--+
|| | -- flg(3) + vtxLen(29), 1st triplet
+--+--+--+--+--+ .. --+
| | -- vertex ID of first triplet
+--+--+--+--+--+ .. --+--+ .. --+
| | -- optional 32 bytes hash key
+--+--+--+--+--+ .. --+--+ .. --+
... -- optional vertex record
N2 +--+--+--+--+
|| | -- flg(3) + vtxLen(29), 2nd triplet
+--+--+--+--+
...
+--+
| | -- marker(8), 0x7d
+--+
where
+ minimum size of an empty filer is 72 bytes
+ the flg(3) represents the tuple (key-mode,vertex-mode) encoding
the serialised storage states
0 -- encoded and present
1 -- not encoded, void => considered deleted
2 -- not encoded, to be ignored
so, when encoded as
flg(3) = key-mode * 3 + vertex-mode
the the tuple (2,2) will never occur and flg(3) < 9
+ the vtxLen(29) is the number of bytes of the optional vertex record
which has maximum size 2^29-1 which is short of 512 MiB
+ the marker(8) is the eight bit array *0111-1101*
4.9 Serialisation of a list of filter IDs
0 +-- ..
... -- some filter ID
+--+--+--+--+--+--+--+--+
| | -- last filter IDs
+--+--+--+--+--+--+--+--+
| | -- marker(8), 0x7e
+--+
where
marker(8) is the eight bit array *0111-1110*
This list is used to control the filters on the database. By holding some IDs in a dedicated list (e.g. the latest filters) one can quickly access particular entries without searching through the set of filters. In the current implementation this list comes in ID pairs i.e. the number of entries is even.
4.10 Serialisation record identifier identification
Any of the above records can uniquely be identified by its trailing marker, i.e. the last byte of a serialised record.
| ** Bit mask** | Hex value | Record type | Chapter reference |
|---|---|---|---|
| 0000 1000 | 0x08 | Branch record | 4.1 |
| 10xx xxxx | 0x80 + x(6) | Extension record | 4.2 |
| 11xx xxxx | 0xC0 + x(6) | Leaf record | 4.3 |
| 0xxx 0yyy | (x(3)<<4) + y(3) | Account payload | 4.4 |
| 0110 1010 | 0x6a | RLP encoded payload | 4.5 |
| 0110 1011 | 0x6b | Unstructured payload | 4.6 |
| 0111 1100 | 0x7c | List of vertex IDs | 4.7 |
| 0111 1101 | 0x7d | Filter record | 4.8 |
| 0111 1110 | 0x7e | List of vertex IDs | 4.9 |
- Patricia Trie implementation notes
5.1 Database decriptor representation
^ +----------+
| | top | active delta layer, application cache
| +----------+
| +----------+ ^
db | stack[n] | |
desc | : | | optional passive delta layers, handled by
obj | stack[1] | | transaction management (can be used to
| | stack[0] | | successively recover the top layer)
| +----------+ v
| +----------+
| | roFilter | optional read-only backend filter
| +----------+
| +----------+
| | backend | optional physical key-value backend database
v +----------+
There is a three tier access to a key-value database entry as in
top -> roFilter -> backend
where only the top layer is obligatory.
5.2 Distributed access using the same backend
There can be many descriptors for the same database. Due to delta layers and filters, each descriptor instance can work with a different state of the database.
Although there is only one of the instances that can write the current state on the physical backend database, this priviledge can be shifted to any instance for the price of updating the roFiters for all other instances.
Example:
db1 db2 db3 -- db1, db2, .. database descriptors/handles
| | |
tx1 tx2 tx3 -- tx1, tx2, ..transaction/top layers
| | |
ø ø ø -- no backend filters yet
\ | /
\ | /
PBE -- physical backend database
After collapse/committing tx1 and saving it to the physical backend database, the above architecture mutates to
db1 db2 db3
| | |
ø tx2 tx3
| | |
ø ~tx1 ~tx1 -- filter reverting the effect of tx1 on PBE
\ | /
\ | /
tx1+PBE -- tx1 merged into physical backend database
When looked at descriptor API there are no changes when accessing data via db1, db2, or db3. In a different, more algebraic notation, the above tansformation is written as
| tx1, ø | (8)
| tx2, ø | PBE
| tx3, ø |
||
\/
| ø, ø | (9)
| tx2, ~tx1 | tx1+PBE
| tx3, ~tx1 |
The system can be further converted without changing the API by committing and saving tx2 on the middle line of matrix (9)
| ø, ø | (10)
| ø, tx2+~tx1 | tx1+PBE
| tx3, ~tx1 |
||
\/
| ø, ~(tx2+~tx1) | (11)
| ø, ø | (tx2+~tx1)+tx1+PBE
| tx3, ~tx1+~(tx2+~tx1) |
The + notation just means the repeated application of filters in left-to-right order. The notation looks like algebraic group notation but this will not be analysed further as there is no need for a general theory for the current implementation.
Suffice to say that the inverse ~tx of tx is calculated against the current state of the physical backend database which makes it messy to formulate boundary conditions.
Nevertheless, (8) can alse be transformed by committing and saving tx2 (rather than tx1.) This gives
| tx1, ~tx2 | (12)
| ø, ø | tx2+PBE
| tx3, ~tx2 |
||
\/
| ø, (tx1+~tx2) | (13)
| ø, ø | tx2+PBE
| tx3, ~tx2 |
As (11) and (13) represent the same API, one has
tx2+PBE =~ tx1+(tx2+~tx1)+PBE because of the middle rows (14)
~tx2 =~ ~tx1+~(tx2+~tx1) because of (14) (15)
which looks like some distributive property in (14) and commutative property in (15) for this example (but it is not straight algebraically.) The =~ operator above indicates that the representations are equivalent in the sense that they have the same effect on the backend database (looks a bit like residue classes.)
It might be handy for testing/verifying an implementation using this example.