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Use a chain cover index to efficiently calculate auth chain difference (
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Improve efficiency of large state resolutions for new rooms. |
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digraph auth { | ||
nodesep=0.5; | ||
rankdir="RL"; | ||
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C [label="Create (1,1)"]; | ||
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BJ [label="Bob's Join (2,1)", color=red]; | ||
BJ2 [label="Bob's Join (2,2)", color=red]; | ||
BJ2 -> BJ [color=red, dir=none]; | ||
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subgraph cluster_foo { | ||
A1 [label="Alice's invite (4,1)", color=blue]; | ||
A2 [label="Alice's Join (4,2)", color=blue]; | ||
A3 [label="Alice's Join (4,3)", color=blue]; | ||
A3 -> A2 -> A1 [color=blue, dir=none]; | ||
color=none; | ||
} | ||
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PL1 [label="Power Level (3,1)", color=darkgreen]; | ||
PL2 [label="Power Level (3,2)", color=darkgreen]; | ||
PL2 -> PL1 [color=darkgreen, dir=none]; | ||
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{rank = same; C; BJ; PL1; A1;} | ||
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A1 -> C [color=grey]; | ||
A1 -> BJ [color=grey]; | ||
PL1 -> C [color=grey]; | ||
BJ2 -> PL1 [penwidth=2]; | ||
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A3 -> PL2 [penwidth=2]; | ||
A1 -> PL1 -> BJ -> C [penwidth=2]; | ||
} |
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# Auth Chain Difference Algorithm | ||
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The auth chain difference algorithm is used by V2 state resolution, where a | ||
naive implementation can be a significant source of CPU and DB usage. | ||
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### Definitions | ||
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A *state set* is a set of state events; e.g. the input of a state resolution | ||
algorithm is a collection of state sets. | ||
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The *auth chain* of a set of events are all the events' auth events and *their* | ||
auth events, recursively (i.e. the events reachable by walking the graph induced | ||
by an event's auth events links). | ||
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The *auth chain difference* of a collection of state sets is the union minus the | ||
intersection of the sets of auth chains corresponding to the state sets, i.e an | ||
event is in the auth chain difference if it is reachable by walking the auth | ||
event graph from at least one of the state sets but not from *all* of the state | ||
sets. | ||
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## Breadth First Walk Algorithm | ||
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A way of calculating the auth chain difference without calculating the full auth | ||
chains for each state set is to do a parallel breadth first walk (ordered by | ||
depth) of each state set's auth chain. By tracking which events are reachable | ||
from each state set we can finish early if every pending event is reachable from | ||
every state set. | ||
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This can work well for state sets that have a small auth chain difference, but | ||
can be very inefficient for larger differences. However, this algorithm is still | ||
used if we don't have a chain cover index for the room (e.g. because we're in | ||
the process of indexing it). | ||
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## Chain Cover Index | ||
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Synapse computes auth chain differences by pre-computing a "chain cover" index | ||
for the auth chain in a room, allowing efficient reachability queries like "is | ||
event A in the auth chain of event B". This is done by assigning every event a | ||
*chain ID* and *sequence number* (e.g. `(5,3)`), and having a map of *links* | ||
between chains (e.g. `(5,3) -> (2,4)`) such that A is reachable by B (i.e. `A` | ||
is in the auth chain of `B`) if and only if either: | ||
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1. A and B have the same chain ID and `A`'s sequence number is less than `B`'s | ||
sequence number; or | ||
2. there is a link `L` between `B`'s chain ID and `A`'s chain ID such that | ||
`L.start_seq_no` <= `B.seq_no` and `A.seq_no` <= `L.end_seq_no`. | ||
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There are actually two potential implementations, one where we store links from | ||
each chain to every other reachable chain (the transitive closure of the links | ||
graph), and one where we remove redundant links (the transitive reduction of the | ||
links graph) e.g. if we have chains `C3 -> C2 -> C1` then the link `C3 -> C1` | ||
would not be stored. Synapse uses the former implementations so that it doesn't | ||
need to recurse to test reachability between chains. | ||
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### Example | ||
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An example auth graph would look like the following, where chains have been | ||
formed based on type/state_key and are denoted by colour and are labelled with | ||
`(chain ID, sequence number)`. Links are denoted by the arrows (links in grey | ||
are those that would be remove in the second implementation described above). | ||
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![Example](auth_chain_diff.dot.png) | ||
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Note that we don't include all links between events and their auth events, as | ||
most of those links would be redundant. For example, all events point to the | ||
create event, but each chain only needs the one link from it's base to the | ||
create event. | ||
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## Using the Index | ||
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This index can be used to calculate the auth chain difference of the state sets | ||
by looking at the chain ID and sequence numbers reachable from each state set: | ||
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1. For every state set lookup the chain ID/sequence numbers of each state event | ||
2. Use the index to find all chains and the maximum sequence number reachable | ||
from each state set. | ||
3. The auth chain difference is then all events in each chain that have sequence | ||
numbers between the maximum sequence number reachable from *any* state set and | ||
the minimum reachable by *all* state sets (if any). | ||
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Note that steps 2 is effectively calculating the auth chain for each state set | ||
(in terms of chain IDs and sequence numbers), and step 3 is calculating the | ||
difference between the union and intersection of the auth chains. | ||
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### Worked Example | ||
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For example, given the above graph, we can calculate the difference between | ||
state sets consisting of: | ||
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1. `S1`: Alice's invite `(4,1)` and Bob's second join `(2,2)`; and | ||
2. `S2`: Alice's second join `(4,3)` and Bob's first join `(2,1)`. | ||
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Using the index we see that the following auth chains are reachable from each | ||
state set: | ||
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1. `S1`: `(1,1)`, `(2,2)`, `(3,1)` & `(4,1)` | ||
2. `S2`: `(1,1)`, `(2,1)`, `(3,2)` & `(4,3)` | ||
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And so, for each the ranges that are in the auth chain difference: | ||
1. Chain 1: None, (since everything can reach the create event). | ||
2. Chain 2: The range `(1, 2]` (i.e. just `2`), as `1` is reachable by all state | ||
sets and the maximum reachable is `2` (corresponding to Bob's second join). | ||
3. Chain 3: Similarly the range `(1, 2]` (corresponding to the second power | ||
level). | ||
4. Chain 4: The range `(1, 3]` (corresponding to both of Alice's joins). | ||
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So the final result is: Bob's second join `(2,2)`, the second power level | ||
`(3,2)` and both of Alice's joins `(4,2)` & `(4,3)`. |
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