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---marp: true---
# Kademlia
<br><br><br>[@arnaucube](https://twitter.com/arnaucube)
2019-04-26
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### Overview
- nodes self sets a random unique ID (UUID)- nodes are grouped in `neighbourhoods` determined by the `node ID` distance- Kademlia uses `distance` calculation between two nodes - distance is computed as XOR (exclusive or) of the two `node ID`s
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- XOR acts as the distance function between all `node ID`s. Why: - distance between a node and itself is zero - is symmetric: distance between A to B is the same to B to A - follows `triangle inequality` - given A, B, C vertices (points) of a triangle - AB <= (AC + CB) - the distance from A to B is shorter or equal to the sum of the distance from A to C plus the distance from C to B - so, we get the shortest path---- with that last 3 properties we ensure that XOR - captures all of the essential & important features of a "real" distance function - is simple and cheap to calculate- each search iteration comes one bit closer to the target - a basic Kademlia network with `2^n` nodes will only take `n` steps (in worst case) to find that node
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### Routing tables
- each node has a routing table, that consists of a `list` for each bit of the `node ID` - each entry holds the necessary data to locate another node - IP address, port, `node ID`, etc - each entry corresponds to a specific distance from the node - for example, node in the Nth position in the `list`, must have a differing Nth bit from the `node ID` - so, the list holds a classification of 128 distances of other nodes in the network---- as nodes are encountered on the network, they are added to the `lists` - store and retrieval operations - helping other nodes to find a key - every node encountered will be considered for inclusion in the lists - keps network constantly updated - adding resilience to failures and attacks---- `k-buckets` - `k` is a system wide number - every `k-bucket` is a `list` having up to `k` entries inside - example: - network with `k=20` - each node will have `lists` containing up to 20 nodes for a particular bit- possible nodes for each `k-bucket` decreases quickly - as there will be very few nodes that are that close- since quantity of possible IDs is much larger than any node population, some of the `k-buckets` corresponding to very short distances will remain empty---- example: - network size: 2^3 - max nodes: 8, current nodes: 7 - let's take 6th node (110) (black leaf) - 3 `k-buckets` for each node in the network (gray circles) - nodes 0, 1, 2 (000, 001, 010) are in the farthest `k-bucket` - node 3 (011) is not participating in the network - middle `k-bucket` contains the nodes 4 and 5 (100, 101) - last `k-bucket` can only contain node 7 (111)
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- Each node knows its neighbourhood well and has contact with a few nodes far away which can help locate other nodes far away.- Kademlia priorizes long connected nodes to remain stored in the `k-buckets` - as the nodes that have been connected for a long time in a network will probably remain connected for a long time in the future---- when a `k-bucket` is full and a new node is discovered for that `k-bucket` - node sends a ping to the last recently seen node in the `k-bucket` - if the node is still alive, the new node is stored in a secondary list (a replacement cache) - replacement cache is used if a node in the `k-bucket` stops responding - basically, new nodes are used only when older nodes disappear
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### Protocol messages
- PING- STORE- FIND_NODE- FIND_VALUEEach `rpc` msg includes a random value from the initiator, to ensure that the response corresponds to the request
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### Locating nodes
- node lookups can proceed asynchronously - `α` denotes the quantity of simultaneous lookups - `α` tipically is 3- node initiates a FIND_NODE request to the `α` nodes in its own `k-bucket` that are closest ones to the desired key---- when the recipient nodes receive the request, they will look in their `k-buckets` and return the `k` closest nodes to the desired key that they know- the requester will update a results list with the results (`node ID`s) that receives - keeping the `k` best ones (the `k` nodes that are closer to the searched key)- the requester node will select these `k` best results and issue the request to them- the proces is repeated again and again until get the searched key---- iterations continue until no nodes are returned that are closer than the best previous results - when iterations stop, the best `k` nodes in the results list are the ones in the whole network that are the closest to the desired key- node information can be augmented with RTT (round trip times) - when the RTT is spended, another query can be initiated - always the query's number are <= `α` (quantity of simultaneous lookups)
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### Locating resources
- data (values) located by mapping it to a key - typically a hash is used for the map- locating data follows the same procedure as locating the closest nodes to a key - except the search terminates when a node has the requested value in his store and returns this value
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#### Data replicating & caching
- values are stored at several nodes (k of them)- the node that stores a value - periodically explores the network to find the k nodes close to the key value - to replicate the value onto them - this compensates the disappeared nodes---- avoiding "hot spots" - for popular values (might have many requests) - near nodes outside the k closest ones, store the value - this new storing is called `cache` - caching nodes will drop the value after a certain time - depending on their distance from the key - in this way the value is stored farther away from the key - depending on the quantity of requests - allows popular searches to find a storer more quickly - alleviates possible "hot spots"- not all implementations of Kademlia have these functionallities (replicating & caching) - in order to remove old information quickly from the system
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### Joining the network
- to join the net, a node must first go through a `bootstrap` process- `bootstrap` process - needs to know the IP address & port of another node (bootstrap node) - compute random unique `node ID` number - inserts the bootstrap node into one of its k-buckets---- `bootstrap` process [...] - perform a node lookup of its own `node ID` against the bootstrap node - this populate other nodes `k-buckets` with the new `node ID` - populate the joining node `k-buckets` with the nodes in the path between that node and the bootstrap node - refresh all `k-buckets` further away than the `k-bucket` the bootstrap node falls in - this refresh is a lookup of a random key that is within that `k-bucket` range - initially nodes have one `k-bucket` - when is full, it can be split
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