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