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+---
+marp: true
+---
+
+
+# Kademlia
+
+
+
+[@arnaucube](https://twitter.com/arnaucube)
+
+2019-04-26
+
+---
+
+### 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
+
+---
+
+- 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
+
+---
+
+### 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)
+
+---
+
+- 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
+
+---
+
+### Protocol messages
+- PING
+- STORE
+- FIND_NODE
+- FIND_VALUE
+Each `rpc` msg includes a random value from the initiator, to ensure that the response corresponds to the request
+
+---
+
+### 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)
+
+---
+
+### 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
+
+---
+
+#### 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
+
+---
+
+### 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