Current implementations


IPFS combines Kademlia + BitTorrent protocol + Git ideas to provide a high-throughput (handling data at a high rate), content-addressed block storage model (stores user data and its information as separate objects in a store that can hold any kind of file system), with content-addressed hyperlinks (making the information retrievable over internet connections based on its content, not its location).

  • Each file and all of the blocks in it have a unique fingerprint (hash).

  • Version history of each file is tracked by IPFS.

  • Duplications are removed across the network.

  • A node stores only content it cares about and some indexing information (for who is storing what).

  • Looking for files means looking for nodes storing the content behind a hash (a unique fingerprint).

  • Every file can be found by its human-readable name using the IPNS naming system.

Use cases

IPFS was designed as a distributed storage technology, useful for archivists, service providers and content creators. The number of users have increased a lot recently, as well as its intended scope and its applications. For example, IPFS makes it possible to run a decentralized application (ÐAPP) and store complex and unstructured content with a distributed file system based on IPFS that is not connected to the public IPFS network, so that its content isn’t spread on other IPFS hosts outside the network.

Currently, IPFS intends to replace the HTTP protocol. There are some serious hurdles to take. Internet standards get set by publishing RFCs, having multiple independent implementations, and going through a standards process by the IETF. The IRTF Decentralized Internet Infrastructure (DIN) has noticed it as distributed storage technology, and in general, the need for distributed technologies.

Merkle DAGs

A future-proof system that allows for multiple different fingerprinting mechanisms (summaries of the content that can be used to address content) to coexist. How? Large files are chunked, hashed, and organised into an Interplanetary Linked Data (IPLD) structure, a Merkle DAG object.

Multihash format

Raw content is run through a hash function, to produce a digest. This digest is said to be guaranteed to be cryptographically unique to the contents of the file, and that file only. The multihash format provides a wrapper around the hash: The hash itself specifies which hash function is used, and the length of the resultant hash in the first two bytes (fn code and length) of the multihash. The rest of it is the hash digest. format

IPFS comes with a default hash algorithm but can be recompiled to use another hash function as default or to change the importer code to add a way to specify the multihash choice. When the hashing algorithm used is changed from SHA256 to BLAKE2b, the prefixes in the wrapper will differ.


Base58 is a group of binary-to-text encoding schemes used to represent large integers as alphanumeric text. It is designed for humans to easily enter the data, copying from some visual source, but also allows easy copy and paste because a double-click will usually select the whole string. It is similar to Base64 but has been modified to avoid both non-alphanumeric characters and letters which might look ambiguous when printed - similar-looking letters are omitted: 0 (zero), O (capital o), I (capital i) and l (lower case L), and non-alphanumeric characters + (plus) and / (slash) are dropped.

The actual order of letters in the alphabet depends on the application, which is the reason why the term Base58 alone is not enough to fully describe the format. Base58Check is a Base58 encoding format that unambiguously encodes the type of data in the first few characters and includes an error detection code in the last few characters.

For example, the base58 letters Qm correspond with hexadecimal 12 (SHA-256 algorithm) and hexadecimal 20 (length 32 bytes).

CID version

A CID is a self-describing content-addressed identifier. It uses cryptographic hashes to achieve content addressing. It uses several multiformats to achieve flexible self-description, namely multihash for hashes, multicodec for data content types, and multibase to encode the CID itself into strings.

Concretely, it’s typed content address: a tuple of (content-type, content-address)

Notes on zero duplication

  • The resulting hash is not only a result of the chosen hash algorithm (hash option), but also affected by the choice of chunking algorithm (chunker option), DAG format (trickle option) and CID version (cid-version option), so it is possible to have completely different hashes even if the format is marked the same.

  • The same file can be duplicated across the network: Someone could add a file, remove it, upgrade the IPFS client (or change to using a different one), add it again, and get a completely different hash. This requires intent and is complicated and time-consuming, so the probability of the existence of multiple hashes for the same file is low.

  • Duplication of files leads to redundancy, something users might want and even may consider necessary.

“Zero duplication” refers to not having wasteful duplicates.

IPFS Components

Incentergy: IPFS the next generation internet procotol technical overviewIncentergy: IPFS the next generation internet procotol technical overview


The IPFS identities technology manages node identity generation and verification. Nodes are identified using a NodeID, which is a cryptographic hash of a public key. Each node stores its public and private keys, with the private key being encrypted with a passphrase.

The NodeId, the cryptographic hash3 of a public-key, is created with S/Kademlia’s static crypto puzzle (pdf).


The IPFS network manages connections to other peers using various underlying network protocols. The network is configurable. IPFS nodes communicate with hundreds of other nodes in the network (or potentially, the entire internet). Some stack features of the IPFS network include transport, reliability, connectivity, integrity, and authenticity systems.

  • IPFS can use any transport protocol and is best suited for WebRTC Data Channels (browser connectivity) or uTP.

  • If underlying networks do not provide reliability, IPFS can provide it using uTP or SCTP.

  • For connectivity it also uses ICE NAT traversal techniques - STUN is a standardized set of methods and a network protocol for NAT hole punching, designed for UDP but which was extended to TCP. TURN is a NAT traversal relay protocol. ICE is a protocol for using STUN and/or TURN to do NAT traversal while picking the best network route available. It fills in some of the missing pieces and deficiencies that were not mentioned in the STUN specification.

  • Integrity of messages can be checked using a hash checksum.

  • Authenticity of messages can be checked using HMAC with the sender’s public key.


The IPFS routing system maintains information to locate specific peers and objects. It responds to both local and remote queries. Routing defaults to a DHT (Dynamic Hash Table), but it’s swappable.

IPFS uses a DHT based on S/Kademlia and Coral (DSHT). Coral stores the addresses of peers who can provide the data blocks taking advantage of data locality. Coral can distribute only subsets of the values to the nearest nodes avoiding hot spots. Coral organises a hierarchy of separate DSHTs called clusters depending on region and size. This enables nodes to query peers in their region first, “finding nearby data without querying distant nodes” and greatly reducing the latency of lookups.

Coral and mainline DHT use DHTs as a place to store – not the value, but – pointers to peers who have the actual value. IPFS uses the DHT in the same way. When a node advertises a block available for download, IPFS stores a record in the DHT with its own Peer.ID. This is called “providing” and the node becomes a “provider”. Requesters who wish to retrieve the content, query the DHT (or DSHT) and need only to retrieve a subset of providers, not all of them. Providing is done once per block because blocks (even sub-blocks) are independently addressable by their hash.


IPFS uses a unique block exchange protocol called BitSwap to govern efficient block distribution. BitSwap is modelled like a market, and users have some minor incentives for data replication. Trade strategies are swappable.

Unlike BitTorrent, BitSwap is not limited to the blocks in one torrent. The blocks can come from completely unrelated files in the filesystem. BitSwap incentivises nodes to seed/serve blocks even when they do not need anything in particular. To avoid leeches (freeloading nodes that never share), peers track their balance (in bytes verified) with other nodes, and peers send blocks to debtor peers according to a function that falls as debt increases.

  • If a node is storing a node that is the parent (root/ancestor) of other nodes, then it is much more likely to also be storing the children. So when a requester attempts to pull down a large DAG, it first queries the DHT for providers of the root. Once the requester finds some and connects directly to retrieve the blocks, BitSwap will optimistically send them the “wantlist”, which will usually obviate any more DHT queries for that DAG.

  • BitSwap only knows about Routing. And it only uses the Provide and FindProviders calls.


Merkle DAGs of content-addressed immutable objects with links are used to represent arbitrary data structures, including file hierarchies and communication systems.

Merkle DAGs provide:

  • Content addressing: All content is uniquely identified by its multihash checksum.

  • Tamper resistance: All content is verified with its checksum.

  • Deduplication: All objects that hold the same content are equal, and only stored once.


IPFS uses a versioned file system hierarchy inspired by Git. On Github, the complete file history and changes over time can be viewed.

IPFS defines a set of objects for modelling a versioned filesystem on top of the Merkle DAG. This object model is similar to the Git model:

  • A block is a variable-size block of data.

  • A list is an ordered collection of blocks or other lists.

  • A tree is a collection of blocks, lists, or other trees.

  • A commit is a snapshot in the version history of a tree.


IPFS uses a self-certifying mutable name system called IPNS. IPNS was inspired by SFS (pdf) and is compatible with given services like DNS.

NodeId is obtained by hash(node.PubKey). Then IPNS assigns every user a mutable namespace at: /ipns/<NodeId>. A user can publish an Object to this /ipns/<NodeId> path signed by his/her private key. When other users retrieve the object, they can check the signature matches the public key and NodeId. This verifies the authenticity of the Object published by the user, achieving mutable state retrieval.

<NodeId> is a hash, it is not human friendly to pronounce and recall. That is where DNS TXT IPNS Records come in. If /ipns/<domain> is a valid domain name, IPFS looks up key ipns in its DNS TXT records. The ipns behaves as a symlink.

  • IPNS uses the Put and GetValue calls.


IPFS - Content Addressed, Versioned, P2P File System (DRAFT 3), Juan Benet



BTFS is a fork of IPFS.


IPFS with FileCoin offers storage on a global network of local providers who have the freedom to set prices based on supply and demand. It implements a generalised version of the BitTorrent exchange protocol and uses Proof-of-Storage (instead of a Proof-of-Work consensus algorithm like Bitcoin). Anyone can join the network, offer unused hard drive space, and get rewarded in FileCoin tokens for data storage and retrieval services. Filecoin is traded on several exchanges and supported by multiple cryptocurrency wallets, allowing the exchange FileCoin for other currencies like Euros, US Dollars, BTC and ETH.



The usual peer crawling


  • Full distribution of every task (no centralized coordination at all): Peers can perform their jobs independently and communicate required data. This will prevent link congestion to a central server. Functionally identically programmed nodes, distinguished by a unique identifier only.

  • No single point of failure and gracefully dealing with permanent and transient failures (identify crawl traps and be tolerant to external failures)

    • Nodes do not crash on the failure of a single peer. ⇒ dynamic reallocation of addresses can be done across other peers.

    • Data sent to a node while it is down still needs to propagate properly throughout the system.

    • Data is still retrievable even with a node failing.

    • When a node comes back online after having failed, data that needs to be stored on that node is propagated to it and can be retrieved from it.

    • If a node failure is permanent, the node needs to be recoverable using data stored by other nodes.

  • Scalability - Not relying on location implies latency can become an issue ⇒ minimising communication.

  • Platform independence of nodes.

  • Locally computable content address assignment based on consistent hashing - IPNS is consistent (check for the presence of DNSLink first for it is faster)

  • Portability: The nodes can be configured to run on any kind of dweb network by just replacing it.

  • Performance: Nodes run on spare CPU processing power and are not to put too large a load on a client machine.

  • Freshness: After an object is initially acquired (processed), it may have to be periodically recrawled and checked for updates. In the simplest case, this could be done by starting another broad breadth-first crawl, or by requesting all items in the collection of a node again. Techniques for optimizing the “freshness” of such collections is usually based on observations about an item’s update history (incremental crawling). Sadly, we cannot use this “as such” as an updated object in IPFS has a new hash. A variety of other heuristics can be used to recrawl as “more important” marked items. Good enough recrawling strategies are essential for maintaining an up-to-date search index with limited crawling bandwidth.

  • Note that security and privacy have no requirements yet as this distributed crawler is a “hello world” function. The component with the distribution function can be further expanded once the distribution of the index is designed.


The below compartmentalised architecture helps to make the system portable. To make it pluggable we will need a high degree of functional independence between the components of the system so that we can run the crawler on top of different underlying dweb networks and different distribution functions (with minimal regression factor) to optimise the distribution function for the underlying dweb network architecture. Combining the three components can be done with a configuration file, created during installation, with the definition of communication interfaces of the dweb network and other required parameters. That means we would only need platform-dependent installers.

The three proposed crawler components:

  • An Overlay Network Layer responsible for formation and maintenance of a distributed search engine network, and communication between peers. These can be unstructured or structured networks. If a network is not scalable, a supernode architecture can be used to improve performance, hence a client must have support for flat as well as supernode architecture.

  • A Peer and Content Distribution Function determines which clients to connect with. Each client has a copy of this function. Hash list & content range associated with a client can change due to joining/leaving of nodes in the network. The function will distribute hashes to crawl as well as content among peers and makes use of the underlying dweb network to provide load balancing and scalability and takes proximity of nodes into account. Initially, we will use a static distribution function, hash list and content range assignment functions can be hash functions.

  • The** Crawler** downloads and extracts dweb objects and sends and receives data from other peers using the underlying network.



  • The Neighbourhood data store contains the identifiers of agents in a node’s neighbourhood.

  • The Seen Hash data store contains a list of hashes that are already processed.

  • The Seen Content data store contains attributes of objects that are already processed.

Whenever a node receives a hash for processing from another node, the Preprocessor adds the hash to one of the Crawl Job Queues.

  • A classifier determines to which crawl job queue a request is added. Hashes from the same node are added to the same crawl job queue.

  • Rate of input is most likely much faster than the rate of output. We can implement an overflow controller that drops hashes after the crawl job queue overflows.

  • Rate throttling to prevent excessive requests to the same node.

  • Checks whether a hash has already been processed by accessing the Seen Hash data structure. If a hash is not already processed then the hash is added to the Seen Hash data structure.

  • We start with just one queue and can then experiment with adding more.

  • Each crawl job queue has one Content Fetcher thread associated with it that streams data into the extractor.

    • Uses the local IPFS gateway to fetch a (named) IPFS resource.

    • Checks file permissions.

    • If we keep the connection open to a node to minimise connection establishment overhead we will need exception handling.

  • The Extractor extracts linked hashes/links from the object and passes them on to the hash validator. It can perhaps be multithreaded and process different pages simultaneously.

  • The Hash Validator checks whether a hash is the responsibility of the node. If so, it sends the hash to the preprocessor. If not, it is sent out on the network.

  • The Content Range Validator checks whether content lies in the range of the node.

  • The Content Processor checks object for duplication from Seen Content data. If the object is not already processed, then it is added to the Seen Content data store.


UbiCrawler: A Scalable Fully Distributed Web Crawler, Paolo Boldi, Bruno Codenotti, Massimo Santini, Sebastiano Vigna

Apoidea: A Decentralized Peer-to-Peer Architecture for Crawling the World Wide Web, Aameek Singh, Mudhakar Srivatsa, LingLiu, and Todd Miller, 2003

Efficient Crawling Through URL Ordering, Junghoo Cho, Hector Garcia-Molina, Lawrence Page, 1998