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Mutable value chains are sequences of immutable values which can be stored in a Key-Value store (KVS) or Distributed hash table (DHT).

Mutable value chains solve the “variable update problem” using sequences of values to represent changes to a variable. Each new value assigned to a variable is represented by adding a new value to the sequence.

I'll assume you know a little about DHTs and Key-Value stores and show how a number of services can be deployed on top of Key-Value stores or DHTs

I'll also assume that the values stored in the KVS or DHT are *immutable* - and look at the central problem of “updating” an immutable value.

This might sound complicated, but really it's very easy and involves the idea of an iterated sequence of SHA keys.

Iterated SHA keys offer a surprisingly simple way to store mutable sequences of data in a Distributed Hash Table (DHT) like Kademlia or massive Key-Value store (like Riak).

How can we modify immutable data?

The short answer is we can't. Suppose we have some data base variable X whose value changes with time. We might say that:

X0 = 10

and some time later:

X1 = 16

and so on. Instead of having a single variable X whose value changes in time we can think of having a sequence of values X0, X1, X2, ... and so on where the values of X0, X1 and so on are immutable (ie never change).

The advantages of having immutable data are many. Firstly the values
are **cacheable** forever. Since we know the values cannot change we
can store them forever in an efficient manner. We can compress the
values and build indexes over them knowing that that data won't
change. We also don't have any problems with update consistency, since
they cannot be updated.

Cacheing works because of immutable data. GIT works because of immutable data, Functional programming works because of immutable data. Functional programs create new values from old values without changing the old values which make it easy to argue about the properties of the program. Things like debugging become easy because old values are not overwritten.

So how can we make mutable sequences of values? If I say x = 10, how can I later on say x = 20, I can't change the *value* of 10 so I can't say “and now there's a new value.”

I've been wondering how to do this for years, and suddenly it occurred to me how to do this. One way that doesn't work is to view the sequence of values as some kind of linked list, where each value has an invisible write-once-only pointer to the next value. The write-once-pointer gets updated when the value is updated, unfortunately this is difficult to implement and there are problems with consistency.

Keys are iterated SHA sequences

The solution is blindingly simple (once you see it) - we represent the sequence as a set Key-Value pairs where the Keys are taken from an iterated sequence of SHA1 checksums. The first key is a random number, thereafter Key[i] = sha(Key[i-1]).

Ann wants to send a stream of messages to Bob.

Assuming we use a DHT like Kademlia to store the data Ann should republish the data (say) every hour and store the data on the 20 nearest nodes to the key.

I haven't said anything about the values that are stored at a particular key. There are several alternatives. Assuming these values are tuples that have been serialized in some way, we could choose:

{sha,Key1} This is an indirection to a second document Key1 is the SHA of a second document stored in the DHT. The document is “self-signed” ie has checksum Key1. The client can fetch this document and check that it has SHA1 checksum of Key1. This is immune to a thing-in-the-middle attack.

{s,Person,Key1,Data,Proof} is a signed document. Person signed the document. Key1 is the SHA of a key in the DHT containing Person's public key. Data is signed with authentication Proof

{e,...} is an encrypted document ...

As you can see there are many possibilities.

Given a sequence of values X0, X1, X2, X3 compute a sequence of values Y0, Y1, Y2, ... where Yi = f(Xi). This is easy:

If the sequence of X's is huge then multiple workers can be started tell them to start computing values at different points in the value chain.

Suppose we have deployed some software on an extremely large number of machines.

Assume that we are at version 23 of some software. The “Key” to this software is K23 (an SHA checksum) to fetch the software we lookup K23 in our DHT, and assume this is the SHA of the software. So:

OurSoftware = lookup(lookup(K23))

Where lookup(Key) retrieves a value from a global DHT.

To publish new software the provider injects two new keys into the software. Something like:

SHA = sha(Blob), inject(SHA, Blob) inject(sha(K23), SHA)

Where inject(Key, Value) inserts a new value into the DHT (or KVS) and Blob represents the software.

Clients at version K23 poll the system by doing lookup(sha(K23)) if a new value is returned then the software needs to be upgraded.

Values can be protected and signed with a layer of cryptography

It should be possible to make small closed groups of agents participating in financial transactions. Once we have established secure message streams between agents, we can add a server and a transaction layer in a manner similar to bitcoin.

By keeping the groups small the transaction chains will be short which simplifies the implementation.

SHA which I have chosen in this article has 160 bits, so after more than 2^160 entries have been put into the DHT collisions will occur.

To put this number in perspective we note that the number of atoms on the planet is about 10^50 (2^166) and the volume of the universe/the Planck volume is about 10^188 (c. 2^624) so we might need a larger key space that that which is provided by SHA.