HyperDB Architecture

HyperDB is a scalable peer-to-peer key-value database.

Filesystem metaphor

HyperDB is structured to be used much like a traditional hierarchical filesystem. A value can be written and read at locations like /foo/bar/baz, and the API supports querying or tracking values at subpaths, like how watching for changes on /foo/bar will report both changes to /foo/bar/baz and also /foo/bar/19.

Set of append-only logs (feeds)

A HyperDB is fundamentally a set of hypercores. A hypercore is a secure append-only log that is identified by a public key, and can only be written to by the holder of the corresponding private key. Because it is append-only, old values cannot be deleted nor modified. Because it is secure, a feed can be downloaded from even untrustworthy peers and verified to be accurate. Any modifications (malicious or otherwise) to the original feed data by someone other than the author can be readily detected.

Each entry in a hypercore has a sequence number, that increments by 1 with each write, starting at 0 (seq=0).

HyperDB builds its hierarchical key-value store on top of these hypercore feeds, and also provides facilities for authorization, and replication of those member hypercores.

Directed acyclic graph

The combination of all operations performed on a HyperDB by all of its members forms a DAG (directed acyclic graph). Each write to the database (setting a key to a value) includes information to point backward at all of the known "heads" in the graph.

To illustrate what this means, let's say Alice starts a new HyperDB and writes 2 values to it:

// Feed

0 (/foo/bar = 'baz')
1 (/foo/2   = '{ "some": "json" }')


// Graph

Alice:  0  <---  1

Where sequence number 1 (the second entry) refers to sequence number 0 on the same feed (Alice's).

Now Alice authorizes Bob to write to the HyperDB. Internally, this means Alice writes a special message to her feed saying that Bob's feed (identified by his public key) should be read and replicated in by other participants. Her feed becomes

// Feed

0 (/foo/bar = 'baz')
1 (/foo/2   = '{ "some": "json" }')
2 (''       = '')


// Graph

Alice: 0  <---  1  <---  2

Authorization is formatted internally in a special way so that it isn't interpreted as a key/value pair.

Now Bob writes a value to his feed, and then Alice and Bob sync. The result is:

// Feed

//// Alice
0 (/foo/bar = 'baz')
1 (/foo/2   = '{ "some": "json" }')
2 (''       = '')

//// Bob
0 (/a/b     = '12')


// Graph

Alice: 0  <---  1  <---  2
Bob  : 0

Notice that none of Alice's entries refer to Bob's, and vice versa. This is because neither has written any entries to their feeds since the two became aware of each other (authorized & replicated each other's feeds).

Right now there are two "heads" of the graph: Alice's feed at seq 2, and Bob's feed at seq 0.

Next, Alice writes a new value, and her latest entry will refer to Bob's:

// Feed

//// Alice
0 (/foo/bar = 'baz')
1 (/foo/2   = '{ "some": "json" }')
2 (''       = '')
3 (/foo/hup = 'beep')

//// Bob
0 (/a/b     = '12')


// Graph

Alice: 0  <---  1  <---  2  <--/  3
Bob  : 0  <-------------------/

Because Alice's latest feed entry refers to Bob's latest feed entry, there is now only one "head" in the database. That means there is enough information in Alice's seq=3 entry to find any other key in the database. In the last example, there were two heads (Alice's seq=2 and Bob's seq=0); both of which would need to be read internally in order to locate any key in the database.

Now there is only one "head": Alice's feed at seq 3.

Authorization

The set of hypercores are authorized in that the original author of the first hypercore in a hyperdb must explicitly denote in their append-only log that the public key of a new hypercore is permitted to edit the database. Any authorized member may authorize more members. There is no revocation or other author management elements currently.

Incremental index

HyperDB builds an incremental index with every new key/value pairs ("nodes") written. This means a separate data structure doesn't need to be maintained elsewhere for fast writes and lookups: each node written has enough information to look up any other key quickly and otherwise navigate the database.

Each node stores the following basic information:

key: the key that is being created or modified. e.g. /home/sww/dev.md
value: the value stored at that key.
seq: the sequence number of this entry in the owner's hypercore. 0 is the first, 1 the second, and so forth.
feed: the ID of the hypercore writer that wrote this
path: a 2-bit hash sequence of the key's components
trie: a navigation structure used with path to find a desired key
clock: vector clock to determine node insertion causality
feeds: an array of { feedKey, seq } for decoding a clock

Vector clock

Each node stores a vector clock of the last known sequence number from each feed it knows about. This is what forms the DAG structure.

A vector clock on a node of, say, [0, 2, 5] means:

when this node was written, the largest seq # in my local fed is 0
when this node was written, the largest seq # in the second feed I have is 2
when this node was written, the largest seq # in the third feed I have is 5

For example, Bob's vector clock for Alice's seq=3 entry above would be [0, 3] since he knows of her latest entry (seq=3) and his own (seq=0).

The vector clock is used for correctly traversing history. This is necessary for the db#heads API as well as db#createHistoryStream.

Prefix trie

Given a HyperDB with hundreds of entries, how can a key like /a/b/c be looked up quickly?

Each node stores a prefix trie that assists with finding the shortest path to the desired key.

When a node is written, its prefix hash is computed. This done by first splitting the key into its components (a, b, and c for /a/b/c), and then hashing each component into a 32-character hash, where one character is a 2-bit value (0, 1, 2, or 3). The prefix hash for /a/b/c is

node.path = [
1, 2, 0, 1, 2, 0, 2, 2, 3, 0, 1, 2, 1, 3, 0, 3, 0, 0, 2, 1, 0, 2, 0, 0, 2, 0, 0, 3, 2, 1, 1, 2,
0, 1, 2, 3, 2, 2, 2, 0, 3, 1, 1, 3, 0, 3, 1, 3, 0, 1, 0, 1, 3, 2, 0, 2, 2, 3, 2, 2, 3, 3, 2, 3,
0, 1, 1, 0, 1, 2, 3, 2, 2, 2, 0, 0, 3, 1, 2, 1, 3, 3, 3, 3, 3, 3, 0, 3, 3, 2, 3, 2, 3, 0, 1, 0,
4 ]

Each component is divided by a newline. 4 is a special value indicating the end of the prefix.

Example

Consider a fresh HyperDB. We write /a/b = 24 and get back this node:

{ key: '/a/b',
  value: '24',
  clock: [ 0 ],
  trie: [],
  feeds: [ [Object] ],
  feedSeq: 0,
  feed: 0,
  seq: 0,
  path:
   [ 1, 2, 0, 1, 2, 0, 2, 2, 3, 0, 1, 2, 1, 3, 0, 3, 0, 0, 2, 1, 0, 2, 0, 0, 2, 0, 0, 3, 2, 1, 1, 2,
     0, 1, 2, 3, 2, 2, 2, 0, 3, 1, 1, 3, 0, 3, 1, 3, 0, 1, 0, 1, 3, 2, 0, 2, 2, 3, 2, 2, 3, 3, 2, 3,
     4 ] }

If you compare this path to the one for /a/b/c above, you'll see that the first 64 2-bit characters match. This is because /a/b is a prefix of /a/b/c.

Since this is the first entry, seq is 0. Since this is the only known feed, feed is also 0. feeds is an array of entries of the form { key: Buffer, seq: Number } that let you map the numeric value feed to a hypercore key and its sequence number head. feeds isn't always set: it only gets included when it changes compared to node.seq - 1, in the interest of storing less data per node.

Now we write /a/c = hello and get this node:

{ key: '/a/c',
  value: 'hello',
  clock: [ 0 ],
  trie: [ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , [ , , [ { feed: 0, seq: 0 } ] ] ],
  feeds: [],
  feedSeq: 0,
  feed: 0,
  seq: 1,
  path:
   [ 1, 2, 0, 1, 2, 0, 2, 2, 3, 0, 1, 2, 1, 3, 0, 3, 0, 0, 2, 1, 0, 2, 0, 0, 2, 0, 0, 3, 2, 1, 1, 2,
     0, 1, 1, 0, 1, 2, 3, 2, 2, 2, 0, 0, 3, 1, 2, 1, 3, 3, 3, 3, 3, 3, 0, 3, 3, 2, 3, 2, 3, 0, 1, 0,
     4 ] }

As expected, this node has the same feed value as before (since we're only writing to one feed). Its seq is 1, since the last was 0. Notice that feeds isn't included, because the mapping of the numeric feed value to a key hasn't changed.

Also, this and the previous node have the first 32 characters of their path in common (the prefix /a).

Notice though that trie is set. It's a long but sparse array. It has 35 entries, with the last one referencing the first node inserted (a/b/). Why?

(If it wasn't stored as a sparse array, you'd actually see 64 entries (the length of the path). But since the other 29 entries are also empty, hyperdb doesn't bother allocating them.)

If you visually compare this node's path with the previous node's path, how many entries do they have in common? At which entry do the 2-bit numbers diverge?

At the 35th entry.

What this is saying is "if the hash of the key you're looking for differs from mine on the 35th entry, you want to travel to { feed: 0, seq: 0 } to find the node you're looking for.

This is how finding a node works, starting at any other node:

Compute the 2-bit hash sequence of the key you're after (e.g. a/b)
Lookup the newest entry in the feed.
Compare its path against the hash you just computed.
If you discover that the path and your hash match, then this is the node you're looking for!
Otherwise, once a 2-bit character from path and your hash disagree, note the index # where they differ and look up that value in the node's trie. Fetch that node at the given feed and sequence number, and go back to step 3. Repeat until you reach step 4 (match) or there is no entry in the node's trie for the key you're after (no match).

What if there are multiple feeds in the HyperDB? The lookup algorithm changes slightly. Replace the above step 2 for:

Fetch the latest entry from every feed. For each head node, proceed to the next step.

The other steps are the same as before.

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