kappa-core
a small core for append-only log based programs
A lot like flumedb, but using
multifeed as an append-only log base,
which is actually a set of append-only logs.
Pronounced "capricorn".
Status
Experimental, but functional.
Usage
var kappa = require('kappa-core')
var memdb = require('memdb')
var core = kappa('./log', { valueEncoding: 'json' })
var idx = memdb()
var sum = 0
var sumview = {
api: {
get: function (core, cb) {
this.ready(function () {
cb(null, sum)
})
}
},
map: function (msgs, next) {
msgs.forEach(function (msg) {
if (typeof msg.value === 'number') sum += msg.value
})
next()
},
storeState: function (state, cb) { idx.put('state', state, cb) },
fetchState: function (cb) { idx.get('state', cb) }
}
core.use('sum', 1, sumview)
core.feed('default', function (err, feed) {
feed.append(1, function (err) {
core.api.sum.get(function (err, value) {
console.log(value)
})
})
})
API
var kappa = require('kappa-core')
var core = kappa(storage, opts)
Create a new kappa-core database.
var feed = core.feed(name, cb)
Create or get a local writable feed called name
. If it already existed, it is
returned. A feed is an instance of
hypercore.
var feeds = core.feeds()
An array of all hypercores in the kappa-core. Check a feed's key
to
find the one you want, or check its writable
/ readable
properties.
Only populated once core.ready(fn)
is fired.
core.use(name[, version], view)
Install a view called name
to the kappa-core instance. A view is an object of
the form
{
api: {
someSyncFunction: function (core) { return ... },
someAsyncFunction: function (core, cb) { process.nextTick(cb, ...) }
},
map: function (msgs, next) {
msgs.forEach(function (msg) {
})
next()
},
fetchState: function (cb) { ... },
storeState: function (state, cb) { ... }
}
The kappa-core instance core
is always is bound to this
in all of the api
functions you define.
version
is an integer that represents what version you want to consider the
view logic as. Whenever you change it (generally by incrementing it by 1), the
underlying data generated by the view will be wiped, and the view will be
regenerated again from scratch. This provides a means to change the logic or
data structures of a view over time in a way that is future-compatible.
The {fetch,store}State
functions are optional: they tell the view where to
store its state information about what log entries have been indexed thus far.
If not passed in, they will be stored in memory (i.e. reprocessed on each fresh
run of the program). You can use any backend you want (like leveldb) to store
the Buffer
object state
.
There are also the following optional opts
:
inedxed
: a function to run whenever a new batch of messages have been
indexed & written to storage. Receives an array of messages.
core.ready(viewNames, cb)
Wait until all views named by viewNames
are caught up. e.g.
// one
core.ready('sum', function () { ... })
// or several
core.ready(['kv', 'refs', 'spatial'], function () { ... })
If viewNames is []
or not included, all views will be waited on.
core.replicate([opts])
Create a duplex replication stream. opts
are passed in to
multifeed's API of the same name.
core.on('error', function (err) {})
Event emitted when an error within kappa-core has occurred. This is very
important to listen on, lest things suddenly seem to break and it's not
immediately clear why.
Install
With npm installed, run
$ npm install kappa-core
Useful view modules
Here are some useful modules that play well with kappa-core for building views:
Why?
flumedb presents an ideal small core API for an append-only log:
append new data, and build (versioned) views over it. kappa-core copies this
gleefully, but with two major differences:
- hypercore is used for feed (append-only log) storage
- views are built in out-of-order sequence
hypercore provides some very useful superpowers:
- all data is cryptographically associated with a writer's public key
- partial replication: parts of feeds can be selectively sync'd between peers,
instead of all-or-nothing, without loss of cryptographic integrity
Building views in arbitrary sequence is more challenging than when order is
known to be topographic, but confers some benefits:
- most programs are only interested in the latest values of data; the long tail
of history can be traversed asynchronously at leisure after the tips of the
feeds are processed
- the views are tolerant of partially available data. Many of the modules
listed in the section below depend on topographic completeness: all entries
referenced by an entry must be present for indexes to function. This makes
things like the equivalent to a shallow clone (think [git][git-shallow]),
where a small subset of the full dataset can be used and built on without
breaking anything.
Acknowledgments
kappa-core is built atop ideas from a huge body of others' work:
Further Reading
License
ISC
[git-shallow]: https://www.git-scm.com/docs/gitconsole.log(one#gitconsole.log(one---depthltdepthgt)