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kappa-core is a minimal peer-to-peer database, based on append-only logs and materialized views.


kappa-core is built on an abstraction called a kappa architecture, or “event sourcing”. This differs from the traditional approach to databases, which is centered on storing the latest value for each key in the database. You might have a table like this:

|id|key|value| |--|--|--| |51387|soup|cold| |82303|sandwich|warm| |23092|berries|room temp|

If you wanted to change the value of soup to warm, you would modify the entry with id=51387 so that the table was now

|id|key|value| |--|--|--| |51387|soup|warm| |82303|sandwich|warm| |23092|berries|room temp|

This table now, once again, represents the current state of the data.

There are some consequences to this style of data representation:

  1. historic data is lost
  2. there is exactly one global truth for any datum
  3. no verifiable authorship information
  4. data is represented in a fixed way (changing this requires “table migrations”)

In contrast, kappa architecture centers on a primitive called the “append-only log” as its single source of truth.

An append-only log is a data structure that can only be added to. Each entry in a log is addressable by its “sequence number” (starting at 0, then 1, 2, 3, …). In the case of kappa-core, which uses hypercore underneath, each log is also identified by a cryptographic public key, which allows each log entry to be digitally signed with that log’s private key, certifying that each entry in the log was indeed authored by the same person or device. A single kappa-core database can have one, ten, or hundreds of append-only logs comprising it.

kappa-core still uses tables like the above, though. However, instead of being the source of truth, these tables are generated (or materialized) from the log data, providing a view of the log data in a new or optimized context. These are called materialized views.

The twin concepts of append-only logs and materialized views are the key concepts of kappa-core. Any kappa-core database does only a few things:

  1. define various materialized views that it finds useful
  2. write data to append-only logs
  3. query those views to retrieve useful information

Let’s look at an example of how the traditional table from the beginning of this section could be represented as a kappa architecture. The three initial rows would begin as log entries first:

    id: 51387,
    key: 'soup',
    value: 'cold'
    id: 82303,
    key: 'sandwich',
    value: 'warm'
    id: 23092,
    key: 'berries',
    value: 'room temp'

These might be written to one log, or perhaps spread across several. They all get fed into materialized views in a nondeterministic order anyway, so it doesn’t matter.

To produce a look-up table like before, a view might be defined like this:

when new log entry E:
  table.put(E.key, E.value)

This would map each key from the full set of log entries to its value, producing this table:

|key|value| |--|--| |soup|cold| |sandwich|warm| |berries|room temp|

Notice id isn’t present. We didn’t need it, so we didn’t bother writing it to the view. It’s still stored in each log entry it came from though.

Now let’s say an entry like { id: 51387, key: 'soup', value: 'warm' } is written to a log. The view logic above the table dictates that the key is mapped to the value for this view, so the a table would be produced:

|key|value| |--|--| |soup|warm| |sandwich|warm| |berries|room temp|

Like the traditional database, the table is mutated in-place to produce the new current state. The difference is that this table was derived from immutable log data, instead of being the truth source itself.

This is all very useful:

  1. log entries are way easier to replicate over a network or USB keys than tables
  2. the log entries are immutable, so they can be cached indefinitely
  3. the log entries are digitally signed, so their authenticity can be trusted
  4. views are derived, so they can be regenerated

#4 is really powerful and worth examination: views can be regenerated. In kappa-core, views are versioned: the view we just generated was version 1, and was defined by the logic

when new log entry E:
  table.put(E.key, E.value)

What if we wanted to change this view at some point, to instead map the entry’s id to its value? Maybe like this:

when new log entry E:
  table.put(, E.value)

With kappa-core, this would mean bumping the view’s version to 2. kappa-core will purge the existing table, and regenerate it from scratch by processing all of the entries in all of the logs all over again. This makes views cheap, and also means no table migrations! Your data structures can evolve as you program evolves, and peers won’t need to worry about migrating to new formats.

Lastly, a kappa-core database is able to replicate itself to another kappa-core database. The replicate API (below) returns a Node Duplex stream. This stream can operate over any stream-compatible transport medium, such as TCP, UTP, Bluetooth, a Unix pipe, or even audio waves sent over the air! When two kappa-core databases replicate, they exchange the logs and the entries in the logs, so that both sides end up with the same full set of log entries. This will trigger your database’s materialized views to process these new entries to update themselves and reflect the latest state.

Because this is all built on hypercore, replication can be done over an encrypted channel.

Thanks for reading! You can also try the kappa-core workshop to use kappa-core yourself, or get support and/or chat about development on

  • IRC: #kappa-db on Freenode


This example sets up an on-disk log store and an in-memory view store. The view tallies the sum of all of the numbers in the logs, and provides an API for getting that sum.

var kappa = require('kappa-core')
var view = require('kappa-view')
var memdb = require('memdb')

// Store logs in a directory called "log". Store views in memory.
var core = kappa('./log', { valueEncoding: 'json' })
var store = memdb()

// View definition
var sumview = view(store, function (db) {

  // Called with a batch of log entries to be processed by the view.
  // No further entries are processed by this view until 'next()' is called.
  map: function (entries, next) {
    db.get('sum', function (err, value) {
      var sum
      if (err && err.notFound) sum = 0
      else if (err) return next(err)
      else sum = value
    entries.forEach(function (entry) {
      if (typeof entry.value === 'number') sum += entry.value
    db.put('sum', sum, next)

  // Whatever is defined in the "api" object is publicly accessible
  api: {
    get: function (core, cb) {
      this.ready(function () {  // wait for all views to catch up
        cb(null, sum)

// the api will be mounted at core.api.sum
core.use('sum', 1, sumview)  // name the view 'sum' and consider the 'sumview' logic as version 1

core.writer('default', function (err, writer) {
  writer.append(1, function (err) {
    core.api.sum.get(function (err, value) {
      console.log(value) // 1


var kappa = require('kappa-core')

var core = kappa(storage, opts)

Create a new kappa-core database.

  • storage is an instance of random-access-storage. If a string is given, random-access-file is used with the string as the filename.
  • Valid opts include:
    • valueEncoding: a string describing how the data will be encoded.
    • multifeed: A preconfigured instance of multifeed

core.writer(name, cb)

Get or create a local writable log called name. If it already exists, it is returned, otherwise it is created. A writer is an instance of hypercore.

var feed = multi.feed(key)

Fetch a log / feed by its public key (a Buffer or hex string).

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

// All are optional except "map"

  // Process each batch of entries
  map: function (entries, next) {
    entries.forEach(function (entry) {
      // ...

  // Your useful functions for users of this view to call
  api: {
    someSyncFunction: function (core) { return ... },
    someAsyncFunction: function (core, cb) { process.nextTick(cb, ...) }

  // Save progress state so processing can resume on later runs of the program.
  // Not required if you're using the "kappa-view" module, which handles this for you.
  fetchState: function (cb) { ... },
  storeState: function (state, cb) { ... },
  clearState: function (cb) { ... }

  // Runs after each batch of entries is done processing and progress is persisted
  indexed: function (entries) { ... },
  // Number of entries to process in a batch
  maxBatch: 100,

NOTE: The kappa-core instance core is always passed as the fist parameter 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 structure of a view over time in a way that is future-compatible.

The fetchState, storeState, and clearState 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. If you use a module like kappa-view, it will handle state management on your behalf.

indexed is an optional function to run whenever a new batch of entries have been indexed and written to storage. Receives an array of entries.

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.pause([viewNames], [cb])

Pause some or all of the views’ indexing process. If no viewNames are given, they will all be paused. cb is called once the views finish up any entries they’re in the middle of processing and are fully stopped.


Resume some or all paused views. If no viewNames is given, all views are resumed.


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.


With npm installed, run

$ npm install kappa-core

Useful helper modules

Here are some useful modules that play well with kappa-core for building materialized views:


kappa-core is built atop two major building blocks:

  1. hypercore, which is used for (append-only) log storage
  2. materialized views, which are built by traversing logs in potentially out-of-order sequence

hypercore provides some very useful superpowers:

  1. all data is cryptographically associated with a writer’s public key
  2. partial replication: parts of logs 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 or sorted in some way, but confers some benefits:

  1. 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 logs are processed
  2. 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), where a small subset of the full dataset can be used and built on without breaking anything.


kappa-core is built atop ideas from a huge body of others’ work:

Further Reading