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An optimized embedded event store for modern node.js, written in ES6.

Disclaimer: This is currently under heavy development and not production ready. See issues/29 for more information.



There is currently only a single embedded event store implementation for node/javascript, namely

It is a nice project, but has a few drawbacks though:

  • its API is fully based around Event Streams, so in order to commit a new event the full existing Event Stream needs to be retrieved first. This makes it unfit for client application scenarios that frequently restart the application.
  • it has backends for quite a few existing databases (TingoDB, NeDB, MongoDB, ...), but none of them are optimized for event storage needs
  • the embeddable storage backends (TingoDB, NeDB) do not persist indexes and hence are very slow on initial load
  • it stores event publishing meta information in the events, so it does updates to event data
  • events are fixed onto one stream and it's not possible to create multiple streams that partially contain the same events. This makes creating projections hard and/or slow.

Use cases

Event sourced client applications running on node.js (electron, node-webkit, etc.). Small event sourced single-server applications that want to get near-optimal write performance. Using it as queryable log storage.

Design goals

  • single node scalability
  • opening/writing to an existing store with millions of events should be as fast as opening/writing an empty store
  • write performance should not be constrained by locking or distributed transaction costs, i.e. single-writer (at least per transaction boundary = stream), so no horizontal write scaling
  • read performance should be optimized for sequential read-forward style reads starting at arbitrary position
  • reads should be scalable to as many readers as necessary (but typically one reader per projection)
  • it should be possible to create high number (thousands) of streams without high resource (memory,cpu) usage
  • re-reading (replaying) an arbitrary stream should be optimized for and cost no more than visiting every document in that stream (no full database scan)
  • consistency
  • writes to a single stream need to be able to guarantee consistency (i.e. every write happens only as of the state immediately before that write)
  • reads from a stream need to be consistent every time, i.e. repeatable read isolation (guaranteed order, read-committed for read-only but read-uncommitted/read your own writes for writers)
  • simplicity
  • the architecture and design should be straight-forward, not more complex than dictated by the goals
  • creating new streams (from existing data) should be easily doable with language-level methods


  • distributed storage/distributed transactions
  • therefore: no network API
  • cross-stream transactions
  • arbitrary querying capabilities - only range scans per stream

Event-Storage and it's specifics

The thing that makes event storages stand out (and also makes them simpler and more performant), is that they have no concept of overwriting or deleting data. They are purely append-only storages and the only querying is sequential (range) reading (possibly with some filtering applied):

This means a couple of things:

  • no write-ahead log or transaction log required - the storage itself is the transaction log!
  • therefore writes are as fast as they can get, but you only can have a single writer (without implementing complex distributed log with RAFT or Paxos)
  • durability comes for free (in complexity) if write caches are avoided
  • reads and writes can happen lock-free, reads don't block writes and are always consistent (natural MVCC)
  • indexes are append-only and hence gain the same benefits
  • since only sequential reading is needed, indexes are simple file position lists - no fancy B+-Tree/fractal tree required
  • indexes are therefore pretty cheap and can be created in high numbers
  • creating backups is easily doable with rsync or by creating file copies on the fly

Using any SQL/NoSQL database for storing events therefore is sub-optimal, as those databases do a lot of work on top which is simply not needed. Write and read performance suffer.


npm install event-storage

Run Tests

npm test


const EventStore = require('event-storage');

const eventstore = new EventStore('my-event-store', { storageDirectory: './data' });
eventstore.on('ready', () => {
    const streamVersion = eventstore.getStreamVersion('my-stream');
    eventstore.commit('my-stream', [{ foo: 'bar' }], streamVersion, () => {

    let stream = eventstore.getEventStream('my-stream');
    for (let event of stream) {

The streamVersion is needed if you do any async work in between the getStreamVersion and commit, that potentially involves other commits to the same stream. See Optimistic Concurrency.

Creating additional streams

Create additional streams that contain only part of another stream, or even a combination of events of other streams.

let myProjectionStream = eventstore.createStream('my-projection-stream', (event) => ['FooHappened', 'BarHappened'].includes(event.type));

for (let event of myProjectionStream) {

Optimistic concurrency

Optimistic concurrency is required when multiple sources generate events concurrently.

Note that having the producer of events behind a HTTP interface automatically implies concurrent operation.

To handle those cases but still guarantee all those producers can have their own consistent view of the current state, you need to track the last streamVersion the producer was at when he generated the event, then send that as expectedVersion with the commit.

const model = new MyConsistencyModel();
const stream = eventstore.getEventStream('my-stream');
stream.forEach((event, metadata) => {
const expectedVersion = stream.version;
// Provide model state and expectedVersion to some state change API or UI that returns a command
// generate new events from the current model, by applying an incoming command
const events = model.handle(command.payload);
try {
    // The expectedVersion is supposed to be given back through the command
    eventstore.commit('my-stream', events, command.expectedVersion, () => {
} catch (e) {
    if (e instanceof EventStore.OptimisticConcurrencyError) {
        // Reattempt command / resolve conflict

Where expectedVersion is either EventStore.ExpectedVersion.Any (no optimistic concurrency check, the default), EventStore.ExpectedVersion.EmptyStream or any version number > 0 that the stream is expected to be at. It will throw an OptimisticConcurrencyError if the given stream version does not match the expected. In that case you should either signal that back to the upstream source, or replay state and reattempt application of the command.


Consumers are durable event-driven listeners on event streams. They provide at-least-once delivery guarantees, meaning that they receive each event in the stream at least once. An event can possibly be delivered twice if the program crashed during the handling of an event, since the current position will only be persisted afterwards.

let myConsumer = eventstore.getConsumer('my-stream', 'my-stream-consumer1');
myConsumer.on('data', event => {
    // do something with event, but be sure to de-duplicate or have idempotent handling

Since a consumer is always bound to a specific stream, you need to create a stream for the specific consumer first, if it needs to listen to events from different write-streams.


The consuming of events will start as soon as a handler for the data event is registered and suspended when the last listener is removed.

As soon as the consumer has caught up the stream, it will emit a caught-up event.

Exactly-Once semantics

Since version 0.6 the consumers can persist their state (a simple JSON object), which allows for achieving exactly-once processing semantics relatively easy. What this means is, that the state of the consumer will always reflect the state of having each event processed exactly once, because if persisting the state fails, the position is also not updated and vice versa.

let myConsumer = eventstore.getConsumer('my-stream', 'my-stream-consumer1');
myConsumer.on('data', event => {
    const newState = { ...myConsumer.state, projectedValue: myConsumer.state.projectedValue + event.someValue };

This is very useful for projecting some data out of a stream with exactly-once processing without a lot of effort. Whenever the state is persisted, the consumer will also emit a persisted event.


Never mutate the consumers state property directly and only use the setState method inside the data handler.

The reason why this works is, that conceptually the state update and the position update happens within a single transaction. So anything you can wrap inside a transaction with storing the position yields exactly-once semantics. However, for example sending an email exactly once for every event is not achievable with this, because you can't wrap a transaction around sending an e-mail and persisting the consumer position in a local file easily.


The EventStore can also be opened in read-only mode since 0.7, by specifying the constructor option readOnly: true. In this mode, any writes to the store are prevented, while all reads and consumers work as normal. The read-only storage will watch the files that back it and automatically update internal state on changes, so the reader is asynchronously fully consistent to the writer state. You can open as many readers as needed and the main use case is to use it for consumers running in a different process than the writer. This way, you can have different processes create projections from the events for different use cases and serve their state out to other systems, e.g. through an HTTP interface or whatever deems useful.

const EventStore = require('event-storage');

const eventstore = new EventStore('my-event-store', { storageDirectory: './data', readOnly: true });
eventstore.on('ready', () => {
    let myConsumer = eventstore.getConsumer('my-stream', 'my-stream-consumer1');
    myConsumer.on('data', event => {
        const newState = { ...myConsumer.state, projectedValue: myConsumer.state.projectedValue + event.someValue };

In theory, it would even be possible with this, to scale the storage to multiple machines, if they are all backed by a common file system. The biggest issue preventing this is, that the nodejs file watcher needs to work on that filesystem. See for more information. Also, you could rsync the files that back the storage to another machine and have a read-only instance running on that. See and the --append option.

Implementation details


Note: All following explanations talk about a single transaction boundary, which is a single write-stream, AKA a storage partition.

The storage engine is not strictly designed to follow ACID semantics. However, it has following properties:


A single document write is guaranteed to be atomic. Unless specifically configured, atomicity spreads to all subsequent writes until the write buffer is flushed, which happens either if the current document doesn't fully fit into the write buffer or on the next node event loop. This can be (ab)used to create a reduced form of transactional behaviour: All writes that happen within a single event loop and still fit into the write buffer will all happen together or not at all. If strict atomicity for single documents is required, you can configure the option maxWriteBufferDocuments to 1, which leads to every single document being flushed directly.


Since the storage is append-only, consistency is automatically guaranteed.


The storage is supposed to only work with a single writer, therefore writes do not influence each other obviously. The single writer is only guaranteed with a simple lock-directory mechanic, which works on NFS. This is of course not a hard guarantee, just a helper to prevent accidentally opening two writers. Reads are guaranteed to be isolated due to the append-only nature and a read only ever seeing writes that have finished (not necessarily flushed - i.e. Dirty Reads) at the point of the read. In a read-only instance, dirty reads are technically impossible, because the reader has no access to the unfinished writes. Multiple reads can happen without blocking writes.

If Dirty Reads are not wanted, they can be disabled with the storage configuration option dirtyReads set to false. That way you will only ever be able to read back documents that where flushed to disk, even on writers. Note though, that this should only be done with in-memory models that keep their own (uncommitted) state, or else you might suffer from inconsistency.

There are no lost updates due to the append-only nature. Phantom reads can be prevented by specifying the maxRevision for streams explicitly (MVCC). All reads are repeatable, as long as no manual truncation happens.


Durability is not strictly guaranteed due to the used write buffering and flushes not being synced to disk by default. All writes happening within a single node event loop and fitting into the write buffer can be lost on application crash. Even after flush, the OS and/or disk write buffers can still limit durability guarantees. This is a trade-off made for increased write performance and can be more finely configured to needs. The write buffer behaviour can be configured with the already mentioned maxWriteBufferDocuments and writeBufferSize options. For strict durability, you can set the option syncOnFlush which will sync all flushes to disk before finishing, but comes at a very high performance penalty of course.

Note: If there are any misconceptions on my side to the ACID semantics, let me know.

Global order

Currently, the storage guarantees a consistent global ordering on all events by managing a global primary index. This makes sure that streams that are made up of multiple write-streams will stay consistent when re-reading all events. This has some issues though, like not being able to consistently reindex a storage, which is discussed in Therefore, this guarantee may be relaxed in later versions.

Event Streams

There are two slightly different concepts of Event Streams:

  • A write stream is a single identifier that an event/document is assigned to on write (see Partitioning). It is therefore a physical separation of the events that happens on write. An event written to a specific write stream can not be removed from it, it can only be linked to from other additional (read) streams.

  • A read stream is an ordered sequence in which specific events are iterated when reading. Every write stream automatically creates a read stream that will iterate the events in the order they were written to that stream. Additional read streams can be created that possibly even sequence events from multiple write streams. Such read streams can be deleted without problem, since they will not actually delete the events, but just the specific iteration sequence.

An Event Stream is implemented as an iterator over an storage index. It is therefore limited to iterating the events at the point the Event Stream was retrieved, but can be limited to a specific range of events, denoted by min/max revision. It implements the node ReadableStream interface.


By default, the Event Store is partitioned on (write) streams, so every unique stream name is written to a separate file. This has several consequences:

  • subsequent reads from a single write stream are faster, because the events share more locality
  • every write stream has it's own write and read buffer, hence interleaved writes/reads will not trash the buffers
  • since writes are buffered, only writes within a single write stream will be flushed together, hence "transactionality" is not spread over streams
  • the amount of write streams is limited by the amount of files the filesystem can handle inside a single folder
  • if hard disk is configured for file based RAID, this will most likely lead to unbalanced load

If required, the partitioning behaviour can be configured with the partitioner option, which is a method with following signature: (string:document, number:sequenceNumber) -> string:partitionName i.e. it maps a document and it's sequence number to a partition name. That way you could for example easily distribute all writes equally among a fixed number of arbitrary partitions by doing (document, sequenceNumber) => 'partition-' + (sequenceNumber % maxPartitions). This is not recommended in the generic case though, since it contradicts the consistency boundary that a single stream should give. Many databases partition the data into Chunks (striding) of a fixed size, which helps with disk performance especially in RAID setups. However, since SSDs become more the standard, the benefit of chunking data is becoming more limited. It does help with incremental backup strategies, or for use cases where old data needs to be archived or even deleted. For those cases, the partitioner could look like (document, sequenceNumber) -> 'partition' + (sequenceNumber / documentsPerChunk) >> 0, which will write documents into an ever increasing number of partitions. Or you partition by the document timestamp, which for an EventStore document could be taken from the committedAt field, which is a javascript timestamp. Optimally, you might want to make sure a commit is not spread among partitions though, so those partitioners are not fool-proof.

Custom Serialization

By default, the serialization will be achieved through JSON.stringify and JSON.parse. Those are plenty fast on recent nodejs versions, but JSON serialization takes more space than more optimized formats. You could use some other library, like @msgpack/msgpack to have performant, but space-safing data format. In benchmarks, @msgpack/msgpack even turns out faster than JSON.parse for deserialization and pretty much on par with JSON.stringify for serialization. The drawback is that the storage files are no longer human readable.

const { encode, decode } = require('@msgpack/msgpack');
const eventstore = new EventStore('my-event-store', {
    storageDirectory: './data',
    storageConfig: {
        serializer: {
            serialize: (doc) => {
                const encoded = encode(doc);
                return Buffer.from(encoded.buffer, encoded.byteOffset, encoded.byteLength).toString('binary');
            deserialize: (string) => {
                return decode(Buffer.from(string, 'binary'));


To apply compression on the storage level, the serializer option of the Storage can be used.

For example to use LZ4:

const lz4 = require('lz4');
const eventstore = new EventStore('my-event-store', {
    storageDirectory: './data',
    storageConfig: {
        serializer: {
            serialize: (doc) => {
                return lz4.encode(Buffer.from(JSON.stringify(doc))).toString('binary');
            deserialize: (string) => {
                return JSON.parse(lz4.decode(Buffer.from(string, 'binary')));

Since compression works on a per document level, compression efficiency is reduced. This is currently necessary to allow fully random access of single documents without having to read a large block before. If available, use a dictionary for the compression library and fill it with common words that describe your event/document schema and the following terms:

  • "metadata":{"commitId":
  • ,"committedAt":
  • ,"commitVersion":
  • ,"commitSize":
  • ,"streamVersion":


When specifying a matcher function for streams/indexes those matcher functions will be serialized into the index file and be eval'd on later loading for convenience to not having to specify the matcher when reopening. In order to prevent some malicious attacker from executing arbitrary code in your application by altering an index file, the matcher function gets fingerprinted with an HMAC. This HMAC is calculated with a secret that you should specify with the hmacSecret option of the storage configuration.

Currently the hmacSecret is an optional parameter defaulting to an empty string, which is unsecure, so always specify an own unique random secret for this in production.

Alternatively you should always explicitly specify your matchers when opening an existing index, since that will check that the specified matcher matches the one in the index file.