• Each transaction efficiently creates a new "copy" of the database, and past copies can still be read from and reverted to.
• Supports storing in a single file as well as purely in-memory use.
• Runs as a library (embedded in process).
• Incrementally reads and writes, so file-based databases can contain larger-than-memory datasets.
• Reads never block writes, and a database can be read from multiple threads/processes without locks.
• No query engine of any kind. You just write data structures (primarily an ArrayList and HashMap) that can be nested arbitrarily.
• No dependencies besides the JavaScript standard library.
• Fully synchronous API — no async/await needed.
• Available on npm.

This database was originally made for the xit version control system, but I bet it has a lot of potential for other projects. The combination of being immutable and having an API similar to in-memory data structures is pretty powerful. Consider using it instead of SQLite for your TypeScript projects: it's simpler, it's pure TypeScript, and it creates no impedance mismatch with your program the way SQL databases do.

• Example
• Initializing a Database
• Types
• Cloning and Undoing
• Sorting and Paginating
• Large Byte Arrays
• Iterators
• Hashing
• Compaction

Example

In this example, we create a new database, write some data in a transaction, and read the data afterwards.

// init the db
using core = new CoreBufferedFile('main.db');
const hasher = new Hasher('SHA-1');
const db = new Database(core, hasher);

// to get the benefits of immutability, the top-level data structure
// must be an ArrayList, so each transaction is stored as an item in it
const history = new WriteArrayList(db.rootCursor());

// this is how a transaction is executed. we call history.appendContext,
// providing it with the most recent copy of the db and a context
// function. the context function will run before the transaction has
// completed. this function is where we can write changes to the db.
// if any error happens in it, the transaction will not complete and
// the db will be unaffected.
//
// after this transaction, the db will look like this if represented
// as JSON (in reality the format is binary):
//
// {"foo": "foo",
//  "bar": "bar",
//  "fruits": ["apple", "pear", "grape"],
//  "people": [
//    {"name": "Alice", "age": 25},
//    {"name": "Bob", "age": 42}
//  ]}
history.appendContext(history.getSlot(-1), (cursor) => {
  const moment = new WriteHashMap(cursor);

  moment.put('foo', new Bytes('foo'));
  moment.put('bar', new Bytes('bar'));

  const fruitsCursor = moment.putCursor('fruits');
  const fruits = new WriteArrayList(fruitsCursor);
  fruits.append(new Bytes('apple'));
  fruits.append(new Bytes('pear'));
  fruits.append(new Bytes('grape'));

  const peopleCursor = moment.putCursor('people');
  const people = new WriteArrayList(peopleCursor);

  const aliceCursor = people.appendCursor();
  const alice = new WriteHashMap(aliceCursor);
  alice.put('name', new Bytes('Alice'));
  alice.put('age', new Uint(25));

  const bobCursor = people.appendCursor();
  const bob = new WriteHashMap(bobCursor);
  bob.put('name', new Bytes('Bob'));
  bob.put('age', new Uint(42));
});

// get the most recent copy of the database, like a moment
// in time. the -1 index will return the last index in the list.
const momentCursor = history.getCursor(-1);
const moment = new ReadHashMap(momentCursor!);

// we can read the value of "foo" from the map by getting
// the cursor to "foo" and then calling readBytes on it
const fooCursor = moment.getCursor('foo');
const fooValue = fooCursor!.readBytes(MAX_READ_BYTES);
assert.strictEqual(new TextDecoder().decode(fooValue), 'foo');

// to get the "fruits" list, we get the cursor to it and
// then pass it to the ReadArrayList constructor
const fruitsCursor = moment.getCursor('fruits');
const fruits = new ReadArrayList(fruitsCursor!);
assert.strictEqual(fruits.count(), 3);

// now we can get the first item from the fruits list and read it
const appleCursor = fruits.getCursor(0);
const appleValue = appleCursor!.readBytes(MAX_READ_BYTES);
assert.strictEqual(new TextDecoder().decode(appleValue), 'apple');

Initializing a Database

A Database is initialized with an implementation of the Core interface, which determines how the i/o is done. There are three implementations of Core in this library: CoreBufferedFile, CoreFile, and CoreMemory.

• CoreBufferedFile databases, like in the example above, write to a file while using an in-memory buffer to dramatically improve performance. This is highly recommended if you want to create a file-based database.
• CoreFile databases use no buffering when reading and writing data. This is almost never necessary but it's useful as a benchmark comparison with CoreBufferedFile databases.
• CoreMemory databases work completely in memory.

Usually, you want to use a top-level ArrayList like in the example above, because that allows you to store a reference to each copy of the database (which I call a "moment"). This is how it supports transactions, despite not having any rollback journal or write-ahead log. It's an append-only database, so the data you are writing is invisible to any reader until the very last step, when the top-level list's header is updated.

You can also use a top-level HashMap, which is useful for ephemeral databases where immutability or transaction safety isn't necessary. Since xitdb supports in-memory databases, you could use it as an over-the-wire serialization format. Much like "Cap'n Proto", xitdb has no encoding/decoding step: you just give the buffer to xitdb and it can immediately read from it.

Types

In xitdb there are a variety of immutable data structures that you can nest arbitrarily:

• HashMap contains key-value pairs stored with a hash
• HashSet is like a HashMap that only sets the keys; it is useful when only checking for membership
• CountedHashMap and CountedHashSet are just a HashMap and HashSet that maintain a count of their contents
• ArrayList is a growable array
• LinkedArrayList is like an ArrayList that can also be efficiently sliced and concatenated
• SortedMap and SortedSet are like a HashMap and HashSet where the keys are byte arrays kept in lexicographic order

The Hash-based data structures and the Arraylist use the hash array mapped trie, invented by Phil Bagwell (originally made immutable and widely available by Rich Hickey in Clojure). The LinkedArrayList, SortedMap, and SortedSet are based on a B-tree.

There are also scalar types you can store in the above-mentioned data structures:

• Bytes is a byte array
• Uint is an unsigned 64-bit int
• Int is a signed 64-bit int
• Float is a 64-bit float

You may also want to define custom types. For example, you may want to store a big integer that can't fit in 64 bits. You could just store this with Bytes, but when reading the byte array there wouldn't be any indication that it should be interpreted as a big integer.

In xitdb, you can optionally store a format tag with a byte array. A format tag is a 2 byte tag that is stored alongside the byte array. Readers can use it to decide how to interpret the byte array. Here's an example of storing a random 256-bit number with bi as the format tag:

const randomBytes = new Uint8Array(32);
crypto.getRandomValues(randomBytes);
moment.put('random-number', new Bytes(randomBytes, new TextEncoder().encode('bi')));

Then, you can read it like this:

const randomNumberCursor = moment.getCursor('random-number');
const randomNumber = randomNumberCursor!.readBytesObject(MAX_READ_BYTES);
assert.strictEqual(new TextDecoder().decode(randomNumber.formatTag!), 'bi');
const randomBigInt = randomNumber.value;

There are many types you may want to store this way. Maybe an ISO-8601 date like 2026-01-01T18:55:48Z could be stored with dt as the format tag. It's also great for storing custom objects. Just define the object, serialize it as a byte array using whatever mechanism you wish, and store it with a format tag. Keep in mind that format tags can be any 2 bytes, so there are 65536 possible format tags.

Cloning and Undoing

A powerful feature of immutable data is fast cloning. Any data structure can be instantly cloned and changed without affecting the original. Starting with the example code above, we can make a new transaction that creates a "food" list based on the existing "fruits" list:

history.appendContext(history.getSlot(-1), (cursor) => {
  const moment = new WriteHashMap(cursor);

  const fruitsCursor = moment.getCursor('fruits');
  const fruits = new ReadArrayList(fruitsCursor!);

  // create a new key called "food" whose initial value is
  // based on the "fruits" list
  const foodCursor = moment.putCursor('food');
  foodCursor.write(fruits.slot());

  const food = new WriteArrayList(foodCursor);
  food.append(new Bytes('eggs'));
  food.append(new Bytes('rice'));
  food.append(new Bytes('fish'));
});

const momentCursor = history.getCursor(-1);
const moment = new ReadHashMap(momentCursor!);

// the food list includes the fruits
const foodCursor = moment.getCursor('food');
const food = new ReadArrayList(foodCursor!);
assert.strictEqual(food.count(), 6);

// ...but the fruits list hasn't been changed
const fruitsCursor = moment.getCursor('fruits');
const fruits = new ReadArrayList(fruitsCursor!);
assert.strictEqual(fruits.count(), 3);

Before we continue, let's save the latest history index, so we can revert back to this moment of the database later:

const historyIndex = history.count() - 1;

There's one catch you'll run into when cloning. If we try cloning a data structure that was created in the same transaction, it doesn't seem to work:

history.appendContext(history.getSlot(-1), (cursor) => {
  const moment = new WriteHashMap(cursor);

  const bigCitiesCursor = moment.putCursor('big-cities');
  const bigCities = new WriteArrayList(bigCitiesCursor);
  bigCities.append(new Bytes('New York, NY'));
  bigCities.append(new Bytes('Los Angeles, CA'));

  // create a new key called "cities" whose initial value is
  // based on the "big-cities" list
  const citiesCursor = moment.putCursor('cities');
  citiesCursor.write(bigCities.slot());

  const cities = new WriteArrayList(citiesCursor);
  cities.append(new Bytes('Charleston, SC'));
  cities.append(new Bytes('Louisville, KY'));
});

const momentCursor = history.getCursor(-1);
const moment = new ReadHashMap(momentCursor!);

// the cities list contains all four
const citiesCursor = moment.getCursor('cities');
const cities = new ReadArrayList(citiesCursor!);
assert.strictEqual(cities.count(), 4);

// ..but so does big-cities! we did not intend to mutate this
const bigCitiesCursor = moment.getCursor('big-cities');
const bigCities = new ReadArrayList(bigCitiesCursor!);
assert.strictEqual(bigCities.count(), 4);

The reason that big-cities was mutated is because all data in a given transaction is temporarily mutable. This is a very important optimization, but in this case, it's not what we want.

To show how to fix this, let's first undo the transaction we just made. Here we use the historyIndex we saved before to revert back to the older database moment:

history.append(history.getSlot(historyIndex)!);

This time, after making the "big cities" list, we call freeze, which tells xitdb to consider all data made so far in the transaction to be immutable. After that, we can clone it into the "cities" list and it will work the way we wanted:

history.appendContext(history.getSlot(-1), (cursor) => {
  const moment = new WriteHashMap(cursor);

  const bigCitiesCursor = moment.putCursor('big-cities');
  const bigCities = new WriteArrayList(bigCitiesCursor);
  bigCities.append(new Bytes('New York, NY'));
  bigCities.append(new Bytes('Los Angeles, CA'));

  // freeze here, so big-cities won't be mutated
  cursor.db.freeze();

  // create a new key called "cities" whose initial value is
  // based on the "big-cities" list
  const citiesCursor = moment.putCursor('cities');
  citiesCursor.write(bigCities.slot());

  const cities = new WriteArrayList(citiesCursor);
  cities.append(new Bytes('Charleston, SC'));
  cities.append(new Bytes('Louisville, KY'));
});

const momentCursor = history.getCursor(-1);
const moment = new ReadHashMap(momentCursor!);

// the cities list contains all four
const citiesCursor = moment.getCursor('cities');
const cities = new ReadArrayList(citiesCursor!);
assert.strictEqual(cities.count(), 4);

// and big-cities only contains the original two
const bigCitiesCursor = moment.getCursor('big-cities');
const bigCities = new ReadArrayList(bigCitiesCursor!);
assert.strictEqual(bigCities.count(), 2);

Sorting and Paginating

The Hash-based structures are great for looking data up by key, but they store their contents in hash order, which is meaningless to a human. You may need to display data in a sensible order (like newest posts first or users by signup date) and show it one page at a time. Relational databases like SQLite have this built-in: you declare a CREATE INDEX, write ORDER BY created_ts LIMIT 20 OFFSET 40, and the query planner maintains the index and seeks into it for you.

In xitdb there are no built-in indexes, so you build and maintain them yourself. That's a little more code, but the index is just another data structure: a SortedMap whose keys are crafted to sort the way you want. You keep it in sync by writing to it in the same transaction that writes the primary data.

Let's model the storage a basic blog would need: a collection of posts we look up by id, plus a secondary index that lets us list them oldest-first with pagination. The primary store is a HashMap from post id to the post's fields (like a row keyed by its primary key). The secondary index is a SortedMap keyed by creation time, whose value is the post id to look up.

The trick is the key. A SortedMap orders its keys lexicographically by their raw bytes, so we encode the timestamp as a big-endian integer (so byte order matches chronological order) and append the post id to break ties between posts created in the same second and keep every key unique:

// build a SortedMap key that sorts by creation time. the big-endian
// timestamp makes byte order match chronological order; the post id is
// appended so two posts with the same timestamp still get distinct keys.
function orderKey(timestamp: number, postId: Uint8Array): Uint8Array {
  const key = new Uint8Array(8 + postId.length);
  new DataView(key.buffer).setBigUint64(0, BigInt(timestamp), false); // false = big-endian
  key.set(postId, 8);
  return key;
}

Now we write some posts. On each insert we write the post into the primary map and add an entry to the secondary index (keeping both in sync is your job, not the database's):

interface Post {
  id: string;
  title: string;
  createdTs: number;
}

// post ids are fixed-length so the timestamp tie-breaker stays aligned
const newPosts: Post[] = [
  { id: 'post000000000001', title: 'Hello, world', createdTs: 1_700_000_000 },
  { id: 'post000000000002', title: 'Second post', createdTs: 1_700_000_500 },
  { id: 'post000000000003', title: 'Third post', createdTs: 1_700_001_000 },
];

history.appendContext(history.getSlot(-1), (cursor) => {
  const moment = new WriteHashMap(cursor);

  // the primary store: a HashMap from post id to the post's fields
  const idToPostCursor = moment.putCursor('id->post');
  const idToPost = new WriteHashMap(idToPostCursor);

  // the secondary index: a SortedMap ordered by creation time. there's
  // no CREATE INDEX here, so we maintain it ourselves on every write.
  const createdTsToPostIdCursor = moment.putCursor('created-ts->post-id');
  const createdTsToPostId = new WriteSortedMap(createdTsToPostIdCursor);

  for (const post of newPosts) {
    // write the post into the primary map under its id
    const postCursor = idToPost.putCursor(post.id);
    const postMap = new WriteHashMap(postCursor);
    postMap.put('title', new Bytes(post.title));
    postMap.put('created-ts', new Uint(post.createdTs));

    // add an entry to the secondary index. the key sorts by time,
    // and the value is the post id we'll use to look the post back up.
    const orderKeyBytes = orderKey(post.createdTs, new TextEncoder().encode(post.id));
    createdTsToPostId.put(orderKeyBytes, new Bytes(post.id));
  }
});

To display a page, we walk the SortedMap instead of the HashMap. A web app would take a pageSize and an after offset from the request (something like /posts?after=20), so this is the xitdb equivalent of ORDER BY created_ts LIMIT pageSize OFFSET after:

const momentCursor = history.getCursor(-1);
const moment = new ReadHashMap(momentCursor!);

const idToPostCursor = moment.getCursor('id->post');
const idToPost = new ReadHashMap(idToPostCursor!);

const createdTsToPostIdCursor = moment.getCursor('created-ts->post-id');
const createdTsToPostId = new ReadSortedMap(createdTsToPostIdCursor!);

// a web request would supply these; here we just grab the first page
const pageSize = 2;
const after = 0;

const count = createdTsToPostId.count();
const end = Math.min(after + pageSize, count);

// seek straight to the start of the page, then walk forward one entry at a
// time. because SortedMap is a count-augmented B+tree, iteratorFromIndex
// finds rank `after` in O(log n) without scanning the entries it skips, so
// jumping to page 500 is just as cheap as page 1.
const decoder = new TextDecoder();
const iter = createdTsToPostId.iteratorFromIndex(after);
for (let i = after; i < end && iter.hasNext(); i++) {
  const idCursor = iter.next()!;
  const idKv = idCursor.readKeyValuePair();

  // the index entry's value is the post id; use it to read the
  // full post out of the primary map
  const postId = decoder.decode(idKv.valueCursor.readBytes(MAX_READ_BYTES));

  const postCursor = idToPost.getCursor(postId);
  const postMap = new ReadHashMap(postCursor!);
  const titleCursor = postMap.getCursor('title');
  const title = decoder.decode(titleCursor!.readBytes(MAX_READ_BYTES));

  // a real app would render this into the page's HTML
  console.log(title);
}

This works for any ordering you need: sort by a username with a string key, by score with a big-endian integer key, or build several SortedMap indexes over the same primary HashMap to offer the data in different orders. With xitdb you "bring your own index". It takes a bit more effort than the declarative convenience of SQL databases, but it gives you more explicit control, and avoids the common problem in SQL where queries silently become inefficient due to not using indexes. In xitdb, inefficiency is hard to miss because you are always writing your queries as imperative code and the indexes are always explicit.

Large Byte Arrays

When reading and writing large byte arrays, you probably don't want to have all of their contents in memory at once. To incrementally write to a byte array, just get a writer from a cursor:

const longTextCursor = moment.putCursor('long-text');
const cursorWriter = longTextCursor.writer();
for (let i = 0; i < 50; i++) {
  cursorWriter.write(new TextEncoder().encode('hello, world\n'));
}
cursorWriter.finish(); // remember to call this!

If you need to set a format tag for the byte array, put it in the formatTag field of the writer before you call finish.

To read a byte array incrementally, get a reader from a cursor:

const longTextCursor = moment.getCursor('long-text');
const cursorReader = longTextCursor!.reader();
let lineCount = 0, line: number[] = [];
const buf = new Uint8Array(1024);
for (let n; (n = cursorReader.read(buf)) > 0; ) {
  for (let i = 0; i < n; i++) {
    if (buf[i] === 0x0A) { lineCount++; line = []; }
    else line.push(buf[i]);
  }
}
if (line.length > 0) lineCount++;
assert.strictEqual(lineCount, 50);

Iterators

All data structures support iteration. Here's an example of iterating over an ArrayList and printing all of the keys and values of each HashMap contained in it:

const peopleCursor = moment.getCursor('people');
const people = new ReadArrayList(peopleCursor!);

const peopleIter = people.iterator();
while (peopleIter.hasNext()) {
  const personCursor = peopleIter.next();
  const person = new ReadHashMap(personCursor!);
  const personIter = person.iterator();
  while (personIter.hasNext()) {
    const kvPairCursor = personIter.next();
    const kvPair = kvPairCursor!.readKeyValuePair();

    const key = new TextDecoder().decode(kvPair.keyCursor.readBytes(MAX_READ_BYTES));

    switch (kvPair.valueCursor.slot().tag) {
      case Tag.SHORT_BYTES:
      case Tag.BYTES:
        console.log(`${key}: ${new TextDecoder().decode(kvPair.valueCursor.readBytes(MAX_READ_BYTES))}`);
        break;
      case Tag.UINT:
        console.log(`${key}: ${kvPair.valueCursor.readUint()}`);
        break;
      case Tag.INT:
        console.log(`${key}: ${kvPair.valueCursor.readInt()}`);
        break;
      case Tag.FLOAT:
        console.log(`${key}: ${kvPair.valueCursor.readFloat()}`);
        break;
    }
  }
}

The above code iterates over people, which is an ArrayList, and for each person (which is a HashMap), it iterates over each of its key-value pairs.

The iteration of the HashMap looks the same with HashSet, CountedHashMap, and CountedHashSet. When iterating, you call readKeyValuePair on the cursor and can read the keyCursor and valueCursor from it. In maps, put sets the key and value. In sets, put only sets the key; the value will always have a tag type of NONE.

ArrayList and LinkedArrayList also have an iteratorFrom method, which starts the iterator from the given index. SortedMap and SortedSet have iteratorFrom and iteratorFromIndex to start the iterator from a key or index respectively. This is especially useful for pagination: you can seek straight to the start of a page and walk forward only as far as you need. See the Sorting and Paginating section for an example.

Hashing

The hashing data structures will create the hash for you when you call methods like put or getCursor and provide the key as a string or Bytes. If you want to do the hashing yourself, there is an overload of those methods that take a Uint8Array as the key, which should be the hash that you computed.

When initializing a database, you tell xitdb how to hash with the Hasher. If you're using SHA-1, it will look like this:

using core = new CoreBufferedFile('main.db');
const hasher = new Hasher('SHA-1');
const db = new Database(core, hasher);

The size of the hash in bytes will be stored in the database's header. If you try opening it later with a hashing algorithm that has the wrong hash size, it will throw an exception. If you are unsure what hash size the database uses, this creates a chicken-and-egg problem. You can read the header before initializing the database like this:

core.seek(0);
const header = Header.read(core);
assert.strictEqual(header.hashSize, 20);

The hash size alone does not disambiguate hashing algorithms, though. In addition, xitdb reserves four bytes in the header that you can use to put the name of the algorithm. You must provide it in the Hasher constructor:

const hasher = new Hasher('SHA-1', Hasher.stringToId('sha1'));

The hash id is only written to the database header when it is first initialized. When you open it later, the hash id in the Hasher is ignored. You can read the hash id of an existing database like this:

core.seek(0);
const header = Header.read(core);
assert.strictEqual(Hasher.idToString(header.hashId), "sha1");

If you want to use SHA-256, I recommend using sha2 as the hash id. You can then distinguish between SHA-256 and SHA-512 using the hash size, like this:

let hasher: Hasher;
const hashIdStr = Hasher.idToString(header.hashId);

switch (hashIdStr) {
  case 'sha1':
    hasher = new Hasher('SHA-1', header.hashId);
    break;
  case 'sha2':
    switch (header.hashSize) {
      case 32:
        hasher = new Hasher('SHA-256', header.hashId);
        break;
      case 64:
        hasher = new Hasher('SHA-512', header.hashId);
        break;
      default:
        throw new Error('Invalid hash size');
    }
    break;
  default:
    throw new Error('Invalid hash algorithm');
}

Compaction

Normally, an immutable database grows forever, because old data is never deleted. To reclaim disk space and clear the history, xitdb supports compaction. This involves completely rebuilding the database file to only contain the data accessible from the latest copy (i.e., "moment") of the database.

using compactCore = new CoreBufferedFile('compact.db');
const compactDb = db.compact(compactCore);

// read from the new compacted db
const history = new ReadArrayList(compactDb.rootCursor());
assert.strictEqual(history.count(), 1);

This compacted database will be in a separate file. If you want to delete the original database and replace it with this one, you'll need to do that yourself. It is not possible to compact a database in-place (using the same file as the target database); doing so would fail and would render your original database unreadable.