JITDB

A database on top of async-append-only-log with automatic index generation and maintenance.

The motivation for this database is that it should be:

• fast
• easy to understand
• run in the browser and in node

Async append only log takes care of persistance of the main log. It is expected to use bipf to encode data. On top of this, JITDB lazily creates and maintains indexes based on the way the data is queried. Meaning if you search for messages of type post an author x two indexes will be created the first time. One for type and one for author. Specific indexes will only updated when it is queried again. These indexes are tiny compared to normal flume indexes. An index of type post is 80kb.

For this to be feasible it must be really fast to do a full log scan. It turns out that the combination of push streams and bipf makes streaming the full log not much slower than reading the file. Meaning a 350mb log can be scanned in a few seconds.

While this is mainly aimed as a query engine, it is possible to base other indexes types on top of this, such as a reduce index on contact messages.

API

Setup

Before using JITDB, you have to setup an instance of async-append-only-log located at a certain path. Then you can instantiate JITDB, and it requires a path to the directory where the indexes will live.

const Log = require('async-append-only-log')
const JITDB = require('jitdb')

const raf = Log('/home/me/path/to/async-log', {
  blockSize: 64 * 1024,
})
const db = JITDB(raf, '/home/me/path/to/indexes')

db.onReady(() => {
  // The db is ready to be queried
})

Operators

JITDB comes with a set of composable "operators" that allow you to query the database. You can load these operators from require('jitdb/operators').

const Log = require('async-append-only-log')
const JITDB = require('jitdb')
const {query, fromDB, where, slowEqual, toCallback} = require('jitdb/operators')

const raf = Log('/home/me/path/to/async-log', {
  blockSize: 64 * 1024,
})
const db = JITDB(raf, '/home/me/path/to/indexes')

db.onReady(() => {
  query(
    fromDB(db),
    where(slowEqual('value.content.type', 'post')),
    toCallback((err, msgs) => {
      console.log(msgs)
    })
  )
})

The essential operators are fromDB, query, and toCallback.

• fromDB specifies which JITDB instance we are interested in
• query wraps all the operators, chaining them together
• where wraps descriptor operators (see below) that narrow down the data
• toCallback delivers the results of the query to a callback

Then there are descriptor operator that help scope down the results to your desired set of messages: and, or, not, equal, slowEqual, and others.

• and(...args) filters for messages that satisfy all args
• or(...args) filters for messages that satisfy at least one of the args
• not(arg) filters for messages that do not safisfy arg
• equal(seek, value, opts) filters for messages where a seeked field matches a specific value:
  • seek is a function that takes a bipf buffer as input and uses bipf.seekKey to return a pointer to the field
  • value is a string or buffer which is the value we want the field's value to match
  • opts are additional configurations:
    • indexType is a name used to identify the index produced by this query
    • prefix boolean or number 32 that tells this query to use prefix indexes
• slowEqual(objPath, value, opts) is a more ergonomic (but slower) way of performing equal:
  • objPath a string in the shape "foo.bar.baz" which specifies the nested field "baz" inside "bar" inside "foo"
  • value is the same as value in the equal operator
  • opts same as the opts for equal()
• includes(seek, value, opts) filters for messages where a seeked field is an array and includes a specific value
• slowIncludes(objPath, value, opts) is to includes what slowEqual is to equal
• predicate(seek, fn, opts) filters for messages where a seeked field is passed to a predicate function fn and the fn returns true
  • opts are additional configurations such as indexType and name. You SHOULD pass opts.name as a simple string uniquely identifying the predicate, OR the fn function should be a named function
• slowPredicate(objPath, fn, opts) is to predicate what slowEqual is to equal
• absent(seek, opts) filters for messages where a seeked field does not exist in the message
• slowAbsent(objPath) is to absent what slowEqual is to equal

Some examples:

Get all messages of type post:

query(
  fromDB(db),
  where(slowEqual('value.content.type', 'post')),
  toCallback((err, msgs) => {
    console.log('There are ' + msgs.length + ' messages of type "post"')
  })
)

Same as above but faster performance (recommended in production):

query(
  fromDB(db),
  where(equal(seekType, 'post', { indexType: 'type' })),
  toCallback((err, msgs) => {
    console.log('There are ' + msgs.length + ' messages of type "post"')
  })
)

// The `seekType` function takes a buffer and uses `bipf` APIs to search for
// the fields we want.
const bValue = Buffer.from('value') // better for performance if defined outside
const bContent = Buffer.from('content')
const bType = Buffer.from('type')
function seekType(buffer) {
  var p = 0 // p stands for "position" in the buffer, offset from start
  p = bipf.seekKey(buffer, p, bValue)
  if (p < 0) return
  p = bipf.seekKey(buffer, p, bContent)
  if (p < 0) return
  return bipf.seekKey(buffer, p, bType)
}

Get all messages of type contact from Alice or Bob:

query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact'),
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId))
    )
  ),
  toCallback((err, msgs) => {
    console.log('There are ' + msgs.length + ' messages')
  })
)

Same as above but faster performance (recommended in production):

query(
  fromDB(db),
  where(
    and(
      equal(seekType, 'contact', 'type')
      or(
        equal(seekAuthor, aliceId, { indexType: 'author' }),
        equal(seekAuthor, bobId, { indexType: 'author' })
      )
    )
  ),
  toCallback((err, msgs) => {
    console.log('There are ' + msgs.length + ' messages')
  })
)

// where seekAuthor is
const bValue = Buffer.from('value') // better for performance if defined outside
const bAuthor = Buffer.from('author')
function seekAuthor(buffer) {
  var p = 0
  p = bipf.seekKey(buffer, p, bValue)
  if (p < 0) return
  return bipf.seekKey(buffer, p, bAuthor)
}

Pagination

If you use toCallback, it gives you all results in one go. If you want to get results in batches, you should use toPullStream, paginate, and optionally startFrom and descending.

• toPullStream creates a pull-stream source to stream the results
• paginate configures the size of each array sent to the pull-stream source
• startFrom configures the beginning seq from where to start streaming
• descending configures the pagination stream to order results from newest to oldest (otherwise the default order is oldest to newest) based on timestamp

Example, stream all messages of type contact from Alice or Bob in pages of size 10:

const pull = require('pull-stream')

const source = query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact')
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId)),
    ),
  ),
  paginate(10),
  toPullStream()
)

pull(
  source,
  pull.drain((msgs) => {
    console.log('next page')
    console.log(msgs)
  })
)

Stream all messages of type contact from Alice or Bob in pages of size 10, starting from the 15th message, sorted from newest to oldest:

const pull = require('pull-stream')

const source = query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact')
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId)),
    ),
  ),
  paginate(10),
  startFrom(15),
  descending(),
  toPullStream()
)

pull(
  source,
  pull.drain((msgs) => {
    console.log('next page:')
    console.log(msgs)
  })
)

Batching with the operator batch() is similar to pagination in terms of performance, but the messages are delivered one-by-one to the final pull-stream, instead of as any array. Example:

const pull = require('pull-stream')

const source = query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact')
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId)),
    ),
  ),
  batch(10), // Note `batch` instead of `paginate`
  descending(),
  toPullStream()
)

pull(
  source,
  // Note the below drain is `msg`, not `msgs` array:
  pull.drain((msg) => {
    console.log('next message:')
    console.log(msg)
  })
)

async/await

There are also operators that support getting the values using await. toPromise is like toCallback, delivering all results at once:

const msgs = await query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact')
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId)),
    ),
  ),
  toPromise()
)

console.log('There are ' + msgs.length + ' messages')

With pagination, toAsyncIter is like toPullStream, streaming the results in batches:

const results = query(
  fromDB(db),
  where(
    and(
      slowEqual('value.content.type', 'contact')
      or(slowEqual('value.author', aliceId), slowEqual('value.author', bobId)),
    ),
  ),
  paginate(10),
  startFrom(15),
  toAsyncIter()
)

for await (let msgs of results) {
  console.log('next page:')
  console.log(msgs)
}

Custom indexes and deferred operator

There may be custom indexes external to JITDB, in which case you should convert the results from those indexes to offsets() or seqs() (read more about these in the low level API section). In those cases, the OFFSETS or SEQS are often received asynchronously. To support piping these async results in the query chain, we have the deferred() operator which postpones the fetching of results from your custom index, but allows you to compose operations nevertheless.

// operator
deferred(task)

where task is any function of the format

function task(meta, cb[, onAbort])

where meta is an object containing an instance of JITDB and other metadata, and onAbort is an optional function that you can pass an abort listener (i.e. onAbort(() => { /* cancel my stuff */ })).

As an example, suppose you have a custom index that returns seqs 11, 13 and 17, and you want to include these results into your operator chain, to AND them with a specific author. Use deferred like this:

query(
  fromDB(db),
  deferred((meta, cb) => {
    // do something asynchronously, then deliver results to cb
    cb(null, seqs([11, 13, 17]))
  }),
  where(slowEqual('value.author', aliceId)),
  toCallback((err, results) => {
    console.log(results)
  })
)

All operators

This is a list of all the operators supported so far:

const {
  fromDB,
  query,
  where,
  and,
  or,
  not,
  equal,
  slowEqual,
  predicate,
  slowPredicate,
  absent,
  slowAbsent,
  includes,
  slowIncludes,
  gt,
  gte,
  lt,
  lte,
  deferred,
  liveSeqs,
  seqs,
  offsets,
  count,
  paginate,
  batch,
  startFrom,
  descending,
  asOffsets,
  sortByArrival,
  debug,
  toCallback,
  toPullStream,
  toPromise,
  toAsyncIter,
} = require('jitdb/operators')

Prefix indexes

Most indexes in JITDB are bitvectors, which are suitable for answering boolean queries such as "is this msg a post?" or "is this msg from author A?". For each of these queries, JITDB creates one file.

This is fine for several cases, but some queries are not boolean. Queries on bitvectors such as "is this msg a reply to msg X?" can end up generating N files if the "msg X" can have N different values. The creation of indexes is this case becomes the overhead.

Prefix indexes help in that case because they can answer non-boolean queries with multiple different values but using just one index file. For example, for N different values of "msg X", just one prefix index is enough for answering "is this msg a reply to msg X?".

The way prefix indexes work is that for each message in the log, it picks the first 32 bits of a field in the message (hence 'prefix') and then compares your desired value with all of these prefixes. It doesn't store the whole value because that could turn out wasteful in storage and memory as the log scales (to 1 million or more messages). Storing just a prefix is not enough for uniqueness, though, as different values will have the same prefix, so queries on prefix indexes will create false positives, but JITDB does an additional check so in the resulting query, you will not get false positives.

Rule of thumb: use prefix indexes in an EQUAL operation only when the target value of your EQUAL can dynamically assume many (more than a dozen) possible values.

An additional option useMap can be provided that will store the prefix as a map instead of an array. The map can be seen as an inverted index that allows for faster queries at the cost of extra space. Maps don't store empty values meaning they are also a good fit for sparce indexes such as vote links.

It is possible specifiy where in the target the prefix buffer should be constructed from using prefixOffset. This is useful for targets that starts with a common prefix such as % in order to increase the information amount.

Low-level API

First some terminology: offset refers to the byte position in the log of a message. Seq refers to the 0-based position of a message in the log.

paginate(operation, seq, limit, descending, onlyOffset, sortBy, latestMsgKeyPrecompaction, cb)

Query the database returning paginated results. If one or more indexes doesn't exist or are outdated, the indexes will be updated before the query is run. onlyOffset can be used to return offset instead of the actual messages. sortBy determines what timestamp to use for ordering messages. Can take values declared or arrival. declared refers to the timestamp for when a message was created, while arrival refers to when a message was added to the database. This can be important for messages from other peers that might arrive out of order compared when they were created.

latestMsgKeyPrecompaction is used internally, and it's safe to just pass null as its value when you're calling paginate.

The result is an object with the fields:

• data: the actual messages
• total: the total number of messages
• duration: the number of ms the query took

Operation can be of the following types:

| type | data | | ------------- | -------------------------------------------- | | EQUAL | { seek, value, indexType, indexAll, prefix } | | GT,GTE,LT,LTE | { indexName, value } | | OFFSETS | { offsets } | | SEQS | { seqs } | | NOT | [operation] | | AND | [operation, operation] | | OR | [operation, operation] |

seek is a function that takes a buffer from the database as input and returns an index in the buffer from where a value can be compared to the value given. value must be a bipf encoded value, usually the equal operator will take care of that. A field not being defined at a point in the buffer is equal to undefined. prefix enables the use of prefix indexes for this operation. indexType is used to group indexes of the same type. If indexAll is specified and no index of the type and value exists, then instead of only this index being created, missing indexes for all possible values given the seek pointer will be created. This can be particular useful for data where there number of different values are rather small, but still larger than a few. One example is author or feeds in SSB, a typical database of 1 million records will have roughly 700 authors. The biggest cost in creating the indexes is traversing the database, so creating all indexes in one go instead of several hundreds is a lot faster.

For GT, GTE, LT and LTE, indexName can be either sequence or timestamp.

SEQS and OFFSETS allow one to use seq and offset (respectively) positions into the log file as query operators. This is useful for interfacing with data indexed by something else than JITDB. Seqs are faster as they can be combined in queries directly.

Example

{
  type: 'AND',
  data: [
    { type: 'EQUAL', data: { seek: db.seekType, value: 'post', indexType: "type" } },
    { type: 'EQUAL', data: { seek: db.seekAuthor, value: '@6CAxOI3f+LUOVrbAl0IemqiS7ATpQvr9Mdw9LC4+Uv0=.ed25519', indexType: "author" } }
  ]
}

I considered adding an option to return raw buffers in order to do some after processing that you wouldn't create and index for, but the overhead of decoding the buffers is small enough that I don't think it makes sense.

all(operation, seq, descending, onlyOffset, sortBy, cb)

Similar to paginate except there is no limit argument and the result will be the messages directly.

count(operation, seq, descending, cb)

Similar to all except it does not fetch records from the log, it only responds with a number that tells the total amount of records matching the operation.

prepare(operation, cb)

Ensures that the indexes in the operation are up-to-date by creating or updating them, if necessary. This is not a query, as it won't return any results. When done, the callback cb is just called with the "duration" of index preparation as the second argument.

lookup(operation, seq, cb)

Given one seq, lookup its corresponding value on the index specified by operation, which is either an operation object or a string for the name of a core index, such as 'seq' and 'timestamp'. The callback cb is called with the index's value (at that seq position) in the 2nd arg.

live(operation, cb)

Will setup a pull stream and this in cb. The pull stream will emit new values as they are added to the underlying log. This is meant to run after paginate or all.

Please note the index is not updated when using this method and only one live seqs stream is supported.

reindex(offset, cb)

Reset all indexes to start from offset. This means that on the next query, messages from that offset and forward will be reindexed.

onReady(cb)

Will call when all existing indexes have been loaded.