Indexing with Custom Tokenizers

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Dgraph comes with a large toolkit of builtin indexes, but sometimes for niche use cases they’re not always enough. Dgraph allows you to implement custom tokenizers via a plugin system in order to fill the gaps.

Caveats

The plugin system uses Go’s pkg/plugin. This brings some restrictions to how plugins can be used.

Plugins must be written in Go.
As of Go 1.9, pkg/plugin only works on Linux. Therefore, plugins will only work on Dgraph instances deployed in a Linux environment.
The version of Go used to compile the plugin should be the same as the version of Go used to compile Dgraph itself. Dgraph always uses the latest version of Go (and so should you!).

Implementing a plugin

You should consider Go’s plugin documentation to be supplementary to the documentation provided here.

Plugins are implemented as their own main package. They must export a particular symbol that allows Dgraph to hook into the custom logic the plugin provides. The plugin must export a symbol named Tokenizer. The type of the symbol must be func() interface{}. When the function is called the result returned should be a value that implements the following interface:

type PluginTokenizer interface {
    // Name is the name of the tokenizer. It should be unique among all
    // builtin tokenizers and other custom tokenizers. It identifies the
    // tokenizer when an index is set in the schema and when search/filter
    // is used in queries.
    Name() string

    // Identifier is a byte that uniquely identifiers the tokenizer.
    // Bytes in the range 0x80 to 0xff (inclusive) are reserved for
    // custom tokenizers.
    Identifier() byte

    // Type is a string representing the type of data that is to be
    // tokenized. This must match the schema type of the predicate
    // being indexed. Allowable values are shown in the table below.
    Type() string

    // Tokens should implement the tokenization logic. The input is
    // the value to be tokenized, and will always have a concrete type
    // corresponding to Type(). The return value should be a list of
    // the tokens generated.
    Tokens(interface{}) ([]string, error)
}

The return value of Type() corresponds to the concrete input type of Tokens(interface{}) in the following way:

`Type()` return value	`Tokens(interface{})` input type
`"int"`	`int64`
`"float"`	`float64`
`"string"`	`string`
`"bool"`	`bool`
`"datetime"`	`time.Time`

Building the plugin

The plugin has to be built using the plugin build mode so that an .so file is produced instead of a regular executable. For example:

go build -buildmode=plugin -o myplugin.so ~/go/src/myplugin/main.go

Running Dgraph with plugins

When starting Dgraph, use the --custom_tokenizers flag to tell Dgraph which tokenizers to load. It accepts a comma separated list of plugins. E.g.

dgraph ...other-args... --custom_tokenizers=plugin1.so,plugin2.so

Plugin validation is performed on startup. If a problem is detected, Dgraph will refuse to initialize.

Adding the index to the schema

To use a tokenization plugin, an index has to be created in the schema. The syntax is the same as adding any built-in index. To add an custom index using a tokenizer plugin named foo to a string predicate named my_predicate, use the following in the schema:

my_predicate: string @index(foo) .

Using the index in queries

There are two functions that can use custom indexes:

Mode	Behavior
`anyof`	Returns nodes that match on any of the tokens generated
`allof`	Returns nodes that match on all of the tokens generated

The functions can be used either at the query root or in filters. There behavior here an analogous to anyofterms/allofterms and anyoftext/alloftext.

Examples

The following examples should make the process of writing a tokenization plugin more concrete.

Unicode Characters

This example shows the type of tokenization that is similar to term tokenization of full-text search. Instead of being broken down into terms or stem words, the text is instead broken down into its constituent unicode codepoints (in Go terminology these are called runes).

This tokenizer would create a very large index that would be expensive to manage and store. That’s one of the reasons that text indexing usually occurs at a higher level; stem words for full-text search or terms for term search.

The implementation of the plugin looks like this:

package main

import "encoding/binary"

func Tokenizer() interface{} { return RuneTokenizer{} }

type RuneTokenizer struct{}

func (RuneTokenizer) Name() string     { return "rune" }
func (RuneTokenizer) Type() string     { return "string" }
func (RuneTokenizer) Identifier() byte { return 0xfd }

func (t RuneTokenizer) Tokens(value interface{}) ([]string, error) {
  var toks []string
  for _, r := range value.(string) {
    var buf [binary.MaxVarintLen32]byte
    n := binary.PutVarint(buf[:], int64(r))
    tok := string(buf[:n])
    toks = append(toks, tok)
  }
  return toks, nil
}

Hints and tips:

Inside Tokens, you can assume that value will have concrete type corresponding to that specified by Type(). It is safe to do a type assertion.
Even though the return value is []string, you can always store non-unicode data inside the string. See this blogpost for some interesting background how string are implemented in Go and why they can be used to store non-textual data. By storing arbitrary data in the string, you can make the index more compact. In this case, varints are stored in the return values.

Setting up the indexing and adding data:

name: string @index(rune) .

{
  set{
    _:ad <name> "Adam" .
    _:ad <dgraph.type> "Person" .
    _:aa <name> "Aaron" .
    _:aa <dgraph.type> "Person" .
    _:am <name> "Amy" .
    _:am <dgraph.type> "Person" .
    _:ro <name> "Ronald" .
    _:ro <dgraph.type> "Person" .
  }
}

Now queries can be performed. The only person that has all of the runes A and n in their name is Aaron:

{
  q(func: allof(name, rune, "An")) {
    name
  }
}
=>
{
  "data": {
    "q": [
      { "name": "Aaron" }
    ]
  }
}

But there are multiple people who have both of the runes A and m:

{
  q(func: allof(name, rune, "Am")) {
    name
  }
}
=>
{
  "data": {
    "q": [
      { "name": "Amy" },
      { "name": "Adam" }
    ]
  }
}

Case is taken into account, so if you search for all names containing "ron", you would find "Aaron", but not "Ronald". But if you were to search for "no", you would match both "Aaron" and "Ronald". The order of the runes in the strings doesn’t matter. It is possible to search for people that have any of the supplied runes in their names (rather than all of the supplied runes). To do this, use anyof instead of allof:

{
  q(func: anyof(name, rune, "mr")) {
    name
  }
}
=>
{
  "data": {
    "q": [
      { "name": "Adam" },
      { "name": "Aaron" },
      { "name": "Amy" }
    ]
  }
}

"Ronald" doesn’t contain m or r, so isn’t found by the search.

Understanding what’s going on under the hood can help you intuitively understand how Tokens method should be implemented.When Dgraph sees new edges that are to be indexed by your tokenizer, it will tokenize the value. The resultant tokens are used as keys for posting lists. The edge subject is then added to the posting list for each token.When a query root search occurs, the search value is tokenized. The result of the search is all of the nodes in the union or intersection of the corresponding posting lists (depending on whether anyof or allof was used).

CIDR Range

Tokenizers don’t always have to be about splitting text up into its constituent parts. This example indexes IP addresses into their CIDR ranges. This allows you to search for all IP addresses that fall into a particular CIDR range. The plugin code is more complicated than the rune example. The input is an IP address stored as a string, e.g. "100.55.22.11/32". The output are the CIDR ranges that the IP address could possibly fall into. There could be up to 32 different outputs ("100.55.22.11/32" does indeed have 32 possible ranges, one for each mask size).

package main

import "net"

func Tokenizer() interface{} { return CIDRTokenizer{} }

type CIDRTokenizer struct{}

func (CIDRTokenizer) Name() string     { return "cidr" }
func (CIDRTokenizer) Type() string     { return "string" }
func (CIDRTokenizer) Identifier() byte { return 0xff }

func (t CIDRTokenizer) Tokens(value interface{}) ([]string, error) {
  _, ipnet, err := net.ParseCIDR(value.(string))
  if err != nil {
    return nil, err
  }
  ones, bits := ipnet.Mask.Size()
  var toks []string
  for i := ones; i >= 1; i-- {
    m := net.CIDRMask(i, bits)
    tok := net.IPNet{
      IP:   ipnet.IP.Mask(m),
      Mask: m,
    }
    toks = append(toks, tok.String())
  }
  return toks, nil
}

An example of using the tokenizer: Setting up the indexing and adding data:

ip: string @index(cidr) .

{
  set{
    _:a <ip> "100.55.22.11/32" .
    _:b <ip> "100.33.81.19/32" .
    _:c <ip> "100.49.21.25/32" .
    _:d <ip> "101.0.0.5/32" .
    _:e <ip> "100.176.2.1/32" .
  }
}

{
  q(func: allof(ip, cidr, "100.48.0.0/12")) {
    ip
  }
}
=>
{
  "data": {
    "q": [
      { "ip": "100.55.22.11/32" },
      { "ip": "100.49.21.25/32" }
    ]
  }
}

The CIDR ranges of 100.55.22.11/32 and 100.49.21.25/32 are both 100.48.0.0/12. The other IP addresses in the database aren’t included in the search result, since they have different CIDR ranges for 12 bit masks (100.32.0.0/12, 101.0.0.0/12, 100.154.0.0/12 for 100.33.81.19/32, 101.0.0.5/32, and 100.176.2.1/32 respectively). Note that we’re using allof instead of anyof. Only allof will work correctly with this index. Remember that the tokenizer generates all possible CIDR ranges for an IP address. If we were to use anyof then the search result would include all IP addresses under the 1 bit mask (in this case, 0.0.0.0/1, which would match all IPs in this dataset).

Anagram

Tokenizers don’t always have to return multiple tokens. If you just want to index data into groups, have the tokenizer just return an identifying member of that group. In this example, we want to find groups of words that are anagrams of each other. A token to correspond to a group of anagrams could just be the letters in the anagram in sorted order, as implemented below:

package main

import "sort"

func Tokenizer() interface{} { return AnagramTokenizer{} }

type AnagramTokenizer struct{}

func (AnagramTokenizer) Name() string     { return "anagram" }
func (AnagramTokenizer) Type() string     { return "string" }
func (AnagramTokenizer) Identifier() byte { return 0xfc }

func (t AnagramTokenizer) Tokens(value interface{}) ([]string, error) {
  b := []byte(value.(string))
  sort.Slice(b, func(i, j int) bool { return b[i] < b[j] })
  return []string{string(b)}, nil
}

In action: Setting up the indexing and adding data:

word: string @index(anagram) .

{
  set{
    _:1 <word> "airmen" .
    _:2 <word> "marine" .
    _:3 <word> "beat" .
    _:4 <word> "beta" .
    _:5 <word> "race" .
    _:6 <word> "care" .
  }
}

{
  q(func: allof(word, anagram, "remain")) {
    word
  }
}
=>
{
  "data": {
    "q": [
      { "word": "airmen" },
      { "word": "marine" }
    ]
  }
}

Since a single token is only ever generated, it doesn’t matter if anyof or allof is used. The result will always be the same.

Integer prime factors

All of the custom tokenizers shown previously have worked with strings. However, other data types can be used as well. This example is contrived, but nonetheless shows some advanced usages of custom tokenizers. The tokenizer creates a token for each prime factor in the input.

package main

import (
    "encoding/binary"
    "fmt"
)

func Tokenizer() interface{} { return FactorTokenizer{} }

type FactorTokenizer struct{}

func (FactorTokenizer) Name() string     { return "factor" }
func (FactorTokenizer) Type() string     { return "int" }
func (FactorTokenizer) Identifier() byte { return 0xfe }

func (FactorTokenizer) Tokens(value interface{}) ([]string, error) {
    x := value.(int64)
    if x <= 1 {
        return nil, fmt.Errorf("Cannot factor int <= 1: %d", x)
    }
    var toks []string
    for p := int64(2); x > 1; p++ {
        if x%p == 0 {
            toks = append(toks, encodeInt(p))
            for x%p == 0 {
                x /= p
            }
        }
    }
    return toks, nil

}

func encodeInt(x int64) string {
    var buf [binary.MaxVarintLen64]byte
    n := binary.PutVarint(buf[:], x)
    return string(buf[:n])
}

Notice that the return of Type() is "int", corresponding to the concrete type of the input to Tokens (which is int64).

This allows you do things like search for all numbers that share prime factors with a particular number. In particular, we search for numbers that contain any of the prime factors of 15, that’s any numbers that are divisible by either 3 or 5. Setting up the indexing and adding data:

num: int @index(factor) .

{
  set{
    _:2 <num> "2"^^<xs:int> .
    _:3 <num> "3"^^<xs:int> .
    _:4 <num> "4"^^<xs:int> .
    _:5 <num> "5"^^<xs:int> .
    _:6 <num> "6"^^<xs:int> .
    _:7 <num> "7"^^<xs:int> .
    _:8 <num> "8"^^<xs:int> .
    _:9 <num> "9"^^<xs:int> .
    _:10 <num> "10"^^<xs:int> .
    _:11 <num> "11"^^<xs:int> .
    _:12 <num> "12"^^<xs:int> .
    _:13 <num> "13"^^<xs:int> .
    _:14 <num> "14"^^<xs:int> .
    _:15 <num> "15"^^<xs:int> .
    _:16 <num> "16"^^<xs:int> .
    _:17 <num> "17"^^<xs:int> .
    _:18 <num> "18"^^<xs:int> .
    _:19 <num> "19"^^<xs:int> .
    _:20 <num> "20"^^<xs:int> .
    _:21 <num> "21"^^<xs:int> .
    _:22 <num> "22"^^<xs:int> .
    _:23 <num> "23"^^<xs:int> .
    _:24 <num> "24"^^<xs:int> .
    _:25 <num> "25"^^<xs:int> .
    _:26 <num> "26"^^<xs:int> .
    _:27 <num> "27"^^<xs:int> .
    _:28 <num> "28"^^<xs:int> .
    _:29 <num> "29"^^<xs:int> .
    _:30 <num> "30"^^<xs:int> .
  }
}

{
  q(func: anyof(num, factor, 15)) {
    num
  }
}
=>
{
  "data": {
    "q": [
      { "num": 3 },
      { "num": 5 },
      { "num": 6 },
      { "num": 9 },
      { "num": 10 },
      { "num": 12 },
      { "num": 15 },
      { "num": 18 }
      { "num": 20 },
      { "num": 21 },
      { "num": 25 },
      { "num": 24 },
      { "num": 27 },
      { "num": 30 },
    ]
  }
}

Getting Started

Connecting

Query Language

GraphQL-based Development

Administration

Tools

Resources

Indexing with Custom Tokenizers

Caveats

Implementing a plugin

Building the plugin

Running Dgraph with plugins

Adding the index to the schema

Using the index in queries

Examples

Unicode Characters

CIDR Range

Anagram

Integer prime factors

Getting Started

Connecting

Query Language

GraphQL-based Development

Administration

Tools

Resources

​Caveats

​Implementing a plugin

​Building the plugin

​Running Dgraph with plugins

​Adding the index to the schema

​Using the index in queries

​Examples

​Unicode Characters

​CIDR Range

​Anagram

​Integer prime factors

Caveats

Implementing a plugin

Building the plugin

Running Dgraph with plugins

Adding the index to the schema

Using the index in queries

Examples

Unicode Characters

CIDR Range

Anagram

Integer prime factors