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README

vellum vellum

Tests Go Reference Go Report Card License

A Go library implementing an FST (finite state transducer) capable of:

  • mapping between keys ([]byte) and a value (uint64)
  • enumerating keys in lexicographic order

Some additional goals of this implementation:

  • bounded memory use while building the FST
  • streaming out FST data while building
  • mmap FST runtime to support very large FTSs (optional)

Usage

Building an FST

To build an FST, create a new builder using the New() method. This method takes an io.Writer as an argument. As the FST is being built, data will be streamed to the writer as soon as possible. With this builder you MUST insert keys in lexicographic order. Inserting keys out of order will result in an error. After inserting the last key into the builder, you MUST call Close() on the builder. This will flush all remaining data to the underlying writer.

In memory:

var buf bytes.Buffer
builder, err := vellum.New(&buf, nil)
if err != nil {
  log.Fatal(err)
}

To disk:

f, err := os.Create("/tmp/vellum.fst")
if err != nil {
  log.Fatal(err)
}
builder, err := vellum.New(f, nil)
if err != nil {
  log.Fatal(err)
}

MUST insert keys in lexicographic order:

err = builder.Insert([]byte("cat"), 1)
if err != nil {
  log.Fatal(err)
}

err = builder.Insert([]byte("dog"), 2)
if err != nil {
  log.Fatal(err)
}

err = builder.Insert([]byte("fish"), 3)
if err != nil {
  log.Fatal(err)
}

err = builder.Close()
if err != nil {
  log.Fatal(err)
}

Using an FST

After closing the builder, the data can be used to instantiate an FST. If the data was written to disk, you can use the Open() method to mmap the file. If the data is already in memory, or you wish to load/mmap the data yourself, you can instantiate the FST with the Load() method.

Load in memory:

fst, err := vellum.Load(buf.Bytes())
if err != nil {
  log.Fatal(err)
}

Open from disk:

fst, err := vellum.Open("/tmp/vellum.fst")
if err != nil {
  log.Fatal(err)
}

Get key/value:

val, exists, err = fst.Get([]byte("dog"))
if err != nil {
  log.Fatal(err)
}
if exists {
  fmt.Printf("contains dog with val: %d\n", val)
} else {
  fmt.Printf("does not contain dog")
}

Iterate key/values:

itr, err := fst.Iterator(startKeyInclusive, endKeyExclusive)
for err == nil {
  key, val := itr.Current()
  fmt.Printf("contains key: %s val: %d", key, val)
  err = itr.Next()
}
if err != nil {
  log.Fatal(err)
}

How does the FST get built?

A full example of the implementation is beyond the scope of this README, but let's consider a small example where we want to insert 3 key/value pairs.

First we insert "are" with the value 4.

step1

Next, we insert "ate" with the value 2.

step2

Notice how the values associated with the transitions were adjusted so that by summing them while traversing we still get the expected value.

At this point, we see that state 5 looks like state 3, and state 4 looks like state 2. But, we cannot yet combine them because future inserts could change this.

Now, we insert "see" with value 3. Once it has been added, we now know that states 5 and 4 can longer change. Since they are identical to 3 and 2, we replace them.

step3

Again, we see that states 7 and 8 appear to be identical to 2 and 3.

Having inserted our last key, we call Close() on the builder.

step4

Now, states 7 and 8 can safely be replaced with 2 and 3.

For additional information, see the references at the bottom of this document.

What does the serialized format look like?

We've broken out a separate document on the vellum disk format v1.

What if I want to use this on a system that doesn't have mmap?

The mmap library itself is guarded with system/architecture build tags, but we've also added an additional build tag in vellum. If you'd like to Open() a file based representation of an FST, but not use mmap, you can build the library with the nommap build tag. NOTE: if you do this, the entire FST will be read into memory.

Can I use this with Unicode strings?

Yes, however this implementation is only aware of the byte representation you choose. In order to find matches, you must work with some canonical byte representation of the string. In the future, some encoding-aware traversals may be possible on top of the lower-level byte transitions.

How did this library come to be?

In my work on the Bleve project I became aware of the power of the FST for many search-related tasks. The obvious starting point for such a thing in Go was the mafsa project. While working with mafsa I encountered some issues. First, it did not stream data to disk while building. Second, it chose to use a rune as the fundamental unit of transition in the FST, but I felt using a byte would be more powerful in the end. My hope is that higher-level encoding-aware traversals will be possible when necessary. Finally, as I reported bugs and submitted PRs I learned that the mafsa project was mainly a research project and no longer being maintained. I wanted to build something that could be used in production. As the project advanced more and more techniques from the BurntSushi/fst were adapted to our implementation.

Are there tools to work with vellum files?

Under the cmd/vellum subdirectory, there's a command-line tool which features subcommands that can allow you to create, inspect and query vellum files.

How can I generate a state transition diagram from a vellum file?

The vellum command-line tool has a "dot" subcommand that can emit graphviz dot output data from an input vellum file. The dot file can in turn be converted into an image using graphviz tools. Example...

vellum dot myFile.vellum > output.dot
dot -Tpng output.dot -o output.png

Related Work

Much credit goes to two existing projects:

Most of the original implementation here started with my digging into the internals of mafsa. As the implementation progressed, I continued to borrow ideas/approaches from the BurntSushi/fst library as well.

For a great introduction to this topic, please read the blog post Index 1,600,000,000 Keys with Automata and Rust

Extension points exported contracts — how you extend this code

Automaton (Interface)
Automaton represents the general contract of a byte-based finite automaton [4 implementers]
automaton.go
Distance (Interface)
Levenshtein Distance computed by a Levenshtein Automaton. Levenshtein automata can only compute the exact Levenshtein d [3 …
levenshtein/levenshtein_nfa.go
Iterator (Interface)
Iterator represents a means of visiting key/value pairs in order. [2 implementers]
fst_iterator.go
Transducer (Interface)
Transducer represents the general contract of a byte-based finite transducer [1 implementers]
transducer.go
MergeFunc (FuncType)
MergeFunc is used to choose the new value for a key when merging a slice of iterators, and the same key is observed with
merge_iterator.go
FuzzyAutomaton (Interface)
(no doc) [1 implementers]
automaton.go
FuzzyIterator (Interface)
(no doc) [2 implementers]
fst_iterator.go

Core symbols most depended-on inside this repo

Close
called by 54
fst_iterator.go
distance
called by 52
levenshtein/levenshtein_nfa.go
Current
called by 32
fst_iterator.go
Next
called by 32
fst_iterator.go
New
called by 28
vellum.go
Iterator
called by 27
fst.go
Insert
called by 22
builder.go
eval
called by 21
levenshtein/dfa.go

Shape

Method 240
Function 180
Struct 46
TypeAlias 12
Interface 10
FuncType 3

Languages

Go100%

Modules by API surface

builder.go34 symbols
levenshtein/levenshtein_nfa.go30 symbols
levenshtein/dfa.go28 symbols
levenshtein/parametric_dfa.go26 symbols
fst.go26 symbols
encoding.go23 symbols
decoder_v1.go22 symbols
utf8/utf8.go20 symbols
fst_iterator.go18 symbols
automaton.go16 symbols
regexp/compile.go14 symbols
vellum_test.go13 symbols

For agents

$ claude mcp add vellum \
  -- python -m otcore.mcp_server <graph>

⬇ download graph artifact