Expandable append-only immutable directed acyclic graph using linked lists.

There's no[1] way to get at the data if you throw away the reference to the entry. Once every entry is garbage collected the underlying data store will be as well, but until then, they all stay in memory.

The references are one-way. Parents can't reference children, so you can only walk up the DAG towards the root, not down it to the leaves.

This is a lowlevel fast implementation, suitable for storing immutable data that is intended to stick around for the lifetime of a task, and really not much else.

It's a tool for that one specific type of job.

"Who's a good dag! YOU ARE!!"

[1]: Technically that's not exactly true, you could poke around in[], but that's very "sticking your fingers into the beast's mouth", and I do not recommend it.


// hybrid module, either works
import { GoodDAG } from 'good-dag'
// or
const { GoodDAG } = require('good-dag')

// default type is string, but you can store anything
const root = new GoodDAG<string>('root value')

// now the root value is set to 'root value'

// get is the root entry
assert.equal(root.value, 'root value')
assert.equal(root.parent(), undefined)

const child = root.append('child')
const otherChild = root.append('other child')

assert.equal(child.parent(), root)
assert.equal(otherChild.parent(), root)

Class: GoodDAG

Represents a directed acyclic graph.

Note that child elements are actually instances of the DAGEntry class, which GoodDAG implements. The two can be used interchangeably, but this may confound instanceof checks, as the two do not inherit from one another classically.

dag = new GoodDAG<T = string>(rootValue: T, blockSize:number = 65536)

Initial root value is set to rootValue.

Block size is set by blockSize, and must be less than or equal to Math.pow(2, 32).

If less than or equal to 256, then the internal linked lists will use a Uint8Array. If less than or equal to Math.pow(2, 16), then it will use a Uint16Array. Otherwise, it will use a Uint32Array.

Keep in mind that using a larger block size will not always improve performance, and can dramatically increase memory footprint! See Performance, Memory, and blockSize Tuning below for guidance on setting this value appropriately.

readonly DAGData

A reference to the underlying data store where this DAGEntry lives.

readonly index: number

The index of this entry in the underlying data store.

dag.parent() => DAGEntry | undefined

Get the parent entry for the dag entry. Will return undefined for the root entry.

dag.value() => T

Return the value set for this entry in the DAG.

dag.isRoot() => boolean

Return true if this is the root entry in the DAG.

dag.root() => DAGEntry<T>

Return the root entry in the DAG.

dag.size() => number

Return count of total number of items stored in the DAG.

dag.append(value: T) => DAGEntry<T>

Adds a value into the DAG with dag as the parent, returning the newly created entry.

Note that values are not unique. Appending the same value multiple times will result in the value appearing in the DAG multiple times.

Also, the links are one-way, meaning that there is no way to get to the children from the parent, only the other direction.

(It's very directed!)

Class: DAGEntry

This is the class used for every entry other than the root.

It is not intended to be instantiated directly, but it is exported for the benefit of class-extension use cases, instanceof type checking, and so on.

DAGEntry instances have all the same methods and properties as GoodDAG. The only difference is that they do not create an underlying data store on creation, and instead use their parent's.

Instantiate DAGEntry classes by calling dag.append(data).

Performance, Memory, and blockSize Tuning

Tuning the blockSize requires some consideration. The default is set to a reasonable middle-ground, suitable for purposes where you expect to have no more than around 50-100k entries in the DAG.

The larger the blockSize, the more memory will be used for each DAG entry. If using a Uint8Array, then blockSize * 2 bytes must be pre-allocated. If using Uint16Array, then blockSize * 4, and if Uint32Array, then blockSize * 8. This allocation is usually quite fast, but if done repeatedly, the time will add up, and a smaller block size will go faster.

However, when adding items to the DAG, once it goes beyond the initial blockSize of items, a new data block must be allocated, and parent lookups in that data block are ever so slightly slower, because they may need to hop between blocks.

Some good rules of thumb: use the following block values based on your best estimate of the most number of items will end up in the DAG in your use case:

  • 2000 items or less, or if you're at all memory constrained: set blockSize to 256, so that each block only has to allocate 512 bytes of memory. (1 byte word size, 2 words per block entry.)
  • between 1000 and 10,000 items: set blockSize to 4096. Each block will allocate 16kb. (2 byte word size, 2 words per entry.)
  • between 10,000 and 50,000 items: set blockSize to 8192. Each block will allocate 32kb.
  • more than 50,000 items, especially if the DAG object isn't likely to stick around for a long time, or if you just don't want to blow more than 128kb per block: set blockSize to 65536. Each block will allocate 128kb. (Set it smaller than that if 128kb is more than you want to spend.)
  • way more than 10m items (like billions), and the DAG object is going to stick around for a long time (like a long-lived module-local variable, for example), so you won't hit the allocation operation very often, and you have plenty of memory to burn, and you really want to optimize lookup speed, and you have a very confident limit on how many items you're ever going to need to store: set the limit to about a quarter of the total count of items you need to support. The total bytes allocated for each block will be this number times 8. For example, setting blockSize to 100,096 will require 782kb per block.

See scripts/block-size-analysis.js in this repo for a script to help understand these trade-offs.

Things You Should Never Do With This Library

Don't pick a blockSize that is bigger than you need, especially if you are creating brand new GoodDAG objects repeatedly.

Don't use this as a cache. There's many other caches that are actually caches and quite good at what they do, so use one of those.

Don't use this as a key-value store, or get frustrated when it's not that.

If your problem is DAG-shaped, this is a DAG-shaped tool. It's a good DAG, it's not a caravan.

That is the joke.

dags, d'ya like dags?

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