A library for highly space efficient Tree cache

How to Install

npm i tree-key-cache

How to use it

First, you need to have a KeyCacheStorage implementation to set where the tree must be persisted. Usually, a simple redis implementation is good enough, like this:

const redis = new Redis(port, host, {
  db: 1,
});

const storage: TreeKeyCacheStorage<string> = {
  async get(key: string) {
    return redis.get(key);
  }
  async set(key: string, value: string, ttl?: number) {
    return ttl ? redis.setex(key, ttl, value) : redis.set(key, value);
  }
  async getCurrentTtl(key: string) {
    return redis.ttl(key);
  }
}

Notice that tree-key-cache works with ttl in seconds, just like redis but not necessarily like some other storage service. Now, with the storage ready, you can instantiate the cache:

const treeKeyCache = new TreeKeyCache<MyEntity>(storage, {
  keyLevelNodes: 4,
});

Notice that we set keyLevelNodes option as 4. This is a required option and is deeply connected with how TreeKeyCache storage persist the data:

• Part of the data goes like simple key-value items, this part we call key level node;
• Part of the data is stored in a tree structure, which we call tree level node;

The option keyLevelNodes states the maximum tree level which we'll keep the data as key, meaning that, starting from that point, everything will be saved as a whole tree in the storage. This strategy really helps to save a lot of memory, but comes with a trade-off: The sequence of keys selected must be very well planned.

Each node of the tree managed by tree-key-cache have an id, which we call key, and we use it to access the node in what we call tree-path, or branch. For example, let's say we want to access the key rootKey1 > subKey1 > nextSubKey1, we can easily access it like this:

const value = await treeKeyCache.getNode(['rootKey1', 'subKey1', 'nextSubKey1']);

This will directly returns a Step object, if there is a value in it, which will look like this:

{
  "key": "nextSubKey1",
  "level": 3,
  "value": "persisted value of any type you want",
  "nodeRef": { /* Here we'll have an object that represents the path to that node */ }
}

If no values exists that the end of the path, undefined is returned. If you want to have a string representing the whole path, you can get it like this:

const fullKey = buildKey(value.nodeRef); // Will result in 'rootKey1:subKey1:nextSubKey1'

Another way to access a node is through the method iteratePath. With this one, you get an async iterable that will yield every node up to the last one in the path:

const iterable = treeKeyCache.iteratePath(['rootKey1', 'subKey1', 'nextSubKey1']);
for await (const step of iterable) {
  if (step.value) {
    console.log(step);
  }
}

To set a node value, you have two options:

setNode

await treeKeyCache.setNode(['rootKey1', 'subKey1', 'nextSubKey1'], 'the value you want');

With this option, only the value at the end of the path will be set to the value you wanted.

deepTreeSet

const iterable = treeKeyCache.deepTreeSet(['rootKey1', 'subKey1', 'nextSubKey'], () => 'initial value');

for await (const step of iterable) {
  if (step.level === 2) {
    step.value = 'different value';
  }
}

With this option, every node on the path is set. The second parameter determines a default value to be assumed, if no value for the node is set yet, but, the catch is that the method returns an async iterable, and you can modify the value of the node before saving it. You can also use this method without specifying a default value:

const iterable = treeKeyCache.deepTreeSet(['rootKey1', 'subKey1', 'nextSubKey']);

for await (const step of iterable) {
  if (step.level === 2) {
    step.value = 'the only value';
  }
}

This way, only the values to be set will be the ones that you specified during the iteration.

Application examples

You can use tree-key-cache to count how many people you have for some criteria. Let's say the keyLevelNodes for the following example is 4. Let's create a function that returns a path for a given person entity:

function getPath(person: Person) {
  return [
    person.country,
    person.state,
    person.city,
    person.birthDay.getUTCFullYear(),
    person.birthDay.getUTCMonth(),
    person.birthDay.getUTCDay(),
  ];
}

Now, let's process a database to categorize all people we have registered.

for (const person of people) {
  const iterable = treeKeyCache.deepTreeSet(getPath(person));
  for await (const step of iterable) {
    step.value ??= 0;
    step.value++;
  }
}

At the end of the iteration, you'll have in your storage the total count of people:

• By country;
• By state;
• By city;
• By year of birth;
• By month of birth;
• By birthday;

In this example, we used keyLevelNodes as 4, for a pretty good reason: the maximum number of tree level nodes will be only 366, which are the maximum number of days in a year. This is very important because, the tree will be entirely loaded to memory when needed, so, always keep that in mind when planning your path sequence, and the keyLevelNodes value.

Depth and breadth first search

Without any additional effort you can perform a depth first search or breadth first search starting at any tree level node. Let's take the people data tree example:

const iterable = treeKeyCache.preOrderDepthFirstSearch(['Brazil', 'Sao Paulo', 'Barueri', '1985']);

for await (const step of iterable) {
  if (step.value !== undefined) {
    console.log(`for ${buildKey(step.nodeRef)} we have ${step.value} people!`);
  }
}

You can also do that at a key level node, but you'll need to have the method getChildren implemented on your storage. This method must me capable of return every child key for a given key with not a tree persisted. Optionally, you can also implement the method registerChild, that will be called every time a child for a given key is set. This is useful to keep track of the children being included for a given node.

Likewise, you can perform a breadth first search using the method preOrderBreadthFirstSearch with the same conditions.

The exclusion of no longer existing children references must be, for now, be managed by the storage implementation itself.

randomIterate

You can also implement the method randomIterate, which must return an AsyncIterable that yields every key in the storage. This method may also receive a pattern string to filter the keys. Implementing this method will give the capability of use the randomIterate method for the treeKeyCache instance.

reprocessAllKeyLevelChildren

This method, when called, can recreate all key level children registration within the storage. To use it, you must implement randomIterate and registerChild. Additionally, if you implement clearAllChildrenRegistry, this method will, first, clear all children registration and only then will create them.

Prune

If you implement the storage with Redis, every key saved there can have its own ttl, but nodes on tree level keys will have the problem that Redis will not control its ttl, as they are literally part of the value. To solve that problem, this library offers the method prune. Calling this method will shrink all the tree level keys, removing expired or empty values, and also removing totally empty or expired branches of the tree. This will help you keeping your storage as compact as possible. You can, for example, create a schedule to call this method once a day.

Versioned values

This library also support dealing with multiple, history values for the same key. The idea is, if you have an use case where you need the last n registered values of a given key to extract some info, like an average, for example, you'll be able to do it over the tree structure. To achieve that, first of all, you need to implement the method getHistory on your storage, to return an AsyncIterable with all the history values of the given key. Next, you need to control how things are saved on the set method, to persist this history. Here are some valid approaches:

• Always including method: Making get always return undefined, so no update is possible, and always saving the value on a new key. The name pattern for those keys must be controlled in a way that it doesn't conflict with the chained keys the library pass to the storage;
• Cycling method: you save the same key on a different redis db, based on a roundtrip based on time (db 5 every even week, db 6 every odd week, for example). The ttl of those keys must be calibrated in a way that, when you change dbs, all the keys of the current one has expired. Finally, getHistory always look up the given key in all dbs to return its AsyncIterable;

These are some implementation suggestions, but of course there are many other ways to implement that!

Custom serializers

By default, tree-key-cache uses JSON to serialize and deserialize either values or trees to be saved on redis, but you can achieve an even greater memory saving by using a custom binary serializer. You can implement your own, by this:

• For value serializers, implement Serializer<YourValueType, YourStorageType>
• For tree serializers, implement: Serializer<Tree, YourStorageType>

Serializer is an interface used for both cases, and, after implementing your own custom serializer, you can inform them during the instance creation:

const treeKeyCache = new TreeKeyCache<YourValueType, YourStorageType>(storage, {
  keyLevelNodes: 4,
  treeSerializer: myCustomTreeSerializerInstance,
  valueSerializer: myCustomValueSerializerInstance
});

We also have implemented two custom serializers for some really popular binary formats:

• @tree-key-cache/avro: A serializer that targets avro data to be saved on the storage. This is a really compact binary format that offers a partially easy payload evolution, as you need to inform older payload versions to do so. Use this to achieve one of the maximum storage economy possible;

• @tree-key-cache/protobuf: A serializer that targets protobuf data to be saved on the storage. This is also a compact format, not so much compared to avro during our own tests, but it offers a more flexible payload evolution, as the retro-compatibility is automatic when evolving the proto file.

License

Licensed under MIT.