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Announcing entcache - a Cache Driver for Ent

· 7 min read

While working on Ariga's operational data graph query engine, we saw the opportunity to greatly improve the performance of many use cases by building a robust caching library. As heavy users of Ent, it was only natural for us to implement this layer as an extension to Ent. In this post, I will briefly explain what caches are, how they fit into software architectures, and present entcache - a cache driver for Ent.

Caching is a popular strategy for improving application performance. It is based on the observation that the speed for retrieving data using different types of media can vary within many orders of magnitude. Jeff Dean famously presented the following numbers in a lecture about "Software Engineering Advice from Building Large-Scale Distributed Systems":

cache numbers

These numbers show things that experienced software engineers know intuitively: reading from memory is faster than reading from disk, retrieving data from the same data center is faster than going out to the internet to fetch it. We add to that, that some calculations are expensive and slow, and that fetching a precomputed result can be much faster (and less expensive) than recomputing it every time.

The collective intelligence of Wikipedia tells us that a Cache is "a hardware or software component that stores data so that future requests for that data can be served faster". In other words, if we can store a query result in RAM, we can fulfill a request that depends on it much faster than if we need to go over the network to our database, have it read data from disk, run some computation on it, and only then send it back to us (over a network).

However, as software engineers, we should remember that caching is a notoriously complicated topic. As the phrase coined by early-day Netscape engineer Phil Karlton says: "There are only two hard things in Computer Science: cache invalidation and naming things". For instance, in systems that rely on strong consistency, a cache entry may be stale, therefore causing the system to behave incorrectly. For this reason, take great care and pay attention to detail when you are designing caches into your system architectures.

Presenting entcache

The entcache package provides its users with a new Ent driver that can wrap one of the existing SQL drivers available for Ent. On a high level, it decorates the Query method of the given driver, and for each call:

  1. Generates a cache key (i.e. hash) from its arguments (i.e. statement and parameters).

  2. Checks the cache to see if the results for this query are already available. If they are (this is called a cache-hit), the database is skipped and results are returned to the caller from memory.

  3. If the cache does not contain an entry for the query, the query is passed to the database.

  4. After the query is executed, the driver records the raw values of the returned rows (sql.Rows), and stores them in the cache with the generated cache key.

The package provides a variety of options to configure the TTL of the cache entries, control the hash function, provide custom and multi-level cache stores, evict and skip cache entries. See the full documentation in https://pkg.go.dev/ariga.io/entcache.

As we mentioned above, correctly configuring caching for an application is a delicate task, and so entcache provides developers with different caching levels that can be used with it:

  1. A context.Context-based cache. Usually, attached to a request and does not work with other cache levels. It is used to eliminate duplicate queries that are executed by the same request.

  2. A driver-level cache used by the ent.Client. An application usually creates a driver per database, and therefore, we treat it as a process-level cache.

  3. A remote cache. For example, a Redis database that provides a persistence layer for storing and sharing cache entries between multiple processes. A remote cache layer is resistant to application deployment changes or failures, and allows reducing the number of identical queries executed on the database by different process.

  4. A cache hierarchy, or multi-level cache allows structuring the cache in hierarchical way. The hierarchy of cache stores is mostly based on access speeds and cache sizes. For example, a 2-level cache that composed of an LRU-cache in the application memory, and a remote-level cache backed by a Redis database.

Let's demonstrate this by explaining the context.Context based cache.

Context-Level Cache

The ContextLevel option configures the driver to work with a context.Context level cache. The context is usually attached to a request (e.g. *http.Request) and is not available in multi-level mode. When this option is used as a cache store, the attached context.Context carries an LRU cache (can be configured differently), and the driver stores and searches entries in the LRU cache when queries are executed.

This option is ideal for applications that require strong consistency, but still want to avoid executing duplicate database queries on the same request. For example, given the following GraphQL query:

query($ids: [ID!]!) {
nodes(ids: $ids) {
... on User {
id
name
todos {
id
owner {
id
name
}
}
}
}
}

A naive solution for resolving the above query will execute, 1 for getting N users, another N queries for getting the todos of each user, and a query for each todo item for getting its owner (read more about the N+1 Problem).

However, Ent provides a unique approach for resolving such queries(read more in Ent website) and therefore, only 3 queries will be executed in this case. 1 for getting N users, 1 for getting the todo items of all users, and 1 query for getting the owners of all todo items.

With entcache, the number of queries may be reduced to 2, as the first and last queries are identical (see code example).

context-level-cache

The different levels are explained in depth in the repository README.

Getting Started

If you are not familiar with how to set up a new Ent project, complete Ent Setting Up tutorial first.

First, go get the package using the following command.

go get ariga.io/entcache

After installing entcache, you can easily add it to your project with the snippet below:

// Open the database connection.
db, err := sql.Open(dialect.SQLite, "file:ent?mode=memory&cache=shared&_fk=1")
if err != nil {
log.Fatal("opening database", err)
}
// Decorates the sql.Driver with entcache.Driver.
drv := entcache.NewDriver(db)
// Create an ent.Client.
client := ent.NewClient(ent.Driver(drv))

// Tell the entcache.Driver to skip the caching layer
// when running the schema migration.
if client.Schema.Create(entcache.Skip(ctx)); err != nil {
log.Fatal("running schema migration", err)
}

// Run queries.
if u, err := client.User.Get(ctx, id); err != nil {
log.Fatal("querying user", err)
}
// The query below is cached.
if u, err := client.User.Get(ctx, id); err != nil {
log.Fatal("querying user", err)
}

To see more advanced examples, head over to the repo's examples directory.

Wrapping Up

In this post, I presented “entcache” a new cache driver for Ent that I developed while working on Ariga's Operational Data Graph query engine. We started the discussion by briefly mentioning the motivation for including caches in software systems. Following that, we described the features and capabilities of entcache and concluded with a short example of how you can set it up in your application.

There are a few features we are working on, and wish to work on, but need help from the community to design them properly (solving cache invalidation, anyone? ;)). If you are interested to contribute, reach out to me on the Ent Slack channel.

For more Ent news and updates: