Rill is a toolkit that brings composable concurrency to Go, making it easier to build concurrent programs from simple, reusable parts. It reduces boilerplate while preserving Go's natural channel-based model.
go get -u github.com/destel/rill-
Make common tasks easier.
Rill provides a cleaner way of solving common concurrency problems, such as processing slices and channels, calling APIs, or making DB queries in parallel. It removes boilerplate and abstracts away the complexities of goroutine orchestration and error handling. At the same time, developers retain full control over the concurrency level of all operations. -
Make concurrent code composable and clean.
Most functions in the library take Go channels as inputs and return new, transformed channels as outputs. This allows them to be chained in various ways to build reusable pipelines from simpler parts, similar to Unix pipes. As a result, concurrent tasks become clear sequences of reusable operations. -
Centralize error handling.
Errors are automatically propagated through the pipeline and can be handled in a single place at the end. For more complex scenarios, Rill also provides tools to intercept and handle errors at any point in the pipeline. -
Simplify stream processing.
Thanks to Go channels, built-in functions can handle potentially infinite streams, processing items as they arrive. This makes Rill a convenient tool for real-time data processing, handling large datasets that don't fit in memory, or building responsive data pipelines. -
Provide solutions for advanced tasks.
Beyond basic operations, the library includes ready-to-use functions for batching, ordered fan-in, map-reduce, stream splitting, merging, and more. Pipelines, while usually linear, can have any topology forming a directed acyclic graph (DAG). -
Support custom extensions.
Since Rill operates on standard Go channels, it's easy to write custom functions compatible with the library. -
Keep it lightweight.
Rill has a small, type-safe, channel-based API, and zero dependencies, making it straightforward to integrate into existing projects. It's also lightweight in terms of resource usage, ensuring that the number of memory allocations and goroutines does not grow with the input size.
Rill makes it easy to process data concurrently. Here's a simple example using ForEach to process items in parallel while handling errors:
func main() {
// Convert a slice of numbers into a channel
numbers := rill.FromSlice([]int{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}, nil)
// Do something with each number and print the result
// Concurrency = 3
err := rill.ForEach(numbers, 3, func(x int) error {
y := doSomethingWithNumber(x)
fmt.Println(y)
return nil
})
// Handle errors
fmt.Println("Error:", err)
}The result as above can also be achieved with WaitGroup or ErrGroup, but Rill shines when building complex multi-stage concurrent pipelines.
The next example demonstrates a multi-stage pipeline that fetches users from an external API in batches, updates their status to active, and saves them back, while controlling the level of concurrency at each step.
func main() {
ctx := context.Background()
// Convert a slice of user IDs into a channel
ids := rill.FromSlice([]int{
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
}, nil)
// Group IDs into batches of 5
idBatches := rill.Batch(ids, 5, 1*time.Second)
// Bulk fetch users from the API
// Concurrency = 3
userBatches := rill.Map(idBatches, 3, func(ids []int) ([]*mockapi.User, error) {
return mockapi.GetUsers(ctx, ids)
})
// Transform the stream of batches back into a flat stream of users
users := rill.Unbatch(userBatches)
// Activate users.
// Concurrency = 2
err := rill.ForEach(users, 2, func(u *mockapi.User) error {
if u.IsActive {
fmt.Printf("User %d is already active\n", u.ID)
return nil
}
u.IsActive = true
err := mockapi.SaveUser(ctx, u)
if err != nil {
return err
}
fmt.Printf("User saved: %+v\n", u)
return nil
})
// Handle errors
fmt.Println("Error:", err)
}When working with external services or databases, batching is a common pattern to reduce the number of requests and improve performance. Rill provides a Batch function that transforms a stream of items into a stream of batches of a specified size. It's also possible to specify a timeout, after which a batch is emitted even if it's not full. This is useful for keeping an application reactive when the input stream is slow or sparse.
Previous examples have already shown how to use Batch to group user IDs for bulk fetching. Let's examine a case where batching with timeout is particularly useful.
In the example below, the UpdateUserTimestamp function updates the last_active_at column in the users table with the current timestamp.
This function is called concurrently from multiple places in the application, such as HTTP handlers.
A large number of such calls would cause a large number of concurrent SQL queries, potentially overwhelming the database.
To mitigate this, it's possible to group the updates and send them to the database in bulk using the Batch function. And when updates are sparse, the timeout setting makes sure they're delayed by at most 100ms, balancing between reducing database load and data freshness.
func main() {
// Start the background worker that processes the updates
go updateUserTimestampWorker()
// Do some updates. They'll be automatically grouped into
// batches: [1,2,3,4,5], [6,7], [8]
UpdateUserTimestamp(1)
UpdateUserTimestamp(2)
UpdateUserTimestamp(3)
UpdateUserTimestamp(4)
UpdateUserTimestamp(5)
UpdateUserTimestamp(6)
UpdateUserTimestamp(7)
time.Sleep(500 * time.Millisecond) // simulate sparse updates
UpdateUserTimestamp(8)
}
// This is the queue of user IDs to update.
var userIDsToUpdate = make(chan int)
// UpdateUserTimestamp is the public API for updating the last_active_at column in the users table
func UpdateUserTimestamp(userID int) {
userIDsToUpdate <- userID
}
// This is a background worker that sends queued updates to the database in batches.
// For simplicity, there are no retries, error handling and synchronization
func updateUserTimestampWorker() {
ids := rill.FromChan(userIDsToUpdate, nil)
idBatches := rill.Batch(ids, 5, 100*time.Millisecond)
_ = rill.ForEach(idBatches, 1, func(batch []int) error {
fmt.Printf("Executed: UPDATE users SET last_active_at = NOW() WHERE id IN (%v)\n", batch)
return nil
})
}Error handling can be non-trivial in concurrent applications. Rill simplifies this by providing a structured approach to the problem. Pipelines typically consist of a sequence of non-blocking channel transformations, followed by a blocking stage that returns a final result and an error. The general rule is: any error occurring anywhere in a pipeline is propagated down to the final stage, where it's caught by some blocking function and returned to the caller.
Rill provides a wide selection of blocking functions. Some of them are:
- ForEach: Concurrently applies a user function to each item in the stream. Example
- ToSlice: Collects all stream items into a slice. Example
- First: Returns the first item or error encountered in the stream. Example
- Reduce: Concurrently reduces the stream to a single value, using a user provided reducer function. Example
- Any: Concurrently checks if at least one item in the stream satisfies a user provided condition. Example
All blocking functions share a common behavior. In case of an early termination (before reaching the end of the input stream or in case of an error), such functions initiate background draining of the remaining items. This is done to prevent goroutine leaks by ensuring that all goroutines feeding the stream are allowed to complete.
Rill is context-agnostic, meaning that it does not enforce any specific context usage. However, it's recommended to make user-defined pipeline stages context-aware. This is especially important for the initial stage, as it allows to stop feeding the pipeline with new items when the context is canceled.
In the example below the FindFirstPrime function uses several concurrent workers to find the first prime number after
a given number. Internally it creates an infinite stream of numbers. When the first prime number is found
in that stream, the context gets canceled, and the pipeline terminates gracefully.
func main() {
p := FindFirstPrime(10000, 3) // Use 3 concurrent workers
fmt.Println("The first prime after 10000 is", p)
}
// FindFirstPrime finds the first prime number after the given number, using several concurrent workers.
func FindFirstPrime(after int, concurrency int) int {
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
numbers := make(chan rill.Try[int])
go func() {
defer close(numbers)
for i := after + 1; ; i++ {
select {
case <-ctx.Done():
return
case numbers <- rill.Wrap(i, nil):
}
}
}()
primes := rill.OrderedFilter(numbers, concurrency, func(x int) (bool, error) {
fmt.Println("Checking", x)
return isPrime(x), nil
})
result, _, _ := rill.First(primes)
return result
}There is a technique that significantly accelerates some seemingly sequential operations by branching them into multiple parallel streams and then merging the results. Common examples include listing S3 objects, querying APIs, or reading from databases.
Example below fetches all users from an external paginated API. Doing it sequentially, page-by-page, would take a long time since the API is slow and the number of pages is large. One way to speed this up is to fetch users from multiple departments at the same time. The code below uses FlatMap to stream users from 3 departments concurrently and merge the results as they arrive, achieving up to 3x speedup compared to sequential processing.
Additionally, it demonstrates how to write a custom reusable streaming wrapper around an existing API function.
The StreamUsers function is useful on its own, but can also be a part of a larger pipeline.
func main() {
ctx := context.Background()
// Convert a list of all departments into a stream
departments := rill.FromSlice(mockapi.GetDepartments())
// Use FlatMap to stream users from 3 departments concurrently.
users := rill.FlatMap(departments, 3, func(department string) <-chan rill.Try[*mockapi.User] {
return StreamUsers(ctx, &mockapi.UserQuery{Department: department})
})
// Print the users from the combined stream
err := rill.ForEach(users, 1, func(user *mockapi.User) error {
fmt.Printf("%+v\n", user)
return nil
})
fmt.Println("Error:", err)
}
// StreamUsers is a reusable streaming wrapper around the mockapi.ListUsers function.
// It iterates through all listing pages and returns a stream of users.
// This function is useful on its own or as a building block for more complex pipelines.
func