dvgica / periodic   0.7.0

Apache License 2.0 GitHub

A low-dependency Scala library providing a cached variable that self-updates periodically, and a periodic function runner

Scala versions: 3.x 2.13 2.12


Maven CI

Periodic is a low-dependency Scala library providing:

  • an in-memory cached variable (AutoUpdatingVar) that self-updates on a periodic basis
  • a periodic runner for a side-effecting function (FnRunner)

It attempts to provide an effect- and runtime-agnostic API which can abstract various implementations as needed.


It is fairly common to need to do something periodically, while a process is running.


AutoUpdatingVar is useful for caching semi-static data in memory and having that data be automatically and periodically updated. The source of the data is typically another process that can be queried to get a new data value. If the cached data becomes stale at a predictable interval, the cached data can be updated before this occurs. If the cached data becomes stale at unpredictable times, the stale data must still be usable. Concrete use cases include:

  • caching a time-limited key or token, and replacing it with a new one before it expires (e.g. an OAuth access token)
  • caching data that changes irregularly and occasionally, such as a list of a country's airports and their codes

For data that changes irregularly but must be up-to-date, you likely want to be subscribing to some kind of change event instead.


FnRunner is useful when you want to do something periodically, but don't need to make any data available. Concrete use cases include:

  • deleting old records in a database
  • triggering calls to an external service


Periodic is available on Maven Central for Scala 2.12, 2.13, and 3. Java 11+ is required.

For the default JDK-based implementation, add the following dependency:

"ca.dvgi" %% "periodic-core" % "<latest>"

For the Pekko Streams-based implementation, use this dependency:

"ca.dvgi" %% "periodic-pekko-stream" % "<latest>"


  • periodic-core depends only on slf4j-api
  • periodic-pekko-stream depends on pekko-stream and periodic-core



All library functionality is based on implementations of Periodic. Therefore all classes require an instance of Periodic in their constructor.

JDK Implementation

JdkPeriodic is the default implementation provided in periodic-core which is suitable for many use cases. Usages of the jdk and jdkFuture methods on the AutoUpdatingVar and FnRunner companion objects create a new, non-shared JdkPeriodic (and thus a new thread) for each invocation. This will work well as long as the number of created threads is not problematic for your application.

Users with many AutoUpdatingVars or FnRunners may wish to share a JdkPeriodic between them to decrease the total number of threads used. In this case, the shared JdkPeriodic may need to be tuned based on workload. Specifically, users may need to provide a ScheduledExecutorService to the shared JdkPeriodic with an increased thread count (the default number of threads used by a JdkPeriodic is one). Threads in the ScheduledExecutorService will be blocked.

The JDK implementation works out of the box with sync (Identity) or async (scala.concurrent.Future) functions. If usage with another effect is desired, provide a typeclass implementation of ca.dvgi.periodic.jdk.Eval.

Pekko Streams Implementation

The Pekko Streams implementation is completely non-blocking and does not need additional resources besides an ActorSystem. A single PekkoStreamsPeriodic can be shared by many AutoUpdatingVars and FnRunners without requiring tuning. It is recommended if you are already using Pekko or don't mind the extra dependency. As usual with Pekko-based code, user-provided functions should not block.

The Pekko Streams implementation only works with scala.concurrent.Future.


Default JDK-based Implementation

import ca.dvgi.periodic._
import scala.concurrent.duration._
import scala.concurrent.Await
import java.time.Instant

def updateData(): String = Instant.now.toString

val data = AutoUpdatingVar.jdk( // or AutoUpdatingVar.jdkFuture if updateData returns a Future
  // can also be dynamic based on the last data
  // can also be finite with configurable behavior for attempt exhaustion

// `ready` returns a `Future[Unit]` which completes when the data initialization is complete
// see also the `blockUntilReadyTimeout` parameter
Await.result(data.ready, 5.seconds)

// the data is cached in memory and can be accessed using `latest`
println(s"Cached data is ${data.latest}")
// data has not been updated yet, same result
println(s"Cached data is still ${data.latest}")


// the `AutoUpdatingVar` fetched new data while this thread was sleeping
println(s"New cached data is ${data.latest}")

This results in the following output:

Cached data is 2023-10-19T02:35:22.467418Z
Cached data is still 2023-10-19T02:35:22.467418Z
New cached data is 2023-10-19T02:35:23.474155Z

For handling errors during update, and other options, see the Scaladocs.

Pekko Streams Implementation

import org.apache.pekko.actor.ActorSystem
import ca.dvgi.periodic.pekko.stream.PekkoStreamsPeriodic
import ca.dvgi.periodic._
import scala.concurrent.duration._
import scala.concurrent.Future
import java.time.Instant

def updateData(): Future[String] = Future.successful(Instant.now.toString)

// generally you should have an ActorSystem in your process already
implicit val actorSystem = ActorSystem() 

val data = AutoUpdatingVar(
  PekkoStreamsPeriodic() // can also be shared by many AutoUpdatingVars or FnRunners


import ca.dvgi.periodic._
import scala.concurrent.duration._
import java.time.Instant

def doSomething(): FiniteDuration = {
  println(s"the time is: ${Instant.now.toString}")

// alternately use FnRunner.jdkFuture or FnRunner.apply(somePeriodic)
val runner = FnRunner.jdk(doSomething, AttemptStrategy.Infinite(1.second), "time printer")


Contributions in the form of Issues and PRs are welcome.