High-performance SLF4J wrapper for Scala.
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Note: version 1.6 now has experimental support for Scala.js. See the table of contents below for documentation.

To get started quickly, you can add this dependency to your build.sbt

libraryDependencies += "org.log4s" %% "log4s" % "1.6.1"



Logging is a generally solved problem on the JVM, thanks largely to the excellent work of Ceki Gülcü and many others. The SLF4J library solves the problem of abstracting logging over different frameworks on the JVM, and frameworks like Logback and Log4j 2 are both flexible and powerful.

On the JVM, Log4s simply sits on top of these existing subsystems. Scala's macro and value classes, enable Log4s provide an idiomatic Scala façade that does not impose runtime overhead and that frequently outperforms the common usage patterns of the JVM APIs.

Log4s also provides some additional functionality to improve the ease of logging-related development, including the Log4s Testing framework for facilitating the testing of logging-related code.

Using Log4s


Scala 2.11, and 2.12 are fully supported. No special settings or compiler options are required: just add the dependency as described above.

Scala 2.10

Scala 2.10 support is still present, but it is beyond its support window: it may be removed in any future minor release if there's a reason. (It will not be removed in a patch release.)

The macro paradise compiler extensions are not required for Scala 2.10.

Scala 2.13

Efforts will be made to support Scala 2.13 milestones and release candidates within a short time after they are available, and it has very few build dependencies so there are usually no complications. If a new Log4s version has not yet been published for a prerelease version of Scala, feel free to file a ticket requesting a build for the new version.

Getting a logger

Most of the time, you simply want to define a logger with a name that matches the enclosing class or module. Log4s makes this common case as easy as possible, transparently deriving the name for you with zero overhead at runtime.

package com.example.project
import org.log4s._

class DemoClass {
  // Retrieves a logger for "com.example.project.DemoClass"
  private[this] val logger = getLogger

There is no requirement that you mark your loggers with private[this], but the compiler may bypass accessors and generate direct field access if you do.

It is not required that you import org.log4s._ into your classes: calling org.log4s.getLogger will also have the same effect. I generally recommend importing the entire log4s package: it doesn't have many symbols that are likely to conflict, and importing the package makes it easy to access other logging features if needed.

Automatic logger naming also works for modules (a.k.a. objects or singletons).

object DemoClass {
  // Will log against category "com.example.project.DemoClass"
  private val logger = org.log4s.getLogger

Notice that by default, Log4s does not include a $ at the end of logger categories for modules. This is slightly different behavior from the common idiom LoggerFactory.getLogger(getClass) used to get a logger for a module, but this behavior is more consistent with Java practices and I suspect is what a majority of users will prefer. Future enhancements may provide a mechanism to allow the user to choose whether to include the trailing $.

Custom Logger Names

There are situations where you may want to use a custom logger name. E.g., you may want to have a special category for some kind of high-level events, or you may want to consolidate the logging of two related classes.

To accomplish this, you can simply pass a name directly to getLogger.

import org.log4s._

object CustomNamed {
  private[this] val queryLogger = getLogger("queries")

Although this is fully supported, I recommend that you use the automatic class-named loggers most of the time. Class-named loggers provide useful debugging information and usually align well with the decisions you'll make about which logging statements you want to enable in which situations. By letting the compiler provide the name for you, you also reduce the chance of errors as you refactor your code.

Instance or static?

My recommendation is that by default you create your loggers as instance variables and mark them as private[this]. This may be more compatible with some complex classloading environments, and this practice is more friendly to principles of encapsulation.

However, if a specific class will be instantiated very frequently, you may want to move its logger to the companion module and mark it private. There are some cases where greater visibility is justified, but these are infrequent for most applications.

The SLF4J FAQ has a good discussion of the tradeoffs between static and instance loggers.

Logging messages

The logger interfaces are extremely simple, but they're more powerful than they look. All the standard loggers take a single argument of type string.

class MyClass(val data: Map[String,Int]) {
  private[this] val logger = org.log4s.getLogger

  logger.debug("Constructing new instance of MyClass")
  logger.trace(s"New instance's data set: $data")

Unlike SLF4J, there are no special methods for parameterized logging, because it turns out to be completely unnecessary. Parameterized logging serves two primary purposes: it provides an easy way to construct complex strings, and it helps avoid some of the costs of building a dynamic message when the message is at a level that is not enabled.

As you can see from the example, Scala 2.10's string interpolation is a much more powerful solution to the first of these issues—and it even saves the runtime work of parsing a format string by splitting up the string into easily concatenated pieces at compile time.

Log4s goes even further in that it uses macros to manipulate the execution so that the string interpolations are not even performed unless the logger is enabled. It does this by inspecting the structure of the argument that you pass into the logger.

If you pass a constant string literal, Log4s will make a direct, in-line call to the underlying SLF4J log method. If you pass in any kind of more complex expression, Log4s will wrap it in an isLevelEnabled call automatically. This is what SLF4J does when you use parameterized logging, but Log4s does it transparently and can even auto-wrap additional calculation.

Compare the following:

class JavaClass {
    logger.trace("Element 1000: {}", linkedList.get(1000));
class ScalaClass {
  logger.trace(s"Element 1000: ${linkedList(1000)}")

In the Java API, parameterized logging is not enough: unless you wrap the call with isTraceEnabled, you will still incur the cost of stepping through the linked list to find element 1000 even if trace-level logging is disabled. Without manual intervention, SLF4J only avoids the cost of string concatenations.

However, Log4s can do better. Its macros discover at compile time that you are constructing a dynamic log statement and automatically wrap the entire calculation with isTraceEnabled.

The string interpolation syntax is not required for this detection, but it is usually the easiest and best-performing approach.

You can also use message nesting with entire code blocks. If logging is not enabled at the provided level, the block is skipped entirely.

class ComplexTrace {
  logger trace {
    def helper(s: String) = ???
    val x = ...
    val y = helper(...)
    s"Combined trace message for $x: $y"

You can, of course, accomplish the same thing using if (logger.isTraceEnabled) .... If the logger is disabled, they will have identical performance. However, the explicit test may perform slightly better than a block in the case where the logger is enabled as a closure may be required to compile the block. (In most situations, these differences are completely negligible, but designing for zero overhead and documenting any usage patterns that do add overhead is a major goal of Log4s.)

Exception logging

When logging an exception, it's always the best practice to send the actual exception object into your logging system. This gives you flexibility in how it's displayed, the ability to do filtering, and additional options for things like database logging.

Log4s allows you to pass exceptions into your logger, while still maintining the simple string-interpolation style API that makes it so convenient. To log an exception, use the following syntax.

try {
} catch {
  case e: Exception => logger.error(e)("Some error message")

There is no method to log an error message without any message, because this is generally not a good practice. You can always feed it an empty string if you really want. It's usually not desirable to use the exception's message, as most logging systems will output this anyway.

Like regular message logging, dynamic arguments are only evaluated if the provided logger is turned on. This includes both the Throwable and the message itself.

This means you could use the following pattern to see who is calling a method, and if you were to disable trace logging you would only pay for the call to isTraceEnabled, which has a cost of only a few nanoseconds (according to the SLF4J FAQ).

object MyObject {
  def xyz() {
    logger.trace(new RuntimeException())("Got call into xyz")

(This is more an illustration of the possibilities of dynamic message processing than a suggestion that this is the best way to get caller information. However, sometimes a low-tech solution like this can be a good complement to more complex solutions like profilers and debuggers.)

Diagnostic contexts

Mapped diagnostic contexts (MDCs) are a great way to put share common contextual information across all log statements. Frameworks like Logback have the ability to not just output them, but also use them in filtering to select the type of logging to perform or even persist certain information in databases.

MDCs in Log4s have the same semantics as those of standard MDCs in SLF4J. In keeping with the design goal of making SLF4J idiomatic to Scala, Log4s's MDCs implement the standard interface for a Scala mutable map.

Though I cover the map-style API first, see the MDC convenience and safety section below for the simpler idiom that I recommend for most situations.

MDC Map-style API

The direct way to manipulate MDCs is through the org.log4s.MDC object.

import org.log4s._

object DiagnosticExample {
  private[this] val logger = getLogger

  def doRequest(user: String) {
    val requestId = java.util.UUID.randomUUID

    // Empty out the MDC for this thread

    /* *************************** */
    /* Set some context in the MDC */
    /* *************************** */

    // Set a single value
    MDC("request-id") = requestId.toString
    // Set multiple values
    MDC += ("request-user" -> user, "request-time" -> System.currentTimeMillis)

    // Note that Log4s requires the caller to do string conversion. This helps
    // ensure that it's really the implementation that you want.

    /* *************** */
    /* Use our context */
    /* *************** */

    // No need to put the request ID in the message: it's in the context
    logger.debug("Processing request")

    /* ************************ */
    /* Remove context variables */
    /* ************************ */

    // Remove a single value
    MDC -= "request-id"
    // Remove multiple values
    MDC -= ("request-user", "request-time")

These are a few common examples, but all the mutator methods of a mutable map will work. It's also possible to intermix calls to SLF4J's MDC methods directly: the Log4s map is backed by the actual SLF4J MDC.

MDC convenience and safety

Note that the example above has a common bug: if some exception happens during request processing, the MDC will not get cleaned up and it will leak to other operations. Because of this common situation, there's a convenience method that does cleanup in a finalizer block. I recommend using this approach for most common settings.

import org.log4s._

object BlockExample {
  def doRequest(user: String) {
    val requestId = java.util.UUID.randomUUID

    // This context operates only for the block, then cleans itself up
    MDC.withCtx ("request-id" -> requestId.toString, "request-user" -> user) {
      logger.debug("Processing request")

Nesting context blocks is permitted. The inner context block retains the values of the outer context. If there are conflicts, the inner block wins, but the outer value is restored when the inner block is completed.

This ability to restore previous values on block exit does require their storage in a map which adds slight memory overhead. If you are in a tight loop with nested contexts, you may have better performance if you add and remove values directly. These performance costs apply only to the block-based API, not the map-style API.

Scala.js Support

Scala.js support is currently experimental. It should be stable enough to use reliably, but there may be API changes in the future. If there are changes, they would likely be to either the configuration system or to the JavaScript native APIs.

Many Scala.js-specific APIs are in the org.log4s.log4sjs package. It is not currently recommended that you import this full package. There may be many public APIs in here that become private later.

Scala-defined usage

Your Scala code that targets JavaScript can retrieve and use loggers exactly the same way that you would when targeting the JVM, fulfilling the basic promise of Scala.js.


Unlike when targeting the JVM, standard frameworks like Logback or Log4j are not available to do the configuration of the logging system. Instead, there is an API that you can call to adjust logging thresholds and appenders.

Normally, you will call this API very early on during your application's startup to set up your logging configuration. However, you can adjust the settings at any time.

import org.log4s._

def initLogging(): Unit = {
  import Log4sConfig._

  /* Set `org.log4s.foo` and any children to log only Info or higher */
  setLoggerThreshold("org.log4s.foo", Info)

  /* Set `org.log4s` to not log anything. This will not override the specific
   * setting we already applied to `org.log4s.foo`. */
  setLoggerThreshold("org.log4s", OffThreshold)

  /* Set to log everything */
  setLoggerThreshold("", AllThreshold)

  /* Unset a previously customized threshold. *Now* this category will inherit
   * from the parent level, which we disabled. */

  /* Add a custom appender */
  val myAppender = { ev: log4sjs.LoggedEvent => ??? }
  /* Add a custom appender, leaving others in place */
  addLoggerAppender("org.example", myAppender)

  /* Set the specific appenders. The `additive` parameter controls whether
   * this is in addition to the appenders of the parent logger. The `false`
   * here means to *not* include any parent appenders. */
  setLoggerAppenders("org.log4s.audit", false, Seq(myAppender))

  /* The `true` here means that myAppender` from the `audit` logger will still
   * be called since it allows additive inheritance. */
  val appender2 = { ev: log4sjs.LoggedEvent => ??? }
  setLoggerAppenders("org.log4s.audit.detailed", true, Seq(appender2))

  /* Resets the logger to default settings. This also adjusts the
   * `org.log4s.audit.detailed` appenders, since that logger inherits
   * appenders from its parents */

JavaScript direct usage

To JavaScript, Log4s exposes a few key methods as a module so that you can access logging facilities from any JavaScript code you might have. You should consult the Scala.js documentation for how to get access to your modules from JavaScript.

Note that all the examples below will assume you have already imported the module under the name log4s, which could look like this for a separately packaged log4s.

var log4s = require('log4s-opt.js')

Basic logging

getLogger is a top-level function that takes a String and gives you back a logger object, just as you'd expect in Scala.

The methods on a logger are straightforward:

var logger = log4s.getLogger("org.log4s")


if (logger.isWarnEnabled) {
  logger.warn("Something went wrong", new Error())

The names of the log levels are the same as in Scala. Note that doing trace-level logging does not trigger the JavaScript's console trace, which automatically dumps a stack trace. if you want this behavior, you can always add a custom appender that inspects the level. If you have advanced needs in this area, please file a feature request.

MDCs in JavaScript

The MDC is available through JavaScript just as it is in Scala. Here's an example

/* Clear the MDC before we start */

log4s.MDC.put("user", "john.doe")
/* Do some logging */
/* Fetch an MDC value (usually not recommended) */
/* Or get a copy of the entire MDC (also not usually recommended) */

Just as in Scala, there's a "with context" method that automatically handles any cleanup for you. It's not quite as convenient in JavaScript as in Scala, but it can still be a good way to ensure your MDC gets cleaned up. These are curried functions: you pass it a context and then it gives you new function to which you pass a zero-argument function that does the work.

var logger = log4s.getLogger("com.example")
/* With a single MDC value */
log4s.MDC.withCtx("user", "jane.roe")(() => {
  /* Do some stuff */
  logger.debug("User did something")
/* With several MDC values */
log4s.MDC.withCtx({"user": "benway", "query": "1234"})(() => {
  /* Do some stuff */
  logger.debug("Use with complext context did something")

JavaScript Configuration Objects

The same basic methods that you would use to do Scala-defined configuration are available through JavaScript. See the documentation above for details on how to use them.

log4s.Config.setLoggerThreshold("org.log4s", log4s.Info)
log4s.Config.addLoggerAppender("org", e => console.log(e.level.name + ": " + e.message))
log4s.Config.setLoggerAppenders("org.test", false, [e => console.log(e.message)])

Appenders can be created by passing in a JavaScript function.

For parameters, there are top-level threshold/level objects available:

  • AllThreshold
  • Trace
  • Debug
  • Info
  • Warn
  • Error
  • OffThreshold


There is a Logback-specific testing library that allows you to do mock-object style testing of your log messages if you'd like. This was built for internal testing of Log4s, but it has been made public by request.


This only works if you are using Logback as your logging framework, at least during testing. (Doing this will not interfere with using a different framework for your runtime logging if you correctly configure the two classpaths.)

SBT config

libraryDependencies += "org.log4s" %% "log4s-testing" % log4sVersion % "test"

I recommend you use a val log4sVersion to match the version number with the main Log4s dependency.

Logback config

You'll then want to add lines like the following in your logback-test.xml

<appender name="TEST" class="org.log4s.TestAppender"/>
<logger name="org.log4s.abc" additivity="false" level="TRACE">
  <!-- Set additivity to `false` if you don't want this logging to actually go to the main output. -->
  <appender-ref ref="TEST" />

Full documentation of this is beyond the scope of this document, but you need to create the custom appender and register it with the appropriate categories. Note that Logback's Groovy-based configuration is more convenient and flexible than the XML for more complicated logging configurations, but it's less familiar and adds an extra runtime dependency on Groovy.


The steps are relatively simple

  1. Get access to a logger that hooked up to your appender
  2. Write one or more events to that logger
  3. Call into the TestAppender object to dequeue the events and inspect them

Here's a simple example of code you might write:

import org.log4s._
import org.scalatest._

/* An auto-named logger would work just as well as long as that logger
 * is hooked up in your Logback configuration. */
val testLogger = getLogger("org.log4s.abc")

TestAppender.withAppender() {
  testLogger.debug("Here's a test message")
  val eventOpt = TestAppender.dequeue
  eventOpt should be ('defined)
  eventOpt foreach { e =>
    e.message should equal ("Here's a test message")
    e.throwable should not be 'defined

More examples are available if you look through the various test classes in this project.

Should I test my logging?

Testing scope and philosophy is a complex topic far beyond the reach of this docuemnt, but I can give some general guidance based on my personal views.

Good tests validate the behavior that callers or users should expect when interacting with your code, but not arbitrary implementation details. Quality tests don't fail just because you changed something—unless that thing impacts the expected behavior of the code. My view is effectively that tests should attempt to fully verify the black-box behavior of a piece of code while ignoring details that do not affect black-box behavior. (However, white-box development techniques may be useful to provide this verification, and judgment is required in the drawing of the lines.)

I'll give two examples.

At one extreme is a trace statement that is used for developer debugging. You probably would not benefit from crying wolf with a test failure just because you added an additional bit of detail to a debug statement or you changed its punctuation. In my opinion, this log statement is probably not part of the code's specification and should not be tested.

At the other extreme would be a scenario where you're using your logging framework to generate audit logs that track access to secured resources. This is important functionality that may be required for regulatory compliance. It is strongly advisable to develop tests that ensure this log is written as expected. I would probably consider a white-box unit test to ensure the messages went to the right logger in the right format and a black-box test that inspected the actual output log files to ensure events were being written in an end-to-end manner.

Most real situations will lie between these two extremes, and you will need to use judgment. In my estimation, most applications probably do not need or want tests that verify the details of their logging, but there are many situations where this testing is approrpiate.

Unsupported features

The following potential or suggested features are not implemented. If some missing feature is particularly valuable to you, feel free to reach out with your requests or suggestions. I'm also—of course—open to pull requests, but please drop me an email first if there are significant new APIs or features so we can agree on the general design.

  • A scalac compiler flag or environment variable to automatically disable all logging below a certain level.
  • Marker support.


Here are all the contributors (chronologically). Thanks to all!