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Contains code to bootstrap your Scala applications.


HOCON configuration is loaded from the system properties, application.conf and reference.conf in the usual way using the Typesafe Config library. However, if there is an environment variable CONFIG_URL defined then config will also be loaded from this and override any local settings except those specified in the system properties. In other words, the order of precedence for config settings is:

  • System properties (e.g. java
  • The configuration at CONFIG_URL
  • application.conf
  • reference.conf

The following URL schemes are supported for CONFIG_URL:

  • http and https, which are ideal for production systems with a centralised configuration service.
  • file, which is ideal for production systems using a configuration management system such as puppet to create application-specific configuration files.
  • classpath, which is intended for development or testing purposes so that you don't need any external dependencies. It's highly recommended that you use file names that won't be mistaken for production configuration such as testing.conf.

Configuration structure

When using this library, the recommended way to structure your configuration is as follows:

  • Put all the settings that have sensible defaults (e.g. log level, timeouts) into reference.conf as a baseline configuration. Do not put any environment-specific settings such as URLs or port numbers in here!
  • Put all the settings that have sensible defaults on a dev workstation into application.conf -- this might include standard local URLs, port numbers, database names, etc. to make it easy for people to work on the project.
  • Use the configuration pointed to by CONFIG_URL for the majority of the runtime configuration (i.e. the environment-specific settings) in environments other than development (e.g. CI build, QA, production).
  • Use the -D overrides for any local overrides. These might be temporary overrides such as log level, or to allow sensitive settings such as private keys to be specified separately from shared configuration.

There are a number of standard configuration setting names, and a recommended structure for application setting names described in Confluence.

Excluding dev config from JARs

Because the dev config settings are in application.conf it's really important that this file is excluded from any JARs that are built or they might accidentally get loaded as the production settings! In your build.sbt file make sure you add something like this which will discard the file when building the JAR:

mergeStrategy in assembly <<= (mergeStrategy in assembly) { old =>
    case "application.conf" => MergeStrategy.discard
    case x => old(x)

Configuring your app

It really couldn't be simpler. Just mix in the Configuration trait and you'll have a new config field available to you with the loaded and merged configuration, e.g.

import com.blinkbox.books.config.Configuration

object MyApp extends App with Configuration {
  println(config) // or do something more useful

To validate the configuration, as well as transforming it into a more usable format, it may be useful to build a strongly-typed eagerly-evaluated configuration wrapper around this loosely typed property bag. An example of this is below, which validates that all the required properties are present and transforms some of them into more useful objects such as gateways or URLs:

case class AppConfig(braintree: BraintreeConfig, service: ServiceConfig)

case class BraintreeConfig(environment: Environment, merchantId: String, publicKey: String, privateKey: String) {
  val gateway = new BraintreeGateway(environment, merchantId, publicKey, privateKey)

case class ServiceConfig(auth: URL)

object AppConfig {
  def apply(config: Config): AppConfig = AppConfig(BraintreeConfig(config), ServiceConfig(config))

object BraintreeConfig {
  def apply(config: Config): BraintreeConfig = {
    val environment = config.getString("braintree.environment") match {
      case "DEVELOPMENT" => Environment.DEVELOPMENT
      case "SANDBOX" => Environment.SANDBOX
      case "PRODUCTION" => Environment.PRODUCTION
      case env => throw new BadValue(config.origin, "braintree.environment", s"Unknown: '$env'.")
    val merchantId = config.getString("braintree.merchantId")
    val publicKey = config.getString("braintree.publicKey")
    val privateKey = config.getString("braintree.privateKey")
    BraintreeConfig(environment, merchantId, publicKey, privateKey)

object ServiceConfig {
  def apply(config: Config): ServiceConfig = ServiceConfig(config.getHttpUrl("service.auth.uri"))

object MyApp extends App with Configuration {
  val appConfig = AppConfig(config)


All logging should be done in GELF format, and in rig environments it is sent via UDP to the log server. To support this the library contains some Logback extensions which can be automatically configured by mixing the Loggers trait into your app, e.g.

import com.blinkbox.books.config._
import com.blinkbox.books.logging._
import com.typesafe.scalalogging.slf4j.StrictLogging
import org.slf4j.MDC

object MyApp extends App with Configuration with Loggers with StrictLogging {
  MDC.put("foo", "bar")      // add a filterable property to log messages"App started") // this will be logged as GELF over UDP

If the MDC context only applies to the particular logging call, and not to subsequent calls, then you can scope the MDC as follows:

object MyApp extends App with Configuration with Loggers with Logging {
  logger.withContext("foo" -> "bar")("App started"))

If you log an MDC property named timestamp containing a UNIX timestamp then it will override the timestamp that the event is reported with. This can be useful for processes where you want to record the event at the start time rather than the finish time. You should only use this for short-running processes, maximum around 10 seconds, to ensure that it doesn't cause problems in the aggregating servers with significantly out-of-order events.

To test the logging out on your machine, the easiest option is to vagrant up the graylog-vagrant virtual machine.

Any information placed in the MDC will be sent as properties in the log messages, which are very useful for filtering them. When using futures, ensure you wrap any execution contexts with DiagnosticExecutionContext to ensure that MDC state is propagated correctly.


The library integrates with Dropwizard Metrics (formerly Codahale Metrics) to improve the ability to monitor software. These can be logged to Graylog and/or New Relic, as well as more mundane places like the console.

Add instrumentation to your thread pools by wrapping them in an InstrumentedThreadPoolExecutor. This will give you metrics useful for tuning them, including:

  • Pool size (gauge)
  • Queue size (gauge)
  • Submitted count (meter)
  • Completed count (meter)
  • Rejected count (meter)
  • Running count (counter)
  • Task duration (timer)

Using the constructor for DiagnosticExecutionContext that takes a MetricRegistry will apply these to the created context for you.


Common code for services, for configuration, logging and metrics.







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