RetryFlow

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def withBackoff[In, Out, Mat](minBackoff: FiniteDuration, maxBackoff: FiniteDuration, randomFactor: Double, maxRetries: Int, flow: Flow[In, Out, Mat])(decideRetry: (In, Out) ⇒ Option[In]): Flow[In, Out, Mat]

API may change!
API may change!
Allows retrying individual elements in the stream with an exponential backoff.
The retry condition is controlled by the decideRetry function. It takes the originally emitted element and the response emitted by flow, and may return a request to be retried.
The implementation of the RetryFlow requires that flow follows one-in-one-out semantics, the Flow may not filter elements, nor emit more than one element per incoming element. The RetryFlow will fail if two elements are emitted from the flow, it will be stuck "forever" if nothing is emitted. Just one element will be emitted into the flow at any time. The flow needs to emit an element before the next will be emitted to it.
minBackoff
minimum duration to backoff between issuing retries
maxBackoff
maximum duration to backoff between issuing retries
randomFactor
adds jitter to the retry delay. Use 0 for no jitter
maxRetries
total number of allowed retries, when reached the last result will be emitted even if unsuccessful
flow
a flow to retry elements from
decideRetry
retry condition decision function
def withBackoffAndContext[In, CtxIn, Out, CtxOut, Mat](minBackoff: FiniteDuration, maxBackoff: FiniteDuration, randomFactor: Double, maxRetries: Int, flow: FlowWithContext[In, CtxIn, Out, CtxOut, Mat])(decideRetry: ((In, CtxIn), (Out, CtxOut)) ⇒ Option[(In, CtxIn)]): FlowWithContext[In, CtxIn, Out, CtxOut, Mat]

API may change!
API may change!
Allows retrying individual elements in the stream with an exponential backoff.
The retry condition is controlled by the decideRetry function. It takes the originally emitted element with its context, and the response emitted by flow, and may return a request to be retried.
The implementation of the RetryFlow requires that flow follows one-in-one-out semantics, the FlowWithContext may not filter elements, nor emit more than one element per incoming element. The RetryFlow will fail if two elements are emitted from the flow, it will be stuck "forever" if nothing is emitted. Just one element will be emitted into the flow at any time. The flow needs to emit an element before the next will be emitted to it.
The wrapped flow and decideRetry take the additional context parameters which can be a context, or used to control retrying with other information.
minBackoff
minimum duration to backoff between issuing retries
maxBackoff
maximum duration to backoff between issuing retries
randomFactor
adds jitter to the retry delay. Use 0 for no jitter
maxRetries
total number of allowed retries, when reached the last result will be emitted even if unsuccessful
flow
a flow with context to retry elements from
decideRetry
retry condition decision function

Related Doc: package backport

object RetryFlow

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def withBackoff[In, Out, Mat](minBackoff: FiniteDuration, maxBackoff: FiniteDuration, randomFactor: Double, maxRetries: Int, flow: Flow[In, Out, Mat])(decideRetry: (In, Out) ⇒ Option[In]): Flow[In, Out, Mat]

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