Quick links:
The Cedi Distributed Trace library provides the capability to instrument effectful programs such that logical traces can be derived and recorded across physical processes and machines. This instrumentation is expressed in a format that is interoperable with Comcast Money. This library consists of immutable data structures which represent the instrumentation and an interpreter - the TraceT[F, A] - which annotates the underlying action (represented as an F[A] where F is the effectful action and A is the result type). The TraceT[F, A] can be thought of as a function from a TraceContext (the cursor into the active trace) to an effectful program whose execution you wish to trace (the effectful program can be any F, such as fs2.Task, though often you'll need implicit fs2.util.Catchable[F] or fs2.util.Suspendable[F] instances if you using something other than Task). Because fs2.Task is often used as the effectful data type, this library provides a type alias TraceTask[A] for TraceT[Task, A] and convenience methods to work with this type alias (the latter included in a TraceTask object).
This library is implemented using functional data structures and techniques and is best used by similarly constructed programs.
It is non-blocking with a small footprint and incurs a reasonably low overhead.
No special thread pools or piggybacking on thread locals and the like are employed.
dtrace is built on Scala and its core constructs use the Functional Streams for Scala / FS2 library.
It is interoperable with Comcast Money. Money is a great library and dtrace was created to complement it, providing a purely functional model where Money has to make some consessions to Java interoperability (it is certainly conceivable that dtrace could at some point be incorporated into Money).
A Money-compliant Distributed Trace is a directed graph of Spans. A Span identifies a branch of the overall Trace representing a logical step or action, executing within the local process. All but the first Span in a Trace has a Parent Span indicating the upstream operation which triggered its child. Span's are identified by a unique Span Identifier (SpanId) along with a parent SpanId (and the overall Distributed Trace GUID). A Trace's first Span has a parent SpanId equal to its own. Each Span also consists of metadata about the action, including whether its action executed successfully or failed (and if a failure, details on it), the duration of the action execution in microseconds, where the Span executed (in which application; on which node; in which process; within what environment, etc), and, optionally, individual Notes specific to the Span (e.g., the Note with the Host Address of a cable settop box for an action issuing an initialize command to the device). A logical Trace (for example, "issue an initialize to a settop box") might originate from a business system with its transmission Span passed in an HTTP header to a microservice running in the cloud which executes Spans to query a persistent data store before making a binary RPC call (recorded in a Span) to a second microservice, passing the current trace information in the RPC context, before that second microservice finally issues the initialize command to the settop, ending the Trace. The dtrace library provides a logging Emitter to record the Spans, as they are executed, to the configured logging system in both JSON and text formats but also provides the means by which custom emitters can be provided.
import fs2.{ Strategy, Task }
import java.time.Instant
import java.util.UUID
import com.ccadllc.cedi.dtrace._
import com.ccadllc.cedi.dtrace.logging.LogEmitter
import TraceSystem._
/*
* Some simple data types for our examples.
*/
case class Region(name: String)
case class SalesReport(total: Double, message: String)
case class SalesFigure(region: String, product: String, units: Int, total: Double)
case class HttpHeader(name: String, value: String)
/*
* We create an implicit strategy for our effectful `fs2.Task`s.
*/
implicit val strategy: Strategy = Strategy.fromFixedDaemonPool(
Runtime.getRuntime.availableProcessors * 2, "dtrace-usage-example"
)
/*
* Near the beginning of the universe, create a `TraceSystem` object to
* hold the top-level information about the program (application and node name,
* deployment and environment names, etc.)
*/
val traceSystem = TraceSystem(
metadata = Map(
"application name" -> "sales-management-system",
"application ID" -> UUID.randomUUID.toString,
"node name" -> "crm.widgetsforsale.com",
"node ID" -> UUID.randomUUID.toString,
"deployment name" -> "us-west-2",
"environment name" -> "production"
),
/* This emitter will write a text entry for each span to the "distributed-trace.txt"
* logger and a JSON entry for each span to the "distributed-trace.json" logger; however,
* it is easy to provide your own emitter by implementing the `TraceSystem.Emitter[F]`
* trait, which requires providing implementations for two methods:
* `def description: String` to provide a description of your emitter and
* `def emit(tc: TraceContext[F]): F[Unit]` to actually do the work of
* emitting the current Span to the destination and in the format of your choosing.
*/
emitter = LogEmitter[Task]
)
val region = Region("Philly")
val httpHeader = HttpHeader("content-type", "application/json")
def retrieveSalesFigures(region: Region): Task[Vector[SalesFigure]] = Task.now(
Vector(SalesFigure("PA", "widget2000", 200000, 850000.0), SalesFigure("NJ", "widget1000", 100000, 550003.50))
)
def calculateSalesReport(figures: Vector[SalesFigure]): Task[SalesReport] = Task.now(SalesReport(figures.map(_.total).sum, "success!"))
def generateSalesReport(region: Region): TraceT[Task, SalesReport] = for {
/*
* Calculate the new quarterly sales figure and generate the report. Note that the import of
* `com.ccadllc.cedi.dtrace._` enriches the `fs2.Task` type by adding a `newSpan`
* method to it using an implicit class. The two lines that follow this comment would,
* without the syntax enrichment, be written as:
* figures <- TraceT.toTraceT(retrieveSalesFigures(region).newSpan(Span.Name("retrieve-sales-figures"), Note.string("region", region.name))
* result <- TraceT.toTraceT(calculateSalesReport(figures)).newSpan(
* Span.Name("calculate-sales-report"), Note.string("region", region.name), Note.long("total-figures", figures.size.toLong)
* )
*/
figures <- retrieveSalesFigures(region).newSpan(Span.Name("retrieve-sales-figures"), Note.string("region", region.name))
report <- calculateSalesReport(figures).newSpan(
Span.Name("calculate-sales-report"), Note.string("region", region.name), Note.long("total-figures", figures.size.toLong)
)
} yield report
/*
* Retrieve the span, in this example, contained in the HTTP header from the originating business system,
* if it exists. This logic may be included an an `akka-http` directive, for example.
*/
val rootSpanEither = SpanId.fromHeader(httpHeader.name, httpHeader.value).right.map {
spanId => Span.newChild[Task](spanId, Span.Name("sales-management-system-root"))
}
/*
* We add a Span to the overall `generateSalesReport` action,
* showing the ability to create Span notes from the traced action result
* with `newAnnotatedSpan`.
*/
val tracedTask: TraceT[Task, SalesReport] = generateSalesReport(region).newAnnotatedSpan(
Span.Name("generate-sales-report"), Note.string("region", region.name)
) { case Right(report) => Vector(Note.string("sales-report", report.toString)) }
/*
* We convert our traced task to a task.
*/
val task: Task[SalesReport] = for {
/* If there was no Span originating from another system found in the HTTP Header, we create a local root Span */
rootSpan <- rootSpanEither.right.getOrElse(Span.root[Task](Span.Name("locally-initiated-report")))
/*
* The tracedTask we've derived earlier around `generateSalesReport` (which includes
* the retrieval and calculate sales figures nested actions, each with their own Spans) is an instance of `TraceT[Task, A]`,
* which is a data structure associating a Span (like "calculate-sales-figures") with its underlying `Task`
* (reiterating that we're using `fs2.Task` in this example, but again, `Task` can be substituted with any
* `F`). When we are done building up these annotated `TraceT` instances, we need to "tie the knot" by
* converting the top-level instance back into a plain `Task` again before we can actually run it. This is
* accomplished by applying the root `Span` for this process (in this example, the one we extracted from an
* HTTP header) using the `trace` method on on our top-level `TraceT` instance (represented here by the
* `tracedTask` value).
*/
result <- tracedTask.trace(TraceContext(rootSpan, traceSystem))
} yield result
/*
* Now, at the end of the universe, we run the task. This will result, in this example using the supplied logging
* framework Emitter, in the following items logged via the `distributed-trace.txt` logger:
* Span: [ span-id=-4268861818882462019 ] [ trace-id=2a71fb7b-f38d-4f6a-a4d1-229c6c5bc963 ] [ parent-id=-6262761813211462065 ]
* [ span-name=retrieve-sales-figures] [ app-name=sales-management-system ] [ start-time=2016-09-26T00:29:14.802Z ]
* [ span-duration=2500 microseconds ] [ span-success=true ] [ failure-detail=N/A ][ notes=[name=region,value=Philly] ]
* [ node-name=crm.widgetsforsale.com ]
*
* Span: [ span-id=-2264899918881452036 ] [ trace-id=2a71fb7b-f38d-4f6a-a4d1-229c6c5bc963 ] [ parent-id=-6262761813211462065 ]
* [ span-name=calculate-sales-report] [ app-name=sales-management-system] [ start-time=2016-09-26T00:29:14.799Z ]
* [ span-duration=2500 microseconds ] [ span-success=true ] [ failure-detail=N/A ]
* [ notes=[name=region,value=Philly], [name=total-figures,value=2] ] [ node-name=crm.widgetsforsale.com ]
*
* Span: [ span-id=-6262761813211462065 ] [ trace-id=2a71fb7b-f38d-4f6a-a4d1-229c6c5bc963 ] [ parent-id=-9466761813211462033 ]
* [ span-name=generate-sales-report] [ app-name=sales-management-system ] [ start-time=2016-09-26T00:29:14.797Z ]
* [ span-duration=5000 microseconds ] [ span-success=true ] [failure-detail=N/A ]
* [ notes=[name=region,value=Philly], [name=report,value=SalesReport(1400000.50, success!)] ]
* [ node-name=crm.widgetsforsale.com ]
*
* Span: [ span-id=-9466761813211462033 ] [ trace-id=2a71fb7b-f38d-4f6a-a4d1-229c6c5bc963 ] [ parent-id=2488084092502843745 ]
* [ span-name=sales-management-root ] [ app-name=sales-management-system ] [ start-time=2016-09-26T00:29:14.793Z ]
* [ span-duration=5110 microseconds ] [ span-success=true ] [ failure-detail=N/A ][ notes=[] ]
* [ node-name=crm.widgetsforsale.com ]
*/
task.unsafeRun()Cedi Distributed Trace supports Scala 2.11 and 2.12. This distribution is published to Maven Central and consists of two library components.
This is the core functionality, recording trace and span information over effectful programs, passing these recorded events to registred emitters for disposition.
libraryDependencies += "com.ccadllc.cedi" %% "dtrace-core" % "1.1.0"This component provides emitters for logging the trace spans in text and/or JSON format using the sf4j logging framework. It uses the circe library for formatting the trace span information as JSON.
libraryDependencies ++= "com.ccadllc.cedi" %% "dtrace-logging" % "1.1.0"This project is made available under the Apache License, Version 2.0. Copyright information can be found in NOTICE.