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Showing posts with label OSS. Show all posts
Showing posts with label OSS. Show all posts

The next chapter

Every end is a new beginning

For the past 6 years I’ve been a part of the CM-Well development team. I’m writing this post with lot’s of mixed feelings. Working on CM-Well has been an awesome experience! I got a chance to work with so many amazing people. But now it’s time to move on, and I’m excited to start a new gig.

CM-Well - the early days

Last year we open-sourced CM-Well, and released the code on github. Doing so involved cleaning up the code, and getting rid off the commit history. Many team members who contributed a lot to the success of the project are not recognized. So, to get it out in the open, I opened the old repo, which has commits up until July last year, and I’m sharing some stats1.

$ git shortlog -sn --all
  2697  Gilad Hoch
  1015  yaakov
   954  michael
   914  israel
   739  Israel
   720  Vadim.Punski
   580  Gilad.Hoch
   450  Mark Zitnik
   310  Tzofia Shiftan
   234  Matan Keidar
   222  Mark.Zitnik
   170  Eli
   125  Yaakov Breuer
   102  Michael.Irzh
    95  Michael
    94  Israel Klein
    87  Tzofia.Shiftan
    58  Eli Orzitzer
    54  Michael Irzh
    44  Builder
    42  gilad
    31  DockerBuilder
    22  dudi
    14  Dudi
    14  Dudi Landau
    14  Yoni.Mataraso
    10  matan
     9  tserver
     8  Liorw
     5  Liya.Katz
     4  israel klein
     2  Shachar.Ben-Zeev
     2  Yoni Mataraso
     2  builder
     1  James Howlett
     1  Yaakov

These are just the commits in the old repo, not including any new commits in github. I also created visualizations using Gource for the old repo.

The first has files fading, and focuses on the contributors:

The second version gives an overview of the entire project:

The project is active, and has a lot of work invested in it, as you can see from the videos. But it doesn’t quite show how CM-Well is used internally. So I fetched some random access.log file from one of the servers, and wrote a little something to convert the log into a logstalgia acceptable format:

https://github.com/hochgi/logstalgia-access-log-converter

I took this opportunity to get to know mill build tool, and some libraries I wanted to experiment with. Long story short, I got 10 minutes of real CM-Well action on a single node (which is part of a cluster that has 20 web servers in it - so you’re only getting 1/20th of the action), and made a visualization using logstalgia with:

$ logstalgia -f -1280x720 --title "CM-Well access.log visualization"                \
    --screen 2 -p 0.2 -u 1 --background 75715e -x -g "meta,URI=/meta/.*$,10"        \
    -g "SPARQL,URI=/_sp?.*$,30" -g "_out,URI=/_out?.*$,10" -g "_in,URI=/_in?.*$,10" \
    -g "misc,URI=/.*,40" --paddle-mode single --paddle-position 0.75                \
    --disable-progress --font-size 25 --glow-duration 0.5 --glow-multiplier 2       \
    --glow-intensity 0.25 converted-access.log

And the output:

I gotta say, it came out pretty neat!2

Goodbye

I suck at goodbyes, so let me just say that I really loved working on CM-Well. It is a great project, and I hope to see it thrive. I will keep track of it, and plan to contribute occasionally on my spare time.


  1. In the early days, we used SVN, and we converted the repo to git at some point, which is why you see some duplicated names (that, and also we may have also committed from multiple users).↩︎

  2. Kinda get me into thinking I should write a logstash appender that streams real-time action directly into a logstalgia end point. It’s gotta be the coolest monitoring one can ask for…!↩︎

Unfolding streams like a boss (part 2)

Parallelizing resumable bulk consumes with CM-Well & akka-stream

In the previous post we introduced CM-Well’s consume API, and showed how it is tailored to be used with akka-http & akka-stream. This post will get into the gory details of how to squeeze the most out of CM-Well, using the bulk-consume API with akka-http & akka-stream.

The consume API in depth

There are two types of “consumes”, consume & bulk-consume. We will focus on the latter. But a few words on consume to not leave you hanging: consume just wraps a regular search, with a few extra filters. in terms of CM-Well’s qp, it translate to the following:

Given filters qp=$QP and an timestamp index-time=${ITIME:-0}, CM-Well generates the following (loosely) equivalent search parameters:

# set to current time minus 30 seconds
NOW=$(
  MILLIS=$(  date +%s%N | cut -b1-13 )
  calc $MILLIS - 30000
)

"?op=search
 &qp=system.indexTime>$ITIME,system.indexTime<$NOW,[$QP]
 &sort-by=system.indexTime
 &length=100"

It then fetches those (up to) 100 (by default) sorted results, and: if all results has the same index time SOME_ITIME, it will replace the previous op=search with op=stream, and previous qp=system.indexTime>$ITIME,system.indexTime<$NOW,[$QP] with qp=system.indexTime:$SOME_ITIME,[$QP]. else it will have multiple values, all sorted. it will drop all the tailing results with index time = $MAX_ITIME, and return the rest, with a token in the header setting the next $ITIME to be $MAX_ITIME - 1.

These are the basics, the are a few more concerns to take into consideration, and if thats interest you, go ahead and check the source code.

Understanding bulk-consume

In contrast to consume API, bulk-consume tries to be more efficient, and retrieve a lot more infotons per request à la stream style. Under the hood it uses Elasticsearch’s scroll API in a similar way to how we described stream API is made in the previous post. The problem is, that you can’t get sorted results with scroll from Elasticsearch. So, instead of advancing the timeline using sorted search, we filter results in advance.

This means there’s a pre-processing phase where we try to find a from and to timestamps, that are not “too far” apart, in terms of number of results, but enough to stream efficiently. CM-Well does it using a simple binary search to do so, and it tries to return a chunked response with O(1M) results (by default). There are many edge cases covered, like an early cutoff, if the binary search doesn’t converged fast enough, And dealing with near current time results, etc’…

Like consume, bulk-consume returns a position token in headers. In fact, the tokens are interchangeable between the 2 APIs, But those returned from a bulk-consume request, might contain some extra attributes. It turns out, that many pre-processing phases can be avoided if previous request stored an optional next “to” timestamp it might have encounterd during the binary search. So, what’s so great about the bulk-consume API? pipelined parallelization! You see, the next position token is given eagerly in the response headers, and a user can use it right away to fire up the next request. Since it will probably take some time to fetch all those O(1M) results, you could end up with as many parallel streams of data that you can handle.

But, you might ask: “What about failures? retrying?”, the answer is, that bulk consume also let’s you set upper bound timestamp explicitly. If your token was an optimized one, you can reuse it safely. If not, a new binary search might yield different time range, and you could end up with duplicate results, or worse, data gaps. To overcome this, you should supply a timestamp explicitly when retrying. But, what should you supply? Well, there’s another header for that. Other than X-CM-WELL-POSITION header, you also get X-CM-WELL-TO header, and the value is the upper bound timestamp found in the binary search. You should supply this timestamp using to-hint query parameter, and retry the bulk-consume request with it. Note that if the position token is optimized, to-hint will be ignored.

OK, got it. let’s write some code

As implied, we will show how to build an akka-stream Source of data from CM-Well, using unfoldFlow, Retry, and other cool constructs you can find on akka-stream & akka-stream-contrib libs.

The easy part (motivation)

Assuming we can somehow get:

type PositionToken = String
val initialPosition: PositionToken = ???
val consume: Flow[PositionToken,(PositionToken,ByteString),_] = ???

The work left is ridiculously easy thanks to unfoldFlow:

SourceGen.unfoldFlow(initialPosition)(consume)

And we’re done! OK… not really… It’s too simplified. unfoldFlow can’t unfold the next element until it gets the previous generated state. This means that all our fancy talk about pipelining parallelization isn’t being taken into consideration here. So let’s try and improve that. How ’bout:

val consume: Flow[PositionToken,(PositionToken,Source[ByteString,_]),_] = ???
SourceGen.unfoldFlow(initialPosition)(consume)
         .flatMapConcat(List.apply)

This is already much better. Each bulk-consume Source is being queried eagerly. But we still have a downside here… bulks are not evenly sized, and size is counted as the number of infotons in the bulk. Not their actual size… Moreover, we mentioned retries are supported using to-hint with X-CM-WELL-TO header’s value. So, if we are going to retry some streams, this means we need to buffer an entire chunk, and only emit once we know it is complete, so we don’t get duplicate results from retries. This implies a single bulk can get us “stuck” waiting for it. The 2 major problems are: * No matter how high we set up our parallelization factor, we could still end up back-pressuring our slow source (by slow, I mean that whatever the use-case, we must assume a fast consumer. e.g: flush to disk, which is much faster than our network calls). * Having $parallelization-factor × O(1M) all buffer into memory, makes our GC inefficient, due to objects kept in memory for long time. And also we cause our downstream to be in starvation until we “unstuck” the current bulk.

So, since bulk are not sorted according to timeline anyway, then no reason not to use merge instead of concat:

SourceGen.unfoldFlow(initialPosition)(consume)
         .flatMapMerge(identity)

Also, we will try to optimize even further. Our queries to bulk-consume are our “bottle-neck”. So, it is better to not pull in all the data with the bulk. let’s use a thin format, like tsv, which won’t return data itself, only a tuple consisting of infoton’s path, date, uuid, and indexTime. This way, we can at a later stage pull in data of small batches of infotons we got from the bulk consume. So our final higher level stream should either look like:

val consume: Flow[PositionToken,(PositionToken,Source[List[ByteString],_]),_] = ???
val addData: Flow[List[ByteString],ByteString,_] = ???
SourceGen.unfoldFlow(initialPosition)(consume)
         .flatMapMerge(identity)
         .mapConcat(identity)
         .via(addData)

where addData flow utilizes Balance to fan out and parallelize data fetching job, and then fan back in to construct a Flow shape which takes care of parallelization internally. Another option, is to use a simpler mapAsync(parallelizationFactor)(...) to get an easier way to parallelize the data fetching job. Or, it can look like:

val consume: Flow[PositionToken,(PositionToken,Source[List[ByteString],_]),_] = ???
val addData: Flow[List[ByteString],ByteString,_] = ???
SourceGen.unfoldFlow(initialPosition)(consume)
         .flatMapMerge(_.via(addData))

OK, armed with our goal in mind, let’s implement the consume flow: ### The detailed part Let’s try to break down & visualize the stream:

  • Retry:
    • bulk-consume ~> fold results into a single element
    • if #lines != X-CM-WELL-N header or any other failure:
      • retry with to-hint=${X-CM-WELL-TO}
    • else
      • emit results
  • Enrich with data:
    • batch paths
    • Balance vs. mapAsync parallelization of fetching (we’ll introduce both approaches)

Wrapping up

This might be covered in a future post. Currently, It has been sitting untouched for too long, and I’m “flushing” it. For now, implementation details are left as an excersize for the reader ;)

A tale of bad framework choices

and how it led to interesting code

One of the most interesting pieces of code in CM-Well, IMO, is the http client util code. In short, it defines an API for a http client, and wraps akka-http which serves as the “http client engine”. Why not just use akka’s API, you might ask…? Well, we’ll get there, but first, a bit of history. (TL;DR: A story of why suffered from tightly coupled code, and how we solved it elegantly + code samples)

A long time ago

(In a galaxy far far away) CM-Well was written mostly in java1 (oh boy…), only the web-service logic was written in scala. The scala eco-system looked very different compared to what it is today. The most popular scala web framework at the time was lift, which was much more relevant than play! Back then play was very new, maybe 1.0 or 1.1, and was still written in java. So it was decided to write the CM-Well web service with lift. But not only the web service… You see, lift classes were already on the classpath. Especially lift’s HttpSupport from the testkit, which had reasonable API. So all of CM-Well’s integration tests were written using it. It served it’s purpose, and tests kept pilling up, until we decided to upgrade scala from 2.9 to 2.10. lift was holding us back, and play started to look more and more attractive. We replaced lift with play! but decided to rewrite the integration tests without using play’s testkit or http client. After all, what if down the road we would want to replace it with something else? We didn’t want to be tied up to much to the web framework, so we minimized our dependency in play as much as possible. For the integration tests, we decided to go with a popular scala http client instead. The infamous Dispatch (don’t judge… back then it was popular). For those who are not familiar with dispatch, it’s a scala http client library, which makes heavy use of symbolic operators (check out the Periodic Table). Other than making the code less readable with all those fancy operators to new-comers, it served it’s purpose pretty good. That is until… Yup. It was time to upgrade scala again, and move from version 2.10 to 2.11. And again, our integration tests (and other code that made use of an http client), were written with a library that didn’t move fast enough. It held us back, causing a “jar hell” of unwanted & outdated dependencies… and we grew tired of all the weird operators. But no one wanted to rewrite all the tests again… that’s a lot of dirty work. We hacked, and managed to get by with using it only in tests, so at least we didn’t had the jar hell on our main artifacts classpath, just in tests. Other code in main artifacts that needed a http client, used whatever was on the classpath directly. Be it play’s WS, or spray’s client, it didn’t really mattered. But time went by, and tests kept pilling up, and it was way due to clean the code. Being a bit wiser from the bad experience, we decided to make sure that tests code will never hold us back again from changing libraries and frameworks. We decided to write a thin wrapper with a sane & simple asynchronous http client API. but wrapper for what? well… it doesn’t matter. That was the whole point; if the wrapping layer is small enough, we can always change the underlying library easily, and won’t have to patch up thousands lines of testing code if we ever switch to another library. Anyway, we needed to pick up something, and at the time, we were really excited about the recent developments in akka. It was 2015, and the akka-stream & akka-http experimental modules came out. We decided to check it out, but the experimental modules were too risky for production code, which made it a perfect candidate to serve in our tests as a dispatch replacement, without affecting production code. This was 2 birds in 1 stone - evaluating an interesting technology in it’s early stages, with real code, without risking anything crucial, and using our thin wrapper to decouple test’s logic from the http client library. P.S. to be on the safe side, and for the sport of it, we started to implement the same thin API on top of ning’s AsyncHttpClient, but never really continued with it, since akka-http got the job done perfectly. But some remnants stayed commented out in the sources, waiting for the day that will never come.

We ❤ Akka-http

Choosing akka was challenging. It introduces this new concept of streams, which, at least in tests, we wanted to abstract away for simplicity. But then again, just consuming everything eagerly, hiding completely the reactive nature of the API, and returning a future of response when everything is done, is asking for performance troubles. We needed a simple default, with an easy way of exploiting the asynchrony & reactive capabilities of akka-http. For that, we made heavy use of type classes, in a slightly adapted version of what is known as the magnet pattern.

The gory details

Let’s see some code, shall we..? starting with the API itself:

object SimpleHttpClient {

  // web sockets API is more complex, and out of the scope for this post,
  // but is shown here for completeness, as it is part of the API.
  // You are more than welcome to check out the source code.
  def ws[T : SimpleMessageHandler](uri: String,
         initiationMessage: T,
         subprotocol: Option[String] = None,
         queryParams: Seq[(String,String)] = Nil,
         headers: Seq[(String,String)] = Nil)(react: T => Option[T])
        (implicit ec: ExecutionContext,
         as: ActorSystem = this.sys,
         mat: Materializer = this.mat) = ...

  def get[T : SimpleResponseHandler](uri: String,
          queryParams: Seq[(String,String)] = Nil,
          headers: Seq[(String,String)] = Nil)
         (implicit ec: ExecutionContext,
          as: ActorSystem = this.sys,
          mat: Materializer = this.mat) = ...

  def put[T : SimpleResponseHandler](uri: String,
          body: Body,
          contentType: Option[String] = None,
          queryParams: Seq[(String,String)] = Nil,
          headers: Seq[(String,String)] = Nil)
         (implicit ec: ExecutionContext,
          as: ActorSystem = this.sys,
          mat: Materializer = this.mat) = ...

  def post[T : SimpleResponseHandler](uri: String,
           body: Body,
           contentType: Option[String] = None,
           queryParams: Seq[(String,String)] = Nil,
           headers: Seq[(String,String)] = Nil)
          (implicit ec: ExecutionContext,
           as: ActorSystem = this.sys,
           mat: Materializer = this.mat) = ...

  def delete[T : SimpleResponseHandler](uri: String,
             queryParams: Seq[(String,String)] = Nil,
             headers: Seq[(String,String)] = Nil)
            (implicit ec: ExecutionContext,
             as: ActorSystem = this.sys,
             mat: Materializer = this.mat) = ...

}

Overall, this looks like a pretty straight forward http client API. Let’s try to clear the fog from the unclear parts: Each of the methods returns a Future[SimpleResponse[T]]. I know what you might be thinking… I said simple API, and here I am, showing some fancy code with weird classes, right…? I’ll list down what might be interesting here:

  • implicit as: ActorSystem = this.sys & mat: Materializer = this.mat
  • Body
  • SimpleResponse[T] & SimpleResponseHandler

ActorSystem & Materializer

In akka-http, in order to handle http requests & reponses, you’ll need to get a hold of a HttpExt, which takes an ActorSystem in Http’s factory method. Also, a connection flow to build the request graph around it is needed. To make things simple, we use superPool which returns a flow that routes requests through cached (per host) connection pools, and is managed by akka. It needs a Materializer. We also need a Materializer for running a simple graph per request. something like:

Source.single(request -> context).via(connectionPool).runWith(Sink.head)

Which performs the request and return a Future[Try[HttpResponse]]. Lastly, we’ll need the Materializer also to handle akka’s HttpResponse, which returns the payload in the form of Source[ByteString,_]. Remember, we wanted to abstract away anything that binds us to the library, so we can’t leak (unless we want to, more on that is to follow) akka’s classes. Not Source nor ByteString. We need to convert it to something else. Anyway, as you can see, it’s needed. But if you pay attention, you’ll see it has default values. This let’s us provide reasonable defaults, which can be configured freely using standard typesafe’s config. The provided reference.conf only defines the bare minimum:

cmwell.util.http {
  akka {
    actor {
      provider = "akka.actor.LocalActorRefProvider"
    }
    http {
      host-connection-pool {
        max-open-requests = 1024
      }
    }
  }
}

And as you might have already guessed, the provided actor system is configured using:

ActorSystem("SimpleHttpClient",ConfigFactory.load().getConfig("cmwell.util.http"))

Also, the provided Materializer & ActorSystem are LAZY (as in lazy val), So it won’t even get instantiated if this code is run within production code which makes sure to supply a fully configured ActorSystem & Materializer. But, you might ask: isn’t this binds us to akka? well, technically, yes. in practice, materializer and actor system are passed implicitly, so it’s not written in code (keeping aside some very rare cases). I.E: in the tests, you don’t see any reference to any materializer or actor system, and we are still loosely coupled, thanks to scala being such a flexible language when it comes to defaults & implicits.

Body

The post & put methods also take a mysterious body: Body, so what is it? Of course, as the name suggests it’s the request body. But, you might ask: Should a user be troubled with creating such objects? The answer is no. The Body companion object hosts some pretty useful implicits:

sealed trait Body {
  def entity(contentType: Option[String]): RequestEntity
  def contentType(ct: String): akka.http.scaladsl.model.ContentType = ...
}

object Body {
  import scala.language.implicitConversions

  implicit def apply(body: String): Body = new BodyFromString(body)
  implicit def apply(body: Array[Byte]): Body = new BodyFromBytes(body)
  implicit def apply(body: ByteString): Body = new BodyFromByteString(body)

  private class BodyFromString(body: String) extends Body {  ...  }
  private class BodyFromBytes(body: Array[Byte]) extends Body {  ...  }
  private class BodyFromByteString(body: ByteString) extends Body {  ...  }
}

This means, that you may pass the body argument as whatever you want, be it a String, a Array[Byte] or even akka’s ByteString. And if we ever need something else, it’s very easy to add more automatically acceptable types. We can just add another implicit conversion in Body’s companion object. or, if it’s a special case, then just instantiate a new Body locally, or write your own implicit conversions.

SimpleResponse[T] & SimpleResponseHandler

SimpleResponse is the reponse we get back from executing the request, it’s a pretty simple case class:

object SimpleResponse {

  type ContentType = String
  type ResponseBody[T] = (ContentType, T)

  ...
}

case class SimpleResponse[T : SimpleResponseHandler](status: Int,
                                                     headers: Seq[(String,String)],
                                                     body: ResponseBody[T]) {
  def contentType = body._1
  def payload = body._2

  override def toString() = ...
}

It has an Int for status, a Seq[(String,String)] for headers, and a ResponseBody[T], which is just a tuple of the mimetype (String) and the body, which can be anything that has a SimpleResponseHandler. All methods in exposed API has a type parameter T that are context bound to SimpleResponseHandler, which is the type class responsible of generating the appropriate response for us from the response returned by underlying library - i.e: akka. It means we need an implicit SimpleResponseHandler[T] in scope. Now, please look carefully at the methods signature; none of the parameters has type T. So, you might think this means the compiler cannot infer the type, and user always have to explicitly write it down? The answer, is no. let’s try it out in the REPL:

scala> val res = SimpleHttpClient.get("http://google.com")
res: scala.concurrent.Future[cmwell.util.http.SimpleResponse[Array[Byte]]] = ...

What happens here, is that there is a single implicit that’s available in SimpleResponseHandler companion, and thus is taken by the compiler (If only one implicit instance of SimpleResponseHandler for some T can be found, than that is what’s being picked up, regardless of what T is). This one implicit has the most general type for a response body. It is simply an Array[Byte]. So, if the reponse can fit in memory, it’ll be returned as an Array[Byte]:

trait SimpleResponseHandler[T] {
  def mkStringRepr(t: T): String
  def mkResponseOf(status: Int,
                   headers: Seq[(String,String)],
                   contentType: String,
                   dataBytes: Source[ByteString,Any])
                  (implicit ec: ExecutionContext): Future[SimpleResponse[T]]
}

object SimpleResponseHandler {

  implicit object ByteArrayHandler extends SimpleResponseHandler[Array[Byte]] {
    ...
  }
}

But, if you want something else, all you have to do is import the appropriate implicit (more sensible implicit instances of SimpleResponseHandler can be found in SimpleResponse.Implicits:

object SimpleResponse {

  ...

  // if you want a SimpleResponse[T] for T != Array[Byte],
  // import a SimpleResponseHandler[T] from here (or implement your own)
  object Implicits {

    implicit object InputStreamHandler extends SimpleResponseHandler[InputStream] {
      ...
    }

    implicit object UTF8StringHandler extends SimpleResponseHandler[String] {
      ...
    }
  }
}

So if response body cannot fit in memory, for instance, simply import the appropriate implicit handler:

scala> import cmwell.util.http.SimpleResponse.Implicits.InputStreamHandler
import cmwell.util.http.SimpleResponse.Implicits.InputStreamHandler

scala> val res = SimpleHttpClient.get("http://google.com")
res: scala.concurrent.Future[cmwell.util.http.SimpleResponse[java.io.InputStream]] = ..

It just works because imported implicits takes precedence over implicits defined in the type class’ companion. Of course, If you import more than one handler, you’ll have to explicitly mention the type, or you’ll get a compiler error for “ambiguous implicit values”.

Nice, so… it seems pretty solid. What else?

The implementation we have in CM-Well’s code base is far from complete:

  • Not all http methods are defined (e.g: HEAD)
  • One can think of way more sensible generic response handlers to add in Implicits object (e.g: one that simply returns the Source[ByteString,_] from akka’s response directly)
  • Classes can be arranged better, in separate files.

Basically, this API is being extended lazily, when we need to add something new, and is not a complete solution.

OK, let’s wrap it up

This post is already getting longer than I thought, and we haven’t covered web sockets API, or how we convert akka’s classes to simple types. So lets leave it as an exercise for the reader 😉. The bottom line of this post, is how we ended up with a nice API, which is very flexible, extendable, and implemented in not too many lines of code. The road was a bit bumpy, but the lesson learned was worth it.

P.S.

If you want to get your hands dirty, we would love to get some PRs! (regarding the code shown here, or any other part of CM-Well).


  1. In fact, CM-Well started out as a POC with python & django, and only later was implemented in java & scala.↩︎

CM-Well is OSS!

Ummm… what is this CM-Well thing?

CM-Well is the project Iv’e worked on in Thomson Reuters for the past few years. I’m not gonna write here about the project itself (there’s more than enough information in the project docs), instead, I’ll write on my personal experience working on it.

So what’s now?

Well, this is just the first post in a series of posts I plan to publish, dealing with CM-Well development. You can expect follow-up posts on, e.g: “A tale of bad framework choises (and how it led to interesting code)”, or “CM-Well & OSS before it became OSS”, and more…

Meanwhile…

Go ahead and check it out:
github.com/thomsonreuters/CM-Well