Danila Matveev optician

Tofu docs (community edition)

It is in the core Tofu dependency - "ru.tinkoff" %% "tofu" % "0.7.0".

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

	import cats.effect.concurrent.Ref
	import cats.effect.{IO, Sync}
	import cats._
	import cats.implicits._
	import fs2.Stream

	def g(r: => IO[Ref[IO, Int]]): Stream[IO, Int] =
	Stream
	.eval(r)
	.flatMap(

	project.git = true
	maxColumn = 120
	lineEndings = unix
	docstring = "ScalaDoc"
	assumeStandardLibraryStripMargin = true

	continuationIndent.callSite = 2
	continuationIndent.defnSite = 4

	style = defaultWithAlign

	import scala.io._
	import java.io._
	import scala.collection.mutable.ArrayBuffer

	object Main {
	def main(args: Array[String]): Unit = {
	var i = 0
	var v1: ArrayBuffer[String] = new ArrayBuffer(10000)
	var v2: ArrayBuffer[String] = new ArrayBuffer(10000)
	Source.stdin.getLines.foreach{

	class A{
	val x: PartialFunction[Any, Int] = {
	case Some(x: Int) => x
	}
	}

	import pushka.json._
	import pushka.annotation._

	// First case.
	// Simple as is.
	@pushka
	case class A(subModel: B, i: Int, str: String)

	@pushka
	case class B(field1: Int, field2: List[Int])

	import org.scalameter._

	import scala.concurrent.ExecutionContext.Implicits.global
	import scala.concurrent.duration.Duration
	import scala.concurrent.{Await, ExecutionContext, Future}


	object Main {

	/*