fsarradin

trait Functor[F[_]] {
  def (fa: F[A]) map[A, B](f: A => B): F[B]
}

case class IO[+A](run: () => A)
object IO {
  def pure[A](a: => A): IO[A] = IO(() => a)

  implied IOFunctor for Functor[IO] {

fsarradin

import scala.reflect._
import scala.compiletime._
import scala.compiletime.Shape._

enum Maybe[+A] derives ToJson {
  case Just(value: A)
  case Nope
}

fsarradin

name := "zkafka"

version := "0.1"

scalaVersion := "2.13.1"

libraryDependencies ++= Seq(
  "dev.zio" %% "zio" % "1.0.0-RC17",
  "dev.zio" %% "zio-streams" % "1.0.0-RC17",
  "com.propensive" %% "magnolia" % "0.12.6"

fsarradin

/**
 * Arithmetic expression representation.
 * 
 * In this expressions you can only have a unique variable.
 * eg. Mult(Var, Add(Const(2), Var)) represents X * (2 + X)
 */
sealed trait Expression
object Expression {
  // represent a variable
  case object Var extends Expression

fsarradin

import java.lang.{Boolean => JBoolean}
import scala.util.{Failure, Success, Try}
import com.typesafe.config._
import zio.config.PropertyTree.{Leaf, Record, Sequence}
import zio.config.{ConfigSource, PropertyTree, ReadError}
import zio.{IO, config}

/**
 * Load a configuration from different sources based on the given
 * configuration schema.

fsarradin

/**
 * Autogenerated by Avro
 *
 * DO NOT EDIT DIRECTLY
 */
package test.entity;
@org.apache.avro.specific.AvroGenerated
public enum Currency implements org.apache.avro.generic.GenericEnumSymbol<Currency> {
  EUR, USD  ;
  public static final org.apache.avro.Schema SCHEMA$ = new org.apache.avro.Schema.Parser().parse("{\"type\":\"enum\",\"name\":\"Currency\",\"namespace\":\"test.entity\",\"symbols\":[\"EUR\",\"USD\"]}");

fsarradin

package io.univalence.aboutfp

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession

object TextMain {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local[*]")
      .appName(getClass.getSimpleName)

fsarradin

import io.univalence.data.{Stock, StockKey, StockKeyBinarySerde, StockBinarySerde}
import java.time.Instant
import java.time.temporal.ChronoUnit
import org.openjdk.jmh.annotations._
import scala.util.Random

abstract class BaseKeyValueStoreBenchmark {

  val products = List("café", "chocolat", "miel")

fsarradin

import scala.util.{Failure, Success, Try}

/**
 * Tools to manage pseudo-binary data.
 *
 * A pseudo-binary data, or ''pseudobin''. Is a (almost) readable binary
 * format. It is not as optimized of pure binary format, but it is
 * easier to analyse and more optimized than the fully readable formats
 * like JSON, CSV, or XML. pseudobin is designed to be a predictable
 * format.

fsarradin

import org.apache.kafka.common.serialization.Serde
import org.apache.kafka.streams.kstream.{Named, ValueTransformerWithKey}
import org.apache.kafka.streams.processor.ProcessorContext
import org.apache.kafka.streams.scala.StreamsBuilder
import org.apache.kafka.streams.scala.kstream.KStream
import org.apache.kafka.streams.state.{
  KeyValueBytesStoreSupplier,
  KeyValueStore,
  Stores
}