4-StaticTyping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ce2109f4-b7dd-4013-8df1-ce7a93bb88f7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "# Preamble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ded01359-9cef-4d8c-87e3-397683500aec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import $ivy.`org.http4s::http4s-ember-client:0.23.23`\n",
    "import $ivy.`org.http4s::http4s-dsl:0.23.23`\n",
    "import $ivy.`org.http4s::http4s-circe:1.0.0-M40`\n",
    "import $ivy.`co.fs2::fs2-core:3.8.0`\n",
    "import $ivy.`co.fs2::fs2-io:3.8.0`\n",
    "import $ivy.`io.circe::circe-parser:0.15.0-M1`\n",
    "import $ivy.`io.circe::circe-generic:0.15.0-M1`\n",
    "import $ivy.`org.slf4j:slf4j-nop:2.0.9`\n",
    "import $ivy.`io.circe::circe-optics:0.15.0`\n",
    "import $ivy.`dev.optics::monocle-macro:3.2.0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "551b505e-fd4b-4f71-8ad6-954fb80fd3c2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import fs2.{Pipe, Pull, Stream}\n",
    "import cats.effect.{IO, Async}\n",
    "import cats.effect.unsafe.implicits.global\n",
    "import org.http4s.*, org.http4s.implicits._\n",
    "import io.circe.Json\n",
    "import io.circe.{`export` as _, Json, *}\n",
    "import io.circe.parser._\n",
    "import io.circe.syntax._\n",
    "import io.circe.optics.JsonPath._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8338c06a-90c5-45c6-97a1-ac5a1710cffd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def stream[F[_]: cats.effect.kernel.Async](u: org.http4s.Uri): Stream[F, Json] = \n",
    "    import org.http4s.ember.client.EmberClientBuilder\n",
    "    import io.circe.jawn.CirceSupportParser\n",
    "    import org.http4s.headers.Authorization\n",
    "    import org.http4s.{Method, Request}\n",
    "    import org.typelevel.jawn.Facade\n",
    "    import org.typelevel.jawn.fs2._\n",
    "\n",
    "    given Facade[Json] = new CirceSupportParser(None, false).facade\n",
    "    val bearerToken = scala.util.Properties.envOrElse(\"BEARER_GITHUB_TOKEN\", \"undefined\")\n",
    "    val authHeader =  Authorization(Credentials.Token(AuthScheme.Bearer, bearerToken))\n",
    "    Stream.resource(EmberClientBuilder.default[F].build).flatMap{\n",
    "        _.stream(Request[F](Method.GET, u).withHeaders(authHeader))\n",
    "         .flatMap(_.body.chunks.parseJsonStream)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b8dd4a4-8ca8-4d9d-9cd1-789510e1e923",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "extension [A](st: Stream[IO, A])\n",
    "    def run: List[A] = \n",
    "        st.compile.toList.unsafeRunSync()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f69da64d-492a-4eca-a3fa-a3259ccfc577",
   "metadata": {},
   "outputs": [],
   "source": [
    "object IsArray: \n",
    "    def unapply(v: Json): Option[Vector[Json]] = \n",
    "        v.asArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84bd32b7-3252-4c5c-abdd-6fb84fb33b1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "object IsObject: \n",
    "    def unapply(v: Json): Option[JsonObject] = \n",
    "        v.asObject"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e9477f8-16c4-4ecd-9abf-9eb62daf2d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "object IsString: \n",
    "    def unapply(v: Json): Option[String] = \n",
    "        v.asString"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68da5da4-b1e8-4d85-9844-8591c6a0425f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def allCommitPages[F[_]: Async](repo: String): Stream[F, Json] =\n",
    "\n",
    "    def newPage(i: Int): Stream[F, Json] = \n",
    "        stream(Uri.unsafeFromString(s\"$repo/commits?page=$i\"))\n",
    "    \n",
    "    def go(i: Int, s: Stream[F,Json]): Pull[F, Json, Unit] =\n",
    "        s.pull.uncons.flatMap:\n",
    "            case Some((hd,tl)) =>\n",
    "                hd(0) match\n",
    "                    case IsArray(Vector()) => Pull.done\n",
    "                    case _ => Pull.output(hd) >> go(i+1, tl ++ newPage(i))\n",
    "            case None => Pull.done\n",
    "\n",
    "    go(1, newPage(0)).stream"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b71288-ea79-4c4b-8846-449a440561a0",
   "metadata": {
    "tags": []
   },
   "source": [
    "# How to embed `jq`?"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e69d6917-b373-4098-9e81-b4c3aefb2c33",
   "metadata": {},
   "source": [
    "![image.png](attachment:330d1b55-36b9-4be8-99af-7c1359895eb1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4251dba6-084b-42ab-b3f9-05c381cd8411",
   "metadata": {},
   "source": [
    "### Jq programs that will _always_ fail"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4347483-82b7-4046-b960-3d298236f594",
   "metadata": {},
   "source": [
    "`.[]` may fail"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6a7f0e2-18e8-4340-b092-1a9ef3776793",
   "metadata": {},
   "source": [
    "This will always fail: `1 | .[]` [Try it!](https://jqplay.org/s/RRyGSQHDmFc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0739d0ef-9632-48de-a849-a5f018f18d92",
   "metadata": {},
   "source": [
    "How can we ruled out these kinds of programs?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f878db66-2c4e-4fd2-8409-c9145afe27cb",
   "metadata": {},
   "source": [
    "### Type rules"
   ]
  },
  {
   "attachments": {
    "1987cd8c-c4c3-4f4e-8b46-e179e08d2688.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "a81b9fbb-cc39-4ac3-a204-f0a9d3bb2f52",
   "metadata": {},
   "source": [
    "![image.png](attachment:1987cd8c-c4c3-4f4e-8b46-e179e08d2688.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60d7e579-d6dc-4950-adfb-10756b26997b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sealed trait Filter[-I <: JsonT, +O <: JsonT]\n",
    "\n",
    "enum JsonT:\n",
    "    case JNullT()\n",
    "    case JBoolT()\n",
    "    case JNumberT()\n",
    "    case JStringT()\n",
    "    case JArrayT[+t <: JsonT]()\n",
    "    case JObjectT()\n",
    "\n",
    "import JsonT._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eff9179-d3ca-42d9-af29-e9cfe2cab0fc",
   "metadata": {},
   "source": [
    "### Type class update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "252c9515-9d2e-40b0-9879-66ed4b10879e",
   "metadata": {},
   "outputs": [],
   "source": [
    "trait Jq[R[+_]]: \n",
    "\n",
    "    /* Primitive Filters */\n",
    "\n",
    "    def id[A <: JsonT]: R[Filter[A, A]]\n",
    "    def str[A <: JsonT](s: String): R[Filter[A, JStringT]]\n",
    "    def iterator[A <: JsonT]: R[Filter[JArrayT[A], A]]\n",
    "    def array[A <: JsonT, B <: JsonT](f: R[Filter[A, B]]): R[Filter[A, JArrayT[B]]]\n",
    "    def error[A <: JsonT](msg: String): R[Filter[A, Nothing]]\n",
    "    \n",
    "    extension [A <: JsonT, B <: JsonT](f1: R[Filter[A, B]])\n",
    "        def |[C <: JsonT](f2: R[Filter[B, C]]): R[Filter[A, C]]\n",
    "        def concat(f2: R[Filter[A, B]]): R[Filter[A, B]]\n",
    "        def `catch`(f2: R[Filter[A, B]]): R[Filter[A, B]]\n",
    "\n",
    "    extension [A <: JsonT](f1: R[Filter[A, JObjectT]])\n",
    "        def index(f2: R[Filter[A, JStringT]]): R[Filter[A, Nothing]]\n",
    "\n",
    "    /* built-in functions */\n",
    "\n",
    "    def map[A <: JsonT, B <: JsonT](f: R[Filter[A, B]]): R[Filter[JArrayT[A], JArrayT[B]]] = \n",
    "        array(iterator | f)\n",
    "\n",
    "object Jq: \n",
    "\n",
    "    def apply[R[+_]](using J: Jq[R]): Jq[R] = J\n",
    "\n",
    "    object syntax: \n",
    "        def id[R[+_], A <: JsonT](using J: Jq[R]) = J.id[A] \n",
    "        def str[R[+_], A <: JsonT](using J: Jq[R])(s: String) = J.str[A](s)\n",
    "        def iterator[R[+_], A <: JsonT](using J: Jq[R]) = J.iterator[A]\n",
    "        def error[R[+_], A <: JsonT](msg: String)(using J: Jq[R]) = J.error[A](msg)\n",
    "\n",
    "        def arr[R[+_], A <: JsonT, B <: JsonT](f: R[Filter[A, B]]*)(using J: Jq[R]) = \n",
    "            J.array(f.reduce(_ concat _))\n",
    "\n",
    "        extension [R[+_]](sc: StringContext)(using J: Jq[R])\n",
    "            def i(args: R[Filter[JsonT, Nothing]]*) = \n",
    "                sc.s().split('.').toList.map(key => J.id.index(J.str(key))).reduce(_ | _)\n",
    "\n",
    "    enum Error: \n",
    "        case CannotIterateOver(j: Json)\n",
    "        case CannotIndexObjectWith(j: Json)\n",
    "        case CannotIndex(j: Json)\n",
    "        case Custom(msg: String = \"\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1eebfb1-f8fd-4de0-9123-6e9561a2c3c6",
   "metadata": {},
   "source": [
    "### Well-formed & malformed programs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13a0962a-0e8c-43be-a828-20ae94a23418",
   "metadata": {},
   "outputs": [],
   "source": [
    "import Jq.syntax._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8c68c7b-ee54-4909-8f7d-2fe191f12354",
   "metadata": {},
   "outputs": [],
   "source": [
    "def program[R[+_]](using J: Jq[R]): R[Filter[JsonT, JStringT /*JsonT*/]] = \n",
    "    arr(str(\"1\"), str(\"2\"), str(\"3\")) | iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cc44428-c41b-4d96-aa4c-8c920289ba7f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def program[R[+_]: Jq] = \n",
    "    str(\"1\") | iterator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1aebf20-c0c1-448a-a806-301f5b58325e",
   "metadata": {},
   "source": [
    "### Fs2 semantics update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07379303-2d04-4162-a6ad-142ef56692e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "object Fs2:\n",
    "\n",
    "    type Filter[F[_]] = \n",
    "        Pipe[F, io.circe.Json, io.circe.Json | Jq.Error]\n",
    "\n",
    "    given [F[_]]: Jq[[t] =>> Filter[F]] with \n",
    "    \n",
    "        def id[A <: JsonT]: Filter[F] = \n",
    "            identity\n",
    "\n",
    "        def str[A <: JsonT](s: String): Filter[F] = \n",
    "            _ map: _ => \n",
    "                Json.fromString(s)\n",
    "\n",
    "        def iterator[A <: JsonT]: Filter[F] = \n",
    "            _ flatMap:\n",
    "                case IsArray(v) => Stream(v*)\n",
    "                case j => Stream(Jq.Error.CannotIterateOver(j))\n",
    "\n",
    "        def array[A <: JsonT, B <: JsonT](f: Filter[F]): Filter[F] =\n",
    "            _ flatMap: json => \n",
    "                f(Stream(json))\n",
    "                    .fold(List[Json|Jq.Error]())(_ :+ _)\n",
    "                    .flatMap: content => \n",
    "                        content.lastOption match\n",
    "                            case Some(error: Jq.Error) => Stream(error)\n",
    "                            case _ => Stream(Json.arr(content.collect{ case j: Json => j }*))\n",
    "\n",
    "        def error[A <: JsonT](msg: String): Filter[F] = \n",
    "            _ map: _ => \n",
    "                Jq.Error.Custom(msg)\n",
    "\n",
    "        extension  [A <: JsonT, B <: JsonT](f1: Filter[F])\n",
    "            def |[C <: JsonT](f2: Filter[F]): Filter[F] = \n",
    "                _ flatMap: v => \n",
    "                    f1(Stream(v)).flatMap:\n",
    "                        case e: Jq.Error => Stream(e)\n",
    "                        case j: Json => \n",
    "                            f2(Stream(j)).takeThrough: \n",
    "                                case j: Json => true\n",
    "                                case _ => false\n",
    "\n",
    "            def concat(f2: Filter[F]): Filter[F] = \n",
    "                _.flatMap: json => \n",
    "                    (f1(Stream(json)) ++ f2(Stream(json)))\n",
    "                        .takeThrough: \n",
    "                            case j: Json => true\n",
    "                            case _ => false\n",
    "\n",
    "            def `catch`(f2: Filter[F]): Filter[F] = \n",
    "                _ flatMap: j => \n",
    "                    f1(Stream(j)) flatMap:\n",
    "                        case j: Json => Stream(j)\n",
    "                        case e: Jq.Error => \n",
    "                            f2(Stream(Json.fromString(e.toString)))\n",
    "\n",
    "        extension [A <: JsonT](f1: Filter[F])\n",
    "            private def indexObj(key: String)(v: Json): Stream[F, Json | Jq.Error] = \n",
    "                f1(Stream(v)) map: \n",
    "                    case IsObject(obj) => obj(key).getOrElse(Json.Null)\n",
    "                    case j: Json => Jq.Error.CannotIndex(j)\n",
    "                    case error => error\n",
    "\n",
    "            def index(f2: Filter[F]): Filter[F] = \n",
    "                _ flatMap: v => \n",
    "                    f2(Stream(v)) flatMap:\n",
    "                        case IsString(s) => indexObj(s)(v)\n",
    "                        case k: Json => Stream(Jq.Error.CannotIndexObjectWith(k))\n",
    "                        case error => Stream(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f236f965-6adf-4287-9a53-6cf7b3df6244",
   "metadata": {},
   "outputs": [],
   "source": [
    "{\n",
    "    import Fs2.given\n",
    "    \n",
    "    Stream(Json.Null).through(program[[t] =>> Fs2.Filter[IO]]).run\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b625c0ff-f43e-47c7-911e-434b5fbbc07e",
   "metadata": {},
   "source": [
    "### A case for `match types`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6341dc0-d936-48ab-8b51-26fea9202cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "sealed trait Filter[-I <: JsonT, +O <: JsonT]\n",
    "\n",
    "enum JsonT:\n",
    "    case JNullT()\n",
    "    case JBoolT()\n",
    "    case JNumberT()\n",
    "    case JStringT()\n",
    "    case JArrayT[+t <: JsonT]()\n",
    "    case JObjectT()\n",
    "\n",
    "import JsonT._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3f94985-f40c-4b2e-9d19-5a113444ef81",
   "metadata": {},
   "outputs": [],
   "source": [
    "type IndexType[T <: JsonT] <: JsonT = T match \n",
    "    case JObjectT => JStringT\n",
    "    case JArrayT[_] => JNumberT    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eefee963-0b52-4300-be15-bcf89f70a8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "type OutputType[T1 <: JsonT] <: JsonT = T1 match \n",
    "    case JArrayT[t] => t\n",
    "    case JObjectT => JsonT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49ef2920-5774-4547-a32c-21ee79e6c1b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "trait Jq[R[+_]]: \n",
    "    extension (i: Int)\n",
    "        def int[T <: JsonT]: R[Filter[T, JNumberT]]\n",
    "\n",
    "    extension (s: String)\n",
    "        def str[T <: JsonT]: R[Filter[T, JStringT]]\n",
    "\n",
    "    def id[T <: JsonT]: R[Filter[T, T]]\n",
    "    \n",
    "    extension [T1 <: JsonT, T <: JsonT, T2 <: JArrayT[T] | JObjectT](\n",
    "        f1: R[Filter[T1, T2]]) \n",
    "        def index(f: R[Filter[T1, IndexType[T2]]]): R[Filter[T1, OutputType[T2]]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61d4220-8d52-4247-97fd-d67c6735f8ec",
   "metadata": {},
   "source": [
    "# References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df55eb1a-8a8e-4f36-9310-1bfb85cb5bc6",
   "metadata": {},
   "source": [
    "* Match types: https://docs.scala-lang.org/scala3/reference/new-types/match-types.html"
   ]
  }
 ],
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