Janis Eccarius
e1d80b9331
Three new Spark jobs demonstrating complementary Spark pillars: LiveDashboardJob (Structured Streaming): - Simulates NowChess game-over event stream via rate source - Watermarking (45 s late-data tolerance) - Tumbling 1-min windows → append-mode Parquet output - Sliding 5-min/1-min windows → update-mode console output - Checkpointing for exactly-once fault tolerance - Production wiring comments show Kafka / spark-redis swap-in PlayerClusteringJob (MLlib): - Derives 4 player features from game_records via JDBC - VectorAssembler + StandardScaler + KMeans inside a Pipeline - ClusteringEvaluator (silhouette score) to measure quality - Per-cluster archetype averages show what each tier represents PlayerGraphJob (GraphX): - Builds directed player graph (vertices=players, edges=games) - PageRank — identifies most influential/active players - ConnectedComponents — finds isolated player communities - Bridges GraphX RDD results back to DataFrames via explicit schema (avoids spark.implicits._ which breaks Scala 3 → Spark 2.13 interop) Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
93 lines
2.9 KiB
Kotlin
93 lines
2.9 KiB
Kotlin
// Standalone Spark batch-analytics module.
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//
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// Spark 3.5.x ships only Scala 2.12/2.13 artifacts; Scala 3 code can consume
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// them via JVM binary compatibility so long as we avoid macro-expanded APIs
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// (spark.implicits._, typed Dataset[T]). We use the untyped DataFrame API
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// exclusively, which is safe to call from Scala 3.
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//
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// Spark is declared compileOnly — the cluster provides it at runtime via
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// spark-submit. Only the PostgreSQL driver and the Scala 3 runtime are
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// bundled into the fat jar produced by the "jar" task.
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//
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// Build the submission jar:
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// ./gradlew :modules:analytics:jar
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//
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// Run a job:
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// spark-submit \
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// --class de.nowchess.analytics.OpeningBookJob \
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// modules/analytics/build/libs/analytics-<version>.jar \
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// [outputDir] [maxPlies]
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//
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// Environment variables consumed:
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// NOWCHESS_JDBC_URL (default: jdbc:postgresql://localhost:5432/nowchess)
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// NOWCHESS_DB_USER (default: nowchess)
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// NOWCHESS_DB_PASS (default: nowchess)
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plugins {
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id("scala")
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application
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}
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group = "de.nowchess"
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version = "1.0-SNAPSHOT"
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@Suppress("UNCHECKED_CAST")
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val versions = rootProject.extra["VERSIONS"] as Map<String, String>
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repositories {
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mavenCentral()
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}
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scala {
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scalaVersion = versions["SCALA3"]!!
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}
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val sparkVersion = "3.5.4"
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dependencies {
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compileOnly("org.scala-lang:scala3-compiler_3") {
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version { strictly(versions["SCALA3"]!!) }
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}
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implementation("org.scala-lang:scala3-library_3") {
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version { strictly(versions["SCALA3"]!!) }
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}
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implementation("org.scala-lang:scala-library") {
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version { strictly(versions["SCALA_LIBRARY"]!!) }
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}
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// Spark is provided by the cluster — compile-only, not bundled.
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compileOnly("org.apache.spark:spark-sql_2.13:$sparkVersion") {
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exclude(group = "org.slf4j", module = "slf4j-log4j12")
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}
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compileOnly("org.apache.spark:spark-core_2.13:$sparkVersion") {
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exclude(group = "org.slf4j", module = "slf4j-log4j12")
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}
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compileOnly("org.apache.spark:spark-mllib_2.13:$sparkVersion") {
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exclude(group = "org.slf4j", module = "slf4j-log4j12")
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}
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compileOnly("org.apache.spark:spark-graphx_2.13:$sparkVersion") {
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exclude(group = "org.slf4j", module = "slf4j-log4j12")
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}
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// PostgreSQL JDBC driver bundled so it is available on executor classpath.
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implementation("org.postgresql:postgresql:42.7.4")
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}
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application {
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mainClass.set("de.nowchess.analytics.OpeningBookJob")
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}
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// Fat jar: includes runtimeClasspath (our code + pg driver + scala3-library)
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// but NOT compileOnly Spark jars.
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tasks.jar {
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manifest {
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attributes["Main-Class"] = "de.nowchess.analytics.OpeningBookJob"
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}
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from(configurations.runtimeClasspath.get().map { if (it.isDirectory) it else zipTree(it) })
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duplicatesStrategy = DuplicatesStrategy.EXCLUDE
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}
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tasks.withType<ScalaCompile> {
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scalaCompileOptions.additionalParameters = listOf("-encoding", "UTF-8")
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}
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