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SPARKC-706: Add basic support for Cassandra vectors
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242 changes: 242 additions & 0 deletions
242
connector/src/it/scala/com/datastax/spark/connector/rdd/typeTests/VectorTypeTest.scala
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| package com.datastax.spark.connector.rdd.typeTests | ||
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| import com.datastax.oss.driver.api.core.cql.Row | ||
| import com.datastax.oss.driver.api.core.{CqlSession, Version} | ||
| import com.datastax.spark.connector._ | ||
| import com.datastax.spark.connector.ccm.CcmConfig | ||
| import com.datastax.spark.connector.cluster.DefaultCluster | ||
| import com.datastax.spark.connector.cql.CassandraConnector | ||
| import com.datastax.spark.connector.datasource.CassandraCatalog | ||
| import com.datastax.spark.connector.mapper.ColumnMapper | ||
| import com.datastax.spark.connector.rdd.{ReadConf, ValidRDDType} | ||
| import com.datastax.spark.connector.rdd.reader.RowReaderFactory | ||
| import com.datastax.spark.connector.types.TypeConverter | ||
| import org.apache.spark.sql.{SaveMode, SparkSession} | ||
| import org.apache.spark.sql.cassandra.{DataFrameReaderWrapper, DataFrameWriterWrapper} | ||
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| import scala.collection.convert.ImplicitConversionsToScala._ | ||
| import scala.collection.immutable | ||
| import scala.reflect.ClassTag | ||
| import scala.reflect.runtime.universe._ | ||
| import scala.reflect._ | ||
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| abstract class VectorTypeTest[ | ||
| ScalaType: ClassTag : TypeTag, | ||
| DriverType <: Number : ClassTag, | ||
| CaseClassType <: Product : ClassTag : TypeTag : ColumnMapper: RowReaderFactory : ValidRDDType](typeName: String) extends SparkCassandraITFlatSpecBase with DefaultCluster | ||
| { | ||
| /** Skips the given test if the cluster is not Cassandra */ | ||
| override def cassandraOnly(f: => Unit): Unit = super.cassandraOnly(f) | ||
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| override lazy val conn = CassandraConnector(sparkConf) | ||
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| val VectorTable = "vectors" | ||
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| def createVectorTable(session: CqlSession, table: String): Unit = { | ||
| session.execute( | ||
| s"""CREATE TABLE IF NOT EXISTS $ks.$table ( | ||
| | id INT PRIMARY KEY, | ||
| | v VECTOR<$typeName, 3> | ||
| |)""".stripMargin) | ||
| } | ||
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| def minCassandraVersion: Option[Version] = Some(Version.parse("5.0-beta1")) | ||
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| def minDSEVersion: Option[Version] = None | ||
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| def vectorFromInts(ints: Int*): Seq[ScalaType] | ||
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| def vectorItem(id: Int, v: Seq[ScalaType]): CaseClassType | ||
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| override lazy val spark = SparkSession.builder() | ||
| .config(sparkConf) | ||
| .config("spark.sql.catalog.casscatalog", "com.datastax.spark.connector.datasource.CassandraCatalog") | ||
| .withExtensions(new CassandraSparkExtensions).getOrCreate().newSession() | ||
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| override def beforeClass() { | ||
| conn.withSessionDo { session => | ||
| session.execute( | ||
| s"""CREATE KEYSPACE IF NOT EXISTS $ks | ||
| |WITH REPLICATION = { 'class': 'SimpleStrategy', 'replication_factor': 1 }""" | ||
| .stripMargin) | ||
| } | ||
| } | ||
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| private def hasVectors(rows: List[Row], expectedVectors: Seq[Seq[ScalaType]]): Unit = { | ||
| val returnedVectors = for (i <- expectedVectors.indices) yield { | ||
| rows.find(_.getInt("id") == i + 1).get.getVector("v", implicitly[ClassTag[DriverType]].runtimeClass.asInstanceOf[Class[Number]]).iterator().toSeq | ||
| } | ||
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| returnedVectors should contain theSameElementsInOrderAs expectedVectors | ||
| } | ||
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| "SCC" should s"write case class instances with $typeName vector using DataFrame API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_write_caseclass_to_df" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
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| spark.createDataFrame(Seq(vectorItem(1, vectorFromInts(1, 2, 3)), vectorItem(2, vectorFromInts(4, 5, 6)))) | ||
| .write | ||
| .cassandraFormat(table, ks) | ||
| .mode(SaveMode.Append) | ||
| .save() | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(1, 2, 3), vectorFromInts(4, 5, 6))) | ||
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| spark.createDataFrame(Seq(vectorItem(2, vectorFromInts(6, 5, 4)), vectorItem(3, vectorFromInts(7, 8, 9)))) | ||
| .write | ||
| .cassandraFormat(table, ks) | ||
| .mode(SaveMode.Append) | ||
| .save() | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(1, 2, 3), vectorFromInts(6, 5, 4), vectorFromInts(7, 8, 9))) | ||
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| spark.createDataFrame(Seq(vectorItem(1, vectorFromInts(9, 8, 7)), vectorItem(2, vectorFromInts(10, 11, 12)))) | ||
| .write | ||
| .cassandraFormat(table, ks) | ||
| .mode(SaveMode.Overwrite) | ||
| .option("confirm.truncate", value = true) | ||
| .save() | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(9, 8, 7), vectorFromInts(10, 11, 12))) | ||
| } | ||
| } | ||
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| it should s"write tuples with $typeName vectors using DataFrame API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_write_tuple_to_df" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
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| spark.createDataFrame(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .toDF("id", "v") | ||
| .write | ||
| .cassandraFormat(table, ks) | ||
| .mode(SaveMode.Append) | ||
| .save() | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(1, 2, 3), vectorFromInts(4, 5, 6))) | ||
| } | ||
| } | ||
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| it should s"write case class instances with $typeName vectors using RDD API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_write_caseclass_to_rdd" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
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| spark.sparkContext.parallelize(Seq(vectorItem(1, vectorFromInts(1, 2, 3)), vectorItem(2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(1, 2, 3), vectorFromInts(4, 5, 6))) | ||
| } | ||
| } | ||
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| it should s"write tuples with $typeName vectors using RDD API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_write_tuple_to_rdd" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
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| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
| hasVectors(session.execute(s"SELECT * FROM $ks.$table").all().iterator().toList, | ||
| Seq(vectorFromInts(1, 2, 3), vectorFromInts(4, 5, 6))) | ||
| } | ||
| } | ||
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| it should s"read case class instances with $typeName vectors using DataFrame API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_read_caseclass_from_df" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
| } | ||
| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
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| import spark.implicits._ | ||
| spark.read.cassandraFormat(table, ks).load().as[CaseClassType].collect() should contain theSameElementsAs | ||
| Seq(vectorItem(1, vectorFromInts(1, 2, 3)), vectorItem(2, vectorFromInts(4, 5, 6))) | ||
| } | ||
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| it should s"read tuples with $typeName vectors using DataFrame API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_read_tuple_from_df" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
| } | ||
| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
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| import spark.implicits._ | ||
| spark.read.cassandraFormat(table, ks).load().as[(Int, Seq[ScalaType])].collect() should contain theSameElementsAs | ||
| Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6))) | ||
| } | ||
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| it should s"read case class instances with $typeName vectors using RDD API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_read_caseclass_from_rdd" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
| } | ||
| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
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| spark.sparkContext.cassandraTable[CaseClassType](ks, table).collect() should contain theSameElementsAs | ||
| Seq(vectorItem(1, vectorFromInts(1, 2, 3)), vectorItem(2, vectorFromInts(4, 5, 6))) | ||
| } | ||
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| it should s"read tuples with $typeName vectors using RDD API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_read_tuple_from_rdd" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
| } | ||
| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
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| spark.sparkContext.cassandraTable[(Int, Seq[ScalaType])](ks, table).collect() should contain theSameElementsAs | ||
| Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6))) | ||
| } | ||
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| it should s"read rows with $typeName vectors using SQL API" in from(minCassandraVersion, minDSEVersion) { | ||
| val table = s"${typeName.toLowerCase}_read_rows_from_sql" | ||
| conn.withSessionDo { session => | ||
| createVectorTable(session, table) | ||
| } | ||
| spark.sparkContext.parallelize(Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6)))) | ||
| .saveToCassandra(ks, table) | ||
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| import spark.implicits._ | ||
| spark.sql(s"SELECT * FROM casscatalog.$ks.$table").as[(Int, Seq[ScalaType])].collect() should contain theSameElementsAs | ||
| Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6))) | ||
| } | ||
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| } | ||
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| class IntVectorTypeTest extends VectorTypeTest[Int, Integer, IntVectorItem]("INT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Int] = ints | ||
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| override def vectorItem(id: Int, v: Seq[Int]): IntVectorItem = IntVectorItem(id, v) | ||
| } | ||
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| case class IntVectorItem(id: Int, v: Seq[Int]) | ||
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| class LongVectorTypeTest extends VectorTypeTest[Long, java.lang.Long, LongVectorItem]("BIGINT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Long] = ints.map(_.toLong) | ||
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| override def vectorItem(id: Int, v: Seq[Long]): LongVectorItem = LongVectorItem(id, v) | ||
| } | ||
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| case class LongVectorItem(id: Int, v: Seq[Long]) | ||
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| class FloatVectorTypeTest extends VectorTypeTest[Float, java.lang.Float, FloatVectorItem]("FLOAT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Float] = ints.map(_.toFloat + 0.1f) | ||
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| override def vectorItem(id: Int, v: Seq[Float]): FloatVectorItem = FloatVectorItem(id, v) | ||
| } | ||
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| case class FloatVectorItem(id: Int, v: Seq[Float]) | ||
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| class DoubleVectorTypeTest extends VectorTypeTest[Double, java.lang.Double, DoubleVectorItem]("DOUBLE") { | ||
| override def vectorFromInts(ints: Int*): Seq[Double] = ints.map(_.toDouble + 0.1d) | ||
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| override def vectorItem(id: Int, v: Seq[Double]): DoubleVectorItem = DoubleVectorItem(id, v) | ||
| } | ||
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| case class DoubleVectorItem(id: Int, v: Seq[Double]) | ||
|
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