SPARKC-706: Add basic support for Cassandra vectors #1366
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,238 @@ | ||
| package com.datastax.spark.connector.rdd.typeTests | ||
|
|
||
| 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} | ||
|
|
||
| import scala.collection.convert.ImplicitConversionsToScala._ | ||
| import scala.collection.immutable | ||
| import scala.reflect.ClassTag | ||
| import scala.reflect.runtime.universe._ | ||
| import scala.reflect._ | ||
|
|
||
|
|
||
| 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) | ||
|
|
||
|
|
||
| override lazy val conn = CassandraConnector(sparkConf) | ||
|
|
||
| val VectorTable = "vectors" | ||
|
|
||
| def createVectorTable(session: CqlSession, table: String): Unit = { | ||
| session.execute( | ||
| s"""CREATE TABLE IF NOT EXISTS $ks.$table ( | ||
| | id INT PRIMARY KEY, | ||
|
There was a problem hiding this comment. Can a vector be a primary key? There was a problem hiding this comment. Perhaps, why do you ask? There was a problem hiding this comment. Wondering if that's supported. |
||
| | v VECTOR<$typeName, 3> | ||
| |)""".stripMargin) | ||
| } | ||
|
|
||
| def minCassandraVersion: Option[Version] = Some(Version.parse("5.0-beta1")) | ||
|
|
||
| def minDSEVersion: Option[Version] = None | ||
|
|
||
| def vectorFromInts(ints: Int*): Seq[ScalaType] | ||
|
|
||
| def vectorItem(id: Int, v: Seq[ScalaType]): CaseClassType | ||
|
|
||
| override def beforeClass() { | ||
| conn.withSessionDo { session => | ||
| session.execute( | ||
| s"""CREATE KEYSPACE IF NOT EXISTS $ks | ||
| |WITH REPLICATION = { 'class': 'SimpleStrategy', 'replication_factor': 1 }""" | ||
| .stripMargin) | ||
| } | ||
| } | ||
|
|
||
| private def hasVectors(rows: List[Row], expectedVectors: Seq[Seq[ScalaType]]): Unit = { | ||
|
There was a problem hiding this comment. Maybe assertVectors would be more clear as the method doesn't return boolean, instead it asserts on the content. |
||
| 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 | ||
| } | ||
|
|
||
| returnedVectors should contain theSameElementsInOrderAs expectedVectors | ||
| } | ||
|
|
||
| "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) | ||
|
|
||
| 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))) | ||
|
|
||
| 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))) | ||
|
|
||
| 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))) | ||
| } | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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))) | ||
| } | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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))) | ||
| } | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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))) | ||
| } | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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))) | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| 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))) | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| spark.sparkContext.cassandraTable[CaseClassType](ks, table).collect() should contain theSameElementsAs | ||
| Seq(vectorItem(1, vectorFromInts(1, 2, 3)), vectorItem(2, vectorFromInts(4, 5, 6))) | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| spark.sparkContext.cassandraTable[(Int, Seq[ScalaType])](ks, table).collect() should contain theSameElementsAs | ||
| Seq((1, vectorFromInts(1, 2, 3)), (2, vectorFromInts(4, 5, 6))) | ||
| } | ||
|
|
||
| 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) | ||
|
|
||
| import spark.implicits._ | ||
| spark.conf.set("spark.sql.catalog.casscatalog", "com.datastax.spark.connector.datasource.CassandraCatalog") | ||
| 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))) | ||
| } | ||
|
|
||
| } | ||
|
|
||
| class IntVectorTypeTest extends VectorTypeTest[Int, Integer, IntVectorItem]("INT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Int] = ints | ||
|
|
||
| override def vectorItem(id: Int, v: Seq[Int]): IntVectorItem = IntVectorItem(id, v) | ||
| } | ||
|
|
||
| case class IntVectorItem(id: Int, v: Seq[Int]) | ||
|
|
||
| class LongVectorTypeTest extends VectorTypeTest[Long, java.lang.Long, LongVectorItem]("BIGINT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Long] = ints.map(_.toLong) | ||
|
|
||
| override def vectorItem(id: Int, v: Seq[Long]): LongVectorItem = LongVectorItem(id, v) | ||
| } | ||
|
|
||
| case class LongVectorItem(id: Int, v: Seq[Long]) | ||
|
|
||
| class FloatVectorTypeTest extends VectorTypeTest[Float, java.lang.Float, FloatVectorItem]("FLOAT") { | ||
| override def vectorFromInts(ints: Int*): Seq[Float] = ints.map(_.toFloat + 0.1f) | ||
|
|
||
| override def vectorItem(id: Int, v: Seq[Float]): FloatVectorItem = FloatVectorItem(id, v) | ||
| } | ||
|
|
||
| case class FloatVectorItem(id: Int, v: Seq[Float]) | ||
|
|
||
| class DoubleVectorTypeTest extends VectorTypeTest[Double, java.lang.Double, DoubleVectorItem]("DOUBLE") { | ||
| override def vectorFromInts(ints: Int*): Seq[Double] = ints.map(_.toDouble + 0.1d) | ||
|
|
||
| override def vectorItem(id: Int, v: Seq[Double]): DoubleVectorItem = DoubleVectorItem(id, v) | ||
| } | ||
|
|
||
| case class DoubleVectorItem(id: Int, v: Seq[Double]) | ||
|
|
||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
use Option instead of Some