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SPARKC-706: Add basic support for Cassandra vectors (#1366)
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‎CHANGES.txt

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3.5.1
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* Support for Vector type available in Cassandra 5.0 (SPARKC-706)
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* Upgrade Cassandra Java Driver to 4.18.1, support Cassandra 5.0 in test framework (SPARKC-710)
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3.5.0
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* Support for Apache Spark 3.5 (SPARKC-704)

‎README.md

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## Quick Links
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| What | Where |
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| ---------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Community | Chat with us at [Apache Cassandra](https://cassandra.apache.org/_/community.html#discussions) |
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| Scala Docs | Most Recent Release (3.5.0): [Connector API docs](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.0/connector/com/datastax/spark/connector/index.html), [Connector Driver docs](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.0/driver/com/datastax/spark/connector/index.html) |
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| Latest Production Release | [3.5.0](https://search.maven.org/artifact/com.datastax.spark/spark-cassandra-connector_2.12/3.5.0/jar) |
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| What | Where |
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| ---------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Community | Chat with us at [Apache Cassandra](https://cassandra.apache.org/_/community.html#discussions) |
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| Scala Docs | Most Recent Release (3.5.1): [Connector API docs](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.1/connector/com/datastax/spark/connector/index.html), [Connector Driver docs](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.1/driver/com/datastax/spark/connector/index.html) |
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| Latest Production Release | [3.5.1](https://search.maven.org/artifact/com.datastax.spark/spark-cassandra-connector_2.12/3.5.1/jar) |
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## News
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### 3.5.1
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- The latest release of the Spark-Cassandra-Connector introduces support for vector types, greatly enhancing its capabilities. This new feature allows developers to seamlessly integrate and work with Cassandra 5.0 and Astra vectors within the Spark ecosystem. By supporting vector types, the connector now provides insights into AI and Retrieval-Augmented Generation (RAG) data, enabling more advanced and efficient data processing and analysis.
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## Features
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| Connector | Spark | Cassandra | Cassandra Java Driver | Minimum Java Version | Supported Scala Versions |
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|-----------|---------------|----------------------------|-----------------------|----------------------|--------------------------|
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| 3.5.1 | 3.5 | 2.1.5*, 2.2, 3.x, 4.x, 5.0 | 4.18 | 8 | 2.12, 2.13 |
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| 3.5.1 | 3.5 | 2.1.5*, 2.2, 3.x, 4.x, 5.0 | 4.18.1 | 8 | 2.12, 2.13 |
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| 3.5 | 3.5 | 2.1.5*, 2.2, 3.x, 4.x | 4.13 | 8 | 2.12, 2.13 |
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| 3.4 | 3.4 | 2.1.5*, 2.2, 3.x, 4.x | 4.13 | 8 | 2.12, 2.13 |
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| 3.3 | 3.3 | 2.1.5*, 2.2, 3.x, 4.x | 4.13 | 8 | 2.12 |
@@ -80,6 +84,9 @@ Currently, the following branches are actively supported:
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## Hosted API Docs
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API documentation for the Scala and Java interfaces are available online:
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### 3.5.1
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* [Spark-Cassandra-Connector](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.1/connector/com/datastax/spark/connector/index.html)
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### 3.5.0
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* [Spark-Cassandra-Connector](https://datastax.github.io/spark-cassandra-connector/ApiDocs/3.5.0/connector/com/datastax/spark/connector/index.html)
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For SBT to download the connector binaries, sources and javadoc, put this in your project
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SBT config:
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libraryDependencies += "com.datastax.spark" %% "spark-cassandra-connector" % "3.5.0"
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libraryDependencies += "com.datastax.spark" %% "spark-cassandra-connector" % "3.5.1"
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* The default Scala version for Spark 3.0+ is 2.12 please choose the appropriate build. See the
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[FAQ](doc/FAQ.md) for more information.

‎connector/src/it/scala/com/datastax/spark/connector/SparkCassandraITFlatSpecBase.scala

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}
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override def withFixture(test: NoArgTest): Outcome = wrapUnserializableExceptions {
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super.withFixture(test)
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super.withFixture(test)
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}
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def getKsName = {
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else report(s"Skipped Because ProtocolVersion $pv < $protocolVersion")
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}
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/** Skips the given test if the Cluster Version is lower or equal to the given `cassandra` Version or `dse` Version
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* (if this is a DSE cluster) */
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def from(cassandra: Version, dse: Version)(f: => Unit): Unit = {
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/** Runs the given test only if the cluster type and version matches.
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*
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* @param cassandra run the test if the cluster is Cassandra >= the given version;
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* if `None`, never run the test for Cassandra clusters
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* @param dse run the test if the cluster is DSE >= the given version;
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* if `None`, never run the test for DSE clusters
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* @param f the test to run
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*/
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def from(cassandra: Version, dse: Version)(f: => Unit): Unit = from(Option(cassandra), Option(dse))(f)
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def from(cassandra: Option[Version] = None, dse: Option[Version] = None)(f: => Unit): Unit = {
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if (isDse(conn)) {
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from(dse)(f)
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dse match {
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case Some(dseVersion) => from(dseVersion)(f)
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case None => report(s"Skipped because not DSE")
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}
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} else {
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from(cassandra)(f)
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cassandra match {
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case Some(cassandraVersion) => from(cassandraVersion)(f)
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case None => report(s"Skipped because not Cassandra")
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}
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}
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}
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/** Skips the given test if the Cluster Version is lower or equal to the given version */
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def from(version: Version)(f: => Unit): Unit = {
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/** Skips the given test if the Cluster Version is lower than the given version */
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private def from(version: Version)(f: => Unit): Unit = {
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skip(cluster.getCassandraVersion, version) { f }
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}
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else f
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}
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/** Skips the given test if the Cluster Version is lower or equal to the given version or the cluster is not DSE */
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/** Skips the given test if the Cluster Version is lower than the given version or the cluster is not DSE */
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def dseFrom(version: Version)(f: => Any): Unit = {
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dseOnly {
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skip(cluster.getDseVersion.get, version) { f }

‎connector/src/it/scala/com/datastax/spark/connector/cql/SchemaSpec.scala

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package com.datastax.spark.connector.cql
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import com.datastax.spark.connector.SparkCassandraITWordSpecBase
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import com.datastax.spark.connector.ccm.CcmConfig
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import com.datastax.spark.connector.cluster.DefaultCluster
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import com.datastax.spark.connector.types._
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import com.datastax.spark.connector.util.schemaFromCassandra
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s"""CREATE INDEX test_d9_m23423ap_idx ON $ks.test (full(d10_set))""")
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session.execute(
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s"""CREATE INDEX test_d7_int_idx ON $ks.test (d7_int)""")
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from(Some(CcmConfig.V5_0_0), None) {
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session.execute(s"ALTER TABLE $ks.test ADD d17_vector frozen<vector<int,3>>")
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}
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for (i <- 0 to 9) {
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session.execute(s"insert into $ks.test (k1,k2,k3,c1,c2,c3,d10_set) " +
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"allow to read regular column definitions" in {
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val columns = table.regularColumns
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columns.size shouldBe 16
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columns.map(_.columnName).toSet shouldBe Set(
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columns.size should be >= 16
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columns.map(_.columnName).toSet should contain allElementsOf Set(
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"d1_blob", "d2_boolean", "d3_decimal", "d4_double", "d5_float",
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"d6_inet", "d7_int", "d8_list", "d9_map", "d10_set",
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"d11_timestamp", "d12_uuid", "d13_timeuuid", "d14_varchar",
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table.columnByName("d14_varchar").columnType shouldBe VarCharType
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table.columnByName("d15_varint").columnType shouldBe VarIntType
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table.columnByName("d16_address").columnType shouldBe a [UserDefinedType]
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from(Some(CcmConfig.V5_0_0), None) {
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table.columnByName("d17_vector").columnType shouldBe VectorType(IntType, 3)
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}
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}
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"allow to list fields of a user defined type" in {

‎connector/src/it/scala/com/datastax/spark/connector/rdd/CassandraRDDSpec.scala

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import com.datastax.oss.driver.api.core.cql.SimpleStatement
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import com.datastax.oss.driver.api.core.cql.SimpleStatement._
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import com.datastax.spark.connector._
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import com.datastax.spark.connector.ccm.CcmConfig.{DSE_V6_7_0, V3_6_0}
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import com.datastax.spark.connector.ccm.CcmConfig.{DSE_V5_1_0, DSE_V6_7_0, V3_6_0}
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import com.datastax.spark.connector.cluster.DefaultCluster
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import com.datastax.spark.connector.cql.{CassandraConnector, CassandraConnectorConf}
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import com.datastax.spark.connector.mapper.{DefaultColumnMapper, JavaBeanColumnMapper, JavaTestBean, JavaTestUDTBean}
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results should contain ((KeyGroup(3, 300), (3, 300, "0003")))
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it should "allow the use of PER PARTITION LIMITs " in from(V3_6_0) {
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it should "allow the use of PER PARTITION LIMITs " in from(cassandra = V3_6_0, dse = DSE_V5_1_0) {
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result.length should be (1)
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‎connector/src/it/scala/com/datastax/spark/connector/rdd/RDDSpec.scala

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import com.datastax.oss.driver.api.core.cql.{AsyncResultSet, BoundStatement}
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import com.datastax.oss.driver.api.core.{DefaultConsistencyLevel, DefaultProtocolVersion}
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import com.datastax.spark.connector._
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import com.datastax.spark.connector.ccm.CcmConfig.V3_6_0
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import com.datastax.spark.connector.ccm.CcmConfig.{DSE_V5_1_0, V3_6_0}
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import com.datastax.spark.connector.cluster.DefaultCluster
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import com.datastax.spark.connector.cql.CassandraConnector
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import com.datastax.spark.connector.embedded.SparkTemplate._
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it should "should be joinable with a PER PARTITION LIMIT limit" in from(V3_6_0){
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it should "should be joinable with a PER PARTITION LIMIT limit" in from(cassandra = V3_6_0, dse = DSE_V5_1_0){
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val source = sc.parallelize(keys).map(x => (x, x * 100))
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val someCass = source
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.joinWithCassandraTable(ks, wideTable, joinColumns = SomeColumns("key", "group"))

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