Spark streaming with cassandra direct join don't work - apache-spark

Question

Hy, guys! I'm trying to dev a spark streaming apps but have some problems.
Some details:
We have Kafka topic, spark 3.2.1 and Cassandra 4.0.4 with datastax spark-cassandra-connector version com.datastax.spark:spark-cassandra-connector_2.12:3.1.0
I need a next route of a data.
Get kafka message and transform to DataFrame in spark -> left join with cassandra existing table on two columns, which bе composite primary key in cassandra table* -> if row with thats keys already exists do nothing, in another way -- write data.
In the documentation wrote about new feature, available since SCC 2.5 in DataFrame API not only from DSE, is a DirectJoin what equal joinWithCassandraTable in RDD API. If i'm trying to use Datasourse V2 API i get usual SortMergeJoin on the spark side. To be frank with you, it's not really "streaming" app, to add data in cassandra i use microbatch way.
== Physical Plan ==
AppendData (12)
+- * Project (11)
+- * Filter (10)
+- * SortMergeJoin LeftOuter (9)
:- * Sort (4)
: +- Exchange (3)
: +- * SerializeFromObject (2)
: +- Scan (1)
+- * Sort (8)
+- Exchange (7)
+- * Project (6)
+- BatchScan (5)
(1) Scan
Output [1]: [obj#342]
Arguments: obj#342: org.apache.spark.sql.Row, MapPartitionsRDD[82] at start at RunnableStream.scala:13
(2) SerializeFromObject [codegen id : 1]
Input [1]: [obj#342]
Arguments: [validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 0, user_id), LongType) AS user_id#343L, if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 1, user_type), StringType), true, false, true) AS user_type#344, if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 2, order_id), StringType), true, false, true) AS order_id#345, if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 3, status_name), StringType), true, false, true) AS status_name#346, if (assertnotnull(input[0, org.apache.spark.sql.Row, true]).isNullAt) null else staticinvoke(class org.apache.spark.sql.catalyst.util.DateTimeUtils$, TimestampType, fromJavaTimestamp, validateexternaltype(getexternalrowfield(assertnotnull(input[0, org.apache.spark.sql.Row, true]), 4, status_dttm), TimestampType), true, false, true) AS status_dttm#347]
(3) Exchange
Input [5]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347]
Arguments: hashpartitioning(user_id#343L, user_type#344, 16), ENSURE_REQUIREMENTS, [id=#793]
(4) Sort [codegen id : 2]
Input [5]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347]
Arguments: [user_id#343L ASC NULLS FIRST, user_type#344 ASC NULLS FIRST], false, 0
(5) BatchScan
Output [2]: [user_id#348L, user_type#349]
Cassandra Scan: keyspace_name.table_name
- Cassandra Filters: []
- Requested Columns: [user_id,user_type]
(6) Project [codegen id : 3]
Output [2]: [user_id#348L, user_type#349]
Input [2]: [user_id#348L, user_type#349]
(7) Exchange
Input [2]: [user_id#348L, user_type#349]
Arguments: hashpartitioning(user_id#348L, user_type#349, 16), ENSURE_REQUIREMENTS, [id=#801]
(8) Sort [codegen id : 4]
Input [2]: [user_id#348L, user_type#349]
Arguments: [user_id#348L ASC NULLS FIRST, user_type#349 ASC NULLS FIRST], false, 0
(9) SortMergeJoin [codegen id : 5]
Left keys [2]: [user_id#343L, user_type#344]
Right keys [2]: [user_id#348L, user_type#349]
Join condition: None
(10) Filter [codegen id : 5]
Input [7]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347, user_id#348L, user_type#349]
Condition : (isnull(user_id#348L) = true)
(11) Project [codegen id : 5]
Output [5]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347]
Input [7]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347, user_id#348L, user_type#349]
(12) AppendData
Input [5]: [user_id#343L, user_type#344, order_id#345, status_name#346, status_dttm#347]
Arguments: org.apache.spark.sql.execution.datasources.v2.DataSourceV2Strategy$$Lambda$3358/1878168161#32616db8, org.apache.spark.sql.connector.write.WriteBuilder$1#1d354f3b
In another way, if i'm trying to use Datasource V1 with explicitly pointing out directJoinSetting when getting cassandra table as DataFrame, like
spark.read.cassandraFormat("tableName", "keyspace").option("directJoinSetting", "on").load
this calls error on join:
Caused by: java.lang.NoSuchMethodError: org.apache.spark.sql.execution.UnaryExecNode.children$(Lorg/apache/spark/sql/execution/UnaryExecNode;)Lscala/collection/Seq;
at org.apache.spark.sql.cassandra.execution.CassandraDirectJoinExec.children(CassandraDirectJoinExec.scala:18)
at org.apache.spark.sql.cassandra.execution.CassandraDirectJoinStrategy$.hasCassandraChild(CassandraDirectJoinStrategy.scala:206)
at org.apache.spark.sql.cassandra.execution.CassandraDirectJoinStrategy$$anonfun$1.applyOrElse(CassandraDirectJoinStrategy.scala:241)
at org.apache.spark.sql.cassandra.execution.CassandraDirectJoinStrategy$$anonfun$1.applyOrElse(CassandraDirectJoinStrategy.scala:240)
full spark-submit command
/opt/spark-3.2.1-bin-hadoop3.2/bin/spark-submit --master yarn --deploy-mode cluster --name "name" \
--conf spark.driver.cores=1 \
--conf spark.driver.memory=1g \
--conf spark.driver.extraJavaOptions="-XX:+UseG1GC -Duser.timezone=GMT -Dfile.encoding=utf-8 -Dlog4j.configuration=name_Log4j.properties" \
--conf spark.executor.instances=1 \
--conf spark.executor.cores=4 \
--conf spark.executor.memory=8g \
--conf spark.executor.extraJavaOptions="-XX:+UseG1GC -Duser.timezone=GMT -Dfile.encoding=utf-8 -Dlog4j.configuration=name_Log4j.properties" \
--conf spark.yarn.queue=default \
--conf spark.yarn.submit.waitAppCompletion=true \
--conf spark.eventLog.enabled=true \
--conf spark.eventLog.dir=hdfs:///spark3-history/ \
--conf spark.eventLog.compress=true \
--conf spark.sql.shuffle.partitions=16 \
--conf spark.sql.extensions=com.datastax.spark.connector.CassandraSparkExtensions \
--conf spark.sql.catalog.cassandracatalog=com.datastax.spark.connector.datasource.CassandraCatalog \
--conf spark.sql.dse.search.enableOptimization=on \
--conf spark.cassandra.connection.host=cassandra_host \
--conf spark.cassandra.auth.username=user_name \
--conf spark.cassandra.auth.password=*** \
--conf spark.sql.directJoinSetting=on \
--class ...
class connector to cassandra
import org.apache.spark.sql._
class CassandraConnector(
val ss: SparkSession,
catalog: String,
keyspace: String,
table: String
) extends Serializable {
def read: DataFrame = ss.read.table(s"$catalog.$keyspace.$table")
def writeDirect(dataFrame: DataFrame): Unit = dataFrame.writeTo(s"$catalog.$keyspace.$table").append()
}
cassadra ddl table
CREATE KEYSPACE IF NOT EXISTS keyspace_name
WITH replication = {'class': 'SimpleStrategy', 'replication_factor': 3};
CREATE TABLE IF NOT EXISTS keyspace_name.table_name
(
user_id BIGINT,
user_type VARCHAR,
order_id VARCHAR,
status_name VARCHAR,
status_dttm timestamp,
PRIMARY KEY (user_id, user_type)
);
method which are making join and writing to cassandra
override def writeBatch(batch: Dataset[Row], batchId: Long): Unit = {
val result =
batch
.as("df")
.join(
cassandraConnector.read
.as("cass"),
col("df.user_id") === col("cass.user_id")
&& col("df.user_type") === col("cass.user_type"),
"left"
)
.withColumn("need_write", when(col("cass.user_id").isNull, true).otherwise(false))
.filter(col("need_write") === true)
.select("df.user_id", "df.user_type", "df.order_id", "df.status_name", "df.status_dttm")
cassandraConnector.writeDirect(result)
}
Can someone explain what i do wrong, please?

Answer 1

Yes, the version of the Spark Cassandra Connector is the source of the problem - advanced functionality, like, Direct Join is heavily dependent on the Spark internal classes that may change between versions. So if you use Spark 3.2, then you need to use corresponding version of the SCC: com.datastax.spark:spark-cassandra-connector_2.12:3.2.0.
Please note that there is no version for Spark 3.3 yet...
P.S. I have a blog post about using direct joins - it could be interesting for you.