Cassandra and Spark: Optimizing for Data Locality-(Russell Spitzer, DataStax)
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This document discusses how the Spark Cassandra Connector optimizes for data locality when performing analytics on Cassandra data using Spark. It does this by using the partition keys and token ranges to create Spark partitions that correspond to the data distribution across the Cassandra nodes, allowing work to be done locally to each data node without moving data across the network. This improves performance and avoids the costs of data shuffling.
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Cassandra and Spark: Optimizing for Data Locality-(Russell Spitzer, DataStax)
- 1. Cassandra and Spark: Optimizing for Data Locality Russell Spitzer Software Engineer @ DataStax
- 2. Lex Luther Was Right: Location is Important The value of many things is based upon it's location. Developed land near the beach is valuable but desert and farmland is generally much cheaper. Unfortunately moving land is generally impossible. Spark Summit
- 3. or lake, or swamp or whatever body of water is "Data" at the time this slide is viewed. Lex Luther Was Wrong: Don't ETL the Data Ocean Spark Summit My House
- 4. Spark is Our Hero Giving us the Ability to Do Our Analytics Without the ETL PARK
- 5. Moving Data Between Machines Is Expensive Do Work Where the Data Lives! Our Cassandra Nodes are like Cities and our Spark Executors are like Super Heroes. We'd rather they spend their time locally rather than flying back and forth all the time.
- 6. Moving Data Between Machines Is Expensive Do Work Where the Data Lives! Our Cassandra Nodes are like Cities and our Spark Executors are like Super Heroes. We'd rather they spend their time locally rather than flying back and forth all the time. Metropolis Superman Gotham Batman Spark Executors
- 7. DataStax Open Source Spark Cassandra Connector is Available on Github https://github.com/datastax/spark-cassandra-connector • Compatible with Spark 1.3 • Read and Map C* Data Types • Saves To Cassandra • Intelligent write batching • Supports Collections • Secondary index pushdown • Arbitrary CQL Execution!
- 8. How the Spark Cassandra Connector Reads Data Node Local
- 9. Cassandra Locates a Row Based on Partition Key and Token Range All of the rows in a Cassandra Cluster are stored based based on their location in the Token Range.
- 10. Metropolis Gotham Coast City Each of the Nodes in a Cassandra Cluster is primarily responsible for one set of Tokens. 0999 500 Cassandra Locates a Row Based on Partition Key and Token Range
- 11. Metropolis Gotham Coast City Each of the Nodes in a Cassandra Cluster is primarily responsible for one set of Tokens. 0999 500 750 - 99 350 - 749 100 - 349 Cassandra Locates a Row Based on Partition Key and Token Range
- 12. Jacek 514 Red The CQL Schema designates at least one column to be the Partition Key. Metropolis Gotham Coast City Cassandra Locates a Row Based on Partition Key and Token Range
- 13. Jacek 514 Red The hash of the Partition Key tells us where a row should be stored. Metropolis Gotham Coast City Cassandra Locates a Row Based on Partition Key and Token Range 830
- 14. With VNodes the ranges are not contiguous but the same mechanism controls row location. Jacek 514 Red Metropolis Gotham Coast City Cassandra Locates a Row Based on Partition Key and Token Range 830
- 15. Loading Huge Amounts of Data Table Scans involve loading most of the data in Cassandra
- 16. Cassandra RDD Use the Token Range to Create Node Local Spark Partitions sc.cassandraTable or sqlContext.load(org.apache.spark.sql.cassandra) Token Ranges spark.cassandra.input.split.size The (estimated) number of C* Partitions to be placed in a Spark Partition
- 17. Cassandra RDD Use the Token Range to Create Node Local Spark Partitions sc.cassandraTable or sqlContext.load(org.apache.spark.sql.cassandra) Token Ranges spark.cassandra.input.split.size The (estimated) number of C* Partitions to be placed in a Spark Partition CassandraRDD Spark Partition
- 18. Cassandra RDD Use the Token Range to Create Node Local Spark Partitions sc.cassandraTable or sqlContext.load(org.apache.spark.sql.cassandra) Token Ranges spark.cassandra.input.split.size The (estimated) number of C* Partitions to be placed in a Spark Partition CassandraRDD
- 19. Cassandra RDD Use the Token Range to Create Node Local Spark Partitions sc.cassandraTable or sqlContext.load(org.apache.spark.sql.cassandra) Token Ranges spark.cassandra.input.split.size The (estimated) number of C* Partitions to be placed in a Spark Partition CassandraRDD
- 20. Cassandra RDD Use the Token Range to Create Node Local Spark Partitions sc.cassandraTable or sqlContext.load(org.apache.spark.sql.cassandra) Token Ranges spark.cassandra.input.split.size The (estimated) number of C* Partitions to be placed in a Spark Partition CassandraRDD
- 21. Spark Driver Spark Partitions Are Annotated With the Location For TokenRanges they Span Metropolis Metropolis Superman Gotham Batman Coast City Green L. The Driver waits spark.locality.wait for the preferred location to have an open executor Assigns Task
- 22. Metropolis Spark Executor (Superman) The Spark Executor uses the Java Driver to Pull Rows from the Local Cassandra Instance On the Executor the task is transformed into CQL queries which are executed via the Java Driver. SELECT * FROM keyspace.table WHERE token(pk) > 780 and token(pk) <= 830 Tokens 780 - 830
- 23. Metropolis Spark Executor (Superman) The Spark Executor uses the Java Driver to Pull Rows from the Local Cassandra Instance SELECT * FROM keyspace.table WHERE token(pk) > 780 and token(pk) <= 830 Tokens 780 - 830 The C* Java Driver pages spark.cassandra.input.page.row.size CQL rows at a time
- 24. Metropolis Spark Executor (Superman) The Spark Executor uses the Java Driver to Pull Rows from the Local Cassandra Instance SELECT * FROM keyspace.table WHERE (Pushed Down Clauses) AND token(pk) > 780 and token(pk) <= 830 Tokens 780 - 830 Because we are utilizing CQL we can also pushdown predicates which can be handled by C*.
- 25. Loading Sizable But Defined Amounts of Data Retrieving sets of Partition Keys can be done in parallel
- 26. joinWithCassandraTable Provides an Interface for Obtaining a Set of C* Partitions Generic RDD Metropolis Superman Gotham Batman Coast City Green L. Generic RDDs can Be Joined But the Spark Tasks will Not be Node Local
- 27. Generic RDD Metropolis Superman Gotham Batman Coast City Green L. Generic RDDs can Be Joined But the Spark Tasks will Not be Node Local joinWithCassandraTable Provides an Interface for Obtaining a Set of C* Partitions
- 28. repartitionByCassandraReplica Repartitions RDD's to be C* Local Generic RDD CassandraPartitionedRDD This operation requires a shuffle
- 29. joinWithCassandraTable on CassandraPartitionedRDDs (or CassandraTableScanRDDs) will be Node local CassandraPartitionedRDDs are partitioned to be executed node local CassandraPartitionedRDD Metropolis Superman Gotham Batman Coast City Green L.
- 30. Metropolis Spark Executor (Superman) The Spark Executor uses the Java Driver to Pull Rows from the Local Cassandra Instance The C* Java Driver pages spark.cassandra.input.page.row.size CQL rows at a time SELECT * FROM keyspace.table WHERE pk =
- 31. DataStax Enterprise Comes Bundled with Spark and the Connector Apache Spark Apache Solr DataStax Delivers Apache Cassandra In A Database Platform
- 32. DataStax Enterprise Enables This Same Machinery with Solr Pushdown Metropolis Spark Executor (Superman) DataStax Enterprise SELECT * FROM keyspace.table WHERE solr_query = 'title:b' AND token(pk) > 780 and token(pk) <= 830 Tokens 780 - 830
- 33. Learn More Online and at Cassandra Summit https://academy.datastax.com/