Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, DataSources API, Spark Cassandra Connector, ORC, Parquet, JSON, CSV, REST, ElasticSearch, DynamoDB, RedShift, Cloudant, DB2
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![IBM | spark.tc
Parquet Data Source!
Configuration!
spark.sql.parquet.filterPushdown=true!
spark.sql.parquet.mergeSchema=true
spark.sql.parquet.cacheMetadata=true!
spark.sql.parquet.compression.codec=[uncompressed,snappy,gzip,lzo]
DataFrames!
val gendersDF = sqlContext.read.format("parquet")
.load("file:/root/pipeline/datasets/dating/genders.parquet")!
gendersDF.write.format("parquet").partitionBy("gender")
.save("file:/root/pipeline/datasets/dating/genders.parquet")
SQL!
CREATE TABLE genders USING parquet
OPTIONS
(path "file:/root/pipeline/datasets/dating/genders.parquet")](https://image.slidesharecdn.com/scotlanddatasciencemeetupdatasourcesapicassandrasparkconnectoroct132015-151013162134-lva1-app6892/75/scotland-data-science-meetup-oct-13-2015-spark-sql-dataframes-catalyst-datasources-api-spark-cassandra-connector-orc-parquet-json-csv-rest-elasticsearch-dynamodb-redshift-cloudant-db2-28-2048.jpg?cb=1668795095)
Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, DataSources API, Spark Cassandra Connector, ORC, Parquet, JSON, CSV, REST, ElasticSearch, DynamoDB, RedShift, Cloudant, DB2
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This talk highlights the Data Sources API which participates in the Spark SQL DataFrame Catalyst Optimizer. We dive deep into the super-advanced Cassandra's open source implementation @ github.com/datastax/spark-cassandra-connector. We discuss data locality, cluster deployment - as well as the pros and cons of mixing OLAP and OLTP workloads. We also implement a SimpleDataSource which is a basic implementation of the DataSources API. All analysis is done with Apache Zeppelin.
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Scotland Data Science Meetup Oct 13, 2015: Spark SQL, DataFrames, Catalyst, DataSources API, Spark Cassandra Connector, ORC, Parquet, JSON, CSV, REST, ElasticSearch, DynamoDB, RedShift, Cloudant, DB2
- 1. IBM | spark.tc Scotland Data Science Meetup Spark SQL + DataFrames + Catalyst + Data Sources API Chris Fregly, Principal Data Solutions Engineer IBM Spark Technology Center Oct 13, 2015 Power of data. Simplicity of design. Speed of innovation.
- 2. IBM | spark.tc Announcements Thanks to ! TechCube Incubator!!! ! Georgia Boyle! Organizer, London Spark Meetup! !
- 3. IBM | spark.tc Who am I?! ! Streaming Data Engineer! Netflix Open Source Committer! ! Data Solutions Engineer! Apache Contributor! ! Principal Data Solutions Engineer! IBM Technology Center! Meetup Organizer! Advanced Apache Meetup! Book Author! Advanced Spark (2016)!
- 4. IBM | spark.tc meetup.com/Advanced-Apache-Spark-Meetup/! Total Spark Experts: 1200+ in only 3 mos!! #5 most active Spark Meetup in the world!! ! Goals! Dig deep into the Spark & extended-Spark codebase! ! Study integrations such as Cassandra, ElasticSearch,! Tachyon, S3, BlinkDB, Mesos, YARN, Kafka, R, etc! ! Surface and share the patterns and idioms of these ! well-designed, distributed, big data components!
- 5. IBM | spark.tc Recent Events Cassandra Summit 2015! Real-time Advanced Analytics w/ Spark & Cassandra! ! ! ! Strata NYC 2015! Practical Data Science w/ Spark: Recommender Systems! ! All Slides Available on ! Slideshare! http://slideshare.net/cfregly!
- 6. IBM | spark.tc Upcoming Advanced Apache Spark Meetups! Project Tungsten Data Structs/Algos for CPU/Memory Optimization! Nov 12th, 2015! Text-based Advanced Analytics and Machine Learning! Jan 14th, 2016! ElasticSearch-Spark Connector w/ Costin Leau (Elastic.co) & Me! Feb 16th, 2016! Spark Internals Deep Dive! Mar 24th, 2016! Spark SQL Catalyst Optimizer Deep Dive ! Apr 21st, 2016!
- 7. IBM | spark.tc Freg-a-palooza Upcoming World Tour London Spark Meetup (Oct 12th)! Scotland Data Science Meetup (Oct 13th)! Dublin Spark Meetup (Oct 15th)! Barcelona Spark Meetup (Oct 20th)! Madrid Spark/Big Data Meetup (Oct 22nd)! Paris Spark Meetup (Oct 26th)! Amsterdam Spark Summit (Oct 27th – Oct 29th)! Delft Dutch Data Science Meetup (Oct 29th) ! Brussels Spark Meetup (Oct 30th)! Zurich Big Data Developers Meetup (Nov 2nd)! High probability! I’ll end up in jail! or married!!
- 8. IBM | spark.tc Slides and Videos Slides! Links posted in Meetup directly! ! Videos! Most talks are live streamed and/or video recorded! Links posted in Meetup directly! ! All Slides Available on Slideshare! http://slideshare.net/cfregly!
- 9. IBM | spark.tc Last Meetup (Spark Wins 100 TB Daytona GraySort) On-disk only, in-memory caching disabled!!sortbenchmark.org/ApacheSpark2014.pdf!
- 10. Spark SQL + DataFrames Catalyst + Data Sources API
- 11. IBM | spark.tc Topics of this Talk! DataFrames! Catalyst Optimizer and Query Plans! Data Sources API! Creating and Contributing Custom Data Source! ! Partitions, Pruning, Pushdowns! ! Native + Third-Party Data Source Impls! ! Spark SQL Performance Tuning!
- 12. IBM | spark.tc DataFrames! Inspired by R and Pandas DataFrames! Cross language support! SQL, Python, Scala, Java, R! Levels performance of Python, Scala, Java, and R! Generates JVM bytecode vs serialize/pickle objects to Python! DataFrame is Container for Logical Plan! Transformations are lazy and represented as a tree! Catalyst Optimizer creates physical plan! DataFrame.rdd returns the underlying RDD if needed! Custom UDF using registerFunction() New, experimental UDAF support! Use DataFrames ! instead of RDDs!!!
- 13. IBM | spark.tc Catalyst Optimizer! Converts logical plan to physical plan! Manipulate & optimize DataFrame transformation tree! Subquery elimination – use aliases to collapse subqueries! Constant folding – replace expression with constant! Simplify filters – remove unnecessary filters! Predicate/filter pushdowns – avoid unnecessary data load! Projection collapsing – avoid unnecessary projections! Hooks for custom rules! Rules = Scala Case Classes! val newPlan = MyFilterRule(analyzedPlan) Implements! oas.sql.catalyst.rules.Rule! Apply to any plan stage!
- 14. IBM | spark.tc Plan Debugging! gendersCsvDF.select($"id", $"gender").filter("gender != 'F'").filter("gender != 'M'").explain(true)! Requires explain(true)! DataFrame.queryExecution.logical! DataFrame.queryExecution.analyzed! DataFrame.queryExecution.optimizedPlan! DataFrame.queryExecution.executedPlan!
- 15. IBM | spark.tc Plan Visualization & Join/Aggregation Metrics! Effectiveness ! of Filter! Cost-based ! Optimization! is Applied! Peak Memory for! Joins and Aggs! Optimized ! CPU-cache-aware! Binary Format! Minimizes GC &! Improves Join Perf! (Project Tungsten)! New in Spark 1.5!!
- 16. IBM | spark.tc Data Sources API! Relations (o.a.s.sql.sources.interfaces.scala)! BaseRelation (abstract class): Provides schema of data! TableScan (impl): Read all data from source, construct rows ! PrunedFilteredScan (impl): Read with column pruning & predicate pushdowns InsertableRelation (impl): Insert or overwrite data based on SaveMode enum! RelationProvider (trait/interface): Handles user options, creates BaseRelation! Execution (o.a.s.sql.execution.commands.scala)! RunnableCommand (trait/interface)! ExplainCommand(impl: case class)! CacheTableCommand(impl: case class)! Filters (o.a.s.sql.sources.filters.scala)! Filter (abstract class for all filter pushdowns for this data source)! EqualTo (impl)! GreaterThan (impl)! StringStartsWith (impl)!
- 17. IBM | spark.tc Creating a Custom Data Source! Study Existing Native and Third-Party Data Source Impls! ! Native: JDBC (o.a.s.sql.execution.datasources.jdbc)! class JDBCRelation extends BaseRelation with PrunedFilteredScan with InsertableRelation ! Third-Party: Cassandra (o.a.s.sql.cassandra)! class CassandraSourceRelation extends BaseRelation with PrunedFilteredScan with InsertableRelation! !
- 18. IBM | spark.tc Contributing a Custom Data Source! spark-packages.org! Managed by! Contains links to externally-managed github projects! Ratings and comments! Spark version requirements of each package! Examples! https://github.com/databricks/spark-csv! https://github.com/databricks/spark-avro! https://github.com/databricks/spark-redshift!
- 20. IBM | spark.tc Demo Dataset (from previous Spark After Dark talks)! RATINGS ! ========! UserID,ProfileID,Rating ! (1-10)! GENDERS! ========! UserID,Gender ! (M,F,U)! <-- Totally -->! Anonymous !
- 21. IBM | spark.tc Partitions! Partition based on data usage patterns! /genders.parquet/gender=M/… /gender=F/… <-- Use case: access users by gender /gender=U/… Partition Discovery! On read, infer partitions from organization of data (ie. gender=F)! Dynamic Partitions! Upon insert, dynamically create partitions! Specify field to use for each partition (ie. gender)! SQL: INSERT TABLE genders PARTITION (gender) SELECT … DF: gendersDF.write.format(”parquet").partitionBy(”gender”).save(…)
- 22. IBM | spark.tc Pruning! Partition Pruning! Filter out entire partitions of rows on partitioned data SELECT id, gender FROM genders where gender = ‘U’ Column Pruning! Filter out entire columns for all rows if not required! Extremely useful for columnar storage formats! Parquet, ORC! SELECT id, gender FROM genders !
- 23. IBM | spark.tc Pushdowns! Predicate (aka Filter) Pushdowns! Predicate returns {true, false} for a given function/condition! Filters rows as deep into the data source as possible! Data Source must implement PrunedFilteredScan!
- 24. Native Spark SQL Data Sources
- 25. IBM | spark.tc Spark SQL Native Data Sources - Source Code!
- 26. IBM | spark.tc JSON Data Source! DataFrame! val ratingsDF = sqlContext.read.format("json") .load("file:/root/pipeline/datasets/dating/ratings.json.bz2") -- or --! val ratingsDF = sqlContext.read.json ("file:/root/pipeline/datasets/dating/ratings.json.bz2") SQL Code! CREATE TABLE genders USING json OPTIONS (path "file:/root/pipeline/datasets/dating/genders.json.bz2") Convenience Method
- 27. IBM | spark.tc JDBC Data Source! Add Driver to Spark JVM System Classpath! $ export SPARK_CLASSPATH=<jdbc-driver.jar> DataFrame! val jdbcConfig = Map("driver" -> "org.postgresql.Driver", "url" -> "jdbc:postgresql:hostname:port/database", "dbtable" -> ”schema.tablename") df.read.format("jdbc").options(jdbcConfig).load() SQL! CREATE TABLE genders USING jdbc OPTIONS (url, dbtable, driver, …)
- 28. IBM | spark.tc Parquet Data Source! Configuration! spark.sql.parquet.filterPushdown=true! spark.sql.parquet.mergeSchema=true spark.sql.parquet.cacheMetadata=true! spark.sql.parquet.compression.codec=[uncompressed,snappy,gzip,lzo] DataFrames! val gendersDF = sqlContext.read.format("parquet") .load("file:/root/pipeline/datasets/dating/genders.parquet")! gendersDF.write.format("parquet").partitionBy("gender") .save("file:/root/pipeline/datasets/dating/genders.parquet") SQL! CREATE TABLE genders USING parquet OPTIONS (path "file:/root/pipeline/datasets/dating/genders.parquet")
- 29. IBM | spark.tc ORC Data Source! Configuration! spark.sql.orc.filterPushdown=true DataFrames! val gendersDF = sqlContext.read.format("orc") .load("file:/root/pipeline/datasets/dating/genders")! gendersDF.write.format("orc").partitionBy("gender") .save("file:/root/pipeline/datasets/dating/genders") SQL! CREATE TABLE genders USING orc OPTIONS (path "file:/root/pipeline/datasets/dating/genders")
- 31. IBM | spark.tc CSV Data Source (Databricks)! Github! https://github.com/databricks/spark-csv! ! Maven! com.databricks:spark-csv_2.10:1.2.0! ! Code! val gendersCsvDF = sqlContext.read .format("com.databricks.spark.csv") .load("file:/root/pipeline/datasets/dating/gender.csv.bz2") .toDF("id", "gender") toDF() defines column names!
- 32. IBM | spark.tc Avro Data Source (Databricks)! Github! https://github.com/databricks/spark-avro! ! Maven! com.databricks:spark-avro_2.10:2.0.1! ! Code! val df = sqlContext.read .format("com.databricks.spark.avro") .load("file:/root/pipeline/datasets/dating/gender.avro") !
- 33. IBM | spark.tc ElasticSearch Data Source (Elastic.co)! Github! https://github.com/elastic/elasticsearch-hadoop! Maven! org.elasticsearch:elasticsearch-spark_2.10:2.1.0! Code! val esConfig = Map("pushdown" -> "true", "es.nodes" -> "<hostname>", "es.port" -> "<port>") df.write.format("org.elasticsearch.spark.sql”).mode(SaveMode.Overwrite) .options(esConfig).save("<index>/<document>")
- 34. IBM | spark.tc Cassandra Data Source (DataStax)! Github! https://github.com/datastax/spark-cassandra-connector! Maven! com.datastax.spark:spark-cassandra-connector_2.10:1.5.0-M1 Code! ratingsDF.write .format("org.apache.spark.sql.cassandra") .mode(SaveMode.Append) .options(Map("keyspace"->"<keyspace>", "table"->"<table>")).save(…)
- 35. IBM | spark.tc Cassandra Pushdown Rules! Determines which filter predicates can be pushed down to Cassandra.! * 1. Only push down no-partition key column predicates with =, >, <, >=, <= predicate! * 2. Only push down primary key column predicates with = or IN predicate.! * 3. If there are regular columns in the pushdown predicates, they should have! * at least one EQ expression on an indexed column and no IN predicates.! * 4. All partition column predicates must be included in the predicates to be pushed down,! * only the last part of the partition key can be an IN predicate. For each partition column,! * only one predicate is allowed.! * 5. For cluster column predicates, only last predicate can be non-EQ predicate! * including IN predicate, and preceding column predicates must be EQ predicates.! * If there is only one cluster column predicate, the predicates could be any non-IN predicate.! * 6. There is no pushdown predicates if there is any OR condition or NOT IN condition.! * 7. We're not allowed to push down multiple predicates for the same column if any of them! * is equality or IN predicate.! spark-cassandra-connector/…/o.a.s.sql.cassandra.PredicatePushDown.scala!
- 36. IBM | spark.tc Special Thanks to DataStax!!!! Russel Spitzer! @RussSpitzer! (He created the following few slides)! (These guys built a lot of the connector.)!
- 37. IBM | spark.tc Spark-Cassandra Architecture!
- 38. IBM | spark.tc Spark-Cassandra Data Locality!
- 39. IBM | spark.tc Spark-Cassandra Node-specific CQL Queries! http://www.slideshare.net/CesareCugnasco/indexing-3dimensional-trajectories-apache-spark-and-cassandra-integration!
- 40. IBM | spark.tc Spark-Cassandra Configuration:input.page.row.size
- 41. IBM | spark.tc Spark-Cassandra Configuration: grouping.key!
- 42. IBM | spark.tc Spark-Cassandra Configuration: size.rows/bytes!
- 43. IBM | spark.tc Spark-Cassandra Configuration: batch.buffer.size!
- 44. IBM | spark.tc Spark-Cassandra Configuration: concurrent.writes!
- 45. IBM | spark.tc Spark-Cassandra Configuration: throughput_mb/s!
- 46. IBM | spark.tc Spark-Cassandra Optimizatins and Next Steps! By-pass CQL front door! Bulk read/write directly to SSTables! Rumored to be in existence! DataStax Enterprise only?! Closed Source Alert!!
- 47. IBM | spark.tc Redshift Data Source (Databricks)! Github! https://github.com/databricks/spark-redshift! Maven! com.databricks:spark-redshift:0.5.0! Code! val df: DataFrame = sqlContext.read .format("com.databricks.spark.redshift") .option("url", "jdbc:redshift://<hostname>:<port>/<database>…") .option("query", "select x, count(*) my_table group by x") .option("tempdir", "s3n://tmpdir") .load(...) Copies to S3 for ! fast, parallel reads vs ! single Redshift Master bottleneck!
- 48. IBM | spark.tc Cloudant Data Source (IBM)! Github! http://spark-packages.org/package/cloudant/spark-cloudant! Maven! com.datastax.spark:spark-cassandra-connector_2.10:1.5.0-M1 Code! ratingsDF.write.format("com.cloudant.spark") .mode(SaveMode.Append) .options(Map("cloudant.host"->"<account>.cloudant.com", "cloudant.username"->"<username>", "cloudant.password"->"<password>")) .save("<filename>")
- 49. IBM | spark.tc DB2 and BigSQL Data Sources (IBM)! Coming Soon!! ! ! ! https://github.com/SparkTC/spark-db2! https://github.com/SparkTC/spark-bigsql! !
- 50. IBM | spark.tc REST Data Source (Databricks)! Coming Soon!! https://github.com/databricks/spark-rest?! Michael Armbrust! Spark SQL Lead @ Databricks!
- 51. IBM | spark.tc Simple Data Source (Me and You Guys)! Coming Right Now!!! Me!
- 52. IBM | spark.tc SparkSQL Performance Tuning (oas.sql.SQLConf)! spark.sql.inMemoryColumnarStorage.compressed=true! Automatically selects column codec based on data! spark.sql.inMemoryColumnarStorage.batchSize! Increase as much as possible without OOM – improves compression and GC! spark.sql.inMemoryPartitionPruning=true! Enable partition pruning for in-memory partitions! spark.sql.tungsten.enabled=true! Code Gen for CPU and Memory Optimizations (Tungsten aka Unsafe Mode)! spark.sql.shuffle.partitions! Increase from default 200 for large joins and aggregations! spark.sql.autoBroadcastJoinThreshold! Increase to tune this cost-based, physical plan optimization! spark.sql.hive.metastorePartitionPruning! Predicate pushdown into the metastore to prune partitions early! spark.sql.planner.sortMergeJoin! Prefer sort-merge (vs. hash join) for large joins ! spark.sql.sources.partitionDiscovery.enabled ! & spark.sql.sources.parallelPartitionDiscovery.threshold!
- 53. IBM | spark.tc Related Links! https://github.com/datastax/spark-cassandra-connector! http://blog.madhukaraphatak.com/anatomy-of-spark-dataframe-api/! https://github.com/phatak-dev/anatomy_of_spark_dataframe_api! https://databricks.com/blog/! https://www.youtube.com/watch?v=uxuLRiNoDio! http://www.slideshare.net/RussellSpitzer!
- 54. IBM | spark.tc Freg-a-palooza Upcoming World Tour London Spark Meetup (Oct 12th)! Scotland Data Science Meetup (Oct 13th)! Dublin Spark Meetup (Oct 15th)! Barcelona Spark Meetup (Oct 20th)! Madrid Spark/Big Data Meetup (Oct 22nd)! Paris Spark Meetup (Oct 26th)! Amsterdam Spark Summit (Oct 27th – Oct 29th)! Delft Dutch Data Science Meetup (Oct 29th) ! Brussels Spark Meetup (Oct 30th)! Zurich Big Data Developers Meetup (Nov 2nd)! High probability! I’ll end up in jail! or married!!
- 55. http://spark.tc/datapalooza IBM Spark Tech Center is Hiring! " JOnly Fun, Collaborative People!! J IBM | spark.tc Sign up for our newsletter at Thank You! Power of data. Simplicity of design. Speed of innovation. Coming to Your City!!!!
- 56. Power of data. Simplicity of design. Speed of innovation. IBM Spark