Code samples, Performance tuning, Building a custom Data Source, Participating in the Catalyst Optimizer, Data Frames, Spark SQL, Spark Cassandra Connector, Spark ElasticSearch Connector, CSV, JSON, Parquet, Avro, ORC, REST, DynamoDB, Redshift

1. IBM | spark.tc
Advanced Apache Spark Meetup
Spark SQL + DataFrames + Catalyst + Data Sources API
Chris Fregly, Principal Data Solutions Engineer
IBM Spark Technology Center
Sept 21, 2015
Power of data. Simplicity of design. Speed of innovation.

2. Meetup Housekeeping

3. IBM | spark.tc
Announcements
Patrick McFadin, Evangelist
DataStax
Steve Beier, Boss Man
IBM Spark Tech Center

4. IBM | spark.tc
Who am I?
Streaming Platform Engineer
Not a Photographer or Model
Streaming Data Engineer
Netflix Open Source Committer
Data Solutions Engineer
Apache Contributor
Principal Data Solutions Engineer
IBM Technology Center

5. IBM | spark.tc
Last Meetup (Spark Wins 100 TB Daytona
GraySort) On-disk only, in-memory caching disabled!sortbenchmark.org/ApacheSpark2014.pdf

6. IBM | spark.tc
Meetup Metrics
Total Spark Experts: ~1000 (+20%)
Mean RSVPs per Meetup: ~300
Mean Attendance: ~50-60% of RSVPs
Donations: $0 (-100%)
This is good!
“Your money is no good here.”
Lloyd from
The Shining
<--- eek!

7. IBM | spark.tc
Meetup Updates
Talking with other Spark Meetup Groups
Potential mergers and/or hostile takeovers!
New Sponsors!!
Looking for more South Bay/Peninsula Hosts
Required: Food, Beer/Soda/Water, Air Conditioning
Optional: A/V Recording and Live Stream
We’re trying out new PowerPoint Animations
Please be patient!

8. IBM | spark.tc
Constructive Criticism from Previous Attendees
“Chris, you’re like a fat version of an
already-fat Erlich from Silicon Valley -
except not funny.”
“Chris, your voice is so annoying that it
actually woke me from the sleep induced
by your boring content.”

9. IBM | spark.tc
Freg-a-palooza Upcoming World Tour
① New York Strata (Sept 29th – Oct 1st)
② London Spark Meetup (Oct 12th)
③ Scotland Data Science Meetup (Oct 13th)
④ Dublin Spark Meetup (Oct 15th)
⑤ Barcelona Spark Meetup (Oct 20th)
⑥ Madrid Spark Meetup (Oct 22nd)
⑦ Amsterdam Spark Summit (Oct 27th – Oct 29th)
⑧ Delft Dutch Data Science Meetup (Oct 29th)
⑨ Brussels Spark Meetup (Oct 30th)
⑩ Zurich Big Data Developers Meetup (Nov 2nd)
High probability
I’ll end up in jail

10. IBM | spark.tc
Topics of this Talk
①DataFrames
②Catalyst Optimizer and Query Plans
③Data Sources API
④Creating and Contributing Custom Data Source
①Partitions, Pruning, Pushdowns
①Native + Third-Party Data Source Impls
①Spark SQL Performance Tuning

11. 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!!

12. 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

13. 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

14. 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!

15. IBM | spark.tc
Data Sources API
Execution (o.a.s.sql.execution.commands.scala)
RunnableCommand (trait/interface)
ExplainCommand(impl: case class)
CacheTableCommand(impl: case class)
Relations (o.a.s.sql.sources.interfaces.scala)
BaseRelation (abstract class)
TableScan (impl: returns all rows)
PrunedFilteredScan (impl: column pruning and predicate pushdown)
InsertableRelation (impl: insert or overwrite data using SaveMode)
Filters (o.a.s.sql.sources.filters.scala)
Filter (abstract class for all filter pushdowns for this data source)
EqualTo
GreaterThan
StringStartsWith

16. 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

17. 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

18. Partitions, Pruning, Pushdowns

19. 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

20. IBM | spark.tc
Partitions
Partition based on data usage patterns
/root/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(…)

21. 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

22. 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

23. Native Spark SQL Data Sources

24. IBM | spark.tc
Spark SQL Native Data Sources - Source Code

25. 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

26. 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, …)

27. 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")

28. 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")

29. Third-Party Data Sources
spark-packages.org

30. 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

31. 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")

32. 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

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"->"dating","table"->"ratings"))
.save()

35. IBM | spark.tc
REST Data Source (Databricks)
Coming Soon!
https://github.com/databricks/spark-rest?
Michael Armbrust
Spark SQL Lead @ Databricks

36. IBM | spark.tc
DynamoDB Data Source (IBM Spark Tech Center)
Coming Soon!
https://github.com/cfregly/spark-dynamodb
Me Erlich

37. 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

38. IBM | spark.tc
Related Links
https://github.com/datastax/spark-cassandra-connector
http://blog.madhukaraphatak.com/anatomy-of-spark-dataframe-api/
https://github.com/phatek-dev/anatomy_of_spark_dataframe_api/
https://databricks.com/blog/…

39. 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

40. Special Thanks to DataStax!!
IBM Spark Tech Center is Hiring!
Only Fun, Collaborative People - No Erlichs!
IBM | spark.tc
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Power of data. Simplicity of design. Speed of innovation.

41. Power of data. Simplicity of design. Speed of innovation.
IBM Spark