1. Spark 1.1 and Beyond
Patrick Wendell

2. About Me
Work at Databricks leading the Spark team Spark 1.1 Release manager Committer on Spark since AMPLab days

3. This Talk
Spark 1.1 (and a bit about 1.2) A few notes on performance Q&A with myself, Tathagata Das, and Josh Rosen

4. A Bit about Spark… Spark Streaming real-time GraphX MLLib
DStream’s: Streams of RDD’s
RDD-Based Graphs
RDD-Based Matrices
RDD-Based Tables
Spark Streaming real-time
GraphX
Graph
(alpha)
MLLib
machine learning
Spark
SQL
Spark RDD API
HDFS, S3, Cassandra
YARN, Mesos, Standalone

5. Spark Release Process
~3 month release cycle, time-scoped 2 months of feature development 1 month of QA Maintain older branches with bug fixes Upcoming release: (previous was 1.0.2)

6. Master
More stable
V1.1.0
For any P.O.C or non production cluster, we always recommend running off of the head of a release branch.
More
features
branch-1.1
V1.0.0
V1.0.1
branch-1.0

7. Spark 1.1
1,297 patches 200+ contributors (still counting) Dozens of organizations To get updates – join our dev list:

8. Roadmap Around ~40% of mailing list traffic is about these libraries.
Spark 1.1 and 1.2 have similar themes Spark core: Usability, stability, and performance MLlib/SQL/Streaming: Expanded feature set and performance
Around ~40% of mailing list traffic is about these libraries.

9. Spark Core in 1.1
Performance “out of the box” Sort-based shuffle Efficient broadcasts Disk spilling in Python YARN usability improvements Usability Task progress and user-defined counters UI behavior for failing or large jobs

10. Spark SQL in 1.1
1.0 was the first “preview” release 1.1 provides upgrade path for Shark Replaced Shark in our benchmarks with 2-3X perf gains Can perform optimizations with X less effort than Hive.

11. Turning an RDD into a Relation
// Define the schema using a case class. case class Person(name: String, age: Int) // Create an RDD of Person objects, register it as a table. val people = sc.textFile("examples/src/main/resources/people.txt") map(_.split(",") map(p => Person(p(0), p(1).trim.toInt)) people.registerAsTable("people")

12. Querying using SQL
// SQL statements can be run directly on RDD’s val teenagers = sql("SELECT name FROM people WHERE age >= 13 AND age <= 19") // The results of SQL queries are SchemaRDDs and support // normal RDD operations. val nameList = teenagers.map(t => "Name: " + t(0)).collect()
// Language integrated queries (ala LINQ) val teenagers =  people.where('age >= 10).where('age <= 19).select('name)

13. Spark SQL in 1.1
JDBC server for multi-tenant access and BI tools Native JSON support Public types API – “make your own” Schema RDD’s Improved operator performance Native Parquet support and optimizations

14. Spark Streaming
Stability improvements across the board Amazon Kinesis support Rate limiting for streams Support for polling Flume streams Streaming + ML: Streaming linear regressions

15. What’s new in MLlib v1.1 Contributors: 40 (v1.0) -> 68
Algorithms: SVD via Lanczos, multiclass support in decision tree, logistic regression with L-BFGS, nonnegative matrix factorization, streaming linear regression
Feature extraction and transformation: scaling, normalization, tf-idf, Word2Vec
Statistics: sampling (core), correlations, hypothesis testing, random data generation
Performance and scalability: major improvement to decision tree, tree aggregation
Python API: decision tree, statistics, linear methods

16. Performance (v1.0 vs. v1.1)

17. Sort-based Shuffle
Old shuffle: Each mapper opens a file for each reducer and writes output simultaneously. Files = # mappers * # reducers New Shuffle: Each mapper buffers reduce output in memory, spills, then sort-merges on disk data.

18. GroupBy Operator
Spark groupByKey != SQL groupBy NO: people.map(p => (p.zipCode, p.getIncome)) .groupByKey() .map(incomes => incomes.sum) YES: people.map(p => (p.zipCode, p.getIncome)) .reduceByKey(_ + _)

19. GroupBy Operator
Spark groupByKey != SQL groupBy NO: people.map(p => (p.zipCode, p.getIncome)) .groupByKey() .map(incomes => incomes.sum) YES: people.groupBy(‘zipCode).select(sum(‘income))

20. GroupBy Operator
Spark groupByKey != SQL groupBy NO: people.map(p => (p.zipCode, p.getIncome)) .groupByKey() .map(incomes => incomes.sum) YES: SELECT sum(income) FROM people GROUP BY zipCode;

21. Other efforts Spark Streaming real-time GraphX MLLib Spark RDD API
Ooyala Job Server
DStream’s: Streams of RDD’s
RDD-Based Graphs
RDD-Based Matrices
RDD-Based Tables
Hive on Spark
Spark Streaming real-time
GraphX
Graph
(alpha)
MLLib
machine learning
Spark
SQL
Pig on Spark
Spark RDD API
HDFS, S3, Cassandra
YARN, Mesos, Standalone

22. Looking Ahead to 1.2+
[Core] Scala 2.11 support Debugging tools (task progress, visualization) Netty-based communication layer [SQL] Portability across Hive versions Performance optimizations (TPC-DS and Parquet) Planner integration with Cassandra and other sources

23. Looking Ahead to 1.2+
[Streaming] Python Support Lower level Kafka API w/ recoverability [MLLib] Multi-model training Many new algorithms Faster internal linear solver

24. Q and A Josh Rosen PySpark and Spark Core Tathagata Das
Spark Streaming Lead