Spark what's new what's coming
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Founding committer of Spark, Patrick Wendell, gave this talk at 2015 Strata London about Apache Spark. These slides provides an introduction to Spark, and delves into future developments, including DataFrames, Datasource API, Catalyst logical optimizer, and Project Tungsten.
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Spark what's new what's coming
- 1. Apache Spark: What’s New, What’s Coming Patrick Wendell - @pwendell May 2015 – Strata London
- 2. About Me Founding committer of Spark at Berkeley AMPLab Co-founder of Databricks, now manage Spark roadmap and community Release manager for Spark 1.3 and 1.4 Set Footer from Insert Dropdown Menu 2
- 3. About Databricks Founded by Apache Spark creators Largest contributor to Spark project, committed to keeping Spark 100% open source End-to-End hosted platform, Databricks Cloud
- 4. Show of Hands A. New to Spark B. Played around with Spark API in training or examples C. Have written a full application on Spark (POC or production) Set Footer from Insert Dropdown Menu 4
- 5. 5
- 6. What Is Spark? A fast and general execution engine for big data processing Fast to write code High level API’s in Python, Java, and Scala Multi paradigm (streaming, batch, and interactive) Fast to run code Low overhead scheduling Optimized engine Can exploit in-memory when available 6
- 7. 7 Spark Core Spark Streaming Spark SQL MLlib GraphX What is Spark? YARN Mesos DatabricksStandalone Packages
- 8. 8 spark-packages.org API Extensions Clojure API Spark Kernel Zepplin Notebook Indexed RDD Deployment Utilities Google Compute Microsoft Azure Spark Jobserver Data Sources Avro CSV Elastic Search MongoDB > ./bin/spark-shell --packages databricks/spark-avro:0.2
- 9. Spark Today Embraced by Hadoop community… But also beyond Hadoop…
- 10. Contributors per Month to Spark 0 20 40 60 80 100 2011 2012 2013 2014 2015 Most active project at Apache, More than 500 known production deployments
- 11. Set Footer from Insert Dropdown Menu 11 What’s New In Spark?
- 12. Some Spark 1.4 and 1.5 Initiatives MLlib Pipelines API for machine learning Dozens of new algorithms/utils Streaming Metric viz and monitoring Deeper Kafka integrations Spark SQL Support all Hive metastore versions HQL/SQL coverage (window functions, etc) Set Footer from Insert Dropdown Menu 12 DataFrames Storage integrations Math and stats functions Code generation Core Runtime Managed memory Cache aware data structures
- 13. From MapReduce to Spark public static class WordCountMapClass extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { String line = value.toString(); StringTokenizer itr = new StringTokenizer(line); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); output.collect(word, one); } } } public static class WorkdCountReduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } } val file = spark.textFile("hdfs://...") val counts = file.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.saveAsTextFile("hdfs://...")
- 14. RDD API Most data is structured (JSON, CSV, Avro, Parquet, Hive …) • Programming RDDs inevitably ends up with a lot of tuples (_1, _2, …) Functional transformations (e.g. map/reduce) are not as intuitive Memory management with arbitrary Java objects is doable, but challenging 14
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- 16. DataFrames in Spark Distributed collection of data grouped into named columns (i.e. RDD with schema) Domain-specific functions designed for common tasks • Metadata • Sampling • Project, filter, aggregation, join, … • UDFs Available in Python, Scala, Java, and R 16
- 17. Demo (Time Permitting) Set Footer from Insert Dropdown Menu 17
- 18. From DataFrames Spring… 1. A DataFrame model for expressive and concise programs 2. A pluggable Datasource API API for reading and writing data frames while minimizing IO 3. The Catalyst logical optimizer for speeding up data frame operations 4. Project Tungsten – optimized physical execution throughout Spark 18
- 19. Data Sources: Input & Output Spark’s Data Source API can read and write DataFrames using a variety of formats. 19 { JSON } Built-In Packages JDBC and more…
- 20. Read Less Data The fastest way to process big data is to never read it. DataFrames can help you read less data automatically: 1Only supported for Parquet and Hive, more support coming in Spark 1.4 - 2Turned off by default in Spark 1.3 20 • Columnar formats can skip fields (i.e. parquet) • Using partitioning (i.e., /year=2014/month=02/…)1 • Skipping data using statistics (i.e., min, max)2 • Pushing predicates into storage systems (i.e., JDBC)
- 21. Plan Optimization & Execution 21 SQL AST DataFrame Unresolved Logical Plan Logical Plan Optimized Logical Plan RDDs Selected Physical Plan Analysis Logical Optimization Physical Planning CostModel Physical Plans Code Generation Catalog DataFrames and SQL share the same optimization/execution pipeline
- 22. Optimization happens as late as possible, therefore Spark SQL can optimize even across functions. 22
- 23. 23 def add_demographics(events): u = sqlCtx.table("users") # Load Hive table events .join(u, events.user_id == u.user_id) # Join on user_id .withColumn("city", zipToCity(u.zip)) # Run udf to add city column events = add_demographics(sqlCtx.load("/data/events", "json")) training_data = events.where(events.city == "New York").select(events.timestamp).collect() Logical Plan filter join events file users table expensive only join relevent users Physical Plan join scan (events) filter scan (users)
- 24. 24 def add_demographics(events): u = sqlCtx.table("users") # Load partitioned Hive table events .join(u, events.user_id == u.user_id) # Join on user_id .withColumn("city", zipToCity(u.zip)) # Run udf to add city column Physical Plan with Predicate Pushdown and Column Pruning join optimized scan (events) optimized scan (users) events = add_demographics(sqlCtx.load("/data/events", "parquet")) training_data = events.where(events.city == "New York").select(events.timestamp).collect() Logical Plan filter join events file users table Physical Plan join scan (events) filter scan (users)
- 25. Physical Execution: Unified Across Languages 25 0 2 4 6 8 10 RDD Scala RDD Python DataFrame Scala DataFrame Python DataFrame SQL Time to Aggregate 10 million int pairs (secs)
- 26. Physical Execution: Fully Managed Memory Spark’s core API uses raw Java objects and Java GC for aggregations and joins DataFrame’s will use a custom binary format and off- heap managed memory Both faster computationally and “GC-free” Set Footer from Insert Dropdown Menu 26
- 27. Set Footer from Insert Dropdown Menu 27 Physical Execution: CPU Efficient Data Structures Keep data closure to CPU cache
- 28. Physical Execution: CPU Efficient Data Structures Set Footer from Insert Dropdown Menu 28
- 29. Other Optimizations Code Generation Avoid interpretation overhead on records Already used in Spark, but will expand Vectorized record processing Process in small batches to avoid function call overhead In some cases can exploit GPU’s 29
- 30. 30 {JSON} Data Source API Spark Core Spark Streaming Spark SQL MLlib GraphX DataFrame API Packages
- 31. 31 Spark Tomorrow vs. Today Today RDD API Hadoop InputFormat Java objects for aggregations Java GC Tomorrow DataFrame API Spark Data Source Spark binary format Spark managed memory
- 32. Set Footer from Insert Dropdown Menu 32 Learn More About Spark Docs: h(p://spark.apache.org/docs/latest/ Site: h(p://spark.apache.org/ Databricks Cloud: h(p://go.databricks.com/register-‐for-‐dbc