Intro to Apache Spark by CTO of Twingo
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http://bit.ly/1BTaXZP – As organizations look for even faster ways to derive value from big data, they are turning to Apache Spark is an in-memory processing framework that offers lightning-fast big data analytics, providing speed, developer productivity, and real-time processing advantages. The Spark software stack includes a core data-processing engine, an interface for interactive querying, Spark Streaming for streaming data analysis, and growing libraries for machine-learning and graph analysis. Spark is quickly establishing itself as a leading environment for doing fast, iterative in-memory and streaming analysis. This talk will give an introduction the Spark stack, explain how Spark has lighting fast results, and how it complements Apache Hadoop. By the end of the session, you’ll come away with a deeper understanding of how you can unlock deeper insights from your data, faster, with Spark.
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Intro to Apache Spark by CTO of Twingo
- 1. An Overview of Apache Spark Ilya Gulman CTO, Twingo June, 2014
- 2. About Twingo • A Big Data Company • Established in 2006 by Golan Nahum • 25 Employees • Reseller and expert integrator in HP VERTICA • Reseller and integrator in MAPR • Reseller and expert integrator in MICROSTRATEGY • Deep knowedge in Phyton and Linux • We did more than 20 Big Data successful Projects • Expertise in SAAS /OEM BIG DATA solutions
- 3. Agenda • What is Spark? • The Difference with Spark • SQL on Spark • Combining the power • Real-World Use Cases • Resources
- 5. Fast and general MapReduce-like engine for large-scale data processing • Fast In memory data storage for very fast interactive queries Up to 100 times faster then Hadoop • General - Unified platform that can combine: SQL, Machine Learning , Streaming , Graph & Complex analytics • Ease of use Can be developed in Java, Scala or Python • Integrated with Hadoop Can read from HDFS, HBase, Cassandra, and any Hadoop data source. What is Spark ?
- 6. Spark is the Most Active Open Source Project in Big Data Projectcontributorsinpastyear Giraph Storm Tez 0 20 40 60 80 100 120 140
- 8. Unified Platform Shark (SQL) Spark Streaming (Streaming) MLlib (Machine learning) Spark (General execution engine) GraphX (Graph computation) Continued innovation bringing new functionality, e.g.: • Java 8 (Closures, Lamba Expressions) • Spark SQL (SQL on Spark, not just Hive) • BlinkDB (Approximate Queries) • SparkR (R wrapper for Spark)
- 9. Supported Languages • Java • Scala • Python • SQL
- 10. Data Sources • Local Files – file:///opt/httpd/logs/access_log • S3 • Hadoop Distributed Filesystem – Regular files, sequence files, any other Hadoop InputFormat • Hbase • Can also read from any other Hadoop data source.
- 11. The Difference with Spark
- 12. Easy and Fast Big Data • Easy to Develop – Rich APIs in Java, Scala, Python – Interactive shell • Fast to Run – General execution graphs – In-memory storage 2-5× less code Up to 10× faster on disk, 100× in memory
- 13. Resilient Distributed Datasets (RDD) • Spark revolves around RDDs • Fault-tolerant collection of elements that can be operated on in parallel – Parallelized Collection: Scala collection which is run in parallel – Hadoop Dataset: records of files supported by Hadoop http://www.cs.berkeley.edu/~matei/papers/2012/nsdi_spark.pdf
- 14. RDD Operations • Transformations – Creation of a new dataset from an existing • map, filter, distinct, union, sample, groupByKey, join, etc… • Actions – Return a value after running a computation • collect, count, first, takeSample, foreach, etc… Check the documentation for a complete list http://spark.apache.org/docs/latest/scala-programming-guide.html#rdd-operations
- 15. RDD Code Example file = spark.textFile("hdfs://...") errors = file.filter(lambda line: "ERROR" in line) # Count all the errors errors.count() # Count errors mentioning MySQL errors.filter(lambda line: "MySQL" in line).count() # Fetch the MySQL errors as an array of strings errors.filter(lambda line: "MySQL" in line).collect()
- 16. RDD Persistence / Caching • Variety of storage levels – memory_only (default), memory_and_disk, etc… • API Calls – persist(StorageLevel) – cache() – shorthand for persist(StorageLevel.MEMORY_ONLY) • Considerations – Read from disk vs. recompute (memory_and_disk) – Total memory storage size (memory_only_ser) – Replicate to second node for faster fault recovery (memory_only_2) • Think about this option if supporting a web application http://spark.apache.org/docs/latest/scala-programming-guide.html#rdd-persistence
- 17. Cache Scaling Matters 69 58 41 30 12 0 20 40 60 80 100 Cache disabled 25% 50% 75% Fully cached Executiontime(s) % of working set in cache
- 18. RDD Fault Recovery RDDs track lineage information that can be used to efficiently recompute lost data msgs = textFile.filter(lambda s: s.startsWith(“ERROR”)) .map(lambda s: s.split(“t”)[2]) HDFS File Filtered RDD Mapped RDD filter (func = startsWith(…)) map (func = split(...))
- 19. Interactive Shell • Iterative Development – Cache those RDDs – Open the shell and ask questions • We have all wished we could do this with MapReduce – Compile / save your code for scheduled jobs later • Scala – spark-shell • Python – pyspark
- 20. SQL on Spark
- 21. Before Spark - Hive • Puts structure/schema onto HDFS data • Compiles HiveQL queries into MapReduce jobs • Very popular: 90+% of Facebook Hadoop jobs generated by Hive • Initially developed by Facebook
- 22. But.. Hive is slow • Takes 20+ seconds even for simple queries • "A good day is when I can run 6 Hive queries” – @mtraverso
- 23. SQL over Spark
- 24. Shark – SQL over Spark • Hive-compatible (HiveQL, UDFs, metadata) – Works in existing Hive warehouses without changing queries or data! • Augments Hive – In-memory tables and columnar memory store • Fast execution engine – Uses Spark as the underlying execution engine – Low-latency, interactive queries – Scale-out and tolerates worker failures
- 25. Shark – SQL over Spark • Hive-compatible (HiveQL, UDFs, metadata) – Works in existing Hive warehouses without changing queries or data! • Augments Hive – In-memory tables and columnar memory store • Fast execution engine – Uses Spark as the underlying execution engine – Low-latency, interactive queries – Scale-out and tolerates worker failures
- 26. Machine Learning
- 27. Machine Learning - MLlib • K-Means • L1 and L2-regularized Linear Regression • L1 and L2-regularized Logistic Regression • Alternating Least Squares • Naive Bayes • Stochastic Gradient Descent * As of May 14, 2014 ** Don’t be surprised if you see the Mahout library converting to Spark soon
- 28. Streaming
- 29. Comparison to Storm • Higher throughput than Storm – Spark Streaming: 670k records/sec/node – Storm: 115k records/sec/node – Commercial systems: 100-500k records/sec/node 0 20 40 100 1000 Throughputper node(MB/s) Record Size (bytes) WordCount Spark Storm 0 20 40 60 100 1000 Throughputper node(MB/s) Record Size (bytes) Grep Spark Storm
- 31. Combining the power • Use Machine Learningg result as table. GENERATE KMeans(tweet_locations) SAVE AS TABLE tweet_clusters; • Combine SQL, ML, and streaming (Scala) val points = sc.runSql[Double, Double]( “select latitude, longitude from historic_tweets”) val model = KMeans.train(points, 10) sc.twitterStream(...) .map(t => (model.closestCenter(t.location), 1)) .reduceByWindow(“5s”, _ + _)
- 33. Spark at Yahoo! • Fast Machine Learning Personalized news pages
- 34. Spark at Yahoo! • Hive on Spark (Shark) Using existing BI tools to view and query advertising analytic data collected in Hadoop. Any tool that plugs into Hive, like Tableau, automatically works with Shark.
- 35. Spark at • One of the largest streaming video companies on the Internet • 4+ billion video feeds per month (second only to YouTube) • CONVIVA uses Spark Streaming to learn network conditions in real time
- 36. Resources
- 37. Remember • If you want to use a new technology you must learn that new technology • For those who have been using Hadoop for a while, at one time you had to learn all about MapReduce and how to manage and tune it • To get the most out of a new technology you need to learn that technology, this includes tuning – There are switches you can use to optimize your work
- 38. Configuration http://spark.apache.org/docs/latest/ Most Important • Application Configuration http://spark.apache.org/docs/latest/configuration.html • Standalone Cluster Configuration http://spark.apache.org/docs/latest/spark-standalone.html • Tuning Guide http://spark.apache.org/docs/latest/tuning.html
- 39. Resources • Pig on Spark – http://apache-spark-user-list.1001560.n3.nabble.com/Pig-on-Spark-td2367.html – https://github.com/aniket486/pig – https://github.com/twitter/pig/tree/spork – http://docs.sigmoidanalytics.com/index.php/Setting_up_spork_with_spark_0.8.1 – https://github.com/sigmoidanalytics/pig/tree/spork-hadoopasm-fix • Latest on Spark – http://databricks.com/categories/spark/ – http://www.spark-stack.org/
- 41. Optional - More Examples
- 42. SparkContext sc = new SparkContext(master, appName, [sparkHome], [jars]); JavaRDD<String> file = sc.textFile("hdfs://..."); JavaRDD<String> counts = file.flatMap(line -> Arrays.asList(line.split(" "))) .mapToPair(w -> new Tuple2<String, Integer>(w, 1)) .reduceByKey((x, y) -> x + y); counts.saveAsTextFile("hdfs://..."); val sc = new SparkContext(master, appName, [sparkHome], [jars]) val file = sc.textFile("hdfs://...") val counts = file.flatMap(line => line.split(" ")) .map(word => (word, 1)) .reduceByKey(_ + _) counts.saveAsTextFile("hdfs://...") Word Count • Java MapReduce (~15 lines of code) • Java Spark (~ 7 lines of code) • Scala and Python (4 lines of code) – interactive shell: skip line 1 and replace the last line with counts.collect() • Java8 (4 lines of code)
- 43. Network Word Count – Streaming // Create the context with a 1 second batch size val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) // Create a NetworkInputDStream on target host:port and count the // words in input stream of n delimited text (eg. generated by 'nc') val lines = ssc.socketTextStream("localhost", 9999, StorageLevel.MEMORY_ONLY_SER) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() ssc.start()
- 44. Deploying Spark – Cluster Manager Types • Standalone mode – Comes bundled (EC2 capable) • YARN • Mesos
Editor's Notes
- Spark is really cool…
- השקענו בפרויקט מעל 12 שנות אדם , פרויקט FIX , בשיתוף עם מטריקס ביי
- Yahoo and Adobe are in production with Spark.
- This sounds a lot like the reason to consider Pig vs. Java MapReduce
- Gracefully
- You can import the MLlib to use here in the shell!
- Don’t forget to share your experiences. This is really what the community is about. Don’t have time to contribute to open source, use it and share your experiences!
- This isn’t all proven out yet, but some of it should just work already.
- Best use case? Standalone followed by Mesos… My personal opinion is that Mesos is where the future will take us.