SparkApplicationDevMadeEasy_Spark_Summit_2015
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The document discusses Spark application development and common problems that can occur. It introduces Unravel Data, a startup that aims to help developers visualize Spark job data, optimize performance through automated analysis and diagnoses, and strategize to prevent problems and meet goals. Key points include discussing common issues like failures, wrong results, poor performance, and resource problems; the difficulty of debugging using logs alone; and demonstrating Unravel's platform to address these challenges.
SparkApplicationDevMadeEasy_Spark_Summit_2015
- 1. Spark Application Development Made Fast and Easy Shivnath Babu Lance Co Ting Keh
- 2. Lance Co Ting Keh Machine Learning @ Box Distributed ML Infrastructure Go Blue Devils! Shivnath Babu Associate Professor @ Duke Chief Scientist at Unravel Data Sys. R&D in Management of Data Systems
- 3. Spark @Box
- 4. What’s so great about Spark? From:
- 6. Spark Execution part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 RDD0 RDD1 RDD2 RDD3 RDD4 HDFS sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath)
- 7. Spark Execution sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath) part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 HDFS Stage 0 Stage 1 RDD0 RDD1 RDD2 RDD3 RDD4
- 8. part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 RDD0 RDD1 RDD2 RDD3 RDD4 HDFS Stage 0 Stage 1 sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath) Spark Execution
- 9. Spark Execution part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 Exec0Exec1Exec2Exec3 RDD0 RDD1 RDD2 RDD3 RDD4 HDFS sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath) Stage 0 Stage 1
- 10. Spark Execution part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 Exec0Exec1Exec2Exec3 RDD0 RDD1 RDD2 RDD3 RDD4 HDFS sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath) Stage 0 Stage 1
- 11. Spark Execution part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 map filter reduceBykey map saveAsTextFile part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 part-0 part-1 part-2 part-3 Exec0Exec1Exec2Exec3 RDD0 RDD1 RDD2 RDD3 RDD4 HDFS sc.textFile(hdfsPath) .map(parseInput) .filter(subThreshold) .reduceByKey(tallyCount) .map(formatOutput) .saveAsTextFile(outPath) Stage 0 Stage 1
- 12. Anything that can go wrong, will go wrong (at some point)
- 13. What can go wrong? Failures • My query failed after 6 hours! • What does this exception mean?
- 14. What can go wrong? • Failures • My query failed after 6 hours! • What does this exception mean? • Wrong results • Result of my job looks wrong • Bad performance • My app is very slow • Pipeline is not meeting the 4hr SLA • Poor scalability • Oh, but it worked on the dev cluster! • Bad App(le)s • Tom’s query brought the cluster down • Application Problems • Poor choice of transformations • Ineffective caching • Bloated data structures • Data/Storage Problems • Skewed data, load imbalance • Small files, poor data partitioning • Spark Problems • Shuffle • Lazy evaluation causes confusion • Resource Problems • Resource contention • Performance degradation And Why?
- 15. How do application developers detect & fix these problems today?
- 17. Too much to look at!
- 18. Look at Logs? Logs in distributed systems are spread out, incomplete, & usually very difficult to understand
- 19. There has to be a better way for application developers to detect & fix problems
- 20. Visualize: Show me all relevant data in one place
- 21. Optimize: Analyze the data for me and give me diagnoses and fixes
- 22. Strategize: Help me prevent the problems from happening and meet my goals
- 23. Demo
- 24. Visualize: Show me all relevant data in one place
- 25. Visualize: Show me all relevant data in one place
- 26. Optimize: Analyze the data for me and give me diagnoses and fixes
- 27. Strategize: Help me prevent the problems from happening and meet my goals
- 28. Strategize: Help me prevent the problems from happening and meet my goals
- 29. We are hiring: jobs@unraveldata.com Sign up for early access at: bit.ly/unravelspark