Better Visibility into Spark Execution for Faster Application Development-(Shivnath Babubabu and Lance Co Ting Keh, Duke)
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Better Visibility into Spark Execution for Faster Application Development-(Shivnath Babubabu and Lance Co Ting Keh, Duke)
- 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. Whatâs 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 â˘âŻ My query failed after 6 hours! â˘âŻ What does this exception mean?
- 14. What can go wrong? â˘âŻ Failures â˘âŻ My query failed after 6 hours! â˘âŻ What does this exception mean? â˘âŻ Wrong results â˘âŻ Result of my job looks wrong â˘âŻ Bad performance â˘âŻ My app is very slow â˘âŻ Pipeline is not meeting the 4hr SLA â˘âŻ Poor scalability â˘âŻ Oh, but it worked on the dev cluster! â˘âŻ Bad App(le)s â˘âŻ Tomâs query brought the cluster down â˘âŻ Application Problems â˘âŻ Poor choice of transformations â˘âŻ Ineffective caching â˘âŻ Bloated data structures â˘âŻ Data/Storage Problems â˘âŻ Skewed data, load imbalance â˘âŻ Small files, poor data partitioning â˘âŻ Spark Problems â˘âŻ Shuffle â˘âŻ Lazy evaluation causes confusion â˘âŻ Resource Problems â˘âŻ Resource contention â˘âŻ 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