Running Presto and Spark on the Netflix Big Data Platform
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This document summarizes Netflix's big data platform, which uses Presto and Spark on Amazon EMR and S3. Key points: - Netflix processes over 50 billion hours of streaming per quarter from 65+ million members across over 1000 devices. - Their data warehouse contains over 25PB stored on S3. They read 10% daily and write 10% of reads. - They use Presto for interactive queries and Spark for both batch and iterative jobs. - They have customized Presto and Spark for better performance on S3 and Parquet, and contributed code back to open source projects. - Their architecture leverages dynamic EMR clusters with Presto and Spark deployed via bootstrap actions for scalability.
![Predicate Pushdown
Dictionary pushdown
column chunk stats [5, 20]
dictionary page <5,11,18,20>
skip this row group
Statistics pushdown
column chunk stats [20, 30]
skip this row group
Example: SELECT… WHERE abtest_id = 10;](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-34-320.jpg)
![Dynamic Allocation [SPARK-6954]](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-47-320.jpg)
![Cached Executor Timeout [SPARK-7955]
val data = sqlContext
.table("dse.admin_genie_job_d”)
.filter($"dateint">=20150601 and $"dateint"<=20150830)
data.persist
data.count](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-49-320.jpg)
![Preemption [SPARK-8167]
Symptom
• Spark tasks randomly fail with “executor lost” error
Cause
• YARN preemption is not graceful
Solution
• Preempted tasks shouldn’t be counted as failures but should be retried](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-50-320.jpg)
![File listing for partitioned table
Partition path
Seq[RDD]
HadoopRDD
HadoopRDD
HadoopRDD
HadoopRDD
Partition path
Partition path
Partition path
Input dir
Input dir
Input dir
Input dir
Sequentially listing input dirs via S3N file system.
S3N
S3N
S3N
S3N](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-54-320.jpg)
![Amazon S3 Bulk Listing
Partition path
ParArray[RDD]
HadoopRDD
HadoopRDD
HadoopRDD
HadoopRDD
Partition path
Partition path
Partition path
Input dir
Input dir
Input dir
Input dir
Amazon S3 listing input dirs in parallel via AmazonS3Client
Amazon
S3Client](https://image.slidesharecdn.com/bdt303tse-reinvent2015-bdt303editjw-151106075812-lva1-app6892/85/Running-Presto-and-Spark-on-the-Netflix-Big-Data-Platform-56-320.jpg)
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Running Presto and Spark on the Netflix Big Data Platform
- 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Eva Tse and Daniel Weeks, Netflix October 2015 BDT303 Running Presto and Spark on the Netflix Big Data Platform
- 2. What to Expect from the Session Our big data scale Our architecture For Presto and Spark - Use cases - Performance - Contributions - Deployment on Amazon EMR - Integration with Netflix infrastructure
- 7. Our Biggest Challenge is Scale
- 8. Netflix Key Business Metrics 65+ million members 50 countries 1000+ devices supported 10 billion hours / quarter
- 9. Our Big Data Scale Total ~25 PB DW on Amazon S3 Read ~10% DW daily Write ~10% of read data daily ~ 550 billion events daily ~ 350 active platform users
- 10. Global Expansion 200 countries by end of 2016
- 12. Cloud apps Kafka Ursula Cassandra Aegisthus Dimension Data Event Data 15 min Daily AWS S3 SS Tables Data Pipelines
- 13. Storage Compute Service Tools AWS S3
- 14. Analytics ETL Interactive data exploration Interactive slice & dice RT analytics & iterative/ML algo and more ... Different Big Data Processing Needs
- 15. Amazon S3 as our DW
- 16. Amazon S3 as Our DW Storage Amazon S3 as single source of truth (not HDFS) Designed for 11 9’s durability and 4 9’s availability Separate compute and storage Key enablement to - multiple heterogeneous clusters - easy upgrade via r/b deployment S3
- 17. What About Performance? Amazon S3 is a much bigger fleet than your cluster Offload network load from cluster Read performance - Single stage read-only job has 5-10% impact - Insignificant when amortized over a sequence of stages Write performance - Can be faster because Amazon S3 is eventually consistent w/ higher thruput
- 18. Presto
- 19. Presto is an open-source, distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes
- 20. Why We Love Presto? Hadoop friendly - integration with Hive metastore Works well on AWS - easy integration with Amazon S3 Scalable - works at petabyte scale User friendly - ANSI SQL Open source - and in Java! Fast
- 22. Usage Stats ~3500 queries/day > 90%
- 23. Expanding Presto Use Cases Data exploration and experimentation Data validation Backend for our interactive a/b test analytics application Reporting Not ETL (yet?)
- 25. Our Deployment Version 0.114 + patches + one non-public patch (Parquet vectorized read integration) Deployed via bootstrap action on Amazon EMR Separate clusters from our Hadoop YARN clusters Not using Hadoop services Leverage Amazon EMR for cluster management
- 26. Two Production Clusters Resource isolation Ad hoc cluster 1 coordinator (r3.4xl) + 225 workers (r3.4xl) Dedicated application cluster 1 coordinator (r3.4xl) + 4 workers + dynamic workers (r3.xl, r3.2xl, r3.4xl) Dynamic cluster sizing via Netflix spinnaker API
- 28. Integration with Netflix Infrastructure Atlas Sidecar Coordinator Amazon EMR Presto Worker Kafka Presto cluster (for data lineage) (for monitoring) Queries + users info
- 30. Our Contributions Parquet File Format Support Schema evolution Predicate pushdown Vectorized read** Complex Types Various functions for map, array and struct types Comparison operators for array and struct types Amazon S3 File System Multi-part upload AWS instance credentials AWS role support* Reliability Query Optimization Single distinct -> group by Joins with similar sub-queries*
- 31. Parquet Columnar file format Supported across Hive, Pig, Presto, Spark Performance benefits across different processing engines Good performance on S3 Majority of our DW is on Parquet FF
- 32. RowGroup Metadata x N row count, size, etc. Dict Page Data Page Data Page Column Chunk Data Page Data Page Data Page Column Chunk Dict Page Data Page Data Page RowGroup x N Footer schema, version, etc. Column Chunk Metadata encoding, size, min, max Parquet Column Chunk Column Chunk Metadata encoding, size, min, max Column Chunk Metadata encoding, size, min, max
- 33. Vectorized Read Parquet: read column chunks in batches instead of row-by-row Presto: replace ParquetHiveRecordCursor with ParquetPageSource Performance improvement up to 2x for Presto Beneficial for Spark, Hive, and Drill also Pending parquet-131 commit before we can publish a Presto patch
- 34. Predicate Pushdown Dictionary pushdown column chunk stats [5, 20] dictionary page <5,11,18,20> skip this row group Statistics pushdown column chunk stats [20, 30] skip this row group Example: SELECT… WHERE abtest_id = 10;
- 35. Predicate Pushdown Works best if data is clustered by predicate columns Achieves data pruning like Hive partitions w/o the metadata overhead Can also be implemented in Spark, Hive and Pig
- 36. Atlas Analytics Use Case 0 20000 40000 60000 80000 100000 Pushdown No Pushdown CPU (s) 0 50 100 150 200 250 300 350 Pushdown No Pushdown Wall-clock Time (s) 0 2000 4000 6000 8000 Pushdown No Pushdown Rows Processed (Mils) 0 100 200 300 400 500 Pushdown No Pushdown Bytes Read (GB) 170x 170x170x 10x Example query: Analyze 4xx errors from Genie for a day High cardinality/selectivity for app name and metrics name as predicates Data staged and clustered by predicate columns
- 37. Stay Tuned… Two upcoming blog posts on techblog.netflix.com - Parquet usage @ Netflix Big Data Platform - Presto + Parquet optimization and performance
- 38. Spark
- 39. Apache Spark™ is a fast and general engine for large-scale data processing.
- 40. Why Spark? Batch jobs (Pig, Hive) • ETL jobs • Reporting and other analysis Interactive jobs (Presto) Iterative ML jobs (Spark) Programmatic use cases
- 41. Deployments @ Netflix Spark on Mesos • Self-serving AMI • Full BDAS (Berkeley Data Analytics Stack) • Online streaming analytics Spark on YARN • Spark as a service • YARN application on Amazon EMR Hadoop • Offline batch analytics
- 42. Version Support $ spark-shell –ver 1.5 … s3://…/spark-1.4.tar.gz s3://…/spark-1.5.tar.gz s3://…/spark-1.5-custom.tar.gz s3://…/1.5/spark-defaults.conf s3://…/h2prod/yarn-site.xml s3://../h2prod/core-site.xml … ConfigurationApplication
- 43. Multi-tenancy
- 44. Multi-tenancy
- 45. Dynamic Allocation on YARN Optimize for resource utilization Harness cluster’s scale Still provide interactive performance
- 46. Container Container Container Container Container Container Container Task Execution Model Container Task Task TaskTaskTaskTask Task Task TaskTaskTask Container Task Pending Tasks Traditional MapReduce Spark Execution Persistent
- 48. Cached Data Spark allows data to be cached • Interactive reuse of data set • Iterative usage (ML) Dynamic allocation • Removes executors when no tasks are pending
- 49. Cached Executor Timeout [SPARK-7955] val data = sqlContext .table("dse.admin_genie_job_d”) .filter($"dateint">=20150601 and $"dateint"<=20150830) data.persist data.count
- 50. Preemption [SPARK-8167] Symptom • Spark tasks randomly fail with “executor lost” error Cause • YARN preemption is not graceful Solution • Preempted tasks shouldn’t be counted as failures but should be retried
- 51. Reading / Processing / Writing
- 52. Amazon S3 Listing Optimization Problem: Metadata is big data • Tables with millions of partitions • Partitions with hundreds of files each Clients take a long time to launch jobs
- 53. Input split computation mapreduce.input.fileinputformat.list-status.num-threads • The number of threads to use list and fetch block locations for the specified input paths. Setting this property in Spark jobs doesn’t help
- 54. File listing for partitioned table Partition path Seq[RDD] HadoopRDD HadoopRDD HadoopRDD HadoopRDD Partition path Partition path Partition path Input dir Input dir Input dir Input dir Sequentially listing input dirs via S3N file system. S3N S3N S3N S3N
- 55. SPARK-9926, SPARK-10340 Symptom • Input split computation for partitioned Hive table on Amazon S3 is slow Cause • Listing files on a per partition basis is slow • S3N file system computes data locality hints Solution • Bulk list partitions in parallel using AmazonS3Client • Bypass data locality computation for Amazon S3 objects
- 56. Amazon S3 Bulk Listing Partition path ParArray[RDD] HadoopRDD HadoopRDD HadoopRDD HadoopRDD Partition path Partition path Partition path Input dir Input dir Input dir Input dir Amazon S3 listing input dirs in parallel via AmazonS3Client Amazon S3Client
- 57. Performance Improvement 0 2000 4000 6000 8000 10000 12000 14000 16000 1 24 240 720 seconds # of partitions 1.5 RC2 S3 bulk listing SELECT * FROM nccp_log WHERE dateint=20150801 and hour=0 LIMIT 10;
- 58. Hadoop Output Committer How it works • Each task writes output to a temp dir. • Output committer renames first successful task’s temp dir to final destination Challenges with Amazon S3 • Amazon S3 rename is copy and delete (non-atomic) • Amazon S3 is eventual consistent
- 59. Amazon S3 Output Committer How it works • Each task writes output to local disk • Output committer copies first successful task’s output to Amazon S3 Advantages • Avoid redundant Amazon S3 copy • Avoid eventual consistency • Always write to new paths
- 61. Next Steps for Netflix Integration Metrics Data lineage Parquet integration
- 62. Key Takeaways
- 63. Our DW source of truth is on Amazon S3 Run custom Presto and Spark distros on Amazon EMR • Presto as stand-alone clusters • Spark co-tenant on Hadoop YARN clusters We are committed to open source; you can run what we run
- 64. What’s Next
- 65. On Scaling and Optimizing Infrastructure… Graceful shrink of Amazon EMR + Heterogeneous instance groups in Amazon EMR + Netflix Atlas metrics + Netflix Spinnaker API = Load-based expand/shrink of Hadoop YARN clusters
- 66. Expand new Presto use cases Integrate Spark in Netflix big data platform Explore Spark for ETL use cases On Presto and Spark…
- 67. Remember to complete your evaluations!
- 68. Thank you! Eva Tse & Daniel Weeks Office hour: 12:00pm – 2pm Today @ Netflix Booth #326