Amazon Elastic Mapreduce Best Practice

Transcript 1. Amazon Elastic MapReduce: Deep Dive and Best Practices Parviz Deyhim November 13, 2013 © 2013 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc. 2. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 3. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 4. Hadoop-as-a-service Map-Reduce engine Integrated with tools What is EMR? Massively parallel Integrated to AWS services Cost effective AWS wrapper 5. HDFS Amazon EMR 6. HDFS Amazon EMR Amazon S3 Amazon DynamoDB 7. Data management Analytics languages HDFS Amazon EMR Amazon S3 Amazon DynamoDB 8. Data management Analytics languages HDFS Amazon EMR Amazon S3 Amazon DynamoDB Amazon RDS 9. Data management Analytics languages HDFS Amazon EMR Amazon Redshift Amazon RDS AWS Data Pipeline Amazon S3 Amazon DynamoDB 10. Amazon EMR Introduction • Launch clusters of any size in a matter of minutes • Use variety of different instance sizes that match your workload 11. Amazon EMR Introduction • Don’t get stuck with hardware • Don’t deal with capacity planning • Run multiple clusters with different sizes, specs and node types 12. Amazon EMR Introduction • Integration with Spot market • 70-80% discount 13. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 14. Amazon EMR Design Patterns Pattern #1: Transient vs. Alive Clusters Pattern #2: Core Nodes and Task Nodes Pattern #3: Amazon S3 as HDFS Pattern #4: Amazon S3 & HDFS Pattern #5: Elastic Clusters 15. Pattern #1: Transient vs. Alive Clusters 16. Pattern #1: Transient Clusters • Cluster lives for the duration of the job • Shut down the cluster when the job is done • Data persist on Amazon S3 • Input & output Data on Amazon S3 17. Benefits of Transient Clusters 1. Control your cost 2. Minimum maintenance • Cluster goes away when job is done 3. Practice cloud architecture • Pay for what you use • Data processing as a workflow 18. When to use Transient cluster? If ( Data Load Time + Processing Time) * Number Of Jobs < 24 Use Transient Clusters Else Use Alive Clusters 19. When to use Transient cluster? ( 20min data load + 1 hour Processing time) * 10 jobs = 13 hours < 24 hour = Use Transient Clusters 20. Alive Clusters • Very similar to traditional Hadoop deployments • Cluster stays around after the job is done • Data persistence model: • Amazon S3 • Amazon S3 Copy To HDFS • HDFS and Amazon S3 as backup 21. Alive Clusters • Always keep data safe on Amazon S3 even if you’re using HDFS for primary storage • Get in the habit of shutting down your cluster and start a new one, once a week or month • Design your data processing workflow to account for failure • You can use workflow managements such as AWS Data Pipeline 22. Benefits of Alive Clusters • Ability to share data between multiple jobs Transient cluster Long running clusters EMR EMR Amazon S3 Amazon S3 EMR HDFS HDFS 23. Benefit of Alive Clusters • Cost effective for repetitive jobs pm EMR pm pm EMR EMR EMR EMR pm 24. When to use Alive cluster? If ( Data Load Time + Processing Time) * Number Of Jobs > 24 Use Alive Clusters Else Use Transient Clusters 25. When to use Alive cluster? ( 20min data load + 1 hour Processing time) * 20 jobs = 26hours > 24 hour = Use Alive Clusters 26. Pattern #2: Core & Task nodes 27. Core Nodes Amazon EMR cluster Master instance group Run TaskTrackers (Compute) Run DataNode (HDFS) HDFS HDFS Core instance group 28. Core Nodes Amazon EMR cluster Master instance group Can add core nodes HDFS HDFS Core instance group 29. Core Nodes Amazon EMR cluster Can add core nodes Master instance group More HDFS space More CPU/mem HDFS HDFS Core instance group HDFS 30. Core Nodes Amazon EMR cluster Can’t remove core nodes because of HDFS Master instance group HDFS HDFS Core instance group HDFS 31. Amazon EMR Task Nodes Amazon EMR cluster Run TaskTrackers Master instance group No HDFS Reads from core node HDFS HDFS HDFS Core instance group Task instance group 32. Amazon EMR Task Nodes Amazon EMR cluster Can add task nodes Master instance group HDFS HDFS Core instance group Task instance group 33. Amazon EMR Task Nodes Amazon EMR cluster More CPU power Master instance group More memory HDFS HDFS Core instance group Task instance group 34. Amazon EMR Task Nodes You can remove task nodes Amazon EMR cluster Master instance group HDFS HDFS Core instance group Task instance group 35. Amazon EMR Task Nodes You can remove task nodes Amazon EMR cluster Master instance group HDFS HDFS Core instance group Task instance group 36. Tasknode Use-Case #1 • Speed up job processing using Spot market • Run task nodes on Spot market • Get discount on hourly price • Nodes can come and go without interruption to your cluster 37. Tasknode Use-Case #2 • When you need extra horse power for a short amount of time • Example: Need to pull large amount of data from Amazon S3 38. Example: HS1 48TB HDFS HS1 48TB HDFS Amazon S3 39. Add Spot task nodes to load data from Amazon S3 Example: HS1 48TB HDFS m1.xl m1.xl m1.xl HS1 48TB HDFS m1.xl Amazon S3 m1.xl m1.xl 40. Example: HS1 48TB HDFS Remove after data load from Amazon S3 m1.xl m1.xl m1.xl HS1 48TB HDFS m1.xl m1.xl m1.xl Amazon S3 41. Pattern #3: Amazon S3 as HDFS 42. Amazon S3 as HDFS Amazon EMR cluster • Use Amazon S3 as your permanent data store • HDFS for temporary storage data between jobs • No additional step to copy data to HDFS HDF S HDF S Core instance group Task instance group Amazon S3 43. Benefits: Amazon S3 as HDFS • Ability to shut down your cluster HUGE Benefit!! • Use Amazon S3 as your durable storage 11 9s of durability 44. Benefits: Amazon S3 as HDFS • No need to scale HDFS • Capacity • Replication for durability • Amazon S3 scales with your data • Both in IOPs and data storage 45. Benefits: Amazon S3 as HDFS • Ability to share data between multiple clusters • Hard to do with HDFS EMR EMR Amazon S3 46. Benefits: Amazon S3 as HDFS • Take advantage of Amazon S3 features • Amazon S3 ServerSideEncryption • Amazon S3 LifeCyclePolicy • Amazon S3 versioning to protect against corruption • Build elastic clusters • Add nodes to read from Amazon S3 • Remove nodes with data safe on Amazon S3 47. What About Data Locality? • Run your job in the same region as your Amazon S3 bucket • Amazon EMR nodes have high speed connectivity to Amazon S3 • If your job Is CPU/memory-bounded data, locality doesn’t make a difference 48. Anti-Pattern: Amazon S3 as HDFS • Iterative workloads – If you’re processing the same dataset more than once • Disk I/O intensive workloads 49. Pattern #4: Amazon S3 & HDFS 50. Amazon S3 & HDFS 1. Data persist on Amazon S3 51. 2. Launch Amazon EMR and copy data to HDFS with S3distcp S3DistCp Amazon S3 & HDFS 52. 3. Start processing data on HDFS S3DistCp Amazon S3 & HDFS 53. Benefits: Amazon S3 & HDFS • Better pattern for I/O-intensive workloads • Amazon S3 benefits discussed previously applies • Durability • Scalability • Cost • Features: lifecycle policy, security 54. Pattern #5: Elastic Clusters 55. Amazon EMR Elastic Cluster (m) 1. Start cluster with certain number of nodes 56. Amazon EMR Elastic Cluster (m) 2. Monitor your cluster with Amazon CloudWatch metrics • Map Tasks Running • Map Tasks Remaining • Cluster Idle? • Avg. Jobs Failed 57. Amazon EMR Elastic Cluster (m) 3. Increase the number of nodes as you need more capacity by manually calling the API 58. Amazon EMR Elastic Cluster (a) 1. Start your cluster with certain number of nodes 59. Amazon EMR Elastic Cluster (a) 2. Monitor cluster capacity with Amazon CloudWatch metrics • Map Tasks Running • Map Tasks Remaining • Cluster Idle? • Avg. Jobs Failed 60. Amazon EMR Elastic Cluster (a) 3. Get HTTP Amazon SNS notification to a simple app deployed on Elastic Beanstalk 61. Amazon EMR Elastic Cluster (a) 4. Your app calls the API to add nodes to your cluster API 62. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 63. Amazon EMR Nodes and Size • Use m1.smal, m1.large, c1.medium for functional testing • Use M1.xlarge and larger nodes for production workloads 64. Amazon EMR Nodes and Size • Use CC2 for memory and CPU intensive jobs • Use CC2/C1.xlarge for CPU intensive jobs • Hs1 instances for HDFS workloads 65. Amazon EMR Nodes and Size • Hi1 and HS1 instances for disk I/Ointensive workload • CC2 instances are more cost effective than M2.4xlarge • Prefer smaller cluster of larger nodes than larger cluster of smaller nodes 66. Holy Grail Question How many nodes do I need? 67. Introduction to Hadoop Splits • Depends on how much data you have • And how fast you like your data to be processed 68. Introduction to Hadoop Splits Before we understand Amazon EMR capacity planning, we need to understand Hadoop’s inner working of splits 69. Introduction to Hadoop Splits • Data gets broken up to splits (64MB or 128) Data 128MB Splits 70. Introduction to Hadoop Splits • Splits get packaged into mappers Data Splits Mappers 71. Introduction to Hadoop Splits • Mappers get assigned to Mappers nodes for processing Instances 72. Introduction to Hadoop Splits • More data = More splits = More mappers 73. Introduction to Hadoop Splits • More data = More splits = More mappers Queue 74. Introduction to Hadoop Splits • Data mappers > cluster mapper capacity = mappers wait for capacity = processing delay Queue 75. Introduction to Hadoop Splits • More nodes = reduced queue size = faster processing Queue 76. Calculating the Number of Splits for Your Job Uncompressed files: Hadoop splits a single file to multiple splits. Example: 128MB = 2 splits based on 64MB split size 77. Calculating the Number of Splits for Your Job Compressed files: 1. Splittable compressions: same logic as uncompressed files 64MB BZIP 128MB BZIP 78. Calculating the Number of Splits for Your Job Compressed files: 2. Unsplittable compressions: the entire file is a single split. 128MB GZ 128MB GZ 79. Calculating the Number of Splits for Your Job Number of splits If data files have unsplittable compression # of splits = number of files Example: 10 GZ files = 10 mappers 80. Cluster Sizing Calculation Just tell me how many nodes I need for my job!! 81. Cluster Sizing Calculation 1. Estimate the number of mappers your job requires. 82. Cluster Sizing Calculation 2. Pick an instance and note down the number of mappers it can run in parallel M1.xlarge = 8 mappers in parallel 83. Cluster Sizing Calculation 3. We need to pick some sample data files to run a test workload. The number of sample files should be the same number from step #2. 84. Cluster Sizing Calculation 4. Run an Amazon EMR cluster with a single core node and process your sample files from #3. Note down the amount of time taken to process your sample files. 85. Cluster Sizing Calculation Estimated Number Of Nodes: Total Mappers * Time To Process Sample Files Instance Mapper Capacity * Desired Processing Time 86. Example: Cluster Sizing Calculation 1. Estimate the number of mappers your job requires 150 2. Pick an instance and note down the number of mappers it can run in parallel m1.xlarge with 8 mapper capacity per instance 87. Example: Cluster Sizing Calculation 3. We need to pick some sample data files to run a test workload. The number of sample files should be the same number from step #2. 8 files selected for our sample test 88. Example: Cluster Sizing Calculation 4. Run an Amazon EMR cluster with a single core node and process your sample files from #3. Note down the amount of time taken to process your sample files. 3 min to process 8 files 89. Cluster Sizing Calculation Estimated number of nodes: Total Mappers For Your Job * Time To Process Sample Files Per Instance Mapper Capacity * Desired Processing Time 150 * 3 min = 11 m1.xlarge 8 * 5 min 90. File Size Best Practices • Avoid small files at all costs • Anything smaller than 100MB • Each mapper is a single JVM • CPU time required to spawn JVMs/mappers 91. File Size Best Practices Mappers take 2 sec to spawn up and be ready for processing 10TB of 100mgB = 100,000 mappers * 2Sec = total of 55 hours mapper CPU setup time 92. File Size Best Practices Mappers take 2 sec to spawn up and be ready for processing 10TB of 1000MB = 10,000 mappers * 2Sec = total of 5 hours mapper CPU setup time 93. File Size on Amazon S3: Best Practices • What’s the best Amazon S3 file size for Hadoop? About 1-2GB • Why? 94. File Size on Amazon S3: Best Practices • Life of mapper should not be less than 60 sec • Single mapper can get 10MB-15MB/s speed to Amazon S3 60sec * 15MB ≈ 1GB 95. Holy Grail Question What if I have small file issues? 96. Dealing with Small Files • Use S3DistCP to combine smaller files together • S3DistCP takes a pattern and target file to combine smaller input files to larger ones 97. Dealing with Small Files Example: ./elastic-mapreduce --jobflow j-3GY8JC4179IOK --jar /home/hadoop/lib/emr-s3distcp-1.0.jar --args '--src,s3://myawsbucket/cf, --dest,hdfs:///local, --groupBy,.*XABCD12345678.([0-9]+-[09]+-[0-9]+-[0-9]+).*, --targetSize,128, 98. Compressions • Compress as much as you can • Compress Amazon S3 input data files – Reduces cost – Speed up Amazon S3->mapper data transfer time 99. Compressions • Always Compress Data Files On Amazon S3 • Reduces Storage Cost • Reduces Bandwidth Between Amazon S3 and Amazon EMR • Speeds Up Your Job 100. Compressions • Compress Mappers and Reducer Output • Reduces Disk IO 101. Compressions • Compression Types: – Some are fast BUT offer less space reduction – Some are space efficient BUT Slower – Some are splitable and some are not Algorithm % Space Remaining Encoding Speed Decoding Speed GZIP 13% 21MB/s 118MB/s LZO 20% 135MB/s 410MB/s Snappy 22% 172MB/s 409MB/s 102. Compressions • If You Are Time Sensitive, Faster Compressions Are A Better Choice • If You Have Large Amount Of Data, Use Space Efficient Compressions • If You Don’t Care, Pick GZIP 103. Change Compression Type • You May Decide To Change Compression Type • Use S3DistCP to change the compression types of your files • Example: ./elastic-mapreduce --jobflow j-3GY8JC4179IOK --jar /home/hadoop/lib/emr-s3distcp-1.0.jar --args '--src,s3://myawsbucket/cf, --dest,hdfs:///local, --outputCodec,lzo’ 104. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 105. Architecting for cost • AWS pricing models: – On-demand: Pay as you go model. – Spot: Market place. Bid for instances and get a discount – Reserved Instance: upfront payment (for 1 or 3 year) for reduction in overall monthly payment 106. Reserved Instances use-case For alive and long-running clusters 107. Reserved Instances use-case For ad-hoc and unpredictable workloads, use medium utilization 108. Reserved Instances use-case For unpredictable workloads, use Spot or on-demand pricing 109. Outline Introduction to Amazon EMR Amazon EMR Design Patterns Amazon EMR Best Practices Amazon Controlling Cost with EMR Advanced Optimizations 110. Adv. Optimizations (Stage 1) • The best optimization is to structure your data (i.e., smart data partitioning) • Efficient data structuring= limit the amount of data being processed by Hadoop= faster jobs 111. Adv. Optimizations (Stage 1) • Hadoop is a batch processing framework • Data processing time = an hour to days • Not a great use-case for shorter jobs • Other frameworks may be a better fit – Twitter Storm – Spark – Amazon Redshift, etc. 112. Adv. Optimizations (Stage 1) • Amazon EMR team has done a great deal of optimization already • For smaller clusters, Amazon EMR configuration optimization won’t buy you much – Remember you’re paying for the full hour cost of an instance 113. Adv. Optimizations (Stage 1) Best Optimization?? 114. Adv. Optimizations (Stage 1) Add more nodes 115. Adv. Optimizations (Stage 2) • Monitor your cluster using Ganglia • Amazon EMR has Ganglia bootstrap action 116. Adv. Optimizations (Stage 2) • Monitor and look for bottlenecks – Memory – CPU – Disk IO – Network IO 117. Adv. Optimizations Run Job 118. Adv. Optimizations Run Job Find Bottlenecks Ganglia CPU Disk Memory 119. Adv. Optimizations Run Job Find Bottlenecks Ganglia CPU Disk Memory Address Bottleneck Fine Tune Change Algo 120. Network IO • Most important metric to watch for if using Amazon S3 for storage • Goal: Drive as much network IO as possible from a single instance 121. Network IO • Larger instances can drive > 600Mbps • Cluster computes can drive 1Gbps -2 Gbps • Optimize to get more out of your instance throughput – Add more mappers? 122. Network IO • If you’re using Amazon S3 with Amazon EMR, monitor Ganglia and watch network throughput. • Your goal is to maximize your NIC throughput by having enough mappers per node 123. Network IO, Example Low network utilization Increase number of mappers if possible to drive more traffic 124. CPU • Watch for CPU utilization of your clusters • If >50% idle, increase # of mapper/reducer per instance – Reduces the number of nodes and reduces cost 125. Example Adv. Optimizations (Stage 2) What potential optimization do you see in this graph? 126. Example Adv. Optimizations (Stage 2) 40% CPU idle. Maybe add more mappers? 127. Disk IO • Limit the amount of disk IO • Can increase mapper/reducer memory • Compress data anywhere you can • Monitor cluster and pay attention to HDFS bytes written metrics • One play to pay attention to is mapper/reducer disk spill 128. Disk IO • Mapper has in memory buffer mapper mapper memory buffer 129. Disk IO • When memory gets full, data spills to disk mapper mapper memory buffer data spills to disk 130. Disk IO • If you see mapper/reducer excessive spill to disk, increase buffer memory per mapper • Excessive spill when ratio of “MAPPER_SPILLED_RECORDS” and “MAPPER_OUTPUT_RECORDS” is more than 1 131. Disk IO Example: MAPPER_SPILLED_RECORDS = 221200123 MAPPER_OUTPUT_RECORDS = 101200123 132. Disk IO • Increase mapper buffer memory by increasing “io.sort.mb” io.sort.mb200< /property> • Same logic applies to reducers 133. Disk IO • Monitor disk IO using Ganglia • Look out for disk IO wait 134. Disk IO • Monitor disk IO using Ganglia • Look out for disk IO wait 135. Remember! Run Job Find Bottlenecks Ganglia CPU Disk Memory Address Bottleneck Fine Tune Change Algo