Lessons Learned Scaling Hadoop and Big Data in the Cloud
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The document discusses lessons learned from scaling Hadoop and big data processing on Amazon EMR. It describes how EMR provides a scalable and cost-effective way to run Hadoop jobs in the cloud without having to manage infrastructure. While EMR enables bootstrapping large clusters easily, performance can vary due to network issues and disk I/O constraints of different instance types. The document outlines best practices for optimizing Hadoop jobs and tuning configurations on EMR.
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Lessons Learned Scaling Hadoop and Big Data in the Cloud
- 1. Lessons Learned : Scaling Hadoop and Big Data in Cloud Vijay Rayapati @amnigos
- 2. Big Data
- 4. What you can do with Data?
- 5. Problem : Local Intelligence
- 6. High Level Architecture Processing Multiple Data Ingest (Hadoop Jobs) Sources Data Storage Output Location Datasets API Intelligence
- 7. Our Challenges • Mul$ple data sources – social, retail, events, news, census, loca$on etc • Spa$al data analysis and querying – loca$on overlay on data • Temporal nature of the input datasets • Large input data sets and hundreds of GB compressed inputs for jobs • Complex processing and business logic based on use cases • Custom output data formats – JSON, XML, XLS, Flat files etc
- 8. Why Amazon EMR? I am interested in using Hadoop to solve business problems and not in building and managing Hadoop infrastructure! Scalable Storage - S3 Flexible Computing – EC2 No Hadoop Management – EMR
- 9. Amazon EMR - Service Architecture
- 10. How to move existing data to Cloud? 10’s of GB 100’s of GB > Terabyte Direct Upload Any S3 tools AWS Import/ Export Any S3 tools Tsunami Aspera, Tsunami
- 11. Solution Architecture EC2 S3 EMR Processing Multiple Data Ingest (Hadoop Jobs) Sources Data Storage Output Location Datasets API Intelligence S3 EC2 EC2
- 12. Amazon EMR – Setup Launching a 500 node and fully configured cluster is as simple as execu$ng one command. > elas$c-‐mapreduce -‐-‐create -‐-‐alive -‐-‐plain-‐output -‐-‐master-‐instance-‐type m1.xlarge -‐-‐slave-‐instance-‐type m2.2xlarge -‐-‐num-‐instances 500 -‐-‐name "Site Analy$cs Cluster" -‐-‐bootstrap-‐ac$on s3://com.bcb11.emr/scripts/bootstrap-‐ custom.sh -‐-‐bootstrap-‐ac$on s3://elas$cmapreduce/bootstrap-‐ac$ons/install-‐ ganglia -‐-‐bootstrap-‐ac$on s3://elas$cmapreduce/bootstrap-‐ac$ons/configure-‐ hadoop -‐-‐args "-‐-‐mapred-‐config-‐file, s3://com.bcb11.emr/conf/custom-‐mapred-‐ site.xml" > elas$c-‐mapreduce -‐j ${jobflow} -‐-‐stream -‐-‐step-‐name “Profile Analyzer" -‐-‐ jobconf mapred.task.$meout=0 -‐-‐mapper s3://com.bcb11.emr/code/mapper.rb -‐-‐reducer s3://com.bcb11.emr/bin/reducer.rb -‐-‐cache s3://com.bcb11.emr/ cache/customdata.dat#data.txt -‐-‐input s3://com.bcb11.emr/input/ -‐-‐output s3:// com.bcb11.emr/output
- 13. EMR Map Reduce Jobs Amazon EMR supports – streaming, custom jar, cascading, pig and hive. Streaming – Write Map Reduce jobs in any scrip$ng language. Custom Jar – Write using Java and good for speed/control. Cascading, Hive and Pig – Higher level of abstrac$on. AWS EMR forums if you need help.
- 14. Hadoop and EMR – Lesson Learned
- 15. EMR – Good, Bad and Ugly Great for bootstrapping large clusters and very cost-‐effec$ve for transient clusters. Most patches are applied and Amazon creates new AMI’s with improvements – but not for everything. Intermiient network issues – Some$mes could cause serious degrada$on of performance. Network/Disk IO is variable based on instance types and streaming jobs will be much sluggish on EMR compared to dedicated setup. Be ready to face variable performance in Cloud.
- 16. Hadoop and EMR – Jobs Use local Hadoop setup for debugging your jobs – there is no easy way on EMR. Capture EMR cluster metrics -‐ always bootstrap with Ganglia. High JVM memory alloca$on lead to long GC pauses. Don’t trust EMR tuned sekngs of Hadoop configura$ons. Benchmark on small cluster for data points.
- 17. Hadoop and EMR – Jobs performance GC Overhead -‐ increase memory and reduce the jvm reuse tasks. Avoid read conten$on at S3 – Have equal or more files in S3 compared to available mappers. Use mapred output compression to save storage, processing $me and bandwidth costs. Set mapred task $meout to 0 if you have long running jobs (> 10 mins) and can disable specula$ve execu$on $me. Always benchmark third party libraries used in your jobs code before pukng them in produc$on – too much sluggish stuff out there.
- 18. Hadoop – High Level Tuning Small files problem – avoid too Tune your sekngs – JVM Reuse, many small files in S3 Sort Buffer, Sort Factor, Map/ Reduce Tasks, Parallel Copies, MapRed Output Compression etc Good thing is that you can use Know what is limi$ng you at a small cluster and sample input node level – CPU, Memory, DISK size for tuning IO or Network IN/OUT
- 19. Performance Tuning Golden Rules When you are operating at very large scale even a 10 ms makes a big difference! Example : Moving away from Simple-‐Json to Jackson JSON Parsing – 600 ms Op$mized Parsing – 500 ms Number of input JSON records – 3 million Time saved by simple op$miza$on – 84 hrs of savings
- 20. We have seen improvements from 10x to 100x in our production clusters – significant money savings.
- 21. Lesson Learned – Saving Time Hadoop Job with complex business logic opera$ng on 350 MB input size Job Language Cluster Size Input Files Processing Time Ruby 6 m1.xlarge 1000 184 mins Java 6 m1.xlarge 1000 69 mins Java 6 m1.xlarge 100 39 mins (1000 files combined) Java 6 m1.xlarge 100 25 mins (EMR tuned) (1000 files combined) Java 6 m1.xlarge 100 13 mins (EMR and Code tuned) (1000 files combined)
- 22. Lesson Learned – Saving Cost A data mining job in produc$on with 50 GB compressed input data Job Cluster Size Processing Each Job 100 Jobs Cost Per Language Time Cost Month Ruby 50 m2.2xlarge 240 mins $242 $24200 Java 20 m1.xlarge 200 mins $68 $6800 Java 20 m1.xlarge $50 $5000 (EMR tuned) 165 mins Java 20 m1.xlarge 50 mins $17 $1700 (EMR and Code tuned)
- 23. EMR Cost Optimization Use a small dedicated/transient cluster Leverage spot instance for Task Nodes Op$mize, profile and tune your code always – code first and config next Tune EMR configura$on based on historical jobs data Always benchmark third party libraries
- 24. Q & A
- 25. Like what we do? – connect with me Kuliza.com | vijay.rayapati@kuliza.com | @kuliza vijay.rayapati@kuliza.com @amnigos