MPP vs Hadoop
•
37 gefällt mir•91,672 views
This is the presentation I made on the Hadoop User Group Ireland meetup in Dublin. It covers the main ideas of both MPP, Hadoop and the distributed systems in general, and also how to chose the best option for you
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Ähnlich wie MPP vs Hadoop
Ähnlich wie MPP vs Hadoop (20)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
MPP vs Hadoop
- 1. 1Pivotal Confidential–Internal Use Only 1Pivotal Confidential–Internal Use Only MPP vs Hadoop Alexey Grishchenko HUG Meetup 28.11.2015
- 2. 2Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Summary
- 3. 3Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Summary
- 4. 4Pivotal Confidential–Internal Use Only Distributed Systems Avoid distributed systems in all the problems that potentially could be solved using non-distributed systems
- 5. 5Pivotal Confidential–Internal Use Only Distributed Systems Ÿ Consensus problem – Paxos – RAFT – ZAB – etc. Ÿ Transaction consistency – 2PC – 3PC
- 6. 6Pivotal Confidential–Internal Use Only Distributed Systems Ÿ CAP Theorem
- 7. 7Pivotal Confidential–Internal Use Only Distributed Systems http://www.cs.cornell.edu/projects/ladis2009/talks/dean-keynote-ladis2009.pdf
- 8. 8Pivotal Confidential–Internal Use Only Distributed Systems Reasons to use Ÿ Performance issues – More than 100’000 TPS – More than 4 GB/sec scan rate – More than 100’000 IOPS Ÿ Capacity issues – More than 50TB of data Ÿ DR and Geo-Distribution
- 9. 9Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Summary
- 10. 10Pivotal Confidential–Internal Use Only MPP Main principles Ÿ Shared Nothing Ÿ Data Sharding Ÿ Data Replication Ÿ Distributed Transactions Ÿ Parallel Processing
- 11. 11Pivotal Confidential–Internal Use Only MPP
- 12. 12Pivotal Confidential–Internal Use Only MPP Works well for Ÿ Relational data Ÿ Batch processing Ÿ Ad hoc analytical SQL Ÿ Low concurrency Ÿ Applications requiring ANSI SQL
- 13. 13Pivotal Confidential–Internal Use Only MPP Not the best choice for Ÿ Non-relational data Ÿ OLTP and event stream processing Ÿ High concurrency Ÿ 100+ server clusters Ÿ Non-analytical use cases Ÿ Geo-Distributed use cases
- 14. 14Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Summary
- 15. 15Pivotal Confidential–Internal Use Only Hadoop Main Components Ÿ HDFS Ÿ YARN Ÿ MapReduce Ÿ HBase Ÿ Hive / Hive+Tez
- 16. 16Pivotal Confidential–Internal Use Only Hadoop HDFS Ÿ Distributed filesystem Ÿ Block-level storage with big blocks Ÿ Non-updatable Ÿ Synchronous block replication Ÿ No built-in Geo-Distribution support Ÿ No built-in DR solution
- 17. 17Pivotal Confidential–Internal Use Only Hadoop HDFS
- 18. 18Pivotal Confidential–Internal Use Only Hadoop YARN Ÿ Cluster resource manager Ÿ Manages CPU and RAM allocation Ÿ Schedulers are pluggable Ÿ Can handle different resource pools Ÿ Supports both MR and non-MR workload
- 19. 19Pivotal Confidential–Internal Use Only Hadoop YARN
- 20. 20Pivotal Confidential–Internal Use Only Hadoop MapReduce Ÿ Framework for distributed data processing Ÿ Two main operations: map and reduce Ÿ Data hits disk after “map” and before “reduce” Ÿ Scales to thousands of servers Ÿ Can process petabytes of data Ÿ Extremely reliable
- 21. 21Pivotal Confidential–Internal Use Only Hadoop MapReduce
- 22. 22Pivotal Confidential–Internal Use Only Hadoop HBase Ÿ Distributed key-value store Ÿ Data is sharded by key Ÿ Data is stored in sorted order Ÿ Stores multiple versions of the row Ÿ Easily scales
- 23. 23Pivotal Confidential–Internal Use Only Hadoop HBase
- 24. 24Pivotal Confidential–Internal Use Only Hadoop Hive Ÿ Query engine with SQL-like syntax Ÿ Translates HiveQL query to MR / Tez / Spark job Ÿ Processes HDFS data Ÿ Supports UDFs and UDAFs
- 25. 25Pivotal Confidential–Internal Use Only Hadoop Hive
- 26. 26Pivotal Confidential–Internal Use Only Hadoop Works well for Ÿ Write Once Read Many Ÿ 100+ server clusters Ÿ Both relational and non-relational data Ÿ High concurrency Ÿ Batch processing and analytical workload Ÿ Elastic scalability
- 27. 27Pivotal Confidential–Internal Use Only Hadoop Not the best choice for Ÿ Write-heavy workloads Ÿ Small clusters Ÿ Analytical DWH cases Ÿ OLTP and event stream processing Ÿ Cost savings
- 28. 28Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Summary
- 29. 29Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open
- 30. 30Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity
- 31. 31Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common
- 32. 32Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K
- 33. 33Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High
- 34. 34Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source
- 35. 35Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source Supportability Easy Complex
- 36. 36Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source Supportability Easy Complex Scalability Up to 100 servers Up to 5000 servers
- 37. 37Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source Supportability Easy Complex Scalability Up to 100 servers Up to 5000 servers Scalability Up to 100-300 TB Up to 100 PB
- 38. 38Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source Supportability Easy Complex Scalability Up to 100 servers Up to 5000 servers Scalability Up to 100-300 TB Up to 100 PB Target Systems DWH Purpose-Built Batch
- 39. 39Pivotal Confidential–Internal Use Only MPP vs Hadoop for Business MPP Hadoop Platform Openness Mostly Closed Open Hardware Options Mostly Appliances Commodity Vendor Lock-in Typical Not Common Technology Price $200K – $10M $50K – $500K Implementation Cost Moderate High Extensibility Vendor-provided APIs Open Source Supportability Easy Complex Scalability Up to 100 servers Up to 5000 servers Scalability Up to 100-300 TB Up to 100 PB Target Systems DWH Purpose-Built Batch Target End Users Business Analysts Developers
- 40. 40Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None
- 41. 41Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard
- 42. 42Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java
- 43. 43Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High
- 44. 44Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High
- 45. 45Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High Query Latency 10-20 ms 10-20 sec
- 46. 46Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High Query Latency 10-20 ms 10-20 sec Query Runtime 5-7 sec 10-15 mins
- 47. 47Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High Query Latency 10-20 ms 10-20 sec Query Runtime 5-7 sec 10-15 mins Query Max Runtime 1-2 hours 1-2 weeks
- 48. 48Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High Query Latency 10-20 ms 10-20 sec Query Runtime 5-7 sec 10-15 mins Query Max Runtime 1-2 hours 1-2 weeks Min Collection Size Megabytes Gigabytes
- 49. 49Pivotal Confidential–Internal Use Only MPP vs Hadoop for Architect MPP Hadoop Query Optimization Good Poor to None Debugging Easy Very Hard Accessibility SQL Mainly Java DBA Skill Level Low High Single Job Redundancy Low High Query Latency 10-20 ms 10-20 sec Query Runtime 5-7 sec 10-15 mins Query Max Runtime 1-2 hours 1-2 weeks Min Collection Size Megabytes Gigabytes Max Concurrency 10-15 queries 70-100 jobs
- 50. 50Pivotal Confidential–Internal Use Only Agenda Ÿ Distributed Systems Ÿ MPP Ÿ Hadoop Ÿ MPP vs Hadoop Ÿ Examples Ÿ Summary
- 51. 51Pivotal Confidential–Internal Use Only Summary Use MPP for Ÿ Analytical DWH Ÿ Ad hoc analyst SQL queries and BI Ÿ Keep under 100TB of data Use Hadoop for Ÿ Specialized data processing systems Ÿ Over 100TB of data
- 52. 52Pivotal Confidential–Internal Use Only 52Pivotal Confidential–Internal Use Only Questions?
- 53. BUILT FOR THE SPEED OF BUSINESS