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Resource Management in the Greenplum Parallel Database

Siva Narayanan (siva@greenplum.com) Consultant Software Engineer, Query Processing, EMC Greenplum. Resource Management in the Greenplum Parallel Database. Why, you ask?. Problem. Finite resources - CPU/memory/IO/network Concurrent activity Different business value (Loads/Reports/Analytics)

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Resource Management in the Greenplum Parallel Database

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  1. Siva Narayanan (siva@greenplum.com) Consultant Software Engineer, Query Processing, EMC Greenplum Resource Management in the Greenplum Parallel Database

  2. Why, you ask?

  3. Problem • Finite resources - CPU/memory/IO/network • Concurrent activity • Different business value (Loads/Reports/Analytics) • Different system impact (Simple/Complex queries) • How can a DBA manage the system and keep everyone happy?

  4. Solution • Determine business value of a query upon arrival • Translate that to fair share of CPU and Memory • Resource reservation / Admission control • Are the resources available? • Run-time resource allocation • Ensure that reservations are honored • Adjust behavior as necessary

  5. CPU vs Memory

  6. CPU Sharing • Every query operator in a execution plan • Continually measures its actual CPU usage and compares it with fair share • If it uses too much, it sleeps for a short while • Rinse, repeat • I/O and network bandwidths are similar

  7. Memory Sharing • Every query operator in a execution plan • Gets a portion of memory reserved for the entire query • Memory intensive operators vs not • Re-use memory between blocking operators • If data is too large, they spill • Net effect, every query uses up to its fair share

  8. Summary • Resource management is a big problem with big data • Align resource allocation with business value • Greenplum Parallel Database has mechanisms for CPU and Memory

  9. Questions? We’re hiring! siva@greenplum.com

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