Yes sql08 inmemorydb


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My talk for Percona Live! April 12th 2012.

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  • Service Reliability. Must be buzzword compliant, as in RFC 2119 Tradeoffs discussed previously.
  • This is really the problem we want to solve. It’s one of the fundamental problems in computer science and doesn’t have a completely satisfactory solution.
  • But is this all really necessary? Or just fashion?
  • This is big myth #1. they are not at all necessarily even related, one could have either or both
  • Mike’s talk last year, only
  • For YesSQL, we needed a more complex set of relations, and transactional support.
  • The rest of this section of our talk will be working through the consequences of these tradeoffs.
  • NDB has a much higher maturity level than most NoSQL systems
  • The CAP Theorem is a limited version of the Systemic Qualities model.
  • Performance is response time.
  • “Conservation of timestamps”
  • Also need to consider updates from versions after 5.1
  • Yes sql08 inmemorydb

    1. 1. A Global In-memory Data System for MySQLDaniel Austin, PayPal Technical StaffPercona Live! MySQL ConferenceSanta Clara, April 12th, 2012 v1.3
    2. 2. Intro: Globalizing NDB Proposed Architecture What We Learned Q&AGlobal In-memory MySQL Confidential and Proprietary 2
    3. 3. Mission YesSQL “Develop a globally distributed DB For user-related data.” • Must Not Fail (99.999%) • Must Not Lose Data. Period. • Must Support Transactions • Must Support (some) SQL • Must WriteRead 32-bit integer globally in 1000ms • MinMax Data Volume: 10-100 TB (working) • Must Scale Linearly with Costs Confidential and Proprietary
    4. 4. THE FUNDAMENTAL PROBLEM INDISTRIBUTED DATA SYSTEMS“How Do We Manage ReliableDistribution of Data Across GeographicalDistances?” Confidential and Proprietary
    5. 5. One Approach: The NoSQL Solution • NoSQL Systems provide a solution that relaxes many of the common constraints of typical RDBMS systems – Slow - RDBMS has not scaled with CPUs – Often require complex data management (SOX, SOR) – Costly to build and maintain, slow to change and adapt – Intolerant of CAP models (more on this later) • Non-relational models, usually key-value • May be batched or streaming • Not necessarily distributed geographically Confidential and Proprietary
    6. 6. Big Data Myth #1: Big Data = NoSQL • „Big Data‟ Refers to a Common Set of Problems – Large Volumes – High Rates of Change • Of Data • Of Data Models • Of Data Presentation and Output – Often Require „Fast Data‟ as well as „Big‟ • Near-real Time Analytics • Mapping Complex Structures Takeaway: Big Data is the problem, NoSQL is one (proposed) solution Confidential and Proprietary
    7. 7. NoSQL Hype Cycle: Where Are We Now? There are currently more than 120+ NoSQL databases listed at! You Are Here ?As the pace of new technology solutions has slowed, some clear winners have emerged. Confidential and Proprietary
    8. 8. 3 Essential Kinds of NoSQL Systems 1. Columnar K-V Systems – Hadoop, Hbase, Cassandra, PNUTs 2. Document-Based – MongoDB, TerraCotta 3. Graph-Based – FlockDB, Voldemort Takeaway: These were originally designed as solutions to specific problems because no commercial solution would work. Confidential and Proprietary
    9. 9. Intro: Globalizing Big Data Proposed Architecture What We Learned Q&AGlobal In-memory MySQL Confidential and Proprietary 9
    10. 10. OUR APPROACH: THE YESSQL SOLUTIONUse MySQL/NDBUse AWSKey Tradeoffs: Cost vs. Performance HA vs. CAP Complexity vs. RDBMS features Confidential and Proprietary
    11. 11. WHY NDB? Pro Con• True HA by design • Some semantic – Fast recovery limitations on fields• Supports (some) X- • Size constraints (2 actions TB?)• Relational Model – Hardware limits• In-memory also architecture = high • Higher cost/byte performance • Requires reasonable• Disk storage for data partitioning non-indexed data • Higher complexity (since 5.1)• APIs, APIs, APIs Confidential and Proprietary
    12. 12. How NDB Works in One SlideGraphics courtesy Confidential and Proprietary
    13. 13. CIRCULAR REPLICATION/FAILOVERGraphics courtesy O’Reilly Confidential and Proprietary
    14. 14. SYSTEM AVAILABILITY DEFINED• Availability of the entire system: n mAsys = 1 – P(1-Pri)j i=1 j=1 V I P• Number of Parallel Components Needed to Parallel Achieve Availability Amin: SerialNmin =[ln(1-Amin)/ln(1-r)] Confidential and Proprietary
    15. 15. Big Data Myth #2: The CAP Theorem Doesn’tSay What You Think It Does • Consistency, Availability, (Network) Partition – Pick any two? Not really. • The Real Story: These are not Independent Variables • AP =CP (Um, what? But…A != C ) • Variations: – PACELC (adds latency tolerance) – VPAC (adds variability) Takeaway: the real story here is about the tradeoffs made by designers of different systems, and the main tradeoff is between consistency and availability, usually in favor of the latter. Confidential and Proprietary
    16. 16. What about “High Performance”? • Maximum lightspeed distance on Earth’s Surface: ~67 ms • Target: data available worldwide in < 1000 ms Sound Easy? Think Again! Confidential and Proprietary
    17. 17. AWS Meets NDB• Why AWS? – Cheap and easy infrastructure-in-a-box (Or so we thought! Ha!)• Services Used: – EC2 (Centos 5.3, small instances for mgm & query nodes, XL for data – Elastic IPs/ELB – EBS Volumes – S3 – Cloudwatch Confidential and Proprietary
    18. 18. ARCHITECTURAL TILES AWS Availability ZonesTiling Rules A B• Never separate NDB & SQL• Ndb:2-SQL:1-MGM:1• Scale by adding more tiles• Failover 1st to nearest AZ• Then to nearest DC• At least 1 replica/AZ C ELB• Don‟t share nodes• Mgmt nodes are redundantLimitations Unused (not present in all locations)• AWS is network-bound @ 250 MBPS – ouch!• Need specific ACL across AZ boundaries• AZs not uniform!• No GSLB NDB MGM SQL• Dynamic IPs• ELB sticky sessions !reliable Confidential and Proprietary
    19. 19. Architecture StackScale by Tiling A B A B A B A B A B A B A B A B 7 AWS Data Centers: US-E, US-W, US-W2, TK, EU, AS, SA-B Confidential and Proprietary
    20. 20. Other Technologies Considered • Paxos – Elegant-but-complex consensus-based messaging protocol – Used in Google Megastore, Bing metadata • Java Query Caching – Queries as serialized objects – Not yet working • Multiple Ring Architectures – Even more complicated = no way Confidential and Proprietary
    21. 21. Intro: Globalizing Big Data Proposed Architecture What We Learned Q&AGlobal In-memory MySQL Confidential and Proprietary 21
    22. 22. SYSTEM READ/WRITE PERFORMANCE (!) What we tested: • 32 & 256 byte char fields In-region replication tests • Reads, writes, query speed vs. volume • Data replication speeds Results: • Global replication < 350 ms • 256 byte writeread < 1000ms worldwide 06/19/2011 06/20/2011 06/21/2011 06/22/2011 06/23/2011 Confidential and Proprietary
    23. 23. The Commit Ordering Problem • Why does commit ordering matter? • Write operators are non-commutative [W(d,t1),W(d,t2)] != 0 unless t1=t2 – Can lead to inconsistency – Can lead to timestamp corruption – Forcing sequential writes defeats Amdahl‟s rule • Can show up in GSLB scenarios Confidential and Proprietary
    24. 24. Dark Side of AWS • Deploying NDB at scale on AWS is hard – Dynamic IPs (use hostfile) – DNS issues – Security groups (ec2-authorize) – Inconsistent EC2 deployments • Are availability zones independent network segments? – No GSLB (!) (rent or buy) Be prepared to struggle a bit! Confidential and Proprietary
    25. 25. Future Directions for YesSQL • Alternate solution using Pacemaker, Heartbeat – From Yves Trudeau @ Percona – Uses InnoDB, not NDB • Implement Memcached plugin – To test NoSQL functionality, APIs • Add simple connection-based persistence to preserve connections during failover • Can we increase the number of data nodes? • As the number of datacenters goes up, sync times go down. What‟s the relation? • So far only limited testing of hard configurations – Production systems will likely need more RAM for MGMs Confidential and Proprietary
    26. 26. Hard Lessons, Shared • Be Careful… – With “Eventual Consistency”-related concepts – ACID, CAP are not really as well-defined as we‟d like considering how often we invoke them • NDB is a good solution – Real HA, real performance, real SQL – Notable limitations around fields, datatypes – Successfully competes with NoSQL systems for most use cases – better in many cases • AWS makes the hard things possible – But not so much the easy things easier Takeaway: You can achieve high performance and availability without giving up relational models and read consistency! Confidential and Proprietary
    27. 27. “In the long run, we are all deadeventually consistent.”Maynard Keynes on NoSQL DatabasesTwitter: @daniel_b_austinEmail: daaustin@paypal.comWith apologies and thanks to the real DB experts, Andrew Goodman, YvesTrudeau, Clement Frazer, Daniel Abadi, and everyone else!