Scaling MySQL: Benefits of Automatic Data Distribution
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Scaling MySQL: Benefits of Automatic Data Distribution

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In this webinar, we cover how ScaleBase provides transparent data distribution to its clients, overcoming caveats, hiding the complexity involved in data distribution, and making it transparent to the ...

In this webinar, we cover how ScaleBase provides transparent data distribution to its clients, overcoming caveats, hiding the complexity involved in data distribution, and making it transparent to the application.

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    Scaling MySQL: Benefits of Automatic Data Distribution Scaling MySQL: Benefits of Automatic Data Distribution Presentation Transcript

    • Webinar: Scaling MySQLBenefits of Automatic Data Distribution December 13, 2012
    • Agenda 1. Who We Are 2. The Scalability Problem 3. Benefits of Automatic Data Distribution 4. Customer ROI/Case Studies 5. Q & A (please type questions directly into the GoToWebinar side panel)2
    • Who We Are Presenters: Paul Campaniello, VP of Global Marketing 25 year technology veteran with marketing experience at Mendix, Lumigent, Savantis and Precise. Doron Levari, Founder A technologist and long-time veteran of the database industry. Prior to founding ScaleBase, Doron was CEO to Aluna.3
    • Pain Points – The Scalability Problem• Thousands of new online and mobile apps launching every day• Demand climbs for these apps and databases can’t keep up• App must provide uninterrupted access and availability• Database performance and scalability is critical4
    • Big Data = Big Scaling Needs Big Data = Transactions + Interactions + Observations Sensors/RFID/Devices Mobile Web User Generated Content Spatial & GPS Coordinates BIG DATAPetabytes User Click Stream Sentiment Social Interactions & Feeds Web Logs Dynamic Pricing Search Marketing WEB Offer History A/B Testing Affiliate NetworksTerabytes External Demographics Segmentation Customer Touches CRM Business Data Offer Details Support Contacts FeedsGigabytes HD Video, Audio, Images Behavioral ERP Purchase Detail Targeting Speech to Text Purchase Record Product/Service Logs Payment Record Dynamic Funnels SMS/MMSMegabytes Increasing Data Variety and Complexity 5 The 451 Group & Teradata
    • Scalability PainInfrastructureCost $ Large You just lost Capital customers Expenditure Predicted Demand Opportunity Traditional Cost Hardware Actual Demand Dynamic Scaling time 6
    • Ongoing “Scaling MySQL” Series • August 16 & September 20, 2012 – Scaling MySQL: ScaleUp versus Scale Out • October 23, 2012 – Methods and challenges to Scale out MySQL • Today – Benefits of Automatic Data Distribution • January 17, 2013 – Catch 22 of read-write splitting7
    • The Database Engine is the Bottleneck... • Every write operation is At Least 4 write operations inside the DB: – Data segment – Index segment – Undo segment – Transaction log • And Multiple Activities in the DB engine memory: – Buffer management – Locking – Thread locks/semaphores – Recovery tasks8
    • The Database Engine is the Bottleneck • Every write operation is At Least 4 write operations inside the DB: – Data segment – Index segment – Undo segment Now multiply – Transaction log by 10TB accessed by • And Multiple Activities in the DB engine memory: 10000 – Buffer management concurrent – Locking sessions – Thread locks/semaphores – Recovery tasks9
    • COI – Customer, Order, Item CUSTOMER ORDER ORDER_ITEM ITEMC_ID NAME LOCATION RANK O_ID C_ID DATE OI_ID O_ID QUANT I_ID I_ID NAME1 John MA 10 1 1 2012-02-01 1 1 3 1 1 iPhone2 James AL 9 2 1 2012-02-01 2 1 6 2 2 iPad3 Peter CA 10 3 2 2012-02-01 3 2 4 1 3 iPad Mini4 Chris FL 8 4 6 2012-02-01 4 2 2 2 4 Kindle5 Oliver MA 9 5 6 2012-02-01 5 2 1 5 5 Kindle Fire6 Allan MA 9 6 8 2012-02-01 6 3 1 1 6 Galaxy S37 Janette CA 8 7 3 6 58 David MD 10 8 4 8 3 9 4 9 4 10 5 2 6 11 6 1 5 10
    • Requirements • Every day: • Updates Throughput – 30,000 new customers – 1,000,000 new orders, average of 5 items per order – Items catalog is updated once a day, nightly, on 11pm Latency • Queries – Top customers, rank 9 and up) – New orders, joins across the board…11
    • Splitting the data • CUSTOMER – random (hash) • ORDER – derivative (C_ID) • ORDER_ITEM – transitive (O_ID -> C_ID) • ITEM – global table12
    • Sliced Database CUSTOMER ORDER ORDER_ITEM ITEMC_ID NAME LOCATION RANK O_ID C_ID DATE OI_ID O_ID QUANT I_ID I_ID NAME1 John MA 10 1 1 2012-02-01 1 1 3 1 1 iPhone4 Chris FL 8 2 1 2012-02-01 2 1 6 2 … …7 Janette CA 8 3 2 4 1 6 Galaxy S3 4 2 2 2 DB - 1 5 2 1 5C_ID NAME LOCATION RANK O_ID C_ID DATE OI_ID O_ID QUANT I_ID I_ID NAME2 James AL 9 3 2 2012-02-01 6 3 1 1 1 iPhone5 Oliver MA 9 6 8 2012-02-01 7 3 6 5 … …8 David MD 10 11 6 1 5 6 Galaxy S3 DB - 2C_ID NAME LOCATION RANK O_ID C_ID DATE OI_ID O_ID QUANT I_ID I_ID NAME3 Peter CA 10 4 6 2012-02-01 8 4 8 3 1 iPhone6 Allan MA 9 5 6 2012-02-01 9 4 9 4 … … 10 5 2 6 6 Galaxy S3 DB - 3 13
    • Requirements Distribution • Every day: • Updates Throughput – 30,000 new customers – 1,000,000 new orders, average of 5 items per order – Items catalog is updated once a day, nightly, on 11pm Parallelism Latency • Queries – Top customers, rank 9 and up) – New orders, joins across the board…14
    • Automatic Data Distribution • The ultimate way to scale • Provides significant performance improvements • The only way to really improve read and also writes • Good for scaling high session-volume reads and writes • Good for scaling high data-volume reads and writes • Home-grown implementations have drawbacks15
    • Scale Out Features and Benefits Feature Benefit Parallel query execution Great performance of cross-db queries & maintenance commands Query result aggregation Support of sophisticated cross-db queries, even with ORDER BY, GROUP BY, LIMIT, Aggregate functions… Online data redistribution Flexibility: no need to over-provision No downtime 100% compatible MySQL proxy Applications unmodified Standard MySQL tools and interfaces MySQL databases untouched Data is safe within MySQL InnoDB/MyISAM/any Data distribution review and analysis Optimization of data distribution policy Data consistency verifier Validate system-wide data consistency Real-time monitoring and alerts Simplify management, reduce TCO16
    • Scale Out Provides Immediate & Tangible Value Application Server Database A Standby A Application Server Database B Standby B Database C Standby C BI Database D Standby D Management17
    • Typical Scale Out (ScaleBase) Deployment Application Server Database A Standby A ScaleBase Central Management Application Server Database B Standby B ScaleBase Data Traffic Manager Database C Standby C BI Database D Standby D Management18
    • Choose Your Scale-out Path Data Distribution Database Size Read/Write Splitting 1 DB? Good for me! # of concurrent sessions19
    • Scaling Out Achieves Unlimited Scalability 160000 140000 120000 100000Throughput 84000 80000 Throughput (TPM) Total DB Size (MB) 60000 60000 # Connections 48000 40000 36000 24000 2500 20000 2000 12000 1500 1500 6000 1000 0 500 500 1 2 4 6 8 10 14 Number of Databases 20
    • Detailed Scale Out Case Studies Nokia AppDynamics Mozilla Solar Edge • Device Apps App • Next gen APM • New Product/ • Next Gen • Availability company Next Gen App/ Monitoring App • Scalability • Scalability for the AppStore • Massive Scale • Geo-clustering Netflix • Scalability • Monitors real implementation • Geo-sharding time data from • 100 Apps thousands of • 300 MySQL DB distributed systems21
    • Summary • Database scalability is a significant problem – App explosion, Big Data, Mobile • Scale Up helps somewhat, but Scale Out provides a long-term, cost-effective solution • ScaleBase has an effective Scale Out solution with a proven ROI – Improves performance & requires NO changes to your existing infrastructure • Choose your scale-out path.... – The ScaleBase platform enables you to start with R/W splitting and grow into automatic data distribution22
    • Questions (please enter directly into the GTW side panel)617.630.2800www.ScaleBase.comdoron.levari@scalebase.compaul.campaniello@scalebase.com23
    • Thank You24