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the NewSQL database you’ll never outgrow Taming the Big Data Fire Hose John Hugg Sr. Software Engineer, VoltDB Big Data Defined Velocity + Moves at very high rates (think sensor-driven systems) + Valuable in its temporal, high velocity state Volume + Fast-moving data creates massive historical archives + Valuable for mining patterns, trends and relationships Variety + Structured (logs, business transactions) + Semi-structured and unstructured VoltDB 2 Example Big Data Use Cases VoltDB Data Source High-frequency operations Lower-frequency operations Capital markets Write/index all trades, store tick data Show consolidated risk across traders Call initiation request Real-time authorization Fraud detection/analysis Inbound HTTP requests Visitor logging, analysis, alerting Traffic pattern analytics Online game Rank scores: • Defined intervals • Player “bests” Leaderboard lookups Real-time ad trading systems Match form factor, placement criteria, bid/ask Report ad performance from exhaust stream Mobile device location sensor Location updates, QoS, transactions Analytics on transactions 3 Big Data and You Incoming data streams are different than traditional business apps Big Data and You + You need to write data quickly and reliably, but … It’s not just about high speed writes + + + + + VoltDB You need to validate in real-time You need to count and aggregate You need to analyze in real-time You need to scale on demand You may need to transact 4 Big Data Management Infrastructure High Velocity Online gaming Ad serving NewSQL Structured data ACID guarantees Relational/SQL Real-time analytics Unstructured data Eventual consistency Schemaless KV, document High Volume Analytic Datastore Sensor data Financial trade Internet commerce SaaS, Web 2.0 Mobile platforms VoltDB Other OLAP data stores NoSQL 5 Big Data Management Infrastructure High Velocity Online gaming NewSQL High Volume Analytic Datastore Ad serving Sensor data Financial trade Internet commerce SaaS, Web 2.0 Mobile platforms VoltDB Other OLAP data stores NoSQL 6 High Velocity Data Management High Velocity DBMS Requirements Ingest at very high speeds and rates Scale easily to meet growth and demand peaks Support integrated fault tolerance Support a wide range of real-time (or “near-time”) analytics Integrate easily with high volume analytic datastores VoltDB 8 High Speed Data Ingestion Support millions of write operations per second at scale Read and write latencies below 50 milliseconds Provide ACID-level consistency guarantees (maybe) Support one or more well-known application interfaces + SQL + Key/Value + Document VoltDB 9 Scale to Meet Growth and Demand Scale-out on commodity hardware Built-in database partitioning + Manual sharding and/or add-on solutions are brittle, require apps to do “heavy lifting”, and can be an operational nightmare Database must automatically implement defined partitioning strategy + Application should “see” a single database instance Database should encourage scalability best practices + For example, replication of reference data minimizes need for multi-partition operations VoltDB 10 A Look Inside Partitioning select count(*) from orders where customer_id = 5 single-partition select count(*) from orders where product_id = 3 multi-partition insert into orders (customer_id, order_id, product_id) values (3,303,2) single-partition update products set product_name = ‘spork’ where product_id = 3 multi-partition Partition 1 VoltDB 1 1 4 101 101 401 1 2 3 knife spoon fork Partition 2 2 3 2 2 5 5 201 501 502 1 2 3 knife spoon fork Partition 3 1 3 2 3 6 6 201 601 601 1 2 3 knife spoon fork 1 1 2 table orders : (partitioned) customer_id (partition key) order_id product_id table products : product_id (replicated) product_name 11 Integrated Fault Tolerance Database should transparently support built-in “Tandem-style” HA + Users should be able to easily increase/decrease fault tolerance levels Database should be easily and quickly recoverable in the event of severe hardware failures Database should be able to automatically detect and manage a variety of partition fault conditions Downed nodes should be “rejoinable” without the need for service windows VoltDB 12 Partition Detection & Recovery Network fault protection Detects partition event Server A Determines which side of fault to disable Server C Snapshots and disables orphaned node(s) Server B Live node rejoin Allows “downed” nodes to rejoin live cluster Server A Automatically re-synchs all node data Server C Coordinates transactions during re-synch Server B VoltDB 13 Real-time Analytics Database should support a wide variety of high performance reads + High-frequency single-partition + Lower-frequency multi-partition Common analytic queries should be optimized in the database + Multi-partition aggregations, limits, etc. Database should accommodate a flexible range of relational data operations + Particularly relevant to structured data VoltDB 14 Integration with Analytic Datastores Database should offer high performance, transactional export Export should allow a wide variety of common data enrichment operations + Normalize and de-normalize + De-duplicate + Aggregate Architecture should support loosely-coupled integrations + Impedance mismatches + Durability VoltDB 15 VoltDB Export Data Flow High Velocity Database Cluster Loosely-coupled, asynchronous Queue must be durable Bi-directional durability VoltDB 16 Summary Big Data infrastructures will usually require more than one engine + High velocity engine for “fast” data + Analytic engine for “deep” data Data characteristics will often determine which high velocity engine to use + NewSQL is often well-suited to structured data + NoSQL is often a good fit for unstructured data Choose solutions that suit your needs and are designed for interoperability VoltDB 17