Real Time Data Analysis


Real Time Data Analysis November 30, 2012 // HBDC, Beijing, China INTRODUCTIONS. Nikita Shamgunov, CTO • BS, MS, PhD in CS • 8 years as a Senior Database Engineer at Microsoft SQL Server, Facebook, MemSQL MAY YOU LIVE IN INTERESTING TIMES. Moor’s law is over But not for data growth All kinds of data Log Image JS Structured DATA IS KEY TO SUCCESS Every mega successful company is a data driven company Google, Facebook, Amazon are obsessed with it What they are doing now, everyone will be doing in 5 years HALF LIFE OF DATA The data you’ve collected recently is usually more important than the data you collected a year ago And the value drops exponentially Half Life of Data SCENARIOUS PERFORMANCE INVESTIGATION Large web destination How does the website perform in every country What is the 99% page load time. How does it correlate with revenue? CONTINIOUS DEPLOYMENT We ship code every week Which commits are regressing the key metrics How can we pinpoint what the problem is? I want to track the performance of every little function and act upon my insights A/B TESTING I want to perform A/B testing and serve ads out of a data store I want to record every impression and every click and make decisions about it in real time. How does it correlate with revenue? HIGH DATA VELOCITY How to store the state of multi-threaded applications and cope with faster-moving data streams? MACHINE LEARNING I want to train my models as fast as possible and test them immediately I need to collect data and push it through a model using convenient tools Once the model is ready I want to use it to make real time decisions when serving web pages. LATENCIES I wish I had a faster machine I wish I had a faster machine I wish I had a faster machine DATA LATENCY. Loading data for analysis is painful. QUERY LATENCY. Queries take too long to run The system cannot handle query volume Cannot sustain predictable performance levels SOLUTIONS Storm by Twitter (Nathan Marz) Cloudera Impala MemSQL BOTTLENECKS BE GONE. MemSQL is a distributed, in-memory SQL database Capable of processing and analyzing the most demanding of workloads Two things we fix: Data latency (the batched load) Query latency MEMSQL FIXES THAT. For data latency, MemSQL provides Ultra-fast data load Real-time stream capture For query latency, MemSQL provides Distributed query execution Efficient SQL-to-C++ conversion Lock-free data structures DISTRIBUTED SYSTEM. Shared-nothing architecture Distributed query optimizer Highly available through leaf-node replication Uses hash-partitioning Aggregator Aggregator Aggregator MemSQL leaf MemSQL leaf MemSQL leaf MemSQL leaf . . . Clie nt Clie nt . . . DURABILITY AND REPLICATION. Logging and snapshotting to disk No buffer pool, hence sequential IO only Random read/write in RAM Sequential IO on disk Native MemSQL replication Ships snapshot to provision, then reads from transaction log Skinny log – no indexes, which are reconstituted on recovery EXECUTION ENGINE. SQL-to-C++ code generation enables efficient execution Auto-parameterization keeps compilation to a minimum Parallel query execution Select * from T where id > 5 and name like “Jen%”; Select * from T where id > @ and name like ^; Consume live application data Issue complex, ad-hoc queries 48-server cluster on EC2 384 cores 2.7 TB of capacity in RAM DEMO OVERVIEW. DEMO TIME. CONTACT ME nikita@memsql.com WEB www.memsql.com 380 10th ST San Francisco, CA 94103 200 Park S Ave New York, NY 10003
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