One Size Fits All: An Idea Whose Time has Come and Gone Michael Stonebraker

Alternate Title The elephants are selling 30 year old “bloatware” That is not good at anything And you should send them to the “home for old software”

Three Financial Services Markets Stream processing (electronic trading) Tick stores (data warehouses) OLTP (transaction processing)

Stream Processing (Electronic Trading) A feed comes out of the wall Compute a “secret sauce” looking for events of interest Trade based on the result But only if you are more nimble than the next guy….

Traditional RDBMS Model Outbound Processing Store the data before processing! Latency What if the data is not important? Too many processes! Optimized for business data processing Where you don’t trust the app. Memory Updates Disk Queries Too slow to be interesting!

Stream Processing Engine with StreamSQL Inbound Processing Database paradigm (SQL) a good one But need a different architecture Straight through processing No task switches Lightweight scheduling StreamBase Application Streambase Application Alerts Actions Event Data Memory Disk Queries

” StreamSQL Application Example Market_Feeds Alerts My_Buys Example: Every minute for every stock I am trading: Calculate VWAP (vol. weighted avg. price) for my trades & all trades Alert whenever my personal trading execution is inferior to market 5 Streambase operators, 30 min to build Streams of “tuples” (time-series data) flow through query Queries run continuously ”

StreamSQL Will Dominate Rule Engines Essentially all applications entail a mix of stored and real-time data StreamSQL covers both kinds of data in a single paradigm A rule engine must switch paradigms StreamSQL amenable to compilation Know what is the next event to process In contrast, hard to figure this out in a rule engine

Performance Benchmark Financial Services Application: Construct a virtual feed of “first arrivers” on a low end Linux machine Relational DB: 11,000 messages/sec Streambase: 300,000 messages/sec Another StreamSQL vendor: 20,000 messages/sec Result: Streambase was a factor of 27 faster

(and Other Warehouse Applications) Tick Stores (and Other Warehouse Applications) Store all market data for the last 10 years To back test “secret sauce” models To answer ad-hoc queries – “how many times has X happened” Typical size – 100 Tbytes Append only

Terminology -- “Row Store” Record 1 Record 2 Record 3 Record 4 E.g. DB2, Oracle, Sybase, SQLServer, …

Rotate Your Thinking 90 Degrees Column stores read only the columns required Not all of them Compression works better By a factor of 2-3 against the elephants No record headers Which are big ticket items No padding to byte or word boundaries

Benchmark Summary Vertica has been baked off about 30 times Typically against the incumbent Has yet to win by less than a factor of 30 against a row store Beats most other column stores by around 10X KX is the only system to come within an order of magnitude

Maybe Elephants are Good at OLTP…… OLTP is a main memory market Not a disk-based one Transactions are short and have no I/O or user stalls Run to completion (single threaded) Disaster Recovery (and HA) a requirement Build it into the bottom of the system

TPC-C Performance on a Low-end Machine Elephant 850 TPS (1/2 the land speed record per processor) H-Store (so far – a university prototype) 70,416 TPS (41X the land speed record per processor) Factor of 82!!!!!

Implications for the Elephants They are selling “one size fits all” Which is 30 year old legacy technology that is good at nothing

Pictorially: Streaming data DBMS apps OLTP Data Warehouse

The DBMS Landscape – Performance Needs Streaming data high low high high OLTP Data Warehouse

One Size Does Not Fit All -- Pictorially Elephants get only “the crevices” Streambase Open source H-Store successors Vertica

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