A Paradigm Shift: The Increasing Dominance of Memory-Oriented Solutions for High Performance Data Access

A Paradigm Shift: TheIncreasing Dominance ofMemory-Oriented Solutions forHigh Performance Data Access!Ben Stopford : RBS

How fast is a HashMap lookup?~20 ns

That’s how long it takes light to travel a room

How fast is a database lookup?~20 ms

That’s how long it takes light to go to Australia and back

3 times

Computers really are very fast!

The problem is we’re quite good atwriting software that slows them down

Desktop Virtualization

We loveabstraction

There are many reasonswhy abstraction is agood idea… …performance just isn’tone of them

Question: is it fair to compare aDatabase with a HashMap?

Not really…

Key Point On one end of ..on the other sitsthe scale sits the the database… HashMap… …but it’s a very very long scale that sits between them.

Times are changing

Database Architecture isAging

The Traditional Architecture

Traditional Shared Shared In Memory Disk NothingDistributed SimplerIn Memory Contract

Simplifying the Contract

How big is the internet? 5 exabytes (which is 5,000 petabytes or 5,000,000 terabytes)

How big is an average enterprise database 80% < 1TB (in 2009)

Simplifying the Contract

Databases have huge operational overheads Taken from “OLTP Through the Looking Glass, and What We Found There” Harizopoulos et al

Avoid that overhead with a simplercontract and avoiding IO

Improving Database Performance !Shared Disk Architecture Shared Disk

Improving Database Performance !Shared Nothing Architecture

Each machine is responsible for a subset of the records. Each record exists on only one machine.! 1, 2, 3… 97, 98, 99… 765, 769… 169, 170… Client 333, 334… 244, 245…

Improving Database Performance (3)! In Memory Databases!(single address-space)

Databases must cache subsets of the data in memory Cache

Not knowing what you don’t know 90% in Cache Data on Disk

If you can ﬁt it ALL in memory youknow everything!!

The architecture of an in memory database

Memory is at least 100x faster than disk ms μs ns ps1MB Disk/Network 1MB Main Memory 0.000,000,000,000Cross Continental Main Memory L1 Cache RefRound Trip Ref Cross Network L2 Cache Ref Round Trip * L1 ref is about 2 clock cycles or 0.7ns. This is the time it takes light to travel 20cm

Memory allows random access.Disk only works well for sequentialreads

This makes them very fast!!

The proof is in the stats. TPC-HBenchmarks on a 1TB data set

So why haven’t in memory databases taken off?

Address-Spaces are relatively small and of a ﬁnite, ﬁxed size

Durability

One solution is distribution

Distributed In Memory (Shared Nothing)

Again we spread our data but this time only using RAM. 1, 2, 3… 97, 98, 99… 765, 769… 169, 170…Client 333, 334… 244, 245…

Distribution solves our two problems

We get massive amounts of parallel processing

But at the cost ofloosing the single address space

Traditional Shared Shared In Memory Disk NothingDistributed SimplerIn Memory Contract

There are three key themes here: Simplify theDistribution No Disk contract Improve Gain scalability by scalability picking All data is through a appropriate held in RAM distributed ACID architecture properties.

ODC – Distributed, Shared Nothing, InMemory, Semi-Normalised, Graph DB 450 processes 2TB of RAM Messaging (Topic Based) as a system of record (persistence)

ODC represents abalance between throughput and latency

What is Latency?

What is Throughput

Which is best for latency? Shared Nothing (Distributed)Traditional In-MemoryDatabase Database Latency?

Which is best for throughput? Shared Nothing (Distributed) Traditional In-Memory Database Database Latency?

So why do we use distributed in memory? Plentiful In Memory hardware Latency Throughput

This is the technology ofthe now. So what is the technologyof the future?

Terabyte Memory Architectures

Fast Persistent Storage

New Innovations on the Horizon

These factors are remolding thehardware landscape to one where memory both vast and durable

This is changing the way we write software

Huge servers in thecommodity space aredriving us towards singleprocess architectures thatutilise many cores andlarge address spaces

We can attain hundreds ofthousands of executionsper second from a singleprocess if it is welloptimised.

“All computers wait at thesame speed” !

We need to optimise for our CPU architecture ms μs ns ps1MB Disk/Network 1MB Main Memory 0.000,000,000,000Cross Continental Main Memory L1 Cache RefRound Trip Ref Cross Network L2 Cache Ref Round Trip * L1 ref is about 2 clock cycles or 0.7ns. This is the time it takes light to travel 20cm

Tools like Vtune allow us tooptimise software to truly leverage our hardware

So what does this all mean?

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A Paradigm Shift: The Increasing Dominance of Memory-Oriented Solutions for High Performance Data Access

by Ben Stopford on Oct 23, 2011

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A Paradigm Shift: The Increasing Dominance of Memory-Oriented Solutions for High Performance Data Access — Presentation Transcript