making the most of in-memory: more than speed

The Briefing Room

Making the Most of In-Memory: More than Speed

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Welcome

Host: Eric Kavanagh

[email protected]


The Briefing Room

!   Reveal the essential characteristics of enterprise software, good and bad

!   Provide a forum for detailed analysis of today’s innovative technologies

!   Give vendors a chance to explain their product to savvy analysts

!   Allow audience members to pose serious questions... and get answers!

Mission


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Topics

This Month: DATA PROCESSING

November: DATA DISCOVERY & VISUALIZATION

December: INNOVATORS


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Data Processing

Efficiency is doing things right; effec2veness is doing the right things.

“~Peter Drucker


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Analyst: Robin Bloor

Robin Bloor is Chief Analyst at The Bloor Group

[email protected]


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Kognitio

!   Founded in 1989, Kognitio is both an in-memory database and an analytical engine

!   The Kognitio Analytical Platform can be deployed as software, as an appliance, or in the cloud

!   The platform enables flexible, ad hoc queries on complex data sets, including data from Hadoop, and it offers scale-up and scale-out capabilities


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Guest: Roger Gaskell

Roger Gaskell is the Chief Technology Officer and one of the founding members of the Kognitio Development Team. He has overall responsibility for all product development, strategic direction and roadmap of new innovation for the Kognitio Analytical Platform. Roger has been instrumental in all generations of the product to date. Over this time, it has evolved from an appliance-based system in the original beta offering in 1989, to a hardware-independent software for x86 processing, then to a cloud-based Platform-as-a-Service offering in in the mid-1990s. Prior to Kognitio, Roger was test and development manager at AB Electronics. During this time his primary responsibility was for the famous BBC Micro Computer and the development and testing of the first mass production of personal computers for IBM.

Making the most of in-memory platforms

October 2013

10

What is an “In-memory” analytical platform

A database where queries are run from data held in computer memory (RAM) rather than mechanical disk

Memory = Fast / Disk = Slow

Analytics go much quicker – SIMPLE? Unfortunately, it’s not as simple as that….

11

Why in-memory: RAM is faster than disk (really!)

Actually, this only part of the story: Analytics completely change the workload characteristics on the database workload

Simple reporting & transactional processing is all about “filtering” the data of interest filtering

Analytics is all about complex “crunching” of the data once it is filtered crunching

Crunching needs processing power & consumes CPU cycles CPU cycles

Storing data on physical disks severely limits the rate at which data can be provided to the CPUs storing

Accessing data directly from RAM allows much more CPU power to be deployed access

12

Analytics is about through data

•  To understand what is happening in the data

“CRUNCHING”

Joins

Sorts

Aggregations

Grouping

Analytical Functions

crunching CPU cycle-intensive & CPU-bound

•  In-memory analytical platforms are therefore CPU-bound –  Assume disk I/O speeds not a bottleneck –  In-memory removes the disk I/O bottleneck

More complex analytics More pronounced this becomes =

13

For analytics, the CPU is king

Being CPU-bound fundamentally changes a system’s design philosophy

Interactive / ad hoc analytics: THINK data to core ratios ≈ <10GB data per CPU core

Disk IO Bound CPUs wait for data from disk No need for efficient coding

Parallelisation ineffective

CPU Bound Every CPU cycle is precious – efficient coding

Parallelization = scalable performance Advanced techniques minimize CPU cycles

14

Why now?

Price of RAM,

Logarithmic (10)

1995 2000 2005 2010 1987

Interest in in-memory

15

Mature BI being overtaken

Numbers, tables, charts, indicators

…accessed with ease and simplicity Historical information, latency

But BI and BI tools have plateaued! Decision Support

Progression into advanced analytics & data science

It’s now all about doing more math …a lot more math

16

Machine learning algorithms

Dynamic Simulation

Statistical Analysis

Clustering

Behaviour modelling

Thus more complex methods – real-time

Reporting & BPM Fraud detection

Dynamic Interaction

Technology/Automation

Ana

lytic

al C

ompl

exity

Campaign Management

#PP_R

17

How to efficiently exploit RAM

•  A large cache is not in-memory –  In-memory platforms hold data in structures that take advantage of the

properties of RAM –  Caches are copies of frequently used disk blocks

•  Platform designed to specifically exploit the random access nature of memory –  Different algorithms –  CPU cycles are precious – code efficiency paramount –  Advanced techniques used to reduce code path length

•  Dynamic Machine Code Generation •  Extended CPU instruction sets

•  Parallelize everything –  Scale-out and Scale-up –  Fully and efficiently use every CPU

core, in every CPU, in every server

18

Analytical Platform Reference Architecture

Persistence Layer

Hadoop Clusters

Enterprise Data Warehouses

Legacy Systems

Kognitio Storage

Reporting

Analytical Platform

Layer Near-line Storage (optional)

Application & Client Layer

All BI Tools All OLAP Clients Excel

Cloud Storage


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Perceptions & Questions

Analyst: Robin Bloor

Big Data, Maybe — Big Parallelism, Yes

Many latency-reducing changes are afoot:

u  Hadoop is a data lake – It’s about latency

u  CPU and memory rule – The old database is dying

u  Grids, not clusters – A server is now a cluster

u  Scaling Up AND Scaling Out – “Only scaling out” is last year’s story

u  SSD will replace spinning disk – But it will never compete with RAM

Why the Excitement?

What are the “new” applications?

BIG DATA capture and staging

BIG DATA ANALYTICS

LITTLE DATA ANALYTICS

OPERATIONAL INTELLIGENCE

A “Modern” Workload

Query Light &

Math Heavy

Where the Rubber Meets the Road

It isn’t really about application latency any more, it’s about business process latency (business time!). This can have many aspects:

u  The collapse of data flows – take the processing to the data

u  Data warehouse offload

u  Full process automation

u  Lower latency = NEW BUSINESS PROCESSES

The Question

Exactly how do we take

advantage of these changes?

This is a BUSINESS question AND a TECHNICAL question.

The question for most organizations is:

u  Low latency is exciting, but where do you see the clear business opportunities?

u  There seems to be a conundrum about where to store “slow” data: Ø  Hadoop? Ø  Traditional data warehouse? Ø  New data warehouse?

u  Is the split between the application and the data real any more?

u  In your opinion, does the Enterprise need a new architecture?

u  How is it possible to define and monitor service levels with in-memory applications?

u  Whither data governance?


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Upcoming Topics

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This Month: DATA PROCESSING

November: DATA DISCOVERY & VISUALIZATION

December: INNOVATORS


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