GaianDB

A dynamic distributed federated database

Dale Lane@dalelane

A massively over-simplified view of data-warehousing...

The “Internet of Things”

GaianDB

a dynamic distributed federated database

Federated data

Network of distributed databases

A dynamic network

A dynamic networkBiologically-Inspired Self-Organisation

Exploit natural selection in nature to build better networks

Robust self-organizing network architectures

Frameworks and algorithms for robust fault-tolerant information dissemination

Robust communications with minimal complexity or human control

Gaian database

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SQL QueryN0

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SQL Query

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SQL Query

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SQL Queries

Queries routed to all database nodes – a flood query, but retrieving only the data required to satisfy a query

Exchanges query traffic in the network for data traffic – aiming to minimize total traffic

Predicated on a concept of ‘store data locally - read data from anywhere’ paradigm

Architecture

GaianDB

Derby Engine: Parsing, Compilation, Execution

GaianPStmtNode VTI:Executes queries on physical leaf nodes +

Propagates the original SQL (+ queryID & steps state info) to linked Gaian nodes

Instantiates Invokes costingmethods

Pushes columns and ‘where’ clausein a structure

MQ(tt) Stream Data

Original SQL

DB2 Oracle MS SQLServer Sybase MySQL Flat files

In-memorytables

Derby

GaianDBGaianDB

GaianDB

propagate

Text Index

Derby tables

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SQL QueryN0

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SQL Query

Expanded Node

Multithreaded, breadth-first query propagation

Loop detection/handling – no duplicates

Performance – with 1,250 nodes

Query time for 1025 nodes, fetching up to 1025 rows from each

y = 4.217x + 349.251

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Row s fetched per node

Tim

e (m

illis

econ

ds)

Query Execute Time

Total Query Time

Linear (Total Query Time)

Query Performance

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53.9

107.8

161.7

215.6

269.5

323.4

377.3

431.2

485.1

539.0

0 200 400 600 800 1000 1200Number of Nodes

Qu

ery

Tim

e(m

illis

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nd

s)

Average Query TimePredicted Max (Layers)Predicted Min (Layers)

Performance questions

The time to propagate a query to all of the nodes in the database, as a function of the number of database nodes (N);

The time to fetch data from across the nodes of the database to a single node, as a function of the volume of data;

The time to fetch data from across the database to multiple nodes concurrently querying, as a function of the number of nodes concurrently querying.

Graph metricsThe eccentricity ε(νi) of a graph vertex νi is the maximum graph distance between νi and any other vertex νj of G i.e. the "longest shortest path" between any two graph vertices (νi , νj) of the graph.

The maximum eccentricity is the graph diameter Gd. The minimum graph eccentricity is the graph radius Gr. We define the size of G as the number of vertices N and the number of connections at each vertex as the vertex degree δi (1 < i ≤ N).

Biologically inspired self-organisation

0123456789

10

0 200 400 600 800 1000Number of Nodes (N)

Grap

h Di

men

sion

(edg

es)

RadiusDiameter(1+e)ln(N)(1-e)ln(N)

Network growth by preferential attachment Using a fitness function at each node

Limit maximum vertex degree =10

Gd = nint [ (1+e) * ln(N) ]

Gr = nint [ (1-e) * ln(N) ]

e = 0.24

Query propagation timeThe predicted maximum (Tmax) and minimum times (Tmin) to execute the flood query are:

TL = link latencyTp = processor delay

Tmax = (Gd + 1)(TL + Tp)Tmin = (Gr + 1)(TL + Tp)

with the predicted execute query time from any node (Tν) being:

Tν = (ε(ν) + 1)(TL + Tp)

Hence substituting for ε(ν) Tν = nint[1 + B * ln(N) * (TL + Tp)]

Measured query propagationIndividual Query Time Scalability

0.0

53.9

107.8

161.7

215.6

269.5

323.4

377.3

431.2

485.1

539.0

592.9

0 200 400 600 800 1000 1200Number of Nodes

Query

Time (

ms)

Average Query TimePredicted Max (Diameter+1)Predicted Min (Radius+1)Queried node eccentricity+1

Individual Query Time Scalability

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53.9

107.8

161.7

215.6

269.5

323.4

0 50 100Number of Nodes

Query

Time

(ms)

Individual Query TimesAverage Query TimeQueried node eccentricity+1

Measured data fetch

Query time to fetch 1 million rows

y = 4.217x + 349.251

y = 1.7383x + 678.141

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0 200000 400000 600000 800000 1000000 1200000Total Rows fetched

Tim

e (m

illis

econ

ds)

Total Query Time 1025 nodes

Total Query Time 1 node

Total Query Time 1 node indexed

Linear (Total Query Time 1025 nodes)

Linear (Total Query Time 1 node)

Example uses

Smart Metering

centralisedwrite

Smart Metering

centralisedread

Smart Metering

distributed federatedwrite

Smart Metering

distributed federatedread

Other uses...

http://www.alphaworks.ibm.com/tech/gaiandb

Image credits

Background: YouTube video “The Internet of Things”, IBMhttp://www.youtube.com/watch?v=sfEbMV295Kk

Icons: DB and envelope icons, Tim Morgan http://flickr.com/photos/timothymorgan/sets/1615269

Microsoft Excel icon, Vincent Garnier (courtesy of IconArchive) http://iconarchive.com/show/softdimension-icons-by-benjigarner/Excel-icon.html

Photo of car mechanics, Tomas http://flickr.com/photos/tma/2264878

All other images original from GaianDB work