straightforward integration of federated data

Straightforward Integrationof Federated Data

Using standard RDBMS servers tolink databases across the internet

1st Annual CASIMIR SymposiumRome, Nov 27–28, 2007

Walter PargentGSF - National Research Center for Environment and Health

1st Ann. CASIMIR symposiumRome, Nov 28, 2007

Straightforward integrationof federated data (2)

Session 3Walter Pargent

Short Glossary

• RDBMS =Relational DataBase Management System

• SQL =Structured Query Language

Aims / Topics

• Show standard RDBMS featuresas options for dynamic data integration

• Discuss databaseLinkage vs. Replication

• Technical, not semantic integration level• How can technology help semantics• In case: Enquire other’s experiences !!!

Janan Eppig @ Corfu

Data integrationETL style

• ETL =– Extraction– Transformation

(and Transfer)– Loading

• => REPLICATION

Thumb ruleof data design

• Redundancy abets inconsistency,so better avoid it …

• … wherever possible– Exceptions for

• Performance issues• (Ease of implementation)

Linkage

Information-Proxy(RDBMS-Server)

Web-Interfaces

DataMart(BioMart)

SQL-Interface

Web-Services

M a s t e r - D a t a b a s e - S e r v e r s

virtual private networks (VPN)

in DemilitarizedFirewall-Zones

The paranoid option:

Linkage Security Aspects

Information-Proxy(RDBMS-Server)

Local-Transact-Work-DBs

Local Proxies

Integration@YourDesk

MS-ACCESS

• … as an RDBMS client• Technical basis

– Data source names (DSNs) defined forindividual table connections

– Open Database Connectivity (ODBC)– Ability to join between heterologous RDBMS

tables and (local) spreadsheets– Relationship manager (for join predefs)

Integration@YourDeskFile -> External Data -> Link Tables

Integration@YourDeskCreate an ODBC link

Integration@YourDeskTools -> Relationships

3 in One

• EMMASTR– mySQL

(Solaris)• cryoDb

(devel)– mySQL

(Windows)• MouseNet

– Sybase ASE(HP-UX)

Integration@YourDeskEMMA_2_MouseNet Query Building

Integration@YourDeskEMMA_2_MouseNet : Results

• Using a desktop database (MS-Access)is suitable for– Small scale and low complexity integration

• Prototyping

– Small scale ad hoc replication– Small scale data migrations / transformations– Ad hoc data editing

• … not suitable for– Handling of large datasets

(no optimization)

BIG & CENTRAL

Large scale &central server

• Rather use a ‘real’ RDBMS …• … on a central performant server• e.g.:

– mySQL FEDERATED engine– Sybase ASE CIS

(Component Integration Services)

• Table defined on the master server# create table xxx ( c1 int, c2 char, … )ENGINE=InnoDB;

• Table definition on proxy server# create table f_xxx (c1 int, c2 char, …)ENGINE=FEDERATEDCONNECTION=

'mysql://usr:PW@host:3306/dbnam/table';

mySQL (5.1)

• Alternative to link many tables(since version 5.1)# CREATE SERVER fedlink

FOREIGN DATA WRAPPER mysqlOPTIONS (

USER 'fed_user',HOST 'remote_host',PORT 9306,DATABASE 'federated' );

# CREATE TABLE test_table (col1, col2, …)ENGINE=FEDERATEDCONNECTION='fedlink.test_table';

mySQL (doc)

• Find mySql documentation for that at:

– “Storage Engines”• “The Federated Storage Engine”

– http://dev.mysql.com/doc/refman/5.1/en/federated-storage-engine.html

Sybase ASE

ToDo on the Master DATABASE• Add entry to interfaces file

# GSFIEG_JAXmaster tcp ether xxx.gsf.de 5555query tcp ether xxx.gsf.de 5555

• As sa user on the MASTERDB# sp_addserver GSFIEG_JAX# sp_addremotelogin GSFIEG_JAX# sp_addremotelogin GSFIEG_JAX, pargent,

Sybase ASEToDo on the Proxy DATABASE• Add entry to interfaces file

# JAX_PUBLICmaster tcp ether yyy.jax.org 4000query tcp ether yyy.jax.org 4000

• As sa user on the ProxyDB# sp_addserver JAX_PUBLIC# sp_addlogin pargent, <lognamAtJaxDb>

,null,null,‘Remote user wapar at jax‘

Sybase ASE

Test it on Proxy DB• Remote Procedure Call (RPC)

# Exec JAX_PUBLIC.mgd..sp_tables

• Example for proxy table# Create proxy_table jax_vocab

at 'JAX_PUBLIC.mgd..VOC_vocab'# Create existing table jax_proc

(c1 int,c2 varchar(10),…)external procedure at 'JAX_PUBLIC.mgd..some_proc'

Other RDBMS’s

• PostgreSQL: contrib/dblink()• IBM DB2 UDB: table nicknames• Oracle: database links• MS SQL-Server: ???

Heterologous Links

• Connections between different RDBMS’s• Implemented using

– ODBC (universal)– protocols specific to individual RDBMS

(esp. towards Oracle, DB2, SQL-Server)

Availability …

• … of features forheterologous database integration:– mySQL and postgreSQL (NOT YET)– Sybase: ECDA options

(Enterprise Connect Data Access)– Oracle: Transparent Gateway option– IBM DB2: Discovery Link– MS SQL-Server: ???

Linkage viaApplication vs. RDBMS

• Webservices and Webapps link onapplication level, which is appropriate forsequentiel / cascading 1:n linkage– Typical web-application (HTML tab with links)– Web-services

• Must have SQL (or similar interface) to dooverall set operations

Why set operations• Scientific inference needs flexibility

– Cannot be confined to pre defined user interfaces(static query forms)

– “Find all mouse lines with traits linked to energymetabolism …”

• Need to use deliberate set oriented queries– Feasibility study / data quality checks in prototype

phase– Pre phase consistency checking to investigate data

curation needs– Prototype functionality and performance checks– Data transformation and masking (views)

Integration atthe input (write) level

• To reduce subsequent need for datacuration, we’ll want to use:– Central Controlled Vocabularies– Ontology servers … (SQL, Webservices or

JavaEnterpriseBeans)– Central Source of Linkage objects

• e.g. a proactive mouseLineDb producingworldwide unique identifiers (codes)

• c.f. internal situation at GSF (ambiguous linecodes)

Replication vs. Federation

• PRO:– Data are at hand for performance tuning and data

curation– Best performance for (client-side) queries

• CON:– Storage needs– Risk of outdated data– Risk of inconsistencies (from duplicates)– Expense of regular uploads

• Network load, script implementation, downtimes

Replication vs. Federation

• PRO:– Always up to date– No denormalization (no add. risk for inconsistencies)– No extra expense for implementation of auto upload

• CON:– Several points of failure for service availability– Inadequate queries may hamper the master server

• However this could be hindered by db usage configuration(available resources, max rowcount, etc … per user …)

Nix is fix(There is more than one way to Rome …)

• Decisions need not /must notbe carved in stone …– Find the currently appropriate mixture– Revalidate decisions regularly

(based on query statistics and user feedback)

– Change easily, whenever ..• Data volatility changes (prototype -> HTP project)

• Service reliability changes (to the better or worse)

• For performance issues

Substitute linkage …

View:XXX

MasterDB

Proxy-table

… by replication

View:XXX

MasterDB

Proxy-table

ReplicatedTable:XXX

Linkage helps replication

• Use linkage of federated data forstraightforward implementation ofreplication# Insert into replica_table

select * from proxy_table• Use master_tab timestamps (ins_on,

upd_on) as filters for incrementalreplication …

• Use diff_log table for master_tab

Where to use what

• Replication– Where master server is not reliably available

or has inadequate performance– The master server is not trusted for

sustainability (long term persistence)– Query load to this part of data is

extraordinarily high

Where to use what

• Federation / Linkage– Master is well maintained by a “buddy”– Query load is intermediate or low– High volatility of master data– … or just in any case, where there is no

important CON

Outlook

• Need to do heterologous tests includingperformance checks

• Adhoc statistics and complex queriesrapidly developed by pure SQL as viewsor stored procedures(by power-users or bio-informaticians …)

• Combine with BioMart ?

Other tools• RDBMS inherent scheduling

and replication options• 3rd party ETL tools

(Extraction, Transformation, Loading)• Usage of client GUI for

SQL stored procedures

THANXto

Thanks to

• Colleagues at the GSF• Martin Hrabé de Angelis• Janan Eppig, Richard Butler and Damian

Smedley• Paul Schofield

straightforward integration of federated data

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