Informix warehouse accelerator update

© 2014 IBM Corporation

Sandor Szabo IBM Informix Development Lab – IBM Software Group

May , 2014

BLU Acceleration for SQL / NoSQL databasesExtreme Speed with In-Memory Analytics


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Agenda: Informix Warehouse Accelerator (IWA)

• Data Warehouse Trends

• Informix Warehouse Accelerator (IWA)

• Technology Overview

• How it Works

• Positioning and Competition

• Customers and Partners

• Reference Architecture

• 12.10 Features & Roadmap

• Q&A


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TRENDSDatabase and Data Warehousing Industry


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Data Warehousing Workload & Optimizations

Data Warehousing/OLAP workload are inherently more complex than OLTP transactions and reasons are well-documented

Ways to overcome that include:– Building Indexes– Partitioning of Data– Building Cubes (MOLAP / ROLAP / HOLAP)– Query Tuning– Appliances that add a new layer of Hardware

to perform I/O for DBMS

Mixed-Workload always a challenge

DBMS needs to be built to handle such a workload


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Third Generation of Database Technology

According to IDC’s Article (Carl Olofson) – Feb. 2010

1st Generation:– Vendor proprietary databases of IMS, IDMS, Datacom

2nd Generation:– RDBMS for Open Systems– Dependent on disk layout, limitations in scalability and disk I/O

3rd Generation: IDC Predicts that within 5 years:– Most data warehouses will be stored in a columnar fashion– Most OLTP database will either be augmented by an in-memory database

(IMDB) or reside entirely in memory– Most large-scale database servers will achieve horizontal scalability through

clustering


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In-Memory DB: Why Now?

64-bit processors can address up to16 exabytes of data

DRAM prices drop by 30% every 18 months

1 Gb of NAND flash memory average price is less than US$0,50

Commodity blades provide 1 terabyte of DRAM

Multicore CPUs enable parallel processing of in-memory data

In-memory-enabling software is amply available and proven


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“By 2012, 70% of Global 1000 organizations will load detailed data into memory as the primary method to optimize BI application performance.”

- Gartner

Conventional DatabasesDisk Read5 milliseconds

In-Memory DatabasesDisk Read5 nanoseconds

The idea: Store & Process Data in Memory instead on Disk to analyze data magnitudes Faster Than with traditional systems


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TECHNOLOGY OVERVIEWIBM Informix Warehouse Accelerator (IWA)


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Introducing IBM Informix Warehouse Accelerator (IWA)

Results

Analyticquery

Linux on Intel / AMD 64-bit

TCP/IP

Query Optimizer

In-Memory Compressed

ColumnarDatabase Partition

Bulk Loader

Query Processor

Yes

Analyticquery

Results

AccelerateQuery?

Most Unix/Linux 64-bit platforms

In-Disk[Compressed]

Relational / Row-basedDatabase

Informix database serverInformix Warehouse

Accelerator

No

POWERFUL HYBRID DATABASE PLATFORMPOWERFUL HYBRID DATABASE PLATFORM

Extreme Performance Transactions Extreme Performance Analytics


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Unique combination of technologies for Speed-of-Thought Analysis

In-Memory Data storage &

Query Processing

Multi-Core Parallelism and Vector

Optimized Algorithm (No Locking)

on Intel 64-bit SIMD technology

Massive Parallel Processing of

Data Load/Refresh & Query

Fast Storage Backup in Disk for

recovery purposes

IWA combines Breakthrough IBM Research & Development Lab Innovations

Row (Informix) &

Column (IWA) data storage

Deep Data Compression

Fits TB of raw data storage

Predicate evaluation done directly

on Compressed data

Intelligent Frequency Partitioning

Full, Partial and Incremental

Refresh (Insert Only on Delta)

IWA

IWA

Informix

IWAInformix

IWA

IWA

File System copy

Memory


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IWA Benefits

Extreme performance for Analytics: 100x+ faster response times for complex BI queries

Leverages existing Informix database, builds on top, to provide instant performance boost

Uses low cost commodity HW: Linux on Intel/AMD 64-bit

Handles Terabytes of data in-memory, thanks to compressed storage and query technology

Works “behind the scenes” in Informix, transparent to client applications

Very simple and flexible installation, configuration and administration

Informix + IWA is a hybrid database platform which provides the best technology and performance for both OLTP and OLAP workloads and support of Big Data solutions

No need to keep doing all this in order to get high performance OLAP queries:

– Indexes– Aggregates / summary tables– Materialized query tables/views– Cubes– Decide on best data partition strategies– Keep different database systems for each type

of workload: OLTP vs OLAP

– Migrate data to another OLAP database– Change your database schema– Change your analytic applications– Tune I/O, memory and CPU for OLAP– Update Statistics– Tune Queries with Optimizer Directives


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You can use IWA’s In-Memory Analytics to Speed Up queries on…


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HOW IT WORKSIBM Informix Warehouse Accelerator (IWA)


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IWA Overview and Seamless Integration with Informix/IDS Before IWA…

Informix– Receives analytic query from client– Spends some time doing intensive I/O– Returns results back to the client

Informix 12.1

Results

SQL


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IWA Overview and Seamless Integration with Informix/IDS Setting up IWA…

Informix– Determine the database subset used in

analytic queries to accelerate (data mart)• Manually or through Workload Analysis

– Deploy an IWA data mart based on DB subset• Stream Load the data from Informix into IWA

– Informix Optimizer is aware of IWA datamart

Informix 12.1

The Accelerator– Install and configure IWA on Linux x86_86– Connect with Informix using custom protocol– IWA compresses and stores a copy of the

Informix DB set into data marts in-memory– IWA data mart is fully loaded, valid and

ready for Informix server to use as needed

Linux on Intel/AMD 64-bit

Bulk Loader CompressedDatabase Partition

TCP/IP

CompressionIn-Memory Columnar Storage

Frequency PartitioningParallelism

Predicate evaluation on compressed dataMulti-core and Vector optimized algorithms

SIMD




SIMD

Query Router

Query Processor


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IWA Overview and Seamless Integration with Informix/IDS Using IWA: Process is transparent to Informix client

Results

SQL

16 Introduction to IBM Informix Warehouse Accelerator

Informix– Receives analytic query from client– If query uses data matching an IWA datamart

and can be accelerated, route/offload it to IWA– Returns results back to the client

Informix 12.1

The Accelerator– Processes the routed SQL query extremely

fast and returns answer back to Informix



TCP/IP




SIMD




SIMD

Query Router

Query Processor

SQL

Results


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IWA Overview and Seamless Integration with Informix/IDS Using IWA: Process is transparent to Informix client

Results

SQL

17 Introduction to IBM Informix Warehouse Accelerator

Informix– Receives analytic query from client– If query uses data matching an IWA datamart

and can be accelerated, route/offload it to IWA– Returns results back to the client– If query is not based on an IWA datamart or

cannot be accelerated, Informix will resolve it

Informix 12.1

The Accelerator– Processes the routed SQL query extremely

fast and returns answer back to Informix



TCP/IP




SIMD




SIMD

Query Router

Query Processor


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CUSTOMERS AND PARTNERSInformix Warehouse Accelerator (IWA)


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Federació Farmacèutica SCCL

50K IWA query requests a day

464 Million rows in biggest fact table

34x faster Global Sales statistics:From 1hr down to 1min 45sec

2x faster Invoicing systems

30 users, 83 processes using IWA

Huge savings in Data Warehouse space in disk:No need for Staging Tables: Size went from 4.5TB to 500GBInformix Storage Optimization Feature (Deep Compression): Size from 500GB to 140GB

“Previous DWH needs a lot of staging tables due to performance issues. With IWA we've dropped all Staging requirements and converted our old 4.5 Terabytes DWH to 500GB on disk. With IWA, our invoicing system has dropped dramatically the time required from 24H to just 12H.”

Santi Pla, IT Director, FedeFarma

“Previous DWH needs a lot of staging tables due to performance issues. With IWA we've dropped all Staging requirements and converted our old 4.5 Terabytes DWH to 500GB on disk. With IWA, our invoicing system has dropped dramatically the time required from 24H to just 12H.”

Santi Pla, IT Director, FedeFarma


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Corporate Name: LABCO S.A.

Brand Name: Labco

Life Sciences, Medical and Laboratory Services

IWA Utilization Country: Belgium, France, Portugal, Spain, Switzerland

IBM Business Partner: Deister S.A.

Solution components:Hardware: IWA runs on 8 cores IBM X Series Intel XeonSoftware: IBM Informix Advanced Enterprise Edition v12

IBM Informix Technologies used:Informix Warehouse Accelerator (IWA)Storage Optimization Feature (Deep Compression)


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LABCO

20K IWA analytic query requests a day

2 big data marts, with these fact tables:1150 Million rows fact table one484 Million rows fact table the other

783GB database (w/ storage optimization)

48x faster response of heavy dashboards:From 24hr down to 30min

Fast deep analysis of laboratory data: Average analysis time: 1min 45sec

15 users, 20 processes using IWA; DWH is becoming source of info for new users

“Without IWA, this project could not been completed successfully. Delivering accurate information of the status of a pan European group to the decision makers staff in minutes was impossible previously. Now with IWA, a lot of new information is available and could be analyzed at the moment it's required.”

Vicente Salvador, Deister S.A.

“Without IWA, this project could not been completed successfully. Delivering accurate information of the status of a pan European group to the decision makers staff in minutes was impossible previously. Now with IWA, a lot of new information is available and could be analyzed at the moment it's required.”

Vicente Salvador, Deister S.A.


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Europe’s Largest Power company tackles the Smart Meter Big Data challenge with Informix TimeSeries + In-Memory Accelerator (IWA)

E.ON Metering (EMTG) is the centre of excellence for the development and commercialization of smart energy solutions and technologies and part of Europe’s largest Power and Gas company E.ON

EMTG operates a sophisticated Smart Meter data infrastructure based on IBM Informix TimeSeries technology in combination with Informix In-Memory Warehouse Accelerator

IBM Information Management products currently used:– Informix 11.70 Ultimate Warehouse Edition– Cognos Business Intelligence 10


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ROADMAP & NEW FEATURESInformix Warehouse Accelerator (IWA)


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Informix 11.70.FC2 (Mar 2011) IWA 1st Release on IUWE On SMP

Informix 11.70.FC3 (Jun 2011) Workload Analysis Tool Support of more Locales Support of Data Currency

Informix 11.70.FC4 (Oct 2011) IGWE (Growth ed. IWA) IWA cluster on Blade Server Non-disruptive Mart Refresh New AQT monitoring options New SQL syntax/functions

Roadmap of IWA-Specific Features

Informix 11.70.FC5 (May 2012) Use w/IDS Secondary Servers New use_dwa options Multiple DISTINCT’s in IDS+IWA Additional SQL functions/syntax Support Solaris Intel 64 on IDS Partition-level Fact tables refresh Support col[x,y] substr notation

Informix 11.70.FC6 (Oct 2012) Partition-level refresh on

Dimension tables

Informix 11.70.FC7(Dec 2012) Enhanced query processing on

sessions with multiple cursors open

Informix 12.10.FC3(Mar 2014) IWA support of synonyms and views,

allows using new and multiple sources of data in a single data mart and query:

Data from local/remote tables NoSQL data (mapped views) Enables Self-Joins (via views)

New function QUARTER in IDS+IWA Extended LIMIT syntax in IDS+IWA IWA accelerates NoSQL data (views) New use_dwa option (uniquecheck)

20112011 20122012 20132013 20142014

Informix 12.10.FC1(Mar 2013) Cognos BI [+SPSS] in Advanced ed. Automatic partition-level refresh Continuous refresh: Trickle Feed,

enabling real-time / right-time analytics

Standard OLAP SQL in IDS+IWA Support of UNION [ALL] queries CTE: SELECT…FROM (SELECT…) Support of INSERT INTO…SELECT Additional SQL and data types Admin w/OAT & built-in ifx_ functions WAREHOUSE security privilege

Informix 12.10.FC2(Sep 2013) Support of data from External Tables TimeSeries data (VTI) support: Real-

time analytics & Big Data on sensor data

IDS (not IWA): Hybrid SQL + NoSQL (non-structured data: JSON/BSON)


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What’s New in IWA 12.10.FC1

Refreshing Data in IWA– Automatic Partition Refresh– Trickle Feed (continuous refresh)

Additional SQL support for analytics in IWA– UNION [ALL] queries– New SQL OLAP functions

Additional interfaces for IWA administration– OpenAdmin Tool (OAT) menu for IWA– New SQL built-in routines for administering IWA

Improved Security– New WAREHOUSE privilege required for administering IWA


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Automatic Partition Refresh

Automatic IWA synchronization with Informix, on-demand

Let Informix find the changed partitions since last refresh and

refresh them in IWA for you

Easy adoption and maintenanceof Real-Time Analytics

With this enhancement…

A single command instructs IWA to refresh only changed data partitions from Informix database to IWA

Applies to Fact and Dimension tables

Benefits…

It removes the potentially error-prone process for manual identification of changed partitions in Informix

Easier administration for keeping current Informix data in IWA


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Low Administration

Automated and fast small updates keep IWA data current

Allows for Real/Right-TimeAnalytics and Operational BI

Continuous Refresh: Trickle Feed

ifx_setupTrickleFeed

Tracks changes in DimensionsTracks inserts in Fact tablesAutomated updates in IWA datamart


Incremental inserts to the Fact tables and changes to Dimensions tables can be continuously updated into IWA

Changes can be at row level, which is more granular than at partition level

Benefits…

We can have “speed of thought” analytics in a real-time data warehouse or mixed workload environment

Actionable analytics on operational data


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Support of UNION [ALL] queries

Benefits…

Fast response for operational and business analytics workloads calling heavy queries in UNION [ALL]

Typical UNION use cases in BI tools: OLAP/Cube operations: ROLL-

UP/ACROSS, AGGREGATE, GROUP ETL: Extract, Slowly-Changing

Dimensions (SCD) Cross-table queries and reports Set operations: union/union all

Some or all of the queries in an UNION, will be accelerated and run much faster

BI tools operations that use UNION behind the scenes will run 100x+ faster


IWA can now accelerate previously long running queries combined in an UNION or UNION ALL operation

SELECT i_item_id, avg(cs_quantity) agg1, avg(cs_list_price) agg2, avg(cs_coupon_amt) agg3, avg(cs_sales_price) agg4FROM catalog_sales, customer_demographics, date_dim, item, promotionWHERE cs_sold_date_sk = d_date_sk andcs_item_sk = i_item_sk and cs_bill_cdemo_sk = cd_demo_sk and cs_promo_sk = p_promo_sk andcd_gender = 'F' and cd_marital_status = 'M' andcd_education_status = 'College' and (p_channel_email = 'N' or p_channel_event = 'N‘) and d_year = 2001GROUP BY i_item_id

UNION ALL

SELECT i_item_id, avg(ws_quantity) agg1, avg(ws_list_price) agg2, avg(ws_coupon_amt) agg3, avg(ws_sales_price) agg4FROM web_sales, customer_demographics, date_dim, item, promotionWHERE ws_sold_date_sk = d_date_sk and ws_item_sk = i_item_sk and ws_bill_cdemo_sk = cd_demo_sk and ws_promo_sk = p_promo_sk and cd_gender = 'F' and cd_marital_status = 'M' and cd_education_status = 'College' and (p_channel_email = 'N' or d_channel_event = 'N') and d_year = 2001GROUP BY i_item_id ;


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Support of standard SQL OLAP functions

In-Database and In-Memory Analytics

Simplified Code for OLAP which can be accelerated, much faster response times

Better Platform Integration and Utilization


Both Informix and IWA support ANSI standard SQL On-Line Analytical Processing (OLAP) functions:

– Ranking: RANK, DENSE_RANK, DENSERANK, CUME_DIST, PERCENT_RANK, NTILE

– Numbering: ROW_NUMBER, ROWNUMBER– Aggregate: SUM, COUNT, AVG, MIN, MAX, STDEV, VARIANCE, RANGE, RATIO_TO_REPORT, RATIOTOREPORT

– First/Last: FIRST_VALUE, LAST_VALUE

Support of windowed aggregates:

Create windows partitions. Apply OLAP function on

each row

FinalOrder by

Join filtersGroup byHaving

Benefits…

Reduce SQL code and accelerate to get much faster performance for OLAP or multidimensional analysis

Better integration and support for BI tools like Cognos BI, and applications that use standard OLAP SQL calls

Increase performance by reducing repeated scans, temporary tables and aggregation needed to do OLAP


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Administering IWA from OAT

to Monitor and Manage IWA

Web and Mobile Interface

As part of Informix administration tools


You can use the standard graphic administration tool OpenAdmin Tool (OAT), for all Informix administration tasks, including the ones for IWA

Benefits…

Easy to use graphic tool OAT can be used to easily setup, integrated administration environment OLTP/OLAP

No need to remember and run commands or stored procedures to manage IWA


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Informix

Informix TimeSeries table Virtual Table Interface (VTI) representation of TimeSeries table

Real-time Analytics

IWA

IWA support for Time Series data

Benefits…

High-performance right-time analytics on big data collected from your sensors, meters, events, GPS/location, RFIDs, to anticipate and improve actions

Combine TimeSeries and IWA for operational actionable analytics based on historic and current sensors data

Unique platform, flexible, fast and scalable, for the most challenging Big Data and Smart Planet solutions

Right-Time Analytics on time-stamped data

Big Data solutions on Sensor data

Operational Intelligence


You can include Time Series data coming from smart sensors into IWA

Data marts in IWA can be defined and loaded from an Informix’ s Virtual Table Interface (VTI) of your TimeSeries data


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IWA data mart supports External Tables

Direct, fast and flexible way to leverage external data for in-memory analytics


We can load data directly from external tables into IWA data marts without having to load it into Informix database first

Large external data in ASCII / binary files or network devices –ex: through named pipes– can be used to quickly populate an IWA data mart

Benefits…

Run extremely fast in-memory analytic queries on operational data from non-Informix external files and devices.

Large amount of external data is quickly loaded and made available in IWA, thanks to high performance reads of Informix External Tables and the no need for the external data to be loaded into Informix database first

Storage savings and flexibility to do in-memory analytics on large data in file systems or devices and integrate it with other SQL and NoSQL data in Informix.


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IWA data mart supports synonyms and views

Fast analytic queries on SQL and NoSQL data

Analytics on data from different sources

More queries and datatypes can be accelerated


Until now, an IWA datamart could only contain regular local tables, all in the same database

We can now create an IWA datamart that uses remote tables in another Informix DB and accelerate queries using those remote tables

– By having a local synonym in the Informix DB of the datamart, which points to the remote Informix table

We can include views as part of an IWA datamart definition, and accelerate queries that use views

– Views could map to a subset of another Informix table or also to NoSQL data

– Use views to accelerate self-joins

Benefits…

Ability to combine and accelerate queries on local with remote tables, no need to make all tables local

Allows to be able to do accelerate self-joins queries by using views

Allows to accelerate data in JSON collections by using views

Fast analytic queries on views, typically slow in SQL DBs due to on-the-fly view materialization


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IWA data mart supports of views and usage in NoSQL query (1)

From MongoDB shell:– Create two collections (JSON): comments and users

From Informix:– Create a view on each JSON collection (comments, users)

– Deploy an IWA data mart by probing a join between them:

– Run the accelerated query.

$ mongo demo_databaseMongoDB shell version: 2.4.9connecting to: demo_database

mongos> db.comments.insert( [ { uid:12345, pid:444, comment:"first" }, { uid:12345, pid:888, comment:"second" }, { uid:99999, pid:444, comment:"third" }] )

mongos> db.users.insert( [ { uid:12345, name:"john" }, { uid:99999, name:"mia" }] )mongos> exit

Example: Accelerating NoSQL data (in a JSON collection) using IWA’s view support

$ dbaccess demo_database -> create view vcomments(uid,pid,comment) as select bson_value_int(data,'uid'), bson_value_int(data,'pid'), bson_value_varchar(data,'comment') from comments;> create view vusers(uid,name) as select bson_value_int(data,'uid'), bson_value_varchar(data,'name') from users;

set environment use_dwa 'probe cleanup';set environment use_dwa 'probe start';

select {+ avoid_execute} * from vcomments c, vusers u where c.uid=u.uid;

set environment use_dwa 'probe stop';

execute procedure ifx_probe2mart('demo_database','noSQL_mart');execute function ifx_createmart('demo_dwa','noSQL_mart');execute function ifx_loadmart('demo_dwa','noSQL_mart','NONE');


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IWA data mart supports of views and usage in NoSQL query (2)

From Informix:

– Run the accelerated query on the JSON collection data:

\

Example: Accelerating NoSQL data (in a JSON collection) using IWA’s view support

set environment use_dwa 'accelerate on';

select c.uid,name,comment from vcomments c, vusers u where c.uid=u.uid and pid=444;

uid 12345name johncomment first

uid 99999name miacomment third


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IWA data mart view support to accelerate SQL-NoSQL query (1)

From Informix:

– We have an Informix table sqldoc1:

– We also have a JSON collection doc1: In Informix using its JSON compatibility or its MongoDB driver:

Example: Accelerating a query combining SQL with NoSQL data (in a JSON collection) using IWA’s view support

create table sqldoc1( name varchar(10), value integer ); insert into sqldoc1 values ("John", 1); insert into sqldoc1 values ("Scott", 2);

Create a view for the NoSQL data:

– Run the accelerated query on the JSON collection data:

create table doc1 ( c1 serial, data BSON );

Insert into doc1(c1,data) values(0, '{ fname:"John", lname:"Miller", age:21, address: { street:"Informix ave" } }'::JSON );Insert into doc1(c1,data) values(0, '{ fname:"Scott", lname:"Lashley", age:21.50, address: { street:"Blazer ave" }, job:"Ref" }'::JSON );

create view viewdoc1 (c1,fname,lname,age,street) asSELECT c1, bson_value_lvarchar(data,"fname"), bson_value_lvarchar(data,"lname"), bson_value_double(data,"age"),bson_value_lvarchar(data,"address.street")FROM doc1;

> select * from viewdoc1;c1 1fname John lname Miller age 21.00000000000street Informix ave

c1 2fname Scott lname Lashley age 21.50000000000street Blazer ave

2 row(s) retrieved.


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IWA data mart view support to accelerate SQL-NoSQL query (2)

Probe the query we want to accelerate– Use workload analysis to find datamart for a query

joining SQL table with NoSQL data (JSON collection)

– Deploy datamart proposed:

Example: Accelerating a query combining SQL with NoSQL data

select a.value,a.name,b.* from sqldoc1 a, viewdoc1 b where a.value=b.c1

Run the query with acceleration. Works fine:

... <mart name="dm_sqlnosql1"> <table name="sqldoc1" schema="root" isFactTable="true"> <column name="name"/> <column name="value"/> </table> <table name="viewdoc1" schema="root" isFactTable="false"> <column name="age"/> <column name="c1"/> <column name="fname"/> <column name="lname"/> <column name="street"/> </table> <reference referenceType="LEFTOUTER" isRuntimeJoin="true" parentCardinality="n" dependentCardinality="n" dependentTableSchema="root" dependentTableName="sqldoc1" parentTableSchema="root" parentTableName="viewdoc1"> <parentColumn name="c1"/> <dependentColumn name="value"/> </reference> </mart></dwa:martModel>

set environment use_dwa '3';Environment set.

select a.value,a.name,b.* from sqldoc1 a, viewdoc1 bwhere a.value=b.c1 ;

value 1name Johnc1 1fname John lname Miller age 21.00000000000street Informix ave

value 2name Scottc1 2fname Scott lname Lashley age 21.50000000000street Blazer ave

Online.log:

01:22:11 SQDWA: select a.value,a.name,b.* from sqldoc1 a, viewdoc1 b where a.value=b.c01:22:11 SQDWA: Identified 1 candidate AQTs for matching01:22:11 SQDWA: matched: aqt46221ac7-8eae-4b9a-9275-bd00dcca357c01:22:11 SQDWA: matching successful (17 msec) aqt46221ac7-8eae-4b9a-9275-bd00dcca357c01:22:11 SQDWA: offloading successful (3036 msec)


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Use workload analysis to find best IWA data mart to accelerate these 3 queries:

– Self-Join using a view (v1) on the table (employee):

– Query on a view to filter rows or columns on the table:

– Query on the base table:

IWA data mart supports of views and usage for Self Join queries (1)

Create 2 views on a base table:– One of the views (v1) will be used to implement a self

join of table ‘employee’ with itself.

create table "root".employee ( employeeid integer, lastname varchar(20), country varchar(20), departmentid integer );

create view "root".v1 (employeeid,lastname,country,departmentid) as select x0.employeeid ,x0.lastname ,x0.country ,x0.departmentid from "root".employee x0 ; create view "root".v2 (employeeid,lastname,country,departmentid) as select x0.employeeid ,x0.lastname ,x0.country ,x0.departmentid from "root".employee x0 where (x0.country = 'United States' ) ;

Example: Accelerating a self-join query, and queries on a table and a given view

SELECT Employee.EmployeeID, Employee.LastName, Employee.EmployeeID, v1.LastName,

Employee.Country FROM Employee INNER JOIN v1 -- self-joinON Employee.Country = v1.Country;

SELECT EmployeeID, LastName, EmployeeID FROM v2;

SELECT EmployeeID FROM Employee;


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Deploy IWA data mart produced– Contains the base table (employee), views (v1) and

(v2), and self-join column relationship (employee, v1)

Run the queries enabling Acceleration:

– Run the accelerated query using view v1 for self-join:

– Run accelerated queries on the view v2 and base table alone:

Example: Accelerating a Self-Join query using Views

<?xml version="1.0" encoding="UTF-8" ?><dwa:martModel xmlns:dwa="http://www.ibm.com/xmlns/prod/dwa" version="1.0"> <mart name="dm_empv1"> <table name="employee" schema="root" isFactTable="true"> <column name="country"/> <column name="employeeid"/> <column name="lastname"/> </table> <table name="v1" schema="root" isFactTable="false"> <column name="country"/> <column name="lastname"/> </table> <table name="v2" schema="root" isFactTable="true"> <column name="employeeid"/> <column name="lastname"/> </table> <reference referenceType="LEFTOUTER" isRuntimeJoin="true" parentCardinality="n" dependentCardinality="n" dependentTableSchema="root" dependentTableName="employee" parentTableSchema="root" parentTableName="v1"> <parentColumn name="country"/> <dependentColumn name="country"/> </reference> </mart></dwa:martModel>

SET ENVIRONMENT use_dwa ‘3’;

SELECT Employee.EmployeeID, Employee.LastName, Employee.EmployeeID, v1.LastName,

Employee.Country FROM Employee INNER JOIN v1 -- self-joinON Employee.Country = v1.Country;





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From Informix:

– Run the accelerated query using view v1 for self-join:

– Run accelerated queries on the view v2 and base table alone:

Example: Accelerating a Self-Join query using Views


SELECT Employee.EmployeeID, Employee.LastName, Employee.EmployeeID, v1.LastName, Employee.Country

FROM Employee INNER JOIN v1 -- self-joinON Employee.Country = v1.Country;





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Date post:	14-May-2015
Category:	Data & Analytics
Upload:	ibm-sverige
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Informix warehouse accelerator update

Data & Analytics