© 2014 ibm corporation simply powerful – informix 12.10.xc1 & xc2 - recap scott pickett ww...

© 2014 IBM Corporation

Simply Powerful – Informix 12.10.xC1 & xC2 - Recap

Scott PickettWW Informix Technical SalesFor questions about this presentation, email to: [email protected]

mailto:[email protected]

Agenda

New Packaging & Pricing Table Compression features Index Compression features New SQL Informix Warehouse Accelerator Time Series HA, ER, SDS, Flexible Grid, Connection Manager

© 2014 IBM Corporation2

Informix Packaging & Pricing Changes


Informix Edition Comparison

* Limits are hard-coded

Components Developer Innovator-C Express WorkgroupAdvanced Workgroup Enterprise

Enterprise Hypervisor

Advanced Enterprise

Pricing Metric Free FreeAUSI, PVULUVS

AUSI, PVULU Socket

PVU AUSI, PVU PVU PVU

Processor/CPU VP Limit

1 core 1 core* 4 cores* 16 cores* 16 cores* Unlimited Unlimited Unlimited

Install Memory Limit 1 GB 2 GB* 8 GB* 16 GB* 16 GB* Unlimited Unlimited Unlimited

Install Data Limit 8 GB 8 GB* Unlimited Unlimited Unlimited Unlimited Unlimited Unlimited

Warehouse Accelerator Memory Limit

N/A N/A 48 GB N/A N/A Unlimited

Replication Limits Unlimited 2 root nodes*

Unlimited Unlimited Unlimited Unlimited Unlimited

HDR/SDS/RSS Limits Unlimited 1 secondary*

2 secondary*

2 secondary*

Unlimited Unlimited Unlimited

Other Limits Parallel Operations*,Distributed Operations,Private Memory Cache for VP,HPL, Compression*,Partitioning*

Parallel Operations*,Distributed Operations,Private Memory Cache for VP,HPL, Compression*,Partitioning*



Platforms

*Note:UNIX is AIX, HP-UX, Solaris

Linux*UNIXWindowsMac-OS




Linux*UNIX

Linux*UNIXWindows Mac-OS

Linux RHELAIX Power

Linux*UNIX

IWA Platform 64-bit Linux 64-bit Linux


Informix Product Features by Edition

Features Developer Innovator-C Express WorkgroupAdvanced

Workgroup EnterpriseEnterprise Hypervisor

Advanced Enterprise

Parallel Operations

Data Partitioning

Private Memory Cache VP

High Performance Loader

*Table, Index and Blob Compression Option Option

Distributed Operations

Enterprise Replication

High Availability Clusters

Shared Disk Secondary

Remote Stand-alone

Updatable Secondary

Informix Warehouse Accelerator

TimeSeries & Spatial Capabilities

Continuous Log Restore

SQL/Instance Administration

Open Administration Tool

Label Based Access Control

Web Feature Services

Column-level Encryption

Virtual Table Interface

DBSpace Prioritization

Direct I/O – Cooked File

Point-in-time Table Restore

Primary Storage Manager

Auto Statistics for Index Build

Last Commit Isolation

Multiple Triggers

Recovery Time Policy


Bundle Additions

SPSS and Cognos– Includes bundling SPSS Modeler V15.0 FP 1 Limited– Includes bundling SPSS Statistics V16.0 1 Limited– Includes bundling 5 Cognos BI 10.2 licenses– Strengthens BI + Predictive Analytics value proposition

• SPSS will be included in any new Informix Advanced Enterprise Edition• Cognos BI will be included for Advanced Growth and Advanced Enterprise Editions

IBM Mobile DB & IBM Mobile Sync – Includes bundling IBM Mobile Database 7.0.1– Includes bundling IBM Mobile Database Sync 1.0– Strengthens Mobile value proposition

• Mobile DB and Sync will be included in all for fee Informix Editions• Help propagate development of mobile applications on Informix and for use with

mobile analytics and BI applications• These were added to 12.10.xC1 and xC2, discontinued permanently starting in xC3.


Auto Compression


Text/Byte Compression

Compression of in-partition data stored in Partition Blobs.

– Partition Blobs are simple large objects (mostly text and byte data types) in which the data is stored outside the row, but in same partition of the same dbspace as the row data.

• So even though the blob data is stored on a separate page from the row data, the partition blob page still belongs to the same partition.


Auto Compression - Overview

Create a compression dictionary for the data rows of a table automatically when it has the required numbers of rows.

Benefits of automatic compression:– Informix compresses data rows as the data is loaded.– Informix sets compression as a property of the table, so any new fragments

added also get compressed automatically.– You use current SQL admin API commands to set a table or fragment for auto

compression.– You have an SQL interface to create a compressed table.


Auto Compression Details

New data will be loaded as compressed.

A minimum of 2000 rows are needed for the compression dictionary to be created.

You can start compression with the same SQL admin API commands that you currently use.

You can also compress a table, using SQL syntax when you create the table.


Auto Compression How To

Using Admin API

– Execute function task(“table compress”, “my_table”, “my_database”);– Execute function task(“fragment compress”, “my_fragid”);

– Even if there are not enough rows to sample and create a compression dictionary, the SQL admin commands will succeed indicating “Auto compression is set”.


Auto Compression - How to

Using SQL

– Create table my_table on (my_col ….) compressed;

– After the table is created, as data is loaded into the table, a compression dictionary will be created when 2000 rows are inserted.


Auto Compression

When a table load uses light append, a dictionary will be automatically created: – The rows that loaded before the dictionary was created will be compressed.

During a normal insert, the new rows inserted will be compressed:– Data rows already in the table (before the compress) will not be compressed.

Auto compression is not supported for indexes and blobs.


Questions


Index Compression


Leaf LeafLeafLeafLeafLeafLeafLeaf LeafLeaf

TwigTwigTwigTwig

Node Node

Root

Basic BTree


create index ix on t1 (last_name, first_name, zipcode)

Key RidList

Smith George 12345Smith Harold 12345Smith Harold 33445

0x400004, 0x256,D0x400004, 0x257,D0x500008, 0x256,D

Key RidList

Token, George, TokenToken , Token, TokenToken , Token, 33445

0x400004, 0x256,D0x400004, 0x257,D0x500008, 0x256,D

Keys get compressed tomake what gets storedlook something like this.


Index Compression Restrictions

Normal detached BTree indexes only Native types No functional indexes Only the leaf level is compressed Can be done as part of index creation or to existing indexes while

online Must contain at least 2000 unique key values Full HDR support

– Log the uncompressed key Uncompress not available at this time for indexes


SQL Syntax

CREATE INDEX idxname ON tabname (column_name1, …) COMPRESSEDEXTENT SIZE # NEXT SIZE #;

EXECUTE FUNCTION sysadmin:task (‘index compress’, ‘index_name’, ‘database’);


Index Compression Benefits

More index data fits into the bufferpool– Reduced I/O

Unlike Oracle, SQL Server, and even DB2, Informix index compression supports composite indexes with either “prefix” and “suffix” keys– Prefix – unique value is last value of composite

• Ex) Red, Honda, Civic, Oregon, VIN #– Suffix – unique value is first value of composite (key only scan)

• Ex) VIN #, Red, Honda, Civic, Oregon


Indexing Varchars vs Indexing Compressed Chars

Varchars are used to save space, in both the data row and the index, by storing only the relevant data and not padding with white space

We did an experiment to compare key-only scan performance of indexed varchars against indexed compressed chars


Varchars vs Compressed Chars

create table people (first_name varchar(64),middle_name varchar(64),last_name varchar(64),address varchar(255),city varchar(64),state char(2),sex varchar(6),ssn char(11),eye_color varchar(24),ancestry varchar(64));


Varchars vs Compressed Chars

select count(*) from people where last_name in ('SMITH', 'JOHNSON', 'WILLIAMS', 'BROWN', 'JONES') AND first_name in ('JAMES', 'JOHN', 'ROBERT', 'MARY', 'PATRICIA', 'LINDA');

Key only scan

create unique index ssn_people on people(ssn);

create index ix1_people on people(last_name, first_name, middle_name, ssn);


Query Results (Several Million Rows)Varchar Uncompressed

Char% better Compressed Char % better

Unbuffered 100s 5.38s 1758% 1.9s 183%

Buffered 6.4s 0.77s 731% 0.77s 0%

# of index nodes 514748 2857165 634095


Why Is Performance Better?

Varchars require IDS to read the data row even if it appears that a key only scan could be performed– Have to compare white space

• White space stored in row• White space removed from index key


Varchar query plan


Char query plan


Varchar index (oncheck –pT)


Char index (oncheck –pT)


Char index compressed (oncheck –pT)


Results

So to look at the earlier slide again:

In both cases, the queries were faster on the compressed character column. Results were not so good on the uncompressed varchar column, due to the byte by byte comparison earlier mentioned.

Index usage: there were almost 120,000 more index nodes on the character (634095) than there were on the varchar (514748).

Here a compressed character column index, loses almost 78% of index nodes (634095 vs uncompressed char 2857165); and it is faster performance wise.


Index Compression

Compress an existing index:– execute function task(“index compress”, “j”, “testdb”);

Repack and shrink an existing index:– execute function task(“index repack shrink”, “j”, “testdb”);

Estimate Compression on an index:– execute function task(“index estimate_compression”,“176_576”,

“stores_demo”, “ifxjson”);– You can estimate compression only for a detached B-tree index on a

fragmented or non-fragmented table.

Pushes all nodes forward on the same page as they are found and compresses them, leaving free space at the end.

Repack consolidates partially filled index pages and fills them, leaving empty pages behind; repack if you intend to shrink.

Shrink reduces an index overall size by returning empty pages to its dbspace; we will not shrink beyond the index first extent size.


Some index repack and shrink results, before and after. An Index before compression, repack and shrink, 20480 pages,

oncheck –Pt output.

After: 2778 Total Pages Allocated, 86.43 % reduction 15.57 % of index Space remains.


Before:

After:

New SQL


Minus, Plus & Intersect Support - Functionality

New Extensions to the existing UNION/UNION ALL SET operation.

Same rules of UNION also applies e.g.

Both query block should have exact same number of columns.

Comparable data types.

Projection clause will not have BYTE or TEXT.


Minus, Plus & Intersect Support - Functionality

Order by should be at the end.

Precedence will be from left to right, unless they are grouped using parentheses.

There are existing restrictions for UNION and they will apply to these operators too.


Intersect and Minus/Except

Intersect returns only those rows which are returned by both queries and results will be distinct.

Minus/Except will returns distinct rows from the left query which are not found in the right side query.

The All keyword is not available for the intersect and minus/except operations.

NULL friendly, means when comparing NULL to NULL they are considered equal.


Minus, Plus & Intersect Support - Examples

create table t1 (col1 int); insert into t1 values (1); insert into t1 values (2); insert into t1 values (2); insert into t1 values (2); insert into t1 values (3); insert into t1 values (4); insert into t1 values (4); insert into t1 values (NULL); insert into t1 values (NULL); insert into t1 values (NULL);

create table t2 (col1 int); insert into t2 values (1); insert into t2 values (3); insert into t2 values (4); insert into t2 values (4); insert into t2 values (NULL);


Minus, Plus & Intersect Support - Examples (cont’d)

Select * from t1 intersect select * from t2;– Returns NULL, 1,3 and 4

Select * from t1 minus/except select * from t2;– Returns 2


Minus, Plus & Intersect Support - Rewrite

Select col1, col2 from t1 intersect select col1, col2 from t2;

Rewrite:

Select distinct * from (select col1, col2 from t1) t11 where exist (select col1, col2 from (select * from t2) t22 where

t11.col1 = t22.col1 and t11.col2 = t22.col2 );


Minus, Plus & Intersect Support - Minus/Except Rewrite

select col1, col2 from t1 minus select col1, col2 from t2;

select distinct * from (select col1, col2 from t1) t11 where not exists (select * from (select col1, col2 from t2) t22 where t11.col1 = t22.col1 and t11.col2 and t11.col2= t22.col2);


Select ….. INTO TABLE

You can now create persistent tables in IDS that can be accessed across sessions using the result set of a SELECT statement.

Combines the functionality of CREATE TABLE and INSERT into a single statement.

Supports all the Storage options of CREATE TABLE on IDS.

You can now change the EXTENT SIZE and NEXT SIZE of an existing table without having to unload the table first.

Allows you to create new schema from existing tables and populate it with only a subset of data for testing purposes.

Supported in a Flexible Grid environment.

Functionality supported via two different syntax enhancements:

– SELECT col1, col2 FROM sourcetab INTO newtab;– CREATE TABLE newtab AS SELECT col1, col2 FROM sourcetab;


4GB Stored Procedure Language

The maximum length of SQL statements and SPL routines is now 4 GB– The actual limit may depend on the available memory on the system.

Valid for Client SDK 3.70.xC5, JDBC 3.70.xC5 or higher.

Transient extended types, collection types are limited to 65535

sysadmin task command length is limited to 2020 (slot table has 28 bytes required).

Perhaps writing SQL or SPL possibly so large should be avoided for programmer maintenance issues …..


CASE Statement in SPL

CASE statement without else clause CASE mod(r,314) WHEN 1 THEN select tabid into r from informix.systables where tabid = 1; WHEN 313 THEN LET r = r + 4; WHEN 3 THEN LET r = r + 3;END CASE;

CASE statement with else clause

CASE r WHEN 317 THEN select tabid into r from informix.systables where tabid = 1; WHEN 312 THEN LET r = r + 4; WHEN 3 THEN LET r = r + 3; ELSE LET r = 1;END CASE;


New Mathematical Functions – Oracle Compatibility

SIGN(num)– Returns the sign of the input numeric value (returns integer):

• -1 for n < 0, 0 for n = 0, and 1 for n > 0 COSH(num)

– Returns hyperbolic cosine (returns float) SINH(num)

– Returns hyperbolic sine (returns float) TANH(num)

– Returns hyperbolic tangent (returns float) ACOSH(num)

– Returns arc hyperbolic cosine (returns float) ASINH(num)

– Returns arc hyperbolic sine (returns float) ATANH(num)

– Returns arc hyperbolic tangent (returns float) LN(num)

– Alias for existing LOGN(num) function (returns float)


Multiple distinct Operators Example

– Legal syntax• Applied to each predicate

– Illegal syntax • Nested operators

select avg(unique col1), count (distinct col2) from my_tab;

select distinct avg(distinct col1) from my_tab;


SQL Enhancements – nulls first / last Example

Default null sort order with the asc keyword

Modified null sort order with the asc nulls last keywords


SQL Enhancements – nulls first / last Example

Default null sort order with the desc keyword

Modified null sort order with the desc nulls first keywords


SQL Enhancements – Windows XML Enhancement

The XML4C and XSLT4C libraries have been ported to Windows 64 enabling XML functionality on that port.


Informix Genero v2.50 - Features

Attachable dialogs & forms UTF-8 support GAS/GWC

– MS Silverlight support for native Genero constructs– HTML5 theme improvements:

• Compatible with GDC (splitters, tabbed MDI, etc.)• Strong dependency on HTML5 standard• Desktop integration, file management, clipboard• Frontcalls to hardware: cameras, etc.• Windows 8 Metro tablets• Windows Phone 8 native browser support

SSO – Security– Extensible framework based on web services:

• Authentication• Authorization (OpenID, SAML)

Web services – WS Security


51

Primary Storage Manager (PSM)Faster and more functional than ISM

Easy way to configure OnBar to take backups and restores Easy way for embedded solutions to take parallel backups

using OnBar Easy way for Ontape customers to migrate to OnBar Replaces ISM

IS M P S M

N um be r o f P ro gra m F ile s 46 2

S ize o f p ro g ra m s s h ippe d w ith ID S 25 M B 2 M B

N um be r o f da e m o n pro gra m s 9 0

S ta rtup S te ps 40 2

M a x im um N um be r o f pa ra lle l s tre a m s

4 U n lim ite d

M a x im um tra ns fe r buff e r s ize 64K 2G B

In te gra te d w ith D a ta ba s e S c he du le r N o Y e s


Informix Warehouse Accelerator - Cluster Environment IWA now has the ability to create an accelerator server across

multiple physical servers as a cluster– An MPP implementation– Enables the creation of significantly larger accelerators and marts

• No longer limited by the amount of memory a single server can support


SQL

Results


TC

P/IP


Informix: Routes SQL queries to accelerator User need not change SQL or apps. Can always run query in Informix,

e.g., if– too short an est. execution time

Bulk Loader

SQL Queries (from apps)


Compressed DB partition

QueryProcessor

Data Warehouse

Informix SQL

(via DRDA)

Query Router

Informix Warehouse Accelerator: Connects to Informix via TCP/IP & DRDA Analyzes, compresses, and loads

Copy of (portion of) warehouse Processes routed SQL query and

returns answer to Informix

Results



Partition Update: Example


Refreshing Data on IWA – Multiple Options

Methods Frequency of Deployment Considerations

Complete Load Nightly or a small number of times per day

For initial load and for periodic complete refresh of mart

Automatic or manual Partition Update

Hourly, nightly or on-demand

For partitioned FACT table

For non-partitioned or partitioned dimension tables

Trickle Feed Continuous For Inserts (Append) to FACT table and I/U/D for dimension tables

Complete reload periodically following Trickle Feed



Partition Update: Refresh data in a data mart at partition level– Provides efficient refresh of data mart data

– Enables fast refresh of fact tables which are usually very large• Dimension table (usually very small) full load refresh is very fast

– Refresh can be done on• The entire table or

• The partition (or subset) that contains data updates


Informix Warehouse Accelerator – Trickle Feed The source database can now be configured to push new data into

the marts every N seconds without any further administrative intervention– Inserts only, deleted / modified fact data is not pushed into the mart

• See next slide for more details– Is controlled by the ifx_setupTrickleFeed() function

• Available in the sysadmin database– In an H/A cluster, this function can only be executed on the cluster primary

Syntax

– Whereaccel_name – the name of the accelerator which supports the martmart_name – the name of the data martbuffer_time – the number of seconds between refreshes

1 – 8639999 (2,400 hours) – refresh both the fact and dimension tables-1 – -8639999 (2,400 hours) – refresh just the fact table

To use this feature, MAX_PDQPRIORITY in the source instance must be set >= 50 for the initial data load and all subsequent loads

ifx_setupTrickleFeed(‘accel_name’, ‘mart_name’, buffer_time )



New SQL Support for query acceleration– Several SQL functions and expressions now support query acceleration.

– Example:• COUNT (DISTINCT CASE …)

• Multiple DISTINCT with aggregates, such as COUNT (DISTINCT…), in a query

• LEN / LENGTH

• SUBSTR / SUBSTRING

• TODAY

• TRIM

• YEAR

• UNITS

• UNION, UNION ALL• RANK, DENSE_RANK, DENSERANK, CUME_DIST, PERCENT_RANK, NTILE,

ROW_NUMBER, ROWNUMBER, FIRST, LAST, RATIO_TO_REPORT, RATIOTOREPORT



Additional platform support with Solaris Intel/AMD

Client

Informix database server

Optimizer

Use Accelerator

?

SQL query

Result set

No


Yes

Linux Intel x86 64-bit

Data Warehouse or mixed OLTP/Warehouse system

TCP/IP

AIX 64-bitHP IA 64-bit Solaris SPARC 64-bitLinux Intel x86 64-bitSolaris x86 64-bit (new support)

W Accelerator supported on Linux Intel/AMD 64-bit.

W Informix Server for the accelerator supported on the 64-bit Aix, HP IA, Linux Intel x86, Solaris Sparc and Solaris x86.

W Adds more cross platform integration.



Using the accelerator with secondary servers: Example


New OLAP Functions

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

Numbering functions– ROW_NUMBER, ROWNUMBER

Aggregate functions– SUM, COUNT, AVG, MIN, MAX– STDEV, VARIANCE, RANGE

FIRST_VALUE/LAST_VALUE functions– FIRST_VALUE, LAST_VALUE

LEAD/LAG functions– LEAD, LAG

RATIO_TO_REPORT function– RATIO_TO_REPORT, RATIOTOREPORT

All of these are accelerated by IWA


Example - Ranking functions

Order by clause is required Window clause is not allowed Duplicate value handling is different between rank() and

dense_rank()– rank() - assigns the next relative ordinal to the next result set value– dense_rank() - assigns the next absolute ordinal to the next result set value

(alias is denserank())


Rank Example

select emp_num, sales, rank() over (order by sales) as rnk, dense_rank() over (order by sales) as dense_rnk from sales;

emp_num sales rnk dense_rnk

101 2,000 1 1 102 2,400 2 2 103 2,400 2 2 104 2,500 4 3 105 2,500 4 3 106 2,650 6 4


Top-N Rank

Use a derived table to obtain top-n rank

select * from (select name, salary rank() over (order by salary desc) as rnk from employee) where rnk <= 5;


Rank in Sports Stats – All Time Baseball Home Runs


Rank Player Home Runs

….

15 Mike Schmidt 548

16 Mickey Mantle 536

17 Jimmy Foxx 534

18 Willie McCovey 521

18 Frank Thomas 521

18 Ted Williams 521

21 Ernie Banks 512

21 Eddie Matthews 512

23 Mel Ott 511

….

26 Albert Pujols 502

Current Home Run Hitters list All Time in Major League Baseball (partial)– Presently, the player Albert

Pujols is active with 502 home runs (as of 05-13-2014) and stands a chance at age 33 to surpass many of the names on this list.

– When he hits his 513th home run, he will rise into 21st place alone, possibly this year.

– The current occupants of 21st place will then be in 22nd place and forever still tied.

Rank in Sports Stats – All Time Women’s Soccer Goals

Rank Name Goals

1 Abby Wambach 165

2 Mia Hamm 158

3 Christine Sinclair 147

4 Kristine Lilly 130

5 Birgiz Prinz 128

6 Julie Fleeting 120

7 Elisabetta Vignotte 107

8 Sun Wen 106

9 Carolina Morace 105

9 Michelle Akers 105

11 Han Duan 101

12 Tiffeny Milbrett 100


Presently, neither Morace nor Akers are active players.

Time Series


Time Series - Write your own program to load data faster

Loader API included in the product Loader APIs can directly be used in SQLs in application Pre-requisites:

– The name of table and TimeSeries column should be in lower case– Primary key is defined for each row– If primary key is CHAR(n), each value should be n bytes long OR

user VARCHAR(n)– Data to be loaded should be compatible to TimeSeries data type– Data must be compatible to Informix data types


Loader API SQL Functions

TSL_Init: Initialize the global and session environments for a specific table and TimeSeries column

TSL_Attach: Initialize a session environment

TSL_SetLogMode: Messages verbosity and destination of messages

TSL_GetLogMessage: Get the generated messages if they were put on a queue

TSL_GetKeyContainer: Return the container used by a specific TimeSeries

TSL_Put: Buffer the records from a lvarchar or a CLOB

TSL_Flush: Flush buffers into the TimeSeries

TSL_SessionClose: Close the loader session

TSL_Shutdown: Close the global environment


IBM Informix TimeSeries Plug-in for Data Studio

Eclipse-based Tools for Working with TimeSeries Data– Enables rapidly loading TimeSeries data– Custom file formats are easily created using graphical interface

New Features– Loading from another database product through JDBC

• Enables extract-load without intermediary files– Non-GUI tooling execution

• Enables automation in production environments


Loading TimeSeries Data from a Database

Select the connection profile for the source database– Defined using New → Database Connection Profile

Specify a query that results in the data to be loaded

A preview of the record structure that results from the query can be seen by clicking the “Preview” button


TS Relational Views in Centaurus

Improvements:– Implement new VTI capabilities to push the predicates down to the

implementation.– Process the new (recursive) predicates in the implementation to push to the

base table

Items in blue are now pushed down:SELECT id, col2, tstamp, value FROM test_tab_vWHERE udr1(id,3,7) = 1AND udr1(id,col2,7) = 1AND (id+1) = 4 ← id = 3 would be pushed downAND udr2(id)-- Now on TS columnAND udr3(value, 2, 3) > 4AND udr4(value)AND (value + 1) > 1.02-- Now on the timestamp as beforeAND tstamp BETWEEN datetime(2010-11-10 01:00:00.00000) year to fraction(5)AND datetime(2010-11-10 03:00:00.00000) year to fraction(5)


Rolling Window Containers

Informix 12.10 adds rolling window containers– Easy control of date driven roll-on/roll-off partitioning– Instant space management – no more slow deletes– Allows active/dormant window control– Windows based on units of a day, a week, a month or a year

Jan Feb Mar Apr May Jun

Jan Jul

Jan Feb Aug

Feb Mar Sep

Dormant Active

6 Month Rolling Window

Feb Mar Apr May Jun

JulMar Apr May Jun

AugJulApr May JunJan

Deleted

Jul

Aug

Sep

Oct

New


HA, SDS, CM, ER & Flexible Grid Enhancements

SDS_ALTERNATE – Parameter allows SDS network communication with a primary in case of primary server failure via smart blobspace.

ER works directly with Storage Provisioning feature

cdr check queue can be used to monitor transaction queues within ER. Supports reporting on the control, send and receive queues

Informix 12.10 introduces the S (send-only) participant type– Can be used in the cdr define replicate command

•In this case g_er1 has a S participant qualifier indicating the replicate can only send data changes from

this replicate, it can not receive updates g_er2 and g_er3 don’t have a participant qualifier indicating the replicate is R/W in these

instances


cdr define repl -c g_er1 -C always -f y price_book \“S stores@g_er1:informix.my_prices" "select * from my_prices where prod_code > 500" \“stores@g_er2:informix.my_prices" "select * from my_prices" \“stores@g_er3:informix.my_prices" "select * from my_prices"

Flexible Grid and Flat File Transfers

Informix 12.10 introduces the capability of replicating flat files to instances in a cluster– Uses the ifx_grid_copy() function to read from / write to a specific directory

on servers• Configured with GRIDCOPY_DIR parameter

Must be $INFORMIXDIR or subdirectory thereof Does not have to be identical across the cluster

The ifx_grid_copy() operation can specify different source and target locations for the files– Can read from $INFORMIXDIR/scripts_repository but send to

$INFORMIXDIR/custom_scripts on all the targets

76 © 2014 IBM Corporation

Grid Queries


SQL Enhancements – Grid Queries In a Flexible Grid environment, it is possible for a cluster of

instances to contain the same dbspaces and tables but no replicated data– Common in retail and or distribution environments where stores / warehouses

have different SKUs (products) and inventory levels

Currently, to gather data from multiple instances requires executing multiple SQL operations into a temporary table or joining the results

Informix 12.10 introduces the grid query operation which, when executed from an administrative node, will gather data from the requested nodes with a single SQL statement


SQL Enhancements – Grid Queries

Grid queries can be executed against the entire cluster or against a subset of the instances – These subsets are defined as “regions” but have nothing to do with geography

The cdr define region command is used to define regions– Syntax

region_name must be unique across all clusters

• Will be used by SQL statements and cdr commands so have to be able to resolve to the correct region

list_of_instances is a whitespace separated list of server group names

cdr define region [--grid | -g ]=grid_name region_name [list_of_instances]


SQL Enhancements – Grid Queries Example

– Instances can be members of more than one region if needed– Regions can contain a subset of instances of another region

• The great_west region can contain g_inst_1, g_inst_2, g_inst_3, g_inst_4• The pac_coast region can contain g_inst_1, g_inst_3

cdr define region –g=my_grid region_1 g_inst_1 g_inst_2;

cdr define region -–grid=my_grid region_2 g_inst_3 g_inst_4;



Regions can not be modified, have to drop and recreate– Use the cdr delete region command

cdr delete region region_1;



After defining any regions that are needed, next step to executing grid queries is to verify the consistency of the tables across the grid– Ensures the table(s) schemas are identical across the grid

• All the columns and data types match so a consistent response to the query can occur

– Prevents table alters from occurring unless executed through a grid operation• Connect to the grid, execute alter, disconnect from the grid



Examples– Notice long and short versions of the commands are used

Add all ds2 database tables

Delete the customer table from the gridtables list

Add the my_tab_1 and my_tab_2 tables to the gridtables list

cdr change gridtable –g=my_grid -D=ds2 -a –all;

cdr change gridtable –g=my_grid -D=ds2 –d customer;

cdr change gridtable –-grid=my_grid --database=ds2 --add my_tab_1 my_tab_2



With any regions defined, and tables added to the gridtable list, can now execute grid queries

Restrictions– Only query operations are supported

– Queries must “simple”, can not contain subqueries, joins, unions or the intersect, minus, or except qualifiers

• The grid query itself *can* be a subquery nested inside another query though

– Queries can only be executed against “normal” tables, not against views, synonyms or external tables

• Exception is sysmaster tables which are allowed

– Queries can only be executed against data types that are supported by distributed operations

• Excludes TimeSeries and any extended types that can’t be used in distributed operations



New syntax to the select operation – the grid clause– Syntax at the statement level

– The optional all keyword determines whether or not all matching rows are returned ( a union all qualifier) from all instances or just the unique values (a basic union qualifier)

• Default is union or unique values

select column_names from table_names grid [all] [‘region_name’ | ‘grid_name’] where . . . . .


SQL Enhancements – Grid Queries New syntax to the select operation – at the session level

– Rather than modifying existing statements, the scope and union qualifier can be set at the session level

• With the set environment select_grid operation

Use the default union operator on the grid queries

Use the union all operator on the grid queries

set environment select_grid [‘grid_name’ | ‘region_name’]

set environment select_grid_all [‘grid_name’ | ‘region_name’]



New syntax to the select operation – at the session level (cont.)

– Once set, all queries will execute as grid queries

– Only one default setting can be active at a time• To change from one setting to the other re-execute the set environment command

– These settings can be changed for any individual statement(s) by including a grid clause within the statement


SQL Enhancements – Grid Queries To turn off grid queries

– Use the option that matches the command enabling grid queries

– Individual statement(s) can still execute as a grid query by including the grid clause in the statement(s)

set environment select_grid default;

set environment select_grid_all default;



But what happens if a node with the grid / region is not available when the grid query executes?– It depends, can either

• Abort the query and return an error• Execute the query with the available nodes

Can query later to see how many and which nodes didn’t participate

Set at a session level with the set environment ifx_node_skip operation


SQL Enhancements – Grid Queries Syntax

– Wheredefault | off – the default setting. Query is aborted and an error returned

on – rows are returned from the nodes available at the time the query was executed

When the environment is set to on, any skipped nodes are captured and can be returned to the calling session– Important – this information is gathered on a statement-by-statement basis

and only lasts until the next grid query is executed in that session

set environment ifx_node_skip [ default | off | on ]



To determine how many nodes, if any, were skipped and their names

Returns an integer value with the number of skipped nodes

Returns an lvarchar with the name of one of the skipped nodes• To get all the node names, you will need to execute this the same number of times

as returned by ifx_gridquery_skipped_nodes_count() All names aren’t returned at once to facilitate easier parsing of the result set The function operates like a fetch against a cursor

execute function ifx_gridquery_skipped_nodes_count()

execute function ifx_gridquery_skipped_nodes()


SQL Enhancements – Grid Queries Example

set environment select_grid my_region;set environment ifx_node_skip on;

select . . . . from . . . . .; {results returned}

execute function ifx_gridquery_skipped_nodes_count();

3

execute function ifx_gridquery_skipped_nodes();g_inst_2





In the event a schema change needs to be made– The change must be made through a grid operation as explained earlier

– A metadata flag will be set indicating an alter operation is in-flight• Prevents any grid queries from executing

– As tables are being altered and acknowledged as completed, a cdr remaster gridtable operation is automatically executed to re-verify consistency across the cluster

– When the cdr remaster gridtable operation returns 0 (zero), the metadata flag is removed and grid queries can be resumed

– The cdr remaster gridtable operation can be invoked by an administrator to check the status of the alter operation

• Can know when to turn on grid query applications


© 2014 ibm corporation simply powerful – informix 12.10.xc1 & xc2 - recap scott pickett ww...

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