Netezza fundamentals for developers

Netezza Fundamentals Introduction to Netezza for Application Developers

Biju Nair

03/02/2013

Version: Draft 1.4

Document provided for information purpose only.

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Preface

As with any subject, one may ask why do we need to write a new document on the subject when there is

so much information available online. This question is more pronounced especially in a case like this

where the product vendor publishes detailed documentation. I agree and for that matter this document

in no way replaces the documentation and knowledge already available on Netezza appliance. The

primary objective of this document is

To be a starting guide for anyone who is looking to understand the appliance so that they can be

productive in a short duration

To be a transition guide for professionals who are familiar with other database management

systems and would like to or need to start using the appliance

Also be a quick reference on the fundamentals for professionals who has some experience with

the appliance

With the simple objective in focus, the book covers the Netezza appliance broadly so that the reader can

be productive in using the appliance quickly. References to other documents are been provided for

interested readers to gain more thorough knowledge. Also joining the Netezza developer community on

the web which is very active is highly recommended. If you find any errors or need to provide feedback,

please notify to books at sieac dot com with the subject “Netezza” and thank you in advance for your

feedback.

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Table of Contents 1. Netezza Architecture ............................................................................................................................ 3

2. Netezza Objects .................................................................................................................................... 9

3. Netezza Security .................................................................................................................................. 16

4. Netezza Storage .................................................................................................................................. 17

5. Statistics and Query Performance ...................................................................................................... 22

6. Netezza Transactions .......................................................................................................................... 26

7. Loading Data, Database Back-up and Restores .................................................................................. 28

8. Netezza SQL ........................................................................................................................................ 33

9. Stored Procedures............................................................................................................................... 36

10. Workload Management .................................................................................................................. 47

11. Best Practices .................................................................................................................................. 51

12. Version 7 Key Features.................................................................................................................... 53

13. Further Reading .............................................................................................................................. 54

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1. Netezza Architecture Building a good foundation helps developing better and beautiful things. Similarly understanding the

Netezza architecture which is the foundation helps develop applications which uses the appliance

efficiently. This section details the architecture at a level which will satisfy the objective to help use the

appliance efficiently.

Netezza uses a proprietary architecture called Asymmetric Massively Parallel Processing (AMPP) which

combines the large data processing efficiency of Massively Parallel Processing (MPP) where nothing

(CPU, memory, storage) is shared and symmetric multiprocessing to coordinate the parallel processing.

The MPP is achieved through an array of S-Blades which are servers on its own running its own

operating systems connected to disks. While there may be other products which follow similar

architecture, one unique hardware component used by Netezza called the Database Accelerator card

attached to the S-Blades. These accelerator cards can perform some of the query processing stages while

data is being read from the disk instead of in the CPU. Moving large amount of data from the disk to the

CPU and performing all the stages of query processing in the CPU is one of the major bottlenecks in the

many of the database management systems used for data warehousing and analytics user cases.

The main hardware components of the Netezza appliance are a host which is a Linux server which can

communicate to an array of S-Blades each of which has 8 processor cores and 16 GB of RAM running

Linux operating system. Each processor in the S-Blade is connected to disks in a disk array through a

Database Accelerator card which uses FPGA technology. Host is also responsible for all the client

interactions to the appliance like handling database queries, sessions etc. along with managing the meta-

data about the objects like database, tables etc. stored in the appliance. The S-Baldes between themselves

and to the host can communicate through a custom built IP based high performance network. The

following diagram provides a high level logical schematic which will help imagine the various

components in the appliance.

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The S-Blades are also referred as Snippet Processing Array or SPA in short and each CPU in the S-

Blades combined with the Database Accelerator card attached to the CPU is referred as a Snippet

Processor.

Let us use a simple concrete example to understand the architecture. Assume an example data warehouse

for a large retail firm and one of the tables store the details about all of its 10 million customers. Also

assume that there are 25 columns in the tables and the total length of each table row is 250 bytes. In

Netezza the 10 million customer records will be stored fairly equally across all the disks available in the

disk arrays connected to the snippet processors in the S-Blades in a compressed form. When a user

query the appliance for say Customer Id, Name and State who joined the organization in a particular

period sorted by state and name the following are the high level steps how the processing will happen

The host receives the query, parses and verifies the query, creates code to be executed to by the

snippet processors in the S-Blades and passes the code for the S-Blades

The snippet processors execute the code and as part of the execution, the data block which

stores the data required to satisfy the query in compressed form from the disk attached to the

snippet processor will be read into memory. The Database Accelerator card in the snippet

processor will un-compress the data which will include all the columns in the table, then it will

remove the unwanted columns from the data which in case will be 22 columns i.e. 220 bytes out

of the 250 bytes, applies the where clause which will remove the unwanted rows from the data

and passes the small amount of the data to the CPU in the snippet processor. In traditional

databases all these steps are performed in the CPU.

The CPU in the snippet processor performs tasks like aggregation, sum, sort etc on the data

from the database accelerator card and passes the result to the host through the network.

The host consolidates the results from all the S-Blades and performs additional steps like sorting

or aggregation on the data before communicating back the final result to the client.

The key takeaways are

The Netezza has the ability to process large volume of data in parallel and the key is to make

sure that the data is distributed appropriately to leverage the massive parallel processing.

Implement designs in a way that most of the processing happens in the snippet processors;

minimize communication between snippet processors and minimal data communication to the

host.

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The simple example helps understand the fundamental components of the appliance and how they work

together, we will build on this knowledge on how complex query scenarios are handled in the relevant

sections.

Terms and Terminology

The following are some of the key terms and terminologies used in the context of Netezza appliance.

Host: A Linux server which is used by the client to interact with the appliance either natively or through

remote clients through OBDC, JDBC, OLE-DB etc. Hosts also stores the catalog of all the databases

stored in the appliance along with the meta-data of all the objects in the databases. It also parses and

verifies the queries from the clients, generates executable snippets, communicates the snippets to the S-

Blades, coordinates and consolidates the snippet execution results and communicates back to the client.

Snippet Processing Array: SPA is an array of S-Blades with 8 processor cores and 16 GB of memory

running Linux operating system. Each S-Blade is paired with Database Accelerator Card which has 8

FPGA cores and connected to disk storage.

Snippet Processor: The CPU and FPGA pair in a Snippet Processing Array is called a snippet

processor which can run a snippet which is the smallest code component generated by host for query

execution.

Netezza Objects

The following are the major object groups in Netezza. We will see the details of these objects in the

following chapters.

Users

Groups

Tables

Views

Materialized View

Synonyms

Database

Procedures and User Defined Function

For anyone who is familiar with other relational database management systems, it will be obvious that

there are no indexes, bufferpools or tablespaces to deal with in Netezza.

Netezza Failover

As an appliance Netezza includes necessary failover components to function seamlessly in the event of

any hardware issues so that its availability is more than 99.99%. There are two hosts in a cluster in all the

Netezza appliances so that if one fails the other one can takes over. Netezza uses Linux-HA (High

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Availability) and Distributed Replicated Block Device for the host cluster management and mirroring of

data between the hosts.

As far as data storage is concerned, one third of every disk in the disk array stores primary copy of user

data, a third stores mirror of the primary copy of data from another disk and another third of the disk is

used for temporary storage. In the event of disk failure the mirror copy will be used and the SPU to

which the disk in error was attached will be updated with the disk holding the mirror copy. In the event

of error in a disk track, the track will be marked as invalid and valid data will be copied from the mirror

copy on to a new track.

If there are any issues with one of the S-Blades, other S-Blades will be assigned the work load. All

failures will be notified based on event monitors defined and enables. Similar to the dual host for high

availability, the appliance also has dual power systems and all the connection between the components

like host to SPA and SPA to disk array also has a secondary. Any issues with the hardware components

can be viewed through the NZAdmin GUI tool.

Netezza Tools

There are many tools available to perform various functions against Netezza. We will look at the tools

and utilities to connect to Netezza here and other tools will be detailed in the relevant sections.

For Administrators one of the primary tools to connect to the Netezza is the NzAdmin. It is a GUI

based tool which can be installed on a Windows desktop and connect to the Netezza appliance. The tool

has a system view which it provides a visual snapshot of the state of the appliance including issues with

any hardware components. The second view the tool provides is the database view which lists all the

databases including the objects in them, users and groups currently defined, active sessions, query history

and any backup history. The database view also provides options to perform database administration

tasks like creation and management of database and database objects, users and groups.

The following is the screen shot of the system view from the NzAdmin tool.

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The following is the screen shot of the database view from the NzAdmin tool.

The second tool which is often used by anyone who has access to the appliance host is the “nzsql”

command. It is the primary tool used by administrators to create, schedule and execute scripts to

perform administration tasks against the appliance. The “nzsql” command invoke the SQL command

interpreter through which all Netezza supported SQL statements can be executed. The command also

had some inbuilt options which can be used to perform some quick look ups like list of list of databases,

users, etc. Also the command has an option to open up an operating system shell through which the user

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can perform OS tasks before exiting back into the “nzsql” session. As with all the Netezza commands,

the “nzsql” command requires the database name, users name and password to connect to a database.

For e.g.

nzsql –d testdb –u testuser –p password

Will connect and create a “nzsql” session with the database “testdb” as the user “testuser” after which

the user can execute SQL statements against the database. Also as with all the Netezza commands the

“nzsql” has the “-h” help option which displays details about the usage of the command. Once the user

is in the “nzsql” session the following are the some of the options which a user can invoke in addition to

executing Netezza SQL statements.

\c dbname user passwd Connect to a new database

\d tablename Describe a table view etc

\d{t|v|i|s|e|x} List tables\views\indexes\synonyms\temp tables\external tables

\h command Help on particular command

\i file Reads and executes queries from file

\l List all databases

\! Escape to a OS shell

\q Quit nzsql command session

\time Prints the time taken by queries and it can be switched off by \time again

One of the third party vendor tools which need to be mentioned is the Aginity Workbench for Netezza

from Aginity LLC. It is a GUI based tool which runs on Windows and uses the Netezza ODBC driver

to connect to the databases in the appliance. It is a user friendly tool for development work and adhoc

queries and also provides GUI options to perform database management tasks. It is highly

recommended for a user who doesn’t have the access to the appliance host (which will be most of the

users) but need to perform development work.

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2. Netezza Objects Netezza appliance comes out of the box loaded with some objects which we are referred to system

objects and users can create objects to develop applications which are referred as user objects. In this

section we will look into the details about the basic Netezza objects which every user of the appliance

need to be aware of.

System Objects:

Users

The appliance comes preconfigured with the following 3 user ids which can’t be modified or deleted

from the system. They are used to perform all the administration tasks and hence should be used by

restricted number of users.

User id Description

root The super user for the host system on the appliance and has all the access as a super user in any Linux system.

nz Netezza system administrator Linux account that is used to run host software on Linux

admin The default Netezza SQL database administrator user which has access to perform all database related tasks against all the databases in the appliance.

Groups

By default Netezza comes with a database group called public. All database users created in the system

are automatically added as members of this group and cannot be removed from this group. The admin

database user owns the public group and it can’t be changes. Permissions can be set to the public group

so that all the users added to the system get those permissions by default.

Databases

Netezza comes with two databases System and a model database both owned by Admin user. The

system database consists of objects like tables, views, synonyms, functions and procedures. The system

database is primarily used to catalog all user database and user object details which will be used by the

host when parsing, validating and creation of execution code for queries from the users.

User Objects:

Database

Users with the required permission or an admin user can create databases using the create database sql

statement. The following is a sample SQL to create a database called testdb and it can be executed in an

nzsql session or other query execution tool.

create database testdb;

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Table

The database owner or user with create table privilege can create tables in a database. The following is a

sample table creation statement which can be executed in an nzsql session or other query execution tool.

create table employee (

emp_id integer not null,

first_name varchar(25) not null,

last_name varchar(25) not null,

sex char(1),

dept_id integer not null,

created_dt timestamp not null,

created_by char(8) not null,

updated_dt timestamp not null,

updated_by char(8) not null,

constraint pk_employee primary key(emp_id)

constraint fk_employee foreign key (dept_id) references department(dept_id)

on update restrict on delete restrict

) distribute on random;

Anyone who is familiar with other DBMS systems the statement will look familiar except for the

“distribute on” clause details of which we will see in a later section. Also there are no storage related

details like tablespace on which the table need to be created or any bufferpool details which are handled

by the Netezza appliance. The following is the list of all the data types supported by the Netezza

appliance which can be used in the column definitions of tables.

Data Type Description/Value Storage

byteint (int1) -128 to 127 1 byte

smallint (int2) -32,768 to 32,767 2 bytes

Integer (int or int4) -35,791,394 to 35,791,394 4 bytes

bigint (int8) -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807 8 bytes

numeric(p,s) Precision p can range from 1 to 38 and scale from 0 to P p < 9 – 4 bytes 10< p<18–8 bytes 19<p<38–16 bytes

numeric Same as numeric(18,0) 4 – 16 bytes

decimal Alias to numeric 4 – 16 bytes

float(p) Floating point number with precision p between 1 & 15 p < 7 – 4 bytes 6 < p < 16 – 8bytes

real Alias for float(6) 4 bytes

double precision Alias for float(15) 8 bytes

Character(n)/char(n) Fixed length, blank padded to length n. The default value of n is 1. The maximum character string size is 64,000.

If n is equal to 16 or less then n bytes. If n is greater than 16, disk usage is the same as varchar (n).

character varying(n) /varchar(n)

Variable length to a maximum length of n. No blank padding, stored as entered. The maximum character string size is 64,000.

N+2 or fewer bytes depending on the actual data.

nchar(n) Fixed length unicode, blank padded to length n. The maximum length of 16,000 characters.

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Data Type Description/Value Storage

nvarchar(n) Variable length unicode to a maximum length of n. The maximum length of 16,000 characters.

Boolean / bool With value true (t) or false (f). 1 byte

date Ranging from January 1, 0001, to December 31, 9999. 4 bytes

timestamp Date part and a time part, with seconds stored to 6 decimal positions. Ranging from January 1, 0001 00:00:00.000000 to December 31, 9999 23:59:59.999999.

8 bytes

Refer the reference document for additional data types and details.

Other than validating the data to be inconsistent with the column data type and the not null constraint,

Netezza doesn’t enforce any of the constraints like the primary key or foreign key when inserting or

loading data into the tables for performance reasons. It is up to the application to make sure that these

constraints are satisfied by the data being loaded into the tables. Even though the constraints are not

enforced by Netezza defining them will provide additional hints to the query optimizer to generate

efficient snippet execution code which in turn helps performance.

Users can also create temporary table in Netezza which will get dropped at the end of the transaction or

session in which the temp table is created. The temporary table can be created by adding the “temporary

or temp” clause as part of the create table statement and the statement can include all the other clauses

applicable for the creation of a regular table. The following is an example of creating a temporary table

create temporary table temp_emp(

id integer constraint pk_emp primary key,

first_name varchar(25)

) distribute on hash(id);

Another way a user can create a table is to model the new table based on a query result. This can be

accomplished in Netezza using the create table as command and are referred as CTAS tables in short.

The following are some sample CTAS statements

create table ctas_emp as select * from emp where dept_id = 1;

create table ctas_emp_dept as select emp.name, dept.name from emp, dept where

emp.dept_id = dept.id;

Since the new table definition is based on the query result which is executed as part of CTAS statement,

there as many possibilities for which the users can use this like redefining the columns in a current table

while populating the new table at the same time. If the user doesn’t want to populate data but create only

the table structure a “limit 0” can be included at the end of the query as in the following example.

create table ctas_emp as select * from emp limit 0;

Tables can be deleted from the database using the DROP sql statement which drops the table and its

content. The following is an example

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drop table employee;

Once tables are created they can be modified using the alter statement. Renaming the table, changing the

owner of the table, adding or dropping a new column, renaming a column, adding or dropping a

constraint are some of the modifications which can be made through the alter statement. The following

are sample alter statements which can be executed through nzsql session or a query execution tool.

alter table employee add column education_level int1;

alter table employee modify column (first_name varchar(30));

alter table employee drop column updated_by;

alter table employee owner to hruser;

The following are some of the points to be aware of when using the alter table statement.

Modifying the column length is only application to columns defined as varchar.

If a table gets renamed the views attached to the table will stop working

Column data types can’t be changed through an alter statement

If a table is referenced by a stored procedure adding or dropping a column is not permitted. The

stored procedure need to be dropped first before adding or dropping a column and then the

stored procedure need to be recreated.

View

The database owner or user with create view privilege can create tables in a database. Netezza stores the

select SQL statement for the view and doesn’t materialize the data and store the data on disk. The SQL

gets executed pulling the data from the base table whenever a user accesses the view creating a virtual

table. The following is a sample view creation statement which can be executed in an nzsql session or

other query execution tool.

create or replace view it_employees as select * from employee where dept_id = 100;

Materialized View

Materialized views are created to project a subset of columns from a table and data sorted on some of

the projected columns. When created the system materializes and stores the sorted projection of the base

table’s data in a new table on disk. Materialized views can be queried directly or can be used to improve

performance against the base table. In the latter case, the materialized view acts like an index since the

system stores an additional column in the view with the details about from where in the base table that

data originated.

A database owner or user with create materialized view privilege can create materialized view in a

database. The following is a sample materialized view creation statement which can be executed in an

nzsql session or query execution tools.

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create materialized view employee_mview as select emp_id, first_name, last_name,

dept_id from employee order by dept_id, first_name;

This materialized view will improve the performance of the following query against the base table since

the view stores where in the base table the data satisfying the query is stored and in turn acts like an

index in a traditional database.

select * from employee where dept_id = 100 and first_name = ‘John’;

The following are the restriction in the creation of materialized views

Only one table can be specified in the FROM clause of the create statement

There can be no where clause in the select clause of the create statement

The columns in the projection list must be columns from the base table and no expressions

The columns in the ORDER BY clause should be one of the columns in the select statement

NULLS LAST or DESC cannot be used in the ORDER BY clause

External, temporary, system or clustered base tables can’t be used as base table for materialized

views

As and when records are inserted into the base table the system also adds data to the materialized view

table. But the new data getting inserted will not be in the sorted order and hence there will be some data

in the materialized data which are not in the sorted order. In order to get the materialized view in sorted

order, the views need to refreshed periodically or threshold of unsorted data as a percentage of total

records can be set so that the system performs the refresh when the percentage of unsorted records in

the table exceeds the threshold.

Synonym

A synonym is an alternate way of referencing tables and views. It allows users to create easy to type

names for long table or view names. An admin user or any user with create synonym privilege can create

synonyms in a database and the following is a sample statement to create synonym through an nzsql

session or other query execution tool.

create synonym techies for it_employees;

Synonyms can be created against for a non-existent table or view since it is not verified during creation.

But if the table or view referred by a synonym is not existent during runtime an error message will be

returned. Synonyms can’t be created for temporary tables, remote databases or other synonyms.

Sequence

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Sequences are database objects through which users can generate unique number and can then be used

in an application like creation of unique keys. The following is a sample sequence creation statement

which can be used to populate the id column in the employee table.

create sequence seq_emp_id as integer start with 1 increment by 1 minvalue 1 no

maxvalue no cycle;

Since no max value is used, the sequence will be able to hold up to the largest value of the sequence type

which in this case is 35,791,394 for integer type. Also in this case once the sequence reaches its

maximum value it doesn’t start reusing the old values since no cycle option is used to prevent reuse.

Values generated by sequences can be accessed using “next value for sequence” and following are some

examples.

select next value for seq_emp_id;

select *, next value for seq_emp_id from emp;

Few points to note about using sequences

No two users of a sequence will ever get the same value from a sequence while requesting for

next value

When a transaction using a sequence rolls back the value read from the sequence will not be

rolled back i.e. there can be gaps in the sequence number due to transaction rollbacks

Netezza appliance assigns sequence number ranges to all SPUs available in the system which gets

cached for performance reasons. That would mean the sequences generated by the various SPUs

will create gaps in the sequence numbers at any point in time since the ranges will not be over

lapping.

System will be forced flush cached values of sequences in situations like stopping of the system,

system or SPU crashes or during some alter sequence statements which will also create gaps in

the sequence number generated by a sequence.

Existing sequences can be modified using “alter sequence” statement. Some of the modifications are

changing the owner, renaming the sequence, restarting the sequence with a new start value, change the

increment value or max value and whether the sequence should or should not reuse the old values once

it has reached its max value. The following is a sample “alter sequence” statement.

alter sequence seq_emp_id increment by 2;

Also an existing sequence can be dropped using a “drop sequence” statement and the following is an

example.

drop sequence seq_emp_id;

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Procedures

We will look into the details about stored procedures when we discuss about application development.

Functions

We will look into the details about user defined functions when we discuss about application

development.

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3. Netezza Security This section deals with security in the context of user access to the appliance. In Netezza there are two

levels of access controls, one at the host OS level and second at the Netezza database level. By setting

the required access restrictions at these two levels, the appliance can be secured effectively.

OS Level Security

Netezza host uses industry standard Linux operating system customized for performance and

functionality required for the appliance. As in any Linux installations, user access restrictions need to be

put in place using user ids, user groups and passwords. Out of the box, the appliance is configured with a

“root” user id which is the Linux super user and the user id “nz” which is the Netezza system

administrator id which is used to run Netezza on the host. The “root” user id can be used to create other

user ids for users who need to access the appliance natively through the host command shell. Since the

host access is required to perform very restricted tasks primarily administration tasks, the number of user

ids created to access the appliance should be fairly small. Restrictions on what users can perform can be

set by creating Linux user groups with different access restrictions and attaching the relevant users to the

groups. Setting password selection rules like mix of alphabets, numbers, special characters, minimum

password length etc. along with password expiry for users is a good practice.

Database Level Security

Access to databases is controlled using user ids and passwords which are separate from the OS level user

id and password. If an user need to be able to access to a Netezza database natively through a “nzsql”

session on the host, the user need to use a OS level user id and password to log in to the host and then

need to invoke the “nzsql” command using the database level user id and password which has access to

the particular database of interest. Access to databases, objects with in objects and the type of activities

which can be performed on them are all controlled by the privileges granted to the user id to perform

the task. Netezza also supports user groups as with the Linux operating system where privileges can be

assigned to groups and similar users can be attached to the group so that it is easier to manage access to

databases. When a user id is attached to more than one group the user id gets combination of all the

privileges assigned to the groups to which the user id is attached to. The following is a sample “create

user” statement

create user dbdev with password ‘zksrfas92834’;

The following is a sample “create group” statement

create group devgrp with user dbdev, scott;

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4. Netezza Storage As discussed earlier, each disk in the appliance is partitioned into primary, mirror and temp or swap

partitions. The primary partition in each disk is used to store user data like database tables, the mirror

stores a copy of the primary partition of another disk so that it can be used in the event of disk failures

and the temp/swap partition is used to store the data temporarily like when the appliance does data

redistribution while processing queries. The logical representation of the data saved in the primary

partition of each disk is called the data slice. When users create database tables and load data into it, they

get distributed across the available data slices. Logical representation of data slices is called the data

partition. For TwinFin systems each S-Blade or SPU is connected to 8 data partitions and some only to 6

disk partitions (since some disks are reserved for failovers). There are situations like SPU failures when a

SPU can have more than 8 partitions attached to it since it got assigned some of the data partitions from

the failed SPU. The following diagram illustrates this concept

The SPU 1001 is connected to 8 data partitions numbered 0 to 7. Each data partition is connected to one

data slice stored on different disks. For e.g., the data partition 0 points to the data slice 17 stored on the

disk with id 1063. The disk 1063 also stores the mirror of the data partition 18 stored on disk 1064. The

following diagram illustrates what happens when the disk 1070 fails.

Immediately after the disk 1070 stops responding, the disk 1069 will be used by the system to satify

queries for which data is required from data slice 23 and 24. Disk 1069 will serve the requests using the

data in both its primary and mirror partition. This will also create a bottleneck which inturn impacts

query performances. In the meantime, the contents in disk 1070 are regenerated on one of the spare

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disks in the disk array which in this case is disk 1100 using the data in disk 1069. Once the regen is

complete the SPU data partition 7 is updated to point to the data slice 24 on disk 1100. The regen

process removes the bottleneck of disk 1069 to perform optimally.

In the situation where a SPU fails, the appliance assigns all the data partitions to other SPUs in the

system. Pair of disks which contains the mirror copy of each others data slice will be assigned to other

SPUs which will result in additional two data partitioned to be managed by the target SPU. If for e.g. if

an SPU currently manages data partitions 0 to 7 and if the appliance reassings two data partitions from a

failed SPU, the SPU will have 10 data partitions to manage and it will be numbered from 0 to 9.

Data Organization

When users create tables in databases and store data into it, data gets stored in disk extents which is the

minimum storage allocated on disks for data storage. Netezza distributes the data in data extents across

all the available data slices based on the distribution key specified during the table creation. A user can

specify upto four columns for data distribution or can specify the data to be distributed randomly or non

at all during the table creation process. If an user provides no distribution specification, Netezza uses

one of the columns to distribute the data and the selection of which can’t be influenced. When the user

specifies particular column for distribution then Netezza uses the column data to distribute the records

being inserted across the dataslices. Netezza uses hashing to determine the dataslice into which the

record need to be stored. When the user selects random as the option for data distribution, then the

appliance uses round robin algorithm to distribute the data uniformly across all the available dataslices.

The following are some sample table create statements using the distribute clause.





sex char(1),









) distribute on hash(emp_id);

create table nation_state (

ns_id integer not null constraint pk_nation_state primary key,

nation varchar(25),

state_code varchar(5),

state varchar(25)

) distribute on random;

The key is to make sure that the data for a table is uniformly distributed across all the data slices so that

there are no data skews. By distributing data across the data slices, all the SPUs in the system can be

utilized to process any query and in turn improves performance. Also the performance of any query

depends on the slowest SPUs handling the query. So if the data is not uniformly distributed then some

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of the SPUs will have more data in the data slice attached to it called data skew which will impact the

processing time and in turn the overall query performance. Selecting columns with high cardinality for

the distribution is a good practice to follow.

Even if a column with high cardinality like a date column is chosen to distribute data, there is a

possibility of creating processing skew. For e.g. using the date column as the distribution key, the data

gets distributed fairly evenly across all the data slices. But if most of the queries are looking for data for a

particular month which is fairly often in a data warehousing environment, then only a particular set of

data slices may need to be processes by the appliance which in turn will only utilize a subset of SPUs

causing the query performing sub optimally. This is called processing skew and needs to be prevented by

understanding the processing requirements and choosing the correct distribution keys.

When creating CTAS tables the following pattern is followed in terms of how the data distribution will

happen in the new table created when an explicit distribution criteria is not defined

If a CTAS table is created on a single source table and the source table is randomly distributed,

then the CTAS table will be distributed in the first column in the CTAS table.

If a CTAS table is created on a single source table with defined distribution on certain columns,

the CTAS table will inherit the source tables distribution

If a CTAS table is created by joining two tables, the distribution key of the resulting CTAS table

will be the join key of the two tables

If a CTAS table is created by joining multiple tables and a group by clause, the distribution key

of the resulting CTAS table will be the keys in the group by clause.

Zone Maps

When table data gets stored in extents on disk, Netezza keeps track of the minimal value and the

maximum value of columns of certain data types in data structures called zone maps. The zone maps are

created and maintained automatically for all the columns in the table which is of following data types.

All Integer types (int1, int2, int4, int8)

Date

Timestamp

By keeping track of the min and max values, Netezza will be able to avoid reading disk extends which

will be most of the disk extends in a large data warehouse environment. For e.g. if one of the fact table

stores one million records for the last 10 years which is more than a billion records and if the queries

process only a month’s data, Netezza will be able to read only the one million record. And if there are 96

snippet processors and if the data is uniformly distributed across all the data slices, then the amount of

data read into the snippet processors will be a little over 100,000 records which is small to process.

Enabling the appliance to utilize the zone map feature by selecting the best data types for columns in the

tables is another key point to take into consideration during design.

When two tables are joined together often like a customer table and order table, the distribution key

selection of the two tables can play an important role in the performance of the queries. If the

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distribution key is on the join column, for e.g. customer id column in both the customer and order table,

the data distribution will result in the records with the same customer id values ending up in the same

data slice for both the tables. When a query joining the table is being processed, since the matching data

from both the tables are in the same data slice the snippet processor will be able to perform the join

locally and the send the result without performing additional work which in turn improves the

performance of the query. If the tables are not distributed on the columns often used for join, matching

data from both the tables will end up in different data slices which means the snippet processor need to

do perform additional work to satisfy the join. The appliance will choose to temporarily redistribute one

of the tables on the join column if the other is already distributed on the join column and then the

snippet processors can perform the join locally. If both the tables are not distributed on the join column,

then the appliance may redistribute both the tables before the snippet processors can perform the join. If

a table stored relatively small number of records then Netezza can decide to broadcast the whole table to

all the SPUs s that each one has its own copy for processing. What this means the host need to

consolidate the table data from all the data slices and send it across to all the SPUs the complete table

data.

Clustered Base Tables (CBT)

Along with the option to distribute data during table definition, Netezza also provides an option how to

organize the distributed data with in a data slice. For e.g., we may have distributed employee table on

employee id but wanted to have employee records from the same department to be stored closely

together, the column dept id column in the table can be specified in the create table statement as in the

following example





sex char(1),









) distribute on hash(emp_id)

organize on(dept_id);

Netezza allows up to four columns to organize on. When data gets stored on the data slice, records with

the same organize on column values will get stored in the same or close by extends. Organize on

improves queries on fact tables when they are defined with the frequently joined columns to organize the

data. All the columns specified in the organize on clause are zone mapped and by knowing the range of

values of these columns stored in each physical extent, Netezza can eliminate reading unwanted extents

during a join query which improves the query performance. Zone mapping of additional data types are

supported when columns are specified in organize on clause and the following is the list of data types

including data types which are by default zone mapped.

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Default zone map data types

Integer - 1-byte, 2-byte, 4-byte, and 8-byte

Date

Timestamp

Additional data types which can be zone mapped due to organize on clause

Char - all sizes, but only the first 8 bytes are used in the zone map

Varchar - all sizes, but only the first 8 bytes are used in the zone map

Nchar - all sizes, but only the first 8 bytes are used in the zone map

Nvarchar - all sizes, but only the first 8 bytes are used in the zone map

Numeric - all sizes up to and including numeric(18)

Float

Double

Bool

Time

Time with timezone

Interval

Organize on column definitions can be modified using the table alter statement. But any column

included in organize on clause can’t be dropped by the table. If a table is altered to be a clustered base

table, any new records inserted into the table will be organized appropriately. In order to organize the

existing records in the table, “GROOM TABLE” needs to be executed to take advantage of the data

reorganization by queries. It is a good practice to have fact tables defined as clustered base tables with

data organized on often joined columns to improve multi-dimensional lookup. At the same time care

needs to be taken on the data organization columns by understanding the often executed queries and

also minimizing the number of columns on which the data need to be organized on. Compared to

traditional indexes or using materialized views to improve performance, CBTs has major advantage in

terms of not using additional space but organizing the table data in place. One point to note is that by

changing the data organization will impact the compression which may result in the increase or decrease

the size of the table storage after converting a table to a CBT.

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5. Statistics and Query Performance Netezza query optimizer relies on the statistics from catalog to come up with an optimal query execution

plan. So it is imperative that the statics need to be kept current without which the execution plan

generated by the optimizer may be sub-optimal resulting in poor query performance. The following are

the statistics the optimizer relies on

Object Type Statistics

Table Number of records in the tables used in the query

Table columns Minimum value in the column

Maximum value in the column

Count of null value in the column

Count of distinct values in the column (dispersion or cardinality)

Some examples of how the statistics can be used by the optimizer

If a column is null able additional code may need to be generated to check whether the value is

null or not

Knowing the number of records in the table, min and max value along with the number of

distinct value can help estimate the number of relevant records which can be returned for the

query assuming there is uniform distribution

Based on the min and max value the optimizer can determine the type of math need to be

performed like 64 or 128 bit computation.

These statistics in the catalog are generated using the “GENERATE STATISTICS” command which

collects them and updated the catalog. Admin users, table owners and users who have the GENSTATS

privilege can execute the generate statistics command on tables in databases. The following are some

examples

generate statistics; -- Generates statistics on all tables in a database – generate statistics on emp; -- Generates statistics on table emp –

generate statistics on emp(emp_id, dept_id);-- Generates statistics on specified columns –

Since generate statistics reads every record in the table to generate the statistics, the data is of very high

accuracy.

Netezza automatically maintains the statistics which may be of lesser accuracy compared to what is

generated by the generate statistics command. The following table lists the various commands and the

type of stat automatically maintained by Netezza. Even though the stat may not be very accurate it is

better than not having any

Command Row Count Min/Max vals Null count Distinct vals Zone Map

Create Table As Y Y Y Y Y

Insert Y Y N N Y

Update Y Y N N Y

Delete N N N N N

Groom N N N N Y

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Note that the groom command just removes the rows deleted logically; there is no change in stats except

for the zone map values.

The following are some recommended scenarios when generate statistics need to be executed for

performance

Significant changes to the database in terms of data

Static tables intended for a long time

Object in queries where is there is three or more joins

Slower query response after changes to tables

On columns used in where, order by, group by and having clauses

When temporary table used in a join contains large volume of data

Netezza also computes just in time statistics using sampling under the following conditions. This is not a

replacement to running generate statistics and also not accurate due to the sampling.

Query has tables that stores more than 5 million records

Query has at least one column restriction like where clause

Only if the query involves user tables

Tables that participate in query join or if a table has an associated materialized view

Along with sampling the JIT statistics collection process utilize zone maps to collect several pieces of

information like

Number of rows to be scanned for the target table

Number of extents need to be scanned for the target table

Max number of extents need to be scanned on the data slices with the greatest skew

Number of rows to be scanned for all the tables in each join

Number of unique values in target table columns involved in join or group by processing

Note that when CTAS tables are created, Netezza schedules generate statistics to collect all required

statistics for the new table. This automatic scheduling of generate statistics can be influenced by the

following two postgresql.conf file settings.

enable_small_ctas_autostats enables or disables auto stats generation on small CTAS tables

ctas_auto_min_rows specifies the threshold number of rows above which the stats generation is

scheduled and the default value is 10000.

Groom

When data is updated in Netezza, the current record is marked for deletion and a new record is created

with updated value instead of updating the current record. Also when a record in a table is deleted, it is

not physically removed from storage instead the record is marked as logically deleted and will not be

visible for future transactions. Similarly when a table is altered to add a drop a column a new version of

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table is created and queries against the table are serviced by using the different versions. Due to the

logical deletes which results in additional data in the tables and joins on versions of altered tables, query

performance gets impacted. In order to prevent the query performance degradation, users of Netezza

appliance need to “GROOM” the table.

The groom command is used to maintain user tables and

Reclaiming physical space by removing logically deleted rows in tables

Migrate records from previous versions of tables into the current version and leave with only

one version of the altered table

Organizing tables according to the organize on columns defined in a alter table command

are some of the key functions of the groom command. By default groom synchronizes with the latest

backup set and what that means is logically deleted data will not be removed if that data is not backed up

until up to the latest backup set. What that means is it is best to schedule grooms after database backups

so that the database objects are kept primed. The following are good practices with groom

Groom tables that often receive large updates and deletes

Schedule “Generate Statistics” after groom on tables so that the stats are up to date and accurate

If there is a need to remove all the records in a table use truncate instead of delete so that there

will be no need for a groom since truncate will reclaim the space

The following are some example groom commands

groom table emp ; -- to delete logically deleted rows and reclaim space --

groom table emp versions; -- to remove older versions of an altered table and copy rows to new –

groom table emp records ready; -- to organize only the records which are ready to be organized –

Note that when groom is getting executed, it doesn’t lock the tables and users will be able to access to

perform data manipulation on the table data.

Explain

In order to understand the plan the Netezza appliance will use to execute a query, users can use the

explain SQL statement. Understanding the plan will also provide an opportunity for the users to make

any changes to the query or the physical data structure of the tables involved to make the query perform

better. The following are some of the sample “explain” statements.

explain select * from emp;

explain distribution select emp.name, dept.name from emp, dept where emp.dept_id =

dept.id;

explain plangraph select * from emp;

The output from the explain statement can be generated in verbose format or graphical format which

can be opened in a web browser window. The output details all the snippets which will be executed in

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sequence on SPUs and also the code which will be executed on the host. The details include the type of

activity like table scan, aggregation etc., the estimated number of rows, the width of the rows, the cost

involved to execute that snippet since the Netezza query optimizer is a cost based optimizer, the object

involved, the confidence on the estimation etc. The following is a sample explain output

QUERY VERBOSE PLAN:

Node 1.

[SPU Sequential Scan table "OWNER" as "B" {}]

-- Estimated Rows = 16, Width = 11, Cost = 0.0.. 0.0, Conf = 100.0

Projections:

1:B.OWNER_NAME 2:B.OWNER_ID

[SPU Broadcast]

[HashIt for Join]

Node 2.

[SPU Sequential Scan table "ORDER" as "A" {}]

-- Estimated Rows = 454718, Width = 8, Cost = 0.0 .. 8.1, Conf = 80.0

Restrictions:

(A.OWNER_ID NOTNULL)

Projections:

1:A.ORDER_ID 2:A.OWNER_ID

Node 3.

[SPU Hash Join Stream "Node 2" with Temp "Node 1" {}]

-- Estimated Rows = 519678, Width = 23, Cost = 0.0 .. 19.7, Conf = 64.0

Restrictions:

(A.OWNER_ID = B.OWNER_ID)

Projections:

1:A.APPLICATION_ID 2:B.OWNER_NAME

Cardinality:

B.ORDER_ID 9 (Adjusted)

[SPU Return]

[Host Return]

QUERY PLANTEXT:

Hash Join (cost=0.0..19.7 rows=519678 width=23 conf=64) {}

(spu_broadcast, locus=spu subject=rightnode-Hash)

(spu_join, locus=spu subject=self)

(spu_send, locus=host subject=self)

(host_return, locus=host subject=self)

l: Sequential Scan table "A" (cost=0.0..8.1 rows=454718 width=8 conf=80) {}

(xpath_none, locus=spu subject=self)

r: Hash (cost=0.0..0.0 rows=16 width=11 conf=0) {}


l: Sequential Scan table "B" (cost=0.0..0.0 rows=16 width=11 conf=100) {}


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6. Netezza Transactions All database systems which allow concurrent processing should support ACID transactions i.e. the

transactions should be atomic, consistent, isolated and durable. All Netezza transactions are ACID in

nature and in this section we will see how ACIDity is maintained by the appliance.

By default Netezza SQLs are executed in auto-commit mode i.e. the changes made by a SQL statement

takes in effect immediately after the completion of the statement as if the transaction is complete. If

there are multiple related SQL statements where all the SQL execution need to fail if any one of them

fails, user can use the BEGIN, COMMIT and ROLLBACK transaction control statements to control

the transaction involving multiple statements. All SQL statements between a BEGIN statement and

COMMIT or ROLLBACK statement will be treated as part of a single transaction i.e if anyone of the

SQL statement fails, all the changes made by the prior statements before the failure will be reverted back

leaving the database data in a state as it was before the start if execution of the BEGIN sql statement.

There are SQL statements like BEGIN which are restricted from including inside a BEGIN,

COMMIT/ROLLBACK block which users need to be aware of.

Traditionally database systems used logs to manage transaction commits and rollbacks. All changes

which are part of a transaction are maintained in a log and based on whether user commits or rolls back

the transaction the changes are made durable i.e. written to the storage. Netezza doesn’t use logs and all

the changes are made on the storage where user data is stored which also helps with the performance. To

accomplish this for table data changes, Netezza maintains three additional hidden columns (createxid,

deletexid and row id) per table row which stores the transaction id which created the row, the transaction

id which deleted the row and a unique row id assigned to the data row by the system. The transaction ids

are uniquely assigned ids for each transaction executed in the system and they are assigned in increasing

order. When a row is created the deletexid is set to “0” and when it gets deleted it is set to the

transaction id which executed the delete request. When an update is made to a table row, the row data is

not updated in place rather a new row is created with the updated values and the old row deletexid is

updated with the id of the transaction which issued the update command. In case a transaction rolls back

a delete operation, the deletexid of the rows deleted is updated with “0” and in the case of rolling back

update operation, the deletexid of the new row created is set to “1” and the deletexid of the old row is

updated with “0”. One key point to note is that the rows are deleted logically and not physically during

delete operations and additional rows are getting created during update operations. What that means is

groom need to be executed regularly on tables which undergo large volume of deletes and updates to

reclaim the space and improve performance.

The transaction isolation is controlled by the transaction isolation levels supported by any database

system and the one used by the individual transaction. By ANSI standards there are four isolation levels

Uncommitted Read

Committed Read

Repeatable Read

Serializable

The following table details the data consistency which can be expected with each isolation levels

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Isolation Level Uncommitted Data Non Repeatable Data Phantom Data

Uncommitted Y Y Y

Committed N Y Y

Repeatable read N N Y

Serializable N N N

Uncommitted Data: Transaction can see data from another transaction which is still not committed

Non Repeatable Data: When a transaction tries to read the same data, the data is changed by another

transaction but committed after the previous read.

Phantom Data: When a transaction tries to read the same data, the data previously read is not changed

but new rows has been added which satisfies the previous query criteria i.e. new rows has been added to

the data rows.

Netezza allows only the “serializable” transaction isolation level and if a transaction is found to be not

serializable then it will not get scheduled. This works fine since the appliance is primarily meant for a

data warehousing environment where there are very minimal or no updates once data is loaded. Data

versioning which can provide a consistent view of data for transactions running concurrently combined

with supporting only serializable transactions, Netezza doesn’t need locks the synchronization

mechanism used in database systems traditionally to maintain concurrency. For some DDL statements

like dropping a table, the appliance will require exclusive access on objects and that would mean the

DDL statement execution will have to wait until any other operation like SELECT on the object is

complete.

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7. Loading Data, Database Back-up and Restores Loading and unloading data, creating back-ups and the process to restore is a vital component for any

database systems and in this section we will look into how there are done in Netezza.

External Tables

Users can define a file as a table and can access them as any other table in the database using SQL

statements. Such tables are called external table. The key difference is that the even though data from

external tables can be accessed through SQL queries the data is stored in files and not in the disks

attached to the SPUs. Netezza allows selects and inserts on external tables but not updates deletes or

truncation. That means users can insert data into the external tables from a normal table in the database

which will get stored in the external file. Also data can be selected from the external table and inserted

into a database table and these two actions are equivalent of data unload and load. Any user with LIST

privilege and CREATE EXTERNAL TABLE privilege can create external tables. The following are

some sample statements to create external tables and how they can be used

-- creates an external table with the same definition as an existing customer table -- create external table customer_ext same as customer using (dataobject

(‘/nfs/customer.data’) delimiter ’|’);

-- create statement used the column definitions provided to create the external table -- create external table product_ext(product_id bigint, product_name varchar(25),

vendor_id bigint) using (dataobject(‘/tmp/product.out’ delimiter ’,’);

-- create statement uses the definition of existing account table to create the external table and stores the data in the account.data file in an Netezza internal format -- create external table ‘/tmp/account.data’ using (format ‘internal’ compress true) as

select * from account;

-- read data from the external table and insert into an existing database table -- insert into temp_account select * from external ‘/tmp/account.data’ using (format

‘internal’ compress true);

-- storing data into an external table which in turn into the file. This file will be readable and used with other DBMS -- insert into product_ext select * from product; Once external tables are created they can be altered or dropped. Statements used to drop and alter a not

mal table can be used to perform these actions on external tables. Note that when an external table is

dropped, the file associated with the table is not removed and need to be removed through OS

commands if that is what is intended. Also queries can’t have more than one external table or unions

can’t be performed on two external tables. When user performs an insert operation on an external table,

the data in the file associated with the external table (if existing) will be deleted before new data is written

i.e. if required backup of the file may need to be made.

External tables are one of the options to backup and restore data in a Netezza database even though it

works at the table level. Since external tables can be used to create data files from table data in a readable

format with user specified delimiters and line escape characters, they can be used to move data between

Netezza and other DMBS systems. Note that the host system is not to store data and hence alternate

arrangements need to be made if the user intends to move large volume of data like using nfs mounts to

store the files created from the external tables.

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NZLoad CLI

NZLoad is a Netezza command line interface which can be used to load data into database tables.

Behind the scenes NZLoad creates an external table for the data file to be loaded, does the select of data

from the external table and inserts the data into the target table and then drops the external table once

the insert of all the records are complete. All these are done by executing a sequence of SQL statements

and the complete load process is considered part of a single transaction i.e. if there is an error in any of

the steps the whole process is rolled back. The utility can be executed on a Netezza host locally or from

a remote client where the Netezza ODBC client is installed. Even though the utility uses the ODBC

driver, it doesn’t require a Data Source Name on the machine it is executed since it uses the ODBC

driver directly bypassing the ODBC driver manager. The following is a sample NZLoad statement

nzload –u nz –pw nzpass –db hr –t employee –df /tmp/employee.dat –delimiter ‘,’ –lf

log.txt –bf bad.txt –outputDir /tmp –fileBufSize 2 –allowReplay 2

A control file can also be used to pass parameters using the –cf option to the nzload utility instead of

passing through command line. This also allows more than one data file to be loaded concurrently into

the database with different options and each load will be considered as different transaction. The control

file option –cf and the data file option –df are mutually exclusive i.e they can’t be used at the same time.

The following is a same nzload command assuming the load parameters are specified in the load.cf file.

nzload –u nz –pw nzpass –host nztest.company.com –cf load.cf

NZLoad exits with an error code of 0 - for successful execution, 1 – for failed execution i.e. no records

were inserted, 2 – for successful loads where the maximum number of rows which were in error during

load did not exceed the maximum specified in the NZLoad command request.

When NZLoad is getting executed against a table, other transactions can still use the table since the load

command will not commit until the end and hence the data being loaded will not be visible to other

transactions executed concurrently. Also the NZLoad sends chunks of data to be loaded along with the

transaction id to all the SPUs which in turn stores the data immediately onto storage and thus taking up

space. If for any reason the transaction performing the load is cancelled or rolled back at any point, the

data will still remain in storage even though it will not be visible to future transactions. In order to claim

the space taken by such records, users need to schedule a groom against the table. Note NZReclaim

utility can also be used on earlier versions of Netezza but it is getting deprecated from version 6. The

user id used to run the NZLoad utility should have the privilege to create external tables along with

select, insert and list access on the database table.

Database Backup and Restore

Unlike traditional database systems, Netezza has a host component where all the database catalogs,

system tables, configuration files etc. are stored and the SPU component where user data is stored. Both

these components need to be backed-up in sync for a successful restore even though in restore situations

like database or table restore the host component may not be required. Netezza provides nzhostbackup

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and nzhostrestore utilities to make host backups and restore along with the nzbackup and nzrestore

utilities to manage the user data backup and restoration. This is in addition to “external tables” which

can be used to backup and restore individual tables which can also be used to make back-ups of all the

tables in a database and hence creating a database backup.

Host Backup and Restore

Netezza host can be backed up using the nzhostbackup command utility. It backs up the Netezza data

directory which stores the system tables, database catalogs, configuration files, query plans, cached

executable code for the SPUs etc which are required for the correct functioning and access to user

databases are stored. When the command is executed, it will pause the system to make a checkpoint

before taking a backup of the /nz/data directory. Once the backup is complete the system is resumed to

its original state and due to this requirement to pause the system, it would be good to schedule the host

backup when there is no or very less activity on the system.

In the event of any issues with the Netezza host, the host can be restored using the nzhostrestore

utility. The restore will restore only the host components like the database catalog entries, system tables

etc to the state when the host backup was made. What that means is if there were actions like dropping

of user tables or truncation or grooming of tables after the host backup, the host restore will not be able

to revert back these actions. This may result in the inconsistent state of the user objects and corrective

action need to be taken to bring them to the state in sync to when the host backup was made. Also in the

case of new tables created after the host backup, when the host restore is done the utility will create SQL

scripts to drop these tables which are orphaned since the entries are not in the catalog. To avoid any

such inconsistencies it is always recommended to schedule the host backup when every there is a major

change to the databases. The following are sample nzhostbackup and nzhostrestore commands.

nzhostbackup /tmp/backup/nz-devhost-bk-01212011.tar.gz

nzhostrestore /tmp/backup/nz-uathost-bk-12212011.tar.gz

During the host restore process the utility checks for the version of the catalog on the host compared to

what is in the backup. If they are not the same or if for some reasons the utility is not able to identify the

versions, then the restore process will fail. This verification step can be skipped using the –catverok of

the nzhostrestore utility but it is not recommended. Note that the nz host backup doesn’t back up the

operating system components or configurations and they need to be backed up separately.

User Database Backup and Restore

User database and data separate from the host components can be backed up and restored using the

nzbackup and nzrestore utilities.

The nzbackup allows the user to make a full, cumulative or differential backup of a database. The full

backup makes a backup of the complete database, while the differential backup makes a backup of the

changes which were made after the previous backup either full or cumulative. The cumulative backup

creates a backup of all the changes which were made after the previous full backup. Users executing the

nzbackup command need to have backup privilege on the specific database level or at the global level.

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The following is an example where a full back up is scheduled every month say on day 1, a differential

back up scheduled every day and a cumulative backup scheduled every week.

When the differential back up is run, it captures all the data after the previous backup. If the database

need to be restored as of day 4 after the monthly backup, the monthly backup need to be used along

with the differential backup 1,2 & 3. If the database needs to be restored as of day 8, the then the

monthly backup, along with the cumulative backup and the differential back 6 need to be used. There is

no point in time recovery in Netezza as with other traditional databases where databases can be

recovered to a point in time between databases backup which uses logs which are not available in

Netezza. The following are some example Netezza backup commands.

-- creates a full backup of the hrdb database -- nzbackup –dir /tmp/backups –u nzbackup –p nzpass –db hrdb

-- creates a differential or incremental backup of the findb database -- nzbackup –dir /nfs/backups –db findb –differential

-- creates a cumulative backup of the hrdb database -- nzbackup –dir /nfs/backups –db hrdb –cumulative

-- creates a backup of the schema from the hrdb database and not the data -- nzbackup –dir /nfs/backups –db hrdb –schema-only

-- creates the backup of the users and permissions at user database and system level -- nzbackup –dir /nfs/backups –users –u nzbackup –p nzpass

As you may have noticed, there are two other uses for the nzbackup utility other than the database data

backup. One is to generate the schema of a database which then can be used to create another database

with only the entities in the source database and not the data. Second is to back up all the users, groups

and the privileges in the system so that it can be used to restore the user details or create the same set of

users and privileges in another system. The history of database backup can be viewed using the –history

option of the nzbackup command.

nzbackup –history

nzbackup –history –db hrdb

A full backup and along with all the differential and cumulative backups after that until the next full

backup is considered park of a backup set. Each backup set is identified by an id along with the backups

made within the backup set identified by a sequence number. All these details are displayed when a user

makes a request for the backup history for a database.

Databases can be restored using the nzrestore command line utility and the user need to have the restore

privilege. A full database restore can’t be made against an existing database which can be done in

traditional databases. Instead the existing database need to be dropped or new database needs to be

Monthly full

Daily Diff 1

iff

Monthly full

Daily Diff 2

iff

Monthly full

Daily Diff 3

iff

Monthly full

Daily Diff 4

iff

Monthly full

Daily Diff 5

iff

Monthly full

Daily Diff 6

iff

Monthly full Weekly Cumulative

iff Monthly full

1 2 3 4 5 6 7 8

Daily Diff 7

iff

Monthly full

9

© sieac llc 32

specified to perform a full database restore. As with nzbackup utility the option of –schema-only can be

used to create only the objects in the new database and not load the data from the backup. Also only the

users and privileges can be restored using the –users option of the nzrestore utility. The following are

some example restore commands

-- Performs a restore of the hrdb database from a full backup -- nzrestore –db hrdb –u nzbackup –p nzpass –dir /tmp/backups –v

-- creates all the objects in fundb into the new database testfindb but doesn’t load any data -- nzrestore –db testfindb –sourcedb findb –schema-only –u nzbackup –p nzpass –dir

/nfs/backups

-- Creates all the users and applies all the privileges as in the backup but doesn’t drop users or alters existing privileges -- nzrestore –users –u nzbackup –p nzpass –dir /nfs/backups

-- Restores the database findb upto and until the backup sequence 2 with in the backupset 200137274 -- nzrestore –db findb –u nzbackup –p nzpass –dir /tmp/backups –backupset 200137274 –

increment 2

Database backups can be used to restore specific table(s) to a point in time using the nzrestore –tables

option. One are more tables can be restored by listing them along with the –tables option. If the table

being restored is in the database then the table will be overwritten and if it doesn’t exist, the table will get

created. Table level restore is applicable only for regular tables.

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8. Netezza SQL As with other popular DBMS systems currently available in the market, SQL in Netezza supports SQL-

92 standard. The SQLs can be executed through the nzsql command line utility or through any JDBC,

ODBC or OLE DB connectivity.

SQL Statements

The following are the SQL statements available for the various object types in Netezza.

Database

SQL Statement Description

Create Database To create a new Netezza database

Drop Database To drop an existing Netezza database

Alter Database To rename or change owner of an existing database. Renaming a database will require recompilation of any views in the database. Also all materialized views will be converted to normal views and will need to be replaced.

The following are some limits at the database level

Limit Type Limit

Name Length Maximum number of characters 128 bytes

Connections Maximum number of connections to the server:2000 default:500

When connected to one database, objects in another database can be accessed by qualifying the object

name with “database name.schema name” for e.g. “hrdb.hradmin.emp” can be used to access the emp

table in hrdb database under hradmin schema. The schema name/object owner name is optional and the

object can be accessed by “database nama..object name” e.g “hrdb..emp” since object names are unique

in a database. While objects in a second database can be queried or used in query joins, no data updates

are allowed across databases.

Tables


CREATE TABLE To create a new Netezza table

DROP TABLE To drop an existing Netezza table

ALTER TABLE To rename or change owner or modify (add, drop) columns or modify constraints in an existing table

SELECT col_name(s) FROM tbl To retrieve data from a database table

INSERT INTO tbl To insert data into a database table

UPDATE tbl SET col_nme = val To update existing data in a table

DELETE FROM tbl To delete data from a table

TRUNCATE TABLE tbl To remove all records from the table. Truncate removes the data and recovers the storage instead of logically deleting the rows as in delete statement

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UNION, UNION ALL To combine data from more than one table or row set UNION can be used in conjunction with SELECT statement

INTERSECT, INTERSECT ALL To retrieve common set of rows in more than one table or row set INTERSECT can be used in conjunction with SELECT statement

EXCEPT[DISTINCT], MINUS[DISTINCT, EXCEPT ALL, MINUS ALL

To retrieve set of rows from one table which are not in another row set/table EXCEPT or MINUS can be used in conjunction with SELECT statement

ALTER VIEWS ON tbl MATERIALIZE REFRESH

To re-materialize all the materialized views which are based on a particular table

The following are some limits at the table level

Limit Type Limit

Name/Column Name Length Maximum number of characters 128 bytes

Max Column Count Maximum number of columns per table: 1600

Distribution Keys Maximum number of distribution columns: 4

Char/Varchar Field length Maximum number of characters in char/varchar type: 64000

Row Size Max row size in a table: 65,535

Synonyms

Synonyms can be used to refer tables and views in the same database with a different name or refer to

objects in a remote database without the “database.schema” qualifier. The following are the supported

SQL statements for Synonyms


CREATE SYNONYM name FOR table/view

To create a synonym and it doesn’t verify whether the table or view for which it is getting created exists or not. If the underlying object is non-existent a runtime error will be thrown when user tries to access the Synonym.

DROP SYNONYM name To drop an existing synonym

ALTER SYNONYM name To rename or change owner of an existing synonym

Views

Views are created to give a different perception of data like restricted table column or join of data from

two sets of data and/or to control access to the data in the database. The following are the supported

SQL statements for views


CREATE OR REPLACE VIEW To create a new Netezza view

DROP VIEW To drop an existing Netezza view

ALTER VIEW To rename or change owner of an existing view

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Materialized Views

The following are the supported SQL statements on Materialized Views


CREATE OR REPLACE MATERIALIZED VIEW

To create a new materialized view or replace an existing one when the base table gets changed that impacts materialized view

DROP VIEW To drop an existing materialized view (same as normal views)

ALTER VIEW vw MATERIALIZE REFRESH|SUSPEND

Use “suspend” option to suspend a view so that activites like base table refresh or groom can run. Use “refresh” option to activate a suspended materialized view. The “refresh” option is also used to re-materialize the view so that the rows are in the correct order.

A system default can be set to specify the percentage of rows in a materialized view that can be out of

order and the default value is 20%. Once set the command “ALTER VIEWS ON MATERIALIZE

REFRESH” will re-materialize all the views in the database which has the set percentage of rows which

are not in the correct order.

Functions

Netezza comes with many built in functions are the following are some of them under various

categories.

Category Functions

Aggregates rollup, cube, grouping sets, count, sum, max, min, avg

Casting to_char, to_date, to_number, to_timestamp, cast(value as type),::

Date Time extract(field from datetime value) where field = epoch, year, month, day,dow,doy

Checking null value nvl(), nvl2()

String Functions Trim, position, substring, lower, upper, like, not like, character_length

Fuzzy String Search le_dst, dle_dst

Value Functions current_date, current_time, current_timestamp, current_user, current_db

Math Functions acos, asin, atan, atan2, cos, cotx, degrees, pi, radiants, sin, tan, random, ceil, abs

This is just a sample list and other built in functions along with the details can be obtained from the

Netezza database user guide.

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9. Stored Procedures Stored procedures are programs written in NZPLSQL language to perform database operations. They

are stored in Netezza databases and runs on Netezza host when executed. NZPLSQL is an interpreted

language based in Postgres’ PL/pgSQL. The key advantages of using stored procedures are

It helps avoid unnecessary network traffic between the appliance and the application requesting

for data which is a major advantage when dealing with large volume of data in a ware housing

environment.

By controlling the access restrictions users can be allowed to perform database operations

through stored procedures without providing access to underlying objects.

By encapsulating business logic in stored procedures there is only one place to make

modifications i.e. helps maintenance

NZPLSQL along with regular SQL statements provides a complete programing language with

capabilities like conditional statement, looping functionality, declaring variables and manipulating it

through expressions, creating and calling sub routines etc. All regular SQL statements except the ones

which are not allowed to be with in a BEGIN/COMMIT block can be used in a stored procedure. NZ

SQL Procedures can accept multiple input variables and return one output either a scalar value or a

result set. The stored procedures can be created, altered, executed and dropped through NZSQL

command utility or through an ODBC, JDBC, OLE-DB connection to a database. The following is a

sample set of SQL statements with respect to stored procedures

/*

Stored procedure to update employee salary

*/

create or replace procedure update_emp_sal(int4) returns int4 execute as owner

language nzplsql as

begin_proc

declare

int4 ret_code;

-- Variable to store the first value passed as input to the stored procedure --

Inc_Percent alias for $1;

begin

update hr..emp set salary = salary+(salary*inc_percent*100);

raise notice ‘Updated employee salary successfully’;

-- Section to handle all unhandled exception --

exception when others the

raise notice ‘Error in updating employee salary’;

End;

End_proc; -- To display the details about the stored procedure --

show procedure update_emp_sal(int4);

-- To execute the stored procedure --

exec procedure update_emp_sal(12);

-- Another way to execute the stored procedure --

call procedure update_emp_sal(-2);

-- To drop the stored procedure --

drop procedure update_emp_sal(int4);

Stored procedures are not case sensitive i.e. they get converted into capital letters irrespective of what

case letter is used. So if there are variables which are spelled the same but use different case letters, they

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all are considered duplicates. Create or replace procedure statement replaces if a procedure with the same

name already in the database or creates new if none exists.

Admin user and database owner by default have all the permissions to perform actions regarding stored

procedures. Users with grant permission option also provide permissions to other users or groups to

alter, create or replace, drop, execute stored procedures. The following are some sample statements

-- grant permission to create stored procedure to the group devgrp --

grant create procedure to group devgrp;

-- grant all permissions regarding stored procedures to user mike --

grant all on procedure to mike;

-- revoke permission to create procedure from the group qagrp --

revoke create procedure from group qagrp;

-- revoke permission to alter on the procedure update_emp_sal(int4) from user tom --

revoke alter on update_emp_sal(int4) from tom;

-- provide execute permission on the procedure update_emp_sal(int4) to user sally --

grant execute on update_emp_sal(int4) to sally;

-- grant drop access on the procedure update_emp_sal(int4) to user ted --

grant drop on update_emp_sal(int4) to ted; Note that a procedure is uniquely identified using the name along with the input parameters. Procedures

with the same name but with different input parameter list are considered different stored procedures.

When a procedure is created it can be defined whether the procedure should execute with the authority

of the user who created it or with the authority of the user who is executing it. The “EXECUTE AS

OWNER” or “EXECUTE AS CALLER” parameter in the CREATE or ALTER procedure statement

determines the authority used during execution. By specifying that the procedure need to be executed as

owner, even if the user executing the procedure doesn’t have the necessary authority on the objects

accessed by the caller of the procedure, the procedure will be executed successfully as long as the owner

who created the procedure has the necessary privileges on the objects. For e.g. if the emp table data can

be updated by only the user in the hrgroup and the stored procedure update_emp_sal(int4) is created by

someone from the hrgroup, the user sally will be able to execute the procedure successfully even if the

user is not part of the hrgroup. The reason is that the procedure was created with “execute as owner”

option and the owner has the privilege to update the table. If the standard is that only the user with the

necessary permissions on the underlying objects can only be able to manipulate data through a

procedure, then procedures can be created using “execute as caller” option.

-- alter procedure update_emp_sal(int4) to execute as caller --

alter procedure update_emp_sal(int4) execute as caller;

After the execution of this alter statement, if the user sally executes the procedure it will fail since the

user is not part of the hrgroup and doesn’t have the update privilege. When a stored procedure is

executed all the objects referred in the procedure will need to be available in the database where it is

getting executed unless the object is fully qualified. If an object from a remote database is used in the

procedure, data can only be queried from the object in the remote database and can’t be inserted,

updated or deleted.

When database backups are restored the stored procedures all get restored and can be without errors

unless the version of Netezza where the restore is done doesn’t support procedures or some of the

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features which came out in a later version. It is a good practice to store the procedure code in other

storage other than just relying on the database backups for recovery purposes.

NZPLSQL Structure

[<<label>>]

[DECLARE

declarations]

BEGIN

statements

[EXCEPTION WHEN OTHERS THEN

handler statements]

END;

Comments

NZPLSQL supports block comments where a block of comments can be enclosed between /* */ and a

line comment where the comment can be added to a line after --.

/*

Block Comments where

Multiple lines of comments can be enclosed

*/

-- Anything until the end of the line is considered as a comment

Variables and Constants

Variables and constants are defined in the DECLARE sections of the procedure and the syntax used is

var_or_const_name [CONSTANT] type [NOT NULL] [ { DEFAULT | := } value ];

if the variable is defined as a constant then the value for the variable can’t be changed during the

execution of the procedure. The default value defines the initial value of the variable and if the type

defines the data type of values which can be stored into the variable. If a value is not assigned to a

variable a sql null value is stored by default into the variable. So in order to define a variable as not null, a

default value need to be assigned without which a null value will get stored and error will get thrown

since null is not an acceptable value in a not null variable.

Variables can be assigned the data type of a table column or the data type of a table row using the

%TYPE and %ROWTYPE attributes. The following are some examples.

user_id users.user_id%TYPE –- sets the data type to the type of the column user_id

Emp_Rec hr..emp%ROWTYPE –- sets the data type to the row of the table emp in hr db

When a variable is set to a row type, the individual columns can be accessed using the dot ‘.’ operator

like emp_rec.emp_id where emp_id is one of the columns in the emp table. The table referred in the

ROWTYPE data type definition should be an existing table in the database where the procedure is

created.

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Array variables are supported in NZPLSQL and can be defined using VARRAY key word. Following is

the syntax to declare an array variable

var_name VARRAY(n) OF data_type;

where var_name is the name of the variable, n is the size of the array and the data_type is the data

type of the values stored in the array. For e.g. the following is an array variable of type int and size 10

vId VARRAY(10) OF INT;

The size of an array variable can be found using the COUNT method. For e.g. vId.COUNT will return

an int value of 10. If required the size of the array can be changed using the EXTEND and TRIM methods

and both methods accepts an integer value with a default value of 1 as input.

Procedure Input Parameters

Procedures can be passed up to 64 parameters and they are not named. Aliases can be created in the

procedures to access the input parameters and they are constants i.e can’t be updated.

CREATE OR REPLACE PROCEDURE update_emp_sal(int, varchar(ANY)) RETURNS int

LANGUAGE NZPLSQL AS

BEGIN_PROC

DECLARE

pId ALIAS FOR $1; -- Can access the first input value passed

pName ALIAS FOR $2; -- Can access the second input value passed

--both the previous variables which are aliases are constants; can’t be updated

BEGIN

UPDATE EMP SELECT * FROM EXT_EMP WHERE id = pId;

END;

If any updates need to be made to the value passed as input parameters, then the values need to be

copied into a local variable, make the required updates and use the local variable for further processing.

CREATE OR REPLACE PROCEDURE p1 (int, varchar(ANY)) RETURNS int

LANGUAGE NZPLSQL AS

BEGIN_PROC

DECLARE

pId ALIAS FOR $1; -- Can access the first input value passed

pName ALIAS FOR $2; -- Can access the second input value passed

--both the previous variables which are aliases are constants; can’t be updated

vId int4; -- local variable which can be updated

BEGIN

vId = pId+1;

UPDATE EMP SELECT * FROM EXT_EMP WHERE id = vId;

END;

Also procedures can be passed arbitrary number of variables with different data types as input

parameters. This is enabled by using VARARGS as the input parameter and during execution the

number of input parameters and the types can be accessed using the predefined array called

PROC_ARGUMENT_TYPES. The total number of parameters passed can be retrieved from the

COUNT of the array as PROC_ARGUMENT_TYPES.COUNT which returns an int value

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corresponding to the number of parameters passed. The types of the variables are passed as array

elements and can be retrieved using the index into the array. For e.g to retrieve the data type of the

second variable passed PROC_ARGUMENT_TYPES(1) can be used which will return a value of type

oid. Note that the first array element indexed with 0 corresponds to the first input parameter passed to

the stored procedure.

CREATE OR REPLACE PROCEDURE p1 (varargs) RETURNS int

LANGUAGE NZPLSQL AS

BEGIN_PROC

DECLARE

type oid;

int argCount;

BEGIN

argCount := PROC_ARGUMENT_TYPES.count; -- number of input parameters passed

typ := PROC_ARGUMENT_TYPES(0); -- type of the first input parameter passed

END;

Scope of Variables

A “DECLARE” section where variables can be defined along with the BEGIN/END section following

the DECLARE section forms a code block and each code block in a procedure can be labeled using

<<label>> where “label” is a name which can be used to referred the block. Any variable declared in a

code block is local to that block and can’t be referred outside of that block. If the same name is used to

define variables in two blocks in the procedure, the code in any block will get to work with the copy of

the variable in that block even though there are other variables with the same name in other blocks.

Other than the declare section, variables are defined when using looping statements like FOR loop. If a

variable is defined in the loop statement with the same name as another variable in the code block, then

the variable in the loop statement will take precedence over other variables with the same name when the

code execute the looping logic. If a variable from another code section need to be referred the variable

can be qualified using the label name of the code section. The following is an example which will help

clarify variable scoping in NZPLSQL.

<<outer>>

DECLARE

val int4;

BEGIN

val := 5;

<<inner>>

DECLARE

val int4;

BEGIN

val := 7;

RAISE NOTICE 'inner val != outer val % %', val, outer.val;-- displays 7,5

FOR val IN 1..10 LOOP

--Note that this is a NEW val variable for the loop.

RAISE NOTICE 'The value of val is %', val; -- displays 1 to 10

END LOOP;

RAISE NOTICE 'inner val is still 7. Value %', inner.val; -- displays 7

END;

RAISE NOTICE 'outer val is still 5. Value %', val; -- displays 5

END;

Expressions and Assignments

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Expressions in NZPLSQL are evaluated internally by executing the “SELECT expression” query using

the query executor. All the expressions in a procedure is compiled and cached when they are first

encountered and all parameter values required for the expressions are passed to the executor in a

parameter array for substitution before the execution of the expression. The cached copy of expressions

will be preserved until there is change in the procedure or the database is stopped or the procedure is

dropped.

While using expressions, it is important to remember about the implicit casting of values apart from the

explicit casting which happens due to variable type definition. For e.g

a int;

a := 1+0.5;

Will generate a result of a = 2 since the variable is explicitly defined as integer and also the value gets

rounded up. At the sametime, the following expression

a = (1+0.3)*2;

will generate a result of 3. This is because Netezza will implicitly convert the 1 to numeric to match the

data type of 0.3 which results in 1.3 for the addition, which again will be multiplied with 2.0 due to

implicit casting of 2 to match the numeric value of 1.3 producing final result of 2.8 for the expression.

Since the variable “a” is defined as int, the value 2.8 will be rounded up to generate the final result of 3.

If unsure about the implicit casting rules, any expression can be executed as a select statement with

proper values to generate a result to understand the implicit casting applied to the expression. The select

statement can also be used to create a table and the resulting column definition from the table created by

the expression will also provide details about the implicit casting. Also NZPLSQL functions like sqrt()

also does some implicit casting which users need to be aware of.

Apart from the implicit casting, users also need to be aware of the maximum values which can be stored

in various data types supported by NZSQL to avoid overflows which will generate incorrect results. For

e.g. if we add 1 to an integer variable storing the value of 2147483647 which is the maximum positive

value an integer can store will result in the incorrect value of -2147483648 due to overflow.

Executing Queries

Apart from being able to execute static queries, queries can be created in the procedures and executed

dynamically in stored procedures. The dynamic queries can be executed using the “EXECUTE

IMMEDIATE query” statement where “query” is a string. When a query gets executed in a procedure,

the system provides status about the query using the ROW_COUNT and LAST_OID status indicators.

ROW_COUNT provides the number of rows processed by the query and the LAST_OID provides the

OID of the last row inserted by the SQL. LAST_OID is relevant only to INSERT queries since it

provides the object id of the last inserted row.

When a select query is executed and to check whether any rows are returned, the ROW_COUNT status

variable can be used. Also the reserved variable “FOUND” which is equivalent to checking

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ROW_COUNT >= 1 can be used immediately after the select query execution to find whether any rows

were retrieved by the query.

Similarly the expression “IS NULL” can be used to verify whether a variable is null or not. For e.g. the

following statement

if dept_id is null then

Dept_id = 999

end if;

will execute the statements in the “if” statement control block when there is no value assigned to the

dept_id or when it is assigned NULL value.

Execution Control Statements

NZPLSQL supports multiple control statement structures and the following provides a brief overview

of the supported structures

Conditional Control

“IF-THEN”, “IF-THEN-ELSE”, “IF-THEN-ELSE-IF”, and “IF-THEN-ELSIF-ELSE” are the

conditional control structures supported.

The following is a sample IF-THEN statement

IF v_sal > 10000 THEN

v_count := v_count + 1;

END IF;

All operators which generates a Boolean result can be used in the IF statement to control the logic flow.

The following is a sample IF-THEN-ELSE control statement



ELSE

v_count_min := v_count_min + 1;

END IF;

The following is a sample IF-THEN-ELSE-IF control statement



ELSE IF v_sal > 5000

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v_count_min := v_count_min + 1;

END IF;

END IF;

The following is a sample IF-THEN-ELSIF-ELSE control statement

IF location.category = ‘H’ THEN

notification := ‘Hazardous’;

ELSIF location.category = ‘R’ THEN

notification := ‘Restricted’;

ELSE

notification := ‘Standard’;

END IF;

Note ELSIF can also be spelled as ELSEIF which produces the same result.

Iterative Control

NZPLSQL supports LOOP-END LOOP, WHILE loop and FOR loop iterative control statements.

Users can terminate out of loops using EXIT statement. The following is an example of LOOP-END

LOOP statement

<<loop1>>

LOOP

Perform tasks

IF v_count > 1000 THEN

EXIT loop1;

END IF;

Perform tasks

END LOOP;

In the sample above IF control statement is used to determine when to exit from the loop. The same can

be accomplished using EXIT WHEN statement as in the following sample. Note that when a label is

used in the EXIT statement, the label should belong to the current loop to which the EXIT statement

belongs to or should be one of the outer loop in which the current loop is part of.

<<loop1>>

LOOP

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Perform tasks

<<loop2>>

LOOP

Perform tasks

IF v_count > 1000 THEN

EXIT loop1; -- The code exits both loop 1 and loop 2

END IF;

Perform tasks

END LOOP; -- This ends loop 2

END LOOP; -- This ends loop 1

The following is an example of WHILE loop statement

WHILE state = ‘MA’ and year = ‘2000’ LOOP

perform tasks

END LOOP;

The following is an example of FOR loop statement

FOR count in 1..100 LOOP –- reverse key word can be used to count backwards

Perform tasks

END LOOP;

Note that if required both the WHILE and FOR loop can be exited out using EXIT statement in

combination with an IF or WHEN control statement before the loops are completed.

Working with table rows

While discussing variable types, we saw the data type of %ROWTYPE which defines the variable to be

the type of a specific table row. Instead being a type of a specific table, a variable can also be defined as a

type of RECORD which can be used to store data from any table. For e.g. a variable r_EMP defined as

EMP%ROWTYPE can store data from the EMP table when used in a SELECT statement like

SELECT * INTO r_EMP WHERE ID = 100;

But when used to select data from a DEPT table into the same variable will result in error. But if a

variable r_REC is defined as RECORD, then the variable can be used to store data from both the EMP

table and the DEPT table with no issues. Another way table row data can be retrieved is by defining

multiple variables with the same data type definitions as the table columns and then use them as comma

separated list in the select statement like the following example

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SELECT * FROM EMP INTO v_ID, v_NAME, v_DEPT WHERE ID = 1000;

So far it is all good to retrieve one record from tables. And as mentioned earlier, the FOUND or

ROW_COUNT variables can be used to verify whether any records were returned from the SELECT

queries executed. But if the query returns a set of rows then a FOR loop is required to traverse through

the result set and act on them. The following is a sample code to handle result sets in NZPLSQL

FOR r_REC in SELECT * FROM EMP LOOP

perform actions

END LOOP;

r_REC can be defined as a RECORD or a ROWTYPE of EMP table type. The column values from the

table row data can be retrieved using the “.” operator and the table column name like r_REC.EMP_ID.

Error Handling and Messages

EXCEPTION statement can be used to handle exceptions during the execution of a NZPLSQL. When

an error occurs the SQLERRM stores the text of the error message. The following is an example

BEGIN

Perform tasks

EXCEPTION WHEN OTHERS THEN

Perform tasks

END;

The exception section of the code gets executed only when there is an error and is placed at the end of

the procedure code. The RAISE statement which can be used to prompt a message complements the

EXCEPTION statement to provide some detailed error messages to the user. The following is an

example

BEGIN

Perform tasks


RAISE NOTICE ‘Exception details %’, SQLERRM;

END;

RAISE can take in different levels and one of them is NOTICE. Others being DEBUG and

EXCEPTION. While RAISE EXCEPTION will abort the transaction, the DEBUG and NOTICE

levels are only used to send messages to logs. Also EXCEPTIONs which occur in a procedure will

propagate through the call chain until it reaches a point where it is handled using EXCEPTION statement

or reaches the main procedure.

Result set as return value

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We have seen how procedures can return unique values and aware of the fact that procedures can also

return result set. In order for a procedure to return a result set, it should be defined with the return type

if REFTABLE(table_name). The following is an example

CREATE OR REPLACE PROCEDURE SP_SELECT_EMP() RETURNS REFTABLE(EMP) LANGUAGE PLSQL AS

The table referred to in the REFTABLE clause should exist in the database even if there is no data in it

during the creation of the procedure. In order to return the data in the table, the RETURN REFTABLE

statement should be used as in this example where all the data in the EM P table will be returned to the

caller of the procedure who needs to handle the result set.

CREATE OR REPLACE PROCEDURE SP_SELECT_EMP() RETURNS REFTABLE(EMP) LANGUAGE PLSQL AS

BEGIN_PROC

BEGIN

Perform tasks

RETURN REFTABLE;


Perform tasks

END;

END_PROC;

Tables referred in the REFTABLE clauses in procedures currently defined in the database can’t be dropped

until the procedure is dropped or the procedure referring to the table is altered to return another table

data as result set.

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10. Workload Management Netezza appliance comes with components to configure the system resource usage so that it can be

utilized efficiently by the various user groups. In order to configure the system for optimal usage the

system usage need to be monitored and understood clearly. In this section we will look into how to

monitor the system usage and how the appliance can be configured for optimal resource usage.

Query History Collection and Reporting

Understanding the queries getting executed and the resources used in the system provides a good

understanding of how the appliance is being utilized by the users of the installation. Netezza can

automatically collect and store the details about the queries getting executed by creating a query history

database and enabling a query history configuration. The query history provides data like

Queries executed, their start and end time and the total execution time

Queries executed by users and user groups

Tables and the columns in the table accessed by the queries and the operations performed

Apart from using the historical query statistics to define the work load management, the data will also

help review and if required redefine the distribution and organization of table data. Enabling query

history data collection involves creating a query history database, creating a query history configuration

which defines the type of data which need to be collected and enabling the query history configuration

so that the system starts populating the query history database. The query history database should be

secured as with any other user defined database so that required privileges are granted only to the

required users.

The query history database can be created using the nzhistcreatedb command utility. The utility creates

the history database with the required tables to store data and views to run queries against them. It is

recommended to use the views to run any queries against the data collected instead of the underlying

tables for future compatibility purposes. The following is an example to create prodhistdb query history

database

nzhistcreatedb –d prodhistdb –t query –u huser –o hadmin –p hadminpass –v 1

The user hadmin will be the owner for the new query database and the user huser will be used to load

query statistic data into the tables in the history database. Both the user ids should be in existence in the

system before the nzhistcreatedb command is executed.

In order to start collecting the query statistics and store into the history database, query history

configurations need to be defined to collect the required level of query data and a configuration need to

be enabled. Multiple configurations can be defined for various levels of data but only one configuration

can be active at any time. When a configuration is enabled it collects by default the data about login

failures, session start and end and query history process startup. In addition to these default data users

can define a configuration to collect data about queries, plan, tables and columns. The following is an

example on creating a query history configuration and how to enable it

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-- To create a history configuration which collects all data regarding queries -- CREATE HISTORY CONFIGURATION prod_hist HISTTYPE QUERY DATABASE prodhistdb USER huser

PASSWORD 'huserpass' COLLECT PLAN,COLUMN LOADINTERVAL 10 LOADMINTHRESHOLD 4

LOADMAXTHRESHOLD 20 STORAGELIMIT 25 LOADRETRY 1 VERSION 1;

-- To set a new history configuration to take in effect when the appliance is started next time -- SET HISTORY CONFIGURATION prod_hist;

-- Stop and start the appliance so that the new history configuration set can take in effect -- nzstop

nzstart

The sample history configuration uses the history database created previously and requires the user id

and password of the user defined as the history database user specified in the –u option in the query

history database creation. The sample history configuration once enabled will collect data for all the areas

since by specifying PLAN in the COLLECT option it also collects for QUERY and also by specifying

COLUMN also collects the data for TABLES implicitly. As you may have noticed in the sequence of

steps to enable a history configuration, the system requires a stop and a start i.e. setting a configuration

will not get the configuration to take in effect. Once a configuration gets enabled, the system will start

collecting data and stores it in a directory. The data stored will get loaded into the history database at

regular interval mentioned in the LOADINTERVAL option of the configuration or when the

LOADMAXTHRESHOLD is reached.

To stop query history data collection a history configuration with histtype of none can be created and

that configuration can be enabled so that no query history data will be collected and loaded into the

query history database. The following is an example

-- To create a history configuration which collects no history data -- CREATE HISTORY CONFIGURATION disable_history HISTTYPE NONE;

-- To set a new history configuration to take in effect when the appliance is started next time -- SET HISTORY CONFIGURATION disable_history;

Overtime history database will grow in size with loading of data and the database data need to be purged

based on the period for which data is required so that the size can be controlled. The command line

utility nzhistcleanupdb can be used to delete data from the history database until up to a certain date and

time. Along with minimizing the storage use, purging unwanted data will also help improve the

performance against the history database. The following is an example to purge records which were

created before 2012 Jan 31 00:00:00 hrs from the prodhistdb database

nzhistcleanupdb -d prodhistdb -u hadmin -pw hadminpass –t "2012-12-31"

Also the history database can be dropped as any user database by using the drop database command. It is

important that no active history configurations are using the database before it is dropped so that there

are no issues with the query history data collection process.

For anyone who is interested in understanding queries executed and the pattern of usage of the system

the following are some of the key views in the query history database.

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Name Description

$v_hist_queries View to query about completed queries

$v_incomplete_queries View to query about queries for which data is not captured completely due to system reset or incomplete load of query statistics

$v_successful_queries Same view as $v_hist_queries but only shows data for successful queries

$v_unsuccessful_queries Same view as $v_hist_queries but only shows data for unsuccessful queries

$v_hist_log_events Shows data about all the events which happened in the system

$v_table_access_stats Shows cumulative stats on all access happened on each table in the system

$v_column_access_stats Shows cumulative stats on all access happened on all table column

Workload Management

Once the pattern of system usage is understood the workload in the system can be managed so that the

system resources can be utilized efficiently. Netezza provides the following features in order to manage

the workload

Feature Description

Short Query Bias (SQB)

This is to reserve system resources for all queries which are estimated to be complete in less than 2 seconds. The query time limit of less than 2 seconds is configurable along with the resources allocated for SQB. For the configuration changes to take in effect, the system would require a pause and resume. The resources which can be configured are number slots in the GRA scheduler and snippet scheduler queues for short queries, memories in snippet processors and host for short queries.

Guaranteed Resource Access (GRA)

User groups can be created called resource sharing groups (RSG) to which a min and max percentage of system resources can be assigned. This will make sure that any job or query executed by a user attached to the resource will be guaranteed the min percentage of the system resources. By default the admin users is defined to get 50% of the system resources and hence it is advisable to use the admin user sparingly. Also for any administrative tasks like backup etc. it is a good practice to have a resource sharing group defined with the required privileges and have users attached to the group so that they can perform the tasks.

Priority Query Execution (PQE)

A user, group or session can be assigned a priority of critical, high, normal or low and the appliance will prioritize the allocation of resources and schedule the queries or jobs executed in the order of priority accordingly. Critical and high priority jobs and queries get more resources than normal or low priority. If multiple jobs/queries with differing priorities are executed with in the same RSG, the jobs will get proportion of the resources assigned to the RSG based on the priority. There are two hidden priorities which are used by the system “System Critical” the highest priority for system operations and the “System Background” for low priority system jobs.

Gate Keeper Unlike the other 3 features, gate keeper is not enabled by default and would require a configuration change. Gate Keeper can be used to throttle the number of queries on various categories which can be executed at the same time in the system. By default configuration parameters are provided to set the maximum number of jobs/queries which can be executed concurrently in the system for the four job priorities of critical, high, normal and low. The number of jobs in

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Feature Description

each priority which can be executed concurrently can be modified using the configuration parameters. Once the number of jobs/queries for a particular priority reaches the number set in the configuration, the gate keeper starts queuing additional jobs in the internal queues for the particular category. If gate keeper is used along with GRA then the jobs gets scheduled based on the resource availability for the job in the RSG to which it belongs and the priority. Gate keeper can also be used to configure additional queues based to throttle queries based on the estimated amount of time to execute queries.

The following are some examples of workload management configuration using GRA, PQE and gate

keeper

-- To create a RSG with minimum resource allocation of 15% and max allocation of 30% -- CREATE GROUP reporting WITH RESOURCE MINIMUM 15 RESOURCE MAXIMUM 30;

-- To create a user group with default priority of high and all the users attached to it gets the priority of high -- CREATE GROUP tableau WITH DEFPRIORITY HIGH;

-- To set the priority if an active session to critical -- ALTER SESSION 501028 SET PRIORITY TO CRITICAL;

-- To alter the default priority and the maximum priority of an user -- ALTER USER mike WITH DEFPRIORITY LOW MAXPRIORITY HIGH;

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11. Best Practices Define all constraints and relationships between objects. Even though Netezza doesn’t enforce

them other than the not null constraint, the query optimizer will still use these details to come-up

with an efficient query execution plan.

When defining columns use data types for which Netezza can create zone maps. Some of the

easy targets are not using columns with numeric(x,0).

If data for a column is known to have a fixed length value, then use char(x) instead of varchar(x).

Varchar(x) uses additional storage which will be significant when dealing with TB of data and

also impacts the query processing since additional data need to be pulled in from disk for

processing.

Use NOT NULL wherever data permits. This will help improve performance by not having to

check for null condition by the appliance and will reduce storage usage.

Use the same data type for columns used in joins so that the query execution can be efficient

which in turn helps queries execute faster.

User the same data type and length for columns with the same name in all the tables in the

database.

Distribute on columns of high cardinality and ones that used to join often. It is best to distribute

fact and dimension table on the same column. This will reduce the data redistribution during

queries improving the performance.

Even if both the fact and dimension table can’t be distributed on the same key, make effort to

avoid redistribution on the fact table by choosing the right column for distribution.

Distribute on one column whenever possible and do not create keys for the sake of distribution.

Use random distribution as the last resort and it is fine to use random distribution on small table

since they may get broadcast.

Define clustered base table when data in a fact table is often looked through multiple

dimensions. Use columns which will used to look into the data through multiple dimensions

when organizing data in a clustered base table.

Create materialized view on a small set of the columns from a large table often used by user

queries.

Create sorted materialized views with the most restricting column in the order by clause of the

view so that it can be used as an index.

Do not drop and recreate materialized view since the OID will get changed and may impact

other dependent objects.

Schedule grooms after major changes like updates, deletes, alters on tables so that space

utilization is optimizes along with increase in query performance.

Schedule regular “generate statistics” jobs particular on larger tables and tables with very activity

so that the optimizer can generate optimal execution plans which improves query performance.

Schedule regular backups which should also include host backup so that data backup and the

catalogs are in sync.

Do not store large quantity of data in the host since it will affect the performance. Use network

mounts or third party vendor products to take and store backups.

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Monitor the system work load at regular intervals and alter the system workload management

accordingly.

Use admin user sparingly. Define a separate group with proper resource allocation along with

required privileges to perform admin related tasks and add users to it to perform admin tasks.

Prefer joins over correlated sub queries.

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12. Version 7 Key Features Page level zone maps instead of extent level.

o Until version 6.0 zone maps were created at the extend level which is 3 MB. But starting

version 7.0 zone maps are created at the page level which is 128 KB and that means less

amount of data will be brought into SPU since the system knows what data is stored at a

much more granular level and eliminate unwanted pages to be read.

Parallelism in query snippet processing

o Currently snippets in a query plan are processed in sequence and with version 7.0 where

possible snippets in query will be executed in parallel which will improve the query

performance significantly.

Restricted distribution of snippets to SPUs

o Currently snippets in query plans are scheduled and executed in all the SPUs. Since the

appliance knows what data is stored in which disk which intron attached to which SPU,

query snippets will only get distributed to the SPU which has the relevant data for the

query to be processed.

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13. Further Reading

Subject Area Reference

Architecture Netezza System Administration Guide

Netezza User Objects Netezza User Guide

Administration Netezza System Administration Guide Netezza User Guide Netezza Advance Security Administration Guide

Data Loading, Backup and Restore Netezza Data Loading Guide Netezza System Administration Guide

Query History, Work Load Mgmt Netezza System Administration Guide

Stored Procedures Netezza Stored Procedures Guide

Netezza fundamentals for developers

Technology

database accelerator

replace procedure

database management

gui based

admin user

query history

query execution

query history