Introduction to Netezza

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Introduction to Netezza

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Topics

• Netezza Architecture

• SQL Differences

• Stored Procedure Differences

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Netezza Architecture – TwinFin 12

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Topics

System Architecture

Snippet Processing Unit (SPU)

System Capacity

Know Your Customers. Grow Your Business.

Distribution in a shared-nothing architecture

Zone maps & data organization

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Database Hardware Architecture Terminology

• Symmetrical Multi Process Architecture (SMP)

• Multiple processors sharing access to disk and memory

• Processors operate asynchronously

• Examples: Sybase, Sybase IQ, Oracle, SQL Server, DB2

• Massive Parallel Process Architecture (MPP)

• Multiple processors, each with separate, dedicated memory and disk

• No hardware is shared between processors

• Processors are slaved to a controller

• Processors operate synchronously

• Implemented many different ways

• Netezza approach is unique

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Netezza Architecture

Host compiles SQL, develops execution plan and sends code Host collects and

returns result sets

Result sent to

user

Host

SQL command sent to host from user

snippets to SPUs based on plan

SPU executes code snippet.

All SPUs execute same code synchronously. SPUs pass data between

each other as needed

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Snippet Processing Unit (SPU)

Each SPU has a dedicated 1TB disk

Disk controlled by FPGA (Field

Programmable Gate Array)

CPU loads query code into FPGA

FPGA executes code using memory

and cache

CPU performs additional processing to

result set

CPU communicates to controller and

other SPUs via NIC

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System Capacity – TwinFin 12

• 12 S-Blades, each have 8 SPUs

• 4 SPUs are spares for fail over – 92 usable SPUs

• Each SPU has a 1TB disk drive

• Disk divided into 3 330GB partitions:

• Active data area

• Workspace

• Redundant copy of another SPU’s active data area

• Nominal 330GB capacity per SPU (uncompressed)

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MPP Shared Nothing Architecture

• A SPU only has visibility to the data on its own disk.

• A SPU will broadcast data to other SPUs as needed (based on

instructions from the controller) via the NIC.

• Broadcast data is received and held in memory or work area by

the SPU for use in resolving the query.

• Each SPU operates synchronously with all other SPUs. Each

execute the same snippet at the same time.

• The controller’s execution plan ensures minimal data transfer

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Data Distribution

• Data distribution to a SPU is defined by the DISTRIBUTE ON

clause of the CREATE TABLE command

• Each table is distributed across all 92 SPUs

• You can distribute RANDOM or specify one or more columns in

the table as a distribution key

• Controller uses distribution key information when deciding the execution plan

• Joins between tables with the same distribution key that are joined using that key will execute entirely on that SPU without the need to broadcast data (table co-location)

• Joins to tables with RANDOM distribution will require data broadcast. The smaller of the two sets is broadcast

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Effects of Poor Data Distribution

• System Capacity

• The system is ‘full’ when any one SPU is full

• A badly skewed distribution of a large table may fill up a SPU prematurely

• Try to keep skew under 10% (5% off average) for very large tables

• System Performance

• SQL code snippets are executed synchronously among all SPUs

• The total execution time for a snippet is the longest time among the SPUs

• The total execution time for a query is the sum of the longest times for each snippet

• Snippet execution time is proportionally related to the amount of data a SPU needs to process

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Data Distribution – Co-Location

• Co-Location is the physical placement of related data across

multiple tables onto the same SPU

• Allows a SPU to join co-located data without interaction with other SPUs

• Speeds queries by eliminating data broadcast steps

• Tables must have identical distribution key columns

• RANDOM distribution will not co-locate

• Any difference in columns will result in a different, unrelated SPU assignment

• Join expression must include all distribution key columns

• Query compiler cannot assume co-location unless the join between two co-located tables includes equi-joins between all columns specified in the distribution key

• Ensure use by defining as few columns as possible as a distribution key

• Don’t bother with small tables

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Data Distribution - Example

• All 3 tables share ID_TS as a

common key

• ID_TS is always used when joining

these tables

• ID_TS is the appropriate column to

use as a distribution key

CREATE TABLE TSVALUES (

ID_TSDATE INTEGER NOT NULL, ID_TS INTEGER NOT NULL,

VALUE DOUBLE PRECISION NOT NULL, ID_SRC INTEGER NOT NULL,

ID_STATUS INTEGER NOT NULL, TS_BEGIN TIMESTAMP NOT NULL, TS_END TIMESTAMP NOT NULL, SIGMA DOUBLE PRECISION NOT NULL )

DISTRIBUTE ON (ID_TS)

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Data Distribution – Example

• Aginity Workbench

• Provides ability to redistribute existing tables

• Allows you to view a table’s distribution

• Distribution display for TSValues:

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Co-Located Queries

• The query will execute in parallel across all SPUs without data

sharing because:

SELECT N.ID_TS, N.TICKER, N.NAME, V.VALUE FROM TSNAMES N, TSVALUES V

WHERE N.ID_TS = V.ID_TS

AND V.TS_BEGIN BETWEEN N.TS_BEGIN AND N.TS_END AND V.TS_END = ‘1/1/3000’;

sharing because:

• Both tables are distributed on the same key (ID_TS)

• ID_TS is used to join the tables

• Identical distribution key definitions across tables ensure rows

with the same distribution key values in those tables reside on

the same disk and SPU.

• Run times (493M row result set):

• Co-located: 22.1 seconds

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Distribution Key Advice

• Keep distribution key as small as possible

• One column is best

• Ensure full use in joins

• Tables with some keys in common will not result in co-location

• Use a key that provides even distribution

• Avoid skews > 10% in very large tables (> 5% nominal system capacity)

• Avoid skews > 5% in multiple same key tables with total size > 5% nominal system capacity

• Avoid skews > 20% in other tables

• May be necessary to add a column to the distribution key to reduce skew

• Don’t worry about skew in small tables (< 0.1 % nominal system capacity)

• Poor Distribution Keys

• Dates – cause query ‘hotspots’ when queries based on date range

• Low cardinality columns

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Zone Maps

• A zone map records the high and low values of columns for

rows in a data block on disk

• Netezza uses zone maps to skip data blocks that do not satisfy

query predicates, speeding query execution

• Zone maps are automatically created for Integer, Date and

Timestamp columns

• You can specify additional zone map columns in the ORGANIZE

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ORGANIZE ON Clause

• Defines a Clustered Base Table

• Data is arranged (sorted) by the ORGANIZE ON columns during

GROOM

• Improves effectiveness of zone maps

• Specify up to 4 columns

• Allowable data types:

• INTEGER, DATE, TIMESTAMP

• CHAR, VARCHAR, NCHAR, NVARCHAR – Only first 8 bytes used

• NUMERIC – up to NUMERIC(18)

• FLOAT, DOUBLE

• BOOL

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Using ORGANIZE ON

• Select columns most likely used in WHERE clause

• Zone map efficiency diminishes for 3

rd

_{and 4}

th

_columns

• Greater chance a wide range of values appearing in a data block

• Arrange lower cardinality or “clumpier” columns first

• Groups larger numbers of rows first, spread over more data blocks

• Increases likelihood subsequent columns spread over multiple blocks

• “Clumpy” – large portion of rows have few unique values

• GROOM table periodically

• Reorganizes data according to ORGANIZE ON specification

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Using ORGANIZE ON - TSValues

• All current rows have ts_end = 1/1/3000 (very clumpy)

• Current rows are of greatest interest (most often selected)

• ts_begin commonly used in most queries

• Specify ts_end first. Clumps all current rows into contiguous

blocks.

CREATE TABLE TSVALUES

(

ID_TSDATE INTEGER NOT NULL, ID_TS INTEGER NOT NULL,

VALUE DOUBLE PRECISION NOT NULL, ID_SRC INTEGER NOT NULL,

ID_STATUS INTEGER NOT NULL, TS_BEGIN TIMESTAMP NOT NULL, TS_END TIMESTAMP NOT NULL, SIGMA DOUBLE PRECISION NOT NULL )

DISTRIBUTE ON (ID_TS)

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GROOM Command

• Organizes rows based on ORGANIZE ON clause

GROOM TABLE TSVALUES

RECORDS ALL

RECLAIM BACKUPSET DEFAULT;

• Organizes rows based on ORGANIZE ON clause

• Reclaims space from deleted rows

• Netezza performs updates by deleting old row and inserting new row

• Deleted rows consume space until table is GROOMed

• Fast – 15 minutes for 4.6 billion rows in TSValues

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SQL Differences – Netezza/Sybase

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Topics

• General Language Characteristics

• Identifiers

• Data Types

• Functions

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General Language Characteristics

• Similar to Oracle syntax and interpretation

• Commands terminated with semi-colon (;)

• Terminator is required at all times

• Uses Oracle’s interpretation of NULL

• A zero length string (‘’) is considered NULL

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Identifiers

• An identifier names a database object

• There are 2 types of Identifiers: regular and delimited

• Regular Identifier

• Is case insensitive

• Must begin with a letter

• Contain letters, digits, underscores, dollar sign ($)

• Delimited Identifier

• Enclosed in double quotes

• Is case sensitive

• May include spaces, other special symbols and reserved words

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Data Types

• Does not support LOB types (CLOB, BLOB)

• Character types

• CHAR, VARCHAR: ASCII data, 64,000 maximum length

• NCHAR, NVARCHAR: Unicode data, 16,000 maximum length

• Exact Numeric Types

• BYTEINT 8 bit signed integer

• SMALLINT 16 bit signed integer

• INTEGER 32 bit signed integer

• BIGINT 64 bit signed integer

• NUMERIC(p,s), NUMERIC(p) Up to 38 digits precision (p), scale (s) from 0 to p

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Data Types (continued)

• Approximate Numeric Types

• FLOAT(p) precision can range from 1 to 15

• REAL same as FLOAT(6) – 4 bytes

• DOUBLE PRECISION same as FLOAT(15) – 8 bytes

• Logical Types

• BOOLEAN (or BOOL) True/false value, 1 byte

• Temporal Types

• Time maintained to microsecond (6 decimal places)

• DATE Date with no time – 4 bytes

• TIME Time with no date – 8 bytes

• TIME WITH TIME ZONE Time with time zone information – 12 bytes

• TIMESTAMP Date and time – 8 bytes

• INTERVAL Time interval, non-standard implementation • Maintained in seconds

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Internal Data Types (pseudo-columns)

• ROWID

• Unique row identifier, assigned when row is inserted

• Not sequential in table

• Value range 100,000 - 9,223,372,036,854,775,807

• Will numbers ever repeat?

• If a table contains 2 billion rows and takes 3 minutes to copy

• And if the table is copied repeatedly and continuously (add 2 billion rows every 3 minutes)

• It will take over 24,000 years to run out of numbers

• Netezza hopes you would have upgraded before then

• CREATEXID, DELETEXID

• Transaction ID that created and deleted the row.

• If DELETEXID > 0, row has been deleted

• You can not see deleted rows using SQL

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Operators

• Differences from Sybase

• Concatenation ||

• Not Equal <> or !=

• Null test ISNULL or IS NULL

NOTNULL or IS NOT NULL

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Conditional Column Expression Functions

• CASE

Searched form:

CASE

WHEN <search-condition-1> THEN <result-1> WHEN <search-condition-2> THEN <result-2> ...

WHEN <search-condition-n> THEN <result-n> ELSE <default-result>

Value form:

CASE <test-value>

WHEN <comparand-value-1> THEN <result-1> WHEN <comparand-value-2> THEN <result-2> ...

WHEN <comparand-value-n> THEN <result-n> ELSE <default-result>

• DECODE

• Same as Oracle DECODE

• decode(<expr>,<search1>,<result1>, …<search N>,<result N>,<default>) ELSE <default-result>

END

Search conditions can be arbitrarily complex and results can be expressions.

ELSE <default-result> END

Test values, comparand values, and results can be expressions.

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Commonly Used Functions

• Current time

• Use NOW() or CURRENT_TIMESTAMP

• TIMEOFDAY() returns a verbose date string. Example: ‘Mon Jan 24 16:12:05 2011 EST’

• Date Conversion

• Date literal is in MM/DD/YYYY format (default). Example: ‘1/1/3000’

• TO_DATE(<text>, <template>) Converts string to DATE data type Template describes format of date string.

Example: TO_DATE(‘24 Jan 2011’, ‘DD Mon YYYY’)

• TO_TIMESTAMP (<text>, <template>) Converts string to TIMESTAMP data type

• TO_CHAR(date or timestamp, <template>) Converts date or timestamp to string

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SQL Differences - Update

Sybase

Netezza

update tempRaw a

Set Sigma = b.Sigma from TSValues b

where a.ID_TS = b.ID_TS

and a.ID_TSDate = b.ID_TSDate update #tempRaw

Set a.Sigma = b.Sigma from #tempRaw a,TSValues b where a.ID_TS = b.ID_TS

and a.ID_TSDate = b.ID_TSDate

• Special characters not used

for temporary tables

• Table only referenced once

and a.ID_TSDate = b.ID_TSDate and now() < b.ts_end

and a.ID_Status >= 1

and a.ID_StatusSigma <= -1 and a.isdelete = 0;

and a.ID_TSDate = b.ID_TSDate and getdate() < b.ts_end

and a.ID_Status >= 1

and a.ID_StatusSigma <= -1 and IsDelete = 0

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SQL Differences - DELETE

• DELETE may only reference one table

• DELETE FROM <table> WHERE <condition>

• Cannot directly convert:

DELETE #tempRaw FROM TSValues B , #tempRaw A

where A.ID_TS = B.ID_TS and A.ID_TSDate = B.ID_TSDate

and ( ( A.Value = B.Value and A.ID_Status = B.ID_Status ) or ( B.ID_Status >= 1 and A.ID_Status <= -1 )) AND ( abs(A.Sigma - B.Sigma) < 0.00000001 or A.ID_StatusSigma <= -1)

and getdate() between B.ts_begin and B.ts_end AND A.Comments IS NULL

• Use UPDATE then DELETE:

Add a boolean column ‘deleteme’ to tempRaw table. Initialize to FALSE. UPDATE tempRaw a

set deleteme = TRUE FROM TSValues B

WHERE A.ID_TS = B.ID_TS AND A.ID_TSDate = B.ID_TSDate

AND ( ( A.Value = B.Value and A.ID_Status = B.ID_Status ) OR ( B.ID_Status >= 1 and A.ID_Status <= -1 )) AND ( abs(A.Sigma - B.Sigma) < 0.00000001 OR A.ID_StatusSigma <= -1)

AND NOW() BETWEEN B.ts_begin AND B.ts_end AND A.Comments IS NULL; DELETE FROM tempRaw WHERE deleteme;

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SQL Differences

• Materialized Views

• View may have only one source table

• Limited to 64 columns

• May include an ORDER BY clause

• Optimize zone maps

• Cannot include an ORGANIZE ON clause

• Sequences

• You may specify data type (byteint, smallint, int, bigint)

• Default is bigint (64 bit signed integer)

• Blocks of numbers are allocated to each SPU during execution

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Stored Procedure Differences

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Topics

• General Differences

• CREATE PROCEDURE statement

• Iterating through rows (cursors)

• Returning result sets

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General Differences

• Statements are terminated with semi-colons (;)

• Variables are not prefixed with special characters

• := symbol used for variable assignment

a := 1;

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CREATE PROCEDURE

Sybase

Netezza

CREATE PROCEDURE sp_TS_ProcessFillValues (@LoaderID int, @ID_SRC int)

AS

DECLARE @err_no int, @err_msg varchar(250), @CurrentTime datetime

<...procedure code ...>

CREATE PROCEDURE sp_TS_ProcessFillValues

(INTEGER, INTEGER)

RETURNS INTEGER EXECUTE AS CALLER LANGUAGE NZPLSQL

AS

begin_proc

DECLARE

<...procedure code ...>

GO

DECLARE

LoaderID alias for $1; ID_SRC ALIAS FOR $2;

CurrentTime datetime; EndDate datetime; StartDate datetime; begin <... procedure code ...> end; end_proc;

Parameters are unnamed, referenced by position: $1, $2…

Alias may be used to name parameters

Procedure enclosed in a BEGIN_PROC -END_PROC block

Executable code enclosed in a BEGIN – END block following declarations.

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Iterating Through Rows

• Netezza does not support cursor data type

• Use looping to iterate through a result set:

FOR record | row IN select_clause LOOP

statements

END LOOP;

Know Your Customers. Grow Your Business. END LOOP;

Alternate form:

FOR record | row IN EXECUTE text_expression LOOP

statements

END LOOP;

• Example

DECLARE myrec RECORD;

mytotal DOUBLE PRECISION; BEGIN

FOR myrec IN SELECT * FROM tsvalues LOOP mytotal := mytotal + myrec.value;

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Returning Sets from Stored Procedures

• Use RETURNS REFTABLE (<table name>) clause

• <table name> refers to a table defined in the schema

• Used to define the format of the result set being returned (metadata only)

• Use REFTABLENAME to refer to the result set

• REFTABLENAME is a system provided variable

• System generates a unique table name

• Essentially a CREATE TEMP TABLE command

• Generate result set using EXECUTE IMMEDIATE

• Example:

EXECUTE IMMEDIATE ‘INSERT INTO ‘ || REFTABLENAME || ‘ VALUES (…);’

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System Variables and Special Functions

• SQLERRM

• Error message relating to last SQL error

• ROW_COUNT

• Number of rows affected by last executed SQL statement

• QUOTE_LITERAL(<expression>)

• Used when building EXECUTE IMMEDIATE strings

• Converts the value of the expression into a properly formatted literal string

• Escapes any special characters as necessary.

• QUOTE_IDENT(<expression>)

• Used when building EXECUTE IMMEDIATE strings

• Converts expression into properly formatted database object name string

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