Teradata Database. SQL Data Types and Literals

Transcription

1 Teradata Database SQL Data Types and Literals Release 15.0 B K September 2015

2 The product or products described in this book are licensed products of Teradata Corporation or its affiliates. Teradata, Active Data Warehousing, Active Enterprise Intelligence, Applications-Within, Aprimo Marketing Studio, Aster, BYNET, Claraview, DecisionCast, Gridscale, MyCommerce, QueryGrid, SQL-MapReduce, Teradata Decision Experts, "Teradata Labs" logo, Teradata ServiceConnect, Teradata Source Experts, WebAnalyst, and Xkoto are trademarks or registered trademarks of Teradata Corporation or its affiliates in the United States and other countries. Adaptec and SCSISelect are trademarks or registered trademarks of Adaptec, Inc. AMD Opteron and Opteron are trademarks of Advanced Micro Devices, Inc. Apache, Apache Avro, Apache Hadoop, Apache Hive, Hadoop, and the yellow elephant logo are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries. Apple, Mac, and OS X all are registered trademarks of Apple Inc. 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3 Preface Purpose SQL Data Types and Literals describes how to use data types and literals within Teradata SQL. Use this book in conjunction with the other books in the SQL book set: See SQL Geospatial Types for information about geospatial types. See Teradata XML for information about the XML type. See Teradata JSON for information about the JSON type. Audience System administrators, database administrators, and security administrators are the principal audience for this book. Teradata field engineers and other technical personnel responsible for designing and maintaining Teradata Database may also find this book useful. Supported Software Releases and Operating Systems This book supports Teradata Database Teradata Database 15.0 is supported on: SUSE Linux Enterprise Server 10 SP3 SUSE Linux Enterprise Server 11 SP1 Teradata Database client applications support other operating systems. Prerequisites You should be familiar with basic relational database management technology and SQL. This manual is not an SQL primer. If you are not familiar with Teradata Database, you may find it useful to read Introduction to Teradata before reading this document. For additional information about developing applications using embedded SQL, see Teradata Preprocessor2 for Embedded SQL Programmer Guide. SQL Data Types and Literals 3

4 Preface Changes to This Book Changes to This Book Release Teradata Database 15.0 September 2015 Teradata Database 15.0 June 2014 Teradata Database 15.0 March 2014 Description Clarified that the maximum length for the column name heading when using the TITLE phrase is 256 Unicode characters. Completed updates based on comments received during translation and QA of the book. Added a cross-reference to Teradata JSON to direct users to information about the JSON data type. Updated the description of the COMPRESS/DECOMPRESS phrase to include support for the JSON type. Added references to ANSI Temporal Table Support. Additional Information URL Description Use the Teradata Information Products Publishing Library site to: View or download a manual: 1 Under Online Publications, select General Search. 2 Enter your search criteria and click Search. Download a documentation CD-ROM: 1 Under Online Publications, select General Search. 2 In the Title or Keyword field, enter CD-ROM, and click Search The Teradata home page provides links to numerous sources of information about Teradata. Links include: Executive reports, white papers, case studies of customer experiences with Teradata, and thought leadership Technical information, solutions, and expert advice Press releases, mentions and media resources Teradata Customer Education delivers training that builds skills and capabilities for our customers, enabling them to maximize their Teradata investment. Use Your Service to access Orange Books, technical alerts, and knowledge repositories, view and join forums, and download software patches. 4 SQL Data Types and Literals

5 Preface Product Safety Information URL developer.teradata.com/ Description Teradata Developer Exchange provides articles on using Teradata products, technical discussion forums, and code downloads. To maintain the quality of our products and services, we would like your comments on the accuracy, clarity, organization, and value of this document. Please Product Safety Information This document might contain several types of product safety statements: Safety Information Type Notice Caution WARNING Description Indicates a situation which, if not avoided, could result in damage to property, such as to equipment or data, but not related to personal injury. Indicates a hazardous situation which, if not avoided, could result in minor or moderate personal injury. Indicates a hazardous situation which, if not avoided, could result in death or serious personal injury. Examples: Notice: Caution: Improper use of the Reconfiguration utility can result in data loss. A drive tray chassis weighs approximately 28.6 kg (63 lb). Do not attempt to remove or install the chassis until all the drives and modules have been removed. WARNING: Risk of electrical shock! Always remove power to the power supply/fan module before servicing it. Teradata Database Optional Features This book may include descriptions of the following optional Teradata Database features and products: Teradata Columnar Teradata Row Level Security Teradata Temporal Teradata Virtual Storage (VS) SQL Data Types and Literals 5

6 Preface Teradata Database Optional Features You may not use these features without the appropriate licenses. The fact that these features may be included in product media or downloads, or described in documentation that you receive, does not authorize you to use them without the appropriate licenses. Contact your Teradata sales representative to purchase and enable optional features. 6 SQL Data Types and Literals

7 Table of Contents Preface Purpose Audience Supported Software Releases and Operating Systems Prerequisites Changes to This Book Additional Information Product Safety Information Teradata Database Optional Features Chapter 1: SQL Data Definition Data Definition in SQL Data Definition Phrases Data Conversion COMPRESS and DECOMPRESS Phrases Constraint Attributes Uniqueness Constraints CHECK Constraints Referential Constraints Chapter 2: Data Literals Hexadecimal Byte Literals Overview of Numeric Literals Integer Literals Hexadecimal Integer Literals Decimal Literals Floating Point Literals NUMBER Literals Overview of Date and Time Literals SQL Data Types and Literals 7

8 Table of Contents DateTime Literals Date Literals Time Literals Timestamp Literals Overview of Interval Literals INTERVAL YEAR Literals INTERVAL YEAR TO MONTH Literals INTERVAL MONTH Literals INTERVAL DAY Literals INTERVAL DAY TO HOUR Literals INTERVAL DAY TO MINUTE Literals INTERVAL DAY TO SECOND Literals INTERVAL HOUR Literals INTERVAL HOUR TO MINUTE Literals INTERVAL HOUR TO SECOND Literals INTERVAL MINUTE Literals INTERVAL MINUTE TO SECOND Literals INTERVAL SECOND Literals Period Literals Character String Literals Unicode Delimited Character Literals Hexadecimal Character Literals Graphic Literals Chapter 3: Numeric Data Types BYTEINT Data Type SMALLINT Data Type INTEGER Data Type BIGINT Data Type DECIMAL/NUMERIC Data Types FLOAT/REAL/DOUBLE PRECISION Data Types NUMBER Data Type Operations on Floating Point Values Rounding SQL Data Types and Literals

9 Table of Contents Chapter 4: DateTime and Interval Data Types DateTime Fields Time Zones DATE Data Type TIME Data Type TIMESTAMP Data Type TIME WITH TIME ZONE Data Type TIMESTAMP WITH TIME ZONE Data Type INTERVAL YEAR Data Type INTERVAL YEAR TO MONTH Data Type INTERVAL MONTH Data Type INTERVAL DAY Data Type INTERVAL DAY TO HOUR Data Type INTERVAL DAY TO MINUTE Data Type INTERVAL DAY TO SECOND Data Type INTERVAL HOUR Data Type INTERVAL HOUR TO MINUTE Data Type INTERVAL HOUR TO SECOND Data Type INTERVAL MINUTE Data Type INTERVAL MINUTE TO SECOND Data Type INTERVAL SECOND Data Type Chapter 5: Period Data Types Period Data Types: Basic Definitions PERIOD(DATE) Data Type PERIOD(TIME) Data Type PERIOD(TIME WITH TIME ZONE) Data Type PERIOD(TIMESTAMP) Data Type PERIOD(TIMESTAMP WITH TIME ZONE) Data Type Chapter 6: Character and CLOB Data Types Character Data CHARACTER Data Type SQL Data Types and Literals 9

10 Table of Contents VARCHAR Data Type CLOB Data Type Default Case Specificity of Character Columns CASESPECIFIC Phrase UPPERCASE Phrase Teradata SQL Character Strings and Client Physical Bytes CHARACTER SET Phrase LATIN Server Character Set UNICODE Server Character Set GRAPHIC Server Character Set KANJISJIS Server Character Set KANJI1 Server Character Set Chapter 7: Byte and BLOB Data Types Data Storage of Byte and BLOB Types BYTE Data Type VARBYTE Data Type BLOB Data Type Chapter 8: UDT Data Type UDT Data Type Chapter 9: ARRAY/VARRAY Data Type ARRAY/VARRAY Data Type Chapter 10: Parameter Data Types TD_ANYTYPE Data Type VARIANT_TYPE Data Type SQL Data Types and Literals

11 Table of Contents Chapter 11: Default Value Control Phrases Using Default Value Control Phrases NOT NULL Phrase DEFAULT Phrase WITH DEFAULT Phrase Chapter 12: Data Type Formats and Format Phrases Data Type Default Formats DATE Formats TIME and TIMESTAMP Formats FORMAT FORMAT Phrase and Character Formats FORMAT Phrase and NUMERIC Formats FORMAT Phrase and DateTime Formats FORMAT Phrase, DateTime Formats, and Japanese Character Sets Naming Columns and Expressions AS NAMED TITLE Appendix A: Notation Conventions Syntax Diagram Conventions Character Shorthand Notation Used In This Book Predicate Calculus Notation Used In This Book Appendix B: External Representations for UDTs Transforms Off and Period Data Type Support UDTTransformsOff Flag PeriodStructOn Flag ArrayTransformsOff Flag Flag Setting Combinations SQL Data Types and Literals 11

12 Table of Contents USING Clause Transform Input/Output Strings for ARRAY/VARRAY UDTs Glossary Index SQL Data Types and Literals

13 CHAPTER 1 SQL Data Definition This chapter describes some of the general principles of SQL data definition. Data Definition in SQL Data definition phrases, also referred to as data description phrases, are used in SQL statements to define how to store data in the columns of a table, how to present the data in the results of queries, and whether to apply column-level integrity constraints. You can also use data definition phrases in expressions to convert data to another type or modify data attributes. Syntax column_name data_type_declaration data_type_attribute column_storage_attribute column_constraint_attribute FF07D233 where: Syntax element Specifies data_type_declaration data_type_attribute column_storage_attribute the data type of a column, such as BYTE or FLOAT. For more information, see Data Type Phrases on page 14. attributes for a column, such as a default value to insert when an INSERT statement omits the value. For more information, see Core Data Type Attributes on page 17. to compress certain values and nulls for one or more columns of a table. For more information, see: Storage and Constraint Attributes on page 18. COMPRESS and DECOMPRESS Phrases on page 22. SQL Data Types and Literals 13

14 Chapter 1: SQL Data Definition Data Definition Phrases Syntax element column_constraint_attribute Specifies column-level integrity constraints, such as PRIMARY KEY. For more information, see: Storage and Constraint Attributes on page 18. Column and Table Constraints on page 30. ALTER TABLE and CREATE TABLE in SQL Data Definition Language. Using Data Definition The following CREATE TABLE statement uses Teradata SQL data definition phrases to define the columns of the Employee table: CREATE TABLE Employee (EmpNo PRIMARY KEY SMALLINT FORMAT '9(5)' CHECK(EmpNo BETWEEN 1000 AND 32001), Name VARCHAR(12) CHARACTER SET LATIN NOT CASESPECIFIC NOT NULL, DeptNo SMALLINT FORMAT '999' CHECK (DeptNo BETWEEN 100 AND 900), JobTitle VARCHAR(12) CHARACTER SET LATIN NOT CASESPECIFIC, Salary DECIMAL(8,2) FORMAT 'ZZZ,ZZ9.99' CHECK (Salary BETWEEN 1.00 AND ), YrsExp BYTEINT FORMAT 'Z9' CHECK (YrsExp BETWEEN -99 AND 99), DOB DATE FORMAT MMMbDDbYYYY NOT NULL, Sex CHAR CHARACTER SET LATIN UPPERCASE NOT NULL, Race CHAR CHARACTER SET LATIN UPPERCASE, MStat CHAR CHARACTER SET LATIN UPPERCASE, EdLev BYTEINT FORMAT 'Z9' CHECK (EdLev BETWEEN 0 AND 2) NOT NULL, HCap BYTEINT FORMAT 'Z9' CHECK (HCap BETWEEN -99 AND 99) INDEX (Name) ; The following SELECT statement uses Teradata SQL data definition phrases to modify the heading of the EmpNo column and the format of the results of the Salary column: SELECT EmpNo (TITLE 'Employee Number'), Salary (FORMAT 'GLLZ(I)D9(F)') FROM Employee; Data Definition Phrases Data Type Phrases A data type phrase (data_type_declaration) determines the type of data to store in a column of a table on Teradata Database. When you create a table, you must specify a data type phrase for each column. A column does not have a default data type. You can also use data type phrases to specify data conversions and casts in expressions. 14 SQL Data Types and Literals

15 Chapter 1: SQL Data Definition Data Definition Phrases Use the data type phrases in the following table to define the data type as byte, numeric, DateTime, interval, character, period, or user-defined type (UDT). Data Type Byte BLOB[(n)] BYTE[(n)] VARBYTE[(n)] Numeric BIGINT BYTEINT DATE a DECIMAL [(n[,m])] DOUBLE PRECISION FLOAT INTEGER NUMBER(n[,m]) NUMBER[(*[,m])] NUMERIC [(n[,m])] REAL SMALLINT DateTime DATE TIME [(n)] TIMESTAMP [(n)] Interval INTERVAL INTERVAL DAY [(n)] INTERVAL DAY [(n)] TO HOUR INTERVAL DAY [(n)] TO MINUTE INTERVAL DAY [(n)] TO SECOND ANSI SQL X X X X X X X X X X X X X X X X X Teradata Extension to ANSI SQL X X X X X X SQL Data Types and Literals 15

16 Chapter 1: SQL Data Definition Data Definition Phrases Data Type INTERVAL HOUR [(n)] INTERVAL HOUR [(n)] TO MINUTE INTERVAL HOUR [(n)] TO SECOND INTERVAL MINUTE [(n)] INTERVAL MINUTE [(n)] TO SECOND [(m)] INTERVAL MONTH INTERVAL SECOND [(n,[m])] INTERVAL YEAR [(n)] INTERVAL YEAR [(n)] TO MONTH Character CHAR[(n)] CHARACTER(n) CHARACTER SET GRAPHIC CLOB CHAR VARYING(n) LONG VARCHAR LONG VARCHAR CHARACTER SET GRAPHIC VARCHAR(n) VARCHAR(n) CHARACTER SET GRAPHIC Period PERIOD(DATE) PERIOD(TIME [(n)]) PERIOD(TIMESTAMP [(n)]) UDT udt_name ANSI SQL X X X X X X X X X X X X X X Teradata Extension to ANSI SQL X X X X X X X Note: DATE is supported both in its Teradata form and in the preferred ANSI DateTime form. For new development, define DATE using ANSI DATE type. The CREATE TYPE statement determines the name of a UDT. For details on the different level of ANSI compliance, see SQL Fundamentals. 16 SQL Data Types and Literals

17 Chapter 1: SQL Data Definition Data Definition Phrases Core Data Type Attributes The following table lists the main Teradata SQL data type attributes. Data Type Attribute ANSI Teradata Extension to ANSI NOT NULL X UPPERCASE [NOT] CASESPECIFIC FORMAT format_string TITLE char_string X X X X AS name X NAMED name X DEFAULT value X DEFAULT USER DEFAULT DATE DEFAULT TIME X X X DEFAULT NULL X WITH DEFAULT X WITH TIME ZONE CHARACTER SET X X The expression data type attributes is non-ansi, as are attributes such as TITLE and FORMAT. Restrictions apply to some attributes. For example, you cannot use the WITH DEFAULT attribute with UDT data types. For further details, see: Chapter 11: Default Value Control Phrases Chapter 12: Data Type Formats and Format Phrases SQL Data Definition Language SQL Data Types and Literals 17

18 Chapter 1: SQL Data Definition Data Definition Phrases Storage and Constraint Attributes The following table lists Teradata SQL storage and constraint attributes. Attribute Family Data Type Attribute ANSI Teradata Extension to ANSI Column Storage COMPRESS COMPRESS NULL COMPRESS constant_value COMPRESS (multivalue_list) COMPRESS USING compress_udf DECOMPRESS USING decompress_udf X X X X X Column Constraints Table Constraints CONSTRAINT CONSTRAINT UNIQUE CONSTRAINT PRIMARY KEY CONSTRAINT CHECK (boolean_condition) CONSTRAINT REFERENCES table_name column_name FOREIGN KEY (column_name_list) PRIMARY KEY UNIQUE CHECK (boolean_expression) REFERENCES table_name [(column_name_list)] X X X X X X X X X X Restrictions apply to some attributes such as COMPRESS. For further details, see: COMPRESS and DECOMPRESS Phrases on page 22. SQL Data Definition Language Data Type Attributes and Views When defining a view, any expression in the SELECT expression list can include a data definition. The data type attributes determine the form of the view display. The data type attributes for a view column can differ from those defined for the associated column of the underlying base tables or views. However, not all data definitions are relevant to view expressions. If you define data type attributes for a view column, these attributes will override any data type attributes defined for the associated column of the underlying base tables or views. For 18 SQL Data Types and Literals

19 Chapter 1: SQL Data Definition Data Definition Phrases example, a TITLE phrase in the CREATE VIEW statement will override a TITLE phrase in the CREATE TABLE statement of the underlying table. Any data type attributes defined for a column in the underlying base tables or views are carried over to the view only if the associated view column is not modified in any way. Example 1 In this example, the title This is the title defined in table tb is automatically carried over to the view since there were no changes to column b in the view. CREATE TABLE tb ( a INTEGER, b INTEGER TITLE 'This is the title'); CREATE VIEW vtb AS (SELECT a, b FROM tb); SELECT TITLE (vtb.b); The SELECT statement produces the following output: Title(This is the title) This is the title Example 2 In this example, the vtb2 view is created from table tb in example 1. The view definition changes the name of column b to c. Therefore, the title This is the title defined in table tb is not carried over to the view. CREATE VIEW vtb2 AS (SELECT a, b AS c FROM tb); SELECT TITLE(vtb2.c); The SELECT statement produces the following output: Title(c) c Example 3 In this example, the vtb3 view is created from table tb in example 1. The title This is the title defined in table tb is not carried over to the view because column b is an expression in the SELECT list of the view definition. CREATE VIEW vtb3 AS (SELECT a, b (SMALLINT) FROM tb); SELECT TITLE(vtb3.b); SQL Data Types and Literals 19

20 Chapter 1: SQL Data Definition Data Conversion The SELECT statement produces the following output: Title(b) b Data Conversion You can use data definition phrases in expressions to convert data to another type or modify data attributes. When used to modify the attributes of returned values, the data description phrase immediately follows the column being modified. expression ( data_type_list ) where: HH01A019 Syntax element expression data_type_list Specifies the data expression to be converted to the new definition defined by data_type_list a data type declaration or data attributes or both. List elements must be separated by commas. Note: This syntax is called Teradata conversion syntax, and is non-ansi. Using Teradata conversion syntax is strongly discouraged. It is a Teradata extension to the ANSI SQL:2011 standard and is retained only for backward compatibility with existing applications. You should always use the CAST function to perform conversions in new applications to ensure ANSI compatibility. For example, in the following SELECT statement, a TITLE phrase overrides the default heading (the column name) for EmpNo, and a FORMAT phrase modifies the display format defined for Salary data in the CREATE TABLE statement. SELECT EmpNo (TITLE 'Emp#'), Salary (FORMAT '$$$,$$9.99') FROM Employee; Restrictions apply to the data types that Teradata conversion syntax supports. For example, you cannot use Teradata conversion syntax to convert data to a UDT. For further details, see SQL Functions, Operators, Expressions, and Predicates. ANSI SQL-Compliant Data Conversion For applications that need to conform to ANSI as well as to modify attributes of a value, use the CAST function instead of Teradata conversion syntax. 20 SQL Data Types and Literals

21 Chapter 1: SQL Data Definition Data Conversion Further Information To select Name as a fixed length field and Salary as an integer value: SELECT CAST (Name AS CHAR(12)), CAST (Salary AS INTEGER) FROM Employee ; For information on Teradata conversion syntax and the CAST function, see SQL Functions, Operators, Expressions, and Predicates. SQL Data Types and Literals 21

22 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases COMPRESS and DECOMPRESS Phrases Purpose Compresses data values and nulls in one or more columns of a table, and decompresses the previously compressed values. Syntax COMPRESS constant, 255 ( constant ) NULL COMPRESS USING dbname. compress_udf DECOMPRESS USING dbname. decompress_udf DECOMPRESS USING dbname. decompress_udf COMPRESS USING dbname. compress_udf 1101A719 where: Syntax element COMPRESS with no arguments Specifies to compress nulls only. Note that if you do not specify COMPRESS, nulls are not automatically compressed. Multi-value Compression (MVC) Specification COMPRESS constant NULL to compress column data using multi-value compression. a set of values to be compressed. You can specify a single constant value, or a multi-valued, comma-separated list of up to 255 distinct constant values enclosed in parentheses. that nulls are to be compressed. NULL can be specified alone, but it must be enclosed in parentheses. This is the same as specifying COMPRESS without an argument. NULL can be specified with up to 255 constant values in a multi-valued, comma-separated list enclosed in parentheses. Algorithmic Compression (ALC) Specification 22 SQL Data Types and Literals

23 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases Syntax element COMPRESS USING DECOMPRESS USING dbname compress_udf decompress_udf Specifies to compress column data using algorithmic compression. to decompress column data that was previously compressed using algorithmic compression. the name of the database in which the compress or decompress user-defined function (UDF) is stored. If a database name is not specified, the default database used is SYSUDTLIB. You should create all compress and decompress UDFs in the SYSUDTLIB database. Note that compression functions supplied by Teradata are located in TD_SYSFNLIB. the name of the UDF used to compress values in the column. the name of the UDF used to decompress values in the column. ANSI Compliance COMPRESS and DECOMPRESS are Teradata extensions to the ANSI SQL:2011 standard. Number of Columns that can be Compressed You can compress as many columns as practical for any table. However, compressing a substantial number of values in a table using MVC can contribute to table header overflow. IF you specify COMPRESS with: no argument NULL enclosed in parentheses COMPRESS with a constant COMPRESS with: a multi-valued, comma-separated list of constant values enclosed in parentheses a multi-valued, comma-separated list of constant values and NULL enclosed in parentheses COMPRESS USING compress_udf DECOMPRESS USING decompress_udf THEN nulls are compressed for nullable columns. NULL cannot be specified in a COMPRESS phrase for a column that is declared NOT NULL. the indicated value is compressed. For nullable columns, nulls are also compressed. nulls and the specified distinct values are compressed for nullable columns. Nulls are compressed for nullable columns regardless of whether NULL appears in the list. NULL cannot appear in a COMPRESS phrase for a column that is declared NOT NULL. nulls are compressed for nullable columns. Non-null column values that are not specified in the value compression list are compressed using the specified compress UDF, and decompressed using the specified decompress UDF. SQL Data Types and Literals 23

24 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases Multi-value Compression (MVC) You can compress data at the column level using multi-value compression, a lossless, dictionary-based compression scheme. With MVC, you specify a list of values to be compressed when defining a column in the CREATE TABLE/ALTER TABLE statement. When you insert a value into the column which matches a value in the compression list, a corresponding compress bit is set instead of storing the actual value, thus saving the disk storage space. The best candidates for compression are the most frequently occurring values in each column. MVC is a good compression scheme when there are many repeating values for a column. If MVC is not an efficient compression scheme for data in a particular column, you can compress the column using algorithmic compression (ALC). You can specify MVC alone, or both MVC and ALC on the same column. If you define both on the same column, ALC is applied only to those non-null values that are not specified in the value compression list of the MVC specification. You can also use MVC together with block-level compression (BLC). You can use MVC to compress columns with these data types: Any numeric data type BYTE VARBYTE CHARACTER VARCHAR DATE To compress a DATE value, you must specify the value as a Date literal using the ANSI DATE format (DATE 'YYYY-MM-DD'). For example: COMPRESS (DATE ' ') TIME and TIME WITH TIME ZONE TIMESTAMP and TIMESTAMP WITH TIME ZONE To compress a TIME or TIMESTAMP value, you must specify the value as a TIME or TIMESTAMP literal. For example: COMPRESS (TIME '15:30:00') COMPRESS (TIMESTAMP ' :30:23') In addition, you can use COMPRESS (NULL) for columns with these data types: ARRAY Period Non-LOB distinct or structured UDT Using MVC with CHARACTER or VARCHAR Data Type When you specify a constant for a CHARACTER or VARCHAR data type without the UPPERCASE option, a data value must match the case of the constant to be compressed. For example, if a column named Doc_Type is defined as follows: 24 SQL Data Types and Literals

25 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases Doc_Type CHARACTER(6) COMPRESS 'Manual' then each value must be entered as 'Manual' in order to be compressed. A value in a different case, such as 'MANUAL' or 'manual,' is not compressed. Rules for Specifying constant Values The following rules apply to a MVC specification that specifies a comma-separated list of constant values: The maximum number of distinct constant values in the list is 255 plus NULL. Values can appear in any order. NULL can appear at any point in the multi-valued constant list. The multi-value list cannot contain duplicate values. Note that case variants are not considered duplicate values if UPPERCASE is not specified. NULL cannot appear in the list if the column is declared NOT NULL. Compress Values of a CHAR or VARCHAR Column in SHOW TABLE When the value compression list of a CHARACTER or VARCHAR column contains characters that are not displayable in the current session character set, a SHOW TABLE query displays a Unicode delimited identifier. Achieving Maximum Benefit with COMPRESS Maximum benefit is achieved when the COMPRESS phrase is applied to a column under the following conditions: Enough rows contain a compressible value (for example, null, zero, blank, or constant value) in the compressed field to exceed the break-even point. For details, see Break-even point for value compression in Database Design. The shortened row length results in the elimination of one or more data blocks. Algorithmic Compression (ALC) In some cases, such as when column values are mostly unique, algorithmic compression can provide better compression results than multi-value compression. Algorithmic compression allows you to define your own compression and decompression algorithms and apply them to data at the column level. You implement the algorithms as external C/C++ scalar UDFs, and then specify them in the column definition of a CREATE TABLE/ALTER TABLE statement. Teradata Database invokes these algorithms to compress and uncompress the column data when the data is moved into the tables or when data is retrieved from the tables. ALC allows you to implement the compression scheme that is most suitable for data in a particular column. The cost of compression and uncompression depends on the algorithm chosen. SQL Data Types and Literals 25

26 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases You can specify ALC alone, or both MVC and ALC on the same column. If you define both on the same column, ALC is applied only to those non-null values that are not specified in the value compression list of the MVC specification. Note: Using ALC together with block-level compression (BLC) may degrade performance, so this practice is not recommended. You can use algorithmic compression to compress table columns with the following data types: ARRAY BYTE VARBYTE BLOB CHARACTER VARCHAR CLOB JSON, with some restrictions listed below TIME and TIME WITH TIME ZONE TIMESTAMP and TIMESTAMP WITH TIME ZONE Period types Distinct UDTs, with some restrictions listed below System-defined UDTs, with some restrictions listed below Teradata Compression and Decompression Functions Teradata provides the following embedded services functions for compressing and uncompressing data. These functions are stored in the TD_SYSFNLIB system database. Function Type UDF Name Description Compress TransUnicodeToUTF8 Takes UNICODE character input and stores it in UTF-8 format. This is useful when the characters are in the ASCII script (U+0000 to U+007F) because UTF-8 uses one byte to represent these characters and UNICODE (UTF-16) uses two bytes. Decompress TransUTF8ToUnicode Takes the data previously compressed using the TransUnicodeToUTF8 function and converts it back to UNICODE. Compress LZCOMP Compresses UNICODE character data using the Lempel-Ziv algorithm. Decompress LZDECOMP Uncompresses UNICODE data that was compressed using LZCOMP. 26 SQL Data Types and Literals

27 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases Function Type UDF Name Description Compress LZCOMP_L Compresses LATIN character data using the Lempel-Ziv algorithm. Decompress LZDECOMP_L Uncompresses LATIN data that was compressed using LZCOMP_L. Compress CAMSET Compresses UNICODE character data into partial byte (for example, 4-bit digits or 5-bit alphabetic letters), one byte, or two byte values using a proprietary Teradata algorithm. Decompress DECAMSET Uncompresses the UNICODE character data that was compressed using CAMSET. Compress CAMSET_L Compresses LATIN character data into partial byte (for example, 4-bit digits or 5-bit alphabetic letters), or one byte values using a proprietary Teradata algorithm. Decompress DECAMSET_L Uncompresses the LATIN character data that was compressed using CAMSET_L. Compress TD_LZ_COMPRESS Compresses any supported ALC data type or predefined type data using Lempel-Ziv coding. Decompress TD_LZ_DECOMPRESS Uncompresses any supported ALC data type or predefined type data that was compressed using TZ_LZ_COMPRESS. Compress TS_COMPRESS Compresses TIME and TIMESTAMP data. Decompress TS_DECOMPRESS Uncompresses TIME and TIMESTAMP data that was compressed using TS_COMPRESS. Compress JSON_COMPRESS Compresses JSON data. Decompress JSON_DECOMPRESS Uncompresses the JSON data that was compressed using JSON_COMPRESS. For more information about these functions, see SQL Functions, Operators, Expressions, and Predicates. If these functions do not provide optimal compression for your data, you may implement your own UDFs for use in compressing and uncompressing table columns. For more information, see Defining Functions for Algorithmic Compression in SQL External Routine Programming. Notice: Restrictions If you create custom ALC UDFs, make sure to test them thoroughly. If the compression or decompression algorithm fails, compressed data may not be recoverable, or may be corrupted. You cannot use ALC to compress columns that have a data type of structured UDT. SQL Data Types and Literals 27

28 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases The TD_LZ_COMPRESS and TD_LZ_DECOMPRESS system functions compress all large UDTs including UDT-based system types such as Geospatial, XML, and JSON. However, if you write your own compression functions, the following restrictions apply: Custom compression functions cannot be used to compress UDT-based system types (except for ARRAY and Period types). Custom compression functions cannot be used to compress distinct UDTs that are based on UDT-based system types (except for ARRAY and Period types). You cannot write your own compression functions to perform algorithmic compression on JSON type columns. However, Teradata provides the JSON_COMPRESS and JSON_DECOMPRESS functions that you can use to perform ALC on JSON type columns. You cannot use ALC to compress temporal columns: A column defined as SYSTEM_TIME, VALIDTIME, or TRANSACTIONTIME. The DateTime columns that define the beginning and ending bounds of a temporal derived period column (SYSTEM_TIME, VALIDTIME, or TRANSACTIONTIME). You can use ALC to compress Period data types in columns that are nontemporal; however, you cannot use ALC to compress derived period columns. For details about temporal tables, see Temporal Table Support and ANSI Temporal Table Support. You cannot specify multi-value or algorithmic compression for a row-level security constraint column. Example 1: Compression Using MVC with a constant Value The following example uses MVC to compress a specified date in a DATE column. CREATE TABLE Duration (DurationID INTEGER, StartDate DATE COMPRESS (DATE ' ')); Example 2: Compression Using MVC with a Multi-valued List The following example uses MVC to compress a list of date values in a DATE column. CREATE TABLE Duration (DurationID INTEGER, StartDate DATE COMPRESS (DATE ' ', DATE ' ', DATE ' ')); Example 3: Compression Using MVC on an INTEGER Column The following example uses MVC to compress a list of numeric values in an INTEGER column. CREATE TABLE ID (IDNum INTEGER, Post INTEGER COMPRESS (44, 45, 63)); 28 SQL Data Types and Literals

29 Chapter 1: SQL Data Definition COMPRESS and DECOMPRESS Phrases Example 4: Compression Using ALC Only In this example, assume that the default server character set is UNICODE. The NULLs in the Description column are compressed. The non-null values of the Description column are compressed using the Teradata-supplied function, TransUnicodeToUTF8. The TransUTF8ToUnicode function uncompresses the values compressed by the TransUnicodeToUTF8 function. CREATE TABLE Pendants (ItemNo INTEGER, Description VARCHAR(1000) COMPRESS USING TD_SYSFNLIB.TransUnicodeToUTF8 DECOMPRESS USING TD_SYSFNLIB.TransUTF8ToUnicode); Example 5: Compression Using MVC and ALC In this example, assume that the default server character set is UNICODE. The NULLs in the Gem column are compressed. MVC is used to compress the values 'amethyst' and 'amber' in the Gem column. Because UPPERCASE is specified, all values of 'amethyst' and 'amber' are compressed regardless of case. The Teradata-supplied function, TransUnicodeToUTF8, compresses all non-null values in the Gem column that are not 'amethyst' or 'amber'. The TransUTF8ToUnicode function uncompresses the values compressed by TransUnicodeToUTF8. CREATE TABLE Pendants (ItemNo INTEGER, Gem CHAR(10) UPPERCASE COMPRESS ('amethyst', 'amber') COMPRESS USING TD_SYSFNLIB.TransUnicodeToUTF8 DECOMPRESS USING TD_SYSFNLIB.TransUTF8ToUnicode); Example 6 ALC Using the LZCOMP Function Related Topics In this example, the NULLs in the Description column are compressed. The non-null UNICODE values in the Description column are compressed using the Teradata-supplied LZCOMP function. The LZDECOMP function uncompresses the values compressed by LZCOMP. CREATE MULTISET TABLE Pendants (ItemNo INTEGER, Gem CHAR(10) UPPERCASE CHARACTER SET UNICODE, Description VARCHAR(1000) CHARACTER SET UNICODE COMPRESS USING TD_SYSFNLIB.LZCOMP DECOMPRESS USING TD_SYSFNLIB.LZDECOMP); For information on the performance and storage capacity savings benefits of value compression using COMPRESS when defining or modifying columns in a table See Database-Level Considerations in Database Design. CREATE TABLE and ALTER TABLE in SQL Data Definition Language. SQL Data Types and Literals 29

30 Chapter 1: SQL Data Definition Constraint Attributes For information on Teradata compression and decompression functions rules and restrictions for compressing column data rules for creating compress and decompress UDFs compression methods supported by Teradata Database and a comparison of the various methods See SQL Functions, Operators, Expressions, and Predicates. CREATE TABLE in SQL Data Definition Language. CREATE TABLE in SQL Data Definition Language. Defining Functions for Algorithmic Compression in SQL External Routine Programming. Reducing Space Usage with Data Compression in Database Administration. Constraint Attributes Constraint attributes specify integrity rules. Constraints can be any of the following types: Uniqueness (see Uniqueness Constraints on page 31). CHECK (see CHECK Constraints on page 31). Referential integrity (see Referential Constraints on page 32). Column and Table Constraints You can specify constraints during table creation and modification. Column constraints apply to single columns as a part of the column definition. Column constraints include: CHECK constraint definition clause on a single column PRIMARY KEY constraint definition clause on a single column REFERENCES constraint definition clause on a single column UNIQUE constraint definition clause Table constraints apply to multiple columns. Table-level constraints include: CHECK constraint definition clause on multiple columns REFERENCES constraint definition clause on multiple columns PRIMARY KEY constraint definition clause on multiple columns UNIQUE constraint definition clause on multiple columns FOREIGN KEY constraint definition clause FOREIGN KEY constraint definitions must also specify a REFERENCES clause. The full syntax for constraints is in ALTER TABLE and CREATE TABLE in SQL Data Definition Language. 30 SQL Data Types and Literals