Managing Tables. About Tables. Tables are the basic unit of data storage in an Oracle Database. Data is stored in rows and columns. You define a table with a table name, such as employees, and a set of columns. You give each column a column name, such as employee. The width can be predetermined by the data type, as in DATE. If columns are of the NUMBER data type, define precision and scale instead of width.
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A row is a collection of column information corresponding to a single record. You can specify rules for each column of a table. These rules are called integrity constraints. One example is a NOT NULL integrity constraint.
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This constraint forces the column to contain a value in every row. You can invoke transparent data encryption to encrypt data before storing it. If users attempt to circumvent the database access control mechanisms by looking inside Oracle data files directly with operating system tools, encryption prevents these users from viewing sensitive data. Tables can also include virtual columns.
A virtual column is like any other table column, except that its value is derived by evaluating an expression. The expression can include columns from the same table, constants, SQL functions, and user- defined PL/SQL functions. You cannot explicitly write to a virtual column. Some column types, such as LOBs, varrays, and nested tables, are stored in their own segments. LOBs and varrays are stored in LOB segments, while nested tables are stored in storage tables. You can specify a STORAGE clause for these segments that will override storage parameters specified at the table level.
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After you create a table, you insert rows of data using SQL statements or using an Oracle bulk load utility. Table data can then be queried, deleted, or updated using SQL. Guidelines for Managing Tables.
This section describes guidelines to follow when managing tables. Following these guidelines can make the management of your tables easier and can improve performance when creating the table, as well as when loading, updating, and querying the table data. The following topics are discussed: Design Tables Before Creating Them. Usually, the application developer is responsible for designing the elements of an application, including the tables. Database administrators are responsible for establishing the attributes of the underlying tablespace that will hold the application tables. Either the DBA or the applications developer, or both working jointly, can be responsible for the actual creation of the tables, depending upon the practices for a site.
Working with the application developer, consider the following guidelines when designing tables: Use descriptive names for tables, columns, indexes, and clusters. Be consistent in abbreviations and in the use of singular and plural forms of table names and columns. Document the meaning of each table and its columns with the COMMENT command.
Normalize each table. Select the appropriate data type for each column.
Consider whether your applications would benefit from adding one or more virtual columns to some tables. Define columns that allow nulls last, to conserve storage space. Cluster tables whenever appropriate, to conserve storage space and optimize performance of SQL statements. Before creating a table, you should also determine whether to use integrity constraints. Integrity constraints can be defined on the columns of a table to enforce the business rules of your database automatically. Specify the Type of Table to Create.
Here are the types of tables that you can create: Type of Table. Description. Ordinary (heap- organized) table. This is the basic, general purpose type of table which is the primary subject of this chapter. Its data is stored as an unordered collection (heap).
Clustered table. A clustered table is a table that is part of a cluster. A cluster is a group of tables that share the same data blocks because they share common columns and are often used together. Besides storing the primary key column values of an index- organized table row, each index entry in the B- tree stores the nonkey column values as well. Each partition can have separate physical attributes, such as compression enabled or disabled, type of compression, physical storage settings, and tablespace, thus providing a structure that can be better tuned for availability and performance.
In addition, each partition can be managed individually, which can simplify and reduce the time required for backup and administration. For partitioned tables, you can optionally identify the tablespace that is to store each partition. Ensure that you have the appropriate privileges and quota on any tablespaces that you use. If you do not specify a tablespace in a CREATE TABLE statement, the table is created in your default tablespace. When specifying the tablespace to contain a new table, ensure that you understand implications of your selection.
By properly specifying a tablespace during the creation of each table, you can increase the performance of the database system and decrease the time needed for database administration. The following situations illustrate how not specifying a tablespace, or specifying an inappropriate one, can affect performance: If users' objects are created in the SYSTEM tablespace, the performance of the database can suffer, since both data dictionary objects and user objects must contend for the same data files. Users' objects should not be stored in the SYSTEM tablespace. To avoid this, ensure that all users are assigned default tablespaces when they are created in the database. If application- associated tables are arbitrarily stored in various tablespaces, the time necessary to complete administrative operations (such as backup and recovery) for the data of that application can be increased.
Consider Parallelizing Table Creation. You can use parallel execution when creating tables using a subquery (AS SELECT) in the CREATE TABLE statement. Because multiple processes work together to create the table, performance of the table creation operation is improved.
Parallelizing table creation is discussed in the section . The NOLOGGING clause causes minimal redo information to be generated during the table creation. This has the following benefits: Space is saved in the redo log files. The time it takes to create the table is decreased. Performance improves for parallel creation of large tables. The NOLOGGING clause also specifies that subsequent direct loads using SQL*Loader and direct load INSERT operations are not logged.
Subsequent DML statements (UPDATE, DELETE, and conventional path insert) are unaffected by the NOLOGGING attribute of the table and generate redo. If you cannot afford to lose the table after you have created it (for example, you will no longer have access to the data used to create the table) you should take a backup immediately after the table is created. In some situations, such as for tables that are created for temporary use, this precaution may not be necessary. In general, the relative performance improvement of specifying NOLOGGING is greater for larger tables than for smaller tables. For small tables, NOLOGGING has little effect on the time it takes to create a table. However, for larger tables the performance improvement can be significant, especially when also parallelizing the table creation. Consider Using Table Compression.
As your database grows in size, consider using table compression. Compression saves disk space, reduces memory use in the database buffer cache, and can significantly speed query execution during reads. Compression has a cost in CPU overhead for data loading and DML. However, this cost might be offset by reduced I/O requirements. Table compression is completely transparent to applications. It is useful in decision support systems (DSS), online transaction processing (OLTP) systems, and archival systems.
You can specify compression for a tablespace, a table, or a partition. If specified at the tablespace level, then all tables created in that tablespace are compressed by default. Compression can occur while data is being inserted, updated, or bulk loaded into a table. Operations that permit compression include: Single- row or array inserts and updates.
The following direct- path INSERT methods: Direct path SQL*Loader. CREATETABLEASSELECT statements.
Parallel INSERT statements. INSERT statements with an APPEND or APPEND.
They are summarized in Table 2. Table 2. 0- 1 Table Compression Methods. Table Compression Method.
Compression Level. CPU Overhead. Applications. Notes. Basic compression.
High. Minimal. DSSNone. OLTP compression.
High. Minimal. OLTP, DSSNone. Warehouse compression (Hybrid Columnar Compression)Higher.
Higher. DSSThe compression level and CPU overhead depend on compression level specified (LOW or HIGH). Archive compression (Hybrid Columnar Compression)Highest. Highest. Archiving.
The compression level and CPU overhead depend on compression level specified (LOW or HIGH). When you use basic compression, warehouse compression, or archive compression, compression only occurs when data is bulk loaded into a table. When you use OLTP compression, compression occurs while data is being inserted, updated, or bulk loaded into a table. Operations that permit compression include: Single- row or array inserts and updates.
Inserts and updates are not compressed immediately. When updating an already compressed block, any columns that are not updated usually remain compressed.
Updated columns are stored in an uncompressed format similar to any uncompressed block. The updated values are re- compressed when the block reaches a database- controlled threshold.
Inserted data is also compressed when the data in the block reaches a database- controlled threshold. The following direct- path INSERT methods: Direct path SQL*Loader.
CREATETABLEASSELECT statements. Parallel INSERT statements. INSERT statements with an APPEND or APPEND. OLTP compression is intended for OLTP applications and compresses data manipulated by any SQL operation.