2 Physical Model Customization

Questions to Answer Before You Customize the Physical Model

When designing the physical model, remember that the logical data model is not one-to-one with the physical data model. Consider the load, query, and maintenance requirements when you convert the logical data model into the physical layer. For example, answer the following questions before you design the physical data model:

• Do you need the physical data model to cover the full scope of the logical data model, or only part of the scope?

  "Common Change Scenarios When Customizing the Foundation Layer of Oracle Airlines Data Model" provides an overview discussion of making physical data model changes when your business needs do not result in a logical model that is the same as the Oracle Airlines Data Model logical model.

• What is the result of the source data profile?

• What is the data load frequency for each table?

• How many large tables are there and which tables are these?

• How will the tables and columns be accessed? What are the common joins?

• What is your data backup strategy?

Conventions When Customizing the Physical Model

When developing the physical model for Oracle Airlines Data Model, the conventions outlined below were followed. Continue to follow these conventions as you customize the physical model.

• General Naming Conventions for Physical Objects

• Use of History (_H) Tables

• Domain Definition Standards

Common Change Scenarios When Customizing the Foundation Layer of Oracle Airlines Data Model

There are several common change scenarios when customizing the foundation layer of the physical data model:

• Additions to Existing Structures

  If you identify business areas or processes that are not supported in the default foundation layer of the physical data model of Oracle Airlines Data Model, add new tables and columns.

  Carefully study the default foundation layer of the physical data model of Oracle Airlines Data Model (and the underlying logical data model) to avoid building redundant structures when making additions. If these additions add high value to your business value, communicate the additions back to the Oracle Airlines Data Model Development Team for possible inclusion in future releases of Oracle Airlines Data Model.

• Deletions of Existing Structures

  If there are areas of the model that cannot be matched to any of the business requirements of your legacy systems, it is safer to keep these structures and not populate that part of the warehouse.

  Deleting a table in the foundation layer of the physical data model can destroy relationships needed in other parts of the model or by applications based on it. Some tables may not be needed during the initial implementation, however you may want to use these structures at a later time. If this is a possibility, keeping the structures now saves re-work later. If tables are deleted, perform a thorough analysis to identify all relationships originating from that entity.

• Changes to Existing Structures

  In some situations some structures in the foundation layer of the physical data model of the Oracle Airlines Data Model may not exactly match the corresponding structures that you use.Before implementing changes, identify the impact that the changes would have on the database design of Oracle Airlines Data Model. Also identify the impact on any applications based on the new design.

Example: Changing the Foundation Layer of the Oracle Airlines Data Model

As an example, let's take a look how Oracle Airlines Data Model supports the various airlines services, what a sample customer might discover during fit-gap analysis, and how that customer might extend Oracle Airlines Data Model to fit the discovered gaps.

Entities supporting airline services

The entities provided with the logical model of Oracle Airlines Data Model that support the airline services are:

• AIRPORT: Airport means an IATA recognized location that serves as an origin or destination of one or more flights. This entity stores the details of the airport like city, country, region of the airport.

• FLIGHT: This entity stores information about the flight (for example, the carrier to which it belongs, and the flight number).

• AIRCRAFT: This entity stores the type of aircraft (for example. Boeing 737).

• AIRCRAFT VERSION: This entity stores the details of the aircraft version used for flights. For example, if the aircraft type is Boeing 737 then the version will be Boeing 737-800.

• CARRIER: This entity stores the details about the carrier (for example, carrier code and description).

• CARRIER TYPE: This entity stores Carrier type details (for example, airline, railway, on road transport, and ship).

The differences discovered during fit-gap analysis

Assume that during the fit-gap analysis, you discover the following needs that are not supported by the logical model delivered with Oracle Airlines Data Model:

• Your company serves more than one service in airline arena. In other words, you discover that you track not only airports as represented in the standard logical model of Oracle Airlines Data Model, but also you need to define airports by categories of airport activities (that is, airport categories). These airport categories are, for example, commercial service, primary, cargo service, reliever, and general aviation airports.

• In the flight operations, the airline needs to maintain a history of airport categories used to operate the various lines of services (for example, commercial, cargo, reliever or general aviation).

Extending the logical and physical model to support the differences

To support the differences, you need to extend the logical and physical model in the following ways:

• To store details about category of the airport you will need to modify the logical model. The classic way to do this is to add another entity to hold this information. For example, you can add an entity named AIRPORT CATEGORY that has a 1:M relationship with the AIRPORT entity. Then, you extend the physical data model in a corresponding manner. The specific steps to extend the physical model are given in Example 2-1, "Extending the Physical Data Model of Oracle Airlines Data Model to Support Multiple Categories for AIRPORT".

• To maintain the history of flight operations which are operated from respective airport categories, you can find the same based on the existing relationship between flight schedule and AIRPORT entity for corresponding Leg entity.

Tablespaces in the Oracle Airlines Data Model

A tablespace consists of one or more data files, which are physical structures within the operating system you are using.

Recommendations: Defining Tablespaces

If possible, define tablespaces so that they represent logical business units.

Use ultra large data files for a significant improvement in very large Oracle Airlines Data Model warehouse.

Changing the Tablespace and Partitions Used by Tables

You can change the tablespace and partitions used by Oracle Airlines Data Model tables. What you do depends on whether the Oracle Airlines Data Model table has partitions:

• For tables that do not have partitions (that is, lookup tables and reference tables), you can change the existing tablespace for a table.

  By default, Oracle Airlines Data Model defines the partitioned tables as interval partitioning, which means the partitions are created only when new data arrives.

  Consequently, for Oracle Airlines Data Model tables that have partitions (that is, Base, Derived, and Aggregate tables), for the new interval partitions to be generated in new tablespaces rather than current ones, issue the following statements.

  ALTER TABLE table_name MODIFY DEFAULT ATTRIBUTES 
  TABLESPACE new_tablespace_name;
   
  

  When new data is inserted in the table specified by table_name, a new partition is automatically created in the tablespace specified by tablespace new_tablespace_name.

• For tables that have partitions (that is, base, derived, and aggregate tables), you can specify that new interval partitions be generated into new tablespaces.

  For Oracle Airlines Data Model tables that do not have partitions (that is, lookup tables and reference tables), to change the existing tablespace for a table then issue the following statement.

  ALTER TABLE table_name MOVE TABLESPACE new_tablespace_name;
  

Data Compression in the Oracle Airlines Data Model

A key decision that you must make is whether to compress your data. Using table compression reduces disk and memory usage, often resulting in better scale-up performance for read-only operations. Table compression can also speed up query execution by minimizing the number of round trips required to retrieve data from the disks. Compressing data however imposes a performance penalty on the load speed of the data. Most of the base tables in the Oracle Airlines Data Model are compressed tables.

Recommendations: Data Compression

In general, choose to compress the data. The overall performance gain typically outweighs the cost of compression.

If you decide to use compression, consider sorting your data before loading it to achieve the best possible compression rate. The easiest way to sort incoming data is to load it using an ORDER BY clause on either your CTAS or IAS statement. Specify an ORDER BY a NOT NULL column (ideally non numeric) that has a large number of distinct values (1,000 to 10,000).

alter table <tablename> move compress;

Tables for Supertype and Subtype Entities in Oracle Airlines Data Model

A supertype is a generic entity type that has a relationship with one or more subtypes.

A subtype is a sub-grouping of the entities in an entity type that is meaningful to the organization and that shares common attributes or relationships distinct from other subgroups.

• Subtypes inherit all supertype attributes

• Subtypes have attributes that are different from other subtypes

For example, in the default Oracle Airlines Data Model, the table DWB_PRTY_INTRATN (Party Interaction) is a supertype that has a number of subtypes including DWB_PRTY_INTRATN_CALL (Party Interaction Call) and DWB_PRTY_INTRATN_EML (Party Interaction Email).

Recommendations: Tables for Supertype and Subtype Entities

Create separate tables for the super type and all sub type entities for the following reasons:

• Data integrity enforced at database level. (using NOT NULL column constraints)

• Relationships can be accurately modeled and enforced including those which apply to only one subtype

• Physical model closely resembles the logical data model.

• It is easier to correlate the logical data model with the physical data model and support the logical data model enhancements and changes.

• Physical data model reflects true business rules (for example, if there are some attributes or relationships mandatory for only one subtype.)

Surrogate Keys in the Physical Model

The surrogate key method for primary key construction involves taking the natural key components from the source systems and mapping them through a process of assigning a unique key value to each unique combination of natural key components (including source system identifier). The resulting primary key value is completely non-intelligent and is typically a numeric data type for maximum performance and storage efficiency.

Advantages of Surrogate keys include:

• Ensure uniqueness: data distribution

• Independent of source systems

• Re-numbering

• Overlapping ranges

• Uses the numeric data type which is the most performant data type for primary keys and joins

Disadvantages of Surrogate keys:

• Have to allocate during ETL

• Complex and expensive re-processing and data quality correction

• Not used in queries – performance impact

• The operational business intelligence requires natural keys to join to operational systems

Integrity Constraints in Oracle Airlines Data Model

Integrity constraints are used to enforce business rules associated with your database and to prevent having invalid information in the tables. The most common types of constraints include:

• PRIMARY KEY constraints, this is usually defined on the surrogate key column to ensure uniqueness of the record identifiers. In general, it is recommended that you specify the ENFORCED ENABLED RELY mode.

• UNIQUE constraints, to ensure that a given column (or set of columns) is unique. For slowly changing dimensions, it is recommended that you add a unique constraint on the Business Key and the Effective From Date columns to allow tracking multiple versions (based on surrogate key) of the same Business Key record.

• NOT NULL constraints, to ensure that no null values are allowed. For query rewrite scenarios, it is recommended that you have an inline explicit NOT NULL constraint on the primary key column in addition to the primary key constraint.

• FOREIGN KEY constraints, to ensure that relation between tables are being honored by the data. Usually in data warehousing environments, the foreign key constraint is present in RELY DISABLE NOVALIDATE mode.

The Oracle Database uses constraints when optimizing SQL queries. Although constraints can be useful in many aspects of query optimization, constraints are particularly important for query rewrite of materialized views. Under some specific circumstances, constraints need space in the database. These constraints are in the form of the underlying unique index.

Unlike data in many relational database environments, data in a data warehouse is typically added or modified under controlled circumstances during the extraction, transformation, and loading (ETL) process.

Indexes and Partitioned Indexes in Oracle Airlines Data Model

Indexes are optional structures associated with tables or clusters. In addition to the classical B-tree indexes, bitmap indexes are very common in data warehousing environments

• Bitmap indexes are optimized index structures for set-oriented operations. Additionally, they are necessary for some optimized data access methods such as star transformations. Bitmap indexes are typically only a fraction of the size of the indexed data in the table.

• B-tree indexes are most effective for high-cardinality data: that is, for data with many possible values, such as customer name or phone number. However, fully indexing a large table with a traditional B-tree index can be prohibitively expensive in terms of disk space because the indexes can be several times larger than the data in the table. B-tree indexes can be stored specifically in a compressed manner to enable huge space savings, storing more keys in each index block, which also leads to less I/O and better performance.

Recommendations: Indexes and Partitioned Indexes

Make the majority of the indexes in your customized Oracle Airlines Data Model bitmap indexes.

Use B-tree indexes only for unique columns or other columns with very high cardinalities (that is, columns that are almost unique). Store the B-tree indexes in a compressed manner.

Partition the indexes. Indexes are just like tables in that you can partition them, although the partitioning strategy is not dependent upon the table structure. Partitioning indexes makes it easier to manage the data warehouse during refresh and improves query performance.

Typically, specify the index on a partitioned table as local. Bitmap indexes on partitioned tables must always be local. B-tree indexes on partitioned tables can be global or local. However, in a data warehouse environment, local indexes are more common than global indexes. Use global indexes only when there is a specific requirement which cannot be met by local indexes (for example, a unique index on a non-partitioning key, or a performance requirement).

Partitioned Tables in the Oracle Airlines Data Model

Partitioning allows a table, index or index-organized table to be subdivided into smaller pieces. Each piece of the database object is called a partition. Each partition has its own name, and may optionally have its own storage characteristics. From the perspective of a database administrator, a partitioned object has multiple pieces that can be managed either collectively or individually. This gives the administrator considerable flexibility in managing partitioned objects. However, from the perspective of the application, a partitioned table is identical to a non-partitioned table. No modifications are necessary when accessing a partitioned table using SQL DML commands.

As discussed in the following topics, partitioning can provide tremendous benefits to a wide variety of applications by improving manageability, availability, and performance:

• Partitioning the Oracle Airlines Data Model for Manageability

• Partitioning the Oracle Airlines Data Model for Easier Data Access

• Partitioning the Oracle Airlines Data Model for Join Performance

Partitioning the Oracle Airlines Data Model for Join Performance

Sub-partitioning by hash is used predominately for performance reasons. Oracle Database uses a linear hashing algorithm to create sub-partitions.

A major performance benefit of hash partitioning is partition-wise joins. Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This significantly reduces response time and improves both CPU and memory resource usage. In a clustered data warehouse, this significantly reduces response times by limiting the data traffic over the interconnect (IPC), which is the key to achieving good scalability for massive join operations. Partition-wise joins can be full or partial, depending on the partitioning scheme of the tables to be joined.

As illustrated by Figure 2-2, "Partitioning for Join Performance", a full partition-wise join divides a join between two large tables into multiple smaller joins. Each smaller join, performs a joins on a pair of partitions, one for each of the tables being joined. For the optimizer to choose the full partition-wise join method, both tables must be equi-partitioned on their join keys. That is, they have to be partitioned on the same column with the same partitioning method. Parallel execution of a full partition-wise join is similar to its serial execution, except that instead of joining one partition pair at a time, multiple partition pairs are joined in parallel by multiple parallel query servers. The number of partitions joined in parallel is determined by the Degree of Parallelism (DOP).

Figure 2-2 Partitioning for Join Performance

Description of "Figure 2-2 Partitioning for Join Performance"

Recommendations: Number of Hash Partitions

In order to ensure that the data gets evenly distributed among the hash partitions it is highly recommended that the number of hash partitions is a power of 2 (for example, 2, 4, 8, and so on). A good rule of thumb to follow when deciding the number of hash partitions a table should have is 2 X # of CPUs rounded to up to the nearest power of 2.

If your system has 12 CPUs, then 32 would be a good number of hash partitions. On a clustered system the same rules apply. If you have 3 nodes each with 4 CPUs, then 32 would still be a good number of hash partitions. However, ensure that each hash partition is at least 16MB in size. Many small partitions do not have efficient scan rates with parallel query. Consequently, if using the number of CPUs makes the size of the hash partitions too small, use the number of Oracle RAC nodes in the environment (rounded to the nearest power of 2) instead.

Parallel Execution in the Oracle Airlines Data Model

Parallel Execution enables a database task to be parallelized or divided into smaller units of work, thus allowing multiple processes to work concurrently. By using parallelism, a terabyte of data can be scanned and processed in minutes or less, not hours or days.

Figure 2-3, "Parallel Execution of a Full Partition-Wise Join Between Two Tables" illustrates the parallel execution of a full partition-wise join between two tables, Sales and Customers. Both tables have the same degree of parallelism and the same number of partitions. They are range partitioned on a date field and sub partitioned by hash on the cust_id field. As illustrated in the picture, each partition pair is read from the database and joined directly.

There is no data redistribution necessary, thus minimizing IPC communication, especially across nodes. Below figure shows the execution plan you would see for this join.

Figure 2-3 Parallel Execution of a Full Partition-Wise Join Between Two Tables

To ensure that you get optimal performance when executing a partition-wise join in parallel, specify a number for the partitions in each of the tables that is larger than the degree of parallelism used for the join. If there are more partitions than parallel servers, each parallel server is given one pair of partitions to join, when the parallel server completes that join, it requests another pair of partitions to join. This process repeats until all pairs have been processed. This method enables the load to be balanced dynamically (for example, 128 partitions with a degree of parallelism of 32).

What happens if only one table that you are joining is partitioned? In this case the optimizer could pick a partial partition-wise join. Unlike full partition-wise joins, partial partition-wise joins can be applied if only one table is partitioned on the join key. Hence, partial partition-wise joins are more common than full partition-wise joins. To execute a partial partition-wise join, Oracle Database dynamically repartitions the other table based on the partitioning strategy of the partitioned table.

After the other table is repartitioned, the execution is similar to a full partition-wise join. The redistribution operation involves exchanging rows between parallel execution servers. This operation leads to interconnect traffic in Oracle RAC environments, since data must be repartitioned across node boundaries.

Figure 2-4 Partial Partition-Wise Join

Description of "Figure 2-4 Partial Partition-Wise Join"

Figure 2-4, "Partial Partition-Wise Join" illustrates a partial partition-wise join. It uses the same example as in Figure 2-3, except that the customer table is not partitioned. Before the join operation is executed, the rows from the customers table are dynamically redistributed on the join key.

• Enabling Parallel Execution for a Session

• Enabling Parallel Execution of DML Operations

• Enabling Parallel Execution at the Table Level

alter session enable parallel query; 

alter session enable parallel dml;

alter table <table_name> parallel 32;