Chapter: Fundamentals of Database Systems - File Structures, Indexing, and Hashing - Indexing Structures for Files

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Some General Issues Concerning Indexing

1. Logical versus Physical Indexes 2. Discussion 3. Column-Based Storage of Relations

Some General Issues Concerning Indexing

 

1. Logical versus Physical Indexes

 

In the earlier discussion, we have assumed that the index entries <K, Pr> (or <K, P>) always include a physical pointer Pr (or P) that specifies the physical record address on disk as a block number and offset. This is sometimes called a physical index, and it has the disadvantage that the pointer must be changed if the record is moved to another disk location. For example, suppose that a primary file organization is based on linear hashing or extendible hashing; then, each time a bucket is split, some records are allocated to new buckets and hence have new physical addresses. If there was a secondary index on the file, the pointers to those records would have to be found and updated, which is a difficult task.

 

To remedy this situation, we can use a structure called a logical index, whose index entries are of the form <K, Kp>. Each entry has one value K for the secondary indexing field matched with the value Kp of the field used for the primary file organiza-tion. By searching the secondary index on the value of K, a program can locate the corresponding value of Kp and use this to access the record through the primary file organization. Logical indexes thus introduce an additional level of indirection between the access structure and the data. They are used when physical record addresses are expected to change frequently. The cost of this indirection is the extra search based on the primary file organization.

 

2. Discussion

 

In many systems, an index is not an integral part of the data file but can be created and discarded dynamically. That is why it is often called an access structure. Whenever we expect to access a file frequently based on some search condition involving a particular field, we can request the DBMS to create an index on that field. Usually, a secondary index is created to avoid physical ordering of the records in the data file on disk.

 

The main advantage of secondary indexes is that—theoretically, at least—they can be created in conjunction with virtually any primary record organization. Hence, a secondary index could be used to complement other primary access methods such as ordering or hashing, or it could even be used with mixed files. To create a B+-tree secondary index on some field of a file, we must go through all records in the file to create the entries at the leaf level of the tree. These entries are then sorted and filled according to the specified fill factor; simultaneously, the other index levels are created. It is more expensive and much harder to create primary indexes and clustering indexes dynamically, because the records of the data file must be physically sorted on disk in order of the indexing field. However, some systems allow users to create these indexes dynamically on their files by sorting the file during index creation.

 

It is common to use an index to enforce a key constraint on an attribute. While searching the index to insert a new record, it is straightforward to check at the same time whether another record in the file—and hence in the index tree—has the same key attribute value as the new record. If so, the insertion can be rejected.

 

If an index is created on a nonkey field, duplicates occur; handling of these duplicates is an issue the DBMS product vendors have to deal with and affects data storage as well as index creation and management. Data records for the duplicate key may be contained in the same block or may span multiple blocks where many duplicates are possible. Some systems add a row id to the record so that records with duplicate keys have their own unique identifiers. In such cases, the B+-tree index may regard a <key, Row_id> combination as the defacto key for the index, turning the index into a unique index with no duplicates. The deletion of a key K from such an index would involve deleting all occurrences of that key K—hence the deletion algorithm has to account for this.

 

In actual DBMS products, deletion from B+-tree indexes is also handled in various ways to improve performance and response times. Deleted records may be marked as deleted and the corresponding index entries may also not be removed until a garbage collection process reclaims the space in the data file; the index is rebuilt online after garbage collection.

 

A file that has a secondary index on every one of its fields is often called a fully inverted file. Because all indexes are secondary, new records are inserted at the end of the file; therefore, the data file itself is an unordered (heap) file. The indexes are usually implemented as B+-trees, so they are updated dynamically to reflect insertion or deletion of records. Some commercial DBMSs, such as Software AG’s Adabas, use this method extensively.

 

We referred to the popular IBM file organization called ISAM in Section 18.2. Another IBM method, the virtual storage access method (VSAM), is somewhat sim-ilar to the B+–tree access structure and is still being used in many commercial systems.


 

3. Column-Based Storage of Relations

 

There has been a recent trend to consider a column-based storage of relations as an alternative to the traditional way of storing relations row by row. Commercial relational DBMSs have offered B+-tree indexing on primary as well as secondary keys as an efficient mechanism to support access to data by various search criteria and the ability to write a row or a set of rows to disk at a time to produce write-optimized systems. For data warehouses (to be discussed in Chapter 29), which are read-only databases, the column-based storage offers particular advantages for read-only queries. Typically, the column-store RDBMSs consider storing each column of data individually and afford performance advantages in the following areas:

 

1.   Vertically partitioning the table column by column, so that a two-column table can be constructed for every attribute and thus only the needed columns can be accessed

 

2.   Use of column-wise indexes (similar to the bitmap indexes discussed in Section 18.5.2) and join indexes on multiple tables to answer queries with-out having to access the data tables

 

3.   Use of materialized views (see Chapter 5) to support queries on multiple columns

 

Column-wise storage of data affords additional freedom in the creation of indexes, such as the bitmap indexes discussed earlier. The same column may be present in multiple projections of a table and indexes may be created on each projection. To store the values in the same column, strategies for data compression, null-value suppression, dictionary encoding techniques (where distinct values in the column are assigned shorter codes), and run-length encoding techniques have been devised. MonetDB/X100, C-Store, and Vertica are examples of such systems. Further discussion on column-store DBMSs can be found in the references mentioned in this chapter’s Selected Bibliography.


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