- Berkeley DB Reference Guide:
- Introduction
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The first step in selecting a database system is figuring out what the choices are. Decades of research and real-world deployment have produced countless systems. We need to organize them somehow to reduce the number of options.
One obvious way to group systems is to use the common labels that vendors apply to them. The buzzwords here include "network," "relational," "object-oriented," and "embedded," with some cross-fertilization like "object-relational" and "embedded network". Understanding the buzzwords is important. Each has some grounding in theory, but has also evolved into a practical label for categorizing systems that work in a certain way.
All database systems, regardless of the buzzwords that apply to them, provide a few common services. All of them store data, for example. We'll begin by exploring the common services that all systems provide, and then examine the differences among the different kinds of systems.
Fundamentally, database systems provide two services.
The first service is data access. Data access means adding new data to the database (inserting), finding data of interest (searching), changing data already stored (updating), and removing data from the database (deleting). All databases provide these services. How they work varies from category to category, and depends on the record structure that the database supports.
Each record in a database is a collection of values. For example, the record for a Web site customer might include a name, email address, shipping address, and payment information. Records are usually stored in tables. Each table holds records of the same kind. For example, the customer table at an e-commerce Web site might store the customer records for every person who shopped at the site. Often, database records have a different structure from the structures or instances supported by the programming language in which an application is written. As a result, working with records can mean:
The second service is data management. Data management is more complicated than data access. Providing good data management services is the hard part of building a database system. When you choose a database system to use in an application you build, making sure it supports the data management services you need is critical.
Data management services include allowing multiple users to work on the database simultaneously (concurrency), allowing multiple records to be changed instantaneously (transactions), and surviving application and system crashes (recovery). Different database systems offer different data management services. Data management services are entirely independent of the data access services listed above. For example, nothing about relational database theory requires that the system support transactions, but most commercial relational systems do.
Concurrency means that multiple users can operate on the database at the same time. Support for concurrency ranges from none (single-user access only) to complete (many readers and writers working simultaneously).
Transactions permit users to make multiple changes appear at once. For example, a transfer of funds between bank accounts needs to be a transaction because the balance in one account is reduced and the balance in the other increases. If the reduction happened before the increase, than a poorly-timed system crash could leave the customer poorer; if the bank used the opposite order, then the same system crash could make the customer richer. Obviously, both the customer and the bank are best served if both operations happen at the same instant.
Transactions have well-defined properties in database systems. They are atomic, so that the changes happen all at once or not at all. They are consistent, so that the database is in a legal state when the transaction begins and when it ends. They are typically isolated, which means that any other users in the database cannot interfere with them while they are in progress. And they are durable, so that if the system or application crashes after a transaction finishes, the changes are not lost. Together, the properties of atomicity, consistency, isolation, and durability are known as the ACID properties.
As is the case for concurrency, support for transactions varies among databases. Some offer atomicity without making guarantees about durability. Some ignore isolatability, especially in single-user systems; there's no need to isolate other users from the effects of changes when there are no other users.
Another important data management service is recovery. Strictly speaking, recovery is a procedure that the system carries out when it starts up. The purpose of recovery is to guarantee that the database is complete and usable. This is most important after a system or application crash, when the database may have been damaged. The recovery process guarantees that the internal structure of the database is good. Recovery usually means that any completed transactions are checked, and any lost changes are reapplied to the database. At the end of the recovery process, applications can use the database as if there had been no interruption in service.
Finally, there are a number of data management services that permit copying of data. For example, most database systems are able to import data from other sources, and to export it for use elsewhere. Also, most systems provide some way to back up databases and to restore in the event of a system failure that damages the database. Many commercial systems allow hot backups, so that users can back up databases while they are in use. Many applications must run without interruption, and cannot be shut down for backups.
A particular database system may provide other data management services. Some provide browsers that show database structure and contents. Some include tools that enforce data integrity rules, such as the rule that no employee can have a negative salary. These data management services are not common to all systems, however. Concurrency, recovery, and transactions are the data management services that most database vendors support.
Deciding what kind of database to use means understanding the data access and data management services that your application needs. Berkeley DB is an embedded database that supports fairly simple data access with a rich set of data management services. To highlight its strengths and weaknesses, we can compare it to other database system categories.
Relational databases are probably the best-known database variant, because of the success of companies like Oracle. Relational databases are based on the mathematical field of set theory. The term "relation" is really just a synonym for "set" -- a relation is just a set of records or, in our terminology, a table. One of the main innovations in early relational systems was to insulate the programmer from the physical organization of the database. Rather than walking through arrays of records or traversing pointers, programmers make statements about tables in a high-level language, and the system executes those statements.
Relational databases operate on tuples, or records, composed of values of several different data types, including integers, character strings, and others. Operations include searching for records whose values satisfy some criteria, updating records, and so on.
Virtually all relational databases use the Structured Query Language, or SQL. This language permits people and computer programs to work with the database by writing simple statements. The database engine reads those statements and determines how to satisfy them on the tables in the database.
SQL is the main practical advantage of relational database systems. Rather than writing a computer program to find records of interest, the relational system user can just type a query in a simple syntax, and let the engine do the work. This gives users enormous flexibility; they do not need to decide in advance what kind of searches they want to do, and they do not need expensive programmers to find the data they need. Learning SQL requires some effort, but it's much simpler than a full-blown high-level programming language for most purposes. And there are a lot of programmers who have already learned SQL.
Object-oriented databases are less common than relational systems, but are still fairly widespread. Most object-oriented databases were originally conceived as persistent storage systems closely wedded to particular high-level programming languages like C++. With the spread of Java, most now support more than one programming language, but object-oriented database systems fundamentally provide the same class and method abstractions as do object-oriented programming languages.
Many object-oriented systems allow applications to operate on objects uniformly, whether they are in memory or on disk. These systems create the illusion that all objects are in memory all the time. The advantage to object-oriented programmers who simply want object storage and retrieval is clear. They need never be aware of whether an object is in memory or not. The application simply uses objects, and the database system moves them between disk and memory transparently. All of the operations on an object, and all its behavior, are determined by the programming language.
Object-oriented databases aren't nearly as widely deployed as relational systems. In order to attract developers who understand relational systems, many of the object-oriented systems have added support for query languages very much like SQL. In practice, though, object-oriented databases are mostly used for persistent storage of objects in C++ and Java programs.
The "network model" is a fairly old technique for managing and navigating application data. Network databases are designed to make pointer traversal very fast. Every record stored in a network database is allowed to contain pointers to other records. These pointers are generally physical addresses, so fetching the record to which it refers just means reading it from disk by its disk address.
Network database systems generally permit records to contain integers, floating point numbers, and character strings, as well as references to other records. An application can search for records of interest. After retrieving a record, the application can fetch any record to which it refers, quickly.
Pointer traversal is fast because most network systems use physical disk addresses as pointers. When the application wants to fetch a record, the database system uses the address to fetch exactly the right string of bytes from the disk. This requires only a single disk access in all cases. Other systems, by contrast, often must do more than one disk read to find a particular record.
The key advantage of the network model is also its main drawback. The fact that pointer traversal is so fast means that applications that do it will run well. On the other hand, storing pointers all over the database makes it very hard to reorganize the database. In effect, once you store a pointer to a record, it is difficult to move that record elsewhere. Some network databases handle this by leaving forwarding pointers behind, but this defeats the speed advantage of doing a single disk access in the first place. Other network databases find, and fix, all the pointers to a record when it moves, but this makes reorganization very expensive. Reorganization is often necessary in databases, since adding and deleting records over time will consume space that cannot be reclaimed without reorganizing. Without periodic reorganization to compact network databases, they can end up with a considerable amount of wasted space.
Database vendors have two choices for system architecture. They can build a server to which remote clients connect, and do all the database management inside the server. Alternatively, they can provide a module that links directly into the application, and does all database management locally. In either case, the application developer needs some way of communicating with the database (generally, an Application Programming Interface (API) that does work in the process or that communicates with a server to get work done).
Almost all commercial database products are implemented as servers, and applications connect to them as clients. Servers have several features that make them attractive.
First, because all of the data is managed by a separate process, and possibly on a separate machine, it's easy to isolate the database server from bugs and crashes in the application.
Second, because some database products (particularly relational engines) are quite large, splitting them off as separate server processes keeps applications small, which uses less disk space and memory. Relational engines include code to parse SQL statements, to analyze them and produce plans for execution, to optimize the plans, and to execute them.
Finally, by storing all the data in one place and managing it with a single server, it's easier for organizations to back up, protect, and set policies on their databases. The enterprise databases for large companies often have several full-time administrators caring for them, making certain that applications run quickly, granting and denying access to users, and making backups.
However, centralized administration can be a disadvantage in some cases. In particular, if a programmer wants to build an application that uses a database for storage of important information, then shipping and supporting the application is much harder. The end user needs to install and administer a separate database server, and the programmer must support not just one product, but two. Adding a server process to the application creates new opportunity for installation mistakes and run-time problems.
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