Database servers can work together to allow a second server to take over quickly if the primary server fails (high availability), or to allow several computers to serve the same data (load balancing). Ideally, database servers could work together seamlessly. Web servers serving static web pages can be combined quite easily by merely load-balancing web requests to multiple machines. In fact, read-only database servers can be combined relatively easily too. Unfortunately, most database servers have a read/write mix of requests, and read/write servers are much harder to combine. This is because though read-only data needs to be placed on each server only once, a write to any server has to be propagated to all servers so that future read requests to those servers return consistent results.
This synchronization problem is the fundamental difficulty for servers working together. Because there is no single solution that eliminates the impact of the sync problem for all use cases, there are multiple solutions. Each solution addresses this problem in a different way, and minimizes its impact for a specific workload.
Some solutions deal with synchronization by allowing only one server to modify the data. Servers that can modify data are called read/write or "master" servers. Servers that can reply to read-only queries are called "slave" servers. Servers that cannot be accessed until they are changed to master servers are called "standby" servers.
Some failover and load balancing solutions are synchronous, meaning that a data-modifying transaction is not considered committed until all servers have committed the transaction. This guarantees that a failover will not lose any data and that all load-balanced servers will return consistent results no matter which server is queried. In contrast, asynchronous solutions allow some delay between the time of a commit and its propagation to the other servers, opening the possibility that some transactions might be lost in the switch to a backup server, and that load balanced servers might return slightly stale results. Asynchronous communication is used when synchronous would be too slow.
Solutions can also be categorized by their granularity. Some solutions can deal only with an entire database server, while others allow control at the per-table or per-database level.
Performance must be considered in any failover or load balancing choice. There is usually a tradeoff between functionality and performance. For example, a full synchronous solution over a slow network might cut performance by more than half, while an asynchronous one might have a minimal performance impact.
The remainder of this section outlines various failover, replication, and load balancing solutions.
Shared disk failover avoids synchronization overhead by having only one copy of the database. It uses a single disk array that is shared by multiple servers. If the main database server fails, the standby server is able to mount and start the database as though it was recovering from a database crash. This allows rapid failover with no data loss.
Shared hardware functionality is common in network storage devices. Using a network file system is also possible, though care must be taken that the file system has full POSIX behavior. One significant limitation of this method is that if the shared disk array fails or becomes corrupt, the primary and standby servers are both nonfunctional. Another issue is that the standby server should never access the shared storage while the primary server is running. It is also possible to use some type of file system mirroring to keep the standby server current, but the mirroring must be done in a way that ensures the standby server has a consistent copy of the file system.
A warm standby server (see Seção 23.4) can be kept current by reading a stream of write-ahead log (WAL) records. If the main server fails, the warm standby contains almost all of the data of the main server, and can be quickly made the new master database server. This is asynchronous and can only be done for the entire database server.
A master-slave replication setup sends all data modification queries to the master server. The master server asynchronously sends data changes to the slave server. The slave can answer read-only queries while the master server is running. The slave server is ideal for data warehouse queries.
Slony-I is an example of this type of replication, with per-table granularity, and support for multiple slaves. Because it updates the slave server asynchronously (in batches), there is possible data loss during fail over.
With statement-based replication middleware, a program intercepts every SQL query and sends it to one or all servers. Each server operates independently. Read-write queries are sent to all servers, while read-only queries can be sent to just one server, allowing the read workload to be distributed.
If queries are simply broadcast unmodified, functions like random(), CURRENT_TIMESTAMP, and sequences would have different values on different servers. This is because each server operates independently, and because SQL queries are broadcast (and not actual modified rows). If this is unacceptable, either the middleware or the application must query such values from a single server and then use those values in write queries. Also, care must be taken that all transactions either commit or abort on all servers, perhaps using two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED. Pgpool and Sequoia are an example of this type of replication.
In synchronous multi-master replication, each server can accept write requests, and modified data is transmitted from the original server to every other server before each transaction commits. Heavy write activity can cause excessive locking, leading to poor performance. In fact, write performance is often worse than that of a single server. Read requests can be sent to any server. Some implementations use shared disk to reduce the communication overhead. Synchronous multi-master replication is best for mostly read workloads, though its big advantage is that any server can accept write requests — there is no need to partition workloads between master and slave servers, and because the data changes are sent from one server to another, there is no problem with non-deterministic functions like random().
For servers that are not regularly connected, like laptops or remote servers, keeping data consistent among servers is a challenge. Using asynchronous multi-master replication, each server works independently, and periodically communicates with the other servers to identify conflicting transactions. The conflicts can be resolved by users or conflict resolution rules.
Data partitioning splits tables into data sets. Each set can be modified by only one server. For example, data can be partitioned by offices, e.g. London and Paris, with a server in each office. If queries combining London and Paris data are necessary, an application can query both servers, or master/slave replication can be used to keep a read-only copy of the other office's data on each server.
Many of the above solutions allow multiple servers to handle multiple queries, but none allow a single query to use multiple servers to complete faster. This solution allows multiple servers to work concurrently on a single query. This is usually accomplished by splitting the data among servers and having each server execute its part of the query and return results to a central server where they are combined and returned to the user. Pgpool-II has this capability.
Because PostgreSQL is open source and easily extended, a number of companies have taken PostgreSQL and created commercial closed-source solutions with unique failover, replication, and load balancing capabilities.