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Figure 10-6 The pgAdmin III Server Status window
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This chapter dove into the world of database security Database security has become a hot topic, and implementing a security plan for a database is a must PostgreSQL allows remote clients to connect to the server using TCP communications You can restrict which clients can connect to the PostgreSQL server by using both a firewall application on the system and the pg_hbaconf configuration file The firewall application is used to block packets from unknown clients from entering the server The pg_hbaconf configuration file is used to restrict what databases and Login Roles can be used from a network client Another aspect of security you must be concerned about is network security When remote clients send data to the PostgreSQL server, that data is contained in TCP packets that can be intercepted To protect data from remote clients, you should enable SSL encryption support on the PostgreSQL server You can either purchase or generate an encryption key and certificate file that are used to communicate with remote clients using the SSL protocol Data transported using SSL is encrypted and is more secure on the network because it cannot be read with a network sniffer The next chapter tackles another important issue for database administrators, the topic of database performance
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Performance
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PostgreSQL 8 for Windows
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atabase performance is often a difficult concept for new database administrators to grasp, and even more difficult to implement There are lots of variables involved in trying to troubleshoot and solve database performance issues This chapter shows some tips that can be useful in trying to get the most performance out of your PostgreSQL database
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ENHANCING QUERY PERFORMANCE
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The words the database seems slow today strike fear in the hearts of database administrators everywhere There are lots of things that can cause a query or database transaction to be slow This section describes some tools you can use to help both monitor and possibly increase performance of your PostgreSQL server The biggest source of performance problems is poorly written queries Unfortunately, it is often the job of the database administrator to help troubleshoot database programmers poorly written query code The best tool for this function is the query explain plan The query explain plan maps out a query into its individual components, then estimates the time each component should take to complete The database programmer can analyze this information to determine which components take the longest time to complete, and possibly alter the query to reduce or sometimes even eliminate the delay This section describes how to use the EXPLAIN SQL command to produce query explain plans, and then shows how to perform query analysis graphically using the pgAdmin III application
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The EXPLAIN Command
Sometimes when entering SQL commands into the database engine, it seems like you are just working with a big black box Queries go into the database engine, and result sets come out of the database engine, without you knowing exactly what is happening inside the database engine The key to improving query performance is getting an idea of what is happening inside the database engine, and determining how to maximize the times each component takes The EXPLAIN SQL command allows us to peek inside the database engine and get a feel for how data is handled If you use the EXPLAIN SQL command in front of a normal SELECT command, PostgreSQL returns estimated statistics from the database engine Here is a simple example:
test=> explain select * from store"Customer"; QUERY PLAN ----------------------------------------------------------Seq Scan on "Customer" (cost=000104 rows=4 width=232) (1 row) test=>
11:
Performance
The SELECT query is not actually run in the database engine The database engine goes through the motions of processing the query, and produces statistics on how it thinks it would handle the query The EXPLAIN command output displays three sets of statistics: The estimated time cost of the SQL command The estimated number of rows the result set would contain The estimated maximum character count in a result set record The cost estimate is provided as two numbers The first value estimates how much time it would take the database engine to return the first record of data for the query In this example, the first value estimates it would take 000 time units to produce one record of the result set The second value estimates how much time it will take the database engine to return the entire result set In this example, PostgreSQL estimates it would take 104 time units to return the entire result set While these values represent time, they do not use standard time units, such as milliseconds These time values are generic time references While this sounds confusing, there is a reason for this Time within the system processor (or processors, if the system is a server with more than one processor) is inexact Each application is provided time slices of the processor as the processor switches between multiple applications At any given time, an application does not necessarily know how many time slices it will have for using the processor Attempting to estimate the exact real time a query would take on any processor on any given system running multiple applications would be nearly impossible Instead, the PostgreSQL developers chose to provide values that represent the amount of time the database engine takes to work on the query These values show how much actual work time the query components take, not necessarily how much real time While these values are not exact, they are consistent You can use these values to compare one query to another Besides the estimated time, you can also choose to view the actual time it takes the database engine to process a query To see the actual time a query takes, use the ANALYZE parameter after EXPLAIN:
test=> explain analyze select * from store"Customer"; QUERY PLAN -----------------------------------------------------------------Seq Scan on "Customer" (cost=000104 rows=4 width=232) (actual time=00820087 rows=5 loops=1) Total runtime: 0135 ms (2 rows) test=>
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