Tag Archives: Database

5 Tips to be a Good JDBC Programmer

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Have you written Java code for your database connections in JDBC that ends up being thrown away? Would using a new database software easily kill your product and set you back months at a time to port? Well, if so, keep reading as the goal of this article is to make you a better JDBC programmer.

Database

    Tip #1: ALWAYS close connections

I wrote an entry about closing database connections back in July so I won’t belabor the point. Suffice it to say, one of the most common mistakes JDBC programmers make is failing to close database resources like result sets, statements, and connections. Unfortunately, because of the nature of the problem a developer will often never see the error until it is discovered in a production environment since most developers test with one process at a time. In short, every time you open a connection you should close it sometime later and that close should be nearly bullet proof, as so:

Connection con;
try {
   con = ...
} finally {
   try { if(con!=null) {con.close();}}
   catch (Exception e1) {}
}

As mentioned in the post about the subject, you can replace the finally code with a call to a method such as closeConnection() which does the same thing.

    Tip #2: Always use PreparedStatements over regular Statements

As you may be aware, PreparedStatements come with many advantages over regular Statements:

  • Sanitizes your database inputs: Great Example
  • Organizes your statements into set of input/output commands
  • Performance boost: The pre-compiled statement can be reused multiple times

The mistake a lot of programmers make is when they say “Well, I have no inputs to the query, so I’m not going to use PreparedStatements”. First off, they may have inputs down the road they needed to ‘tack on’ to the query, and it will save a lot of time rewriting the code if the query is already structured as a PreparedStatement. Second, perhaps there are some hard-coded input values that you can’t imagine changing, but could change down the road. Better to use a PreparedStatement and a static object, than to hard-code it, as shown in this example:

Statement statement = con.createStatement();
statement.executeQuery("SELECT name FROM widgets WHERE type = 'WidgetB'");

versus:

final String widgetType = "WidgetB";
Statement pStatement = con.prepareStatement("SELECT name FROM widgets WHERE type = ?");
pStatement.setString(1,widgetType);
pStatement.executeQuery();

Sure, the first example is shorter, but the second gives you the freedom to easily change the Widget Type down the road as well as supporting additional inputs since the setup work is all ready done.

    Tip #3: Be Prepared To Change Databases

It’s not a myth; database platforms do change. You may be working on a project using Oracle and you get a request from your manager for an estimate to port the code to MySQL. It can and does happen, the question is are you going to respond with “a couple of days/weeks” or “12-24 months”. Your answer should shed some light on how good your code really is.

    Tip #4: Never Reference Database-Specific Libraries

If you ever find yourself writing a line of code such as this:

OracleCallableStatement cst = (OracleCallableStatement)conn.prepareCall ...

Stop! This is an example where a regular Callable Statement is likely called for, but you’re using a database-specific line in your code. The whole purpose of JDBC driver pattern is that you can drag and drop new JDBC driver libraries for MySQL, Oracle, etc into your application and the software will still compile and run just fine. Lines of code like the one above require you have the JDBC library available at compile-time and guarantee you’ll have to rewrite code to port to another language. In other words, writing a line of code like this is a developer admitting to themselves “I will definitely need to rewrite this line of code if I want to port to another database”.

    Tip #5: Never use Database Stored Procedures*

Never, ever, write a Java application that relies, in large part, on stored procedures. Not only can they never be ported to a different database, 9 times out of 10, you can’t even recall exactly what they did when you wrote them. They fall into the same category of Perl code, which is WORN (write once, read never), since most of the time reading them is harder than rewriting them, mostly because TSQL/PSQL are not very pretty languages to work with.

* There some cases where stored procedures are allowed, such as reporting statistics or materialized views, but these are few and far between. The bulk of your main application logic, such as adding new users, performing basic transactions, and editing data should not rely on stored procedures in any way. Reporting is allowed here because often times performing metrics on millions of records in JDBC would require too many round trips over the network. In other words, reporting requires the kind of direct network access stored procedures guarantee since they run on the database itself. But if you are stuck using them, you should insulate them so they don’t interact with any of your java code directly, such as an independent nightly build of statistics.

    Bonus Tip #6: Never put JDBC code inside JSPs

I started with 5 tips but our reader’s comments reminded me of a 6th tip: Never put JDBC code of any kind inside a JSP! The purpose of a 3-tiered architecture is to get good separation of layers, and putting JDBC code inside the presentation tier skips the middle layer all together. Some developers reduce the impact of this mistake by putting their connection code inside a Java file and only passing the result set to a JSP. This is still really bad! JSPs should have no JDBC code whatsoever since most of the time queries can be reused by multiple modules and JSPs don’t exactly lend themselves to resusability.

Database Performance – Block Updates over the Internet

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When you run a website within a hosted web environment, you often do not have the luxury of direct or intranet access to your database anymore. With remoting tools such as phpMyAdmin becoming increasingly popular among web hosting providers, remote connections over the internet may be the only access a user has to their database. Every once in a while a user may have an application that needs to perform a large number of inserts into their database, and in many situations this can be become a daunting task. The purpose of this article is to bring the problem of large updates to light and discuss some solutions.

Key

  • The Problem

Let’s say I’m writing a script within my application to initialize a new table in my database, and this table requires the insertion of 100,000 items. For anyone who’s ever tried something like this over the internet, they probably are aware of some of the limitations. Web hosting providers tend to insert a number of timeouts throughout the system, such as the time a user can maintain a web connection open or the time a user can maintain a database connection. Both of these timeouts, often set at 60 seconds, can be easily reached if the 100,000 records are being transmitted over the internet.

For example, you may have tried to insert a large file in phpMyAdmin and seen the screen go white and the transfer stop. This is often because you have reached one of the server’s predefined timeouts. Other times, the page may explicitly throw a timeout exception.

The core of the problem is that the server is unwilling to open a connection for the length of time required to perform the update. So what is a developer to do? Well, let’s address 3 potential solutions for dealing with the problem:

  • 1. Naive Approach: One Update at a Time

It’s a reasonable guess that 99% of most web applications perform updates one at a time, with commands immediately sent to the database upon execution. Most, if not all, users will stick to this pattern until they have reason to do so otherwise.

Going back to our example with inserting 100,000 items, how would this approach handle it? Well, it would create 100,000 connections to the database, one at a time, of course. The problem is the overhead of creating each connection means this script, while executing correctly, is actually the most time and resource consuming approach of any that we will discuss in this article. While establishing a connection to a database is normally a trivial thing, doing it 100,000 times is not.

Note: By connection, I’m referring to total round trips to the database, not necessarily individual connection objects you create within your application

If done as part of a transaction, this script will execute perhaps 20,000 items before throwing a timeout exception, at which point all previous inserts will be rolled back. Furthermore, if the inserts do go through, it can be frustrating from an application perspective to modify the application to pick up where it left off. Even when this approach is capable of completing successfully, the overhead of connecting to the database 100,000 individual times will often make this script run very slowly in the real world.

  • 2. Risky Approach: All Updates at Once

One potentially good solution is to upload the entire set of records in a single database connection. If the total size is of the records is not too big (5 megabytes for example), the update will likely succeed and at an extremely fast rate when compared to the first approach.

So where does this solution go wrong? Well, lets say the total record size of the 100,000 records is 100 megabytes. It is often the case that the file can never finish uploading to the server before the timeout is reached. Such as with the example with phpMyAdmin going to a white screen, the server won’t maintain a connection long enough to transfer the target file to the database.

Keep in mind, uploading the large set of records to application server may not solve this problem. I’ve seen cases where files local to the application server failed, because the connection between the application server and database within a hosted environment were simply not fast enough to transfer the file and perform the update.

  • 3. Powerful Approach: Block Updates

In the first solution we saw the overhead of creating thousands of database connections cause the time required to perform the update grow drastically, whereas in the second solution the time required to perform the update was great but not within the boundaries of most database connection timeouts. The third and last solution I’ll discuss is to perform updates as set of blocks.

For example, lets say we took the 100,000 records and divided them into 20 blocks of 5,000 records. A quick comparison of perform yields:

Solution Database Connections Count Largest File Size Per Database Connection
1 Update at a Time 100,000 1 kilobyte
All Updates at Once 1 100 megabytes
Block Updates (n=5000) 20 5 megabytes

From this table we see the block solution has the performance advantage of the second solution, namely few database connections since 1 versus 20 connections is quite negligible, but never has a file size bigger than 5 megabytes and is less likely to fail transferring a large file. Furthermore, we can double the block size to 40 blocks of 2,500 records and still have great performance (40 connections versus 1) with a file size of half. In general, you would implement such a solution with the block size, n, determined at runtime or in a properties file so that it can be easily changed. Also keep in mind the last block is most likely never filled. For example, if I had 99,995 records, the last block of 5,000 records would only have 4,995 items and it would be important to make sure the code did make a mistake and assume a full block.

  • Real World Application: Does this work in practice?

Yes, most definitely. I can recall a situation where I was faced with such an issue. The first solution which inserted one record at a time took over an hour to run. The second solution of inserting everything at once almost never completely because it would timeout long before it was finished. The third solution of inserting things as blocks always completed and often within a 5 minutes.

While this solution describes one type of scenario where block updates is the best approach, there are lot of factors that could affect what you do in your application, such as having the ability to increase the timeout values within your hosted application. There are also more advanced solutions such as Batch Updates provided by JDBC as well as the ability to run SQL scripts locally if you have shell access to your database. Overall, this article is meant to remind you that while it’s common to ignore performance consideration of large files in the real world (we’ll just get faster internet!), there are some excellent gains to be made if you spend some time considering handling large files in your application.

Database Key Generation in Java Applications

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All JDBC programmers at one time or another have had to deal with key generation for new records, or more precisely, how to retrieve keys for newly created records. This article discusses some of the techniques and limitations offered to Java JDBC programmers.

Key

  • Unique Keys

Most (good) database systems assign a unique key, usually a large number, to every record in the database. For example, a user “Bob Jones” might have a User Id associated with the record of 808182313713. This key is unique for Bob and is a lot easier to pass around a system then Bob’s full name, since there may be other Bob Jones’s in the system. We could spend time discussing Natural Keys, although I would argue there is no such thing as natural keys. As an example, Bob’s social security number might seem like a natural key since it is unique to Bob but it may be the case Bob is a visiting foreigner (or the application is deployed outside the country) in which a US social security number is not applicable. Also, it’s been reported the government has been known to recycle social security numbers, as well as the fact that people can be issued new social security numbers during their lifetime. Even if unique natural keys did exist – e-mail for example – what if a user wants to change their address down the road?

This leads us to our second requirement of unique keys: Unique keys should not change throughout the life of the system. Changing unique keys is often an extremely difficult, highly risk process. A third, less followed, requirement is that unique keys should be private and only used internally by the application server. This is a huge boon to security and prevents users from attacking the system. In the case the system does need to expose a key, such as an eBay item number found in the URL of the auction, the system may create two keys: an Internal Id that is used for table relations within a database, and an External Id used to perform lookups on behalf of the user. The key here is that the External Id the user sees only exists in one record, whereas the Internal Id is used throughout the system to connect different records.

For the purpose of this article, we’ll assume key generation is the common approach of assigning long integers to each record in a table. In most database systems, this corresponds to the BIGINT type.

Helpful Advice: Always use the largest integer type the database has to offer for unique keys – numeric wrap-around is something to be concerned about, especially on smaller data types.

  • Defining the Problem

Most systems generate keys sequentially starting with a low number such as 1000. Why? Well, for starters it makes it easy to find new unused key value. Just find the max value of the table, and any value above this will be an unused available key. You could randomly generate a key, but then creating a new key would be a time consuming processing of generating a random number that has not been used.

Now, let’s say a user is in the process of creating a new record in your system. For each user record, you also have a set of postal addresses. For example, Bob may be purchasing items on NewEgg and have a home address and a work address. Furthermore, Bob enters his two addresses at the time he creates his account, so the application server receives the information to create all 3 records at once. In such a situation, you would normally have 3 records: 1 user record for Bob and 2 address records. You could add the address info in the user table, although then you have to restrict the number of addresses Bob can have and/or have a user table with a lot of extra columns.

Inserting Bob into the user table is straight forward enough, but there is a problem when you go to insert users into the address table, namely that you need Bob’s newly generated User Id in order to insert any records into the address table. After all, you can’t insert addresses without being connected to a specific user, lest chaos ensue in your data management system.

Most database systems provide a technique for the database to create keys for you in the event you do not specify them. In this situation, the first insert would leave the User Id for Bob as blank, or more precisely null, and the database would fill in a new value on insertion. The problem is the database isn’t really obligated to tell you what this newly created value is. The rest of this article will be devoted to discussing how you can get this generated key in your application.

  • Technique 1: Get the max id

Probably the most common approach is the quick and dirty one; perform a select query immediately after your insert and retrieve the max value:

SELECT max(id) FROM users

The good: In a lot of systems this actually works pretty well. It’s easy to implement and is extremely database independent.
The bad: It’s extremely risky in high concurrency environments. For example, if two users perform inserts at nearly the same time, they will both get the same max value from this query, leading to a huge breach in security (both users may have access to one of the users records if the application server returns the same id). There are ways to set your transaction isolation level to avoid problems like this, but that can be tricky and often requires more skill and experience than someone implementing this solution regularly is likely to have.

Also, you have to be *very* sure the system always creates keys strictly in ascending order. A database system that reused old or skipped key values would be incompatible with this approach.

  • Technique 2: JDBC to the Rescue!

Many JDBC programmers are unaware that there is built-in JDBC method for retrieving the newly generated keys immediately after an insert. It is appropriately named getGeneratedKeys() and can be used as follows:

Connection con = // connect to database
final PreparedStatement ps = con.prepareStatement("INSERT INTO users (...) VALUES (...)");
ps.setInt(1,...)
...
final int insertStatus = ps.executeUpdate();
int newKey = -1;
if(insertStatus == 1) {
     final ResultSet rs = ps.getGeneratedKeys();
     if(rs.next()) {
          newKey = rs.getInt(1);
          if(rs.next()) {
               // Probably should throw some exception if rs returns a second, unexpected record
          }
     } else {
          // Probably should throw some exception if no generated keys created
     }
} else {
     // Definitely should throw an error if insert failed
}
System.out.println("The key value for the newly created record is: "+newKey);

A reminder: JDBC starts with 1 (instead of 0) for accessing columns based on an index number.

The good: Don’t need to write any special or tricky logic to get the key. Works in systems that use ascending and non-ascending key value generation. No risk of getting the wrong key value even if you fail to set transaction or isolation levels properly.
The bad: Not supported by all JDBC drivers. Need to test it out in your real system to verify it works before relying on it.

  • Technique 3: Application Server Controlled Keys

One approach used in most large J2EE systems is to build a key generation service within the application tier. In this manner, all generated keys are created ahead of time by the application server without the use of any database-specific features. In the example above, the system would generate all the keys for the user and addresses before the first insert even occurs. Then it would insert the 3 records, one after another, using the keys it had at the start of the service. Keep in mind, the order of insert still matters, i.e. you can’t insert an address for a user that does not yet exist; but in this case, you don’t need to stop after the first insert to figure out information for the next insert.

Such a key generation module would have to be application-wide (static) and thread safe to prevent two requests from getting the same key at the same time. Also, such a system would have to be fault tolerant; if the system crashes it should not reassign any of the same keys after the next restart.

Often this is implemented with a database table that keeps track of the most recently assigned key for each record. To make the service perform well, keys are often released in blocks. For example, the system might say “The last key used was 4100, and I’m going to request 4101-4200”. In this manner the system would update the database record for that table to 4200 and only change this value when it used up all 100 keys in memory. It’s fault tolerant because even if not all keys from 4101-4200 are used, if the system crashes, it knows it can just request 4201-4300 next time.

The good: Performs better than any of the other solutions since it takes the load off of the database entirely. All inserts can be performed immediately without requesting information from the database in between inserts. Safer than any of the solutions because its is 100% database independent. Supports composite keys and distributed database systems.
The bad: Requires the most overhead of any of the solutions since you need to implement a server wide key generation tool. Requires the application server to know the name of each table (or some reference to each table) to track the key generation values for each table. Also, you could skip a lot of keys (sparse key range) if your system restarts frequently, although servers shouldn’t be restarting frequently! Lastly, it tends to require an experienced developer to implement properly (prevent errors) and perform well (using blocking properly).

  • Technique 4: Database-specific techniques

Lastly, you may want to rely on database-specific techniques. For example, Oracle allows you to write queries such as:

INSERT INTO Users (...) VALUES (...) RETURNING (id) INTO newUserId

In this case you would perform an executeQuery() instead of an executeUpdate(), so that the results of the insert could be read in a Result Set.

There are numerous other database-specific techniques for retrieving newly generated keys, but since I advocate against database-specific code in Java applications, I’ll leave a more detailed discussion for someone else.

The good: It’s safer than technique 1 since it works in systems with non-sequential keys.
The bad: It’s database-specific. Not only that but as shown above, all of your insert statements would be database specific. Would make moving the code to another database system very difficult (often impossible).

  • Final Thoughts

Generally, for smaller systems Technique #2 using getGeneratedKeys() is the best solution. Larger systems with composite key generation or distributed databases often require Technique #3 for an application controlled key generator.

As for the other two approaches, I would strongly advise against ever using Technique #4, lest you want to give future developers of your source code a huge headache in management and portability. Lastly, Technique #1 is sometimes safe to use, especially in single threaded systems, although is often evidence of a beginner JDBC developer.