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DECLARE @keycol AS INT, @filler AS CHAR(200); SELECT TOP (1) @keycol = keycol, @filler = filler FROM dbo.T1 ORDER BY keycol; WHILE @@rowcount = 1 BEGIN -- Process data here -- Get next row SELECT TOP (1) @keycol = keycol, @filler = filler FROM dbo.T1 WHERE keycol > @keycol ORDER BY keycol; END

Concurrency and correctness Providing reliability is not just a matter of ensuring that the system does not crash. An equal measurement of your system s reliability is the extent to which it operates consistently. Regardless of load, time of day, and other factors, your system must always keep itself in a valid state and behave in a predictable way. The integrity of your system s data is not hard to achieve in most distributed applications, because they rely at some point on a database management system (DBMS) that guarantees such integrity. The state and behavior of a running application, however, is the responsibility of its designer and developers. Ensuring that any logic-intensive application will run correctly in all situations is a complicated task. In a distributed system, it is even more so. This is because servers in distributed systems must provide access to shared resources to various clients, often concurrently. It is the responsibility of each service implementer to ensure that information updates are coordinated and synchronized across all client invocations. To address this, each distributed component should have a detailed state model and be tested thoroughly. Assume nothing works properly until proven otherwise. You will thank yourself when your system goes live and you still have your weekends. Ensuring that individual J2EE components work together like they should can be achieved by using the aforementioned JTA API and the transactional capabilities of the EJB container. Your application can also lean on the transactional capabilities of its relational database in some situations.

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You use a TOP (1) query to grab the first row based on key order . Within a loop, when a row is found in the previous iteration, you process the data and request the next row (the row with the next key) . This code ran for about 25 seconds against a cold cache and 17 seconds

against a warm cache similar to the cursor code . However, keep in mind that I created a clustered index on keycol to improve performance by accessing the desired row in each iteration with minimal I/O . You can see the plan for this code in Figure 8-1 .

FiguRe 8-1 Execution plan for the code in Listing 8-1

Even with the index in place, this approach involves a very high I/O cost compared to the cursor approach . The plan in Figure 8-1 performs a seek operation in the index for each row in the table . With the number of rows in our table the index has three levels . This means that the approach shown in Listing 8-1 involves 3 reads per row, amounting in a total of 3,000,000 reads for 1,000,000 rows . That s compared to about 25,000 reads that the cursor approach involves because it simply scans the data once . Without that index, matters are even worse for the approach shown in Listing 8-1 . The code would run substantially slower because each invocation of the query would need to rescan large portions of data . The cursor solution would still perform the same because it would still scan the data only once . Note that a cursor solution based on sorted data would also benefit from an index and would run substantially slower without one because it would need to sort the data after scanning it . With large tables and no index on the sort columns, the sort operation can be expensive because sorting in terms of complexity is O(n log n), whereas scanning is only O(n) . Before you proceed, make sure you turn off the Discard results after execution option in SSMS .

Error handling Dealing with error conditions in distributed systems is a real challenge. This is because the failures that occur do not crash the entire system. A part of the system fails, and it is up to the other components to detect the failure and take appropriate action. And since certain types of failures can t be detected easily or at all, individual components need to be overly suspicious of errors when interacting with each other. There are various types of distributed system failures, which can be grouped as follows:

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