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The putThread() method requires that the caller thread be the same thread it is attempting to pool. This requirement is enforced using an assert statement1 (provided you have enabled the assertions, of course). It adds the thread to the list of waiting threads, where it will wait until notified by the wakeupThread() method, which is typically called after someone obtains it from the pool using getThread(). The getThread() method provides a thread to the caller by returning an available thread from the pool. The thread still continues to be in a waiting state until it is woken up. The client will change the thread s state to that needed by the task it is about to perform much like initializing a newly created thread before waking it up. The wakeupThread() method wakes up the thread. Once woken up, the thread starts performing its given task.

Hash partitioning is designed to achieve a good spread of data across many different devices (disks), or just to segregate data out into more manageable chunks. The hash key chosen for a table should be a column or set of columns that are unique, or at least have as many distinct values as possible to provide for a good spread of the rows across partitions. If you choose a column that has only four values, and you use two partitions, then all the rows could quite easily end up hashing to the same partition, obviating the goal of partitioning in the first place! We will create a hash table with two partitions in this case. We will use a column named HASH_KEY_COLUMN as our partition key. Oracle will take the value in this column and determine the partition this row will be stored in by hashing that value: ops$tkyte@ORA10G> CREATE TABLE hash_example 2 ( hash_key_column date, 3 data varchar2(20) 4 ) 5 PARTITION BY HASH (hash_key_column) 6 ( partition part_1 tablespace p1, 7 partition part_2 tablespace p2 8 ) 9 / Table created. Figure 13-2 shows that Oracle will inspect the value in the HASH_KEY_COLUMN, hash it, and determine which of the two partitions a given row will appear in:

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However, the benefit you can derive from these depends very much on the type of system you are using.

PART_1

7.4.5 Testing our solution Now that we have the implementation, it is time to test our thread pooling using the AspectJ-based solution. Let s first write a test client to exercise the pooling functionality, as shown in listing 7.15.

You should not look toward partitions as a way to massively improve query performance in an OLTP system In fact, in a traditional OLTP system, you must apply partitioning with care so as to not negatively affect runtime performance In a traditional OLTP system, most queries are expected to return virtually instantaneously, and most of the retrievals from the database are expected to be via very small index range scans Therefore, the main performance benefits of partitioning listed previously would not come into play Partition elimination is useful where you have full scans of large objects, because it allows you to avoid full scanning large pieces of an object However, in an OLTP environment, you are not full scanning large objects (if you are, you have a serious design flaw).

PART_2

Even if you partition your indexes, any increase in performance achieved by scanning a smaller index will be miniscule if you actually achieve an increase in speed at all If some of your queries use an index and they cannot eliminate all but one partition from consideration, you may find your queries actually run slower after partitioning since you now have 5, 10, or 20 small indexes to probe, instead of one larger index We will investigate this in much more detail later when we look at the types of partitioned indexes available to us Having said all this, there are opportunities to gain efficiency in an OLTP system with partitions For example, they may be used to increase concurrency by decreasing contention They can be used to spread the modifications of a single table out over many physical partitions.

import java.io.*; import java.net.*; public class EchoClient { public static void main(String[] args) throws Exception { if (args.length != 2) { System.out.println( "Usage: java EchoClient <server> <portNum>"); System.exit(1); } String serverName = args[0]; int portNum = Integer.parseInt(args[1]); setup(serverName, portNum); } private static void setup(String serverName, int portNum) throws IOException { Socket requestSocket = new Socket(InetAddress.getByName(serverName), portNum);

Instead of having a single table segment with a single index segment, you might have 20 table partitions and 20 index partitions It could be like having 20 tables instead of 1, hence contention would be decreased for this shared resource during modifications As for parallel operations, as we ll investigate in more detail in the next chapter Parallel Execution, you do not want to do a parallel query in an OLTP system You would reserve your use of parallel operations for the DBA to perform rebuilds, create indexes, gather statistics, and so on The fact is that in an OLTP system, your queries should already be characterized by very fast index accesses, and partitioning will not speed that up very much, if at all This does not mean that you should avoid partitioning for OLTP; it means that you shouldn t expect partitioning to offer massive improvements in performance.

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