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CHAPTER 14 PARALLEL EXECUTION
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Note If a parallel execution is not occurring in your system, do not expect to see the parallel execution
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servers in V$SESSION. They will be in V$PROCESS, but will not have a session established unless they are being used. The parallel execution servers will be connected to the database, but will not have a session established. See 5 for details on the difference between a session and a connection.
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In a nutshell, that is how parallel query and, in fact, parallel execution in general works. It entails a series of parallel execution servers working in tandem to produce subresults that are fed either to other parallel execution servers for further processing or to the coordinator for the parallel query. In this particular example, as depicted, we had BIG_TABLE spread across four separate devices, in a single tablespace (a tablespace with four data files). When implementing parallel execution, it is generally optimal to have your data spread over as many physical devices as possible. You can achieve this in a number of ways: Using RAID striping across disks Using ASM, with its built-in striping Using partitioning to physically segregate BIG_TABLE over many disks Using multiple data files in a single tablespace, thus allowing Oracle to allocate extents for the BIG_TABLE segment in many files In general, parallel execution works best when given access to as many resources (CPU, memory, and I/O) as possible. However, that is not to say that nothing can be gained from parallel query if the entire set of data were on a single disk, but you would perhaps not gain as much as would be gained using multiple disks. The reason you would likely gain some speed in response time, even when using a single disk, is that when a given parallel execution server is counting rows it is not reading them, and vice versa. So, two parallel execution servers may well be able to complete the counting of all rows in less time than a serial plan would. Likewise, you can benefit from parallel query even on a single CPU machine. It is doubtful that a serial SELECT COUNT(*) would use 100 percent of the CPU on a single CPU machine it would be spending part of its time performing (and waiting for) physical I/O to disk. Parallel query would allow you to fully utilize the resources (the CPU and I/O, in this case) on the machine, whatever those resources may be. That final point brings us back to the earlier quote from Practical Oracle8i: Building Efficient Databases: parallel query is essentially nonscalable. If you allowed four sessions to simultaneously perform queries with two parallel execution servers on that single CPU machine, you would probably find their response times to be longer than if they just processed serially. The more processes clamoring for a scarce resource, the longer it will take to satisfy all requests. And remember, parallel query requires two things to be true. First, you need to have a large task to perform for example, a long-running query, the runtime of which is measured in minutes, hours, or days, not in seconds or subseconds. This implies that parallel query is not a solution to be applied in a typical OLTP system, where you are not performing longrunning tasks. Enabling parallel execution on these systems is often disastrous. Second, you
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CHAPTER 14 PARALLEL EXECUTION
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need ample free resources such as CPU, I/O, and memory. If you are lacking in any of these, then parallel query may well push your utilization of that resource over the edge, negatively impacting overall performance and runtime. In the past, parallel query was considered mandatory for many data warehouses simply because in the past (say, in 1995) data warehouses were rare and typically had a very small, focused user base. Today in 2005, data warehouses are literally everywhere and support user communities that are as large as those found for many transactional systems. This means that you may well not have sufficient free resources at any given point in time to enable parallel query on these systems. That doesn t mean parallel execution in general is not useful in this case it just might be more of a DBA tool, as we ll see in the section Parallel DDL, rather than a parallel query tool.
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