Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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CHAPTER 13 ■ PARTITIONING 565 Enhanced Statement Performance The last general (potential) benefit of partitioning is in the area of enhanced statement (SELECT, INSERT, UPDATE, DELETE, MERGE) performance. We’ll take a look at two classes of statements—those that modify information and those that just read information—and discuss what benefits we might expect from partitioning in each case. Parallel DML Statements that modify data in the database may have the potential to perform parallel DML (PDML). During PDML, Oracle uses many threads or processes to perform your INSERT, UPDATE, or DELETE instead of a single serial process. On a multi-CPU machine with plenty of I/O bandwidth, the potential increase in speed may be large for mass DML operations. In releases of Oracle prior to 9i, PDML required partitioning. If your tables were not partitioned, you could not perform these operations in parallel in the earlier releases. If the tables were partitioned, Oracle would assign a maximum degree of parallelism to the object, based on the number of physical partitions it had. This restriction was, for the most part, relaxed in Oracle9i and later with two notable exceptions. If the table you wish to perform PDML on has a bitmap index in place of a LOB column, then the table must be partitioned in order to have the operation take place in parallel, and the degree of parallelism will be restricted to the number of partitions. In general, though, you no longer need to partition to use PDML. ■Note We will cover parallel operations in more detail in Chapter 14. Query Performance In the area of strictly read query performance (SELECT statements), partitioning comes into play with two types of specialized operations: • Partition elimination: Some partitions of data are not considered in the processing of the query. We have already seen an example of partition elimination. • Parallel operations: Examples of this are parallel full table scans and parallel index range scans. However, the benefit you can derive from these depends very much on the type of system you are using. OLTP Systems 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
566 CHAPTER 13 ■ PARTITIONING 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). 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 shortly 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 out the modifications of a single table over many physical partitions. 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, 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. Most OLTP applications are not able to take advantage of the times where partitioning is able to enhance query performance, but you can still benefit from the other two possible partitioning benefits; administrative ease and higher availability. Data Warehouse Systems In a data warehouse/decision-support system, partitioning is not only a great administrative tool, but also something that will speed up processing. For example, you may have a large table on which you need to perform an ad hoc query. You always do the ad hoc query by sales quarter, as each sales quarter contains hundreds of thousands of records and you have millions of online records. So, you want to query a relatively small slice of the entire data set, but it is not really feasible to index it based on the sales quarter. This index would point to hundreds of thousands of records, and doing the index range scan in this way would be terrible (refer to Chapter 11 for more details on this). A full table scan is called for to process many of your queries, but you end up having to scan millions of records, most of which won’t apply to our query. Using an intelligent partitioning scheme, you can segregate the data by quarter such that when you query the data for any given quarter, you will full scan only that quarter’s data. This is the best of all possible solutions. In addition, in a data warehouse/decision-support system environment, parallel query is used frequently. Here, operations such as parallel index range scans or parallel fast full index scans are not only meaningful, but also beneficial to us. We want to maximize our use of all available resources, and parallel query is a way to do it. So, in this environment, partitioning stands a very good chance of speeding up processing.
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Expert Oracle Database Architecture
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Contents Foreword . . . . . . . . .
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■CONTENTS v Block Buffer Cache .
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■CONTENTS vii ■CHAPTER 9 Redo a
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■CONTENTS ix ■CHAPTER 12 Dataty
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Foreword “THINK.” In 1914, Thom
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About the Technical Reviewers ■JO
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Introduction The inspiration for th
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■INTRODUCTION xix • Exposure to
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Setting Up Your Environment In this
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338 CHAPTER 10 ■ DATABASE TABLES
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CHAPTER 11 ■ ■ ■ Indexes Inde
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Apress.Expert.Oracle.Database.Architecture.9i.and.10g.Programming.Techniques.and.Solutions.Sep.2005

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