Making Oracle run faster 2-database design and optimization based on massive data

Making Oracle run faster 2-database design and optimization based on massive data

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The first domestic project was created based on the author's 10 years of work experience.
Books on Database Design and Optimization Based on massive data

Basic Information

Author:Tan Huaiyuan

Series Name:Itpub Technology series

Press: Electronic Industry Press

ISBN:9787121139215

Mounting time: 2011-8-1

Published on: February 1, July 2011

Http://product.china-pub.com/198426

Making Oracle run faster 2-database design and optimization based on massive data online reading of e-books

Introduction

Database Design is a technical field that has only emerged in recent years. Early on, databases are affiliated to a system in the form of a black box, and developers have very little attention to it.
In recent years, due to the surge in the amount of data in many systems, various performance problems have become increasingly prominent, and most of these performance problems have fallen on the data carrier-database. Therefore, people are paying more and more attention to database performance. The performance of a database is usually determined at the system design stage. Therefore, the database is designed separately from the system design and becomes more and more mainstream.
This is a book discussing the design and optimization of Oracle databases in a massive data environment. It is also the author's experience in Oracle Database work over the past 10 years. It is the crystallization of the author's work experience, such books are rare.
This book focuses on how to construct an efficient Oracle database in a massive data environment and brings many related technologies into this core topic. These technologies include: partitioning, indexing, database object attributes, parallel technology, read-only tablespace, initialization parameters, several common database architectures, and database backup and recovery under a specific database architecture.
Making Oracle run faster 2-database design and optimization based on massive data is suitable for Oracle DBAs, developers, project managers, and others interested in database performance.

 
Directory
Making Oracle run faster 2-database design and optimization based on massive data
Article 1 partition
Chapter 2 source of partition 2
1.1 Delete and system resource 3
1.2 Delete and release space 10
Chapter 1 Application of partitioning in massive databases 15
2.1 partition type 16
2.1.1 range partition 16
2.1.2 hash partition 35
2.1.3 list partition 42
2.1.4 combined partition 43
2.2 Comparison of Three Types of partitions 46
2.2.1 applicability of range partitions 47
2.2.2 Hash partition efficiency 47
2.2.3 advantages of list partitions and range partitions 54
2.3 Summary 61
Article 2
Chapter 2 B-tree index 64
3.1 Overview of B-tree indexes 65
3.2 Efficiency of B-tree index 66

. 3.2.1 primary key or uniqueness constraint 66
3.2.2 The B-tree index is suitable for fields with low key-value repetition rate
Chapter 2 bitmap index 85
4.1 bitmap index overview 86
4.2 When to use bitmap index 97
Chapter 2 full-text index 5th
5.1 full-text index overview 109
5.1.1 comparison between full-text indexes and general indexes 110
5.1.2 object of full-text index 114
5.1.3 Application of full-text indexing in massive databases 123
5.2 full-text index space 138
5.3 full-text indexing and DML operations 145
5.3.1 insert operation 146
5.3.2 delete operation 149
5.3.3 update operation 150
5.4 ctx_report toolkit 154
5.4.1 ctx_report.describe_index 158
5.4.2 describe_policy function 161
5.4.3 create_index_script function 162
5.4.4 create_policy_script function 164
5.4.5 index_size function 165
5.4.6 index_stats Stored Procedure 168
5.4.7 query_log_summary storage process 170
5.5 conclusion 175
Article 3 object ownership
Chapter 2 object attributes 6th
6.1 object attributes in Oracle Database 178
6.2 relationship between object attributes and system 183
Chapter 2 parallelism degree 7th
7.1 parallel 186 on Object Attributes
7.2 use hint for parallel execution 189
7.3 The degree of parallelism on the index is 193
Chapter 1 Data Compression 8th
8.1 application of Data Compression Technology 199
8.2 partition index compression 203
8.3 advantages of Data Compression 205
8.3.1 space saving 205
8.3.2 Performance Improvement: 212
8.4 DML operations and data compression 219
Chapter 2 read-only tablespace 9th
9.1 relationship between read-only tablespace and data backup and recovery 228
9.2 impact of read-only tablespace on Database startup and shutdown 231
9.3 read-only tablespace can prevent accidental deletion and modification of Data 237
9.4 read-only tablespace makes it easier to manage tablespaces 240
Chapter 1 Analysis of database objects 10th
10.1 CBO and RBO 246
10.2 Analysis and Dynamic sampling 251
10.3 object analysis frequency 254
10.3.1 data will not be changed by 255 after it is imported into the database
10.3.2 data already exists in the table and often changes by 271
Article 4 database architecture design of massive data
Chapter 2 RAC architecture 11th
11.1 application of RAC in massive databases 293
11.2 RAC architecture-Business Division 295
11.3 RAC architecture-Load Balancing 308
11.3.1 Client Server Load balancer 308
11.3.2 Server Load balancer 313
11.4 RAC architecture-failover 316
Chapter 2 distributed database architecture 12th
12.1 advantages of distributed databases 319
12.2 Distributed Database Data Processing 321
12.3 distributed database character set 323
Chapter 2 Data guard architecture 13th
13.1 data guard overview 326
13.2 data guard protection mode 329
13.2.1 maximum data protection mode: 329
13.2.2 maximum performance mode 329
13.2.3 maximum availability mode 330
13.3 data guard and Rac 331
13.4 standby database type in data guard 332
13.4.1 physical standby database 332
13.4.2 logical standby database 333
Chapter 2 Storage Architecture Design (ASM + San) 14th
14.1 ASM 336
14.1.1 convenient storage management 337
14.1.2. A management tool of ASM-asmcmd 345
14.1.3 high storage availability of 348
14.1.4 improves storage performance by 350
14.2 San 352
Chapter 1 backup and restoration of massive data 15th
15.1 traditional data backup solution 354
15.1.1 back up data 355 through data export
15.1.2 database cold backup 355
15.1.3 Database Hot Backup 356
15.2 database backup and recovery solutions for massive data 357
15.2.1 use distributed database 357
15.2.2 backup and restore the table space by transferring the tablespace 358
15.2.3 back up the loaded data 368
Article 5 initialization parameters
Chapter 1 memory-related parameters 16th
16.1 sga_target 373
16.2 pga_aggregate_target 379
Chapter 1 I/O-related parameters 17th
17.1 db_file_multiblock_read_count 383
17.2 other four parameters related to database I/O and data files 388
17.2.1 db_writer_processes 388
17.2.2 disk_asynch_io 389
17.2.3 dbwr_io_slaves 389
17.2.4 db_files 390
Chapter 1 optimizer-related parameters 18th
18.1 cursor_sharing 391
18.2 optimizer_dynamic_sampling 394
18.3 optimizer_mode 398
18.3.1 all_rows 398
18.3.2 first_rows_n 398
18.3.3 first_rows 399
Chapter 1 other parameters 19th
19.1 parallel parameters 400
19.1.1 set parallel 401 in Object Attributes
19.1.2 using parallel 401 in hint
19.2 Distributed Database-related parameters 404
Appendix A Oracle Character Set 406
Appendix B show_space Stored Procedure 415
Appendix C design scheme of a typical OLAP system database 419