NOTE OF ANALYZE

本文建议不要使用ANALYZE语句中的COMPUTE和ESTIMATE子句来收集Oracle优化器统计信息,因为这些子句仅为了向后兼容而保留。推荐使用DBMS_STATS包来并行收集统计信息、收集分区对象的全局统计信息,并以其他方式调整统计信息收集。对于与成本基础优化器无关的统计信息收集,仍需使用ANALYZE语句。

Do not use the COMPUTE and ESTIMATE clauses of
ANALYZE to collect optimizer statistics. These clauses are supported
for backward compatibility. Instead, use the DBMS_STATS package,
which lets you collect statistics in parallel, collect global statistics
for partitioned objects, and fine tune your statistics collection in
other ways. The optimizer, which depends upon statistics, will
eventually use only statistics that have been collected by DBMS_STATS.

You must use the ANALYZE statement (rather than DBMS_STATS)
for statistics collection not related to the cost-based optimizer, such
as:
■ To use the VALIDATE or LIST CHAINED ROWS clauses
■ To collect information on freelist blocks

[@more@]

来自 “ ITPUB博客 ” ,链接:http://blog.itpub.net/10599713/viewspace-1001633/,如需转载,请注明出处,否则将追究法律责任。

转载于:http://blog.itpub.net/10599713/viewspace-1001633/

3306没有被占用,log文件打印025-08-08 16:57:45 810c InnoDB: Warning: Using innodb_additional_mem_pool_size is DEPRECATED. This option may be removed in future releases, together with the option innodb_use_sys_malloc and with the InnoDB's internal memory allocator. 2025-08-08 16:57:45 33036 [Note] InnoDB: innodb_empty_free_list_algorithm has been changed to legacy because of small buffer pool size. In order to use backoff, increase buffer pool at least up to 20MB. 2025-08-08 16:57:45 33036 [Note] InnoDB: Using mutexes to ref count buffer pool pages 2025-08-08 16:57:45 33036 [Note] InnoDB: The InnoDB memory heap is disabled 2025-08-08 16:57:45 33036 [Note] InnoDB: Mutexes and rw_locks use Windows interlocked functions 2025-08-08 16:57:45 33036 [Note] InnoDB: _mm_lfence() and _mm_sfence() are used for memory barrier 2025-08-08 16:57:45 33036 [Note] InnoDB: Compressed tables use zlib 1.2.3 2025-08-08 16:57:45 33036 [Note] InnoDB: Using generic crc32 instructions 2025-08-08 16:57:45 33036 [Note] InnoDB: Initializing buffer pool, size = 16.0M 2025-08-08 16:57:45 33036 [Note] InnoDB: Completed initialization of buffer pool 2025-08-08 16:57:45 33036 [Note] InnoDB: Highest supported file format is Barracuda. 2025-08-08 16:57:45 33036 [Note] InnoDB: Starting crash recovery from checkpoint LSN=32714859625 2025-08-08 16:57:45 33036 [ERROR] InnoDB: Space id in fsp header 2268439514,but in the page header 821414241 2025-08-08 16:57:45 33036 [ERROR] InnoDB: Invalid flags 0xa0697901 in tablespace 4294967295 2025-08-08 16:57:45 33036 [ERROR] InnoDB: invalid tablespace flags in tablespace .\testlink\tlexecutions.ibd (table testlink/tlexecutions) 2025-08-08 16:57:45 33036 [Note] InnoDB: Page size:1024 Pages to analyze:64 2025-08-08 16:57:45 33036 [Note] InnoDB: Page size: 1024, Possible space_id count:0 2025-08-08 16:57:45 33036 [Note] InnoDB: Page size:2048 Pages to analyze:64 2025-08-08 16:57:45 33036 [Note] InnoDB: Page size: 2048, Possible space_id count:0
08-09
根据原作 https://pan.quark.cn/s/459657bcfd45 的源码改编 Classic-ML-Methods-Algo 引言 建立这个项目,是为了梳理和总结传统机器学习(Machine Learning)方法(methods)或者算法(algo),和各位同仁相互学习交流. 现在的深度学习本质上来自于传统的神经网络模型,很大程度上是传统机器学习的延续,同时也在不少时候需要结合传统方法来实现. 任何机器学习方法基本的流程结构都是通用的;使用的评价方法也基本通用;使用的一些数学知识也是通用的. 本文在梳理传统机器学习方法算法的同时也会顺便补充这些流程,数学上的知识以供参考. 机器学习 机器学习是人工智能(Artificial Intelligence)的一个分支,也是实现人工智能最重要的手段.区别于传统的基于规则(rule-based)的算法,机器学习可以从数据中获取知识,从而实现规定的任务[Ian Goodfellow and Yoshua Bengio and Aaron Courville的Deep Learning].这些知识可以分为四种: 总结(summarization) 预测(prediction) 估计(estimation) 假想验证(hypothesis testing) 机器学习主要关心的是预测[Varian在Big Data : New Tricks for Econometrics],预测的可以是连续性的输出变量,分类,聚类或者物品之间的有趣关联. 机器学习分类 根据数据配置(setting,是否有标签,可以是连续的也可以是离散的)和任务目标,我们可以将机器学习方法分为四种: 无监督(unsupervised) 训练数据没有给定...
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