PolarDB PostgreSQL版(以下简称 PolarDB-PG)是一款阿里云自主研发的企业级数据库产品,采用计算存储分离架构,兼容 PostgreSQL 与 Oracle。PolarDB-PG 的存储与计算能力均可横向扩展,具有高可靠、高可用、弹性扩展等企业级数据库特性。同时,PolarDB-PG 具有大规模并行计算能力,可以应对 OLTP 与 OLAP 混合负载;还具有时空、向量、搜索、图谱等多模创新特性,可以满足企业对数据处理日新月异的新需求。
Most Common Elements
/*
* A "most common elements" slot is similar to a "most common values" slot,
* except that it stores the most common non-null *elements* of the column
* values. This is useful when the column datatype is an array or some other
* type with identifiable elements (for instance, tsvector). staop contains
* the equality operator appropriate to the element type, and stacoll
* contains the collation to use with it. stavalues contains
* the most common element values, and stanumbers their frequencies. Unlike
* MCV slots, frequencies are measured as the fraction of non-null rows the
* element value appears in, not the frequency of all rows. Also unlike
* MCV slots, the values are sorted into the element type's default order
* (to support binary search for a particular value). Since this puts the
* minimum and maximum frequencies at unpredictable spots in stanumbers,
* there are two extra members of stanumbers, holding copies of the minimum
* and maximum frequencies. Optionally, there can be a third extra member,
* which holds the frequency of null elements (expressed in the same terms:
* the fraction of non-null rows that contain at least one null element). If
* this member is omitted, the column is presumed to contain no null elements.
*
* Note: in current usage for tsvector columns, the stavalues elements are of
* type text, even though their representation within tsvector is not
* exactly text.
*/
#define STATISTIC_KIND_MCELEM 4
复制
与 MCV 类似,但是保存的是列中的 最常见元素,主要用于数组等类型。同样,在 staop
中保存了等值运算符用于判断元素出现的频率高低。但与 MCV 不同的是这里的频率计算的分母是非空的行,而不是所有的行。另外,所有的常见元素使用元素对应数据类型的默认顺序进行排序,以便二分查找。
Distinct Elements Count Histogram
/*
* A "distinct elements count histogram" slot describes the distribution of
* the number of distinct element values present in each row of an array-type
* column. Only non-null rows are considered, and only non-null elements.
* staop contains the equality operator appropriate to the element type,
* and stacoll contains the collation to use with it.
* stavalues is not used and should be NULL. The last member of stanumbers is
* the average count of distinct element values over all non-null rows. The
* preceding M (>=2) members form a histogram that divides the population of
* distinct-elements counts into M-1 bins of approximately equal population.
* The first of these is the minimum observed count, and the last the maximum.
*/
#define STATISTIC_KIND_DECHIST 5
复制
表示列中出现所有数值的频率分布直方图。stanumbers
数组的前 M 个元素是将列中所有唯一值的出现次数大致均分到 M - 1 个桶中的边界值。后续跟上一个所有唯一值的平均出现次数。这个统计信息应该会被用于计算 选择率。
Length Histogram
/*
* A "length histogram" slot describes the distribution of range lengths in
* rows of a range-type column. stanumbers contains a single entry, the
* fraction of empty ranges. stavalues is a histogram of non-empty lengths, in
* a format similar to STATISTIC_KIND_HISTOGRAM: it contains M (>=2) range
* values that divide the column data values into M-1 bins of approximately
* equal population. The lengths are stored as float8s, as measured by the
* range type's subdiff function. Only non-null rows are considered.
*/
#define STATISTIC_KIND_RANGE_LENGTH_HISTOGRAM 6
复制
长度直方图描述了一个范围类型的列的范围长度分布。同样也是一个长度为 M 的直方图,保存在 stanumbers
中。
Bounds Histogram
/*
* A "bounds histogram" slot is similar to STATISTIC_KIND_HISTOGRAM, but for
* a range-type column. stavalues contains M (>=2) range values that divide
* the column data values into M-1 bins of approximately equal population.
* Unlike a regular scalar histogram, this is actually two histograms combined
* into a single array, with the lower bounds of each value forming a
* histogram of lower bounds, and the upper bounds a histogram of upper
* bounds. Only non-NULL, non-empty ranges are included.
*/
#define STATISTIC_KIND_BOUNDS_HISTOGRAM 7
复制
边界直方图同样也被用于范围类型,与数据分布直方图类似。stavalues
中保存了使该列数值大致均分到 M - 1 个桶中的 M 个范围边界值。只考虑非空行。
「喜欢这篇文章,您的关注和赞赏是给作者最好的鼓励」
关注作者
【版权声明】本文为墨天轮用户原创内容,转载时必须标注文章的来源(墨天轮),文章链接,文章作者等基本信息,否则作者和墨天轮有权追究责任。如果您发现墨天轮中有涉嫌抄袭或者侵权的内容,欢迎发送邮件至:contact@modb.pro进行举报,并提供相关证据,一经查实,墨天轮将立刻删除相关内容。
评论
相关阅读
2025年2月国产数据库大事记
墨天轮编辑部
963次阅读
2025-03-05 12:27:34
神州数码携手云原生数据库 PolarDB,共筑国产数据库新生态
神州数码集团
168次阅读
2025-03-03 18:04:27
IDC:2024上半年中国分布式事务数据库软件市场规模为1.5亿美元,同比增长18.5%,阿里、腾讯与华为位列前三
通讯员
162次阅读
2025-03-03 10:01:48
阿里云Tair KVCache:打造以缓存为中心的大模型Token超级工厂
阿里云瑶池数据库
82次阅读
2025-03-25 10:37:41
正式公测|阿里云数据库Tair Serverless KV,轻松应对流量波动
阿里云瑶池数据库
47次阅读
2025-03-05 11:09:23
阿里云谈AI下半场 数据库已经开始比拼性价比
通讯员
43次阅读
2025-03-06 09:56:21
庖丁解InnoDB之B+Tree (三)
olep
42次阅读
2025-03-04 11:14:02
心智观察所|若前方无路,便踏出一条路:中国数据库产业迎来“哪吒时刻”
通讯员
39次阅读
2025-03-04 09:47:59
PostgreSQL LRU刷脏简析
PolarDB
38次阅读
2025-03-06 09:27:04
PolarSearch使用指南
快点好起来
37次阅读
2025-03-19 15:32:53