Oracle B-Tree索引大小估算详解

Oracle
B-Tree
Index
需积分: 31 0 下载量 100 浏览量 更新于2024-07-25 收藏 960KB PDF 举报
Oracle的B-Tree索引估计大小是一项重要的任务，特别是在数据库设计和优化过程中。B-Tree是一种自平衡的数据结构，广泛用于Oracle数据库管理系统中，特别是用于存储大型表的索引。本资源提供了一个名为"EstimatesizeofB-treeIndexes"的工具或指南，版本为0.327，发布于2005年12月。作者是Guy Lambregts，他持有Oracle Certified Professional (OCP) DBA资格，并代表 Steria Benelux公司。 **1. 引言**： 首先，介绍B-Tree索引的原理和在Oracle中的应用。B-Tree的高效性在于其分层结构，通过将数据分布在多个节点上，实现了快速查找、插入和删除操作。索引的大小估算有助于决定表空间的分配，避免性能瓶颈，并确保足够的空间以支持数据增长。 **2. 叶子块数量估算**： - **2.1 叶子块级别的 overhead**：这部分讨论了叶子节点（包含实际键值对的节点）的额外开销，包括存储元数据（如键值的指针）、可能的扩展空间预留以及B-Tree的平衡因子。叶子节点的大小会因配置参数（比如ORDER BY列数）而变化。 - **2.2 每个索引项的overhead**：除了叶子节点的开销外，每个索引项（即键值对）自身也有固定大小的存储需求，包括键的长度、指针或其他辅助数据。索引项的大小会根据所使用的数据类型和索引策略来计算。 **3. 索引结构的细节**： 该部分深入解析了如何计算B-Tree的高度和平均分支因子，以便估算所需的整体叶子块数量。这涉及考虑索引的分布特性，如是否使用了范围或唯一性约束，以及是否有重复键值等因素。 **4. 实践指导**： 资源提供了一些实用的步骤和公式，帮助用户根据表的列数、数据分布、键值长度以及可能的索引策略，进行准确的B-Tree索引大小估算。此外，还可能涉及如何考虑未来的数据增长和索引更新的影响。 本资源是一个详细的指南，对于理解Oracle数据库中B-Tree索引的内部结构、估算其大小以及优化索引设计至关重要。这对于数据库管理员和开发者来说，是优化存储和提高查询性能不可或缺的知识。通过遵循这里的建议，可以更有效地管理数据库资源，确保系统的稳定性和效率。

数学建模-F93036-PIPE Estimate the Value of Private Information.zip

2022-01-18 上传
《数学建模-F93036-PIPE Estimate the Value of Private Information》这个压缩包文件主要探讨了在信息技术日益发达的今天，如何利用数学建模方法来评估私人信息的价值。数学建模是一种将实际问题转化为数学模型的...

Point-Estimate-Methods-and-Samplers-master_two-pointestimate_

2021-09-30 上传
在标题"Point-Estimate-Methods-and-Samplers-master_two-pointestimate_"和描述"two point estimate method"中，我们可以看到这个主题的核心——即对两种主要的点估计方法进行深入探讨。 点估计的目标是用一个单一...

F93036-PIPE Estimate the Value of Private Information.PDF

2023-05-25 上传
标题"F93036-PIPE Estimate the Value of Private Information"和描述中提到的主要知识点是关于个人隐私数据价值的评估和管理。以下是对这一主题的详细分析： 在当今信息化社会，尽管数据产生量巨大，但人类个体...

Improve the following code:def estimate_variance(xs: np.ndarray, ys: np.ndarray, affine: np.ndarray, translation: np.ndarray, responsibility: np.ndarray) -> float: """ Estimate the variance of GMM. For simplification, we assume all the Gaussian distributions share the same variance, and each feature dimension is independent, so the variance can be represented as a scalar. :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points :param affine: an affine matrix with size (D, D) :param translation: a translation vector with size (1, D) :param responsibility: the responsibility matrix with size (N, M) :return: the variance of each Gaussian distribution, a float """ # TODO: change the code below and compute the variance of each Gaussian return

117 浏览量
:param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of ...

def estimate_variance(xs: np.ndarray, ys: np.ndarray, affine: np.ndarray, translation: np.ndarray, responsibility: np.ndarray) -> float: """ Estimate the variance of GMM. For simplification, we assume all the Gaussian distributions share the same variance, and each feature dimension is independent, so the variance can be represented as a scalar. :param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of points :param affine: an affine matrix with size (D, D) :param translation: a translation vector with size (1, D) :param responsibility: the responsibility matrix with size (N, M) :return: the variance of each Gaussian distribution, a float """ # TODO: change the code below and compute the variance of each Gaussian return 1

2023-05-29 上传
:param xs: a set of points with size (N, D), N is the number of samples, D is the dimension of points :param ys: a set of points with size (M, D), M is the number of samples, D is the dimension of ...

请为我解释以下命令 ns-export pointcloud --load-config outputs\output\nerfacto\2023-07-11_170210/config.yml --output-dir exports/pcd/ --num-points 1000000 --remove-outliers True --estimate-normals False --use-bounding-box True --bounding-box-min -1 -1 -1 --bounding-box-max 1 1 1

365 浏览量
- `--estimate-normals False`：这个参数表示是否估计点云数据中每个点的法线向量，设置为False表示不估计。 - `--use-bounding-box True`：这个参数表示是否使用边界框限制点云数据的范围，设置为True表示使用。 ...

(a) Consider the case of a European Vanilla Call option which is path independent. Examine the convergence of the Monte Carlo Method using the programme given in ‘MC Call.m’. How does the error vary with the number of paths nP aths? The current time is t = 0 and the Expiry date of the option is t = T = 0.5. Suppose that the current value of the underlying asset is S(t = 0) = 100 and the Exercise price is E = 100, with a risk free interest rate of r = 0.04 and a volatility of σ = 0.5. (b) Now repeat part (a) above but assume that the volatility is σ = 0.05. Does the change in the volatility σ influence the convergence of the Monte Carlo Method? (c) Now repeat part (a) but instead of taking one big step from t = 0 to t = T divide the interval into nSteps discrete time steps by using the programme given in ‘MC Call Small Steps.m’. Confirm that for path independent options, the value of nP aths determines the rate of convergence and that the value of nSteps can be set to 1. (d) Now let us consider path dependent options. The programme given in ‘MC Call Small Steps.m’ is the obvious starting point here. We assume that the current time is t = 0 and the expiry date of the option is t = T = 0.5. The current value of the underlying asset is S(t = 0) = 100 and the risk free interest rate is r = 0.05 and the volatility is σ = 0.3. (i) Use the Monte Carlo Method to estimate the value of an Arithematic Average Asian Strike Call option with Payoff given by max(S(T) − S, ¯ 0). (ii) Use the Monte Carlo Method to estimate the value of an Up and Out Call option with Exercise Price E = 100 and a barrier X = 150. (iii) Comment on the the rate of convergence for part (i) and (ii) above with respect to the parameters nP aths and nP aths使用matlab编程

103 浏览量
(b) The change in volatility does affect the convergence of Monte Carlo simulation. When volatility is lower, the convergence is faster because the option price has less variance. (c) For path-...

pandapower.estimation的属性和方法

156 浏览量
- estimate_voltage_angles - 估计电压角度 - estimate_branches - 估计支路 - estimate_buses - 估计母线 - estimate_trafo - 估计变压器 - estimate_trafo3w - 估计三绕组变压器 这些属性和方法提供了进行潮流...

oracle explain

102 浏览量
The optimizer considers factors such as the available indexes, the size of the tables, and the complexity of the query. The Explain Plan can be obtained using the EXPLAIN PLAN statement or by using ...

how to estimate the ordered probit model,please give the code

2023-05-30 上传
The "summary" function is used to print the summary of the model, which includes the estimated coefficients, standard errors, and p-values. Note that the exact code may vary depending on the dataset...
HiDB
  • 粉丝: 0
上传资源 快速赚钱

最新资源