Summarizing Data with CUBE and ROLLUP

本文展示了如何使用SQL中的CUBE和ROLLUP命令来快速获取数据库数据的概览,通过实例分析了如何利用这些命令来汇总特定表格的数据,并提供了不同命令在输出结果上的区别。

Looking for a quick, efficient way to summarize the data stored in your database?  The SQL ROLLUP and CUBE commands offer a valuable tool for gaining some quick and dirty insight into your data.  ROLLUP and CUBE are SQL extensions and they're available in SQL Server 6.5 (and above) and Oracle 8i (and above).

The CUBE command is added to an SQL 

To provide an example, let's imagine a table that contains the number and type of pets available for sale at our chain of pet stores:

Pets

Type

Store

Number

Dog

Miami

12

Cat

Miami

18

Turtle

Tampa

4

Dog

Tampa

14

Cat

Naples

9

Dog

Naples

5

Turtle

Naples

1


As the proud owners of this Florida pet superstore, we'd like to take a quick look at various aspects of our inventory.  We could hire an SQL programmer to sit down and write a number of queries to retrieve the exact data that we're looking for.  However, our dataset isn't very large and we enjoy looking at the raw numbers.  Our hunger for data can be appeased using the CUBE command.  Here's the sample SQL:

SELECT Type, Store, SUM(Number) as Number
FROM Pets 
GROUP BY type,store 
WITH CUBE

And the results of the query:

Type

Store

Number

Cat

Miami

18

Cat

Naples

9

Cat

NULL

27

Dog

Miami

12

Dog

Naples

5

Dog

Tampa

14

Dog

NULL

31

Turtle

Naples

1

Turtle

Tampa

4

Turtle

NULL

5

NULL

NULL

63

NULL

Miami

30

NULL

Naples

15

NULL

Tampa

18

Wow!  That's a lot of data!  Notice that we are presented with a number of additional groupings that contain NULL fields that wouldn't appear in the results of a normal GROUP BY command.  These are the summarization rows added by the CUBE statement.  Analyzing the data, you'll notice that our chain has 27 cats, 31 dogs and 5 turtles spread among our three stores.  Our Miami store has the largest number of pets in stock with a whopping inventory of 30 pets.

We're not particularly interested in the total number of pets at each store -- we'd just like to know our statewide inventory of each species along with the standard GROUP BY data.  Utilizing the ROLLUP operator instead of the CUBE operator will eliminate the results that contain a NULL in the first column. 

Here's the SQL:

SELECT Type, Store, SUM(Number) as Number
FROM Pets 
GROUP BY type,store 
WITH ROLLUP

And the results:

Type

Store

Number

Cat

Miami

18

Cat

Naples

9

Cat

NULL

27

Dog

Miami

12

Dog

Naples

5

Dog

Tampa

14

Dog

NULL

31

Turtle

Naples

1

Turtle

Tampa

4

Turtle

NULL

5

NULL

NULL

63

And that's CUBE and ROLLUP in a nutshell!  Be sure to check back next week for another exciting journey into the world of databases!

Data preprocessing involves several key steps and techniques. One area relevant to data preprocessing is the Extraction, Transformation, and Loading (ETL) process [^3]. ### Data Extraction Data extraction is the initial step where data is retrieved from multiple, heterogeneous, and external sources. This allows for the collection of data from various places to be used in subsequent analysis [^3]. ### Data Cleaning Data cleaning is an important technique. It focuses on detecting errors in the data and rectifying them when possible. This ensures that the data used for analysis is of high - quality and free from obvious inaccuracies [^3]. ### Data Transformation Data transformation converts data from legacy or host format to warehouse format. This step is crucial for making the data compatible with the data warehouse and subsequent analysis tools [^3]. ### Loading and Refresh After transformation, the data is loaded. This involves sorting, summarizing, consolidating, computing views, checking integrity, and building indices and partitions. Additionally, the refresh process propagates the updates from the data sources, ensuring that the data in the warehouse is up - to - date [^3]. ### Example in a Specific Domain In the context of researching suicidality on Twitter, data preprocessing also plays a role. When collecting tweets using the public API as done by O’Dea et al., the data needs to be preprocessed before applying machine - learning models like logistic regression and SVM on TF - IDF features. This may involve cleaning the text data, removing special characters, and normalizing the text [^2]. ### Tools and Techniques in Genome 3D Structure Research In the study of the 3D structure of the genome, data preprocessing is also essential. For Hi - C data, specific preprocessing steps include dealing with chimeric reads, mapping, representing data as fixed or enzyme - sized bins, normalization, and detecting A/B compartments and TAD boundaries. Tools such as HiC - Pro, HiCUP, HOMER, and Juicer are used for Hi - C analysis, which includes preprocessing steps [^4]. ```python # A simple example of data cleaning in Python import pandas as pd # Assume we have a DataFrame with some data data = {'col1': [1, 2, None, 4], 'col2': ['a', 'b', 'c', None]} df = pd.DataFrame(data) # Drop rows with missing values cleaned_df = df.dropna() print(cleaned_df) ```
评论
成就一亿技术人!
拼手气红包6.0元
还能输入1000个字符
 
红包 添加红包
表情包 插入表情
 条评论被折叠 查看
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值