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MSSQL database. I have issue to create database using old databases data. Old database structure is thousands tables conected with each other by ID. In this tables data duplicated many times. Old database tables have more than 50 000 rows (users). Structure like this table
Users (id, login, pass, register-date, update-date),
Users-detail (id, users_id, some data)
Users-some-data (id, users_is, some data)
and this kind of tables is hundreds.
And the question is, which design of db structure to choose, one table with all of this data, or hundreds of tables separated by some theme.
Which type of db structure would be with better performance?
Select id, login, pass from ONE_BIG_TABLE
or
Select * from SMALL_ONLY_LOGINS_TABLE.
Answer really depends on the use. No one can optimize your database for you if they don't know the usage statistics.
Correct DB design dictates that an entity is stored inside a single table, that is, the client with their details for example.
However this rule can change on the occasion you only access/write some of the entity data multiple times, and/or of there is optional info you store about a client (eg, some long texts, biography, history, extra addresses etc) in which cases it would be optimal to store them on a child-table.
If you find yourself a bunch of columns with all-null values, that means you should strongly consider a child table.
If you only need to try login credentials against the DB table, a stored procedure that returns a bool value depending on if the username/password are correct, will save you the round-trip of the data.
Without indexes the select on the smaller tables will be faster. But you can create the same covering index (id, login, pass) on both tables, so if you need only those 3 columns performance will probably be the same on both tables.
The general question which database structure is better can not be answered without knowing the usage of your database.
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I'm trying to build an OLAP database with this data set about the Olympics, the problem is that datasets are in csv format and they are usually in one single table, I've imported the data in access as I was told that Access has a tool to split the data in different tables but I have not found anything related to that. This is my current table:
Id1 is the one created in access so it could include the duplicated data, ID is the original one in the data set.
I want to normalize the data into the following schema:
I've tried to split data manually, but since there are lots of data, It's risky and prone to a lot of mistakes and errors.
Any idea on how to do this on Access or is there a better method to do it?
Since you have already imported your data into access and that data still needs to be normalized you can use an access wizard under database tools - analyze table. This wizard will help you normalize a table by splitting the original table into multiple tables. Here is one link to get you started with the table analyzer:
https://support.office.com/en-us/article/normalize-your-data-using-the-table-analyzer-8edbb763-5bab-4fbc-b62d-c17b1a40bbe2
The table analyzer will create new tables and copy the data from the original table into the new tables resulting in a structure like the following:
The table analyzer will even save the query it uses so you can reuse it later. However if you just choose defaults the wizard will not give you appropriate names for keys and tables. Also you might want to adjust the relationship structure access chooses. You can do all these things in the wizard once you are familiar with it. In this case I just renamed all the tables and keys but left seasons as the top of the relationships pile.
Alternately you can Import the data one table at a time but you will have to clean it first (particularly adding primary keys) or you will have problems. The data import wizard in access has the option to skip variables under one of the advanced tabs.
You can skip the table analyzer wizard and create the tables and write the queries to transfer the data yourself but The wizard is faster :)
Data Cleaning Commentary: Under the heading a picture is worth a thousand words it helps if you post your data and what you want. I found the dataset online and I have a couple comments that may be helpful. ID has a one to many relationship with Country so it cannot be used as a primary key. So let access provide primary keys. Age has missing data so a decision will need to be made on how to handle that, I just put the problem off by converting age to a text variable.
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I have a program where the user fills data into a table. The tables all have different sizes, some are 3x2, others are 30x20.
Its just like an excel sheet and looks like this, where the user can add rows and columns as much as she needs them. These are charts related to products so each table has a unique product number
Whats the best way to organize this data in a SQL database?
Is it one SQL table per user generated table?
This seems excessive as i would end up with a database with 10000s of tables. Are there other, better ways to store the data? Can i combine it into one SQL table?
You can make SQL tables like
table_header
- id
- rows
- cols
table_detail
- header_id
- row
- col
- value
Then to store a table you create a header record and add the contents one entry at a time. To read the table back you would basically just pull all data for the table, maybe with an ORDER BY if you want to make parsing back into the table easier. Something like that.
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Each of the salesmen should make a forecast of his sales. I know how he may input data directly from Excel sheet to SQL table. Do I need to create different tables - one table per salesman? At the end I need to aggregate all the forecasts. Is it possible to make it with just one table?
The condition is that one salesman is not allowed to see the other salesmen forecasts.
It seems to be a common problem of inputting data to database by many different users with restrictions on access.
Update. Each salesman is in different town. Say we have 500 salesmen so it is not the way to gather data from 500 Excel files into one big Excel file and then load it to SQL.
actually you don't need to create different tables for each salesmen. one table is enough to load all your salesman info Excel data. to find each salesmen's forecast sales simple transmission query will help u
You need at least two tables. You need a staging table to receive the excel data and perform the necessary validation, transformation, etc. You need at least one table for data storage. Given that you are talking about people and sales, you probably want a normalized database. If you don't know what that means, I've heard good things about the book, Database Design for Mere Mortals.
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Sorry about the vague title.
An example: I'm guessing SO has one large table that lists all answers, in a schema like:
[ Ques No, Ans No, Text , Points ]
[ 22, 0 , "Win", 3 ],
[ 22, 1 , "Tin", 4 ],
[ 23, 0 , "Pin", 2 ]
My question is would it be better if there were two tables: Table_Ques22 and Table_Ques23? Can someone please list the pros and cons?
What comes to my mind:
Cons of multiple tables: Overhead of meta storage.
Pros of multiple tables: Quickly answer queries like, find all answers to Ques 22. (I know there are indices, but they take time to build and space to maintain).
Databases are designed to handle large tables. Having multiple tables with the same structure introduces a lot of problems. These come to mind:
Queries that span multiple rows ("questions" in your example) become much more complicated and performance suffers.
Maintaining similar entities is cumbersome. Adding an index or partitioning a single table is one thing. Doing it to hundreds of tables is much harder.
Maintaining triggers is cumbersome.
When a new row appears (new question), you have to incur the overhead of creating a table rather than just adding to an existing table.
Altering a table, say to add a new column or rename an existing one, is very cumbersome.
Although putting all questions in one table does use a small additional amount of storage, you have to balance that against the overhead of having very small tables. A table with data has to occupy at least one data page, regardless of whether the data is 10 bytes or 10 Gbytes. If a data page is 16 kbytes, that is a lot of wasted space to support multiple tables for a singe entity.
As for database limits. I'm not even sure a database could support a separate table for each question on Stack Overflow.
There is one case where having parallel table structures is useful. That is when security requirements require that the data be separated, perhaps for client confidentiality reasons. However, this is often an argument for separate databases, not just separate tables.
What about: SQL Servers are not made for people ignoring the basics of the relational theoream.
You ahve a ton of problems with cross question queries in your part, which will totally kill all the gains. Typical beginner mistake - I suggest a good book about SQL basics.
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I have a lot of databases with relatively large amounts of columns ranging from 5 to 300. Each table has at least 50,000 rows in it.
What is the most effective way to store this data? Presently the data has just been dumped into an indexed sql database.
It was suggested to me to create 3 columns as follows.
Column Name, Column category, Row ID, Row Data.
example data would be
Male, 25-40, 145897, 365
Would this be faster? Would this be slower? Is there better ways to store such large and bulky databases?
I will almost never be updating or changing data. It simply be outputted to a 'datatables' dynamic table where it will be sorted, limited and ect. The category column will be used to break up the columns on the table.
Normalize your db!
I have struggled with this "theory" for a long time and experience has proven that if you can normalize data across multiple tables it is better and performance will not suffer.
Do Not try to put all the data in one row with hundreds of columns. Not because of performance but because of development ease.
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