Technology – The Difference Between Float Vs. Double Data Types

It would be incorrect to say that floating-point numbers should never be used as an SQL data type for arithmetic. I will stick to double-precision floating-point data types for SQL Server that are suitable for my requirements.

The double-precision floating-point data type is ideal for modeling weather systems or displaying trajectories but not for the type of calculations the average organization may use in the database. The biggest difference is in the accuracy when creating the database. You need to analyze the data types and fields to ensure no errors and insert the data values for maximum accuracy. If there is a large deviation, the data will not be processed during the calculation. If you detect incorrect use of the data type with double precision, you can switch to a suitable decimal or number type.

What are the differences between numeric, float, and decimal data types, and should they be used in which situations?

  • Approximate numeric data types do not store the exact values specified for many numbers; they store an extremely close approximation of the value
  • Avoid using float or real columns in WHERE clause search conditions, especially the = and <> operators

For example, suppose the data that the report has received is summarized at the end of the month or end of the year. In that case, the decimal data for calculation becomes integer data and is added to the summary table.

In SQL Server, the data type float _ n corresponds to the ISO standard with a value from n = 1 to 53. The floating-point data is approximated not by the data type’s value but by the range of what can be represented. Both float- and float-related numeric SQL types consist of a significant numeric value and an exponent, a signed integer that indicates the size of the numeric value.

And float-related numeric SQL data types are precise positive integers that define the number of significant digits and exponents of a base number. This type of data representation is called floating-point representation. A float is an approximate number, meaning that not all values can be displayed in the data type range because it is a rounded value.

You can’t blame people for using a data type called Money to store the money supply. In SQL Server, decimal, number, Money, and SmallMoney data types have a decimal place to store values. Precision means the total number of digits after the decimal point.

From a mathematical point of view, there is a natural tendency to use floats. People who use float spend their lives rounding up values and solving problems that shouldn’t exist. As I mentioned earlier, there are places where it makes sense to hover above the real, but these are for scientific calculations, not business calculations.

SmallMoney (2144783647, 4 bytes) We can use this data type for Money- or currency values. The double type can be used as a data type with real values for dealing with Money.

Type Description Memory bits Integer 0 1 null TinyInt allows integers 0 to 255 1 bytes TinyInt allows integers 32767 2 bytes Int allows integers 2147483647 4 bytes BigInt allows integers 9223372036854775807 8 bytes Decimal P is a precisely scaled number. The parameter p specifies the maximum total number of digits stored to the left or right of the decimal point. The data type low and upper range storage observations Real 340E 38 4 Bytes We can use float924 as an ISO synonym for real.

In MariaDB, the number of seconds has elapsed since the beginning of the 1970s (01-01) with a decimal accuracy of 6 digits (0 is the default). The same range of precision is the SQL Server type range (bytes) MariaDB type range size (bytes) Precision notes Date 0001 01-01-99.99 12: 31: 3 They cover the same range: Date 0.001-03-01 9.99912: 31 8: 0: 3 Round DateTime 0.01 0.1-02.9999 12: 31 8 0: 6 In MariaDB the value is near impossible to specify (see below). We can insert a value that requires fewer bits than that assigned to the null-bit pad on the left.

A binary string is a sequence of octets, not a character set, and the associated sorting is described by the binary data type descriptor. Decimal (p) is the exact numerical precision (p scale (n)) of a decimal number that is any number with a decimal point. A Boolean data type consists of different truth values (true, false, and boolean), and it supports unknown truth values, zeroes, and forbidden (not zero) constraints.

This syntax was deprecated in MySQL 8.0.17.7 and will be removed in future versions of MySQL: float (p) A floating-point number. MySQL uses the p-value to specify whether to use a float or a double due to the data type.

Creating data types in PostgreSQL is done with the create-type command. For example, the following commonly used data types are organized into categories with a brief description of the value range and memory size. The native data type is the text data type, the numeric data type, and the date/time Boolean data type.

To understand what floating-point SQL is and what numerical data types are, you need to study computer science a little. Floating-point arithmetic was developed when saving memory was a priority and was used as a versatile method for calculating large numbers. The SQL Prompt Code Analysis Rule (BP023) warns you when using Floating over Real data types. It introduces significant inaccuracies into the type of calculations that many companies do with their SQL Server data.

The difference between a float and a p is that a real float is binary (not decimal) and has an accuracy equal to or greater than the defined value.

The reason for this difference is that the SQL standard specifies a default from 0 to D. Still, the implementation is free to choose a default M. This means that an operation of this type will result in a result different from the result it would produce for MariaDB type if you use enough decimal places. It is important to remember that numerical SQL data types sacrifice precision ranges to approximate the names.

Technology – What Are The SQL Minus and Except Clauses

Structured Query Language (SQL) is the de-facto query language used in most database management systems (DBMS) such as Oracle and Microsoft SQL Server. This domain-specific language is used in programming to query and return the desired data from a database. We use SQL to write queries that declare what data to expect from a dataset without really indicating how to obtain it. We can also use SQL to update and delete information from a database.

Ideally, in a relational database management system, the database usually runs on the “back end” side of a server in its structured form. By itself, this data is hard to interpret. So, users often have programs on a client computer that help to manipulate that data using rows, columns, fields, and tables. These programs are designed to allow users to send SQL statements to the server. The server then processes these statements by filtering data from the enormous, complex databases and returns results to the user.

Each query begins with finding the data needed then refining it down into something that can be processed and understood easily. To do this, you must use an organized set of operations to get meaningful data from a dataset. This article will explore the Minus Vs except SQL clauses to help you write optimized queries that run fast across various DBMS.

SQL EXCEPT clause

The SQL EXCEPT clause is one of the most commonly used statements that work together with two SELECT statements to return unique rows from a dataset. The SQL EXCEPT combines two SELECT statements to return the row that is present in the first select statement and not in the second.

If you’ve noticed, most SQL clauses do the same thing represented in standard spoken language. For instance, exception literally means not included. SQL EXCEPT is also very similar to the same concept.

The EXCEPT statement returns the distinct row from the left input query that is not output by the right input query. I.e., Returns the resultant rows that appear in query_expression_1 and not in query_expression_2.

SQL EXCEPT Clause Example

Consider a simple situation where you have two tables, one with dog names and the other one with cat names.

Cats Data Set

+———+———–+

| CatId | CatName |

|———+———–|

| 1 | Boss |

| 2 | Scarlet |

| 3 | Fluffy |

| 4 | Fluffy |

+———+———–+

Dogs Data Set

+———+———–+

| DogId | DogName |

|———+———–|

| 1 | Yelp |

| 2 | Woof |

| 3 | Boss |

| 4 | Boss |

+———+———–+

Using the SQL Except statement, we can filter the dataset and return only the distinct rows from the left SELECT query that have not been returned by the SELECT query on the right side of the EXCEPT statement.

An example SQL syntax query would look like this:

SELECT CatName FROM Cats

EXCEPT

SELECT DogName FROM Dogs;

In a typical scenario, a client program will send this query to the “back-end” server. This statement is then processed and only returns the values available in the “cats” dataset that don’t appear in the “dogs” dataset. When two rows are similar, as is the case with “fluffy,” only one row is returned. This is because the SQL query only returns distinct rows.

Here’s the result of the above query:

+———–+

| CatName |

|—————|

| Fluffy |

| Scarlet |

+————– +

Common SQL Except Rules
  • You must have the same number of columns in both queries
  • The column order must be the same in all queries
  • The column data types must be compatible with each other. The data types really don’t have to be the same, but they MUST be comparable through implicit conversion.
  • The EXCEPT statement returns all records from the 1st SELECT statement not available in the second SELECT statement
  • The EXCEPT operator in the SQL server is similar to the MINUS operator in Oracle.
  • MySQL does not support SQL Except clause. The workaround is to use the LEFT JOIN clause when using MySQL.

SQL MINUS Clause

MINUS operator does the same thing as the EXCEPT clause. But unlike the EXCEPT clause, the MINUS operator is only supported by Limit number of databases:

Database NameMinusExcept
Amazon RedshiftNoYes
VQL (denodo)YesNo
ElasticsearchYesNo
MariaDBYesYes
IBM Db2NoYes
Microsoft SQL ServerNoYes
MongoDBNoNo
MySQLNoNo
OracleYesNo
PostgreSQL No Yes
SOQL (salesforce)NoNo
snowflakeYesYes
SQLiteNoYes

The MINUS operator compares two queries and only returns the rows present in the first dataset but are not output by the second set. The result usually contains the distinct rows available in the left Select statement that aren’t included in the results of the right select statement.

Here is a typical MINUS syntax:

SELECT column_list_1 FROM T1

MINUS

SELECT column_list_2 FROM T2;

Common Oracle Minus Operator Rules

For MINUS operator to work, the dataset must conform with rules similar to those of SQL EXCEPT clause:

  • The data type of corresponding columns must be similar (Either Numeric or Character)
  • The order and number of columns must be the same.
  • The column used for ordering can be defined by the column number.
  • Duplicates are automatically eliminated in the final result.

Conclusion

The Minus Vs Except SQL Clause comparison can be confusing for many people. These two clauses are synonymous with each other and have similar syntax and results. Both Minus and Except help users skim through datasets to identify unique rows available only in the first SELECT query and not returned by the second SELECT query.

SQL Server Length Function Equivalent

The purpose of the Length function in SQL

The SQL LENGTH function returns the number of characters in a string. The LENGTH function is available in many Database Management Systems (DBMS).

The LENGTH Function Syntax

  • LENGTH(string)

LENGTH Function Notes

  • If the input string is empty, the LENGTH returns 0.
  • If the input string is NULL, the LENGTH returns NULL.

Length Function Across Databases

When working as a technical consultant, one has to work with customer’s databases and as you move from one database to another you will find that the function commands may vary–assuming the database has an equivalent function.

Working with VQL and SQL Server got me thing about the LENGTH() function, so, here is a quick references list, which does include the SQL Server.  

IBM DB2

  • LENGTH( )

IBM Informix

  • CHAR_LENGTH() Or CHARACTER_LENGTH()

MariaDB

  • LENGTH( )

Microsoft SQL Server

  • LEN( )

MySQL

  • CHAR_LENGTH() Or CHARACTER_LENGTH()

Netezza

  • LENGTH( )

Oracle

  • LENGTH( )

PostgreSQL

  • CHAR_LENGTH() Or CHARACTER_LENGTH()

SOQL (SalesForce)

  • SOQL has no LENGTH function

VQL (Denodo)

  • LEN( )

What is the difference between left join and left outer join in SQL

When working with different databases syntax can cause questions and confusion.  Recent having been asked what the difference was between a left join and a left outer join, a subject which I hadn’t thought about in a while, I thought a simple explanation might be in order.   Actually, there is no difference between a left join and a left outer join, other than syntax.  Both perform the exact same operation in SQL, where the Left (Outer) Join will retain those rows for which there was a match in both tables and, also, retain those rows which exist only in the left (controlling) table of the join.