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Definition of the PostgreSQL column does not exist exception.Hadoop, Data Science, Statistics & others
Syntax:
Below is the column name syntax that does not exist except in PostgreSQL.
Column name does not exist exception using select
Select name_of_column from name_of_table limit (number);
Error: column name_of_column does not exist
Line1: select name_of_column from name_of_table limit (number);
Column name does not exist exception using insert
Insert into name_of_table (name_of_column1, name_of_column2, name_of_column3, …, name_of_columnN) values (Value_of_column1, Value_of_column2, Value_of_column3, …, Value_of_columnN);
ERROR: column “name_of_column” does not exist
LINE 1: insert into name_of_table (name_of_column1, name_of_column2, name_of_column3, …, name_of_columnN) values (Value_of_column1, Value_of_column2, Value_of_column3, …, Value_of_columnN)
Column name does not exist exception using update
update name_of_table set name_of_column = (value_of_column) where condition;
ERROR: column “name_of_column” does not exist
LINE 1: update name_of_table set name_of_column = value_of_column where name_of_column = ‘value_of_column’;
Column name does not exist exception using delete
Delete from name_of_table where name_of_column = value_of_column;
ERROR: column “name_of_column” does not exist
LINE 1: delete from name_of_table where name_of_column = value_of_column;
Below is the parameter description syntax of column name does not exist exception in PostgreSQL.
Select –Column name does not exist, an exception will display while we have to execute a select operation on the specified column.
Insert – Column name does not exist exception will display while we have to execute insert operation on a specified column.
Update – Column name does not exist exception will display while we have to execute update operation on the specified column.
Delete – Column name does not exist exception will display while we have to execute delete operation on the specified column.
Name of the column –This is defined as the column name from which we have received the exception that the column name does not exist.
Name of the table –This is defined as the table name from which we have received the exception that the column name does not exist.
How column does not exist exception raised in PostgreSQL?
The column does not exist exception occurs when a column does not exist in the table. If the searching column does not exist in the table, then it will raise the exception that the column does not exist in the table.
The below example shows that if a searching column does not exist in the table, it will give the exception that the column name does not exist.
d+ test_col; select ID_Name from test_col;
In the above example, we have used the ID_Name column for searching the data from the test_col table, but it will issues an error that the column name does not exist in the table because the ID_Name column does not exist in the table.
To select, update, delete, and insert the data into the table, we need to define the correct column name, which we searched for.
Also, we need to define the column name in a double quote if our column name contains a mixed letter.
The example below shows that we need to define the double quote when using a mixed letter column in operations.
select address from test_col; d+ test_col;
In the above example, we have used the address column, which contains the mixed letter, after using this column, it will issue an error because it will contain the mixed column letter.
Examples
Below is an example of a column that does not exist exception. We have using a test_col table to describe an example of a column name that does not exist exception.
Below are the data and table structure of the test_col table.
select * from test_col; d+ test_col;1. Column name does not exist exception using select
The below example shows that the column name does not exception using select operations.
select Name, ID from test_col; select "Name", "ID" from test_col;2. Column name does not exist exception using insert
The below example shows that the column name does not exception using insert operations.
insert into test_col (ID, Name, AddRess, PhoNe) values (3, 'ABC', 'Mumbai', 1234567890); insert into test_col ("ID", "Name", "AddRess", "PhoNe") values (4, 'PQR', 'Mumbai', 1234567890);3. Column name does not exist exception using update
The below example shows that the column name does not exception using update operations.
update test_col set ID = 5 where Name = 'ABC'; update test_col set "ID" = 5 where "Name" = 'ABC'; select * from test_col;
The below example shows that the column name does not exception using delete operations.
delete from test_col where ID = 4; delete from test_col where "ID" = 4; select * from test_col; How to avoid a column that does not exist exception?
We can avoid the column does not exist exception by specifying the name of the column. Below is the example to avoid the column does not exist exception.
select test_name from test_col; select "Name" from test_col;
We can also avoid the exception by using the double quote in the column. Below example shows that use a double quote to avoid exception.
select Name from test_col; select "Name" from test_col; ConclusionThe column does not exist exception occurs in PostgreSQL when we have not used a specified column name while doing any operations. Also, it occurs when we have not used a double quote to the mismatch case letter column name in PostgreSQL.
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How To Convert A Numerical Column Into Factor Column In R?
Often, we find that the values that represent factor levels are recorded as numerical values, therefore, we need to convert those numerical values to factor. In this way, we can use the factor column properly in our analysis otherwise R program will treat the factors as numerical values and the analysis output will be incorrect.
ExampleLive Demo
data(mtcars) str(mtcars) Output 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : num 6 6 4 6 8 6 8 4 4 6 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ... mtcars$cyl<-as.factor(mtcars$cyl) str(mtcars) 'data.frame': 32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ cyl : Factor w/ 3 levels "4","6","8": 2 2 1 2 3 2 3 1 1 2 ... $ disp: num 160 160 108 258 360 ... $ hp : num 110 110 93 110 175 105 245 62 95 123 ... $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ... $ wt : num 2.62 2.88 2.32 3.21 3.44 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ vs : num 0 0 1 1 0 1 0 1 1 1 ... $ am : num 1 1 1 0 0 0 0 0 0 0 ... $ gear: num 4 4 4 3 3 3 3 4 4 4 ... $ carb: num 4 4 1 1 2 1 4 2 2 4 ... ExampleLive Demo
data(ToothGrowth) str(ToothGrowth) Output 'data.frame': 60 obs. of 3 variables: $ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ... $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ... $ dose: num 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ... ExampleLive Demo
head(ToothGrowth,20) Output len supp dose 1 4.2 VC 0.5 2 11.5 VC 0.5 3 7.3 VC 0.5 4 5.8 VC 0.5 5 6.4 VC 0.5 6 10.0 VC 0.5 7 11.2 VC 0.5 8 11.2 VC 0.5 9 5.2 VC 0.5 10 7.0 VC 0.5 11 16.5 VC 1.0 12 16.5 VC 1.0 13 15.2 VC 1.0 14 17.3 VC 1.0 15 22.5 VC 1.0 16 17.3 VC 1.0 17 13.6 VC 1.0 18 14.5 VC 1.0 19 18.8 VC 1.0 20 15.5 VC 1.0 ToothGrowth$dose<-as.factor(ToothGrowth$dose) str(ToothGrowth) 'data.frame': 60 obs. of 3 variables: $ len : num 4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ... $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ... $ dose: Factor w/ 3 levels "0.5","1","2": 1 1 1 1 1 1 1 1 1 1 ... ExampleLive Demo
data(morley) str(morley) Output 'data.frame': 100 obs. of 3 variables: $ Expt : int 1 1 1 1 1 1 1 1 1 1 ... $ Run : int 1 2 3 4 5 6 7 8 9 10 ... $ Speed: int 850 740 900 1070 930 850 950 980 980 880 ... ExampleLive Demo
morley$Expt<-as.factor(morley$Expt) str(morley) Output 'data.frame': 100 obs. of 3 variables: $ Expt : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ... $ Run : int 1 2 3 4 5 6 7 8 9 10 ... $ Speed: int 850 740 900 1070 930 850 950 980 980 880 ... ExampleLive Demo
x1<-sample(1:4,20,replace=TRUE) x2<-rnorm(20,2,3) df<-data.frame(x1,x2) str(df) Output 'data.frame':20 obs. of 2 variables: $ x1: int 1 4 1 2 3 1 4 1 4 2 ... $ x2: num 1.56 1.64 2.83 2.2 4.23 ... Example df$x1<-as.factor(df$x1) str(df) Output 'data.frame': 20 obs. of 2 variables: $ x1: Factor w/ 4 levels "1","2","3","4": 4 3 2 2 4 4 1 2 4 2 ... $ x2: num 3.82 1.13 2.99 5.8 3.3 ... ExampleLive Demo
data(BOD) str(BOD) ' Output data.frame': 6 obs. of 2 variables: $ Time : num 1 2 3 4 5 7 $ demand: num 8.3 10.3 19 16 15.6 19.8 - attr(*, "reference")= chr "A1.4, p. 270" ExampleLive Demo
BOD$Time<-as.factor(BOD$Time) str(BOD) Output 'data.frame': 6 obs. of 2 variables: $ Time : Factor w/ 6 levels "1","2","3","4",..: 1 2 3 4 5 6 $ demand: num 8.3 10.3 19 16 15.6 19.8 - attr(*, "reference")= chr "A1.4, p. 270"What Does User Not Found Mean On Instagram?
We often take for granted just how easy it is to find someone on the internet. With people having established their presence on pretty much every social media platform, it’s only a matter of typing in a username and running a search till you find the person you were looking for. Of course, platforms have provided settings and provisions to avoid the abuse of this feature; especially to curb the undesirable consequences that come with it. Nevertheless, the ability to find a user is an essential feature of Social Media platforms.
Indeed it can seem odd when the one-off ‘User not found’ error pops-up when you expect to see a profile instead. What does this error mean when and why are you seeing it? Here’s everything you need to know.
What does ‘user not found’ mean?
It’s probable that despite typing in the correct username you will be reckoned with the ‘user not found’ in the search results. There are multiple reasons for this error popping up so keep in mind that Instagram has made the ‘user not found’ provision for two reasons. Either the user really does not exist or they are intentionally not available on Instagram for an array of reasons. Unfortunately, there is not much you can do if you see the ‘user not found’ error, however, you can verify the possible reasons for why you might be seeing this error.
Why do you see ‘user not found’ error on Instagram
There are specific reasons that the ‘user not found’ error has been set in place.
Scenario #1: You might have been blocked by the user
In this scenario, ‘User not found’ acts as a polite way of letting the finder know that they have been blocked by the user they are looking for. This is also a general norm on Social Media platforms where you are met simply with the absence of the user instead of an explicit notification stating that you are being intentionally avoided. If you want to confirm that you have been blocked, ask another user (who has not given the user any reason to block them) to check whether the profile is visible to them when they run the same search as you. If the profile is visible to them, then you have your answer.
Scenario #2: User changed their name
As obvious as this scenario may seem, it’s something we tend to overlook and it’s a good idea to triple check whether the user has changed their name. Considering the fact that social media is not a true representation of the actual person, but rather a mood or persona, it’s very likely that the user simply changed their name. Make sure that you confirm their most current username and only then search for the username again.
Scenario #3: User may have temporarily disabled their account
Temporarily disabling one’s IG account is an essential activity when performing a Social Media detox. When an account is temporarily disabled, it’s basically made to appear as if the user does not exist. This also means that anyone looking for them will be unable to find them. Once the user logs back into their Instagram Account and chooses to reenable their Instagram account, their username will appear when you search for them.
Scenario #3: User may have permanently deleted their account
While on one hand users may disappear temporarily, there are also those who have permanently deleted their account. In this case, the user has been erased from Instagram forever and their username will never appear on a search ever again.
Scenario #4: User is banned by Instagram
Instagram can ban users for violation of its Terms of service. When a ban has been implemented, Instagram implements certain restrictions on the account that is in violation and it will not appear on the search list when you search for them as part of said restrictions. Once the ban is revoked, you will not see the user not found error and instead just see the user profile.
We hope this article clarified what you need to know about the user not found error. Just as Social Media platforms like Instagram ensure that users have as much visibility as possible, they also make provisions to allow users to disappear when they want. So do keep in mind that the user not found errors has a much deeper function than meets the eye and all you need to do is identify what is the function that it’s fulfilling.
How Json Works In Postgresql?
Definition of PostgreSQL JSON
JSON is an abbreviation of JavaScript Object Notation. JSON stores value in key-value pair; it is an open standard format. We generally prefer JSON for sending/receiving or exchanging data between servers and in web applications. The data within JSON is in text format, which is easily human-readable. PostgreSQL version 9.2 introduced support for the native JSON data type. PostgreSQL provides various methods and operators to work with JSON data.
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Syntax:
column_name json How JSON Works in PostgreSQL?
We need to make sure the given data is in a valid JSON format before adding it to the table.
If JSON data is incorrect, then it will throw an error.
PostgreSQL provides the two native operators to work with JSON data.
How to Insert JSON Data?To understand the insertion of JSON data, let us create a ‘student’ table with the following structure.
The student table consists of two columns:
stud_id: The column is the primary key column that uniquely identifies the student.
stud_data: The column which stores the student’s information in the form of JSON.
Let’s create the table by using the CREATE TABLE statement:
CREATE TABLE student ( stud_id serial NOT NULL PRIMARY KEY, stud_data json NOT NULL );Now we will insert the data into the stud_data column, which is of type JSON. Before adding JSON data to the table, we need to ensure the given data is invalid in JSON format. Now insert the JSON data with the help of the following INSERT statement, which will add a new row into the ‘student’ table.
INSERT INTO student (stud_data) VALUES ( '{ "name": "Oliver Jake", "information": { "mobile_number": "9999999999", "branch": "Computer", "rank":12 } }' );After executing the above statement, illustrate the student table’s content using the following snapshot and SQL statement.
select * from student;Output:
We can insert multiple rows in the table using the following INSERT statement:
INSERT INTO student (stud_data) VALUES ( '{ "name": "Jack Connor", "information": { "mobile_number": "9999999910", "branch": "Computer", "rank":1 } }' ), ( '{ "name": "Harry Callum", "information": { "mobile_number": "9999999911", "branch": "Civil", "rank":2 } }' ), ( '{ "name": "Jacob John", "information": { "mobile_number": "9999999912", "branch": "Electrical", "rank":6 } }' ); select * from student;We can fetch the data from the student table by using the following snapshot and SQL statements.
Output:
Examples of PostgreSQL JSONWe have created a student table in the above section; let’s use the same for understanding the following examples.
Example #1 – Get all students in the form of JSON key SELECT FROM student;Output:
Example #2 – Get all students in the form of JSON text SELECT FROM student;Output:
Example #3 – Get specific JSON node using operators SELECT FROM student ORDER BY rank;Output:
Example #4 – Use JSON operator in WHERE clauseIn order to filter rows from the result set, we can use the JSON operators in the WHERE clause. Consider the following example, which gives us the record whose branch is Computer by using the following statement.
SELECT FROM student WHEREOutput:
Example #5 – PostgreSQL JSON functionsPostgreSQL provides us with some functions to handle JSON data.
json_each functionBy using the json_each() function, we can expand the outermost JSON object into a set of key-value pairs as follows:
SELECT json_each (stud_data) FROM student;We can use the json_each_text() function to get a set of key-value pairs as text.
json_object_keys functionWe can use the json_object_keys() function to get a set of keys in the outermost JSON object as follows:
SELECT FROM student; json_typeof functionWith the help of the function json_typeof(), we can get the type of the outermost JSON value as a string. The type of JSON value can be a boolean, number null, string, object, and array.
We can get the data type of the information using the following statement:
SELECT FROM student;Output:
We can get the data type rank field of the nested information JSON object using the following statement:
SELECT FROM student;Output:
Advantages of using JSON in PostgreSQLAdvantages of using JSON in PostgreSQL are given below:
Avoid complicated joins.
Parsing of JSON data is quite easier and faster execution.
Compatible with various database management systems.
Javascript Notation Objects are faster and very easy to read and understand.
The data within the JSON object is separated by a comma, making it easily understandable.
JSON is lightweight for data exchange.
ConclusionFrom the above article, we hope you understand how to use the PostgreSQL JSON data type and how the PostgreSQL JSON data type works to store the data in key-value pair. Also, we have added some examples of PostgreSQL JSON to understand it in detail.
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Learn The Examples Of Postgresql Subquery
Introduction to PostgreSQL Subquery
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Syntax 1. With a select statement Select column_name1, .., column_nameN From table_name1 [, table_name2] Where column_name operator Select column_name from table_name1 [, table_name2] [Where] condition) 2. With Insert statement INSERT INTO table_name [ (column_name1 [, column_name2 ]) ] FROM table_name1 [, table_name2] [WHERE VALUE OPERATOR] 3. With update statement UPDATE table_name SET column_name = new_value [WHERE OPERATOR [VALUE] (SELECT COLUMN_NAME FROM TABLE_NAME) [WHERE) ] 4. With delete statement DELETE FROM TABLE_NAME [ WHERE OPERATOR [ VALUE ] (SELECT COLUMN_NAME FROM TABLE_NAME) [ WHERE) ]Below is the parameter description of the above syntax as follows.
Select – Used to select the statement.
Column_name1 to column_nameN – It specifies the Column name.
From – You use the “From” clause to retrieve data from the chosen table.
TABLE_NAME – Used to specify the Table name.
Where condition – Where condition specified to fetch data per the query described to fetch the data.
Insert – Used to Insert statement.
Delete – Used to Delete statement.
Update – Used to Update statement.
Working of PostgreSQL SubqueryBelow is the working as follows.
A nested subquery, also known as an inner query, is what this refers to.
We have used the PostgreSQL subquery to select, insert, update, and delete statements.
It’s important to note that when working with PostgreSQL, you cannot use a subquery between operators with another subquery. However, you can use it within the Subquery itself.
Enclose it with parentheses.
We have used only one column in the select clause and multiple columns in the main query to compare it with the selected columns. Subqueries in PostgreSQL do not support the use of the “Order by” clause, but they can still be utilized in the main query.
Instead of the order by, we have used group by to perform the same operation as order by.
It will return more than one row.
You can use a subquery to return data that serves as a condition to restrict further the data retrieved by the main query.
Types of PostgreSQL SubqueryBelow is the type as follows. We have used Employee_test1 and Employee_test2 tables to describe types.
1. Table1 – Employee_test1 CREATE TABLE Employee_Test1 ( emp_id INT NOT NULL, emp_name character(10) NOT NULL, emp_address character(20) NOT NULL, emp_phone character(14), emp_salary INT NOT NULL, date_of_joining date NOT NULL); INSERT INTO Employee_Test1 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (1, 'ABC', 'Pune', '1234567890', 20000, '01-01-2023'); INSERT INTO Employee_Test1 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (2, 'PQR', 'Pune', '1234567890', 20000, '01-01-2023'); INSERT INTO Employee_Test1 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (3, 'XYZ', 'Mumbai', '1234567890', 35000, '02-01-2023'); INSERT INTO Employee_Test1 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (4, 'BBS', 'Mumbai', '1234567890', 45000, '02-01-2023'); INSERT INTO Employee_Test1 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (5, 'RBS', 'Delhi', '1234567890', 50000, '03-01-2023'); select * from Employee_Test1; 2. Table2 – Employee_test2 CREATE TABLE Employee_Test2 ( emp_id INT NOT NULL, emp_name character(10) NOT NULL, emp_address character(20) NOT NULL, emp_phone character(14), emp_salary INT NOT NULL, date_of_joining date NOT NULL); INSERT INTO Employee_Test2 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (1, 'PQR', 'Pune', '1234567890', 20000, '01-01-2023'); INSERT INTO Employee_Test2 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (2, 'XYZ', 'Mumbai', '1234567890', 35000, '02-01-2023'); INSERT INTO Employee_Test2 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (3, 'BBS', 'Mumbai', '1234567890', 45000, '02-01-2023'); INSERT INTO Employee_Test2 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (4, 'RBS', 'Delhi', '1234567890', 50000, '03-01-2023'); INSERT INTO Employee_Test2 (emp_id, emp_name, emp_address, emp_phone, emp_salary, date_of_joining) VALUES (6, 'ABC', 'Pune', '1234567890', 20000, '01-01-2023'); select * from Employee_test2;
Subqueries with the SELECT Statement
Below is the example of the Subqueries with the SELECT Statement as follows.
Subqueries with the INSERT Statement
Below is the example of the Subqueries with the INSERT Statement as follows.
INSERT INTO Employee_Test1 SELECT * FROM Employee_Test2 WHERE EMP_ID IN (SELECT EMP_ID FROM Employee_Test2) ; select * from Employee_test1;
Subqueries with the UPDATE Statement
Below is the example of the Subqueries with the UPDATE Statement as follows.
select * from Employee_test1;
Subqueries with the DELETE Statement
Below is the example of the Subqueries with the DELETE Statement as follows.
select * from Employee_test1; ConclusionA subquery in PostgreSQL is also called a nested or inner subquery. It does not use the ORDER BY clause, but the main query can use it to order the results. We used to group by clause instead of the order by clause in the PostgreSQL subquery.
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Complete Guide To Postgresql Blob With Examples
Introduction to PostgreSQL blob
PostgreSQL blob data type is defined as binary large object, basically blob data type is not available in PostgreSQL instead of blob we have using bytea data type. Blob data type in PostgreSQL is basically used to store the binary data such as content of file in PostgreSQL. The storage size of blob data type in PostgreSQL is 1 to 4 bytes plus the string of actual binary, input format of blob data type is different in PostgreSQL.
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Syntax
Below is the syntax :
Create table name_of_table (name_of_column1 data_type, name_of_column2 data_type, name_of_column3 data_type, …, name_of_columnN bytea); Name_of_variable or name_of_column bytea; Alter table name_of_tableadd columnname_of_column bytea;Below is the parameter description syntax of blob/bytea data type in PostgreSQL:
Create table: We create a table in PostgreSQL by using the “CREATE TABLE” statement and define the column with the data type “BYTEA”. We can create any table and defining data type as bytea to the column.
Name of table: When creating a table in PostgreSQL, we define the name of the table and assign the “BYTEA” data type to the column. We can define bytea data type to the column at the time of table creation. Also, we are defining the data type as bytea after table creation using alter command.
Name of column 1 to name of column N: This is defined as create a column on the table and defined bytea data type for the same.
Data type: When creating a table in PostgreSQL, we assign the data type to the table column. We can define data type as per which data we are storing into the table.
blob/bytea: The data type “BLOB”/”BYTEA” is specified when creating the column in the table. We store the binary data or file into the table using the blob data type.
Name of variable: This is nothing but the column name which we used at the time of table creation.
Alter table: We use the ALTER TABLE command in PostgreSQL to modify a table. With the ALTER command, we can change the data type of a column from one type to another.
Add column: We use the ADD COLUMN command in PostgreSQL to add a new column to a table. In this case, we are adding a new column and defining its data type as BLOB/BYTEA.
How blob Data Type works in PostgreSQL?
From PostgreSQL 7.2 version, we can store binary type of data into a table by using the bytea data type.
The blob data type’s storage size is 1 to 4 bytes, but it depends on the string used at the time of insertion.
For inserting data into the blob column, we need to create a function first. After creating the function, we call the same and restore data into the table.
PostgreSQL supports the binary data type, but we need to be careful while using the same.
At the time of insertion SQL statement in the blob data type column, we need to have a binary string with the values. Also, we have to escape some characters from the binary string as per the rules provided in PostgreSQL.
In PostgreSQL, binary strings can be divided into character strings in two ways. PostgreSQL bytea supports two external format for output and input.
Output format of bytea data type depends on the configuration parameter, the default value of bytea data type parameter is hex in PostgreSQL.
There is no specific data type of blob available in PostgreSQL; instead of a blob, we are using a bytea data type.
Example:
Below example shows that we are using bytea data type instead of blob data type in PostgreSQL.
Code:
create table test_blob (text_blob blob, id int, address varchar, phone int); create table test_blob (text_blob bytea, id int, address varchar, phone int); d+ test_blob;Output:
In the above example, we have used the blob data type at the time of table creation, but it will issue an error that the blob data type does not exist.
In the second example, instead of a blob, we have used bytea data type, using bytea data type, we have created a table. Using bytea data type, we can restore binary type data in PostgreSQL.
ExamplesGiven below are the examples mentioned :
Example #1Create a table by using blob/bytea data type.
Below example shows that create a table using blob/bytea data type.
We have defined bytea data type to blob_test column.
Code:
create table test_blob1 (blob_test bytea, id int, address varchar, phone int, name varchar); d+ test_blob1Output:
Example #2Create a table by defining blob/bytea data type on multiple column.
Below example shows that create a table by using blob/bytea data type on multiple column.
We have defined bytea data type to blob_test, blob_test1, and blob_test2 column.
Code:
create table test_blob2 (blob_test bytea, blob_test1 bytea, blob_test2 bytea,id int, address varchar, phone int, name varchar); d+ test_blob2 Example #3Add a new column and define the data type as blob/bytea.
Below example shows that add a new column and define the data type as blob/bytea.
In the below example, we are adding the column name as blob_test1 and defining the data type as blob/bytea for the same.
Code:
Alter table test_blob1 ADD COLUMN blob_test1 bytea; d+ test_blob1;Output:
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