DEV Community: samali0121

Boosting PostgreSQL Performance

samali0121 — Thu, 30 Nov 2023 18:07:20 +0000

Introduction

As applications grow and handle increasingly complex datasets, ensuring the performance of the underlying database becomes paramount. PostgreSQL, a powerful and feature-rich open-source relational database management system, offers a variety of techniques to optimize query performance. In this blog, we will delve into the significance of indexing and explore various optimization techniques to unleash the full potential of your PostgreSQL database.

Understanding Indexing in PostgreSQL

In PostgreSQL, an index is a data structure that acts as a roadmap to quickly locate specific rows within a table. It is akin to the index of a book, allowing you to find information faster without scanning the entire content. The database engine utilizes indexes to efficiently retrieve data, making them indispensable for enhancing query performance.

Types of Indexes in PostgreSQL

1. B-Tree Indexes: These are the default and most common type of index in PostgreSQL. Suitable for single-column and composite indexing, B-Tree indexes excel in range queries and equality lookups.
2. Hash Indexes: Ideal for exact-match queries but unsuitable for range scans, hash indexes work well with columns having discrete values.
3. GiST (Generalized Search Tree): GiST indexes are suitable for complex data types like geometric data or full-text search.
4. GIN (Generalized Inverted Index): GIN indexes are excellent for indexing arrays or performing full-text search operations.
5. SP-GiST (Space-Partitioned Generalized Search Tree): An efficient choice for indexing space-related data and custom data types.
6. BRIN (Block Range INdex): Designed for large tables with sorted data, BRIN indexes are best suited for time-series data.

Indexing Best Practices

1. Identify High-Impact Queries: Begin by analyzing the most frequently executed and time-consuming queries. Focus on indexing the columns used in these queries to achieve the most significant performance improvements.
2. Avoid Over-Indexing: While indexes improve read performance, they come with overhead. Avoid creating unnecessary indexes that can slow down write operations and inflate storage requirements.
3. Create Composite Indexes: Leverage composite indexes to cover multiple columns used together in frequent joins or filtered conditions. This approach reduces the number of individual indexes and streamlines performance.
4. Regularly Analyze and Vacuum: Keep PostgreSQL database statistics up to date using the ANALYZE command. Additionally, schedule regular vacuuming to reclaim space and optimize table performance.
5. Consider Partial Indexes: For large tables with a small subset of frequently accessed rows, consider using partial indexes. These indexes only cover rows that meet specific conditions, reducing the index size and maintenance overhead.
6. Monitor Index Usage: PostgreSQL provides tools to monitor index usage. Identify and remove or update indexes that are not being utilized to eliminate unnecessary overhead.

Performance Optimization Techniques

1. Query Optimization: Improve query performance by rewriting queries to be more efficient, employing appropriate join techniques, and minimizing the use of unnecessary subqueries or nested loops.
2. Connection Pooling: Implement connection pooling to reduce the overhead of establishing new connections. By reusing existing connections, you can significantly improve the database's ability to handle concurrent requests.
3. Cache Management: Implement caching mechanisms to store frequently accessed data in memory. This reduces the need for frequent database lookups, leading to faster response times.

PostgreSQL extensions

PostgreSQL extensions are additional features or functionalities that can be added to a PostgreSQL database to extend its capabilities beyond the core features provided by the database management system. These extensions are designed to provide specialized functionalities, allowing users to tailor PostgreSQL to their specific needs. Extensions can be developed by the PostgreSQL community or by third-party developers and are distributed separately from the main PostgreSQL distribution.

Here are some key points about PostgreSQL extensions:

Types of Extensions: PostgreSQL supports various types of extensions, including procedural languages, data types, indexing methods, full-text search capabilities, and more. Some common types of extensions include:

a. Procedural Language Extensions: These extensions allow you to add new procedural languages to PostgreSQL, such as PL/pgSQL (the default language), PL/Python, PL/Perl, and PL/Tcl.

b. Data Type Extensions: Data type extensions enable the creation of custom data types that are not present in the standard PostgreSQL installation.

c. Indexing Extensions: These extensions provide alternative indexing methods to improve the performance of specific types of queries. Examples include PostGIS for spatial indexing and pg_trgm for text search indexing.

d. Full-Text Search Extensions: Extensions like pg_trgm, pg_bigm, and unaccent enhance the full-text search capabilities of PostgreSQL, enabling more sophisticated text search operations.

e. Foreign Data Wrapper Extensions: Foreign data wrappers (FDWs) allow PostgreSQL to interact with external data sources, such as other databases or APIs. Extensions like postgres_fdw and dblink facilitate this interaction.

2. Installation: Installing an extension in PostgreSQL is a straightforward process. Many extensions come bundled with PostgreSQL distributions, while others can be easily installed using package managers or via SQL commands.

**3. CREATE EXTENSION: **To enable an extension in a specific PostgreSQL database, you can use the CREATE EXTENSION SQL command. For example, to enable the "hstore" extension, you would execute: CREATE EXTENSION hstore;

4. Managing Extensions: PostgreSQL provides commands to list, install, uninstall, and update extensions. You can use the \dx meta-command in the psql interactive terminal to view a list of installed extensions.

5. Updating Extensions: When you upgrade PostgreSQL to a new version, you may need to update the extensions as well. Many extensions have version-specific releases to ensure compatibility with the latest PostgreSQL version.

6. Third-Party Extensions: In addition to the extensions maintained by the PostgreSQL community, there are numerous third-party extensions developed and maintained by external contributors. These extensions can offer specialized functionalities tailored to specific use cases.

7. Security Considerations: While extensions can be powerful and useful, it is essential to review and assess their security implications before installing them in your PostgreSQL database. Only install extensions from trusted sources and ensure they are compatible with your PostgreSQL version.

PostgreSQL extensions are a powerful way to extend the functionality of the database to suit your specific application requirements. Before using an extension, it's essential to understand its purpose, features, and potential impact on your database's performance and security. By leveraging extensions, you can enhance the capabilities of PostgreSQL and build more sophisticated and customized database solutions.

Conclusion

Optimizing the performance of your PostgreSQL database is a continuous and iterative process. By understanding the different types of indexes and employing various optimization techniques, you can unlock the true potential of your database and provide a seamless experience for your users. Regularly monitor the performance, analyze query patterns, and fine-tune your indexing strategies to ensure your PostgreSQL database operates at peak efficiency. With a well-optimized database, your applications can handle increasing loads and complex queries with ease. Happy optimizing!

Exploring the Power of Apache Age in Graph Database Management

samali0121 — Tue, 03 Oct 2023 19:03:46 +0000

Apache Age is an open-source project that provides a native graph extension for the PostgreSQL database management system. It enables users to model, store, and manage graph data using the relational database paradigm, making it a powerful tool for managing graph databases.

Graph databases are specialized databases designed to handle graph data structures, such as nodes and edges, and the relationships between them. These databases are becoming increasingly popular due to their ability to model complex relationships and perform advanced queries efficiently. However, graph databases often require specialized tools and expertise to manage, which can be a barrier to adoption for some organizations.

Apache Age addresses this challenge by providing a graph database management system that builds on top of the well-established and widely used PostgreSQL database. It leverages the scalability, robustness, and reliability of PostgreSQL to provide a high-performance and flexible graph database solution.

One of the key advantages of Apache Age is its ability to seamlessly integrate with existing PostgreSQL databases. Users can easily add graph data to their existing database schema and query both relational and graph data using SQL or the Apache TinkerPop Gremlin language. This enables organizations to manage both graph and non-graph data within a single database, simplifying their data management workflows and reducing costs.

Another key advantage of Apache Age is its compatibility with popular programming languages such as Python, Java, Node.js, and Go, which makes it easy for developers to integrate graph database functionality into their applications. This compatibility, combined with the performance and scalability benefits of PostgreSQL, makes Apache Age an excellent choice for a wide range of graph database use cases.

In conclusion, Apache Age plays an important role in managing graph databases by providing a powerful, flexible, and scalable graph database solution that builds on top of the widely used PostgreSQL database. Its seamless integration with existing PostgreSQL databases, rich set of graph data management features, and compatibility with popular programming languages make it an excellent choice for organizations looking to manage and query their graph data efficiently and effectively.

You can study more in depth from Official Apache Age Documentation

Standard SQL Grammars & Syntaxes

samali0121 — Fri, 04 Aug 2023 13:31:25 +0000

SQL (pronounced “ess-que-el”) stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems. SQL statements are used to perform tasks such as update data on a database, or retrieve data from a database. Some common relational database management systems that use SQL are: Oracle, Sybase, Microsoft SQL Server, Access, Ingres, etc.

Why SQL?
SQL is very popular because it offers the following:

It allows users to access data in the relational database management systems.
Users can describe and define the data in a database and manipulate it.
It allows to embed within other languages using SQL modules, libraries & pre-compilers.
Users can create and drop databases and tables.
Users can create view, stored procedure, functions in a database.
It allows users to set permissions on tables, procedures and views.

SQL Commands & Syntax

SQL is a programming language designed for accessing, modifying and extracting information from relational databases. As a programming language, SQL has commands and a syntax for issuing those commands. These commands can be classified into the following groups based on their nature:

DDL - Data Definition Language

The DML commands are used for interacting with data within the database tables.
CREATE: Creates a new table, a view of a table, or other object in the database.
ALTER: Modifies an existing database object, such as a table.
DROP: Deletes an entire table, a view of a table or other objects in the database.

DML - Data Manipulation Language

The DDL commands are responsible for defining and managing the database schema.
SELECT: Retrieves certain records from one or more tables.
INSERT: Creates a record.
UPDATE: Modifies records.
DELETE: Deletes records.

DCL - Data Control Language

DCL commands manage database security, granting or revoking user access privileges.
GRANT: Gives a privilege to user.
REVOKE: Takes back privileges granted from user.

Common SQL Commands With Examples:

SQL SELECT: The SELECT command is used to get some or all data in a table.

SELECT title, author, pub_date
FROM catalog
WHERE pub_date = 2021;

SQL CREATE: The CREATE command is used to create a new SQL database or SQL table.
The following CREATE DATABASE statement creates a new SQL database named my_table:

CREATE DATABASE my_table;

SQL DELETE: The DELETE command removes rows from a named table.

DELETE FROM Employees WHERE last_name='Doe';

SQL INSERT INTO: The INSERT INTO command is used to add records into a database table.

INSERT INTO Employees (
    last_name,
    first_name
)
VALUES (
    'Jhon',
    'Doe'
);

SQL UPDATE: The UPDATE command is used to make changes to rows or records in a specific table.

UPDATE Employees
SET last_name = 'edison',
WHERE last_name = 'jhon';

Advanced SQL Commands:

JOIN: The JOIN clause is used to combine rows from two or more tables based on a related column between them. Common types of joins include INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

GROUP BY: The GROUP BY clause is used to group rows that have the same values in specified columns. It is often used in conjunction with aggregate functions like SUM, COUNT, AVG, etc.

ORDER BY: The ORDER BY clause is used to sort the query result based on one or more columns, either in ascending or descending order.

Subqueries: A subquery is a query nested within another query. Subqueries can be used in various clauses like WHERE, FROM, and SELECT.

Suggested links: https://forcedotcom.github.io/phoenix/

Ending Note:
In this article, I tried to explained the SQL definition. If you are going to work with database its good to learn the standard SQL commands and grammar to enhance the skills.
However, SQL is a vast subject, and continuous learning and practice will further enhance your understanding and expertise.

Performance optimization of Node js

samali0121 — Wed, 02 Aug 2023 06:02:41 +0000

What is Node
Node.js is a cross-platform, open-source server environment that can run on Windows, Linux, Unix, macOS, and more. Node.js is also a back-end JavaScript runtime environment, runs on the V8 JavaScript Engine, and executes JavaScript code outside a web browser. It enables server-side scripting and the development of highly scalable and efficient network applications. Node.js was created by Ryan Dahl and was first released in 2009.

Key Features Of Node:

Asynchronous and Non-blocking I/O
V8 JavaScript Engine
NPM (Node Package Manager)
Event-Driven Architecture
Cross-Platform Compatibility
Real-time Applications
Scalability

Let discuss One of the best way to optimize Node.js performance.
Using Node’s built-in profiler: Node has a high-quality profiler built into the Node debugger. We can enable it by passing the --inspect flag when running an application.

A sample application below can be use to test the Node.js profiler. It includes both a fast route that only sends a response and a slow route that loops, allocates, and sleeps.

Save the code as index.js in a new project directory:

const express = require('express');
const app = express();

app.get('/', slowRoute);
app.get('/fast', fastRoute);

function slowRoute(req, res) {
  loop();
  allocate();
  setTimeout(() => res.send('Success'), 100);
}

function fastRoute(req, res) {
  res.send('Success');
}

function loop() {
  for (let i = 0; i <= 1e8; i++) {}
}

function allocate() {
  const items = [];

  for (let i = 0; i < 1e6; i++) {
    items.push({ count: i });
  }
}

app.listen(3030, 'localhost', () => console.log(`Listening on localhost:3030`));

There are many other way to optimize as well which are:

1. Monitor & Profile With APM
2. Use Caching to Reduce Latency
3. Use Timeouts
Timeouts are a useful tool in Node.js for measuring the performance of your code and identifying potential bottlenecks. A timeout is simply a mechanism that allows you to set a timer for a certain amount of time, after which a callback function is called.
Here’s an example:

setTimeout(() => {
  console.log('Timeout complete.');
}, 1000);

4. Ensuring Secure Client-Side Authentications: Web applications require a secure storage mechanism for session data to sustain user authentication and monitor user sessions. Saving session data on the client-side can trigger severe security risks. So for scalability, it is important to ensure secure client-side authentication.
Enabling secure cookies, establishing session timeouts, and regularly rotating session IDs can ensure secure session data storage. Additionally, using HTTPS for the web application can furnish an extra layer of security for session data.

5. Improve Clustering through Throughput: To implement clustering in Node.js, the cluster module can be utilized. These module enables the creation of worker processes, each running on a separate CPU core. The master process, which oversees the worker processes, can communicate with them through inter-process communication (IPC).

By default, the master process listens to incoming connections and distributes them to the worker processes using a round-robin algorithm. However, other load-balancing algorithms such as IP hash or least connection can also be utilized.

6. Use CDN(Content Delivery Network): CDN is an effective way to improve the performance and speed of web applications. A CDN is a distributed network of servers that store cached versions of web content, such as images, videos, and other static assets. When a user requests a web page, the CDN delivers the content from the server closest to the user, reducing the latency and improving the page load time.

Some benefits include:

Faster content delivery to users worldwide due to distributed servers
Reduced load on origin server leading to improved website performance and availability
Improved user experience due to reduced latency and improved page load times
Improved scalability to handle large traffic spikes and accommodate growth
Advanced security features such as DDoS protection and SSL encryption
Reduced bandwidth costs through caching and content optimization.

7. Use Asynchronous Programming: Asynchronous programming techniques in Node.js can significantly enhance your application’s performance by enabling multiple tasks to run simultaneously without blocking the event loop. This is accomplished through the usage of callbacks, Promises, or async/await functions.

Some examples of asynchronous include:

Reading and writing files asynchronously using the fs module
Making HTTP requests using the request or Axios modules
Interacting with databases using the MySQL or mongoose modules
Processing vast amounts of data using streams

8. Using Load Balancer: A load balancer serves to prevent a single server being overburdened with traffic, leading to a sluggish response or even unavailability.

NGINX, HAProxy, and Amazon ELB are some of the sought-after load balancers for Node.js.

8. Database Queries Optimization: In Node.js, indexing your queries is a great way to start. It allows the database engine to quickly locate the data required for a given query. It’s essential to limit the amount of data retrieved from the database and avoid costly operations such as full table scans to optimize your queries.

9. Reducing Dependencies: By reducing the number of dependencies in your Node.js application, you can enhance performance, reduce startup times, and minimize the possibility of security vulnerabilities.

11. Organized Code Practices: The last tip may be the simplest, but the most actionable, and relevant to every developer. To keep an eye on efficiency while developing your application, here are just a few good habits:

Employ efficient algorithms and data structures
Reduce I/O operations
Leverage middleware

Ending Note: Node optimization is a vast topic but this article only covered the tip of some of the common practices you can adopt to get higher-performance results.

Regression Testing (Part-2)

samali0121 — Fri, 21 Jul 2023 11:53:24 +0000

Why Need Of Regression Testing?

Change in requirements and code is modified as per the changed requirements
Added new features in product
Bug fixing
Fixing of performance related issues

Techniques of Regression Testing

Retest All: Re execute all the tests in the existing test bucket. This requires huge time and resources and that’s why it is very expensive.
Regression Test Selection: Execute the selected part of test suites i.e. re-usable test cases or obsolete test cases.
Prioritization Of Test Case: Prioritization of test cases depends on business impact and used functionalities.

Types of Regression Testing:

Corrective: Corrective regression testing is used when there are no changes introduced in the existing software/application/product specification. The existing test cases can be used to conduct the desired test.

Progressive: This testing is used in situations when the modifications are added in the specifications of the product and new test cases are designed. Progressive regression testing works effectively when there are changes done in the software/application specifications as well as new test cases are designed.

Selective: We utilize a subset of the current experiments to cut down the retesting cost and effort in selective regression testing. In this case if any changes are done to any part or module of software application/product, e.g. capacities, factors and so on and afterward a test cases must be re-executed. Here is the difficult part to determine the conditions between an experiment and the program elements it covers. It is done to observe the effect of new code development to the existing code of the application. When this type of relapse testing is directed, a subset from existing experiments is utilized, to reduce the effort needed for retesting.

Retest-All: Retest-all strategy is very time taking because in this testing we reuse all tests which results in the execution of avoidable test cases. This strategy is not much useful when any small modification or change is done to the application. Retest-all strategy involves the testing of all aspects of a particular application as well as reusing all test cases even where the changes have not been made. This testing is not advisable when there is a small change in the existing code.

Complete: Complete regression testing is very useful when multiple changes have been done in the existing code. This testing is specially used when multiple new changes has certain impact on software’s root code. Performing this testing is highly valuable to identify unexpected bugs.

Best Practices For Regression Testing
We may adopt following practices to get the good results:

Get a Fresh Perspective: Remember we emphasized on the importance of test cases? So make sure to keep them up-to-date so they reflect the current state of your application. That also includes new features added and specification changes

Know What Works Best: Make sure to keep running the tests that gives you consistent results on a regular basis.

Prioritizing Task or Cases: The high-load areas require more thorough testing as they incorporate crucial functionality to the system.

Conclusion: Regression testing is an important part for improving software product quality and enhancing the end-user experience by making sure that code changes do not impact the older or existing product functionality. Using appropriate regression testing tools eliminates software defects early in the software deployment cycle.

*Recommendations: *
https://www.spiceworks.com/tech/devops/articles/what-is-regression-testing/
https://www.javatpoint.com/regression-testing

Regression Testing (Part-1)

samali0121 — Thu, 20 Jul 2023 22:16:10 +0000

Brief Introduction

Regression Testing is a type of software testing is the type of testing which verify that recent changes or updates to a software application have no new bugs. This is because new code may bring in new logic that conflict with the existing code. Normally QA teams have a series of regression test cases for important features that they will re-execute each time these code changes occur to save time and maximize test efficiency.

Why is Regression Testing Important?
Regression Testing is important for several reasons i.e. overall quality, reliability, and maintainability of software applications. Here are some key reasons why regression testing holds significant importance:

Detecting Defects Early
Ensuring Software Stability
Verifying Fixes of Bugs
Supporting Agile Development (https://www.atlassian.com/agile)
Facilitating Continuous Integration and Deployment (CI/CD)
Reduces Risks and Costs & Supports Long-Term Maintenance
Compliance, Standards & Enhancing Confidence in Code Changes

Regression Testing Tools and Frameworks
Selenium: Selenium is used to run regression tests. Selenium Browser Automation helps run tests on different browsers and find bugs.

Cypress: You can perform E2E Testing and Components Testing on Cypress. This helps you run Regression Tests on browsers using Cypress effortlessly.

Appium: It is used to run regression tests on iOS and Android apps. You can check the functionality of the mobile apps using Appium.

Playwright:Playwright is used for running regression tests for web applications on different browsers.

Puppeteer: You can use Playwright to run regression tests for web applications on different browsers.

BrowserStack App Automate: You can integrate different web testing frameworks, like Appium, Espresso, and XCUITest, with BrowserStack App Automate. It helps you run tests on real Android and iOS devices for better accuracy.

Conclusion: Regression Testing is a fundamental practice in software development that safeguards against regressions in the quality and reliability of software applications throughout their lifecycle. By catching defects early, ensuring stability, and supporting agile practices, regression testing plays a vital role in delivering high-quality software products to end-users.

Suggestion to get more information:
https://katalon.com/resources-center/blog/regression-testing
https://en.wikipedia.org/wiki/Regression_testing

To be Continue...

GDB Installation On Ubuntu

samali0121 — Thu, 20 Jul 2023 20:40:48 +0000

Introduction:

GDB stands for "GNU Debugger." It is a powerful and popular debugger tool used primarily for debugging programs written in various programming languages.
The main purpose of GDB is to help developers find and fix bugs in their programs. It allows them to inspect the running state of a program, control its execution, and examine variables and memory.

Installation of GDB

You can easily install GDB on Ubuntu by running the following command on terminal:

1. Updating Package Listing:

sudo apt update

2. Installing "GBD"

sudo apt install gdb

3. Installation Confirmation:

gdb --version

That's it! You have now installed GDB on your Ubuntu system. To use it, simply run it followed by the executable you want to debug, like this:

gdb your_executable

Key Feature Of GDB

Designers can set breakpoints at explicit lines or works in the source code to end the program's execution and assess its state at that point.
GDB gives different commands to step through the program's execution, including stepping line by line, stepping into functions, and stepping out of functions.
It allows developers to obtain a backtrace of the program's call stack by identify the sequence of function calls leading to an issue.
Developers can set breakpoints that trigger just when certain conditions are met, allowing for more fine-grained debugging.
GDB helps developers to examine the values of variables and data stored in memory during program execution.
GDB can be used to analyses core dumps generated when a program crashes, aiding in post-mortem debugging.

GDB package Information:

Conclusion: GDB is a powerful tool used for debugging, and you should have a good understanding of debugging concepts to use it effectively.

Apache AGE Installation

samali0121 — Thu, 20 Jul 2023 20:24:54 +0000

Brief Introduction:
If you are about to install PostgreSQL and Apache AGE on a Windows machine in a step-by-step manner, then this is the right post for you. This post will help you to have a fully functional Apache AGE graph & PostgreSQL, ready to use application.

PostgreSQL:It is the one of the famous open-source database management system refer to as simply "Postgres". It is designed to handle a wide range of workloads, from single-machine applications to large-scale enterprise databases.

Apache AGE
Apache AGE (Advanced Graph Extensions) is a graph database that is built on top of Postgres SQL. AGE allows you to store, query, and analyze graph data using familiar SQL syntax and the PostgreSQL ecosystem.

Apache AGE Installation

samali0121 — Tue, 18 Jul 2023 12:11:13 +0000

Apache AGE

Apache AGE (Advanced Graph Extensions) is a graph database that is built on top of Postgres SQL. AGE allows you to store, query, and analyze graph data using familiar SQL syntax and the PostgreSQL ecosystem.

Installation Guidelines:
There is a step-by-step guide to installing Apache AGE (Advanced Graph Extensions) on a Linux-based system, specifically Ubuntu:

Step 1: Install PostgreSQL

Open terminal & run the following command

sudo apt-get update
sudo apt-get install postgresql

Step 2: Dependencies Installatio**

Run the following command in the terminal

sudo apt-get install libpq-dev build-essential git

Step 3: Cloning Apache AGE Repository

https://github.com/apache/incubator-age.git

git clone https://github.com/apache/incubator-age.git

Step 4: Downloading & Installing AGE

cd age/

# install
sudo make PG_CONFIG=/home/<your pc name>/age_installation/pg/postgresql-11.18/bin/pg_config install

# install check
make PG_CONFIG=/home/<your pc name>//age_installation/pg/postgresql-11.18/bin/pg_config installcheck

Note: PG_CONFIG require the path to the pg_config file. Give the path from the recently installed PostgreSQL.

Step 5: Configuration of Apache AGE

sudo nano /etc/postgresql/<postgresSQL version>/main/postgresql.conf

Add shared_preload_libraries = 'age' to enable Apache AGE.

Step 6: Starting Postgres SQL

sudo service postgresql restart

Verify the installation SELECT age_version();
If Apache AGE is installed properly, it will show the version information.

If you are done with all the above steps then you are successful in installing ApacheAGE on your Ubuntu.

Good luck with that...

Duel Boot (Ubuntu + window)

samali0121 — Mon, 17 Jul 2023 12:04:19 +0000

You have to be sure about following points, before installing dual boot:

Make sure you have backup of current data.
Ensure that your computer meets the minimum system requirements for both Ubuntu and Windows.
Before proceeding, it is a good idea to create recovery drive or system image. It helps to restore windows in case of lose.
download ubuntu setup from https://ubuntu.com/download/desktop, and make a bootable Usb using https://rufus.ie/en/.
Shrink the windows partition by searching "Disk Management" option from windows search.
Point to focus: Configuration settings used be in UEFI not legacy else you may have issues.
Reinstall or shutdown the system and then start and press "F12,F5 etc.." to enter in boot menu & follow the installation steps.
Once the Ubuntu installation is complete, restart your computer. 9.If you want to change the default operating system that loads by default, you can usually do so through the boot menu or the BIOS/UEFI settings.

DEV Community: samali0121

Boosting PostgreSQL Performance

Introduction

Understanding Indexing in PostgreSQL

Types of Indexes in PostgreSQL

Indexing Best Practices

Performance Optimization Techniques

PostgreSQL extensions

Conclusion

Exploring the Power of Apache Age in Graph Database Management

Standard SQL Grammars & Syntaxes

SQL Commands & Syntax

DDL - Data Definition Language

DML - Data Manipulation Language

DCL - Data Control Language

Common SQL Commands With Examples:

Advanced SQL Commands:

Performance optimization of Node js

Regression Testing (Part-2)

Regression Testing (Part-1)

GDB Installation On Ubuntu

Introduction:

Installation of GDB

1. Updating Package Listing:

2. Installing "GBD"

3. Installation Confirmation:

Key Feature Of GDB

GDB package Information:

Recommended Reading:

Apache AGE Installation

Apache AGE Installation

Apache AGE

Step 1: Install PostgreSQL

Step 2: Dependencies Installatio**

Step 3: Cloning Apache AGE Repository

Step 4: Downloading & Installing AGE

Step 5: Configuration of Apache AGE

Step 6: Starting Postgres SQL

Duel Boot (Ubuntu + window)