#GraphQL APIs

[govindhtech]

Google Cloud SQL’s Linear Optimization Vector Search Magic

On Google Cloud SQL, linear optimizes data and scalability with vector search capability

The goal of Linear is to enable product teams to deliver excellent software. In order to assist customers in streamlining processes throughout the product development process, They have dedicated the previous several years to developing an extensive project and problem tracking system. Although began as an issue tracker, have developed software into a potent project management tool for users worldwide and cross-functional teams.

For example, Linear Asks streamlines cooperation for users of her platform who don’t have Linear accounts by enabling businesses to handle request processes like bug and feature requests over Slack. Furthermore,Have included a feature called Similar Issues, which keeps duplicate or overlapping tickets at bay and guarantees more accurate and clearer data representation for expanding businesses.

Workflow and product management tools are becoming more and more necessary as clients’ companies expand and they have more users on the platform and problems to monitor. Her goal is to facilitate this expansion while maintaining her high standards for performance, stability, quality, and features that enable intricate technical setups and excellent user experiences.

Looking for a vector search database that is scalable

Google had a PostgreSQL database with the pgvector extension hosted on a PaaS during early development period, however it wasn’t indexed or used for production workloads.They had to update databases to handle production workloads and find a solution that supported vector search, since this is the most effective approach to categorize and detect related problems based on common traits or patterns. They can easily detect related or duplicate concerns by identifying commonalities across the vector representations of the issues. By streamlining bug tracking and assisting customers in resolving problems more successfully, this feature improves processes overall and saves time and resources.

Google Cloud SQL’s Vector Search

Google looked into and ultimately tried a number of the recently launched database products that are specifically designed to store vectors. However, in addition to the comparatively expensive cost for a function that wasn’t essential to the offering, google also encountered issues with indexing speed and excessive downtime while growing. They decided on Google Cloud SQL for PostgreSQL once pgvector support was enabled, considering Linear’s current data volume and objectives for finding an affordable solution. Its dependability and scalability pleased us. The learning curve for team was nonexistent since this decision was also in line with how to now use databases, models, ORMs, etc.

The magnitude and number of vectors had to deal with for the production dataset initially made migration procedure from development to production difficult. But were able to correctly index each division after divided the problems table into 300 chunks. The migration went well and used the typical procedure of building a follower from the current PostgreSQL database.

Linear uses Google Cloud to enable its real-time sync

Today, Google Cloud SQL for PostgreSQL powers main working database.Similarity-search features’ vectors might be stored in a separate database as Google Cloud SQL for PostgreSQL comes with the pgvector extension. This is accomplished by employing OpenAI ada embeddings to encode the semantic meaning of problems into a vector, which is then combined with additional filters to assist in the identification of related and pertinent things.

Linear’s online and desktop clients synchronize with backend in real-time, according to architectural design. they run synchronized WebSocket servers, public and private GraphQL APIs, and background job task runners on Google Cloud.

All of them operate as independently scalable Kubernetes workloads. They are confident in choice of Google Cloud SQL for PostgreSQL as major database option, and whole technological stack is built using NodeJS and Typescript. Google’s managed Memorystore for Redis as a cache and event bus.

Cloud SQL for PostgreSQL allows for future innovation and seamless scalability

For Linear,Google Cloud SQL for PostgreSQL has been quite helpful.they must depend on managed services since lack a dedicated operations staff. It’s great for operations and frees up engineering time to focus on creating user-facing features since it lets us extend database seamlessly into tens of terabytes of data without needing significant technical work.

In addition, They have received positive comments from clients, particularly with respect to Linear’s capability to recognize duplicate problems when they file a bug report. The program now makes suggestions for possible duplication when a user opens a new problem. Furthermore, Linear shows potential relevant defects that have previously been noted while managing client tickets using customer support application connections like Zendesk.

In the future, want to include machine learning (ML) into Linear to improve the user experience, automate processes, and provide smart recommendations within the product. Additionally, dedicated to improving similarity search features and going beyond vector similarity to include other signals in computations. have no doubt that Google Cloud will play a crucial role in assisting us in realizing this goal.

Read more on Govindhtech.com

[lucid-outsourcing-solutions]

Implementing GraphQL APIs in ColdFusion: Best Practices and Strategies

[esolznet]

Shopify's GraphQL API for Advanced Data Management and Queries

[mp3monsterme]

Open API's Arazzo and overlay specifications

The OpenAPI Specification OAS and its Open API Initiative (OAI)—the governing body—have been around for 10 years, and of course, OAS’s foundation, Swagger, has been around a lot longer. OpenAPI is very much a mature proposition. But the OAI community hasn’t stood still. Two standards have been developed, the first being Overlays and the latter being Arazzo. Overlays Overlays support the Arazzo…

[simple-logic]

#Guess Let's play 'Guess The Logo'! 🤔

Can you recognize this logo? Drop your guesses below👇

[orbitwebtech]

GraphQL vs REST API in Node.js: Which One Should You Use?

🔗 APIs are the backbone of modern web development, but choosing the right one can feel overwhelming. Let’s break it down and make your decision easier!

🌟 REST API: The Traditional Powerhouse

REST (Representational State Transfer) has been the gold standard for years. Here's why: ✅ Simplicity: Easy to implement and understand. ✅ HTTP Protocol: Leverages HTTP methods like GET, POST, PUT, DELETE. ✅ Widely Supported: Works seamlessly across platforms. But REST has a downside — over-fetching or under-fetching data, especially when your clients need only a specific subset of information.

🚀 GraphQL: The Modern Challenger

GraphQL, developed by Facebook, is revolutionizing API development. Here’s what makes it unique: ✨ Single Endpoint: One endpoint to rule them all! ✨ Client-Controlled Data: Fetch exactly what you need, no more, no less. ✨ Strong Typing: Built-in schema ensures data consistency. ✨ Flexibility: Perfect for complex queries and large-scale applications. However, it requires a steeper learning curve and might be overkill for simple use cases.

🤔 Which One Should You Use?

Use REST API if your project is simple, time is limited, or you’re working with a small dataset.

Choose GraphQL if your project is complex, you need efficient data fetching, or you're building a large-scale application.

💡 Pro Tip: Combine the best of both worlds! REST and GraphQL can coexist, providing flexibility where needed.

👉 Ready to make your Node.js development smoother? Whether it's GraphQL or REST, the right choice will save you time and effort.

Visit for More: GraphQL vs REST API in Node.js: Which One Should You Use?

[lineeshkumar]

How Can a Multilingual Content Moderation API Help Your Business?

The artificial intelligence startup Mistral has announced the launch of a new API for content moderation, designed to ensure greater security and adequacy on various digital platforms. This API is the same technology that powers moderation on Mistral's Le Chat platform and stands out for being adaptable to different applications and security standards.

AI Image

A Powerful and Flexible Moderation Model

At the core of this tool is Ministral 8B, a model tuned and trained to classify texts into nine specific categories: sexual content, hate and discrimination, violence and threats, dangerous and criminal content, self-harm, health, financial, legal issues, and personally identifiable data (PII). This approach allows the API to be applied to both general texts and more complex conversations, expanding its scope for developers and businesses looking to protect their online communities.

In a statement, Mistral underscored the importance of offering more robust and scalable moderation solutions. "We have observed a growing enthusiasm in the industry and the research community for AI-based moderation systems that can be more robust and adaptable to different applications," they said in their blog.

Advantages and Challenges of Automated Moderation

The use of AI-powered moderation systems promises to make it easier to manage large volumes of content while maintaining a safe environment for users. However, they also present challenges. Previous models of moderation have been criticized for biases, such as misinterpreting phrases in African-American Vernacular English (AAVE) as disproportionately "toxic." In addition, some systems tend to mark discussions about disability as negative or problematic, which can perpetuate stigmas and limit online expression.

Mistral acknowledges that although his model shows high levels of accuracy, there is still room for improvement.

Competition and Possible Comparisons

An interesting point is that Mistral did not compare its API directly with other well-known ones, such as Jigsaw Perspective's moderation API or OpenAI's, which leaves open curiosity about how it positions itself in terms of accuracy and effectiveness against these options. This detail may indicate that the company seeks to first establish itself in the market before making public comparisons or simply that it considers its most specialized and unique approach.

A Path to Customizable Moderation

One of the most attractive aspects of this API is its ability to be customized. Companies and developers can adapt the tool to meet their own security standards and policies, making the moderation process not only automated but also aligned with their values and needs.

[anandshivam2411]

Unlocking the Skills of a Full Stack Developer

To excel as a full stack developer, embark on a journey that begins with mastering the foundational technologies of web development: HTML, CSS, and JavaScript. These essential skills will empower you to build and style dynamic web pages. Next, immerse yourself in front-end frameworks such as React, Angular, or Vue, which will enable you to create engaging and interactive user experiences.

On the back end, dive into server-side languages like Node.js, Python (utilizing powerful frameworks like Django or Flask), Ruby on Rails, and PHP. Understanding how to leverage databases is equally vital, so familiarize yourself with both SQL (using systems like PostgreSQL and MySQL) and NoSQL options like MongoDB.

A strong grasp of APIs is crucial for modern development, so learn how to create and utilize RESTful services and explore GraphQL for efficient data retrieval. Equip yourself with Git for version control, allowing you to track changes and collaborate seamlessly with others.

Enhance your problem-solving abilities through coding challenges on platforms like LeetCode, and build an impressive portfolio by undertaking personal projects or contributing to open-source initiatives. Embrace a mindset of continuous learning by following industry blogs, enrolling in online courses, and participating in workshops to stay ahead of the curve. There is also a masterclass to help you understand these concepts better.

Don’t overlook the importance of soft skills; effective communication and teamwork are vital in today’s collaborative work environment. Finally, seek out mentorship to guide your development and engage in code reviews to benefit from the insights of experienced developers. By diligently following these steps, you can cultivate the expertise and confidence necessary to thrive as a full stack developer in a competitive landscape.

[newcodesociety]

[robomad]

Node.js and GraphQL: An Introduction

Node.js and GraphQL: An Introduction to Building APIs

Introduction GraphQL is a query language for APIs developed by Facebook in 2012 and released as an open-source project in 2015. It provides a more efficient, powerful, and flexible alternative to REST. With GraphQL, clients can request exactly the data they need, making it easier to evolve APIs over time. Node.js, with its asynchronous nature and powerful libraries, is an excellent choice for…

[lucid-outsourcing-solutions]

ColdFusion with AWS AppSync: GraphQL API Management for Modern Applications

[getegecourse]

Understanding the Basics of APIs

Application Programming Interfaces (APIs)

crucial components in modern software development. They allow different software systems to communicate with each other and facilitate the integration of various services and functionalities. This article explores the basics of APIs, their types, and their importance in today’s tech landscape.

What is an API? for more........

An API (Application Programming Interface) is a set of rules and protocols that allows one piece of software to interact with another. It defines the methods and data formats that applications can use to request and exchange information. APIs enable different software systems to communicate, share data, and perform operations without needing to understand each other's underlying code.

Key Concepts:

Endpoint: A specific URL where an API service can be accessed. Endpoints represent different functionalities or resources offered by the API.

Request: The action taken by a client to interact with an API, such as retrieving data or sending information. Requests typically include a method (GET, POST, PUT, DELETE), headers, and sometimes a body.

Response: The data or acknowledgment returned by the API after processing a request. Responses usually include status codes, headers, and a body containing the requested information or result.

Authentication: A process used to verify the identity of a user or application accessing the API. Common methods include API keys, OAuth tokens, and Basic Auth.

Types of APIs

Web APIs: Also known as HTTP APIs or REST APIs, these are accessed over the web using HTTP/HTTPS protocols. They are widely used for web services and allow different systems to communicate via the internet.

RESTful APIs: Representational State Transfer (REST) APIs use standard HTTP methods and are designed to be stateless. They operate on resources identified by URLs and support various formats like JSON and XML.

SOAP APIs: Simple Object Access Protocol (SOAP) APIs use XML-based messaging protocols for exchanging information. SOAP APIs are known for their robustness and are often used in enterprise environments.

GraphQL APIs: A query language for APIs that allows clients to request specific data. GraphQL APIs provide a more flexible and efficient way to interact with data compared to REST APIs.

Library APIs: These APIs are part of software libraries or frameworks that provide predefined methods and functionalities for developers to use in their applications.

Click now

For those interested in deepening their understanding of APIs, including practical applications and advanced concepts, Getege offers a comprehensive course. This course provides detailed insights into API design, implementation, and best practices, helping you master the fundamentals and more complex aspects of API development.

[jblogify]

[govindhtech]

REST API vs GraphQL API: Choosing an API Which You Need

REST API vs. GraphQL APIs: A Developer Showdown

APIs power modern online services by connecting software components and transferring data across the internet. API technologies like SOAP, REST, and GraphQL facilitate software development by integrating third-party data and services. APIs allow firms to securely communicate data and provide services to workers, business partners, and users.

Recent disputes have focused on REST (representational state transfer) and GraphQL, despite the various APIs. Both have several advantages and are used in networking projects worldwide. Data traffic management differs greatly between them. IBM compare REST and GraphQL APIs and explain how corporations may optimise their networks.

Explain REST and GraphQL APIs Comparing REST and GraphQL APIs requires knowledge of each.

REST The early 2000s-developed REST architecture paradigm for networked hypermedia applications uses a stateless, client/server, cacheable communication protocol. RESTful APIs drive REST designs.

REST APIs address resources using URIs. REST APIs use endpoints to execute CRUD (“create,” “read,” “update,” and “delete”) network resource activities. They shape and size client resources based on a media type or MIME type. Most formats are JSON and XML (occasionally HTML or plain text).

The server handles client requests for resources and returns all related data. HTTP response codes include “200 OK” for successful REST queries and “404 Not Found” for missing resources.

GraphQL In 2012, Facebook created GraphQL, a query language and API runtime that became open source in 2015.

API schema published in GraphQL schema definition language defines GraphQL. Each schema lists data types and associations the user may query or alter. The resolver backs each schema field. The resolver converts GraphQL queries, modifications, and subscriptions into data and fetches it from databases, cloud services, and other sources. Resolvers specify data formats and let the system combine data from several sources.

Instead of utilising several endpoints to acquire data and conduct network operations like REST, GraphQL exposes data models by using a single endpoint for all GraphQL queries. Using resource attributes and resource references, the API gets the client all the data they need from a single query to the GraphQL server.

GraphQL and REST APIs are resource-based data exchanges that employ HTTP methods like PUT and GET to limit client actions. However, important contrasts explain why GraphQL has grown and why RESTful systems have endured.

REST API vs GraphQL API GraphQL APIs are typically considered an enhancement over RESTful settings due to their ability to promote front-end-back-end team cooperation. An organization’s API journey should continue with GraphQL, which solves REST difficulties.

REST was the norm for API designs, and many developers and architects still use RESTful settings to manage IT networks. Understanding the differences is crucial to any IT management plan.

REST and GraphQL APIs handle differently:

Data retrieval REST uses many endpoints and stateless interactions to handle each API request as a new query, so clients obtain all resource data. Over-fetching occurs when a client only wants a portion of the data. To make up for under-fetching, a RESTful system frequently requires clients query each resource individually if they need data from numerous resources. Single-endpoint GraphQL APIs eliminate over- and under-fetching by providing clients with an accurate, full data response in one round trip from a single request.

Versioning Teams must version APIs to adjust data structures and avoid end-user errors and service outages in a REST architecture. Developers must establish a new endpoint for every update, producing several API versions and complicating maintenance. GraphQL eliminates versioning since clients may express data needs via queries. Clients without need for new server fields are unaffected. Clients may request deprecated fields until queries are updated.

Error handling HTTP status codes indicate request success or failure in REST APIs. Each status code has a purpose. A successful HTTP request returns 200, whereas a client error returns 400 and a server error returns 500.

This status reporting method sounds simpler, however HTTP status codes are typically more valuable to online users than APIs, particularly for mistakes. REST does not specify errors, therefore API failures may display as transport problems or not at all with the status code. This may compel staff to study status documentation to understand faults and how infrastructure communicates failures.

Because HTTP status codes don’t transmit problems (excluding transport faults), GraphQL APIs return 200 OK for every request. The system sends faults in the response body with the data, so clients must interpret it to determine whether the request was successful.

However, GraphQL specifies errors, making API problems easier to identify from transport failures. The response body’s “errors” item describes errors, making GraphQL APIs easier to develop upon.

REST lacks built-in functionality for real-time data changes. Long-polling and server-sent events are required for real-time functionality, which might complicate a programme.

Subscriptions provide real-time changes in GraphQL. Subscriptions enable the server to notify clients when events occur by maintaining a continuous connection.

Environment and tools Developers have several tools, libraries, and frameworks in the REST environment. Teams must browse several endpoints and learn each API’s norms and practices while using REST APIs.

GraphQL APIs are young, but the ecosystem has evolved greatly since their inception, with several server and client development tools and modules. GraphiQL and GraphQL Playground are powerful in-browser IDEs for discovering and testing GraphQL APIs. GraphQL also supports code generation, simplifying client-side development.

Caching REST APIs use eTags and last-modified headers to cache API requests. While effective, some caching solutions are difficult to implement and may not be suited for all use situations.

Dynamic queries make GraphQL APIs harder to cache. Persisted queries, response caching, and server-side caching may alleviate these issues and simplify GraphQL caching.

When to utilise GraphQL and REST APIs REST and GraphQL APIs are distinct tools for various goals, not better.

For public-facing e-commerce sites like Shopify and GitHub, REST is easy to deploy and provides a simple, cacheable communication protocol with strict access constraints. Due to under- and over-fetching issues, REST APIs are ideal for:

Businesses with simpler data profiles and smaller applications Businesses without complicated data queries Businesses where most customers utilise data and processes similarly GraphQL APIs increase system efficiency and developer ease-of-use by providing flexible, fast data fetching. This makes GraphQL ideal for APIs in complicated setups with quickly changing front-end needs. This includes:

Business with limited bandwidth wants to restrict calls and answers Companies who seek to aggregate data at one endpoint Businesses with diverse client requirements Though they employ distinct methods, GraphQL and REST APIs may improve network scalability and server speed.

Control your API environment using IBM API Connect Whether you use REST or GraphQL APIs or a mix of both your business can benefit from a wide range of applications, including JavaScript implementations and integration with microservices and serverless architectures. Use both API types to optimise your IT infrastructure with IBM API Connect.

IBM API Connect lets you establish, manage, protect, socialise, and monetise APIs and encourage digital transformation in data centres and clouds. This lets organisations and consumers power digital applications and innovate in real time.

API Connect helps organisations stay ahead in API management, which will be crucial in a computing ecosystem that will get bigger, more complicated, and more competitive.

Read more on Govindhtech.com

[cryptrz-security]

#PentestGround, free pentesting labs

Preparing a lab for practicing hashtag#pentest can take time. Depending of the vulnerable and attacker machines, you may need to download a vulnerable #virtualMachine, install it, fix eventual issues on #VirtualBox or #VMware, start services, maybe prepare the #database, #tools, #proxy, #certificates, etc… Pentest-Tools.com provides ready to use free pentesting labs, including #webApps,#Redis, #RestAPI and #GraphQL.

Enjoy!