#PostgreSQL database

[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.

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[delphintechnologies1]

Postgresql Development Services misses out on certain features however the developer can either create them or choose to install them through third-party extensions. The database makes use of a language known as PGSQL for creating procedures and also implementing other programming languages.

[nixcraft]

Wait what... ? this is dangerous knowledge.

[simple-logic]

Simple Logic transformed a global enterprise’s database performance with seamless cloud migration!

Challenges: Frequent FDW failures disrupting operations 🔌 Downtime from limited resources ⏳ Delays causing customer dissatisfaction 😟 Manual workarounds slowing tasks 🐢

Our Solution: Migrated DB to AWS for scalable performance ☁️ Fixed FDW stability issues 🔧 Optimized PostgreSQL & Oracle integration 🚀 Resolved resource bottlenecks 🛠️

The Results: Stable cloud setup with zero downtime ✅ Faster processing, happier users ⚡😊

Ready to boost your database performance? 📩 marketing@simplelogic.in 📞 +91 86556 16540

💻 Explore insights on the latest in #technology on our Blog Page 👉 https://simplelogic-it.com/blogs/

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[nubecolectiva]

5 Companies or Projects That Use PostgreSQL.

5 Empresas o Proyectos Que Usan PostgreSQL.

👉 https://nubecolectiva.com/blog/5-empresas-o-proyectos-que-usan-postgresql/

[techdirectarchive]

How to change the default user password in PostgreSQL

In this guide, we shall discuss how to change the default user password in PostgreSQL. PostgreSQL is a relational database management system (RDBMS) that provides an open-source solution for storing and managing data. It offers a variety of features for database administration, queries, and security. This blog post was birthed by the following question: “Resetting the Veeam built-in PostgreSQL…

[teamfemmebabe]

Hey Friends - what's good (via A woman with a black and white wig. - Glam Girl X)

[govindhtech]

Database Center GCP: Smarter Fleet Management with AI

Database Centre GCP

The AI-powered Database Centre, now GA, simplifies database fleet management.

Database Centre, an AI-powered unified fleet management system, streamlines database fleet security, optimisation, and monitoring. Next 25 Google Cloud announced its general availability.

Google Cloud Database Centre is an AI-powered fleet management system. It is commonly available.

Database Centre GCP simplifies database fleet administration, including security, optimisation, and monitoring. This AI-enabled dashboard provides a unified picture of your database fleet. We want to unleash your data's power and organise your database fleet.

It replaces disconnected tools, complex scripts, APIs, and other arduous database fleet monitoring methods. Database Centre offers a complete experience using Google's AI models.

AlloyDB Aiven is also available. Omni simplifies multi-cloud AI

The main Database Centre characteristics and capabilities are:

Unified view: It eliminates information silos and the need to seek customised tools and spreadsheets by showing your whole database fleet. This provides unparalleled database knowledge.

Database Centre GCP uses AI to make intelligent insights. It actively reduces fleet risk with smart performance, reliability, cost, compliance, and security advice.

Optimise your database fleet with AI-powered support. Using natural language chat, fleet issues may be resolved quickly and optimisation ideas given. This interface uses Gemini for usability.

Database Centre GCP improves health and performance tracking for several Google Cloud databases, including:

For PostgreSQL, AlloyDB

Bigtable

Memorystore

Firestore

MySQL, PostgreSQL, and SQL Server Cloud SQL Tracking Health Issues It summarises your fleet's most pressing health issues from your Security Command Centre and Google Cloud projects. It then suggests investigating affected projects or situations. You can monitor several health issues:

Used database engines and versions;

Important databases' availability and outage risk.

How well backups protect critical databases from errors and calamities.

If resources follow security best practices.

Find databases that don't meet industry requirements. The dashboard shows category problem counts. Your Google Cloud database footprint.

Database Centre GCP improves recommendations for supported databases with general availability, addressing issues like ineffective queries/indexes, high resource usage, hotspot detection, costly commands, deletion protection not enabled, and no automated backup policy.

Gemini Integration: Gemini provides clever ideas and an easy-to-use chat interface. Gemini Chat answers database fleet health questions, makes project-specific advice, and helps determine and implement the appropriate practices. It helps troubleshoot aid performance.

Saveable Views: Users can create, store, and share persona-specific views.

Historical Data: Users can track weekly issues and new database resources.

Alerting: Centralises occurrences and database alerting policies.

Database Governance Risks: Database Centre GCP reduces database governance risks, including procedures and tools for monitoring and protecting sensitive data access throughout its lifecycle. It helps enforce best practices and identify compliance issues.

Database Centre benefits enterprises with cloud resources across several projects and products. It protects database resources against outages.

Price and Database Centre Access

Database Centre is accessible from the Google Cloud managed database services console for Cloud SQL, AlloyDB, Spanner, and Bigtable. Users with IAM rights have it enabled by default.

Google Cloud users can access Database Centre GCP for free. Natural language chat and Gemini-backed recommenders (cost and performance) require Gemini Cloud Assist. Google Security Command Central (SCC) membership is required for sophisticated security and compliance monitoring capabilities.

Database Centre data takes a few minutes to update, but sometimes it takes 24 hours.

[msrlunatj]

Bases de Datos Relacionales vs No Relacionales: Diferencias Clave y Qué Necesitas Saber

Introducción

En el mundo del desarrollo de software, las bases de datos juegan un papel crucial en el almacenamiento y gestión de datos. Existen dos categorías principales de bases de datos: relacionales y no relacionales. Cada tipo tiene sus propias características, ventajas y desventajas. En este blog, exploraremos las diferencias clave entre estas dos categorías y qué necesita saber un programador sobre cada una para tomar decisiones informadas.

1. Bases de Datos Relacionales

a) ¿Qué es una Base de Datos Relacional?

Una base de datos relacional (RDBMS, por sus siglas en inglés) organiza los datos en tablas relacionadas entre sí mediante claves primarias y foráneas. Utiliza el lenguaje SQL (Structured Query Language) para la gestión y consulta de datos.

b) Características Clave:

Estructura Tabular: Los datos se almacenan en tablas con filas y columnas. Cada tabla representa una entidad, y cada fila una instancia de esa entidad.

Relaciones entre Tablas: Las tablas pueden estar relacionadas entre sí mediante claves primarias y foráneas.

Transacciones ACID: Asegura que las transacciones sean Atómicas, Consistentes, Aisladas y Duraderas, garantizando la integridad de los datos.

c) Ventajas:

Integridad de Datos: Garantiza la precisión y consistencia de los datos mediante restricciones y reglas.

Consultas Complejas: El lenguaje SQL permite realizar consultas complejas y combinaciones de datos.

Escalabilidad Vertical: Se pueden aumentar los recursos del servidor para manejar mayores cargas de trabajo.

d) Desventajas:

Escalabilidad Horizontal Limitada: Puede ser más difícil escalar horizontalmente (distribuir datos en múltiples servidores).

Rigidez del Esquema: Requiere un esquema fijo, lo que puede hacer que los cambios en la estructura de datos sean complicados.

e) Ejemplos Populares:

MySQL: Amplamente utilizado en aplicaciones web y sistemas de gestión de contenido.

PostgreSQL: Conocido por su robustez y cumplimiento de estándares SQL.

Microsoft SQL Server: Ofrece herramientas avanzadas de análisis y administración de datos.

Oracle Database: Ampliamente utilizado en grandes empresas por su escalabilidad y características avanzadas.

2. Bases de Datos No Relacionales

a) ¿Qué es una Base de Datos No Relacional?

Las bases de datos no relacionales (NoSQL) están diseñadas para manejar grandes volúmenes de datos no estructurados o semi-estructurados. No utilizan un esquema fijo y pueden almacenar datos en varios formatos, como documentos, pares clave-valor, columnas o grafos.

b) Características Clave:

Flexibilidad del Esquema: No requieren un esquema fijo, permitiendo la adaptación de datos según sea necesario.

Escalabilidad Horizontal: Diseñadas para escalar horizontalmente mediante la distribución de datos en múltiples servidores.

Tipos de Datos Diversos: Pueden manejar datos en formatos como JSON, XML, o BSON.

c) Ventajas:

Escalabilidad Horizontal: Fácil de escalar distribuyendo datos en múltiples servidores.

Flexibilidad del Esquema: Permite agregar y modificar datos sin necesidad de un esquema rígido.

Alto Rendimiento: Adecuado para aplicaciones con grandes volúmenes de datos y requisitos de alta disponibilidad.

d) Desventajas:

Consistencia Eventual: En algunos casos, las bases de datos NoSQL sacrifican la consistencia inmediata en favor de la disponibilidad y particionamiento.

Consultas Limitadas: La capacidad de realizar consultas complejas puede ser limitada en comparación con SQL.

e) Ejemplos Populares:

MongoDB: Una base de datos orientada a documentos que utiliza JSON-like para el almacenamiento de datos.

Cassandra: Una base de datos orientada a columnas, conocida por su escalabilidad y disponibilidad.

Redis: Una base de datos en memoria basada en pares clave-valor, utilizada para almacenamiento en caché y procesamiento en tiempo real.

Neo4j: Una base de datos orientada a grafos que permite consultas sobre relaciones entre datos.

3. Cuándo Usar Cada Tipo de Base de Datos

a) Bases de Datos Relacionales:

Aplicaciones Transaccionales: Cuando la integridad y consistencia de los datos son críticas, como en sistemas financieros o de gestión de inventarios.

Consultas Complejas: Si necesitas realizar consultas complejas o combinaciones de datos con SQL.

Aplicaciones con Esquema Fijo: Cuando el esquema de los datos es estable y no se espera que cambie con frecuencia.

b) Bases de Datos No Relacionales:

Big Data y Aplicaciones Web: Para manejar grandes volúmenes de datos y aplicaciones que requieren alta disponibilidad y rendimiento.

Datos No Estructurados: Cuando trabajas con datos no estructurados o semi-estructurados, como contenido de redes sociales o datos de sensores.

Escalabilidad: Si tu aplicación requiere escalar horizontalmente para manejar un crecimiento rápido en la carga de trabajo.

4. Recursos Adicionales

Documentación y Tutoriales:

Documentación de MySQL

Documentación de MongoDB

Tutorial de PostgreSQL

Introducción a NoSQL

Libros Recomendados:

“SQL: El lenguaje de consulta estructurado” de Jesús García

“MongoDB: The Definitive Guide” de Kristina Chodorow (en inglés)

“Designing Data-Intensive Applications” de Martin Kleppmann (en inglés)

Conclusión

Entender las diferencias entre bases de datos relacionales y no relacionales es esencial para elegir la solución adecuada para tus proyectos de desarrollo. Las bases de datos relacionales son ideales para aplicaciones que requieren integridad y consultas complejas, mientras que las bases de datos no relacionales ofrecen flexibilidad y escalabilidad para manejar grandes volúmenes de datos. Conociendo las características y ventajas de cada tipo, puedes tomar decisiones más informadas y construir aplicaciones más efectivas.

[unicloudm]

Migration of PostgreSQL Database from EC2 to AWS RDS Aurora

Migrating a PostgreSQL database from EC2 to AWS RDS Aurora involves transferring data from a self-managed instance to a fully managed service. This process improves scalability, availability, and performance by leveraging Aurora's distributed, fault-tolerant architecture. Key steps include data backup, schema conversion, data transfer, and validation, ensuring minimal downtime and enhanced database reliability.

[mydbops]

​The Mydbops Blog offers expert insights and practical guidance on managing open-source databases such as MySQL, MariaDB, MongoDB, PostgreSQL, TiDB, and Cassandra. It covers topics like performance optimization, security hardening, replication strategies, and the latest trends in database technology. The blog serves as a valuable resource for database administrators and developers seeking to enhance their knowledge and stay updated with industry best practices. ​

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Using Django with PostgreSQL Database

Learn how to set up and configure Django to use PostgreSQL, a powerful open-source database. Follow this step-by-step guide to integrate PostgreSQL with your Django application.

Introduction PostgreSQL is a powerful, open-source object-relational database system that is widely used in the industry. Combining PostgreSQL with Django, a high-level Python web framework, provides a robust and scalable solution for web application development. This guide will walk you through the process of setting up a Django application with a PostgreSQL database, covering everything from…

[simple-logic]

#QuizTime

Which database is your go to for high availability?

A) PostgreSQL 🐘

B) MySQL 🧬

C) MongoDB 🍃

D) OracleDB 🏛️

Comments your answer below👇

💻 Explore insights on the latest in #technology on our Blog Page 👉 https://simplelogic-it.com/blogs/

🚀 Ready for your next career move? Check out our #careers page for exciting opportunities 👉 https://simplelogic-it.com/careers/

[jstarkd00110]

Postgresql database backup

Upback! The effortless database backup and recovery solution. We make it simple and secure to safeguard your data, focusing on essential database backup processes for MySQL, MariaDB, and PostgreSQL. Our easy-to-use management console allows you to perform scheduled database backups without the fuss and all the functionalities!