Best of NoSQL — 2025

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   Datadog·22w

   How microservice architectures have shaped the usage of database technologies

   Microservices have transformed database usage from monolithic, single-database architectures to distributed systems where organizations run multiple database technologies simultaneously. Analysis of 2.5 million services shows over half of organizations now use both SQL and NoSQL databases side by side, with many adopting 3+ different database technologies. This shift enables teams to choose the right tool for each service but introduces new challenges: fragmented schemas require data integration layers like GraphQL, analytics demands OLAP systems like Snowflake, and service communication relies heavily on message queues like Kafka and RabbitMQ for asynchronous decoupling.

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   Community Picks·47w

   When to Choose NoSQL Over SQL

   Explores when to choose NoSQL over SQL databases from a backend developer's perspective, using practical examples like e-commerce product catalogs and IoT sensor data. Covers real-world trade-offs including schema flexibility, performance considerations, and eventual consistency. Discusses polyglot persistence architectures that combine multiple database technologies for optimal results, emphasizing that the choice depends on specific system requirements rather than one being universally better.

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   Towards Dev·1y

   Introduction to Redis (Part 1)

   Redis is an open-source, in-memory data structure store used as a database, cache, and message broker. It supports various data structures such as strings, hashes, and lists. Redis is known for its high performance, flexibility, and durability. It offers features like in-memory storage with persistence options, multi-language support, and scalability through master-slave replication. Redis can act as both a secondary database for caching and a primary database for modern applications.

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   DEV·1y

   📍The Art of Choosing the Right Database

   Choosing the right database involves understanding the underlying storage engines and transaction protocols. This post explores the differences between SQL and NoSQL databases, focusing on B-Trees and LSM Trees. It discusses the strengths and use cases for databases like MySQL, MongoDB, Cassandra, and PostgreSQL, and highlights the importance of considering read vs. write-heavy workloads and transaction requirements.

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   LogRocket·1y

   Migrating to Valkey from Redis

   The post discusses migrating from Redis to Valkey, highlighting key differences such as licensing changes, performance improvements, and new features in Valkey 8.0. It covers the similarities in basic features, improvements in multi-threading, RDMA support, and cost advantages. Detailed migration steps are provided, along with the recommendation to test applications thoroughly before making the switch.

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   Article

   MongoDB·34w

   The 10 Skills I Was Missing as a MongoDB User

   A developer shares lessons learned from transitioning from relational databases to MongoDB, covering common mistakes like improper data modeling (over-normalizing then over-embedding), ineffective indexing strategies, and reactive monitoring. The piece walks through how understanding document modeling patterns, query optimization with explain plans, aggregation pipelines, and proactive monitoring transformed their MongoDB experience from frustrating to efficient. MongoDB's new Skill Badges program is highlighted as a structured learning path that covers these fundamental skills.

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   Baeldung·48w

   Introduction to DiceDB

   DiceDB is an open-source, high-performance in-memory database that offers Redis-like commands with modern features such as reactive queries. The tutorial covers installation via Docker, connecting through the DiceDB CLI, and performing basic operations like SET, GET, DEL, PING, ECHO, TYPE, and EXISTS commands. DiceDB runs on port 7379 by default and provides a developer-friendly experience for real-time applications with familiar key-value operations.

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   Article

   System Design Codex·26w

   5 Million Profiles Reads/Second - Caching at LinkedIn Scale

   LinkedIn scaled their profile service to handle 5 million reads per second by implementing an integrated caching architecture using Couchbase, achieving a 99% cache hit rate. The solution combines Couchbase cache with Espresso (their NoSQL database) and Brooklin (data streaming platform) to handle both read and write paths efficiently. Key design decisions include resilience mechanisms for cache failures, high availability through data center replication, and data consistency using System Change Numbers (SCN) with Compare-And-Swap operations. This resulted in 90% reduction in storage nodes, 60% lower tail latency, and 10% cost savings.

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   ByteByteGo·1y

   System Design Was HARD - Until You Knew the Trade-Offs

   System design involves making critical trade-offs to achieve the right balance for a project's specific needs. Understanding these trade-offs in data management, like choosing between SQL and NoSQL for consistency versus scalability, or normalization versus denormalization for data integrity versus performance, is vital. The CAP theorem highlights the need to prioritize between consistency, availability, and partition tolerance in distributed systems. Ultimately, the chosen approach depends on the application's requirements and priorities.

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    ByteByteGo·1y

    How LinkedIn Scaled User Restriction System to 5 Million Queries Per Second

    LinkedIn's restriction enforcement system, CASAL, combines machine learning models, rule-based systems, and human reviews to prevent abuse and ensure a safe environment. Initially reliant on relational databases, LinkedIn faced challenges in scaling, leading to the adoption of server-side and client-side caching, and eventually NoSQL systems like Espresso. The third generation introduced off-heap memory and tools like DaVinci and Venice to enhance performance and resilience. Each iteration focused on improving scalability, reducing latency, and maintaining data consistency while supporting a growing user base.

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    Article

    P99 Conf·51w

    Migrating from Postgres to ScyllaDB, with 349X Faster Query Processing

    Coralogix migrated their metadata store from PostgreSQL to ScyllaDB, achieving a 349x performance improvement by reducing query processing time from 30 seconds to 86 milliseconds. The migration involved redesigning data models for NoSQL, implementing bloom filter chunking strategies, and optimizing partition keys. Despite challenges with data modeling and EBS storage decisions, the team successfully deployed a 3-node ScyllaDB cluster handling 10K writes per second with sub-millisecond P99 latency, processing terabytes of Parquet metadata for their observability platform.

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    The Polymathic Engineer·27w

    Scaling the data storage layer in system design

    Explores three fundamental approaches to scaling database systems: replication using leader-follower topology to increase read capacity, partitioning to handle both read and write operations across multiple machines, and NoSQL databases designed for horizontal scalability. Covers the trade-offs of synchronous vs asynchronous replication, the complexity of implementing partitioning in relational databases, and how NoSQL systems sacrifice features like joins and strong consistency for availability and scale. Emphasizes that NoSQL requires more upfront data modeling than relational databases despite common misconceptions about flexibility.

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    Community Picks·1y

    Forget SQL vs NoSQL - Get the Best of Both Worlds with JSON in PostgreSQL

    The post explains how to leverage JSON within PostgreSQL to combine the benefits of both SQL and NoSQL databases. It details the evolution of JSON support in PostgreSQL, from the introduction of the JSON data type to more advanced features like JSONB and JSONPath. Additionally, it covers how to create and query JSON data, the importance of indexing for performance, and common anti-patterns to avoid. The post concludes by comparing JSON in PostgreSQL with traditional NoSQL databases and outlines when it's best to use each approach.

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    AppSignal·47w

    How to Use MongoDB in Python Flask

    MongoDB is a flexible NoSQL database that integrates well with Flask applications. The tutorial covers setting up a Flask project with MongoDB using PyMongo, creating a User model with CRUD operations, and implementing API endpoints for creating, reading, updating, and deleting users. Key benefits include flexible data modeling, horizontal scalability through sharding, and seamless integration with Python frameworks.

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