# NoSQL A category of non-relational databases that use data models other than traditional relational tables. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## What Matters Most for NoSQL Migrations DevFeed: [What Matters Most for NoSQL Migrations]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-21T13:44:46Z Content type: tutorial Language: en Sources: [ScyllaDB]() Topics: [NoSQL](), [Database Migration](), [migration](), [data-architecture](), [data](), [App]() Tags: [apps](), [community](), [complexity](), [data](), [data-modeling](), [database](), [database-migration](), [databases](), [how-to](), [migration](), [offline](), [performance](), [scale](), [validation](), [what-matters]() ### AI overview This article presents practical guidance for planning, executing, and de-risking NoSQL database migrations. It compares online and offline approaches and emphasizes schema migration, data movement, and validation, with attention to performance, scale, complexity, and disruption. ### Source excerpt How to prioritize the things that matter most for planning, executing and de-risking your NoSQL database migration ## ScyllaDB vs Aerospike, Wide-Column vs. Key/Value DevFeed: [ScyllaDB vs Aerospike, Wide-Column vs. Key/Value]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-06T13:13:23Z Content type: comparison Language: en Sources: [ScyllaDB]() Topics: [NoSQL](), [Database](), [Benchmark](), [Time Series](), [Availability](), [Replication](), [Encryption](), [Cloud](), [Latency]() Tags: [article](), [availability](), [benchmark](), [database](), [encryption](), [latency](), [leadership-team](), [performance](), [product](), [replication](), [security](), [time-series]() ### AI overview This comparison explains how wide-column databases differ from key-value stores, using ScyllaDB and Aerospike as examples. It argues that ScyllaDB's broader data model, clustering keys, replication, availability, and operational features can support more complex access patterns while maintaining strong performance and low latency. ### Source excerpt Wide-column flexibility doesn't have to come at the expense of performance -- see where the two models differ, where each one wins, and why you no longer have to choose ## How to Design Nested Documents for a Blogging App DevFeed: [How to Design Nested Documents for a Blogging App]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [NoSQL](), [data-modeling](), [Content Management System]() Tags: [cms](), [data-modeling](), [database](), [how-to](), [mongodb](), [nosql]() ### AI overview A tutorial on designing nested documents for a blogging application in MongoDB. It examines document modeling choices for authors, blog posts, and comments, and explains why modeling these relationships as separate documents may not provide the best MongoDB experience. ### Source excerpt So you want to build your own content management system (CMS), also sometimes known as a blog? This is a classic example when learning how to use a database, whether it be a relational database manage... The post How to Design Nested Documents for a Blogging App appeared first on DataCamp. ## Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation DevFeed: [Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation]() Original publisher: [Read original article]() Author: Morgan Chen; Minh Nguyen Published: 2026-04-27T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [NoSQL](), [Database](), [Serverless](), [serverless architecture]() Tags: [database](), [firebase](), [firestore](), [full-text-search](), [geo-queries](), [geospatial](), [javascript](), [nosql](), [real-time](), [serverless](), [serverless-architecture]() ### AI overview Firestore pipeline operations reached general availability on the enterprise edition, adding full-text search, geospatial queries, JOIN capabilities through subqueries, and data manipulation while retaining Firestore's NoSQL and serverless features. ### Source excerpt With support for full-text search, joins, and geospatial queries, Firestore is ready to meet all of your app's querying needs. ## Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story DevFeed: [Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story]() Original publisher: [Read original article]() Author: Mark Surnin and Muhammad Junaid Muzammil, Software Engineer Published: 2026-04-07T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Apache Cassandra](), [upgrade](), [NoSQL](), [Database](), [observability](), [Kubernetes]() Tags: [2026](), [apache](), [cassandra](), [clusters](), [downtime](), [kubernetes](), [nosql](), [observability](), [reliability-engineering](), [upgrade]() ### AI overview Yelp's Database Reliability Engineering team describes upgrading more than a thousand Cassandra nodes from 3.11 to 4.1 on Kubernetes without downtime. The article covers the motivation, expected reliability and performance improvements, operational guardrails, certificate handling, repairs, logging, and compatibility work for related components. ### Source excerpt The Database Reliability Engineering team at Yelp seamlessly upgraded more than a thousand Cassandra nodes with zero downtime. This post takes you behind the scenes of our upgrade strategy, from planning sessions to flawless rollouts. Background Motivation Apache Cassandra is a distributed wide-column NoSQL datastore and is used widely at Yelp for storing both primary and derived data. Yelp orchestrates Cassandra clusters on Kubernetes with the help of operators, as explained in our operator overview post. Upgrading from Cassandra 3.11 to 4.1 offered several observability and reliability improvements, in addition to performance gains. Based on public benchmarks, we expected to... ## Different Types of Database Management Systems DevFeed: [Different Types of Database Management Systems]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-20T10:30:23Z Content type: article Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [Databases](), [Database](), [data](), [Software](), [Scalability](), [NoSQL](), [MongoDB](), [MySQL](), [Oracle Database](), [sql-server]() Tags: [applications](), [backend](), [backup](), [big-data-and-dbms](), [choosing-the-right-dbms-for-your-needs](), [cloud-based-dbms](), [cloud-native](), [comparison-of-dbms-types](), [conclusion](), [consistency](), [data](), [database](), [definition-and-importance-of-dbms](), [different-types-of-database-management-systems](), [emerging-types-in-database-management-systems](), [evolution](), [evolution-of-database-management-systems](), [examples](), [hierarchical-database-management-systems](), [industries](), [integrity](), [management](), [mysql](), [network-database-management-systems](), [nosql](), [nosql-databases](), [object-oriented-database-management-systems-oodbms](), [recovery](), [relational-database-management-systems-rdbms](), [relational-databases](), [scalability](), [systems](), [types](), [types-of-database-management-systems]() ### AI overview This article explains what database management systems are, why they matter, how they support storage, queries, security, integrity, backup, and recovery, and how major DBMS categories have evolved. It also introduces examples including MySQL, Oracle Database, Microsoft SQL Server, and MongoDB. ### Source excerpt Data is one of the most valuable assets for organizations in this AI-powered digital era. From banking systems and e-commerce [...] The post Different Types of Database Management Systems appeared first on Talent500 blog. ## Firestore introduces Pipeline operations and an advanced query engine DevFeed: [Firestore introduces Pipeline operations and an advanced query engine]() Original publisher: [Read original article]() Author: Morgan Chen; Tyler Crowe; Minh Nguyen Published: 2026-01-15T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [Databases](), [NoSQL](), [Google](), [SDKs]() Tags: [admin-sdk](), [databases](), [firebase](), [firestore](), [javascript](), [nosql-databases](), [offline](), [pipeline](), [real-time](), [sdks]() ### AI overview Firestore announces Pipeline operations and a redesigned query engine for its Enterprise edition. The update adds chained query stages, aggregations, map operations, regular-expression string matching, and optional indexes. ### Source excerpt With a new advanced query engine and querying methods in the SDKs, Firestore is more powerful than ever. ## KVS: The key-value storage engine that powers the Fury ecosystem DevFeed: [KVS: The key-value storage engine that powers the Fury ecosystem]() Original publisher: [Read original article]() Author: Ariel Zach Published: 2025-10-01T14:09:08Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [NoSQL](), [Persistence](), [Scalability](), [Low Latency](), [Availability](), [Data Management]() Tags: [architecture](), [availability](), [data](), [database](), [databases](), [development](), [distributed](), [fury](), [infrastructure](), [key-value-store](), [kvs](), [low-latency](), [nosql](), [persistence](), [resilience](), [scalability](), [security](), [storage]() ### AI overview This article explains KVS, a key-value storage system in the Fury ecosystem. It describes how unique key-value pairs enable fast access, and reports that KVS supports large-scale distributed persistence with automatic scaling, low latency, availability, and recovery mechanisms. ### Source excerpt In modern software development, the efficiency, scalability, and resilience of data storage systems are critical to any platform's success. In this context, KVS (Key-Value Store) emerges as a fundamental component within the Fury ecosystem, offering a robust and flexible solution for data management. This article explores what KVS is, how it works, and why it's so important for Fury and its users. What is KVS? KVS, or Key-Value Store, is a data storage system based on the key-value paradigm. Unlike traditional relational databases, which organize data in tables and rows, KVS stores each data element as a unique key-value pair. This approach simplifies access and manipulation of information, enabling extremely fast and efficient operations. In Fury, KVS isn't just another database -- it's the fundamental engine that drives distributed, scalable, and fault-tolerant data persistence and retrieval. Currently, KVS processes over 642 million operations per minute, including 572 million reads and 70 million writes. The underlying infrastructure scales automatically and maintains low latency even under high-demand scenarios. The service is used by more than 7,600 applications within Mercado Libre (around 25% of the total), making it a key component of the ecosystem. Overall, it stores over 9 petabytes of data with mechanisms that ensure availability and recovery in case of failures. How does KVS work? Each piece of data is stored under a unique key. To retrieve or modify a value, you only need to know its key, eliminating the need for complex queries and relationships. This speeds up both read and write operations. KVS is built on top of the Fury ecosystem, which provides key guarantees such as scalability, security, and traffic control. It uses an architecture based on a fully managed NoSQL solution, designed to automatically adapt to demand. Thanks to this infrastructure, KVS inherits advanced scalability capabilities, allowing it to grow automatically and transparently, c ## Dynamo, DynamoDB, and Aurora DSQL DevFeed: [Dynamo, DynamoDB, and Aurora DSQL]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-08-15T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Amazon DynamoDB](), [DSQL](), [Dynamo](), [NoSQL](), [Replication](), [Availability](), [Serverless](), [SQL](), [Amazon Web Services](), [Transactions]() Tags: [amazon-dynamodb](), [availability](), [aws](), [data](), [database](), [databases](), [dsql](), [dynamo](), [replication](), [serverless](), [sql]() ### AI overview This article compares the architectures of Amazon Dynamo, Amazon DynamoDB, and Aurora DSQL, beginning with how they provide durability and availability when a single host fails. It explains Dynamo's replication across successor nodes in a consistent-hashing ring and contrasts it with DynamoDB's replica groups, multi-AZ servers, Paxos replication, quorum acknowledgments, and scaling behavior. ### Source excerpt Dynamo, DynamoDB, and Aurora DSQL Names are hard, ok? People often ask me about the architectural relationship between Amazon Dynamo (as described in the classic 2007 SOSP paper), Amazon DynamoDB (the serverless distributed NoSQL database from AWS), and Aurora DSQL (the serverless distributed SQL database from AWS). There's a ton to say on the topic, but I'll start off on comparing how the systems achieve a few key properties. The key references for this post are: For Dynamo, Dynamo: Amazon's Highly Available Key-value Store from SOSP'07. For DynamoDB, Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service from ATC'22, Distributed Transactions at Scale in Amazon DynamoDB from ATC'23, and Lessons learned from 10 years of DynamoDB from the Amazon Science blog. For DSQL, my blog series on DSQL. Durability The databases we're looking at offer different levels of durability, but all three are designed not to lose data when a single host fails. Dynamo does this by taking advantage of its consistent hashing approach, replicating the data across multiple hosts in order in the hash ring: To achieve high availability and durability, Dynamo replicates its data on multiple hosts. Each data item is replicated at N hosts. ... Each key, k, is assigned to a coordinator node[]. The coordinator is in charge of the replication of the data items that fall within its range. In addition to locally storing each key within its range, the coordinator replicates these keys at the N-1 clockwise successor nodes in the ring. Like Dynamo, DynamoDB assigns a node in a hash ring to each individual item. But that's where the similarities stop. Instead of replicating across multiple nodes in the ring, in DynamoDB each node consists of a replica group with multiple servers in multiple AZs using Paxos to replicate the data. Instead of appearing the ring N times, each item appears once, and takes advantage of fault-tolerant nodes rather than spreading over multiple ## New Chainguard Images December 2024: Adoptium, AWX, CouchDB DevFeed: [New Chainguard Images December 2024: Adoptium, AWX, CouchDB]() Original publisher: [Read original article]() Published: 2025-01-16T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [Adoptium](), [openjdk](), [Ansible](), [Java](), [NoSQL](), [Open Source](), [Erlang](), [.NET MAUI](), [Process]() Tags: [adoptium](), [ansible](), [apache](), [awx](), [chainguard](), [chainguard-images](), [container-images](), [couchdb](), [erlang](), [helm](), [java](), [jdk](), [linkerd](), [net-11](), [nosql](), [open-source](), [opentofu]() ### AI overview Chainguard describes its December 2024 batch of 39 new Chainguard Images, highlighting images for Adoptium, AWX, and Apache CouchDB. The article notes FIPS-enabled variants and summarizes the intended uses and image characteristics. ### Source excerpt Chainguard released several new images in December 2024, including images for Adoptium, AWX, CouchDB, and others. Check out the full list. ## DynamoDB For System Design Interviews DevFeed: [DynamoDB For System Design Interviews]() Original publisher: [Read original article]() Author: Evan King Published: 2024-11-19T21:30:02Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [DynamoDB](), [Amazon Web Services](), [NoSQL](), [Database](), [Transactions](), [Provisioning](), [Application Development](), [configuration](), [Learning](), [vendor lock-in]() Tags: [aws](), [configuration](), [data-storage](), [database](), [dynamodb](), [learning](), [nosql](), [provisioning](), [system-design-interview](), [transactions]() ### AI overview This tutorial explains DynamoDB for system design interviews. It covers DynamoDB's managed operation, scalability, key-value and NoSQL data model, transaction support, lack of open-source internals, and practical considerations including vendor lock-in and whether interviewers permit its use. ### Source excerpt By the Co-founder of www.hellointerview.comIntro DynamoDB is a fully-managed, highly scalable, key-value service provided by AWS. Cool, buzz-words. But what the hell does that mean and why does it matter? Fully-Managed -- This means that AWS takes care of all the operational aspects of the database. The fully-managed nature allows AWS to handle all operational tasks -- hardware provisioning, configuration, patching, and scaling -- freeing developers to concentrate on application development. Highly Scalable -- DynamoDB can handle massive amounts of data and traffic. It automatically scales up or down to adjust to your application's needs, without any downtime or performance degradation. Key-value -- DynamoDB is a NoSQL database, which means it doesn't use the traditional relational database model. Instead, it uses a key-value model that allows for flexible data storage and retrieval. The moral of the story is that DynamoDB is a super easy to use and can scale to support a wide variety of applications. For system design interviews in particular, it has just about everything you'd ever need from a database. It even supports transactions now! Which neutralizes one of the biggest criticisms of DynamoDB in the past. Importantly, DynamoDB is not open-source, so we can't as easily describe its internals like we did with breakdowns of open source technologies like Kafka and Redis. Instead, we'll focus more on you interact with it. In order to look under the hood, we'll rely on the limited information AWS provides via documentation and the DynamoDB Whitepaper. In this deep dive, we'll break down exactly what you need to know about DynamoDB in order to field any question about it in a system design interview. Along the way, you'll also acquire practical learning that you can later apply in your own projects. Let's break it down! Candidates often ask me, "am I even allowed to use DynamoDB in an interview?"The answer is simple, ask your interviewer! Many will say yes, and just expec ## Query DynamoDB tables with SQL DevFeed: [Query DynamoDB tables with SQL]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-30T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [SQL](), [NoSQL]() Tags: [dynamodb](), [nosql](), [sql](), [the-data-base]() ### AI overview The article presents querying DynamoDB tables with SQL and says Tinybird makes NoSQL data accessible to people who know SQL, avoiding PartiQL. ### Source excerpt Query DynamoDB with SQL instead of fighting with PartiQL. Tinybird makes your NoSQL data accessible to anyone who knows SQL. ## How Discord Processes 30+ Petabytes of Data DevFeed: [How Discord Processes 30+ Petabytes of Data]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2024-09-25T13:02:10Z Content type: article Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Discord](), [data](), [scylladb](), [NoSQL](), [C++](), [Machine learning]() Tags: [c-plus-plus](), [data](), [discord](), [nosql](), [scylladb]() ### AI overview This article explains how Discord analyzes billions of daily messages and manages large-scale data processing. It describes message storage in ScyllaDB, data cleaning, machine-learning use cases, and Discord's development of Derived to automate data transformation for analysis. ### Source excerpt Discord's genius approach to automating insights from billions of messages ## SQL vs NoSQL Explained DevFeed: [SQL vs NoSQL Explained]() Original publisher: [Read original article]() Author: Nelson Djalo Published: 2024-07-02T16:00:57Z Content type: comparison Language: en Sources: [Amigoscode Newsletter]() Topics: [NoSQL](), [SQL](), [Databases](), [clickhouse]() Tags: [acid](), [business-intelligence](), [consistency](), [data-analysis](), [database](), [database-scalability](), [durability](), [microsoft](), [mysql](), [nosql](), [olap](), [oracle](), [postgresql](), [rdbms](), [relational-database-management-systems-rdbms](), [relational-databases](), [schema](), [sql]() ### AI overview This comparison explains the differences between SQL and NoSQL databases, including their characteristics, scalability models, and common use cases. It covers SQL database features such as structured schemas and ACID compliance, along with relational and OLAP database categories. ### Source excerpt Choosing the Right Database for Your Needs ## NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB DevFeed: [NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB]() Original publisher: [Read original article]() Author: Thor Schaeff Published: 2024-01-31T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [MongoDB](), [Supabase](), [NoSQL](), [Docker](), [fly.io](), [Node.js]() Tags: [docker](), [fly-io](), [mongodb](), [node-js](), [nosql](), [postgres]() ### AI overview This tutorial explains how to use FerretDB to add MongoDB compatibility to Postgres-backed Supabase projects. It covers running FerretDB locally with Docker, connecting with MongoDB tools and the Node.js client, performing basic document operations, and deploying FerretDB to Fly.io. ### Source excerpt NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB ## How Apple built iCloud to store billions of databases DevFeed: [How Apple built iCloud to store billions of databases]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2024-01-14T21:53:05Z Content type: article Language: en Sources: [Engineer's Codex]() Topics: [Apache Cassandra](), [Database](), [Multitenancy](), [Cloud](), [NoSQL](), [Architecture & Design](), [Scalability]() Tags: [architecture](), [cassandra](), [cloud](), [database](), [nosql](), [scalability](), [separate]() ### AI overview This article examines how Apple built iCloud and CloudKit using FoundationDB and Cassandra. It discusses the systems' extreme multi-tenant architecture and engineering choices such as asynchronous processing, stateless design, resource isolation, and layered abstractions to support scalability, reliability, availability, and developer experience. ### Source excerpt Apple uses Cassandra and FoundationDB for CloudKit, their cloud backend service. We take a look into how exactly each is used within their cloud and the problems they've solved. ## NoSQL's Role Evolves as SQL and SQLite Gain Favor for Modern Web Workloads DevFeed: [NoSQL's Role Evolves as SQL and SQLite Gain Favor for Modern Web Workloads]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2023-12-21T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [NoSQL](), [SQL](), [SQLite](), [Web]() Tags: [nosql](), [sql](), [sqlite](), [web]() ### AI overview The article argues that SQL databases, especially SQLite, are regaining popularity for modern web workloads because hardware improvements and simpler usage have changed the trade-offs that previously favored NoSQL. It also notes that specialized NoSQL systems continue to serve particular purposes. ### Source excerpt NoSQL's role evolves as SQL, especially SQLite, gains favor for modern web workloads due to hardware advances and ease of use. ## DynamoDB Transactions: An E-Commerce with Amazon DynamoDB DevFeed: [DynamoDB Transactions: An E-Commerce with Amazon DynamoDB]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2023-11-09T18:42:01Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [Amazon DynamoDB](), [Database](), [NoSQL](), [Amazon Web Services](), [Back end]() Tags: [acid](), [amazon-dynamodb](), [amazon-web-services](), [architecture](), [aws](), [backend](), [customers](), [database](), [databases](), [dynamodb](), [e-commerce](), [nosql](), [operations](), [products]() ### AI overview This tutorial explains how to use DynamoDB transactions in an e-commerce application to atomically verify product stock, decrement inventory, and create an order. It describes the race condition that can occur when separate read and write operations process simultaneous purchases. ### Source excerpt We're building an e-commerce app with DynamoDB for the database, pretty similar to the one we built for the DynamoDB Database Design article. No need to go read that issue (though I think it came up great), here's how our database works: Customers a... ## Understanding How DynamoDB Scales DevFeed: [Understanding How DynamoDB Scales]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2023-10-19T14:40:53Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [NoSQL](), [Database](), [Serverless](), [Availability](), [Resilience](), [Transactions](), [Provisioning]() Tags: [architecture](), [availability](), [aws](), [databases](), [distributed](), [dynamodb](), [nosql](), [partition](), [provisioning](), [resilience](), [serverless](), [transactions]() ### AI overview This tutorial explains how DynamoDB scales horizontally through partitioning and replication across nodes. It compares provisioned capacity for reads and writes and explains read and write capacity units, consistency, data size limits, and the doubled capacity use of transactions. ### Source excerpt Note: This content was originally published at the Simple AWS newsletter. As you probably know, DynamoDB is a NoSQL database. It's a managed, serverless service, meaning you just create a Table (that's the equivalent of a Database in Postgres), and A... ## NoSQL: The Baby and the Bathwater DevFeed: [NoSQL: The Baby and the Bathwater]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2023-01-30T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [NoSQL](), [Scalability](), [Databases](), [SQL](), [Replication]() Tags: [blog-post](), [replication](), [scalability](), [sql]() ### AI overview An introduction to a series examining what makes databases scale, using the NoSQL movement as its central subject. The article focuses on scalability and considers whether abandoning features such as explicit schemas, transactions, strong consistency, joins, secondary indexes, unique keys, and SQL actually improved database scalability. It also introduces replication and sharding as scalability techniques. ### Source excerpt NoSQL: The Baby and the Bathwater Is this a database? This is a bit of an introduction to a long series of posts I've been writing about what, fundamentally, it is that makes databases scale. The whole series is going to take me a long time, but hopefully there's something here folks will enjoy. On March 12 2006, Australia set South Africa the massive target of 434 runs to chase in a one-day international at the Wanderers in Johannesburg. South Africa, in reply, set a record that stands to this day: 438 runs in a successful chase. It's hard to overstate what an outlier this was. The previous record for a successful run chase was only 332. Despite nearly two decades of bigger is better scores in cricket, nothing has come close. It wasn't just cricket scores that were getting bigger in the mid 2000s. Databases were too. The growth of the web, especially search and online shopping, were driving systems to higher scales than they had ever seen before. With this trend towards size came a repudiation of the things that had come before. No longer did we want SQL. No, now we wanted NoSQL. There are various historical lenses we can apply to the NoSQL movement, from branding (No SQL or Not Only SQL), to goals (scalability vs write availability2 vs open source), to operations (should developers or DBAs own the schema? Should DBAs still exist?), but there was clearly a movement1 with at least some set of common goals. In this blog post I'm going to single-mindedly focus on one aspect of NoSQL: scalability. We'll look at some of the things the NoSQL movement threw out, and ask ourselves whether those things actually helped achieve better scalability. On the way, we'll start exploring the laws of scalability physics, and what really matters. So what did NoSQL throw out? Again, that varies from database to database, but it was approximately these things: Explicit schema Transactions Strong consistency Joins, secondary indexes, unique keys, etc. The SQL language itself Looking thro ## SQL or NoSQL? Why not use both (with PostgreSQL)? DevFeed: [SQL or NoSQL? Why not use both (with PostgreSQL)?]() Original publisher: [Read original article]() Author: Mark Burggraf Published: 2022-11-24T07:00:00Z Content type: article Language: en Sources: [Supabase Blog]() Topics: [SQL](), [NoSQL](), [PostgreSQL](), [Databases](), [JSON]() Tags: [data](), [database](), [developer](), [how-to](), [json](), [postgresql](), [sql]() ### AI overview The article compares SQL and NoSQL approaches for storing application data and explains why PostgreSQL can support both relational SQL and document-oriented NoSQL patterns. It uses an example of daily food, water, exercise, weight, and notes data to discuss storage structure, querying, and tradeoffs. ### Source excerpt How to turn Postgres into an easy-to-use NoSQL database that retains all the power of SQL ## The DynamoDB paper DevFeed: [The DynamoDB paper]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2022-07-12T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Amazon DynamoDB](), [NoSQL](), [Database](), [systems](), [API](), [Dynamo](), [Amazon Web Services]() Tags: [amazon-dynamodb](), [api](), [architecture](), [aws](), [customers](), [database](), [dynamo](), [performance](), [scale](), [systems]() ### AI overview The article discusses the DynamoDB paper, highlighting the database service's massive scale, evolution to meet customer needs, and lessons from real-world distributed-system design. It explains how non-uniform workloads caused partition hot spots and throttling despite sufficient total provisioned throughput, and contrasts DynamoDB's architecture with the earlier Dynamo system. ### Source excerpt The DynamoDB paper The other database called Dynamo This week at USENIX ATC'22, a group of my colleagues1 from the AWS DynamoDB team are going to be presenting their paper Amazon DynamoDB: A Scalable, Predictably Performant, and Fully Managed NoSQL Database Service. This paper is a rare look at a real-world distributed system that runs at massive scale. From the paper: In 2021, during the 66-hour Amazon Prime Day shopping event, Amazon systems ... made trillions of API calls to DynamoDB, peaking at 89.2 million requests per second 89 million requests per second is a big database by any standards (and that's just Amazon's use of DynamoDB)! What's exciting for me about this paper is that it covers DynamoDB's journey, and how it has changed over time to meet customers' needs. There are relatively few papers that cover this kind of change over time. For example: The uniform distribution of throughput across partitions is based on the assumptions that an application uniformly accesses keys in a table and the splitting a partition for size equally splits the performance. However, we discovered that application workloads frequently have non-uniform access patterns both over time and over key ranges. When the request rate within a table is non-uniform, splitting a partition and dividing performance allocation proportionately can result in the hot portion of the partition having less available performance than it did before the split. Since throughput was allocated statically and enforced at a partition level, these non- uniform workloads occasionally resulted in an application's reads and writes being rejected, called throttling, even though the total provisioned throughput of the table was sufficient to meet its needs. This is the kind of assumption in a system design--that splitting makes performance better--that's really easy to overlook when designing a system, and potentially difficult to fix when you're in production. A lot of what makes systems like DynamoDB so useful is ## Sponsored Post: G-Core Labs, Close, Wynter, Pinecone, Kinsta, Bridgecrew, IP2Location, StackHawk, InterviewCamp.io, Educative, Stream, Fauna, Triplebyte DevFeed: [Sponsored Post: G-Core Labs, Close, Wynter, Pinecone, Kinsta, Bridgecrew, IP2Location, StackHawk, InterviewCamp.io, Educative, Stream, Fauna, Triplebyte]() Original publisher: [Read original article]() Author: HighScalability Team Published: 2022-06-10T15:39:03Z Content type: article Language: en Sources: [High Scalability](), [High Scalability]() Topics: [DevOps](), [site-reliability-engineer](), [Databases](), [Monitoring](), [Ansible](), [hosting](), [Linux](), [PHP](), [JavaScript](), [NoSQL](), [WordPress](), [Ubuntu]() Tags: [ansible](), [databases](), [debian](), [devops](), [hosting](), [javascript](), [linux](), [lxc](), [monitoring](), [nosql](), [php](), [site-reliability-engineer](), [sponsored](), [sponsored-post](), [ubuntu](), [wordpress]() ### AI overview A sponsored post presents hiring and promotional opportunities from several technology companies, including Site Reliability Engineer, DevOps Engineer, SysOps Engineer, and JavaScript Developer roles. 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If you are a talented JavaScript developer and you would like to work with an owner ## Sponsored Post: G-Core Labs, Close, Wynter, Pinecone, Kinsta, Bridgecrew, IP2Location, StackHawk, InterviewCamp.io, Educative, Stream, Fauna, Triplebyte DevFeed: [Sponsored Post: G-Core Labs, Close, Wynter, Pinecone, Kinsta, Bridgecrew, IP2Location, StackHawk, InterviewCamp.io, Educative, Stream, Fauna, Triplebyte]() Original publisher: [Read original article]() Author: High Scalability Published: 2022-06-10T14:39:03Z Content type: article Language: en Sources: [High Scalability]() Topics: [site-reliability-engineering](), [Databases](), [Monitoring](), [cloud-infrastructure](), [NoSQL](), [Back end](), [Linux](), [Ansible](), [hosting](), [PHP](), [WordPress]() Tags: [ansible](), [backend](), [databases](), [hosting](), [important](), [infrastructure](), [linux](), [monitoring](), [nosql](), [php](), [site-reliability](), [sponsored](), [sponsored-post]() ### AI overview A sponsored post listing hiring and recruitment opportunities from Close, Wynter, Kinsta, InterviewCamp.io, and Triplebyte. The listings seek site reliability, system administration, DevOps, SysOps, JavaScript, and related technology candidates, mentioning databases, infrastructure, Linux, PHP, Ansible, LXC, hosting, and WordPress. ### Source excerpt Who's Hiring? Close is building the sales communication platform of the future and we're looking for a Site Reliability Engineer to help us accomplish that goal. If you have a passion for databases (both SQL and NoSQL), significant experience building, managing, and monitoring infrastructure, databases, and [Next page]()