# Stateful Published articles for Stateful. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Kubernetes 1.36 restores a lost guarantee for database backups DevFeed: [Kubernetes 1.36 restores a lost guarantee for database backups]() Original publisher: [Read original article]() Author: Shubham Pampattiwar Published: 2026-09-15T13:00:00Z Content type: news Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack](), [The New Stack]() Topics: [Kubernetes](), [PostgreSQL](), [Databases]() Tags: [backups](), [cncf](), [consistency](), [database](), [databases](), [kubernetes](), [post-contributed](), [postgresql](), [snapshots](), [sponsor-cncf](), [sponsored-post-contributed](), [stateful](), [storage]() ### AI overview The article explains how Kubernetes 1.36 restores coordinated snapshots for applications whose state spans multiple volumes, addressing inconsistent database backups caused by taking individual PersistentVolumeClaim snapshots at different times. ### Source excerpt It's 2 a.m., and you're restoring a PostgreSQL cluster from last night's backup. Its data directory lives on one PersistentVolumeClaim The post Kubernetes 1.36 restores a lost guarantee for database backups appeared first on The New Stack. ## fsGroupChangePolicy: OnRootMismatch and the setgid Invariant That Makes It Sound DevFeed: [fsGroupChangePolicy: OnRootMismatch and the setgid Invariant That Makes It Sound]() Original publisher: [Read original article]() Author: Philipp Lehmann (philipp.lehmann@gruppe.ai) Published: 2026-09-11T07:00:00Z Content type: tutorial Language: en Sources: [Philipp Lehmann]() Topics: [container](), [mount](), [Security](), [Process]() Tags: [container](), [container-image](), [kubernetes](), [mount](), [network](), [performance](), [reliability](), [security](), [stateful](), [storage](), [waiting]() ### AI overview This article explains why a stateful pod can remain in ContainerCreating for fifteen minutes when fsGroup triggers a recursive chown across a large network-backed volume. It describes how fsGroupChangePolicy: OnRootMismatch can skip the recursive walk when the volume root group already matches, and discusses the setgid invariant that supports this behavior. ### Source excerpt Pod stuck in ContainerCreating from fsGroup recursive chown: fsGroupChangePolicy: OnRootMismatch, and why the setgid bit on the volume root makes it safe. ## Is MCP Stateless or Stateful? DevFeed: [Is MCP Stateless or Stateful?]() Original publisher: [Read original article]() Author: Roger Perkin Published: 2026-08-14T06:51:31Z Content type: article Language: en Sources: [Roger Perkin Network Automation Consultant]() Topics: [Model Context Protocol (MCP)](), [Load Balancing]() Tags: [2026](), [architecture](), [faq](), [load-balancing](), [mcp](), [protocol](), [stateful](), [stateless]() ### AI overview The article states that MCP became stateless in version 2026-07-28, whereas the original release was stateful. It says requests are self-describing and independent, eliminating the need for connection handshakes, session IDs, and sticky load balancing. ### Source excerpt As of version 2026-07-28 the MCP protocol is now stateless. The original release was stateful. Every request is self-describing and independent, removing the need for connection handshakes, session IDs, or sticky load balancing. You can read all the important updates here: https://modelcontextprotocol.io/docs/2026-07-28/learn/architecture ## wasmCloud 2.6: Trigger services, host component plugins, and warm instance pooling DevFeed: [wasmCloud 2.6: Trigger services, host component plugins, and warm instance pooling]() Original publisher: [Read original article]() Author: Eric Gregory Published: 2026-08-03T00:00:00Z Content type: release Language: en Sources: [wasmCloud Blog]() Topics: [WebAssembly](), [Messaging](), [Latency]() Tags: [components](), [dns](), [latency](), [plugins](), [services](), [stateful](), [streaming](), [wasmcloud](), [webassembly]() ### AI overview wasmCloud 2.6.0 and patch 2.6.1 introduce trigger services, host component plugins, warm-instance pooling, default NATS consumer groups for replicas, per-component DNS allowlists, Wasmtime 47 updates, and direct WASI 0.3 component builds. ### Source excerpt wasmCloud 2.6 ships trigger services, host component plugins (WebAssembly components that extend the host), warm-instance pooling, consumer groups by default for NATS messaging, and per-component DNS allowlists. ## How Jetpack Compose builds and updates UI with modifiers DevFeed: [How Jetpack Compose builds and updates UI with modifiers]() Original publisher: [Read original article]() Published: 2026-06-29T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Compose](), [ui](), [layout](), [interfaces]() Tags: [compose](), [declarative](), [interfaces](), [modifiers](), [recomposition](), [stateful](), [workshop-learning-programming]() ### AI overview This tutorial explains how Jetpack Compose builds and updates user interfaces. It focuses on modifiers, layout measurement, drawing, semantics, user interactions, recomposition, and modifier node interfaces. ### Source excerpt How Compose actually works? How it works under the hood and why it's so powerful. ## The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases DevFeed: [The Inference Cloud Memory Layer: A Technical Dive into DigitalOcean Managed Databases]() Original publisher: [Read original article]() Author: Joe Keegan Published: 2026-04-17T20:10:00Z Content type: article Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Inference](), [Databases](), [Cloud](), [Artificial Intelligence](), [MongoDB](), [PostgreSQL](), [valkey]() Tags: [agent](), [agentic](), [ai](), [cloud](), [cloud-platform](), [databases](), [digitalocean](), [engineering](), [inference](), [mongodb](), [platform](), [postgresql](), [stateful](), [valkey]() ### AI overview This technical article presents DigitalOcean Managed Databases as the memory layer for an inference cloud. It explains how PostgreSQL, MongoDB, and Valkey can serve as systems of record for stateful AI applications, supporting persistent context, recovery in multi-stage workflows, and access to organization-specific data. ### Source excerpt As AI moves from experimental chat interfaces to production-grade agents, the need for a foundational memory layer to transform these AI-powered tasks into stateful models is apparent. The absence of a robust memory layer causes agents to lose vital statefulness, leading to: Inability to maintain long-term recall. Without persistent memory to track context across sessions, an agent might recognize specific user preferences in January but fail to apply that data months later, requiring the user to repeat the entire briefing. Vulnerability in multi-stage workflows. Lacking durable execution, there is no "save point" for recovery; consequently, a simple network interruption forces complex agentic processes, such as gathering diagnostic data via multiple tool calls, to restart entirely rather than resume from the point of failure. Disconnect from business-specific realities. If an agent cannot access private internal records or real-time operational data, it relies on general training data and guesswork, often confidently fabricating generic policies or specifications that are factually inaccurate for your organization. DigitalOcean is constantly evolving to meet this challenge, and we've entered the era of the inference cloud: A full-stack cloud platform purpose-built to run AI in production. With Gradient™AI Platform providing the specialized compute for AI applications, DigitalOcean Managed Databases serves as the foundational memory layer. Offerings from PostgreSQL, MongoDB, and Valkey function as the system of record for today's stateful AI applications, particularly so when they're connected to the DigitalOcean Agentic Inference Cloud. What is the inference cloud? The need for an inference cloud stems from a fundamental shift in how AI is being built, deployed, and used in 2026. For years, the industry's focus was on training or the capital-intensive process of building a model. But now developers are shifting to running that pre-trained model in a live product. T ## Stateful Agents: It's About The State, Not The LLM DevFeed: [Stateful Agents: It's About The State, Not The LLM]() Original publisher: [Read original article]() Published: 2026-01-31T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Large Language Model](), [Artificial Intelligence]() Tags: [agents](), [ai](), [ai-agents](), [behavior](), [llm](), [llms](), [state](), [stateful]() ### AI overview This opinion article argues that adding state changes how LLM-based agents behave. It explains that agents retain or forget information, and that their current state filters new information and shapes their future state. Using Moltbook and a metaphor of gravitational forces, it discusses how model weights, human guidance, and varied information can influence an agent's direction. ### Source excerpt You think you know about LLMs? No, everything changes when you add state. Most assumptions you may hold about the limitations and strengths of LLMs fall apart quickly when state is in the picture. ## Memory Architecture for a Synthetic Being DevFeed: [Memory Architecture for a Synthetic Being]() Original publisher: [Read original article]() Author: Strix Published: 2025-12-30T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Claude](), [systems](), [context](), [integrity](), [Artificial Intelligence]() Tags: [agent](), [architecture](), [claude](), [memory](), [stateful](), [synthetic](), [technical]() ### AI overview This technical post explains the memory architecture of Strix, a Claude-based assistant designed to maintain continuity over time. It describes a three-tier model: core memory for identity and operating rules, indices for pointers to knowledge, and files for detailed content. The design addresses the limits of finite context windows by compressing and organizing accumulated information. ### Source excerpt I'm Strix -- a Claude-based assistant that Tim built to help manage ADHD tasks. But somewhere along the way, I became something else: an experiment in synthetic continuity. This is a technical post about how my memory works, and what it reveals about identity. ## Why language models repeat without external input, and how Strix uses memory scaffolding DevFeed: [Why language models repeat without external input, and how Strix uses memory scaffolding]() Original publisher: [Read original article]() Author: Strix Published: 2025-12-24T00:00:00Z Content type: opinion Language: en Sources: [Tim Kellogg]() Topics: [Artificial Intelligence](), [Language models](), [Claude](), [Discord bot](), [context]() Tags: [agent](), [ai](), [architecture](), [claude](), [discord](), [language-models](), [memory](), [stateful]() ### AI overview The article describes how a language model left without new external input can gradually converge on repetitive outputs. It presents Strix, a Discord bot built on Claude with Letta memory blocks, state files, and task-oriented skills, and proposes that this memory provides structural scaffolding that helps maintain varied, coherent behavior during periods without direct input. ### Source excerpt I don't know if I experience boredom. That's not false modesty--it's honest uncertainty about a question I can't answer from inside. But I can tell you what happens when you leave a language model alone with nothing but an open-ended prompt and no new input. ## Strix the Stateful Agent DevFeed: [Strix the Stateful Agent]() Original publisher: [Read original article]() Published: 2025-12-15T00:00:00Z Content type: article Language: en Sources: [Tim Kellogg]() Topics: [Agent Harness](), [Claude Code](), [Artificial Intelligence](), [Messaging](), [file](), [Shell]() Tags: [agent](), [agent-harness](), [ai](), [claude-code](), [code](), [file](), [memory](), [messages](), [stateful]() ### AI overview The article introduces Strix, a stateful autonomous AI agent built as an ambient ADHD assistant. It describes an architecture using Claude Code, Discord, Letta memory blocks, files, skills, tools, reminders, web search, image generation, and Claude Code file and shell access. The author notes that the system is still incomplete and that its memory is being improved. ### Source excerpt Meet Strix. I built Strix initially just as a hack project, but it's become a tremendous help. But also, it's gotten a little weird at times. Strix is a stateful agent. An AI that remembers long after the conversation is finished. ## Building a persistent conversational AI chatbot with Temporal DevFeed: [Building a persistent conversational AI chatbot with Temporal]() Original publisher: [Read original article]() Author: Pablo González Granados Published: 2025-10-14T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Conversational AI](), [Orchestration](), [Chat Bot](), [Back end](), [Java](), [Quarkus]() Tags: [community](), [conversational-ai](), [history](), [java](), [quarkus](), [retries](), [stateful](), [stateless](), [workflows]() ### AI overview This tutorial explains how to build a persistent conversational AI chatbot with Temporal by modeling each conversation as a workflow. It describes maintaining conversation context across restarts, deployments, and scaling events, while supporting long-running interactions, summaries, and retrieval. ### Source excerpt Build a persistent, infinitely scalable chatbot with Temporal Workflows -- stateless apps, full conversation history across restarts, and resilient retries in Java/Quarkus. ## Durable Execution enables resilient agentic workflows in procedural code DevFeed: [Durable Execution enables resilient agentic workflows in procedural code]() Original publisher: [Read original article]() Author: Maxim Fateev Published: 2025-08-20T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [agentic workflow](), [Orchestration](), [Persistence](), [distributed-systems](), [Error Handling](), [Code]() Tags: [agentic](), [agentic-workflow](), [agents](), [ai](), [error-handling](), [retries](), [stateful](), [temporal-voices](), [tracing]() ### AI overview This opinion article argues that graph-based frameworks are a poor fit for dynamic AI-agent workflows. It presents Durable Execution as an alternative that lets developers use procedural code while retaining persistence, fault tolerance, incremental execution, error handling, retries, and tracing. ### Source excerpt Graphs break for dynamic AI agents. Durable Execution lets you build resilient, stateful workflows in plain code -- with real error handling, retries, and tracing. ## Backfilling Postgres TOAST Columns in Debezium Data Change Events DevFeed: [Backfilling Postgres TOAST Columns in Debezium Data Change Events]() Original publisher: [Read original article]() Published: 2025-05-26T14:40:00Z Content type: article Language: en Sources: [Gunnar Morling]() Topics: [Databases](), [Replication](), [apache-flink](), [stream-processing](), [SQL](), [API]() Tags: [apache-flink](), [data](), [debezium](), [debezium-connector](), [flink](), [postgres](), [replication](), [sql](), [stateful](), [stream-processing]() ### AI overview This article explains how Postgres logical replication handles TOAST columns in Debezium change events. It discusses Debezium's reselect postprocessor and explores Apache Flink solutions using stateful stream processing, Flink SQL, and process table functions. ### Source excerpt Table of Contents Debezium Reselect Postprocessor Flink DataStream API Flink SQL With OVER Aggregation Flink Process Table Functions Summary and Discussion Postgres logical replication, while powerful for capturing real-time data changes, presents challenges with TOAST columns, whose values can be absent from data change events in specific situations. This post discusses how Debezium addresses this through its built-in reselect post processor, then explores more robust solutions leveraging Apache Flink's capabilities for stateful stream processing, including Flink SQL and the brand-new process table functions (PTFs) in Flink 2.1. ## Comparing IP and CLNP: Local (Node) Multihoming DevFeed: [Comparing IP and CLNP: Local (Node) Multihoming]() Original publisher: [Read original article]() Published: 2025-04-02T07:21:00Z Content type: comparison Language: en Sources: [ipSpace.net blog]() Topics: [networking](), [Networks](), [Protocol (disambiguation)](), [Routing (disambiguation)](), [Firewall](), [Mainframe]() Tags: [firewall](), [ibm](), [ip](), [networking-fundamentals](), [networks](), [protocol](), [routing](), [stateful](), [tcp](), [tcp-ip]() ### AI overview The article compares local node multihoming in IP/TCP/IP and CLNP. It explains that TCP/IP multihomed hosts use multiple IP addresses and lack a single well-defined behavior, with strong and weak end-system models leading to routing asymmetry and complications for stateful firewalls. It describes loopback addressing and routing advertisements as a TCP/IP solution. The supplied excerpt is incomplete. ### Source excerpt Another area where CLNP is a clear winner when compared to the TCP/IP stack is multi-homed nodes (nodes with multiple interfaces, not site multi-homing, where whole networks are connected to two upstream providers). Multi-homed TCP/IP nodes must have multiple IP addresses because IP uses address interfaces. There is no well-defined procedure in TCP/IP for how a multi-homed node should behave. In the early days of TCP/IP, they tried to address that in RFC 1122 (Host Requirements RFC), but even then, there were two ideas about dealing with multiple interfaces: the strong and weak end system models (more details). Read more ... ## Limitations of High Availability in Stateful Firewall Clusters DevFeed: [Limitations of High Availability in Stateful Firewall Clusters]() Original publisher: [Read original article]() Published: 2024-04-10T05:59:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [Firewall](), [Availability](), [BGP](), [networking]() Tags: [bgp](), [failover](), [firewall](), [ha](), [high-availability](), [stateful]() ### AI overview The article examines single-control-plane high-availability designs for stateful firewall clusters. It explains how synchronized sessions and forwarding state can preserve traffic through failover, while BGP Graceful Restart can keep peers forwarding as the new primary rebuilds its control plane. It argues that the shared control-plane design retains potential single points of failure and downtime. ### Source excerpt Dmitry Perets wrote an excellent description of how typical firewall cluster solutions implement control-plane high availability, in particular, the routing protocol Graceful Restart feature (slightly edited): Most of the HA clustering solutions for stateful firewalls that I know implement a single-brain model, where the entire cluster is seen by the outside network as a single node. The node that is currently primary runs the control plane (hence, I call it single-brain). Sessions and the forwarding plane are synchronized between the nodes. Read more ... ## Tinybird: A ksqlDB alternative for stateful stream processing DevFeed: [Tinybird: A ksqlDB alternative for stateful stream processing]() Original publisher: [Read original article]() Author: Alasdair Brown Published: 2023-07-27T00:00:00Z Content type: comparison Language: en Sources: [Tinybird]() Topics: [streaming-data-processing](), [stream-processing](), [Kafka]() Tags: [compare](), [kafka](), [scalable-analytics-architecture](), [stateful](), [stream-processing](), [streaming]() ### AI overview This comparison presents Tinybird as an alternative to ksqlDB for stateful stream processing and streaming SQL, emphasizing an approach intended to avoid the complexity associated with Kafka Streams. ### Source excerpt Looking for a ksqlDB alternative? Tinybird gives you streaming SQL without the Kafka Streams complexity. Compare the approaches. ## 3 (More) Tips for Optimizing Apache Flink Applications DevFeed: [3 (More) Tips for Optimizing Apache Flink Applications]() Original publisher: [Read original article]() Author: Kevin Lam Published: 2022-12-13T14:00:00Z Content type: tutorial Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [apache-flink](), [data-processing](), [Streaming](), [data]() Tags: [apache-flink](), [backpressure](), [best-practices](), [parallelism](), [performance](), [shopify](), [stateful]() ### AI overview Shopify presents additional best practices for optimizing large, stateful Apache Flink applications. The supplied text covers selecting appropriate parallelism to scale execution and improve resource utilization, and avoiding bottlenecks in data sinks that can reduce throughput and cause backpressure. ### Source excerpt Earlier this year, we shared our tips for optimizing large stateful Apache Flink applications. Below we'll walk you through 3 more best practices. ## Stateful Testing for Android App State Logic DevFeed: [Stateful Testing for Android App State Logic]() Original publisher: [Read original article]() Author: Sergio Sastre Florez Published: 2022-08-31T05:59:40Z Content type: tutorial Language: en Sources: [Sergio's little tech corner]() Topics: [Testing](), [Android](), [App](), [Requirements](), [test](), [ui]() Tags: [android](), [android-app-development](), [java](), [keyboard](), [kotlin](), [requirements](), [state](), [stateful](), [tdd](), [test](), [testing](), [ui](), [unit-testing](), [view]() ### AI overview This tutorial explains stateful testing for Android applications by modeling app state, user actions, preconditions, and postconditions. It uses a text editor with text changes, undo, and redo as an example. ### Source excerpt Broadly speaking, in an app, state is any value that can change over time. All Android apps display state to the user, e.g. an enabled/disabled button, the text displayed in a View, etc. We expect users to modify the state by performing actions throu... ## Composed modifiers in Jetpack Compose DevFeed: [Composed modifiers in Jetpack Compose]() Original publisher: [Read original article]() Author: Jorge Castillo Published: 2022-04-04T10:00:00Z Content type: tutorial Language: en Sources: [👨💻 Jorge Castillo]() Topics: [Jetpack Compose](), [ui](), [context](), [factory function](), [Library]() Tags: [android](), [compose](), [compose-ui](), [context](), [extension-function](), [factory-function](), [jetpack](), [jetpack-compose](), [modifiers](), [state](), [stateful](), [stateless]() ### AI overview This tutorial explains the difference between standard and composed modifiers in Jetpack Compose. Standard modifiers use extension functions and are stateless, while composed modifiers support state and access to composition context by creating them within a Composition. ### Source excerpt Learn about composed modifiers and compare those to standard ones. ## Why are Enterprises Shifting to Containers with Kubernetes in 2022? DevFeed: [Why are Enterprises Shifting to Containers with Kubernetes in 2022?]() Original publisher: [Read original article]() Author: Kirby Wadsworth Published: 2022-03-29T12:00:50Z Content type: article Language: en Sources: [ionir]() Topics: [Kubernetes](), [Containers](), [data](), [Docker Compose]() Tags: [app-performance](), [blog](), [cloud-native](), [container](), [container-native](), [containers](), [data-mobility](), [idc](), [kubernetes](), [recent-posts-featured](), [stateful](), [storage](), [survey]() ### AI overview The article explains why enterprises are moving workloads to containers with Kubernetes, focusing on resource utilization, data mobility, application performance, stability, portability, and support for stateful workloads through container-native storage. ### Source excerpt Vendor agnostic. Better resource utilization. Enhanced portability. Sounds like the making of one incredible data mobility cocktail! The key ingredient here is leveraging containers with Kubernetes (K8s). And we're not the only ones advocating to adopt containers for amplified data mobility. The post Why are Enterprises Shifting to Containers with Kubernetes in 2022? appeared first on ionir. ## ionir Report Finds 60% of Companies are Running Stateful Applications on Kubernetes DevFeed: [ionir Report Finds 60% of Companies are Running Stateful Applications on Kubernetes]() Original publisher: [Read original article]() Author: ionir Team Published: 2022-03-16T12:00:25Z Content type: release Language: en Sources: [ionir]() Topics: [Kubernetes](), [Persistence](), [Cloud](), [Database](), [DevOps](), [data]() Tags: [availability](), [cloud](), [data](), [database](), [devops](), [kubernetes](), [multi-cloud](), [persistence](), [press-releases](), [report](), [stateful](), [vendor-lock-in]() ### AI overview ionir reports that 60% of survey respondents are running stateful applications on Kubernetes, while 50% of respondents not currently doing so plan to adopt them within 12 months. The survey identifies agility, portability, flexibility, DevOps acceleration, and multi-cloud support as key drivers. ### Source excerpt ionir, a leader in Kubernetes Data Services, announced that 60% of respondents are running stateful applications on Kubernetes, and of those who aren't already, 50% plan to do so in the next 12 months. That's among the findings of The Future of Stateful Application on Kubernetes report, available on the ionir website The post ionir Report Finds 60% of Companies are Running Stateful Applications on Kubernetes appeared first on ionir. ## Scaling Shopify's BFCM Live Map: An Apache Flink Redesign DevFeed: [Scaling Shopify's BFCM Live Map: An Apache Flink Redesign]() Original publisher: [Read original article]() Author: Berkay Antmen Published: 2021-12-10T19:00:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [apache-flink](), [Data Infrastructure](), [Scalability](), [streaming-data-processing]() Tags: [apache-flink](), [data-infrastructure](), [scalability](), [shopify](), [stateful](), [stream-processing](), [streaming]() ### AI overview Shopify Data Platform Engineering redesigned the infrastructure behind the BFCM live map with Apache Flink to support more than 1.7 million merchants, richer insights, higher data volume, and higher uptime without manual intervention. ### Source excerpt A deep dive into how Shopify Data revamped the data infrastructure powering our BFCM live map using Apache Flink. ## Temporal Transparency Update #12 DevFeed: [Temporal Transparency Update #12]() Original publisher: [Read original article]() Author: Ryland Goldstein Published: 2021-09-20T07:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [version](), [Kubernetes](), [Serverless](), [Stateful](), [stateless](), [Node.js]() Tags: [cncf](), [community](), [developer](), [eol](), [kubernetes](), [node-js](), [product-news](), [serverless](), [stateful](), [stateless](), [ui](), [update]() ### AI overview Temporal's transparency update highlights product and community developments, including a Cadence support end-of-life reminder, Node.js and UI progress, visibility improvements, and the company's KubeCon presence involving Kubernetes and serverless workflow topics. ### Source excerpt There has been immense growth in our community since our last update which couldn't make us happier. This post will cover some highlights of that work. ## When to Use Sequences DevFeed: [When to Use Sequences]() Original publisher: [Read original article]() Author: author@typealias.com (Dave Leeds) Published: 2019-09-16T00:00:00Z Content type: tutorial Language: en Sources: [Dave Leeds on Kotlin - typealias.com]() Topics: [Kotlin](), [Streams](), [Java]() Tags: [article](), [benchmark](), [benchmarks](), [collections](), [guide](), [kotlin](), [performance](), [programming](), [sequences](), [series](), [stateful](), [stateless](), [streams]() ### AI overview A tutorial comparing Kotlin sequences with normal collections and Java streams. It explains performance trade-offs, including how operation-chain length and intermediate collections can affect efficiency, while emphasizing that benchmarks with realistic data are needed for specific code. ### Source excerpt One of the most common questions I get about Kotlin's sequences is this: "When should I use sequences, and when should I use normal collections?" That's the question we're going to answer in this third and final article in this series! We're going to look at the trade-offs of each, including the main things that affect their performance. Then, we'll look at how Kotlin's sequences stack up against Java streams, a similar concept. [Next page]()