# sharding Published articles for sharding. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Top System Design Performance Metrics DevFeed: [Top System Design Performance Metrics]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-14T08:36:41Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [benchmarking](), [Availability](), [Architecture & Design](), [Scalability](), [Load Balancing](), [health checks](), [Concurrency](), [Disaster Recovery](), [Database](), [sharding](), [IO](), [API]() Tags: [api](), [availability](), [blocking](), [concurrency](), [database](), [disaster-recovery](), [health-checks](), [load-balancing](), [performance](), [performance-metrics](), [scalability](), [sharding](), [system-design]() ### AI overview A tutorial on system design performance metrics, focusing on availability and throughput. It explains how these metrics are measured and outlines techniques such as load balancing, health checks, failover, redundancy, disaster recovery, query optimization, sharding, and asynchronous processing. ### Source excerpt Must Know Metrics ## Scaling a distributed cache: Why consistent hashing is mandatory DevFeed: [Scaling a distributed cache: Why consistent hashing is mandatory]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-05-17T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [consistent hashing](), [distributed-systems](), [Caching](), [sharding](), [scaling](), [Database](), [Memcached](), [Redis]() Tags: [caching](), [consistent-hashing](), [database](), [distributed](), [distributed-cache](), [distributed-systems](), [memcached](), [redis](), [scaling](), [sharding]() ### AI overview The article explains why modulo-based cache sharding can remap nearly every key when a node fails, causing a large cache miss surge and database overload. It presents consistent hashing on a fixed ring as a way to limit movement to keys assigned to the failed node, and notes that virtual nodes improve distribution across physical cache nodes. ### Source excerpt Sharding a cache with hash(key) modulo N means the denominator changes when a node dies, remapping nearly every key. Dropping from five nodes to four invalidates about 80 percent of the cache instantly and routes that miss storm straight at the primary database. Consistent hashing maps nodes and keys onto a fixed ring so only the dead node's keys move, and virtual nodes fix the uneven distribution a handful of physical nodes would otherwise produce. ## PGKeeper: Building the bouncer we needed for Postgres DevFeed: [PGKeeper: Building the bouncer we needed for Postgres]() Original publisher: [Read original article]() Author: Lihao He; Tim Goh; Mehant Baid Published: 2026-05-04T14:52:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [PostgreSQL](), [Scalability](), [Architecture & Design](), [benchmarking](), [Databases](), [Network]() Tags: [architecture](), [database-scalability](), [performance](), [postgres](), [postgresql](), [queue](), [routing](), [scalability](), [sharding]() ### AI overview Figma describes why it built PGKeeper, a scalable PostgreSQL connection and load management service that replaced PgBouncer in front of its Postgres fleet. The article covers the database workload pressures, request routing, connection pooling, and rollout of the new service. ### Source excerpt This is the story of why and how we built PGKeeper, a scalable and reliable service to support Figma's rapidly growing products and database workload. ## Ten Years of ITNOG DevFeed: [Ten Years of ITNOG]() Original publisher: [Read original article]() Published: 2026-04-22T05:25:00Z Content type: opinion Language: en Sources: [ipSpace.net blog]() Topics: [networking](), [VXLAN](), [BGP](), [evpn](), [5G](), [Ethernet](), [Latency]() Tags: [5g](), [bgp](), [ethernet](), [evpn](), [latency](), [networking](), [sharding](), [threading](), [vxlan]() ### AI overview A personal recap of ITNOG 10 in Bologna highlights the event's networking presentations, including VXLAN-based 5G-to-Ethernet services, security functions in EVPN/VXLAN, BGP routing optimization for 26 million paths, and hollow-core fiber technology. ### Source excerpt I spent the last two days in Bologna at ITNOG 10 in the excellent company of Italian networking engineers (many of them personal friends) and a few guests from around the world. As always, the organizers and the program committee didn't disappoint - it was a smoothly organized, lovely event full of interesting presentations. Thanks a million to everyone involved; I'll definitely be back! Now for the highlights, starting with the ultimate catnip for the differently attentive: running two presentations in parallel on the same screen with the soundtrack distributed via headphones. I've never seen anything like that, and while it looked weird (I have no idea how the presenters took it), it turned out to be very useful, as you could easily tune out AI-washing presentations and switch to something more interesting. On the other hand, you could be faced with a hard choice of having to select one of two excellent presentations: Read more ... ## Kubo 0.40.0 adds reproducible CID Profiles, import cleanup, diagnostics, and gateway improvements DevFeed: [Kubo 0.40.0 adds reproducible CID Profiles, import cleanup, diagnostics, and gateway improvements]() Original publisher: [Read original article]() Author: Ipfs Published: 2026-02-25T00:00:00Z Content type: release Language: en Sources: [IPFS]() Topics: [Software](), [sharding](), [Go Language](), [configuration]() Tags: [gc](), [go](), [memory](), [release](), [release-notes](), [sharding]() ### AI overview Kubo 0.40.0 introduces reproducible file imports through CID Profiles, automatic cleanup of interrupted flatfs operations, improved connectivity diagnostics and gateway behavior, and reduced memory usage and garbage-collection overhead through its Go 1.26 toolchain. ### Source excerpt Just released: Kubo 0.40.0! ## Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism DevFeed: [Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism]() Original publisher: [Read original article]() Author: Cen Zhao; Xiaodong Wang; Jianyu Huang Published: 2025-10-17T16:00:50Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Large Language Model](), [AI Inference](), [Inference Performance](), [LLM Techniques](), [GPU](), [sharding](), [long-context]() Tags: [ai-research](), [gpu](), [inference](), [kv-cache](), [large-language-models-llms](), [latency](), [llms](), [parallelism](), [performance](), [sharding]() ### AI overview Meta describes three forms of parallelism--tensor, context, and expert parallelism--for scaling large language model inference across GPUs. The article explains how prefill and decoding differ computationally and how these techniques target resource efficiency, throughput, and latency. ### Source excerpt At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App. We're sharing how we developed and implemented advanced parallelism techniques to optimize key performance metrics related to resource efficiency, throughput, and latency. The rapid evolution of large language models (LLMs) has ushered in a [...] Read More... The post Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism appeared first on Engineering at Meta. ## How Klarna Migrated the KRED System from Mnesia to Postgres with Zero Downtime DevFeed: [How Klarna Migrated the KRED System from Mnesia to Postgres with Zero Downtime]() Original publisher: [Read original article]() Author: Onno Vos Dev Published: 2025-02-26T09:04:27Z Content type: article Language: en Sources: [Klarna Engineering]() Topics: [Databases](), [Erlang](), [Replication](), [sharding](), [Transactions](), [systems]() Tags: [cluster](), [databases](), [erlang](), [klarna](), [memory](), [postgresql](), [production](), [replication](), [scaling](), [sharding](), [software-development](), [software-engineering](), [transactions]() ### AI overview This article describes Klarna's migration of KRED, an Erlang servicing system, from Mnesia to Postgres with zero downtime. It explains the original replicated seven-node architecture, the system's scaling challenges, and a plan involving sharding across multiple clusters. ### Source excerpt How we migrated from Mnesia to Postgres with zero downtime Back in December 2004, an Erlang application was born called KRED (referring to the freshly-started company called Kreditor, now known as Klarna). KRED is one of the "servicing systems" at Klarna and keeps track of consumer debt (among other things). It was powered by Mnesia and consisted of a cluster of 7 nodes, each holding a full copy of the database on disk. The data was replicated using a custom replication mechanism built in-house by Klarna. One node was elected as the leader and its database was considered the source of truth in the system. All database transactions were executed on the leader and writes were replicated to the rest of the nodes, the so-called followers. The Mnesia database was around 15 TB and at its peak in 2018 around 1.3 TB was held in memory at all times. Considering that few suppliers were selling hardware with such specs, it's easy to claim the crown of one of the biggest Mnesia databases in terms of in-memory storage, that was running in production. The rest of the data was offloaded to disk using mnesia_eleveldb. KRED has been a stable workhorse at Klarna so why change a winning concept? Get ready, for a two part blog post where we'll first go through our journey of how we went about this and secondly, how we made Mnesia behave just like Postgres and implemented our version serializable isolation level on top of Postgres! How the journey started Three engineers, sat down in a bar in Stockholm, Sweden and asked this question: 'When Klarna truly takes off, will KRED survive? Assuming "no", and presented with a blanco check, how would we tackle this problem?' The answer quickly revolved around the issues of running Mnesia on an even larger cluster and with leveldb compaction hitting some hot tables during peak times. One can only imagine how that problem would just continue to get worse over time. Considering the three engineers had worked on KRED for a long time, scaling KRED wa ## Comparing CockroachDB and PostgreSQL DevFeed: [Comparing CockroachDB and PostgreSQL]() Original publisher: [Read original article]() Author: Cassie McAllister Published: 2025-01-06T00:00:00Z Content type: comparison Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [PostgreSQL](), [Databases](), [cloud-infrastructure](), [Open Source]() Tags: [cloud-infrastructure](), [cockroachdb](), [comparison](), [consensus](), [databases](), [distributed](), [postgresql](), [sharding](), [sql]() ### AI overview This comparison examines architectural differences between PostgreSQL and CockroachDB. It explains PostgreSQL's vertical scaling and manual sharding approach, then describes CockroachDB's distributed SQL architecture, horizontal scaling, distributed consensus, and triplicated data storage. ### Source excerpt Before we begin a comparison blog post about PostgreSQL, we must first acknowledge that it is one of the most reliable and widely used databases in the history of software. The world owes a debt of gratitude to the open source community that has built and supported this important project for the last 35 years. In this post, we unpack some of the architectural differences between PostgreSQL and CockroachDB. We'll point out where the limitations of single server, single instance architecture might pose challenges for modern cloud infrastructure, and how a distributed foundation can be a better solution for your apps and services. ## How Stripe Used MongoDB Sharding and Replication to Scale Payments with Minimal Downtime DevFeed: [How Stripe Used MongoDB Sharding and Replication to Scale Payments with Minimal Downtime]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2024-12-04T14:02:44Z Content type: article Language: en Sources: [Hacking Scale by Better Stack]() Topics: [MongoDB](), [Databases](), [Replication](), [Database]() Tags: [database](), [developer](), [mongodb](), [replication](), [sharding]() ### AI overview The article explains how Stripe used MongoDB, sharding, replication sets, and query routing to scale payment data while maintaining reliability during migrations. It describes the database architecture and the roles of shards, replicas, Mongos instances, and config servers. ### Source excerpt Stripe's hand-crafted system that guarantees their data never gets lost ## What's new in Maestro: 1.39.0 DevFeed: [What's new in Maestro: 1.39.0]() Original publisher: [Read original article]() Author: Bartek Pacia Published: 2024-10-16T07:00:00Z Content type: release Language: en Sources: [mobile.dev - Medium]() Topics: [Testing](), [Artificial Intelligence](), [Large Language Model](), [API](), [iOS](), [API keys](), [OpenAI](), [anthropic]() Tags: [ai](), [api](), [bugfixes](), [improvements](), [ios](), [llms](), [maestro](), [new-features](), [release](), [sharding]() ### AI overview Maestro 1.39.0 introduces AI-powered commands for UI assertions, along with improved sharding, landscape iOS support, bug fixes, and stability improvements. The release uses LLMs to help address screenshot-testing and baseline-maintenance challenges. ### Source excerpt New features including the first AI-powered commands, improved sharding, landscape iOS support, and many bugfixes and stability improvements. ## Figma's 100x Approach to Scaling Its Collaborative Experience DevFeed: [Figma's 100x Approach to Scaling Its Collaborative Experience]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2024-09-11T13:03:05Z Content type: article Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Figma](), [Scalability](), [Databases](), [GraphQL](), [React](), [Ruby](), [WebSocket](), [Redux](), [.NET 11 Preview 7]() Tags: [database](), [figma](), [graphql](), [react](), [real-time](), [redux](), [ruby](), [scalability](), [sharding](), [web]() ### AI overview The article explains how Figma scaled real-time collaboration by developing LiveGraph, an in-house system inspired by GraphQL that acts as a shared data store or cache and manages database queries, updates, and client subscriptions. It also describes the scaling pressures that led to the LiveGraph 100x project. ### Source excerpt How Figma destroyed their old setup and used the pieces to build a better one ## Implementing Multi-GPU Distributed Training for Stitch Fix's Personalized Recommendations DevFeed: [Implementing Multi-GPU Distributed Training for Stitch Fix's Personalized Recommendations]() Original publisher: [Read original article]() Published: 2023-06-08T09:00:00Z Content type: article Language: en Sources: [Stitch Fix]() Topics: [distributed-training](), [recommendations](), [GPU](), [sharding](), [Training AI Models](), [Multi-GPU](), [PyTorch]() Tags: [distributed-training](), [gpu](), [model-training](), [multi-gpu](), [parallel](), [performance](), [pytorch](), [recommendations](), [sharding]() ### AI overview This Stitch Fix engineering article explains how the company implemented multi-GPU distributed training for its Client Time Series Model (CTSM), a PyTorch-based model used in personalized recommendations. It describes sharding training data across GPUs and training mini-batches in parallel to reduce training time, along with the surrounding retraining and deployment workflow. ### Source excerpt Stitch Fix uses a cutting-edge multi-tiered recommender system stack to personalize styling recommendations at scale. This stack comprises several critical components, including feature generation, scoring, ranking, and inventory optimization techniques. Our scoring module is based on the Client Time Series Model (CTSM) which is an award winning novel sequence based model that uses temporally masked encoders. CTSM is built using PyTorch, and was initially trained on a single Graphics Processing Unit (GPU) instance. Since we first put this model into production last year, we have launched several updates to the model that improved its performance. Many of these improvements involved adding new features or increasing the time window of our training data. As a result, the model training time increased significantly, making it harder for us to iterate quickly and get feedback on new ideas we want to try for improving the model. We needed a way to reduce the model training time. This blog delves into the steps we followed to overcome this challenge and our journey to implement multi-GPU distributed model training for CTSM. By sharding the training data across multiple GPUs and training multiple mini-batches in parallel, we aimed to achieve significant reductions in training time. We present empirical results showcasing the observed reduction in training time when we scaled up resources from 1 to N GPUs, and share some future directions we are considering in our continued effort to speed up model training. Model Training Workflow The scores generated by CTSM are leveraged by multiple downstream services to get insight into what items a client is likely to purchase. The model is retrained at a regular cadence to ensure that it is using the most updated information about each client when making predictions and does not degrade in its performance. We leverage configuration driven machine learning pipelines to set up a Directed Acyclic Graph (DAG) that automatically retrains ## Consistent hashing algorithm DevFeed: [Consistent hashing algorithm]() Original publisher: [Read original article]() Author: NK Published: 2023-02-22T16:39:15Z Content type: tutorial Language: en Sources: [High Scalability]() Topics: [consistent hashing](), [Algorithm](), [distributed-systems](), [Cache](), [Scalability](), [Server]() Tags: [algorithm](), [architecture](), [cache](), [consistent-hashing](), [distributed-system](), [hashing](), [high-availability](), [load-balancing](), [low-latency](), [scalability](), [sharding](), [system-design]() ### AI overview The article explains how consistent hashing places nodes and data keys on a virtual hash ring, then assigns data by traversing the ring clockwise to the next node. It discusses cache-server scaling, hotspot reduction, dynamic load, replication, availability, and latency in distributed systems. ### Source excerpt You can subscribe to the system design newsletter to excel in system design interviews and software architecture. You can view the original article Consistent hashing explained on systemdesign.one website. How does consistent hashing work? At a high level, consistent hashing performs the following operations: The output of the hash ## Consistent hashing algorithm DevFeed: [Consistent hashing algorithm]() Original publisher: [Read original article]() Author: NK Published: 2023-02-22T16:39:15Z Content type: tutorial Language: en Sources: [High Scalability](), [High Scalability]() Topics: [hashing](), [Algorithm](), [Cache](), [Scalability](), [distributed-systems](), [Software](), [Availability]() Tags: [algorithm](), [availability](), [cache](), [consistent-hashing](), [distributed-system](), [hashing](), [hotspot](), [load-balancing](), [low-latency](), [scalability](), [sharding](), [software-architecture]() ### AI overview A tutorial explaining consistent hashing: nodes and data keys are mapped onto a virtual hash ring, and clockwise traversal assigns data to nodes. It discusses terminology, cache-server scaling requirements, hotspot reduction, dynamic load, replication, availability, and latency. ### Source excerpt This is a guest article by NK. You can view the original article Consistent hashing explained on systemdesign.one website. How does consistent hashing work? At a high level, consistent hashing performs the following operations: The output of the hash function is placed on a virtual ring structure (known as the hash ring) The hashed IP addresses of the nodes are used to assign a position for the nodes on the hash ring The key of a data object is hashed using the same hash function to find the position of the key on the hash ring The hash ring is traversed in the clockwise direction starting from the position of the key until a node is found The data object is stored or retrieved from the node that was found Terminology ## Sharing Control of a Dependency Manager's Database DevFeed: [Sharing Control of a Dependency Manager's Database]() Original publisher: [Read original article]() Published: 2022-12-14T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Database]() Tags: [audits](), [data-ownership](), [data-rights](), [database](), [gatekeeping](), [go-blockchain](), [golang-blockchain](), [load-balancing](), [master](), [privacy](), [replication](), [sharding](), [slave](), [sqlite]() ### AI overview This episode explores different methods for sharing control of a dependency manager's database. It examines the benefits, drawbacks, bottlenecks, and disadvantages of each approach, and explains why these methods fall short of solving the database ownership problem. ### Source excerpt Introduction In episode 3, Bill needed to figure out how to share ownership of his dependency manager's database in a secure and efficient manner. Bill is the only stakeholder with full access to his dependency manager's database. That is, he is the only person with the ability to mutate the database. This is problematic because he can freely edit the database which will make it harder for other users to confidently perform audits. And as a result, reduce the perceived transparency of his dependency manager. ## How trivago Maintains Global Availability During a Google Cloud Regional Outage DevFeed: [How trivago Maintains Global Availability During a Google Cloud Regional Outage]() Original publisher: [Read original article]() Author: Arne Claus Follow Published: 2022-08-15T00:00:00Z Content type: tutorial Language: en Sources: [Trivago]() Topics: [Google Cloud Platform (GCP)](), [Kubernetes](), [Kafka](), [migration](), [Back end](), [sharding](), [Replication](), [systems](), [MySQL]() Tags: [backend](), [cloud](), [datacenters](), [failover](), [gke](), [google-cloud](), [how-to](), [kafka](), [migration](), [mysql](), [replication](), [sharding](), [systems]() ### AI overview This article explains trivago's regional failover testing and its effort to maintain global availability when a Google Cloud region becomes unavailable. It describes the move from locale-based sharding toward services that can handle equivalent traffic and data across regions. ### Source excerpt How trivago manages to stay globally available, even if a Google Cloud region goes down ## EF-Supported Teams: Research & Development Roundup DevFeed: [EF-Supported Teams: Research & Development Roundup]() Original publisher: [Read original article]() Author: Ethereum Foundation Published: 2021-08-12T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Development](), [Ethereum](), [Security]() Tags: [community](), [devcon](), [development](), [ethereum](), [network](), [organizational](), [research-development](), [security-research](), [sharding](), [stateless]() ### AI overview This roundup reports on progress from EF-supported Ethereum teams, including community grants, research into statelessness, custody proofs, sharding, scaling and security, and ethereum.org roadmap work. It also describes Ethereum network activity around the London update, Altair and the Merge. ### Source excerpt Welcome to London! This is an exciting time for the Ethereum ecosystem, and the pace will only pick up further as we approach Altair and the Merge in the months ahead. The Beacon Chain now has 6.5+ million Ether staked, and 200K+ active validators online across five clients, and... ## Understanding Monarch, Google's Planet-Scale Monitoring System DevFeed: [Understanding Monarch, Google's Planet-Scale Monitoring System]() Original publisher: [Read original article]() Published: 2021-05-20T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Monitoring](), [Time Series](), [Google](), [Databases](), [sharding](), [systems](), [Latency](), [Network]() Tags: [database](), [google](), [latency](), [load-balancing](), [logs](), [monitoring](), [series](), [sharding](), [spanner](), [time-series]() ### AI overview This article explains Monarch, Google's planet-scale in-memory time series database and monitoring system. It covers its availability and partitioning goals, regional data placement, in-memory leaves, persistent logs, ingestion routers, range assignment, lexicographic sharding, and metric formats. ### Source excerpt Monarch is a planet-scale in-memory time series database developed by Google. It is mainly used by as a reliable monitoring system by most of Google's internal systems like Spanner, BigTable, Colossus, BlobStore... ## eth2 quick update no. 22 DevFeed: [eth2 quick update no. 22]() Original publisher: [Read original article]() Author: Danny Ryan Published: 2021-02-11T00:00:00Z Content type: news Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [client]() Tags: [community](), [ethereum](), [merge](), [projects](), [research](), [research-development](), [sharding](), [update]() ### AI overview An Ethereum Foundation eth2 research workshop covered planned Beacon Chain upgrades, including the eth1+eth2 merge and sharding. The update also announces staking community grants and links to a January 2021 State of Eth2 post. ### Source excerpt tl;dr Successful R&D workshop on future beacon chain upgrades Staking community grants announced Quick plug for my State of Eth2, Jan 2021 post... ## Dgraph Database Semantics DevFeed: [Dgraph Database Semantics]() Original publisher: [Read original article]() Published: 2020-06-26T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [Database](), [graph-database](), [Scalability](), [Architecture & Design](), [Latency](), [Availability](), [Replication](), [Transactions](), [Resilience]() Tags: [acid](), [architecture](), [ardan-labs](), [availability](), [blog](), [database](), [distributed](), [engineering](), [go](), [go-programming](), [golang](), [graph-database](), [high-availability](), [latency](), [low-latency](), [programming](), [replication](), [resilience](), [scalability](), [servers](), [sharding](), [software](), [transactions]() ### AI overview This article explains the semantics behind Dgraph's description as a distributed graph database. It provides a high-level mental model for claims involving horizontal scalability, cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience, while leaving lower-level mechanics to the referenced paper. ### Source excerpt Introduction In this paper written by Manish Jain (the founder of Dgraph) he describes Dgraph as: a distributed graph database which provides horizontal scalability, distributed cluster-wide ACID transactions, low-latency arbitrary-depth joins, synchronous replication, high availability, and crash resilience. There are many claims being stated here which frankly I didn't understand the meaning of the first time I read this sentence. I decided I wanted to better understand these claims with the hope that it would help me understand Dgraph's architecture and engineering decisions better. ## Validated, staking on eth2: #3 - Sharding Consensus DevFeed: [Validated, staking on eth2: #3 - Sharding Consensus]() Original publisher: [Read original article]() Author: Carl Beekhuizen Published: 2020-03-27T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Protocol (disambiguation)](), [Security](), [Network](), [Database]() Tags: [consensus](), [database](), [improvements](), [network](), [protocol](), [research-development](), [security](), [sharding](), [transactions]() ### AI overview The article explains how eth2 uses sharding to divide storage and computation across shard chains and describes sharded consensus, in which validators are organized into committees and attest to beacon-chain blocks over slots and epochs. It presents these mechanisms as ways to improve scaling while distributing consensus work. ### Source excerpt Special thanks to Sacha Yves Saint-Leger & Joseph Schweitzer for review. Sharding is one of the many improvements that eth2 has over eth1. The term was borrowed from database research where a shard means a piece of a larger whole. In the context of databases and eth2, sharding means... ## Validated: Staking on eth2 #0 -- Beacon Chain, Sharding, and Validator Roles DevFeed: [Validated: Staking on eth2 #0 -- Beacon Chain, Sharding, and Validator Roles]() Original publisher: [Read original article]() Author: Carl Beekhuizen Published: 2019-11-27T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Architecture & Design](), [Scalability](), [Security](), [systems]() Tags: [platform](), [pos](), [research-development](), [scalability](), [security](), [sharding]() ### AI overview This article introduces eth2 and explains the validator-focused role of its beacon chain. It describes the phased design: Phase 0 manages validators and shard coordination, Phase 1 adds shard data, and Phase 2 adds execution to create a decentralized computing platform. ### Source excerpt Thanks to Danny and Joe for review. As the launch of the beacon chain grows nearer and eth2 becomes ever more final, the time has come to fast-sync the community with the latest on the inner workings of eth2 and on the concrete requirements, incentives and experience of being... ## Increasing the scaling limits of the Firebase Realtime Database DevFeed: [Increasing the scaling limits of the Firebase Realtime Database]() Original publisher: [Read original article]() Author: Jamie Niemasik Published: 2019-09-19T00:00:00Z Content type: news Language: en Sources: [Firebase Blog]() Topics: [Realtime Database](), [Scalability](), [sharding](), [Firebase](), [Firestore]() Tags: [firebase](), [firestore](), [iot](), [launch](), [low-latency](), [realtime-database](), [scalability](), [serverless](), [sharding]() ### AI overview Firebase announces that it has doubled the Firebase Realtime Database concurrent connections limit from 100,000 to 200,000 for existing databases and new projects. The article also explains sharding across database instances for higher aggregate capacity and discusses use cases such as collaborative applications and IoT sensors. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Ethereum scalability research and development subsidy programs DevFeed: [Ethereum scalability research and development subsidy programs]() Original publisher: [Read original article]() Author: Vitalik Buterin Published: 2018-01-02T17:42:21Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Scalability](), [Blockchain](), [Development](), [Python]() Tags: [blockchain](), [development](), [ethereum](), [python](), [research](), [research-development](), [scalability](), [sharding](), [strategy]() ### AI overview The article presents Ethereum scalability as a major technical challenge and describes two complementary approaches: sharding at the base layer and layer 2 protocols that move most transactions off-chain. It also announces subsidy programs supporting research, development, and sharding client implementations. ### Source excerpt The Ethereum community, key developers and researchers and others have always recognized scalability as perhaps the single most important key technical challenge that needs to be solved in order for blockchain applications to reach mass adoption. Blockchain scalability is difficult primarily because a typical blockchain design requires every node in... [Next page]()