# Apache Cassandra Apache Cassandra is an open-source distributed NoSQL database that uses a partitioned wide-column storage model with eventually consistent semantics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Yelp Upgraded More Than 1,000 Cassandra Nodes from 3.11 to 4.1 Without Downtime DevFeed: [How Yelp Upgraded More Than 1,000 Cassandra Nodes from 3.11 to 4.1 Without Downtime]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-11T15:31:13Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Apache Cassandra](), [distributed-systems](), [Kubernetes](), [Latency](), [Tool]() Tags: [cassandra](), [distributed-systems](), [kubernetes](), [latency](), [migrations](), [stargate]() ### AI overview Yelp's Database Reliability Engineering team upgraded more than 1,000 Cassandra nodes from version 3.11 to 4.1 without downtime. The article describes compatibility work, staged automation, proxy routing, gossip management, and CDC changes used during the migration, and reports up to a 58% reduction in p99 latency and an 11% throughput improvement on key clusters. ### Source excerpt PLUS: OpenAI phone by 2027 πŸ“±, Shadow testing Flink πŸ‘¨πŸ’», Computer use 45x expensive πŸ’° ## 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... ## The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once DevFeed: [The RUM Conjecture: You Cannot Optimize Reads, Updates, and Memory at Once]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-17T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [systems](), [Databases](), [Apache Cassandra](), [rocksdb](), [MySQL](), [PostgreSQL]() Tags: [b-tree](), [capacity](), [cassandra](), [database](), [databases](), [dram](), [indexing](), [latency](), [memory](), [mysql](), [node](), [performance](), [rocksdb](), [storage-engine](), [system-design]() ### AI overview The article explains the RUM Conjecture, which describes a tradeoff among read overhead, update overhead, and memory overhead in database indexes and storage engines. It compares B-Trees, LSM-Trees, and hash indexes to show how each optimizes different tradeoffs. ### Source excerpt You can strictly optimise at most two of read overhead, update overhead, and memory overhead; the third will be expensive. B-Trees optimise reads and memory and pay on writes. LSM-Trees optimise writes and memory and pay on reads. Hash indexes optimise reads and writes and pay in RAM. The useful question is not whether a database is good but which corner it optimises and what you are willing to pay for the other two. ## Engineering Features at Scale: Inside the Darwin Feature Store DevFeed: [Engineering Features at Scale: Inside the Darwin Feature Store]() Original publisher: [Read original article]() Author: Dream Blog Published: 2026-01-27T15:38:25Z Content type: article Language: en Sources: [Dream11 Engineering]() Topics: [Feature Engineering](), [Low Latency](), [Apache Cassandra](), [Machine learning](), [Open Source]() Tags: [cassandra](), [data](), [engineering](), [feature-engineering](), [feature-store](), [inference](), [kafka](), [latency](), [machine-learning](), [offline](), [open-source](), [pipelines](), [software-engineering](), [tech]() ### AI overview Dream Horizon's Darwin Feature Store is an open-source, low-latency feature platform designed to support real-time machine learning and offline training at Dream11. The article describes the challenges that led to its development, including batch-only pipelines, manual schema changes, and scaling issues. It reports serving more than 200 million feature requests per minute with p99 read latency below 5 milliseconds and no production incidents over the preceding year. ### Source excerpt By Mohit Jain and Ujjwal Bagrania Dream Horizon, our open-source effort to make Dream11's battle-tested tech available to every developer, brings you the Darwin Feature Store -- a unified, low-latency feature platform built to power real-time ML at scale, and shaped to help teams build, manage, and trust features in production. Explore the Darwin Feature Store here. At Dream11, data and ML have always powered how millions of users experience sports in real time. From personalisation and relevance to ensuring efficiency under peak match traffic, ML sits deep in the critical path of the product. But models are only half the story. The real challenge is managing features -- how they're defined, versioned, and accessed consistently across training and live traffic, especially under peak sports pressure. And at the scale we operate at, feature access doesn't mean the occasional lookup; it means hundreds of millions of requests per minute, spanning both real-time inference and offline training pipelines. As Dream11 grew, feature engineering stopped being something we could manage with pipelines and tables, and became infrastructure that everything else depended on. That shift forced us to build the Darwin Feature Store: a system designed not just to tackle sports-scale traffic, but to make feature engineering predictable, reliable, and developer-friendly in production. Today, that translates into serving 200M+ feature requests per minute, delivering p99 read latencies under 5 ms, and doing so reliably through the most demanding live sports moments, without a single production incident over the last year. In this post, we'll walk through how we built the Feature Store, and what it takes to serve features reliably. The Early Days: Batch-Only, Fragile, and Manual Before the Darwin Feature Store existed, our feature engineering was mostly a few pipelines, a lot of Cassandra tables, and an increasing number of engineers building ML models. Here's what the first version looked li ## Fork yeah: We're adding ten new open source projects to EmeritOSS DevFeed: [Fork yeah: We're adding ten new open source projects to EmeritOSS]() Original publisher: [Read original article]() Published: 2026-01-15T00:00:00Z Content type: news Language: en Sources: [Chainguard: Unchained]() Topics: [Open Source](), [Prometheus](), [Apache Cassandra](), [Amazon S3](), [observability](), [Grafana](), [data-processing]() Tags: [amazon-s3](), [cassandra](), [chainguard-containers](), [chainguard-emeritoss](), [chainguard-images](), [chainguard-open-source](), [dashboards](), [maintenance](), [minio](), [observability](), [open-source](), [open-source-chainguard-images](), [pgcat](), [prometheus](), [security](), [source]() ### AI overview Chainguard announces ten additional open source projects joining EmeritOSS, a program intended to provide long-term maintenance and stability for mature projects. The supplied text highlights MinIO, Prometheus-related exporters, and integrations involving Apache Cassandra and RabbitMQ, covering object storage, monitoring, metrics, and observability. ### Source excerpt We added 10 open source projects to EmeritOSS--including MinIO, Prometheus exporters, and PgCat--to provide long-term, stability-focused maintenance and security. ## ВрСйсинг Π² hh.ru: ΠΊΠ°ΠΊ ΠΌΡ‹ выросли ΠΎΡ‚ 1 тысячи Π΄ΠΎ 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° событий Π² сСкунду Π±Π΅Π· сСмплирования DevFeed: [ВрСйсинг Π² hh.ru: ΠΊΠ°ΠΊ ΠΌΡ‹ выросли ΠΎΡ‚ 1 тысячи Π΄ΠΎ 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½Π° событий Π² сСкунду Π±Π΅Π· сСмплирования]() Original publisher: [Read original article]() Author: Heruvimka (hh.ru, ΠšΠΎΠ½Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠΈ ОлСга Π‘ΡƒΠ½ΠΈΠ½Π° (ΠžΠ½Ρ‚ΠΈΠΊΠΎ)) Published: 2025-09-16T09:00:45Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [observability](), [telemetry](), [SRE](), [Kafka](), [nginx](), [Apache Cassandra](), [OpenTelemetry](), [RabbitMQ]() Tags: [cassandra](), [devops](), [jaeger](), [kafka](), [nginx](), [observability](), [opentelemetry](), [operational-intelligence](), [rabbitmq](), [request](), [sre](), [tag-1e4ee1f65f5a](), [tag-68e701e78517](), [tag-73eb9b712998](), [tag-75f84211cab4](), [tag-8f8626975338](), [tag-9abb13e52060](), [tag-b92bf5906bbd](), [tag-d9df843a1803](), [tag-dfac9042ce7b](), [tracing]() ### AI overview This Russian developer article describes how hh.ru built and rebuilt its tracing and observability architecture as its data volume increased. The supplied text states that the system handles 24,000 RPS, one million spans per second, and 5,000 service instances, and introduces a log-based tracing design using Request-Id propagation. ### Source excerpt Π’ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ Π΅ΡΡ‚ΡŒ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒ Π²Ρ‹ΡΡ‚Ρ€ΠΎΠΈΡ‚ΡŒ систСму observability. Π’ hh.ru ΠΌΡ‹ пСрСстраивали Π°Ρ€Ρ…ΠΈΡ‚Π΅ΠΊΡ‚ΡƒΡ€Ρƒ ΠΏΠΎΠ΄ большСС количСство Π΄Π°Π½Π½Ρ‹Ρ… нСсколько Ρ€Π°Π· -- сСйчас ΠΈΠΌΠ΅Π΅ΠΌ Π½Π° Π²Ρ…ΠΎΠ΄Π΅ 24ΠΊ RPS, 1 ΠΌΠΈΠ»Π»ΠΈΠΎΠ½ спанов Π² сСкунду, 5ΠΊ инстансов сСрвисов. Если Π²Ρ‹ -- ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹ΠΉ находится Π² процСссС построСния ΠΈΠ»ΠΈ пСрСстройки собствСнной систСмы трСйсинга, этот Π΄ΠΎΠΊΠ»Π°Π΄ -- для вас. ΠŸΡ€ΠΈΠ²Π΅Ρ‚, Π₯Π°Π±Ρ€! Π― -- АлСксандр ΠšΠ°Π·Π°Π½Ρ†Π΅Π², ΡƒΠΆΠ΅ Π±ΠΎΠ»Π΅Π΅ дСсяти Π»Π΅Ρ‚ Π² Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅. Когда-Ρ‚ΠΎ Π±Ρ‹Π» ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€ΠΎΠΌ Π½Π° ΠΏΠΈΠ²Π·Π°Π²ΠΎΠ΄Π΅ ΠΈ ΠΌΠΎΠ³Ρƒ Ρ€Π°ΡΡΠΊΠ°Π·Π°Ρ‚ΡŒ, ΠΈΠ· Ρ‡Π΅Π³ΠΎ Π΄Π΅Π»Π°ΡŽΡ‚ ΠΏΠ΅Π½Π½ΠΎΠ΅; Π½ΠΎ сСгодня -- ΠΎ Π΄Ρ€ΡƒΠ³ΠΎΠΌ. Π§ΠΈΡ‚Π°Ρ‚ΡŒ Π΄Π°Π»Π΅Π΅ ## The simplest way to count 100 billion unique IDs: Part 1 DevFeed: [The simplest way to count 100 billion unique IDs: Part 1]() Original publisher: [Read original article]() Author: Ariel PΓ©rez Published: 2025-03-25T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [real-time](), [Apache Cassandra](), [Kafka](), [Redis](), [Reddit]() Tags: [cassandra](), [i-built-this](), [kafka](), [real-time](), [reddit](), [redis]() ### AI overview A tutorial on building a simpler real-time system for counting unique IDs at very large scale, using Kafka, Redis, and Cassandra as part of the design. ### Source excerpt How to build a simpler, real-time version of Reddit's complex system for counting unique IDs, involving Kafka, Redis, and Cassandra. ## FIPS-ing the Un-FIPS-able: Apache Cassandra DevFeed: [FIPS-ing the Un-FIPS-able: Apache Cassandra]() Original publisher: [Read original article]() Published: 2025-03-05T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Apache Cassandra](), [Cryptography](), [container images](), [Encryption](), [chainguard]() Tags: [apache](), [cassandra](), [chainguard](), [compliance](), [container-images](), [cryptography](), [encryption](), [fedramp](), [fips](), [security]() ### AI overview Chainguard announces FIPS-validated container images for Apache Cassandra 4.0, 4.1, and 5.0, built entirely from source. The work makes Cassandra deployable with FIPS-approved cryptography in regulated industries and government environments, helping customers strengthen software supply chains and meet compliance requirements. ### Source excerpt Chainguard was able to create FIPS container images for Apache Cassandra 4.0, 4.1, and 5.0. See how we did it. ## Evolving the Backend Storage for Platform Metrics DevFeed: [Evolving the Backend Storage for Platform Metrics]() Original publisher: [Read original article]() Author: David Murray Published: 2024-05-09T22:00:00Z Content type: article Language: en Sources: [Heroku]() Topics: [Heroku](), [Back end](), [Time Series](), [Apache Cassandra](), [Kafka](), [stream-processing](), [autoscaling]() Tags: [apache-kafka](), [app-architecture](), [autoscaling](), [aws](), [backend](), [cassandra](), [cloud-infrastructure](), [data-analytics](), [database](), [developer-tools](), [engineering](), [heroku](), [kafka](), [metrics](), [performance-optimization](), [platform](), [platform-updates](), [stream-processing](), [time-series]() ### AI overview Heroku describes an infrastructure upgrade that migrated the storage backend for platform metrics. The article explains how MetaaS collects application observations, aggregates them into time-series statistics, and uses Kafka, stream-processing jobs, and Cassandra to support dashboard metrics, alerting, autoscaling, retention, and querying. ### Source excerpt One of our most important goals at Heroku is to be boring. Don't get us wrong, we certainly hope that you're excited about the Heroku developer experience -- as heavy users of Heroku ourselves, we certainly are! But, even more so, we hope that you don't have to spend all that much time thinking about [...] The post Evolving the Backend Storage for Platform Metrics appeared first on Heroku. ## 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. ## Durable Digest: March 2023 DevFeed: [Durable Digest: March 2023]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2023-03-30T00:00:00Z Content type: news Language: en Sources: [Temporal Blog]() Topics: [Development](), [Command-line interface](), [SDKs](), [Replication](), [MySQL](), [Apache Cassandra](), [PostgreSQL]() Tags: [announcements](), [cassandra](), [cli](), [mysql](), [postgresql](), [release](), [replication](), [sdk](), [updates](), [v1]() ### AI overview Temporal's March 2023 Durable Digest summarizes Temporal v1.20 updates, including the Temporal CLI, Scheduled Actions, SQL-based Advanced Visibility, archival changes, Workflow Update, cross-shard replication, authorizer improvements, and Batch API support for DeleteWorkflow. It also highlights SDK updates, events, and community content. ### Source excerpt Explore Temporal v1.20 updates, new SDK features, upcoming events, and community highlights in our March newsletter. Stay informed and engaged! ## How Temporal Auto-Setup works DevFeed: [How Temporal Auto-Setup works]() Original publisher: [Read original article]() Author: Shawn Wang Published: 2022-01-05T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Deployment](), [Docker](), [Databases](), [elasticsearch](), [Apache Cassandra](), [Kubernetes]() Tags: [cassandra](), [deployment](), [docker](), [elasticsearch](), [how-to](), [kubernetes](), [migrations](), [schema](), [self-hosted]() ### AI overview This article explains how Temporal's auto-setup process supports deployment. It describes the Docker entrypoint, schema setup for SQL databases and Cassandra, optional Elasticsearch visibility-schema setup, and Temporal Server namespace registration, with context for self-hosted users moving from auto-setup to production deployments. ### Source excerpt Discover how Temporal's auto-setup feature simplifies deployment, offering easy installation with Docker images and zipped binaries for the Temporal server ## Temporal Transparency Update #10 DevFeed: [Temporal Transparency Update #10]() Original publisher: [Read original article]() Author: Ryland Goldstein Published: 2021-02-19T04:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Apache Cassandra](), [Security](), [Authentication](), [Authorization](), [OpenID connect (OIDC)](), [Open Source](), [bug](), [upgrade](), [soc2]() Tags: [authentication](), [authorization](), [bug](), [cassandra](), [community](), [oidc](), [open-source](), [security](), [soc2](), [upgrade]() ### AI overview Temporal's tenth transparency update reports a Cassandra persistence bug affecting users before version 1.6.0 and recommends upgrading before changing Cassandra clusters. It also describes progress on security, including open-source authentication beta support for OIDC-compliant identity providers, authorization, and SOC2 preparations. ### Source excerpt Things have been very busy at Temporal over the last few months and there are some nice updates I can share. ## Temporal Transparency Update #7 DevFeed: [Temporal Transparency Update #7]() Original publisher: [Read original article]() Author: Ryland Goldstein Published: 2020-09-04T04:00:00Z Content type: news Language: en Sources: [Temporal Blog]() Topics: [Code](), [Testing](), [Deployment](), [Apache Cassandra](), [Helm charts]() Tags: [cassandra](), [charts](), [deployment](), [product-news](), [temporal](), [testing](), [update]() ### AI overview Temporal's seventh transparency update reports stabilization progress after code completion across its components. It covers deployment infrastructure work, scale-testing scenarios, fault-injection testing, and the investigation of issues found by tests. ### Source excerpt Hey Temporal community, hope everyone is having a fantastic Friday. In our previous update we announced code completion for JavaSDK which also meant reaching the V1 code complete milestone. ## Avoiding Overengineering: Choose Architecture and Databases for the Job DevFeed: [Avoiding Overengineering: Choose Architecture and Databases for the Job]() Original publisher: [Read original article]() Author: umer Published: 2017-08-19T00:00:00Z Content type: opinion Language: en Sources: [Code Ahoy - Articles]() Topics: [Databases](), [Architecture & Design](), [MySQL](), [NoSQL](), [Microservice](), [Apache Cassandra]() Tags: [architecture](), [cassandra](), [databases](), [microservices-architecture](), [mysql](), [nosql]() ### AI overview The author reflects on choosing technologies based on actual needs rather than hype. They describe replacing a monolith and MySQL with service-oriented architecture and Cassandra, concluding that the resulting system was too complex for a small startup team to maintain. ### Source excerpt It was April. Year was probably was 2010. The cold, snowy winter was finally coming to an end and the spring was almost in the air. I was preparing for my final exams. The review lectures were going on for the RDBMS course that I was enrolled in at my university. Around the same time, I had started hearing and reading about the shiny, new technology that was going to change the way we use databases. The NoSQL movement was gaining momentum. I was reading blogs about how MongoDB is big time outperforming ancient, non web scale relational databases. After the lecture, I asked my professor: Me: So, between RDBMS and NoSQL databases, which one do you think is the best? Professor: Well, it depends. Me: Depends on what? Professor: Depends on what you are trying to achieve. Both have their pros and cons. You pick the right tool for the job. Me: But MySQL can't really scale. Professor: How do you think we got this far? Send me an email and I'll send you some papers and practical uses in the industry. SQL was hard for my brain, especially the joins. I loved NoSQL. Simple key->value model without any joins! RDBMS systems that were designed in 1960's were simply not enough to keep up with modern demands. I had lost all interest in RDBMS and predicted they'll just fade off in the next few years. It's 2012. We're redesigning my employer's flagship product. The first version was a monolith that used the boring MySQL. Spending too much time reading blogs and Hacker News comments section, we convinced ourselves that we need to go big and modern: Break monolith into service-oriented architecture, aka, the SOA. Replace MySQL with Cassandra (MySQL to Redis to Cassandra) And we built it. There was nothing wrong with the new system... except one major flaw. It was too complex for a small startup team to maintain. We had built a Formula One race car, that makes frequent pit-stops and requires very specialized maintenance, when we needed a Toyota Corolla that goes on for years and years on j ## From Hadoop and Cassandra to Kafka Streams DevFeed: [From Hadoop and Cassandra to Kafka Streams]() Original publisher: [Read original article]() Author: Nicolas Yann Couturier Published: 2017-07-31T12:00:00Z Content type: article Language: en Sources: [Finn.no]() Topics: [Kafka](), [Apache Cassandra](), [Hadoop]() Tags: [cassandra](), [hadoop](), [kafka]() ### AI overview The article describes FINN.no's existing pipeline for computing classified-ad statistics from user actions published to Apache Kafka, stored in Cassandra, and processed in batches by Hadoop. It introduces the need to join these events with demographic data to provide more detailed viewer statistics. ### Source excerpt Some context People who publish their classified ads on FINN.no get access to various statistics to see how their ads are performing. For a user it can look something like this: Statistics the owner of a realestate ad gets to see The top left bar chart shows the repartition of the incoming traffic by day and the legend to the right of it shows the total numbers for each type of incoming traffic. The lower section is divided in 3 parts: the left part shows the number of views by unique users; the one to the center shows how many users have been notified of the ad by email and how many added the ad to their favorites; the right part shows how many viewers out of the total come from a specific type of traffic. This gives the user basic insight into the reach of their ad, such as how many views it has and how many unique users have viewed it. Around November 2016 there was a request to show more detailed information about the viewers, such as the age, gender and location distribution of the viewers (demographic information) so the owner could potentially change an ad to better fit the audience they wanted. Existing solution As users view ads, do actions (such as send a message to an ad's owner or scroll down and read the whole page, for example), these actions are gathered and published internally on Apache Kafka. These streams of data then become the basis for computing the statistics above. The plan was then to also publish demographic data about the viewers (such as location, age and gender) on Kafka and join the user actions with this demographic data to provide enhanced statistics. At the time, the action events published on Kafka were saved to Cassandra Apache Cassandra clusters, and statistics were being computed as batches on an aging Apache Hadoop cluster reading from Cassandra. Both our Hadoop and Cassandra clusters had not receive much love recently and were all on end-of-life versions. The old system also had an increasing tendency to fail, so we were also i ## New I/O flavors in ePouta DevFeed: [New I/O flavors in ePouta]() Original publisher: [Read original article]() Author: Jukka Nousiainen (noreply@blogger.com) Published: 2017-07-07T12:43:00Z Content type: release Language: en Sources: [CSC - IT Center For Science - Cloud Team]() Topics: [cloud-infrastructure](), [IO](), [Persistence](), [Apache Cassandra](), [datasets](), [Server]() Tags: [cassandra](), [clusters](), [cpouta](), [epouta](), [persistence](), [ssd](), [storage]() ### AI overview The I/O Virtual Machine flavors previously released in cPouta are now available in ePouta. They use SSD-backed striped RAID0 storage, providing high I/O performance without persistence or VM health guarantees if a node or disk fails. They are suited to clustered software such as Hadoop and Cassandra, with anti-affinity server groups recommended for node separation. ### Source excerpt About a year ago we released the I/O Virtual Machine flavors in cPouta. As of July, similar flavors are now also available in ePouta; you can find an exhaustive list here. The I/O flavors are provided with the same disclaimer as with their cPouta counterparts: both the root disk and the ephemeral disk for data storage are hosted on a striped (RAID0) array of SSD disks. That means you get a lot of I/O, but absolutely no guarantees for the persistence or health of VMs in case a node or a disk breaks. Thus these flavors are most natural to use with software which clusters nicely, such as Hadoop or Cassandra. To further ensure each VM in a cluster ends up on separate nodes, we recommend creating an anti-affinity server group. ## How (and Why) We Moved to Spinnaker DevFeed: [How (and Why) We Moved to Spinnaker]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2017-04-07T05:00:00Z Content type: article Language: en Sources: [Target]() Topics: [CI/CD](), [Cloud](), [Jenkins](), [Provisioning](), [Terraform](), [Microservices](), [Apache Cassandra](), [Persistence](), [Cache](), [Git]() Tags: [automation](), [cassandra](), [ci-cd](), [cloud](), [deployment](), [engineering](), [git](), [jenkins](), [microservices](), [redis](), [spinnaker](), [terraform]() ### AI overview Target describes moving portions of target.com from a homegrown cloud deployment platform to Spinnaker after evaluating CI/CD platforms. The team selected Spinnaker for its Jenkins integration, multi-cloud deployment support, and use of immutable images, then adapted its architecture with Terraform, cloud storage, and managed caching to support resilient, highly available operation. ### Source excerpt Background Just after the middle of last year, Target expanded beyond its on-prem infrastructure and began deploying portions of target.com to the cloud. The deployment platform was homegrown (codename Houston), and was backed wholly by our public cloud provider. While in some aspects that platform was on par with other prominent continuous deployment offerings, the actual method of deploying code was cumbersome and not adherent to cloud best practices. These shortcomings led to a brief internal evaluation of various CI/CD platforms, which in turn led us to Spinnaker. We chose Spinnaker because it integrates with CI tools we already use at scale (Jenkins), supports deploying to all major public cloud providers, and compels software deployment best practices - all deployments are performed via immutable images, a snapshot of config + code. Supporting a Platform The primary goal of Target's cloud platform is to enable product teams to deploy and manage their applications across multiple cloud providers. We provide CI/CD, monitoring, and service discovery as services, and any application deployed via our platform gets those capabilities via a base image that is pre-configured for connectivity to each service's respective endpoint. Since these components are essentially products we provide to internal customers, we had to ensure the new CD platform was operationally supportable and highly-available. So, as soon as we decided on Spinnaker, a handful of engineers from the Cloud Platform group set about making this happen. Default Spinnaker scripts make it easy to standup a single self-contained server with the microservices and persistence layer all together, but that wasn't conducive to doing blue-green deployments - allowing updates of Spinnaker without downtime to our internal customers. We built jobs for building packages based off the master branch of each Spinnaker component's upstream git repository, and wrote Terraform plans to manage the deployment of each stack. ## Apache Cassandra in a Microservices Enterprise Platform DevFeed: [Apache Cassandra in a Microservices Enterprise Platform]() Original publisher: [Read original article]() Author: mick Published: 2015-04-28T14:00:00Z Content type: article Language: en Sources: [Finn.no]() Topics: [Apache Cassandra](), [Microservice](), [Architecture & Design](), [Temporal data]() Tags: [apis](), [architecture](), [cassandra](), [coupling](), [enterprise](), [event-driven](), [microservices](), [rest](), [time-series]() ### AI overview The article discusses Apache Cassandra as a persistence layer for microservices platforms, particularly for systems that need scalability and time-series data models. It also outlines microservices practices involving isolated runtime APIs, reduced coupling, REST, and producer-defined schemas for event-driven designs. ### Source excerpt In this article we'll explore how Apache Cassandra, the world's most popular wide column store and 8th most popular database overall, will only grow as a cornerstone technology in a microservices platform. With a little theory to microservices, to some examples of microservices and underlying required infrastructure, we'll show that any solution both capable of scaling and dealing with time-series data-models is going to need to depend upon Apache Cassandra as a persistence layer, despite having a polyglot persistence model at large. microservices Microservices is a term that's come out of ThoughtWorks' Martin Fowler and James Lewis. It's a bit of a buzzword, basically a fresh revival of the parts of service orientated architecture that you should be focusing on and getting right. A lot of it hopefully is obvious to you already. If you've been doing service orientated architecture or even generally just unix programming properly over the years it might well be frustrating just how buzz "microservices" has become. But it's worth keeping in mind how much garbage we've collected and how many aspects of service orientated architecture that we've gotten badly wrong over the years. Younger programmers certainly deserve the clarity that ThoughtWorks is giving us here. Microservices, following the tips and guidelines from Sam Newman, can basically be broken down into four groups. interfaces Ensure that you standardise the systems architecture at large and especially the gaps or what we know as the APIs between services. Standardise upon practices and protocols that minimise coupling. Move from tightly coupled systems with many compile time dependencies and distributed published client libraries, to clearly defined and isolated runtime APIs. Take advantage of REST, especially level 3 in richardson's maturity model, for the synchronous domain driven designed parts of your system. When it comes to event driven design use producer defined schemas, like that offered by Apache Th ## How FINN Redesigned Its Big Data Statistics System DevFeed: [How FINN Redesigned Its Big Data Statistics System]() Original publisher: [Read original article]() Author: mick Published: 2015-04-27T09:00:00Z Content type: tutorial Language: en Sources: [Finn.no]() Topics: [big-data](), [Architecture & Design](), [Scalability](), [Apache Cassandra](), [Hadoop](), [web applications](), [Back end]() Tags: [architecture](), [backend](), [big-data](), [cassandra](), [hadoop](), [scalability](), [web-applications]() ### AI overview This technical article explains how FINN redesigned its statistics system for asynchronous processing, fault tolerance, linear scalability, and durability. The design separates event collection from statistics display, using Scribe, Cassandra, Thrift, and Hadoop MapReduce. ### Source excerpt In the previous article we introduced our own introduction into the world of Big Data, and explored what it meant for FINN. Here we'll go into the technical depth about the implementation of our Big Data needs. rehashing the previous article FINN is a busy site, the busiest in Norway, and we display over 80 million ad pages each day. Back when it was around 50 million views per day, the old system responsible for collecting statistics was performing up to a thousand database writes per second during peak traffic. Like a lot of web applications we had a modern scalable presentation and logic tier based upon ten tomcat servers but just one not-so-scalable monster database sitting in the data tier. The procedure responsible for writing to the statistics table in the relational database was our biggest thorn. It had indeed gotten so bad that: during peak traffic; operations had to at the first sign of trouble turn off this database procedure - that is when users were getting the most traffic to their ads we had to stop collecting their statistics. At this time we were also in the process of modularising the FINN web application. The time was right to turn our statistics system into something modern and modular. We wanted an asynchronous, fault-tolerance, linearly scaling, and durable solution. the design The new design uses the Command Query Separation pattern by using two separate modules: one for the collecting of events and one for displaying statistics. The event collecting system achieves asynchronousity, scalability, and durability by using Scribe. The backend persistence and statistics module achieves all goals by using Cassandra and Thrift. As an extension of the push-on-change model: the event collection stores denormalised data and it is later aggregated and normalised to the views the statistics module requires; we use MapReduce jobs within a Hadoop cluster. the statistics module At FINN all our modular architecture is built either upon REST or interfaces def ## ApacheCon Budapest 2014 DevFeed: [ApacheCon Budapest 2014]() Original publisher: [Read original article]() Author: mick Published: 2014-11-25T13:00:00Z Content type: opinion Language: en Sources: [Finn.no]() Topics: [Hadoop](), [Apache Spark](), [Docker](), [hdfs](), [Apache Cassandra](), [Streaming](), [couchdb](), [Data structures]() Tags: [apache](), [cassandra](), [couchdb](), [data-structures](), [docker](), [hadoop](), [hdfs](), [spark](), [streaming]() ### AI overview A firsthand account of ApacheCon Budapest 2014 discusses Apache communities and several technologies, including Docker, Spark, Cassandra, CouchDB, Hadoop, Mesos, Aurora, YARN, Solr, and OpenOffice. It highlights considerations around scalability, testing, streaming, production utilisation, and data processing. ### Source excerpt Last week was Europe's ApacheCon, held in Budapest. A refreshingly "laid-back and no-nonsense" conference free from the douchebaggery that comes from big sponsors and marketing. This year it was held in the awesome 5-star Corinthia Hotel, and consisted of a welcoming and cheerful vibe, a place for apache members, committers, and all, to get together and better know each other. Apache continues to grow as the foundation for strong communities building trusted open sourced solutions, for running half the internet, and providing a substantial amount of code to your java stack. Some hot topics were Docker, Mesos, Spark, Cassandra, CouchDB, Hadoop, Solr, OpenOffice, and the need for greater diversity in our communities. Spark is all the rage because of its brevity and simplicity, but isn't really a complete solution yet because it doesn't scale in many situations. Docker is awesome, and looks to take over the testing domain, but it remains limited and out of production as long as it has no network stack implementation. There was a very cool demonstration of Mesos and Aurora increasing production utilisation, even allowing in quiet periods for development/testing servers to come in. The next release of YARN (2.6) will see support for long-lived services, whereby we can transform many of our hadoop jobs with very little effort into true streaming solutions. YARN will also be able to deploy docker containers through its clusters, this could be a very nice solution for our batch jobs. Putting these together and you see that the technical separation between streaming and aggregating solutions fades away and it really just boils down to what data structures you solve each use case with. Talking with a hadoop committer it came to light that FINN could be running the fastest hadoop cluster that they know of, given HDFS runs on SSDs and is dedicated just for hadoop internals. Of no surprise Solr dealt a lot with scaling and performance, while Cassandra presented use-cases from ac ## Roshi: a CRDT system for timestamped events DevFeed: [Roshi: a CRDT system for timestamped events]() Original publisher: [Read original article]() Published: 2014-05-09T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [Core Infra](), [Apache Cassandra](), [Streams](), [Sorting](), [data]() Tags: [announcements](), [cassandra](), [data](), [event](), [go](), [infrastructure](), [open-source](), [scale](), [sorting](), [streams]() ### AI overview SoundCloud describes Roshi, a CRDT system for timestamped events, in the context of building a large-scale social stream. The article contrasts the existing Cassandra-based fan-out-on-write model with fan-in-on-read, which stores events in users' outboxes and merges them when streams are requested. The alternative reduces writes and storage costs while making reads more challenging because many event streams must be time-sorted and merged. ### Source excerpt Let's talk about the stream. The SoundCloud stream represents stuff that's relevant to you primarily via your social graph, arranged in time... ## Hackergarten Paris - ce qu'il faut savoir sur les projets DevFeed: [Hackergarten Paris - ce qu'il faut savoir sur les projets]() Original publisher: [Read original article]() Author: brice.dutheil@gmail.com (Brice Dutheil) Published: 2012-03-05T01:54:28Z Content type: article Language: fr Sources: [The Coffee Workshop]() Topics: [ide](), [Maven](), [IntelliJ IDEA](), [Apache Cassandra](), [Jenkins](), [Git](), [NoSQL]() Tags: [cassandra](), [git](), [ide](), [intellij](), [jenkins](), [maven](), [nosql](), [opensource]() ### AI overview This French announcement presents the second Hackergarten Paris session and explains what participants should install or prepare in advance. It covers project work involving Hibernate OGM, Hibernate Search, Maven, Jenkins, Infinitest, and Mockito, with setup guidance for IDEs, Java, Cassandra, Git, SVN, and dependencies. ### Source excerpt Hello Γ  tous, Mercredi 7 mars (Γ  19h) aura lieu la 2Γ¨me session Hackergarten. AprΓ¨s Soat, on a le plaisir d'Γͺtre hΓ©bergΓ© par Valtech (103 Rue de Grenelle, 75007 Paris) et il y aura des pizzas, gros merci Γ  eux. ## SoundCloud Signs Apache Corporate Contributor License Agreement DevFeed: [SoundCloud Signs Apache Corporate Contributor License Agreement]() Original publisher: [Read original article]() Published: 2011-10-14T00:00:00Z Content type: news Language: en Sources: [SoundCloud Backstage Blog]() Topics: [Open Source](), [Apache Cassandra](), [Hadoop]() Tags: [apache](), [cassandra](), [contribute](), [corporate](), [developers](), [hadoop](), [open-source](), [production](), [scale](), [software](), [source](), [work]() ### AI overview SoundCloud announces that it has signed the Apache Corporate Contributor License Agreement. The company says it relies extensively on open source software and Apache projects, and plans to contribute code to Flume first while enabling developers to contribute during work time when the project is used by SoundCloud. ### Source excerpt We just signed the corporate contributor license agreement (CCLA). SoundCloud always was big on open source - we nearly exclusively use open... [Next page]()