# consul Published articles for consul. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Investigating Redis Client Failures After a Node Loss at hh.ru DevFeed: [Investigating Redis Client Failures After a Node Loss at hh.ru]() Original publisher: [Read original article]() Author: nicholasgribanov (hh.ru) Published: 2026-08-26T05:30:49Z Content type: article Language: ru Sources: [HeadHunter RU]() Topics: [Redis]() Tags: [consul](), [downtime](), [java](), [jedis](), [redis](), [sre](), [tag-1d4e49052118](), [tag-68e701e78517](), [tag-b0a411324cb6](), [tag-b92bf5906bbd](), [tag-dfac9042ce7b](), [timeout]() ### AI overview A Platform team at hh.ru investigates incidents in its internal Redis client, including a 57-minute outage after a Redis node was lost. The team used controlled-load testing, Jedis updates, timeout and pool experiments, new metrics, and logs to trace the failures to a code issue and improve client stability. ### Source excerpt Пару лет назад мы устроили настоящее расследование серии инцидентов в поисках скрытого дефекта нашего Redis-клиента. Команда воспроизводила сбои под контролируемой нагрузкой, проверяла одну гипотезу за другой, обновляла Jedis, экспериментировала с таймаутами и размерами пулов -- но ничего не помогало. А помогли новые метрики и логи, настойчивость команды, ночные эксперименты и готовность разбирать поведение системы до последнего соединения. Получилась история с неожиданными поворотами, ложными следами и одной лишней строчкой кода в роли главного подозреваемого -- а её итогом стал Redis-клиент, который оказался устойчивее, чем был до начала расследования. Меня зовут Коля Грибанов, я тимлид команды "Платформа" в hh.ru. В статье расскажу, почему потеря одной ноды Redis вызывала шторм из десятков тысяч соединений, и как мы шаг за шагом искали причину инцидентов. Читать далее ## Update for Chainguard Images users on HashiCorp license changes DevFeed: [Update for Chainguard Images users on HashiCorp license changes]() Original publisher: [Read original article]() Published: 2023-09-01T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [chainguard images](), [chainguard](), [Security](), [Open Source](), [Terraform]() Tags: [chainguard-images](), [consul](), [hardened-container-image](), [hashicorp](), [open-source](), [opentf](), [releases](), [security](), [terraform](), [vault]() ### AI overview Chainguard explains how HashiCorp's switch from the Mozilla Public License to the Business Source License affects Chainguard Images users. Paying customers receive six additional months of security patches for MPL-licensed HashiCorp tools, while free users retain access to the final MPL-based versions and may receive BUSL-based images through other distribution options. ### Source excerpt Chainguard's proactive approach to HashiCorp license changes: Secure image solution for hassle-free adaptation. ## From pipeline to beyond DevFeed: [From pipeline to beyond]() Original publisher: [Read original article]() Author: GameChanger Published: 2021-05-05T09:00:18Z Content type: article Language: en Sources: [GameChanger]() Topics: [Kafka](), [Amazon S3](), [data](), [streaming-data-processing](), [Back end]() Tags: [analysis](), [backend](), [consul](), [data](), [infrastructure](), [kafka](), [pipeline](), [redshift](), [s3](), [terraform](), [warehouse]() ### AI overview The article examines options for moving data out of Kafka into a data warehouse and archive. It discusses off-the-shelf tools such as Kafka Connect, Secor, and Gobblin, the limitations encountered, and the development of a custom solution. The requirements include preserving Avro data and schemas, writing to S3, and partitioning data by processing time or event time. ### Source excerpt An overview of off-the-shelf solutions for moving data out of Kafka, problems we had making those systems work, and how we wrote our own solution and stood it up for those in a similar situation. You cannot know everything a system will be used for when you start: it is only at the end of its life you can have such certainty. [1] Many moons ago I wrote about our design for upgrading our data pipeline, which lightly touched on how we'd move data out of our pipeline (Kafka) to downstream systems, namely our data warehouse and data archive. At that time we hadn't really been able to dive into focusing on getting the data out of Kafka, because getting data in to Kafka is often much more custom and complex, and we thought we'd be able to use an off the shelf solution like Kafka Connect to move data out, don't even worry about it. We were, uh -- we were wrong. Let me take you on our journey, in case you're on this journey too. The problem space Solution 1: Kafka Connect Solution 2: Secor or Gobblin Solution 3: we'll do this ourselves Tangent: naming things How you can do this yourselves, code edition How you can do this yourselves, infrastructure and metrics edition Takeaways The problem space Programmers are not to be measured by their ingenuity and their logic but by the completeness of their case analysis. [4] At a high level, the problem we needed a solution for was as follows: Data enters the data pipeline from numerous backend systems. This crossover point is producers into the pipeline, which we'd already implemented. Data from the data pipeline needs to move into the data warehouse and the data archive. This crossover point would be a consumer on the pipeline. Ideally we'd like the same consumer for both needs that we can simply configure differently. We want to preserve our data's Avro format along side its schemas. This would allow every system that interacts with the data to use the same language. We want to write our data to S3. data warehouse: This will be our ## Using Terraform, Consul, and Ansible to Define and Deploy a Data Pipeline DevFeed: [Using Terraform, Consul, and Ansible to Define and Deploy a Data Pipeline]() Original publisher: [Read original article]() Author: GameChanger Published: 2019-12-18T12:00:00Z Content type: tutorial Language: en Sources: [GameChanger]() Topics: [Infrastructure as code](), [Ansible](), [Terraform](), [configuration](), [JSON](), [YAML](), [Git](), [systems]() Tags: [ansible](), [collection](), [consul](), [data](), [git](), [iac](), [infrastructure](), [infrastructure-as-code](), [json](), [pipeline](), [terraform](), [yaml]() ### AI overview The article explains how infrastructure as code can document and reproduce a data pipeline across environments. It describes combining Terraform, Consul, and Ansible so infrastructure, machine configuration, and related settings can be managed together. ### Source excerpt You've finally got everything working in your staging environment: the new systems talk to each other, everything is running smoothly, your dashboards are beautiful and pristine. Now you need to get it into production with zero down time and no interruptions. And then you realize, you've no idea what you actually did in your staging environment. Let's talk about infrastructure I don't consider myself an "infrastructure lady;" it's just not my jam, and that's fine. However I have had to learn a lot about our infrastructure for setting up our new data pipeline system -- and I don't just show up to learn the minimum. I come to m a s t e r. IaC, or infrastructure as code, is the idea of setting up your infrastructure using definition files, code, and standard programming practices. In a way, it brings infrastructure to those of us who might otherwise be overwhelmed by what's going on and what we're suppose to do. Why manually enter configuration values when you can store them in a YAML or JSON file in a git repo? Suddenly you can see its history, you can search it, and it documents itself to a certain extent. This last point is of particular interest to me, as so much of the data pipeline is just how the systems work together, what the configurations are to facilitate this, and and documenting how all this works. If -- if -- we could have our data pipeline and all its friends live in some straightforward, self documenting IaC setup, it would make it easy not only to remember what we did but also to onboard someone new to the system and to deploy it to different environments with high confidence that it'll work. At GameChanger, that meant making three systems work together: Terraform, Consul, and Ansible. Terraform sets up what we want the landscape of our machines to look like. Ansible sets up what we want the landscape on our machines to look like. And Consul is the new kid in the collection, just here to have a good time and be helpful. Leibnizian optimism Alright, so we ## Target and Elasticsearch: Maintaining an ELK stack over Peak Season DevFeed: [Target and Elasticsearch: Maintaining an ELK stack over Peak Season]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2017-05-25T05:00:00Z Content type: article Language: en Sources: [Target]() Topics: [elasticsearch](), [Logging](), [Cloud](), [Monitoring](), [dashboards](), [Scalability](), [Kafka]() Tags: [apache](), [apache-kafka](), [cloud](), [clusters](), [consul](), [contribute](), [dashboards](), [elasticsearch](), [elk](), [hashicorp](), [infrastructure](), [kafka](), [logging](), [logging-and-metrics](), [logs](), [make](), [metrics](), [monitoring](), [open](), [open-source](), [scalability]() ### AI overview This article describes how Target operated and evolved an Elasticsearch-based ELK stack for large-scale log aggregation, search, analytics, and multi-tenant logging during peak production periods. It discusses cloud scalability, operational challenges, and the open-source tools used alongside Elasticsearch. ### Source excerpt One of the strongest benefits of launching an application into the cloud is the pure on-demand scalability that it provides. I've had the privilege of working with the ELK stack (Elasticsearch, Logstash, Kibana) for purposes of log aggregation for the past two years. When we started at that time, we were pleased with our performance on search and query times with 10's of gigabytes of data in the cluster in production. When Peak time hit, we reveled as our production clusters successfully managed half a terabyte of data(!). During peak, Target hosted 14 Elasticsearch clusters in the cloud containing more than 83 billion documents across nearly 100 terabytes in production environments alone. Consumers of these logs are able to get access to queries in blazing fast times with excellent reliability. It wasn't always that way though, and our team learned much about Elasticsearch in the process. What's The Use Case At Target? In a word, "vast." The many teams that use our platform for log aggregation and search are often times looking for a variety of things. Simple Search This one is easy, and the least resource intensive. Simply doing a match query and searching for fields within our data. Metrics / Analytics This one can be harder to accommodate at times, but some teams use our Elasticsearch clusters for near-realtime monitoring and Analytics using Kibana dashboards. Multi-tenant Logging Not necessarily consumer facing, but an interesting use for Elasticsearch is that we can aggregate many teams and applications into one cluster. In essence, this saves money over individual applications paying for infrastructure to log themselves. Simple search is the least of our concerns here. Queries add marginal load on the cluster, but often they are one-offs or otherwise infrequently used. However, the largest challenge faced here is multi-tenant demand. Different teams have very different needs for logging/metrics; designing a robust and reliable 'one-size-fits-all' platform is