# scheduling Published articles for scheduling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) DevFeed: [Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta)]() Original publisher: [Read original article]() Author: Harsh Vardhan Published: 2026-09-16T16:00:00Z Content type: tutorial Language: en Sources: [Postman Blog]() Topics: [gRPC](), [Postman](), [Monitoring](), [Authentication]() Tags: [beta](), [general](), [graphql](), [graphql-monitoring](), [how-to](), [monitor-grpc-streaming](), [monitoring](), [monitors](), [postman](), [postman-monitors-grpc-graphql](), [scheduling](), [test]() ### AI overview This tutorial explains how to schedule gRPC and GraphQL requests with Postman Monitors in beta. It covers supported request types, streaming caveats, response testing, authentication, and current feature limitations. ### Source excerpt Postman Monitors now run gRPC and GraphQL requests in beta. Learn how to schedule them, test responses, and send feedback. Start monitoring today. The post Scheduling gRPC and GraphQL Requests with Postman Monitors (Beta) appeared first on Postman Blog. ## New data pipeline management platform at Khan Academy DevFeed: [New data pipeline management platform at Khan Academy]() Original publisher: [Read original article]() Author: Khan Academy Published: 2018-04-30T22:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [data](), [DataOps](), [systems]() Tags: [batch](), [bigquery](), [cloud-dataflow](), [data](), [data-pipelines](), [debugging](), [engineering](), [infrastructure](), [news](), [pipeline](), [scheduling]() ### AI overview Khan Academy developed Khanalytics to manage its growing collection of data pipelines. The platform provides a sandboxed environment for batch jobs, a web interface, automatic parallelization, centralized logs, and pipeline scheduling with dependencies. ### Source excerpt By Ragini Gupta Data is very crucial to Khan Academy and is itself an internal product for the ... Read more ## libosmocore - libosmocore 1.14.1 released DevFeed: [libosmocore - libosmocore 1.14.1 released]() Original publisher: [Read original article]() Author: osmith Published: 2026-07-22T10:37:33Z Content type: release Language: en Sources: [Open Source Mobile Communications: News]() Topics: [changelog](), [IO](), [cpu](), [scheduling](), [make]() Tags: [2026](), [bug](), [changelog](), [cpu](), [io](), [issue](), [make](), [scheduling](), [tracker]() ### AI overview libosmocore 1.14.1 has been released with fixes to I/O queue handling, CPU scheduling initialization and platform-dependent scheduling support, utility issues, and a missing crypto header in the build configuration. ### Source excerpt Today we've released libosmocore 1.14.1. The changelog over 1.14.0 is as follows: libosmocore (1.14.1) unstable; urgency=medium [ Vadim Yanitskiy ] * osmo_io: iofd_txqueue_dequeue(): fix double next->io_len decrement [ Timur Davydov ] * vty: fix osmo_cpu_sched_vty_init() prototype * vty: make CPU scheduling optional based on platform support [ Oliver Smith ] * utils: fix typos * utils/gsmtap-logsend: close infile before exit [ Alexander Couzens ] * gsm/Makefile.am: add missing kdf/crypto.h -- Oliver Smith Wed, 22 Jul 2026 11:30:55 +0200 ## Task Queue Priority and Fairness: Your Task Queue, your way DevFeed: [Task Queue Priority and Fairness: Your Task Queue, your way]() Original publisher: [Read original article]() Author: Conna Lanzafane Published: 2026-05-12T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [SDKs]() Tags: [fairness](), [priority](), [product-news](), [queue](), [queues](), [scheduling](), [sdks]() ### AI overview Temporal Task Queue Priority and Fairness is generally available for all SDKs. Priority controls task execution order, while Fairness prevents a single Workflow, user, or tenant from monopolizing Workers. ### Source excerpt Task Queue Priority and Fairness are GA for all Temporal SDKs, helping teams prioritize critical work and prevent tenant starvation without custom infra. ## Managing an Engineer's Week as a Queueing System DevFeed: [Managing an Engineer's Week as a Queueing System]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-15T00:00:00Z Content type: opinion Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [queueing theory](), [scheduling](), [systems](), [Server]() Tags: [email](), [focus](), [minutes](), [notifications](), [productivity](), [queueing-theory](), [scheduling](), [servers](), [slack](), [software-engineering](), [system](), [time-management](), [work]() ### AI overview The article argues that software engineers' schedules behave like queueing systems: near-total utilization increases waiting time, while interruptions evict working context. It recommends maintaining schedule headroom, batching communications, and limiting work in progress. ### Source excerpt An engineer's week behaves like a queueing system: book yourself at 100% utilization and the wait time for anything new goes vertical, exactly as it does for a server. The fixes are the ones we already use in systems: keep headroom, coalesce interrupts instead of taking them one by one, cap work in progress because Little's law applies to you, and stop letting urgent-but-small tasks hold the lock while important work starves. ## Paper #196. The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling DevFeed: [Paper #196. The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2025-04-14T03:31:33Z Content type: article Language: en Sources: [Aleksey Charapko]() Topics: [scheduling](), [cpu](), [data centers](), [distributed-systems](), [Hardware](), [Computing](), [Benchmark]() Tags: [benchmark](), [carbon](), [computing](), [cpu](), [data-center](), [data-centers](), [distributed](), [distributed-systems](), [efficiency](), [emissions](), [hardware](), [reading-group](), [scheduling]() ### AI overview This article presents a reading-group discussion of a paper about carbon-aware scheduling. It explains how Software Carbon Intensity combines operational and embodied carbon, and argues that allocating embodied carbon to jobs can make scheduling decisions misleading, particularly in heterogeneous clusters. ### Source excerpt The last paper we covered in the Distributed Systems Reading group discussed CPUs, data centers, scheduling, and carbon emissions--we read "The Sunk Carbon Fallacy: Rethinking Carbon Footprint Metrics for Effective Carbon-Aware Scheduling." Below is my improvised presentation of this paper for the reading group. This paper was an educational read for me, as I learned [...] ## Explainable Linear Programs DevFeed: [Explainable Linear Programs]() Original publisher: [Read original article]() Published: 2025-02-07T05:24:38Z Content type: opinion Language: en Sources: [Jeremy Kun]() Topics: [Optimization](), [scheduling](), [Routing (disambiguation)](), [Large Language Model](), [Microsoft]() Tags: [google](), [large-language-models](), [microsoft](), [optimization](), [routing](), [scheduling](), [shortform]() ### AI overview The author describes a 2020 side project for explaining linear-programming solutions by interactively changing constraints or objectives, re-solving models, and aggregating differences using semantic metadata. The article connects this approach to a 2023 Microsoft paper that uses a large language model to translate natural-language queries into structured queries for supply-chain optimization. ### Source excerpt Back in 2020, when I worked in the supply chain side of Google, I had a fun and impactful side project related to human-level explanations of linear programs. A linear program is a mathematical model that defines some number of variables, linear constraints, and a linear objective function. When some variables are forced to be integer (ILPs), you can solve a lot of useful problems like scheduling, routing, and packing. That's basically how all supply chain optimization works. ## How Request processing has changed over the years DevFeed: [How Request processing has changed over the years]() Original publisher: [Read original article]() Published: 2025-02-03T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [processing](), [Computer science](), [Concurrency](), [scheduling](), [Kernel](), [Processes](), [Ruby](), [Python]() Tags: [concurrency](), [cpu](), [kernel](), [process](), [processing](), [python](), [request](), [ruby](), [scheduling]() ### AI overview This article explains how web request processing works, distinguishing blocking and non-blocking tasks and introducing concepts such as CPU control, kernel behavior, and cooperative versus preemptive scheduling. It begins comparing request-processing architectures in the context of Ruby, Python, NodeJS, and Rails services. ### Source excerpt . [How Request Processing Has Changed Over the Years](how-request-processing-has-changed-over-the-years-cover... ## How to convert your job scheduling system to Temporal Schedules DevFeed: [How to convert your job scheduling system to Temporal Schedules]() Original publisher: [Read original article]() Author: Irina Belova Published: 2024-04-15T04:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Job](), [migration](), [systems](), [retry](), [Script]() Tags: [how-to](), [job](), [migration](), [replacement](), [retry](), [scheduling](), [scripts](), [worker](), [workflow]() ### AI overview This tutorial explains how to migrate existing cron and other scheduled jobs to Temporal Schedules. It covers wrapping non-Temporal scripts in Activities, assessing schedule and overlap requirements, handling retries and failures, and deploying workflows in a Temporal Worker. ### Source excerpt Temporal Schedules offer a reliable, efficient alternative to traditional job schedulers, replacing Cron, systemd timers, and task queues like Celery. ## Apache Airflow 2.9.0: Dataset and UI Improvements DevFeed: [Apache Airflow 2.9.0: Dataset and UI Improvements]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2024-04-08T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [dataset](), [datasets](), [releases](), [Release notes](), [ui](), [Python]() Tags: [2-9-0](), [airflow](), [apache-airflow](), [dataset](), [datasets](), [python](), [release](), [release-notes](), [scheduling](), [ui]() ### AI overview Apache Airflow 2.9.0 introduces expanded data-aware scheduling with logical OR and arbitrary AND/OR combinations, plus a timetable combining dataset events with time-based schedules. It also adds dataset event REST API endpoints, dataset UI enhancements, Python 3.12 support with Pendulum 3, and custom names for dynamically mapped tasks. ### Source excerpt I'm happy to announce that Apache Airflow 2.9.0 has been released! This time around we have new features for data-aware scheduling and a bunch of UI-related improvements. Apache Airflow 2.9.0 contains over 550 commits, which include 38 new features, 70 improvements, 31 bug fixes, and 18 documentation changes. Details: 📦 PyPI: https://pypi.org/project/apache-airflow/2.9.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/2.9.0/ 🛠 Release Notes: https://airflow.apache.org/docs/apache-airflow/2.9.0/release_notes.html 🐳 Docker Image: "docker pull apache/airflow:2.9.0" 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-2.9.0 Airflow 2.9.0 is also the first release that supports Python 3.12. However, Pendulum 2 does not support Python 3.12, so you'll need to use Pendulum 3 if you upgrade to Python 3.12. New data-aware scheduling options Logical operators and conditional expressions for DAG scheduling When Datasets were added in Airflow 2.4, DAGs only had scheduling support for logical AND combinations of Datasets. Simply, you could schedule against more than one Dataset, but a DAG run would only be created once all the Datasets were updated after the last run. Now in Airflow 2.9, we support logical OR and even arbitrary combinations of AND and OR. As an example, you can schedule a DAG whenever dataset_1 or dataset_2 are updated : with DAG(schedule=(dataset_1 | dataset_2), ...): ... You can have arbitrary combinations: with DAG(schedule=((dataset_1 | dataset_2) & dataset_3), ...): ... You can read more about this new functionality in the data-aware scheduling docs. Combining Dataset and Time-Based Schedules Airflow 2.9 comes with a new timetable, DatasetOrTimeSchedule, that allows you to schedule DAGs based on both dataset events and a timetable. Now you have the best of both worlds. For example, to run whenever dataset_1 updates and at midnight UTC: with DAG( schedule=DatasetOrTimeSchedule( timetable=CronTriggerTimetable("0 0 * * *", timezone="UTC"), ## How do I convert my Cron into a Schedule? DevFeed: [How do I convert my Cron into a Schedule?]() Original publisher: [Read original article]() Author: Quinn Klassen Published: 2023-12-18T05:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Development](), [SDKs](), [CRUD](), [Command-line interface](), [Python](), [Go Language](), [Java](), [.NET]() Tags: [cli](), [crud](), [execution](), [go](), [how-to](), [java](), [net](), [python](), [scheduling](), [sdk](), [tutorial](), [visibility](), [workflow]() ### AI overview This tutorial explains how to replace traditional cron jobs with Temporal Schedules. It covers the migration process, Schedule CRUD operations through the SDKs, CLI, and UI, and examples for Python, Go, Java, and .NET. ### Source excerpt Converting a Cron into a Temporal schedule will will improve the durabililty of task execution and the visibility of schedule and workflow runs. ## Temporal Schedules: Reliable, scalable, and more flexible than Cron jobs DevFeed: [Temporal Schedules: Reliable, scalable, and more flexible than Cron jobs]() Original publisher: [Read original article]() Author: Irina Belova Published: 2023-11-15T07:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Job](), [Scalability](), [Monitoring](), [observability]() Tags: [execution](), [monitoring](), [observability](), [product-news](), [progress](), [scalability](), [scheduling](), [temporal]() ### AI overview Temporal announces the general availability of Temporal Schedules, a replacement for traditional Cron jobs. The feature supports durable task execution, workflow controls, progress insight, observability, flexible intervals, and reduced reliance on external scheduling systems. ### Source excerpt Replace traditional Cron jobs with Temporal Schedules for durable task execution, progress insights, and enhanced scalability. ## Using the Eisenhower Matrix to Manage Overwhelm DevFeed: [Using the Eisenhower Matrix to Manage Overwhelm]() Original publisher: [Read original article]() Author: Pedro Gil Carvalho Published: 2023-06-23T06:46:57Z Content type: opinion Language: en Sources: [Pedro Gil Carvalho]() Topics: [Matrix](), [systems](), [scheduling](), [meetings]() Tags: [delegation](), [matrix](), [meetings](), [scheduling](), [systems]() ### AI overview The author describes using management systems to handle recurring problems and reduce decision-making overhead. When competing priorities become overwhelming, they use the Eisenhower Matrix to classify tasks by urgency and importance, identify delegation opportunities, and review priorities on a Miro board. ### Source excerpt How the Eisenhower Matrix saves my day ## Temporal v1.20: Simpler dev environments & Workflow scheduling DevFeed: [Temporal v1.20: Simpler dev environments & Workflow scheduling]() Original publisher: [Read original article]() Author: Justin Pirie Published: 2023-03-20T07:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Development](), [releases](), [Command-line interface](), [distributed-systems](), [Automation](), [Containers](), [elasticsearch](), [datadog](), [cURL](), [Homebrew](), [Quartz]() Tags: [automation](), [cli](), [containers](), [curl](), [datadog](), [dev](), [distributed](), [distributed-systems](), [elasticsearch](), [homebrew](), [product-news](), [quartz](), [scheduling](), [v1](), [workflow]() ### AI overview Temporal v1.20 simplifies local development environments by packaging Temporalite into the core product and adding a Temporal CLI with installation options including curl, Homebrew, and binary downloads. The release also adds Advanced Visibility to the CLI, deprecates direct Advanced Visibility on Cassandra without Elasticsearch, and makes Scheduled Workflows generally available. ### Source excerpt Explore Temporal v1.20's improvements in dev environments and Workflow scheduling, making it easier to adopt distributed systems best practices. ## The Value of Deep Work for Peak Performance DevFeed: [The Value of Deep Work for Peak Performance]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2023-03-11T06:00:00Z Content type: opinion Language: en Sources: [Tobias Mende]() Topics: [code productivity](), [Software](), [Developer experience](), [Tech Lead]() Tags: [culture-deep-work-employee-happiness-developer-productivity-engineering-excellence](), [deep](), [developer-experience](), [meetings](), [performance](), [productivity](), [scheduling](), [software-engineer](), [tech-lead](), [work]() ### AI overview This article explains why uninterrupted deep work matters for software engineers and teams, how distractions and meetings reduce available focus time, and how scheduling protected work blocks can improve productivity and quality. It also introduces a four-part series on balancing deep work with approachability in a developer experience team. ### Source excerpt The Value of Deep Work for Peak Performance As somebody who juggled various roles and responsibilities as a software engineer, architect and tech lead, I certainly understand the importance of deep work and how difficult it can be to get it when working with other people. ## How to schedule jobs with Quartz on Ktor DevFeed: [How to schedule jobs with Quartz on Ktor]() Original publisher: [Read original article]() Author: Marco Gomiero Published: 2022-04-22T00:00:00Z Content type: tutorial Language: en Sources: [Posts on Marco Gomiero]() Topics: [Ktor](), [Quartz](), [Back end](), [Database](), [Persistence](), [MySQL]() Tags: [backend](), [database](), [how-to](), [ktor](), [mysql](), [persistence](), [quartz](), [scheduler](), [scheduling]() ### AI overview This tutorial explains how to schedule recurring jobs in a Ktor backend project using Quartz. It covers Quartz's persistence of job state, database table setup, logging configuration, and scheduler creation, with MySQL used in the example. ### Source excerpt SERIES: Building a backend with Ktor Part 1: Structuring a Ktor project Part 2: How to persist Ktor logs Part 3: How to use an in-memory database for testing on Ktor Part 4: How to handle database migrations with Liquibase on Ktor Part 5: Generate API documentation from Swagger on Ktor Part 6: How to schedule jobs with Quartz on Ktor Part 7: Moving from mobile to backend development with Ktor Sometimes, on a backend project, there is the need to run one or more tasks periodically, like for system administration, maintenance, backup, syncing content in the background, etc. These types of tasks can be scheduled "manually" with a cron job or with a scheduling library like Quartz, that makes easy for example the persistence of the task's state even after a reboot of the server. ## What's new in Apache Airflow 2.2.0 DevFeed: [What's new in Apache Airflow 2.2.0]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2021-10-11T00:00:00Z Content type: release Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [async](), [data]() Tags: [airflow](), [apache-airflow](), [async](), [bug-fixes](), [new-features](), [release](), [scheduling](), [sensors]() ### AI overview Apache Airflow 2.2.0 is presented as a release with more than 600 commits since version 2.1.4, including new features, improvements, bug fixes, and documentation changes. Highlighted features include pluggable custom timetables for DAG scheduling and deferrable tasks that can return worker resources while waiting for asynchronous checks. ### Source excerpt I'm proud to announce that Apache Airflow 2.2.0 has been released. It contains over 600 commits since 2.1.4 and includes 30 new features, 84 improvements, 85 bug fixes, and many internal and doc changes. Details: 📦 PyPI: https://pypi.org/project/apache-airflow/2.2.0/ 📚 Docs: https://airflow.apache.org/docs/apache-airflow/2.2.0/ 🛠 Changelog: https://airflow.apache.org/docs/apache-airflow/2.2.0/changelog.html 🐳 Docker Image: docker pull apache/airflow:2.2.0 🚏 Constraints: https://github.com/apache/airflow/tree/constraints-2.2.0 As the changelog is quite large, the following are some notable new features that shipped in this release. Custom Timetables (AIP-39) Airflow has historically used cron expressions and timedeltas to represent when a DAG should run. This worked for a lot of use cases, but not all. For example, running daily on Monday-Friday, but not on weekends wasn't possible. To provide more scheduling flexibility, determining when a DAG should run is now done with Timetables. Of course, backwards compatibility has been maintained - cron expressions and timedeltas are still fully supported, however, timetables are pluggable so you can add your own custom timetable to fit your needs! For example, you could write a timetable to schedule a DagRun execution_date has long been confusing to new Airflowers, so as part of this change a new concept has been added to Airflow to replace it named data_interval, which is the period of data that a task should operate on. The following are now available: logical_date (aka execution_date) data_interval_start (same value as execution_date for cron) data_interval_end (aka next_execution_date) If you write your own timetables, keep in mind they should be idempotent and fast as they are used in the scheduler to create DagRuns. More information can be found at: Customizing DAG Scheduling with Timetables Deferrable Tasks (AIP-40) Deferrable tasks allows operators or sensors to defer themselves until a light-weight async check pass ## Apache Airflow For Newcomers DevFeed: [Apache Airflow For Newcomers]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2020-08-17T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Development]() Tags: [airflow](), [apache-airflow](), [community](), [dependencies](), [python](), [scheduler](), [scheduling](), [tasks](), [workflows]() ### AI overview This tutorial introduces Apache Airflow for newcomers, explaining how to author workflows with Python scripts, represent them as DAGs, define task dependencies, and use the scheduler to execute tasks when dependencies are met. ### Source excerpt Apache Airflow is a platform to programmatically author, schedule, and monitor workflows. A workflow is a sequence of tasks that processes a set of data. You can think of workflow as the path that describes how tasks go from being undone to done. Scheduling, on the other hand, is the process of planning, controlling, and optimizing when a particular task should be done. Authoring Workflow in Apache Airflow. Airflow makes it easy to author workflows using python scripts. A Directed Acyclic Graph (DAG) represents a workflow in Airflow. It is a collection of tasks in a way that shows each task's relationships and dependencies. You can have as many DAGs as you want, and Airflow will execute them according to the task's relationships and dependencies. If task B depends on the successful execution of another task A, it means Airflow will run task A and only run task B after task A. This dependency is very easy to express in Airflow. For example, the above scenario is expressed as task_A >> task_B Also equivalent to task_A.set_downstream(task_B) That helps Airflow to know that it needs to execute task A before task B. Tasks can have far more complex relationships to each other than expressed above and Airflow figures out how and when to execute the tasks following their relationships and dependencies. Before we discuss the architecture of Airflow that makes scheduling, executing, and monitoring of workflow an easy thing, let us discuss the Breeze environment. Breeze Environment The breeze environment is the development environment for Airflow where you can run tests, build images, build documentations and so many other things. There are excellent documentation and video on Breeze environment. Please check them out. You enter the Breeze environment by running the ./breeze script. You can run all the commands mentioned here in the Breeze environment. Scheduler The scheduler is the component that monitors DAGs and triggers those tasks whose dependencies have been met. It watc ## ⏰Scheduling FCM Push Notifications🔔 On Device -- Android📱 DevFeed: [⏰Scheduling FCM Push Notifications🔔 On Device -- Android📱]() Original publisher: [Read original article]() Author: Shreyas Patil Published: 2019-12-19T02:44:55Z Content type: tutorial Language: en Sources: [Shreyas Patil's Blog]() Topics: [Android](), [Firebase]() Tags: [android](), [firebase](), [how-to](), [on-device](), [others](), [push-notifications](), [scheduling]() ### AI overview A tutorial on scheduling Firebase Cloud Messaging push notifications locally on Android. It describes sending a data payload to an FCM topic in advance, processing it on the device, and scheduling the notification so it can appear even if the user is offline at the scheduled time. ### Source excerpt Learn how to schedule FCM push notifications on-device in Android without expensive cloud cron jobs, using AlarmManager and WorkManager. ## Using a Cron Job to Run Rake Tasks Inside Your Docker Container DevFeed: [Using a Cron Job to Run Rake Tasks Inside Your Docker Container]() Original publisher: [Read original article]() Author: Fuzzygroup Published: 2019-11-29T00:00:00Z Content type: tutorial Language: en Sources: [Scott Johnson]() Topics: [Docker](), [container](), [Bash](), [Job]() Tags: [bash](), [container](), [docker](), [job](), [rails](), [rake](), [run](), [scheduling](), [shell](), [shell-script]() ### AI overview This tutorial explains how to use an OS-level cron job to run a Rake task inside a Docker container. It shows how to execute a shell in the container, run the task with Docker, and use a shell script to obtain the container hash dynamically because the hash changes when the container restarts. ### Source excerpt Despite cron being an age old *nix technology, it alway seems like scheduling tasks is an annoying thing. Note: Cron is the standard *nix technology for running tasks on a recurring basis like "every monday at 5 am execute this program". We are now doing things like running tasks within containers and that raises the question of where does cron exist: within the container? at the OS level? what about multiple instances of the container across different machines? I'm not going to try and address the bigger picture questions here like multiple instances but I can show you how to use cron at the OS level to drive a rake task within a container. How Do You Run Something Within in a Container The first thing to understand is that you can easily execute a shell with in a container, from the outside, with this syntax: docker exec -it CONTAINER_HASH /bin/bash The CONTAINER_HASH is the docker equivalent of a *nix pid (process identifier). How to Run a Rake Task And if you can run a shell like /bin/bash within a container from the outside then, drum roll please, you can easily run anything from outside including a Rake task. And if you have that then you can easily schedule it. Here's an example: docker exec -it 8d76da4ab481 bundle exec rake data_source:update_or_initial_load --trace The 8d76da4ab481, shown above, is an example of the CONTAINER_HASH. But Docker Container Hashes Change... If you think about this a bit there is one real problem - that damn CONTAINER_HASH isn't consistent. It changes every time the container is restarted. The trick to making a cron job that runs your rake task is to use a shell script which pulls the container hash dynamically. And to figure that you we are going to start with my dockerbash blog post. Here's the shell script from the dockerbash post: #!/bin/bash if [ -z $1 ]; then echo "You need to specify the name of the container you want to get into like:" echo "dockerbash police" else pid=`docker ps | grep $1 | awk '{print $1}'` docker exec -i ## Come over for dinner DevFeed: [Come over for dinner]() Original publisher: [Read original article]() Author: Map Published: 2019-05-01T20:55:56Z Content type: opinion Language: en Sources: [Craig Kerstiens]() Topics: [hosting](), [trust](), [scheduling]() Tags: [hosting](), [philosophies](), [remote](), [scheduling](), [trust](), [work]() ### AI overview The author describes regularly inviting coworkers, former colleagues, and friends to dinner as an alternative to going out. They argue that shared meals build rapport and trust, support more effective teamwork, and can include remote workers and team members through planned scheduling and rotation. ### Source excerpt When I first moved to the Bay area I was fresh out of grad school. I was frequently heading out to dinner or to happy hour after work with colleagues. I was young and single, so why not of course. As time passed, marriage, kids, etc. the ability to go out for a quick drink or dinner was competing with various priorities. Dinner and drinks with co-workers was always a great time. It wasn't just about hanging out, it built rapport and trust which I found made me a more effective teammate and product manager. It was about 8 years ago that I started to implement a variation of heading out for dinner and drinks. I started inviting people over for dinner. I still do this regularly. Roughly once a week we end up hosting someone for dinner. Sometimes it is a single person, sometimes it is a group of people. Sometimes it is co-workers, sometimes former colleagues, often friends that don't work in tech. Growing up in the south it was common to have people over, I'd said we did that just as much as going out to dinner with folks. You'd get an invite to go to someone elses place and you'd show up with a bottle of wine or flowers in hand. Initially when I asked people in the Bay area over for dinner I'd get weird looks. Over? Like to your house? The reaction from folks at the end of the night was very often... that was really fun. Thanks for the invite, I can't remember the last time I just sat down at someones place, had a good meal, and conversation. Once I found early success with this I started implementing it pretty methodically. When remote workers were in town I'd make sure to place them at thet top of the list to come if the scheduling worked. Same when friends visit from out of town. I'd also try to regularly rotate through my teams and those that report to me. At one point when I had 22 engineers that I was leading product for I had to do a bit of juggling and stagger things a bit, groups of 4 folks or so at a time and each would be over about once every 6 months. I made ## Announcing Our Connect v2 Labor API DevFeed: [Announcing Our Connect v2 Labor API]() Original publisher: [Read original article]() Author: Dina Spitzer Published: 2019-04-04T21:38:53Z Content type: release Language: en Sources: [Square Corner Blog - Medium]() Topics: [API](), [data](), [import](), [Job]() Tags: [announce](), [api](), [blog](), [business](), [data](), [developer](), [employment](), [import](), [integrations](), [job](), [release](), [scheduling](), [search](), [web-development]() ### AI overview Square announces the release of the Connect v2 Labor API, renamed from the Timecard API. The release adds functionality for employee shifts, break tracking, multiple jobs and wages, labor data import/export, and shift search. ### Source excerpt Capture employee working hours with breaks and hourly pay rateHeads up, we've moved! If you'd like to continue keeping up with the latest technical content from Square please visit us at our new home https://developer.squareup.com/blog We're very excited to announce the release of the Square Connect v2 Labor API. Having visibility into daily employee operations and labor costs is a critical part of managing a business. It helps business owners better understand the performance of their business, employees, and ultimately make more informed & cost-effective staffing decisions. With the Labor API, partners can build stronger, more accurate, and more comprehensive labor management integrations on behalf of our shared customers. To highlight the changes and additional functionality recently implemented (i.e. break tracking, multiple wages, etc...), we updated the name from Timecard API to Labor API. The new name better aligns with the breadth of labor management functionalities our partners can power with the Labor API. You can more easily manage employee's hours, breaks, shift wages, and import/export labor data. How to create a Shift (including break and wage information):https://medium.com/media/281fb8863abf67cb66c58e404143445e/hrefHow to query a set of closed shifts from a workweek:https://medium.com/media/b15dbea66bdaaf0f8f8a3a6a6cf48d29/href You will find below a list of functionalities we implemented with this release: Employee shifts: View/create shifts worked by any employee, including the business location where the shift was worked, shift start/end times, and regular, overtime, and double time hours worked during the shift. Employee break tracking: Track employee breaks over the course of a worked shift, including break start/end times, durations, and whether they were paid/unpaid. Employee job tracking: View/edit the job (and wage) worked by an employee for any given shift, including the ability for one employee to have multiple jobs (and wages). Search: Searc ## Calendar Tetris is an antipattern DevFeed: [Calendar Tetris is an antipattern]() Original publisher: [Read original article]() Author: Jack Tarantino Published: 2017-06-12T17:55:10Z Content type: opinion Language: en Sources: [Jacopo Tarantino]() Topics: [meetings]() Tags: [agile](), [antipattern](), [best-practices](), [calendar-tetris](), [meetings](), [productivity](), [scheduling]() ### AI overview The article argues that "Calendar Tetris"--an overloaded calendar requiring constant rescheduling and causing frequent double-booking--harms lunch breaks and productivity. It discusses how meetings and interruptions can contribute to cognitive errors and lost working time, and questions the assumption that managers and senior employees must attend every meeting. ### Source excerpt It's time that we took back our work day from our calendars and Calendar Tetris is the first thing that has got to go. Calendars are a tool and like every tool they should be used appropriately. What is "Calendar Tetris"? Does your calendar look like a Jackson Pollack painting?