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Cron is a time-based job scheduler that lets you schedule tasks and run scripts periodically at a fixed time, date, or interval. Moreover, these tasks are called cron jobs. With cron jobs, you can efficiently perform repetitive tasks like clearing cache, synchronizing data, system backup and maintenance, etc. These cron jobs also have other features like command automation, which can significantly reduce the chances of human errors. However, many Linux users face multiple issues while setting up a cron job. So, this article provides examples of how to set up a cron job in Linux. How To Set up a Cron Job Firstly, you must know about the crontab file to set up a cron job in Linux. You can access this file to view information about existing cron jobs and edit it to introduce new ones. Before directly opening the crontab file, use the below command to check that your system has the cron utility: sudo apt list cron If it does not provide an output as shown in the given image, install cron using: sudo apt-get install cron -y Now, verify that the cron service is active by using the command as follows: service cron status Once you are done, edit the crontab to start a new cron job: crontab -e The system will ask you to select a particular text editor. For example, we use the nano editor by entering ‘1’ as input. However, you can choose any of the editors because the factor affecting a cron job is its format, which we’ll explain in the next steps. After choosing an editor, the crontab file will open in a new window with basic instructions displayed at the top. Finally, append the following crontab expression in the file: * * * * * /path/script Here, each respective asterisk(*) indicates minutes, hours, daily, weekly, and monthly. This defines every aspect of time so that the cron job can execute smoothly at the scheduled time. Moreover, replace the terms path and script with the path containing the target script and the script’s name, respectively. Time Format to Schedule Cron Jobs As the time format discussed in the above command can be confusing, let’s discuss its format in brief: In the Minutes field, you can enter values in the range 0-59, where 0 and 59 represent the minutes visible on a clock. For an input number, like 9, the job will run at the 9th minute every hour. For Hours, you can input values ranging from 0 to 23. For instance, the value for 2 PM would be ’14.’ The Day of the Month can be anywhere between 1 and 31, where 1 and 31 again indicate the first and last Day of the Month. For value 17, the cron job will run on the 17th Day of every Month. In place of Month, you can enter the range 1 to 12, where 1 means January and 12 means December. The task will be executed only during the Month you specify here. Note: The value ‘*’ means every acceptable value. For example, if ‘*’ is used in place of the minutes’ field, the task will run every minute of the specified hour. For example, below is the expression to schedule a cron job for 9:30 AM every Tuesday: 30 9 * * 2 /path/script For example, to set up a cron job for 5 PM on weekends in April: 0 17 * 4 0,6-7 /path/script As the above command demonstrates, you can use a comma and a dash to provide multiple values in a field. So, the upcoming section will explain the use of various operators in a crontab expression. Arithmetic Operators for Cron Jobs Regardless of your experience in Linux, you’ll often need to automate jobs to run twice a year, thrice a month, and more. In this case, you can use operators to modify a single cron job to run at different times. Dash(-): You can specify a range of values using a dash. For instance, to set up a cron job from 12 AM to 12 PM, you can enter * 0-12 * * * /path/script. Forward Slash(/): A slash helps you divide a field’s acceptable values into multiple values. For example, to make a cron job run quarterly, you’ll enter * * * /3 * /path/script. Comma(,): A comma separates two different values in a single input field. For example, the cron expression for a task to be executed on Mondays and Wednesdays is * * * * 1,3 /path/script. Asterisk(*): As discussed above, the asterisk represents all values the input field accepts. It means an asterisk in place of the Month’s field will schedule a cron job for every Month. Commands to Manage a Cron Job Managing the cron jobs is also an essential aspect. Hence, here are a few commands you can use to list, edit, and delete a cron job: The l option is used to display the list of cron jobs. The r option removes all cron jobs. The e option edits the crontab file. All the users of your system get their separate crontab files. However, you can also perform the above operations on their files by adding their username between the commands– crontab -u username [options]. A Quick Wrap-up Executing repetitive tasks is a time-intensive process that reduces your efficiency as an administrator. Cron jobs let you automate tasks like running a script or commands at a specific time, reducing redundant workload. Hence, this article comprehensively explains how to create a cron job in Linux. Furthermore, we briefed the proper usage of the time format and the arithmetic operators using appropriate examples. View the full article

What is Apache Airflow? Apache Airflow addresses the need for a robust, scalable, and flexible solution for orchestrating data workflows.View the full article

In the rapidly evolving landscape of container orchestration, Kubernetes has emerged as the de facto standard, offering a robust framework for deploying, managing, and scaling containerized applications. One of the cornerstone features of Kubernetes is its powerful and flexible scheduling system, which efficiently allocates workloads across a cluster of machines, known as nodes. This article delves deep into the mechanics of Kubernetes scheduling, focusing on the pivotal roles of pods and nodes, to equip technology professionals with the knowledge to harness the full potential of Kubernetes in their projects. Understanding Kubernetes Pods A pod is the smallest deployable unit in Kubernetes and serves as a wrapper for one or more containers that share the same context and resources. Pods encapsulate application containers, storage resources, a unique network IP, and options that govern how the container(s) should run. A key concept to grasp is that pods are ephemeral by nature; they are created and destroyed to match the state of your application as defined in deployments. View the full article

This post series is about mastering offline data pipeline's best practices, focusing on the potent combination of Apache Airflow and data processing engines like Hive and Spark. In Part 1 of our series explored the strategies for enhancing Airflow data pipelines using Apache Hive on AWS EMR. Our primary objective was to attain cost efficiency and establish effective job configurations. In this concluding Part 2, we will extensively explore Apache Spark, another pivotal element in our comprehensive data engineering toolkit. By optimizing the Airflow job parameters specifically for Spark, there is a substantial potential for enhancing performance and realizing substantial cost savings. Why Apache Spark in Airflow? Apache Spark is a really important framework and tool for data processing in companies all about data. It's genuinely outstanding at processing massive amounts of data quickly and efficiently. It's especially great for complex data analytics with fast query performance and advanced analytics capabilities. This makes Spark a preferred choice for enterprises handling vast amounts of data and requiring real-time analytics. View the full article

At Meta, Bento is our internal Jupyter notebooks platform that is leveraged by many internal users. Notebooks are also being used widely for creating reports and workflows (for example, performing data ETL) that need to be repeated at certain intervals. Users with such notebooks would have to remember to manually run their notebooks at the required cadence – a process people might forget because it does not scale with the number of notebooks used. In this post, we’ll explain how we married Bento with our batch ETL pipeline framework called Dataswarm (think Apache Airflow) in a privacy and lineage-aware manner... View the full article

Cloud-native technologies are becoming increasingly ubiquitous, and Kubernetes is at the forefront of this movement. Today, Kubernetes is seeing widespread adoption across organizations in a variety of different industries. When implemented properly, Kubernetes can help these organizations achieve higher availability, scalability, and resiliency for their workloads. Combining Kubernetes with the attributes of cloud computing—such as unparalleled scalability and elasticity—can help organizations enhance their containerized applications’ resiliency and availability. As detailed in this introductory post, Karpenter‘s objective is to make sure that your cluster’s workloads have the compute they need, no more and no less, right when they need it. In its most recent updates, Karpenter added support for more advanced scheduling constraints, such as pod affinity and anti-affinity, topology spread, node affinity, node selection, and resource requests. This post will specifically delve into podAffinity, podAntiAffinity, and volume topology awareness and elaborate on the use cases that they’re best suited for... View the full article

Amazon Connect forecasting, capacity planning, and scheduling (preview) are now available in the Asia Pacific (Sydney) AWS region. Machine-learning powered capabilities make it easier for contact center managers to help predict contact volumes and average handle time with high accuracy, determine ideal staffing levels, and optimize agent schedules to ensure they have the right agents at the right time. This helps businesses optimize their operations, meet service level goals, and improve agent and customer satisfaction. Getting started takes just a click, eliminating the need to build custom applications or integrate with third-party products. View the full article

Amazon Connect forecasting, capacity planning, and scheduling (preview) are now available in the Europe (London) AWS Region. Machine-learning powered capabilities make it easier for contact center managers to help predict contact volumes and average handle time with high accuracy, determine ideal staffing levels, and optimize agent schedules to ensure they have the right agents at the right time. This helps businesses optimize their operations, meet service level goals, and improve agent and customer satisfaction. Getting started takes just a click, eliminating the need to build custom applications or integrate with third-party products. View the full article

Today AWS Batch introduced the ability for customers to specify AWS Fargate as a compute resource for their AWS Batch jobs. With AWS Batch support for AWS Fargate, customers now have a way to run jobs on serverless compute resources, fully managed from job submission to completion. Now, you only need to submit your analytics, map reduce, and other batch workloads and let AWS Batch and AWS Fargate handle the rest. View the full article

Zeit is an open-source GUI tool for scheduling jobs via “crontab” and “at”. It is written in C++ and released under GPL-3.0 License. It is an easy to use tool that provides a simple The post Zeit – A GUI Tool to Schedule Cron and At Jobs in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides. View the full article

Today, the AWS Copilot CLI for Amazon Elastic Container Service (Amazon ECS) launched version 0.5.0. Starting with this release, you can deploy applications or jobs that need to run only on a particular schedule. AWS Copilot has built in timeouts and retries to provide more flexibility for how your scheduled jobs run. AWS Copilot will also deploy all the required infrastructure and settings, while you just provide the application and the schedule to be run. This allows you to focus on development instead of manually setting up rules and infrastructure to ensure your scheduled jobs run when needed. View the full article

Amazon Redshift now allows you to schedule your SQL queries for executions in recurring schedules and enables you to build event-driven by integrating with Amazon EventBridge. You can now schedule time sensitive or long running queries, loading or unloading your data, or refreshing your materialized views on a regular schedule. View the full article