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by: Guest Contributor Tue, 31 Oct 2023 00:55:00 GMT In the realm of database offerings, where data is the lifeblood of modern businesses, constructing resilient systems isn't just a best practice; it's a strategic imperative. Disaster recovery planning has become a cornerstone in ensuring the continuity of operations, safeguarding valuable data, and minimizing the impact of unexpected events. This article delves into the critical factors of disaster recovery planning in database services, highlighting the essential requirements and strategies to build resilient systems that can withstand the challenges of unexpected disruptions. Understanding the Need for Disaster Recovery Planning Unpredictable Nature of Disasters Disasters, whether natural or human-triggered, are inherently unpredictable. From earthquakes and floods to cyber attacks and hardware failures, a myriad of events can threaten the availability, integrity, and security of database systems. Business Continuity and Data Integrity Database services play a pivotal role in the daily operations of organizations. Ensuring business continuity and maintaining data integrity are paramount, as disruptions can cause financial losses, reputational damage, and operational setbacks. Key Principles of Disaster Recovery Planning Risk Assessment and Impact Analysis Conduct a thorough risk assessment to identify potential threats and vulnerabilities. Additionally, perform an impact analysis to understand the effects of different disaster scenarios on database services. This foundational step guides the development of a focused and effective recovery plan. Define Recovery Objectives Clearly define recovery objectives, such as Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO). RTO outlines the acceptable downtime, while RPO determines the maximum acceptable data loss in the event of a disaster. These objectives serve as benchmarks for the effectiveness of the recovery plan. Data Backup and Redundancy Implement robust data backup and redundancy strategies. Regularly back up critical data and store copies in geographically diverse locations. This ensures that, in the event of a disaster, businesses can quickly restore operations using the most recent available data. While both terms are often used in the same conversations, this isn’t an either/or decision. Both backups and redundancy offer two distinct and equally valuable solutions to ensuring business continuity in the face of unplanned accidents, unexpected attacks, or system failures. Redundancy is designed to increase your operational time, boost workforce productivity, and reduce the amount of time a system is unavailable due to a failure. Backup, however, is designed to kick in when something goes wrong, allowing you to completely rebuild regardless of what caused the failure. Moreover, if you use ELT tools for regular updating of critical data across backup and redundancy systems, maintaining seamless data access and continuity will become much easier. This becomes especially important when you stream your data to databases or data warehouses through such ELT solutions as BigQuery connectors. In short, redundancy prevents failure while backups prevent loss. In a modern business environment that is inherently dependent on access to large volumes of data, it’s clear that operational redundancy and backups are both critical elements of an effective continuity strategy. Comprehensive Documentation Document all aspects of the disaster recovery plan comprehensively. This includes procedures for data backup, system restoration, communication protocols, and the roles and responsibilities of the recovery team. Well-documented plans facilitate a smooth and coordinated response during crises. Strategies for Building Resilient Systems Geographical Distribution and Cloud Services Leverage the geographical distribution capabilities of cloud services. Distributing data across multiple regions and utilizing cloud-based databases enhances redundancy and ensures data availability even if one region is impacted by a disaster. Redundant Infrastructure Implement redundant infrastructure at both the hardware and software levels. Redundant servers, storage systems, and network components can mitigate the impact of hardware failures. Additionally, consider using load balancing and failover mechanisms to distribute workloads and ensure continuous service availability. Regular Testing and Simulation Conduct regular testing and simulation exercises to validate the effectiveness of the disaster recovery plan. Simulating different disaster scenarios, such as data corruption, network failures, or system outages, helps organizations identify weaknesses and fine-tune their recovery strategies. Automated Monitoring and Alerts Implement automated monitoring tools that continuously track the health and performance of database services. Set up alerts for critical thresholds and potential issues, enabling proactive identification of anomalies and rapid response to emerging problems. Incident Response and Communication Incident Response Team Form an incident response team responsible for executing the disaster recovery plan. Clearly define the roles and responsibilities of team members, ensuring that each member is well-trained and familiar with their specific duties during a disaster. Communication Protocols Establish clear communication protocols for disseminating information during a disaster. Define channels, responsibilities, and escalation procedures to ensure that stakeholders, including employees, customers, and relevant authorities, are informed promptly and accurately. Continuous Improvement and Adaptability Post-Incident Review and Analysis Conduct post-incident reviews and analysis after each simulation or actual disaster. This retrospective examination allows organizations to identify areas for improvement, refine recovery strategies, and enhance the overall resilience of database services. Adaptability to Evolving Threats Recognize that the threat landscape is dynamic, with new risks emerging over time. Disaster recovery plans need to be adaptable and evolve alongside technological advancements and changing security threats. Regularly update and refine the plan to address new challenges effectively. Scaling Disaster Recovery with Business Growth As businesses expand, data volume grows, and infrastructure becomes more complex. Old disaster recovery strategies and plans may now fall short. It becomes essential for businesses to evaluate and improve their disaster recovery plans to adapt to growing needs. This includes scaling resources and updating recovery objectives. Conclusion Building resilient systems through comprehensive disaster recovery planning is a crucial investment in the long-term success and viability of database services. By adhering to key principles, implementing strategic recovery strategies, and fostering a culture of continuous improvement, organizations can make their databases more robust against unexpected events. As the digital landscape evolves, the ability to recover quickly and efficiently from disasters will become a hallmark of organizations that prioritize data integrity, business continuity, and trust within their stakeholders.

by: Scott Robinson Mon, 23 Oct 2023 14:12:00 GMT Deleting a file in Python is fairly easy to do. Let's discuss two methods to accomplish this task using different Python modules. Using the 'os' Module The os module in Python provides a method called os.remove() that can be used to delete a file. Here's a simple example: import os # specify the file name file_name = "test_file.txt" # delete the file os.remove(file_name) In the above example, we first import the os module. Then, we specify the name of the file to be deleted. Finally, we call os.remove() with the file name as the parameter to delete the file. Note: The os.remove() function can only delete files, not directories. If you try to delete a directory using this function, you'll get a IsADirectoryError. Using the 'shutil' Module The shutil module, short for "shell utilities", also provides a method to delete files - shutil.rmtree(). But why use shutil when os can do the job? Well, shutil can delete a whole directory tree (i.e., a directory and all its subdirectories). Let's see how to delete a file with shutil. import shutil # specify the file name file_name = "test_file.txt" # delete the file shutil.rmtree(file_name) The code looks pretty similar to the os example, right? That's one of the great parts of Python's design - consistency across modules. However, remember that shutil.rmtree() is more powerful and can remove non-empty directories as well, which we'll look at more closely in a later section. Deleting a Folder in Python Moving on to the topic of directory deletion, we can again use the os and shutil modules to accomplish this task. Here we'll explore both methods. Using the 'os' Module The os module in Python provides a method called os.rmdir() that allows us to delete an empty directory. Here's how you can use it: import os # specify the directory you want to delete folder_path = "/path/to/your/directory" # delete the directory os.rmdir(folder_path) The os.rmdir() method only deletes empty directories. If the directory is not empty, you'll encounter an OSError: [Errno 39] Directory not empty error. Using the 'shutil' Module In case you want to delete a directory that's not empty, you can use the shutil.rmtree() method from the shutil module. import shutil # specify the directory you want to delete folder_path = "/path/to/your/directory" # delete the directory shutil.rmtree(folder_path) The shutil.rmtree() method deletes a directory and all its contents, so use it cautiously! Wait! Always double-check the directory path before running the deletion code. You don't want to accidentally delete important files or directories! Common Errors When dealing with file and directory operations in Python, it's common to encounter a few specific errors. Understanding these errors is important to handling them gracefully and ensuring your code continues to run smoothly. PermissionError: [Errno 13] Permission denied One common error you might encounter when trying to delete a file or folder is the PermissionError: [Errno 13] Permission denied. This error occurs when you attempt to delete a file or folder that your Python script doesn't have the necessary permissions for. Here's an example of what this might look like: import os try: os.remove("/root/test.txt") except PermissionError: print("Permission denied") In this example, we're trying to delete a file in the root directory, which generally requires administrative privileges. When run, this code will output Permission denied. To avoid this error, ensure your script has the necessary permissions to perform the operation. This might involve running your script as an administrator, or modifying the permissions of the file or folder you're trying to delete. FileNotFoundError: [Errno 2] No such file or directory Another common error is the FileNotFoundError: [Errno 2] No such file or directory. This error is thrown when you attempt to delete a file or folder that doesn't exist. Here's how this might look: import os try: os.remove("nonexistent_file.txt") except FileNotFoundError: print("File not found") In this example, we're trying to delete a file that doesn't exist, so Python throws a FileNotFoundError. To avoid this, you can check if the file or folder exists before trying to delete it, like so: import os if os.path.exists("test.txt"): os.remove("test.txt") else: print("File not found") OSError: [Errno 39] Directory not empty The OSError: [Errno 39] Directory not empty error occurs when you try to delete a directory that's not empty using os.rmdir(). For instance: import os try: os.rmdir("my_directory") except OSError: print("Directory not empty") This error can be avoided by ensuring the directory is empty before trying to delete it, or by using shutil.rmtree(), which can delete a directory and all its contents: import shutil shutil.rmtree("my_directory") Similar Solutions and Use-Cases Python's file and directory deletion capabilities can be applied in a variety of use-cases beyond simply deleting individual files or folders. Deleting Files with Specific Extensions Imagine you have a directory full of files, and you need to delete only those with a specific file extension, say .txt. Python, with its versatile libraries, can help you do this with ease. The os and glob modules are your friends here. import os import glob # Specify the file extension extension = "*.txt" # Specify the directory directory = "/path/to/directory/" # Combine the directory with the extension files = os.path.join(directory, extension) # Loop over the files and delete them for file in glob.glob(files): os.remove(file) This script will delete all .txt files in the specified directory. The glob module is used to retrieve files/pathnames matching a specified pattern. Here, the pattern is all files ending with .txt. Deleting Empty Directories Have you ever found yourself with a bunch of empty directories that you want to get rid of? Python's os module can help you here as well. import os # Specify the directory directory = "/path/to/directory/" # Use listdir() to check if directory is empty if not os.listdir(directory): os.rmdir(directory) The os.listdir(directory) function returns a list containing the names of the entries in the directory given by path. If the list is empty, it means the directory is empty, and we can safely delete it using os.rmdir(directory). Note: os.rmdir(directory) can only delete empty directories. If the directory is not empty, you'll get an OSError: [Errno 39] Directory not empty error.

by: Chris Coyier Thu, 26 Jan 2023 01:30:59 +0000 Stephen and I hop on the podcast to chat about some of our recent tooling, local development, and DevOps work. A little while back, we cleaned up our entire monorepo’s circular dependency problems using Madge and elbow grease. That kind of thing usually isn’t the biggest of deals and the kind of thing a super mature bundler like webpack deals with, but other bundlers might choke on. Later, we learned that we had more dependency issues like inter-package circular dependencies (nothing like production deployments to keep you honest) and used more tooling (shout out npx depcheck) to clean more of it up. Workspaces in a monorepo can also paper over missing dependencies — blech. Another change was moving off using a .dev domain for local development, which oddly actually caused some strange and hard-to-diagnose DNS issues sometimes. We’re on .test now, which should never be a public TLD. Time Jumps 00:26 Dev ops spring cleaning 01:25 Local dev with .dev, wait, no, .test 06:58 Sponsor: Notion 07:54 Circular dependency 11:41 Monorepo update 13:35 Interpackage and unused packages 16:25 TypeScript 17:54 Upgrading packages 20:35 Hierarchy of packages Sponsor: Notion Notion is an amazing collaborative tool that not only helps organize your company’s information but helps with project management as well. We know that all too well here at CodePen, as we use Notion for countless business tasks. Learn more and get started for free at notion.com. Take your first step toward an organized, happier team, today.