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Gossip Protocol in Social Media Networks: Instagram and Beyond

By Arun Pandey

Gossip protocol is a communication scheme used in distributed systems for efficiently disseminating information among nodes. It is inspired by the way people gossip, where information spreads through a series of casual conversations. This article will discuss the gossip protocol in detail, followed by its potential implementation in social media networks, including Instagram. We will also include code snippets to provide a deeper technical understanding. Gossip Protocol The gossip protocol is based on an epidemic algorithm that uses randomized communication to propagate information among nodes in a network. The nodes exchange information about their state and the state of their neighbors. This process is repeated at regular intervals, ensuring that the nodes eventually become aware of each other's states. The key features of gossip protocol include: Fault-tolerance: The protocol can handle node failures effectively, as it does not rely on a central authority or a single point of failure. Scalability: Gossip protocol can efficiently scale to large networks with minimal overhead. Convergence: The system converges to a consistent state quickly, even in the presence of failures or network delays. Gossip Protocol in Social Media Networks: Instagram Social media networks are distributed systems that need to handle massive amounts of data and user interactions. One of the critical aspects of such networks is efficiently propagating updates and notifications to users. Gossip protocol can be employed to achieve this goal by allowing user nodes to exchange information about their state and the state of their connections. For instance, consider Instagram, a social media platform where users can post photos and follow other users. When a user posts a new photo, it needs to be propagated to all their followers. Using the gossip protocol, the photo can be efficiently disseminated across the network, ensuring that all followers receive the update in a timely manner. Technical Implementation of Gossip Protocol in Social Media Networks To illustrate the implementation of gossip protocol in a social media network, let's consider a simplified example using Python. In this example, we will create a basic network of users who can post updates and follow other users, similar to Instagram. First, let's define a User class to represent a user in the network: Python class User: def __init__(self, user_id): self.user_id = user_id self.followers = set() self.posts = [] def post_photo(self, photo): self.posts.append(photo) def follow(self, user): self.followers.add(user) Next, we'll implement the gossip protocol to propagate updates among users. We will create a GossipNetwork class that manages the user nodes and initiates gossip communication: Python import random class GossipNetwork: def __init__(self): self.users = {} def add_user(self, user_id): self.users[user_id] = User(user_id) def post_photo(self, user_id, photo): self.users[user_id].post_photo(photo) self.gossip(user_id, photo) def gossip(self, user_id, photo): user = self.users[user_id] for follower in user.followers: # Propagate the photo to the follower self.users[follower].posts.append(photo) # Continue gossiping with a random subset of the follower's followers if len(self.users[follower].followers) > 0: next_follower = random.choice(list(self.users[follower].followers)) self.gossip(next_follower, photo) The main method to test the behavior: Python if __name__ == "__main__": # Create a gossip network network = GossipNetwork() # Add users to the network for i in range(1, 6): network.add_user(i) # Establish follower relationships network.users[1].follow(2) network.users[2].follow(3) network.users[3].follow(4) network.users[4].follow(5) # Post a photo by user 1 network.post_photo(1, "photo1") # Print the posts of each user for i in range(1, 6): print(f"User {i}: {network.users[i].posts}") This code creates a simple network of five users with a chain of follower relationships (1 -> 2 -> 3 -> 4 -> 5). When user 1 posts a photo, it will be propagated through the gossip protocol to all users in the chain. The output will show that all users have received the posted photo: Plain Text User 1: ['photo1'] User 2: ['photo1'] User 3: ['photo1'] User 4: ['photo1'] User 5: ['photo1'] In this example, when a user posts a photo, the GossipNetwork.post_photo() method is called. This method initiates gossip communication by propagating the photo to the user's followers and their followers using the GossipNetwork.gossip() method. Conclusion The gossip protocol is an efficient and robust method for disseminating information among nodes in a distributed system. Its implementation in social media networks like Instagram can help propagate updates and notifications to users, ensuring timely delivery and fault tolerance. By understanding the inner workings of the gossip protocol in social media networks, developers can better appreciate its role in maintaining a consistent and reliable distributed platform. More

Garbage Collection: Unsung Hero

By Ram Lakshmanan

CORE

Garbage Collection is a facet often disregarded and underestimated, yet beneath its surface lies the potential for profound impacts on your organization that reach far beyond the realm of application performance. In this post, we embark on a journey to unravel the pivotal role of Garbage Collection analysis and explore seven critical points that underscore its significance. Improve Application Response Time Without Code Changes Automatic garbage collection (GC) is a critical memory management process, but it introduces pauses in applications. These pauses occur when GC scans and reclaims memory occupied by objects that are no longer in use. Depending on various factors, these pauses can range from milliseconds to several seconds or even minutes. During these pauses, no application transactions are processed, causing customer requests to be stranded. However, there’s a solution. By fine-tuning the GC behavior, you can significantly reduce GC pause times. This reduction ultimately leads to a decrease in the overall application’s response time, delivering a smoother user experience. A real-world case study from one of the world’s largest automobile manufacturers demonstrates the impact of GC tuning without making a single line of code change. Read the full case study here. They were able to reduce their response time by 50% just by tuning their GC settings without a single line of code change. Efficient Cloud Cost Reduction In the world of cloud computing, enterprises often unknowingly spend millions of dollars on inefficient garbage collection practices. A high GC Throughput percentage, such as 98%, may initially seem impressive, like achieving an ‘A grade’ score. However, this seemingly minor difference carries substantial financial consequences. Imagine a mid-sized company operating 1,000 AWS t2.2x.large 32G RHEL on-demand EC2 instances in the US West (North California) region. The cost of each EC2 instance is $0.5716 per hour. Let’s assume that their application’s GC throughput is 98%. Now, let’s break down the financial impact of this assumption: With a 98% GC Throughput, each instance loses approximately 28.8 minutes daily due to garbage collection. In a day, there are 1,440 minutes (equivalent to 24 hours x 60 minutes). Thus, 2% of 1,440 minutes equals 28.8 minutes. Over the course of a year, this adds up to 175.2 hours per instance. (i.e. 28.8 minutes x 365 days) For a fleet of 1,000 AWS EC2 instances, this translates to approximately $100.14K in wasted resources annually (calculated as 1,000 EC2 instances x 175.2 hours x $0.5716 per hour) due to garbage collection delays. This calculation vividly illustrates how seemingly insignificant pauses in GC activity can amass substantial costs for enterprises. It emphasizes the critical importance of optimizing garbage collection processes to achieve significant cost savings. Trimming Software Licensing Cost In today’s landscape, many of our applications run on commercial vendor software solutions like Dell Boomi, ServiceNow, Workday, and others. While these vendor software solutions are indispensable, their licensing costs can be exorbitant. What’s often overlooked is that the efficiency of our code and configurations within these vendor software platforms directly impacts software licensing costs. This is where proper Garbage Collection (GC) analysis comes into play. It provides insights into whether there is an overallocation or underutilization of resources within these vendor software environments. Surprisingly, overallocation often remains hidden until we scrutinize GC behavior. By leveraging GC analysis, enterprises gain the visibility needed to identify overallocation and reconfigure resources accordingly. This optimization not only enhances application performance but also results in significant cost savings by reducing the licensing footprint of these vendor software solutions. The impact on the bottom line can be substantial. Forecast Memory Problems in Production Garbage collection logs hold the key to vital predictive micrometrics that can transform how you manage your application’s availability and performance. Among these micrometrics, one stands out: ‘GC Throughput.’ But what is GC Throughput? Imagine your application’s GC throughput is at 98% — it means that your application spends 98% of its time efficiently processing customer activity, with the remaining 2% allocated to GC activity. The significance becomes apparent when your application faces a memory problem. Several minutes before a memory issue becomes noticeable, the GC throughput will begin to degrade. This degradation serves as an early warning, enabling you to take preventive action before memory problems impact your production environment. Troubleshooting tools like yCrash closely monitor ‘GC throughput’ to predict and forecast memory problems, ensuring your application remains robust and reliable. Unearthing Memory Issues One of the primary reasons for production outages is encountering an OutOfMemoryError. In fact, there are nine different types of OutOfMemoryErrors: java.lang.OutOfMemoryError: Java heap space java.lang.OutOfMemoryError: PermGen space java.lang.OutOfMemoryError: GC overhead limit exceeded java.lang.OutOfMemoryError: Requested array size exceeds VM limit java.lang.OutOfMemoryError: Unable to create new native thread java.lang.OutOfMemoryError: Metaspace java.lang.OutOfMemoryError: unable to create new native thread java.lang.OutOfMemoryError: Direct buffer memory java.lang.OutOfMemoryError: Compressed class space GC analysis provides valuable insights into the root cause of these errors and helps in effectively triaging the problem. By understanding the specific OutOfMemoryError type and its associated details, developers can take targeted actions to debug and resolve memory-related issues, minimizing the risk of production outages. Spotting Performance Bottlenecks During Development In the modern software development landscape, the “Shift Left” approach has become a key initiative for many organizations. Its goal is to identify and address production-related issues during the development phase itself. Garbage Collection (GC) analysis enables this proactive approach by helping to isolate performance bottlenecks early in the development cycle. One of the vital metrics obtained through GC analysis is the ‘Object Creation Rate.’ This metric signifies the average rate at which objects are created by your application. Here’s why it matters: If your application, which previously generated data at a rate of 100MB/sec, suddenly starts creating 150MB/sec without a corresponding increase in traffic volume, it’s a red flag indicating potential problems within the application. This increased object creation rate can lead to heightened GC activity, higher CPU consumption, and degraded response times. Moreover, this metric can be integrated into your Continuous Integration/Continuous Deployment (CI/CD) pipeline to gauge the quality of code commits. For instance, if your previous code commit resulted in an object creation rate of 50MB/sec and a subsequent commit increases it to 75MB/sec for the same traffic volume, it signifies an inefficient code change. To streamline this process, you can leverage the GCeasy REST API. This integration allows you to capture critical data and insights directly within your CI/CD pipeline, ensuring that performance issues are identified and addressed early in the development lifecycle. Efficient Capacity Planning Effective capacity planning is vital for ensuring that your application can meet its performance and resource requirements. It involves understanding your application’s demands for memory, CPU, network resources, and storage. In this context, analyzing garbage collection behavior emerges as a powerful tool for capacity planning, particularly when it comes to assessing memory requirements. When you delve into garbage collection behavior analysis, you gain insights into crucial micro-metrics such as the average object creation rate and average object reclamation rate. These micro-metrics provide a detailed view of how your application utilizes memory resources. By leveraging this data, you can perform precise and effective capacity planning for your application. This approach allows you to allocate resources optimally, prevent resource shortages or overprovisioning, and ensure that your application runs smoothly and efficiently. Garbage Collection analysis, with its focus on memory usage patterns, becomes an integral part of the capacity planning process, enabling you to align your infrastructure resources with your application’s actual needs. How To Do Garbage Collection Analysis While there are monitoring tools and JMX MBeans that offer real-time Garbage Collection metrics, they often lack the depth needed for thorough analysis. To gain a complete understanding of Garbage Collection behavior, turn to GC logs. Once you have GC logs, select a free GC log analysis tool that suits your needs. With your chosen GC log analysis tool, examine Garbage Collection behavior in the logs, looking for patterns and performance issues. Pay attention to key metrics, and based on your analysis, optimize your application to reduce GC pauses and enhance performance. Adjust GC settings, allocate memory efficiently, and monitor the impact of your changes over time. Conclusion In the fast-paced world of software development and application performance optimization, Garbage Collection (GC) analysis is often the unsung hero. While it may be considered an underdog, it’s high time for this perception to change. GC analysis wields the power to enhance performance, reduce costs, and empower proactive decision-making. From improving application response times to early issue detection and precise capacity planning, GC analysis stands as a pivotal ally in optimizing applications and resources. More

Your Codebase Is a Cluttered Garage

By Erik Costlow

Avoid Merge Conflicts, Don't Manage Them

By Jonathan Hall

The Systemic Process of Debugging

By Shai Almog

CORE

Future-Proofing Data Architecture for Better Data Quality

This is an article from DZone's 2023 Data Pipelines Trend Report.For more: Read the Report Data quality is an undetachable part of data engineering. Because any data insight can only be as good as its input data, building robust and resilient data systems that consistently deliver high-quality data is the data engineering team's holiest responsibility. Achieving and maintaining adequate data quality is no easy task. It requires data engineers to design data systems with data quality in mind. In the hybrid world of data at rest and data in motion, engineering data quality could be significantly different for batch and event streaming systems. This article will cover key components in data engineering systems that are critical for delivering high-quality data: Monitoring data quality – Given any data pipeline, how to measure the correctness of the output data, and how to ensure the output is correct not only today but also in the foreseeable future. Data recovery and backfill – In case of application failures or data quality violations, how to perform data recovery to minimize impact on downstream users. Preventing data quality regressions – When data sources undergo changes or when adding new features to existing data applications, how to prevent unexpected regression. Monitoring Data Quality As the business evolves, the data also evolves. Measuring data quality is never a one-time task, and it is important to continuously monitor the quality of data in data pipelines to catch any regressions at the earliest stage possible. The very first step of monitoring data quality is defining data quality metrics based on the business use cases. Defining Data Quality Defining data quality is to set expectations for the output data and measure the deviation in the actual data from the established expectations in the form of quantitative metrics. When defining data quality metrics, the very first thing data engineers should consider is, "What truth does the data represent?" For example, the output table should contain all advertisement impression events that happened on the retail website. The data quality metrics should be designed to ensure the data system accurately captures that truth. In order to accurately measure the data quality of a data system, data engineers need to track not only the baseline application health and performance metrics (such as job failures, completion timestamp, processing latency, and consumer lag) but also customized metrics based on the business use cases the data system serves. Therefore, data engineers need to have a deep understanding of the downstream use cases and the underlying business problems. As the business model determines the nature of the data, business context allows data engineers to grasp the meanings of the data, traffic patterns, and potential edge cases. While every data system serves a different business use case, some common patterns in data quality metrics can be found in Table 1. METRICS FOR MEASURING DATA QUALITY IN A DATA PIPELINE Type Limitations Application health The number of jobs succeeded or running (for streaming) should be N. SLA/latency The job completion time should be by 8 a.m. PST daily. The max event processing latency should be < 2 seconds (for streaming). Schema Column account_id should be INT type and can't be NULL. Column values Column account_id must be positive integers. Column account_type can only have the values: FREE, STANDARD, or MAX. Comparison with history The total number of confirmed orders on any date should be within +20%/-20% of the daily average of the last 30 days. Comparison with other datasets The number of shipped orders should correlate to the number of confirmed orders. Table 1 Implementing Data Quality Monitors Once a list of data quality metrics is defined, these metrics should be captured as part of the data system and metric monitors should be automated as much as possible. In case of any data quality violations, the on-call data engineers should be alerted to investigate further. In the current data world, data engineering teams often own a mixed bag of batched and streaming data applications, and the implementation of data quality metrics can be different for batched vs. streaming systems. Batched Systems The Write-Audit-Publish (WAP) pattern is a data engineering best practice widely used to monitor data quality in batched data pipelines. It emphasizes the importance of always evaluating data quality before releasing the data to downstream users. Figure 1: Write-Audit-Publish pattern in batched data pipeline design Streaming Systems Unfortunately, the WAP pattern is not applicable to data streams because event streaming applications have to process data nonstop, and pausing production streaming jobs to troubleshoot data quality issues would be unacceptable. In a Lambda architecture, the output of event streaming systems is also stored in lakehouse storage (e.g., an Apache Iceberg or Apache Hudi table) for batched usage. As a result, it is also common for data engineers to implement WAP-based batched data quality monitors on the lakehouse table. To monitor data quality in near real-time, one option is to implement data quality checks as real-time queries on the output, such as an Apache Kafka topic or an Apache Druid datasource. For large-scale output, sampling is typically applied to improve the query efficiency of aggregated metrics. Helper frameworks such as Schema Registry can also be useful for ensuring output events have a compatible as-expected schema. Another option is to capture data quality metrics in an event-by-event manner as part of the application logic and log the results in a time series data store. This option introduces additional side output but allows more visibility into intermediate data stages/operations and easier troubleshooting. For example, assuming the application logic decides to drop events that have invalid account_id, account_type, or order_id, if an upstream system release introduces a large number of events with invalid account_id, the output-based data quality metrics will show a decline in the total number output events. However, it would be difficult to identify what filter logic or column is the root cause without metrics or logs on intermediate data stages/operations. Data Recovery and Backfill Every data pipeline will fail at some point. Some of the common failure causes include: Incompatible source data updates (e.g., critical columns were removed from source tables) Source or sink data systems failures (e.g., sink databases became unavailable) Altered truth in data (e.g., data processing logic became outdated after a new product release) Human errors (e.g., a new build introduces new edge-case errors left unhandled) Therefore, all data systems should be able to be backfilled at all times in order to minimize the impact of potential failures on downstream business use cases. In addition, in event streaming systems, the ability to backfill is also required for bootstrapping large stateful stream processing jobs. The data storage and processing frameworks used in batched and streaming architectures are usually different, and so are the challenges that lie behind supporting backfill. Batched Systems The storage solutions for batched systems, such as AWS S3 and GCP Cloud Storage, are relatively inexpensive and source data retention is usually not a limiting factor in backfill. Batched data are often written and read by event-time partitions, and data processing jobs are scheduled to run at certain intervals and have clear start and completion timestamps. The main technical challenge in backfilling batched data pipelines is data lineage: what jobs updated/read which partitions at what timestamp. Clear data lineage enables data engineers to easily identify downstream jobs impacted by problematic data partitions. Modern lakehouse table formats such as Apache Iceberg provide queryable table-level changelogs and history snapshots, which allow users to revert any table to a specific version in case a recent data update contaminated the table. The less queryable data lineage metadata, the more manual work is required for impact estimation and data recovery. Streaming Systems The source data used in streaming systems, such as Apache Kafka topics, often have limited retention due to the high cost of low-latency storage. For instance, for web-scale data streams, data retention is often set to several hours to keep costs reasonable. As troubleshooting failures can take data engineers hours if not days, the source data could have already expired before backfill. As a result, data retention is often a challenge in event streaming backfill. Below are the common backfill methodologies for event streaming systems: METHODS FOR BACKFILLING STREAMING DATA SYSTEMS Method Description Replaying source streams Reprocess source data from the problematic time period before those events expire in source systems (e.g., Apache Kafka). Tiered storage can help reduce stream retention cost. Lambda architecture Maintain a parallel batched data application (e.g., Apache Spark) for backfill, reading source data from a lakehouse storage with long retention. Kappa architecture The event streaming application is capable of streaming data from both data streams (for production) and lakehouse storage (for backfill) Unified batch and streaming Data processing frameworks, such as Apache Beam, support both streaming (for production) and batch mode (for backfill). Table 2 Preventing Data Quality Regressions Let's say a data pipeline has a comprehensive collection of data quality metrics implemented and a data recovery mechanism to ensure that reasonable historical data can be backfilled at any time. What could go wrong from here? Without prevention mechanisms, the data engineering team can only react passively to data quality issues, finding themselves busy putting out the same fire over and over again. To truly future-proof the data pipeline, data engineers must proactively establish programmatic data contracts to prevent data quality regression at the root. Data quality issues can either come from upstream systems or the application logic maintained by data engineers. For both cases, data contracts should be implemented programmatically, such as unit tests and/or integration tests to stop any contract-breaking changes from going into production. For example, let's say that a data engineering team owns a data pipeline that consumes advertisement impression logs for an online retail store. The expectations of the impression data logging should be implemented as unit and/or regression tests in the client-side logging test suite since it is owned by the client and data engineering teams. The advertisement impression logs are stored in a Kafka topic, and the expectation on the data schema is maintained in a Schema Registry to ensure the events have compatible data schemas for both producers and consumers. As the main logic of the data pipeline is attributing advertisement click events to impression events, the data engineering team developed unit tests with mocked client-side logs and dependent services to validate the core attribution logic and integration tests to verify that all components of the data system together produce the correct final output. Conclusion Data quality should be the first priority of every data pipeline and the data architecture should be designed with data quality in mind. The first step of building robust and resilient data systems is defining a set of data quality metrics based on the business use cases. Data quality metrics should be captured as part of the data system and monitored continuously, and the data should be able to be backfilled at all times to minimize potential impact to downstream users in case of data quality issues. The implementation of data quality monitors and backfill methods can be different for batched vs. event streaming systems. Last but not least, data engineers should establish programmatic data contracts as code to proactively prevent data quality regressions. Only when the data engineering systems are future-proofed to deliver qualitative data, data-driven business decisions can be made with confidence. This is an article from DZone's 2023 Data Pipelines Trend Report.For more: Read the Report

By Xinran Waibel

Revolutionizing Software Testing

This is an article from DZone's 2023 Automated Testing Trend Report.For more: Read the Report Artificial intelligence (AI) has revolutionized the realm of software testing, introducing new possibilities and efficiencies. The demand for faster, more reliable, and efficient testing processes has grown exponentially with the increasing complexity of modern applications. To address these challenges, AI has emerged as a game-changing force, revolutionizing the field of automated software testing. By leveraging AI algorithms, machine learning (ML), and advanced analytics, software testing has undergone a remarkable transformation, enabling organizations to achieve unprecedented levels of speed, accuracy, and coverage in their testing endeavors. This article delves into the profound impact of AI on automated software testing, exploring its capabilities, benefits, and the potential it holds for the future of software quality assurance. An Overview of AI in Testing This introduction aims to shed light on the role of AI in software testing, focusing on key aspects that drive its transformative impact. Figure 1: AI in testing Elastically Scale Functional, Load, and Performance Tests AI-powered testing solutions enable the effortless allocation of testing resources, ensuring optimal utilization and adaptability to varying workloads. This scalability ensures comprehensive testing coverage while maintaining efficiency. AI-Powered Predictive Bots AI-powered predictive bots are a significant advancement in software testing. Bots leverage ML algorithms to analyze historical data, patterns, and trends, enabling them to make informed predictions about potential defects or high-risk areas. By proactively identifying potential issues, predictive bots contribute to more effective and efficient testing processes. Automatic Update of Test Cases With AI algorithms monitoring the application and its changes, test cases can be dynamically updated to reflect modifications in the software. This adaptability reduces the effort required for test maintenance and ensures that the test suite remains relevant and effective over time. AI-Powered Analytics of Test Automation Data By analyzing vast amounts of testing data, AI-powered analytical tools can identify patterns, trends, and anomalies, providing valuable information to enhance testing strategies and optimize testing efforts. This data-driven approach empowers testing teams to make informed decisions and uncover hidden patterns that traditional methods might overlook. Visual Locators Visual locators, a type of AI application in software testing, focus on visual elements such as user interfaces and graphical components. AI algorithms can analyze screenshots and images, enabling accurate identification of and interaction with visual elements during automated testing. This capability enhances the reliability and accuracy of visual testing, ensuring a seamless user experience. Self-Healing Tests AI algorithms continuously monitor test execution, analyzing results and detecting failures or inconsistencies. When issues arise, self-healing mechanisms automatically attempt to resolve the problem, adjusting the test environment or configuration. This intelligent resilience minimizes disruptions and optimizes the overall testing process. What Is AI-Augmented Software Testing? AI-augmented software testing refers to the utilization of AI techniques — such as ML, natural language processing, and data analytics — to enhance and optimize the entire software testing lifecycle. It involves automating test case generation, intelligent test prioritization, anomaly detection, predictive analysis, and adaptive testing, among other tasks. By harnessing the power of AI, organizations can improve test coverage, detect defects more efficiently, reduce manual effort, and ultimately deliver high-quality software with greater speed and accuracy. Benefits of AI-Powered Automated Testing AI-powered software testing offers a plethora of benefits that revolutionize the testing landscape. One significant advantage lies in its codeless nature, thus eliminating the need to memorize intricate syntax. Embracing simplicity, it empowers users to effortlessly create testing processes through intuitive drag-and-drop interfaces. Scalability becomes a reality as the workload can be efficiently distributed among multiple workstations, ensuring efficient utilization of resources. The cost-saving aspect is remarkable as minimal human intervention is required, resulting in substantial reductions in workforce expenses. With tasks executed by intelligent bots, accuracy reaches unprecedented heights, minimizing the risk of human errors. Furthermore, this automated approach amplifies productivity, enabling testers to achieve exceptional output levels. Irrespective of the software type — be it a web-based desktop application or mobile application — the flexibility of AI-powered testing seamlessly adapts to diverse environments, revolutionizing the testing realm altogether. Figure 2: Benefits of AI for test automation Mitigating the Challenges of AI-Powered Automated Testing AI-powered automated testing has revolutionized the software testing landscape, but it is not without its challenges. One of the primary hurdles is the need for high-quality training data. AI algorithms rely heavily on diverse and representative data to perform effectively. Therefore, organizations must invest time and effort in curating comprehensive and relevant datasets that encompass various scenarios, edge cases, and potential failures. Another challenge lies in the interpretability of AI models. Understanding why and how AI algorithms make specific decisions can be critical for gaining trust and ensuring accurate results. Addressing this challenge requires implementing techniques such as explainable AI, model auditing, and transparency. Furthermore, the dynamic nature of software environments poses a challenge in maintaining AI models' relevance and accuracy. Continuous monitoring, retraining, and adaptation of AI models become crucial to keeping pace with evolving software systems. Additionally, ethical considerations, data privacy, and bias mitigation should be diligently addressed to maintain fairness and accountability in AI-powered automated testing. AI models used in testing can sometimes produce false positives (incorrectly flagging a non-defect as a defect) or false negatives (failing to identify an actual defect). Balancing precision and recall of AI models is important to minimize false results. AI models can exhibit biases and may struggle to generalize new or uncommon scenarios. Adequate training and validation of AI models are necessary to mitigate biases and ensure their effectiveness across diverse testing scenarios. Human intervention plays a critical role in designing test suites by leveraging their domain knowledge and insights. They can identify critical test cases, edge cases, and scenarios that require human intuition or creativity, while leveraging AI to handle repetitive or computationally intensive tasks. Continuous improvement would be possible by encouraging a feedback loop between human testers and AI systems. Human experts can provide feedback on the accuracy and relevance of AI-generated test cases or predictions, helping improve the performance and adaptability of AI models. Human testers should play a role in the verification and validation of AI models, ensuring that they align with the intended objectives and requirements. They can evaluate the effectiveness, robustness, and limitations of AI models in specific testing contexts. AI-Driven Testing Approaches AI-driven testing approaches have ushered in a new era in software quality assurance, revolutionizing traditional testing methodologies. By harnessing the power of artificial intelligence, these innovative approaches optimize and enhance various aspects of testing, including test coverage, efficiency, accuracy, and adaptability. This section explores the key AI-driven testing approaches, including differential testing, visual testing, declarative testing, and self-healing automation. These techniques leverage AI algorithms and advanced analytics to elevate the effectiveness and efficiency of software testing, ensuring higher-quality applications that meet the demands of the rapidly evolving digital landscape: Differential testing assesses discrepancies between application versions and builds, categorizes the variances, and utilizes feedback to enhance the classification process through continuous learning. Visual testing utilizes image-based learning and screen comparisons to assess the visual aspects and user experience of an application, thereby ensuring the integrity of its look and feel. Declarative testing expresses the intention of a test using a natural or domain-specific language, allowing the system to autonomously determine the most appropriate approach to execute the test. Self-healing automation automatically rectifies element selection in tests when there are modifications to the user interface (UI), ensuring the continuity of reliable test execution. Key Considerations for Harnessing AI for Software Testing Many contemporary test automation tools infused with AI provide support for open-source test automation frameworks such as Selenium and Appium. AI-powered automated software testing encompasses essential features such as auto-code generation and the integration of exploratory testing techniques. Open-Source AI Tools To Test Software When selecting an open-source testing tool, it is essential to consider several factors. Firstly, it is crucial to verify that the tool is actively maintained and supported. Additionally, it is critical to assess whether the tool aligns with the skill set of the team. Furthermore, it is important to evaluate the features, benefits, and challenges presented by the tool to ensure they are in line with your specific testing requirements and organizational objectives. A few popular open-source options include, but are not limited to: Carina – AI-driven, free forever, scriptless approach to automate functional, performance, visual, and compatibility tests TestProject – Offered the industry's first free Appium AI tools in 2021, expanding upon the AI tools for Selenium that they had previously introduced in 2020 for self-healing technology Cerberus Testing – A low-code and scalable test automation solution that offers a self-healing feature called Erratum and has a forever-free plan Designing Automated Tests With AI and Self-Testing AI has made significant strides in transforming the landscape of automated testing, offering a range of techniques and applications that revolutionize software quality assurance. Some of the prominent techniques and algorithms are provided in the tables below, along with the purposes they serve: KEY TECHNIQUES AND APPLICATIONS OF AI IN AUTOMATED TESTING Key Technique Applications Machine learning Analyze large volumes of testing data, identify patterns, and make predictions for test optimization, anomaly detection, and test case generation Natural language processing Facilitate the creation of intelligent chatbots, voice-based testing interfaces, and natural language test case generation Computer vision Analyze image and visual data in areas such as visual testing, UI testing, and defect detection Reinforcement learning Optimize test execution strategies, generate adaptive test scripts, and dynamically adjust test scenarios based on feedback from the system under test Table 1 KEY ALGORITHMS USED FOR AI-POWERED AUTOMATED TESTING Algorithm Purpose Applications Clustering algorithms Segmentation k-means and hierarchical clustering are used to group similar test cases, identify patterns, and detect anomalies Sequence generation models: recurrent neural networks or transformers Text classification and sequence prediction Trained to generate sequences such as test scripts or sequences of user interactions for log analysis Bayesian networks Dependencies and relationships between variables Test coverage analysis, defect prediction, and risk assessment Convolutional neural networks Image analysis Visual testing Evolutionary algorithms: genetic algorithms Natural selection Optimize test case generation, test suite prioritization, and test execution strategies by applying genetic operators like mutation and crossover on existing test cases to create new variants, which are then evaluated based on fitness criteria Decision trees, random forests, support vector machines, and neural networks Classification Classification of software components Variational autoencoders and generative adversarial networks Generative AI Used to generate new test cases that cover different scenarios or edge cases by test data generation, creating synthetic data that resembles real-world scenarios Table 2 Real-World Examples of AI-Powered Automated Testing AI-powered visual testing platforms perform automated visual validation of web and mobile applications. They use computer vision algorithms to compare screenshots and identify visual discrepancies, enabling efficient visual testing across multiple platforms and devices. NLP and ML are combined to generate test cases from plain English descriptions. They automatically execute these test cases, detect bugs, and provide actionable insights to improve software quality. Self-healing capabilities are also provided by automatically adapting test cases to changes in the application's UI, improving test maintenance efficiency. Quantum AI-Powered Automated Testing: The Road Ahead The future of quantum AI-powered automated software testing holds great potential for transforming the way testing is conducted. Figure 3: Transition of automated testing from AI to Quantum AI Quantum computing's ability to handle complex optimization problems can significantly improve test case generation, test suite optimization, and resource allocation in automated testing. Quantum ML algorithms can enable more sophisticated and accurate models for anomaly detection, regression testing, and predictive analytics. Quantum computing's ability to perform parallel computations can greatly accelerate the execution of complex test scenarios and large-scale test suites. Quantum algorithms can help enhance security testing by efficiently simulating and analyzing cryptographic algorithms and protocols. Quantum simulation capabilities can be leveraged to model and simulate complex systems, enabling more realistic and comprehensive testing of software applications in various domains, such as finance, healthcare, and transportation. Parting Thoughts AI has significantly revolutionized the traditional landscape of testing, enhancing the effectiveness, efficiency, and reliability of software quality assurance processes. AI-driven techniques such as ML, anomaly detection, NLP, and intelligent test prioritization have enabled organizations to achieve higher test coverage, early defect detection, streamlined test script creation, and adaptive test maintenance. The integration of AI in automated testing not only accelerates the testing process but also improves overall software quality, leading to enhanced customer satisfaction and reduced time to market. As AI continues to evolve and mature, it holds immense potential for further advancements in automated testing, paving the way for a future where AI-driven approaches become the norm in ensuring the delivery of robust, high-quality software applications. Embracing the power of AI in automated testing is not only a strategic imperative but also a competitive advantage for organizations looking to thrive in today's rapidly evolving technological landscape. This is an article from DZone's 2023 Automated Testing Trend Report.For more: Read the Report

By Tuhin Chattopadhyay

CORE

How To Handle Technical Debt in Scrum

If you work in software development, you likely encounter technical debt all the time. It accumulates over time as we prioritize delivering new features over maintaining a healthy codebase. Managing technical debt, or code debt, can be a challenge. Approaching it the right way in the context of Scrum won’t just help you manage your tech debt. It can allow you to leverage it to strategically ship faster and gain a very real competitive advantage. In this article, I’ll cover: The basics of technical debt and why it matters How tech debt impacts Scrum teams How to track tech debt How to prioritize tech debt and fix it Why continuous improvement matters in tech debt Thinking About Tech Debt in Scrum: The Basics Scrum is an Agile framework that helps teams deliver high-quality software in a collaborative and iterative way. By leveraging strategies like refactoring, incremental improvement, and automated testing, Scrum teams can tackle technical debt head-on. But it all starts with good issue tracking. Whether you're a Scrum master, product owner, or developer, I’m going to share some practical insights and strategies for you to manage tech debt. The Impact of Technical Debt on Scrum Teams Ignoring technical debt can lead to higher costs, slower delivery times, and reduced productivity. Tech debt makes it harder to implement new features or updates because it creates excessive complexity. Product quality suffers in turn. Then maintenance costs rise. There are more customer issues, and customers become frustrated. Unmanaged technical debt has the potential to touch every part of the business. Technical debt also brings the team down. It’s a serial destroyer of morale. Ignoring tech debt or postponing it is often frustrating and demotivating. It can also exacerbate communication problems and create silos, hindering project goals. Good management of tech debt, then, is absolutely essential for the modern Scrum team. How to Track Tech Debt Agile teams who are successful at managing their tech debt identify it early and often. Technical debt should be identified: During the act of writing code. Scrum teams should feel confident accruing prudent tech debt to ship faster. That’s so long as they track that debt immediately and understand how it could be paid off. Backlog refinement. This is an opportunity to discuss and prioritize the product backlog and have nuanced conversations about tech debt in the codebase. Sprint planning. How technical debt impacts the current sprint should always be a topic of conversation during sprint planning. Allocate resources to paying back tech debt consistently. Retrospectives. An opportunity to identify tech debt that has been accrued or which needs to be considered or prioritized. Use an in-editor issue tracker, which enables your engineers to track issues directly linked to code. This is a weakness of common issue-tracking software like Jira, which often undermines the process entirely. Prioritising Technical Debt in Scrum There are many ways to choose what to prioritize. I suggest choosing a theme for each sprint. Allocate 15-20% of your resources to fixing a specific subset of technical debt issues. For example, you might choose to prioritize issues based on… Their impact on a particular part of the codebase needed to ship new features Their impact on critical system functionality, security, or performance Their impact on team morale, employee retention, or developer experience The headaches around issue resolution often stem from poor issue tracking. Once your Scrum team members have nailed an effective issue-tracking system that feels seamless for engineers, solving tech debt becomes much easier. The Importance of Good Issue Tracking in Managing Technical Debt in Scrum Good issue tracking is the foundation of any effective technical debt management strategy. Scrum teams must be able to track technical debt issues systematically to prioritize and address them effectively. Using the right tools can make or break a tech debt management strategy. Modern engineering teams need issue-tracking tools that: Link issues directly to code. Make issues visible in the code editor Enable engineers to visualize tech debt in the codebase Create issues from the code editor in Stepsize Continuous Improvement in Scrum Identify tech debt early and consistently. Address and fix tech debt continuously. Use Scrum sessions such as retrospectives as an opportunity to reflect on how the team can improve their process for managing technical debt. Consider: Where does tech debt tend to accumulate? Is everybody following a good issue-tracking process? Are issues high-quality? Regularly review and update the team's “Definition of Done” (DoD), which outlines the criteria that must be met for a user story to be considered complete. Refining the DoD increases their likelihood of shipping high-quality code that is less likely to result in technical debt down the line. Behavioral change is most likely when teams openly collaborate, supported by the right tools. I suggest encouraging everybody to reflect on their processes and actively search for opportunities to improve. Wrapping Up Managing technical debt properly needs to be a natural habit for modern Scrum teams. Doing so protects the long-term performance of the team and product. Properly tracking technical debt is the foundation of any effective technical debt management strategy. By leveraging the right issue-tracking tools and prioritizing technical debt in the right way, Scrum teams can strategically ship faster. Doing so also promotes better product quality and maintains team morale and collaboration. Remember, technical debt is an unavoidable part of software development, but with the right approach and tools, it’s possible to drive behavioral change and safeguard the long-term success of your team.

By Ruth Dillon-Mansfield

How to Debug an Unresponsive Elasticsearch Cluster

Elasticsearch is an open-source search engine and analytics store used by a variety of applications from search in e-commerce stores, to internal log management tools using the ELK stack (short for “Elasticsearch, Logstash, Kibana”). As a distributed database, your data is partitioned into “shards” which are then allocated to one or more servers. Because of this sharding, a read or write request to an Elasticsearch cluster requires coordinating between multiple nodes as there is no “global view” of your data on a single server. While this makes Elasticsearch highly scalable, it also makes it much more complex to setup and tune than other popular databases like MongoDB or PostgresSQL, which can run on a single server. When reliability issues come up, firefighting can be stressful if your Elasticsearch setup is buggy or unstable. Your incident could be impacting customers which could negatively impact revenue and your business reputation. Fast remediation steps are important, yet spending a large amount of time researching solutions online during an incident or outage is not a luxury most engineers have. This guide is intended to be a cheat sheet for common issues that engineers running that can cause issues with Elasticsearch and what to look for. As a general purpose tool, Elasticsearch has thousands of different configurations which enables it to fit a variety of different workloads. Even if published online, a data model or configuration that worked for one company may not be appropriate for yours. There is no magic bullet getting Elasticsearch to scale, and requires diligent performance testing and trial/error. Unresponsive Elasticsearch Cluster Issues Cluster stability issues are some of the hardest to debug, especially if nothing changes with your data volume or code base. Check Size of Cluster State What Does It Do? Elasticsearch cluster state tracks the global state of our cluster, and is the heart of controlling traffic and the cluster. Cluster state includes metadata on nodes in your cluster, status of shards and how they are mapped to nodes, index mappings (i.e. the schema), and more. Cluster state usually doesn’t change often. However, certain operations such as adding a new field to an index mapping can trigger an update. Because cluster updates broadcast to all nodes in the cluster, it should be small (<100MB). A large cluster state can quickly make the cluster unstable. A common way this happens is through a mapping explosion (too many keys in an index) or too many indices. What to Look For Download the cluster state using the below command and look at the size of the JSON returned.curl -XGET 'http://localhost:9200/_cluster/state' In particular, look at which indices have the most fields in the cluster state which could be the offending index causing stability issues. If the cluster state is large and increasing. You can also get an idea looking at individual index or match against an index pattern like so:curl -XGET 'http://localhost:9200/_cluster/state/_all/my_index-*' You can also see the offending index’s mapping using the following command:curl -XGET 'http://localhost:9200/my_index/_mapping' How to Fix Look at how data is being indexed. A common way mapping explosion occurs is when high-cardinality identifiers are being used as a JSON key. Each time a new key is seen like “4” and”5”, the cluster state is updated. For example, the below JSON will quickly cause stability issues with Elasticsearch as each key is being added to the global state. { "1": { "status": "ACTIVE" }, "2": { "status": "ACTIVE" }, "3": { "status": "DISABLED" } } To fix, flatten your data into something that is Elasticsearch-friendly:{ [ { "id": "1", "status": "ACTIVE" }, { "id": "2", "status": "ACTIVE" }, { "id": "3", "status": "DISABLED" } ] } Check Elasticsearch Tasks Queue What Does It Do? When a request is made against elasticsearch (index operation, query operation, etc), it’s first inserted into the task queue, until a worker thread can pick it up. Once a worker pool has a thread free, it will pick up a task from the task queue and process it. These operations are usually made by you via HTTP requests on the :9200 and :9300 ports, but they can also be internal to handle maintenance tasks on an index At a given time there may be hundreds or thousands of in-flight operations, but should complete very quickly (like microseconds or milliseconds). What to Look For Run the below command and look for tasks that are stuck running for a long time like minutes or hours. This means something is starving the cluster and preventing it from making forward progress. It’s ok for certain long running tasks like moving an index to take a long time. However, normal query and index operations should be quick.curl -XGET 'http://localhost:9200/_cat/tasks?detailed' With the ?detailed param, you can get more info on the target index and query. Look for patterns in which tasks are consistently at the top of the list. Is it the same index? Is it the same node? If so, maybe something is wrong with that index’s data or the node is overloaded. How to Fix If the volume of requests is higher than normal, then look at ways to optimize the requests (such as using bulk APIs or more efficient queries/writes) If not change in volume and looks random, this implies something else is slowing down the cluster. The backup of tasks is just a symptom of a larger issue. If you don’t know where the requests come from, add the X-Opaque-Id header to your Elasticsearch clients to identify which clients are triggering the queries. Checks Elasticsearch Pending Tasks What Does It Do? Pending tasks are pending updates to the cluster state such as creating a new index or updating its mapping. Unlike the previous tasks queue, pending updates require a multi step handshake to broadcast the update to all nodes in the cluster, which can take some time. There should be almost zero in-flight tasks in a given time. Keep in mind, expensive operations like a snapshot restore can cause this to spike temporarily. What to Look For Run the command and ensure none or few tasks in-flight.curl curl curl -XGET 'http://localhost:9200/_cat/pending_tasks' If it looks to be a constant stream of cluster updates that finish quickly, look at what might be triggering them. Is it a mapping explosion or creating too many indices? If it’s just a few, but they seem stuck, look at the logs and metrics of the master node to see if there are any issues. For example, is the master node running into memory or network issues such that it can’t process cluster updates? Hot Threads What Does It Do? The hot threads API is a valuable built-in profiler to tell you where Elasticsearch is spending the most time. This can provide insights such as whether Elasticsearch is spending too much time on index refresh or performing expensive queries. What to Look For Make a call to the hot threads API. To improve accuracy, it’s recommended to capture many snapshots using the ?snapshotsparamcurl -XGET 'http://localhost:9200/_nodes/hot_threads?snapshots=1000' This will return stack traces seen when the snapshot was taken. Look for the same stack in many different snapshots. For example, you might see the text 5/10 snapshots sharing following 20 elements. This means a thread spends time in that area of the code during 5 snapshots. You should also look at the CPU %. If an area of code has both high snapshot sharing and high CPU %, this is a hot code path. By looking at the code module, disassemble what Elasticsearch is doing. If you see wait or park state, this is usually okay. How to Fix If a large amount of CPU time is spent on index refresh, then try increasing the refresh interval beyond the default 1 second. If you see a large amount in cache, maybe your default caching settings are suboptimal and causing a heavy miss. Memory Issues Check Elasticsearch Heap/Garbage Collection What Does It Do? As a JVM process, the heap is the area of memory where a lot of Elasticsearch data structures are stored and requires garbage collection cycles to prune old objects. For typical production setups, Elasticsearch locks all memory using mlockall on-boot and disables swapping. If you’re not doing this, do it now. If Heap is consistently above 85% or 90% for a node, this means we are coming close to out of memory. What to Look For Search for collecting in the last in Elasticsearch logs. If these are present, this means Elasticsearch is spending higher overhead on garbage collection (which takes time away from other productive tasks). A few of these every now and then ok as long as Elasticsearch is not spending the majority of its CPU cycles on garbage collection (calculate the percentage of time spent on collecting relative to the overall time provided). A node that is spending 100% time on garbage collection is stalled and cannot make forward progress. Nodes that appear to have network issues like timeouts may actually be due to memory issues. This is because a node can’t respond to incoming requests during a garbage collection cycle. How to Fix The easiest is to add more nodes to increase the heap available for the cluster. However, it takes time for Elasticsearch to rebalance shards to the empty nodes. If only a small set of nodes have high heap usage, you may need to better balance your customer. For example, if your shards vary in size drastically or have different query/index bandwidths, you may have allocated too many hot shards to the same set of nodes. To move a shard, use the reroute API. Just adjust the shard awareness sensitivity to ensure it doesn’t get moved back.curl -XPOST -H "Content-Type: application/json" localhost:9200/_cluster/reroute -d ' { "commands": [ { "move": { "index": "test", "shard": 0, "from_node": "node1", "to_node": "node2" } } ] }' If you are sending large bulk requests to Elasticsearch, try reducing the batch size so that each batch is under 100MB. While larger batches help reduce network overhead, they require allocating more memory to buffer the request which cannot be freed until after both the request is complete and the next GC cycle. Check Elasticsearch Old Memory Pressure What Does It Do? The old memory pool contains objects that have survived multiple garbage collection cycles and are long-living objects. If the old memory is over 75%, you might want to pay attention to it. As this fills up beyond 85%, more GC cycles will happen but the objects can’t be cleaned up. What to Look For Look at the old pool used/old pool max. If this is over 85%, that is concerning. How to Fix Are you eagerly loading a lot of field data? These reside in memory for a long time. Are you performing many long-running analytics tasks? Certain tasks should be offloaded to a distributed computing framework designed for map/reduce operations like Apache Spark. Check Elasticsearch FieldData Size What Does It Do? FieldData is used for computing aggregations on a field such as terms aggregation Usually, field data for a field is not loaded in memory until the first time an aggregation is performed on it. However, this can also be precomputed on index refresh if eager_load_ordinals is set. What to Look For Look at an index or all indices field data size, like so:curl -XGET 'http://localhost:9200/index_1/_stats/fielddata' An index could have very large field data structures if we are using it on the wrong type of data. Are you performing aggregations on very high-cardinality fields like a UUID or trace id? Field data is not suited for very high-cardinality fields as they will create massive field data structures. Do you have a lot of fields with eager_load_ordinals set or allocate a large amount to the field data cache. This causes the field data to be generated at refresh time vs query time. While it can speed up aggregations, it’s not optimal if you’re computing the field data for many fields at index refresh and never consume it in your queries. How to Fix Make adjustments to your queries or mapping to not aggregate on very high cardinality keys. Audit your mapping to reduce the number that have eager_load_ordinals set to true. Elasticsearch Networking Issues Node Left or Node Disconnected What Does It Do? A node will eventually be removed from the cluster if it does not respond to requests. This allows shards to be replicated to other nodes to meet the replication factor and ensure high availability even if a node was removed. What to Look For Look at the master node logs. Even though there are multiple masters, you should look at the master node that is currently elected. You can use the nodes API or a tool like Cerebro to do this. Look if there is a consistent node that times out or has issues. For example, you can see which nodes are still pending for a cluster update by looking for the phrase pending nodes in the master node’s logs. If you see the same node keep getting added but then removed, it may imply the node is overloaded or unresponsive. If you can’t reach the node from your master node, it could imply a networking issue. You could also be running into the NIC or CPU bandwidth limitations How to Fix Test with the setting transport.compression set to true This will compress traffic between nodes (such as from ingestion nodes to data nodes) reducing network bandwidth at the expense of CPU bandwidth. Note: Earlier versions called this setting transport.tcp.compression If you also have memory issues, try increasing memory. A node may become unresponsive due to a large time spent on garbage collection. Not Enough Master Node Issues What Does It Do? The master and other nodes need to discover each other to formulate a cluster. On the first boot, you must provide a static set of master nodes so you don’t have a split brain problem. Other nodes will then discover the cluster automatically as long as the master nodes are present. What to Look For Enable Trace logging to review discovery-related activities.curl -XPUT -H "Content-Type: application/json" localhost:9200/_cluster/_settings -d ' { "transient": {"logger.discovery.zen":"TRACE"} }' Review configurations such as minimum_master_nodes (if older than 6.x). Look at whether all master nodes in your initial master nodes list can ping each other. Review whether you have a quorum, which should be number of master nodes / 2 +1. If you have less than a quorum, no updates to the cluster state will occur to protect data integrity. How to Fix Sometimes network or DNS issues can cause the original master nodes to not be reachable. Review that you have at least number of master nodes / 2 +1 master nodes currently running. Shard Allocation Errors Elasticsearch in Yellow or Red State (Unassigned Shards) What Does It Do? When a node reboots or a cluster restore is started, the shards are not immediately available. Recovery is throttled to ensure the cluster does not get overwhelmed. Yellow state means primary indices are allocated, but secondary (replica) shards have not been allocated yet. While yellow indices are both readable and writable, availability is decreased. The yellow state is usually self-healable as the cluster replicates shards. Red indices mean primary shards are not allocated. This could be transient such as during a snapshot restore operation, but can also imply major problems such as missing data. What to Look For See the reason behind why allocation has stopped.curl -XGET 'http://localhost:9200/_cluster/allocation/explain' curl -XGET 'http://localhost:9200/_cat/shards?h=index,shard,prirep,state,unassigned.reason' Get a list of red indices, to understand which indices are contributing to the red state. The cluster state will be in the red state as long as at least one index is red.curl -XGET 'http:localhost:9200/_cat/indices' | grep red For more detail on a single index, you can see the recovery status for the offending index.curl -XGET 'http:localhost:9200/index_1/_recovery' How to Fix If you see a timeout from max_retries (maybe the cluster was busy during allocation), you can temporarily increase the circuit breaker threshold (Default is 5). Once the number is above the circuit breaker, Elasticsearch will start to initialize the unassigned shards.curl -XPUT -H "Content-Type: application/json" localhost:9200/index1,index2/_settings -d ' { "index.allocation.max_retries": 7 }' Elasticsearch Disk Issues Index Is Read-Only What Does It Do? Elasticsearch has three disk-based watermarks that influence shard allocation. The cluster.routing.allocation.disk.watermark.low watermark prevents new shards from being allocated to a node with disk filling up. By default, this is 85% of the disk used. The cluster.routing.allocation.disk.watermark.high watermark will force the cluster to start moving shards off of the node to other nodes. By default, this is 90%. This will start to move data around until below the high watermark. If Elasticsearch disk exceeds the flood stage watermark cluster.routing.allocation.disk.watermark.flood_stage, is when the disk is getting so full that moving might not be fast enough before the disk runs out of space. When reached, indices are placed in a read-only state to avoid data corruption. What to Look For Look at your disk space for each node. Review logs for nodes for a message like below:high disk watermark [90%] exceeded on XXXXXXXX free: 5.9gb[9.5%], shards will be relocated away from this node Once the flood stage is reached, you’ll see logs like so:flood stage disk watermark [95%] exceeded on XXXXXXXX free: 1.6gb[2.6%], all indices on this node will be marked read-only Once this happens, the indices on that node are read-only. To confirm, see which indices have read_only_allow_delete set to true.curl -XGET 'http://localhost:9200/_all/_settings?pretty' | grep read_only How to Fix First, clean up disk space such as by deleting local logs or tmp files. To remove this block of read-only, make the command:curl -XPUT -H "Content-Type: application/json" localhost:9200/_all/_settings -d ' { "index.blocks.read_only_allow_delete": null }' Conclusion Troubleshooting stability and performance issues can be challenging. The best way to find the root cause is by using the scientific method of hypothesis and proving it correct or incorrect. Using these tools and the Elasticsearch management API, you can gain a lot of insights into how Elasticsearch is performing and where issues may be.

By Derric Gilling

CORE

Maximizing Uptime: How to Leverage AWS RDS for High Availability and Disaster Recovery

In today's digital era, businesses depend on their databases for storing and managing vital information. It's essential to guarantee high availability and disaster recovery capabilities for these databases to avoid data loss and reduce downtime. Amazon Web Services (AWS) offers a remarkable solution to meet these goals via its Relational Database Service (RDS). This article dives into implementing high availability and disaster recovery using AWS RDS. Grasping AWS RDS Amazon RDS is a managed database service, making database deployment, scaling, and handling more straightforward. It accommodates database engines like MySQL, PostgreSQL, Oracle, and SQL Server. AWS RDS oversees regular tasks such as backups, software patching, and hardware provisioning, thus enabling users to concentrate on their applications instead of database management. Achieving High Availability Through Multi-AZ Deployments High availability refers to the system's capacity to maintain operation and accessibility despite component failures. AWS RDS provides Multi-AZ (Availability Zone) deployments to ensure your database instances retain high availability. What Do Availability Zones Represent? AWS data centers span several geographic regions, each comprising at least two Availability Zones. These Zones represent distinct locations outfitted with duplicate power, networking, and connectivity. They offer fault tolerance and guarantee that shortcomings in one zone fail to influence others. How Multi-AZ Deployments Work Multi-AZ deployments operate on a system where AWS duplicates your principal database to a backup instance within a separate Availability Zone. Any alterations on the primary instance have synchronous replication on the standby instance. If an outage, planned or unplanned, impacts the primary instance, AWS promotes the standby instance to assume the role of the new primary, thus reducing downtime. Understanding Multi-AZ Deployments Setup Establishing Multi-AZ deployments is done directly via the AWS Management Console or Command Line Interface (CLI). When creating an RDS instance, one chooses the "Multi-AZ Deployment" option. AWS takes over from there, overseeing synchronization, failover, and monitoring tasks. Disaster Recovery Utilizing Read Replicas While Multi-AZ deployments ensure high availability inside one region, disaster recovery necessitates a strategy for managing regional shutdowns. As a solution, AWS RDS presents Read Replicas. Understanding Read Replicas Read Replicas represent asynchronous duplicates of the primary database instance. They permit the creation of numerous read-only copies in varied regions, distributing read traffic and offering options for disaster recovery. Functioning of Read Replicas The primary instance replicates data to the Read Replicas in an asynchronous manner. Although Read Replicas only allow read operations, they hold the potential to be elevated to standalone database instances during a disaster. By channeling read traffic toward the Read Replicas it lightens the load of read operations on the primary instance, enhancing overall performance. Establishing Disaster Recovery Using Read Replicas Through the AWS Management Console or CLI, one can create Read Replicas. The replication settings include the region and are configurable. Managing the promotion process is feasible manually or via automation using AWS tools such as AWS Lambda. Conclusion High availability and disaster recovery form the cornerstone of database management strategies. AWS RDS enables businesses to quickly deploy Multi-AZ for regional high availability and Read Replicas for cross-regional disaster recovery. Utilizing these features and monitoring tools, companies can maintain the resilience and accessibility of their databases, even when encountering unforeseen challenges. Therefore, AWS RDS is a dependable and sturdy option for managing databases in the cloud.

By Raghava Dittakavi

7 Essential Software Quality Metrics for Project Success

Let's talk about something crucial in software projects—software quality metrics. These metrics can help us ensure our projects are successful and meet the needs of the people using them. In this article, our primary purpose is to provide you with a comprehensive guide to the essential software quality metrics that can significantly contribute to the success of your project. We want you to walk away with a solid understanding of these metrics so you can make well-informed decisions and ensure the best outcomes for your projects. We'll explain software quality metrics, software testing services, and their importance to project success. Then, we'll discuss their role in managing projects, including how they can help us measure progress and make necessary adjustments. Understanding these metrics is critical. With this knowledge, we can make our projects more likely to succeed and meet user needs. Next, we'll delve into the essential software quality metrics themselves. We'll provide a detailed explanation of each metric, discussing how these metrics contribute to the overall success of your project. By the end of this article, you'll have a firm grasp of these metrics and why these are crucial to the software development process. We aim to empower you to take control of your projects and make the best decisions possible, using software quality metrics as your guide. So, dive into the software quality metrics world and discover how they help you achieve project success! What Are Software Quality Metrics? Software quality metrics are like measuring tools to check our software projects' performance. We use these metrics to measure different aspects of our software, like how easy it is to understand the code, how fast it runs, if it's doing what it's supposed to do, and how safe it is from hackers. Importance of Tracking and Measuring Software Quality Metrics Tracking and measuring these metrics is essential because they help us see if our software is on the right track. By monitoring these numbers, we can spot problems early and fix them before they become significant. For instance, if we discover that our software is running too slowly, we can figure out what's causing the problem and make changes to speed it up. Checking these metrics also helps us ensure we're meeting the goals we set for our project. Role of Software Quality Metrics in Project Management Software quality metrics play a significant role in managing software projects. When working on a project, we must keep track of many things, including schedules and budgets, and ensure everyone is doing their part. These metrics help us do these by giving us a clear picture of how our project is doing so we can make wise decisions and adjustments as needed. It helps us ensure that our project stays on track and succeeds. How To Choose the Suitable Metrics for Your Project Let's discuss choosing the right metrics for your software project! Picking the right metrics is important because it helps us focus on what matters for our project's success. Here's what you need to know: Factors to Consider When Selecting Software Quality Metrics When selecting software quality metrics, there are a few factors to remember: First, think about your project's goals. What do you want to achieve with your software? If you want to create a game with great graphics or an app that's super easy to use, choose metrics to help you measure if you're reaching those goals. Next, consider the people who will be using your software. What do they care about most? For example, if your users are worried about security, you should choose metrics that measure your software's security. Finally, think about the resources you have available. Some metrics require special tools or extra time to measure, so make sure you can realistically track the metrics you choose. Aligning Software Quality Metrics With Project Objectives and Requirements To ensure your metrics are helpful, they should align with your project's objectives and requirements. If you aim to create a fast, user-friendly app, choose metrics that measure how quickly it runs and how easy it is for users to complete tasks. By selecting metrics matching your project's goals and requirements, you can focus on the most essential and make better decisions throughout development. To choose the right metrics for your project, consider your goals, users, and resources. Ensuring the metrics you select align with your project's objectives and requirements will help you focus on what's most vital and contribute to your project's success! 7 Essential Software Quality Metrics for Project Success These quality metrics help ensure your software is excellent and meets user needs. Here's a breakdown of each category and the specific metrics within them: 1. Code Quality Metrics Code quality metrics measure the quality of your code and its readability, maintainability, and reusability. It includes metrics like lines of code (LOC), cyclomatic complexity (CC), McCabe's score, comment-to-code ratio, and duplicated lines of code. Complexity: Code quality metric measures how complicated your code is. When code is too complex, it can be hard to understand and change. Keeping complexity low helps make your code easier to work with and reduces the chances of errors. Maintainability: Maintainability is about how easy it is to update and fix your code. When your code is maintainable, finding and fixing problems, adding new features, and running your software is more effortless. Code coverage: This metric tells you what percentage of your code is tested by your automated tests. High code coverage means that most of your code is being tested, which helps ensure that your software is working correctly and is less likely to have bugs. 2. Performance Metrics Performance metrics measure how quickly your software reacts to user actions or requests. These consist of response time, throughput, errors per second, user sessions, and page load time. Fast response times mean your users won't have to wait long for your software to do what they want, which makes for a better experience. Throughput: Throughput is about how much work your software can handle in a certain amount of time. High throughput means your software can take on several tasks quickly, which is especially important when many people use your software simultaneously. Resource Utilization: This metric examines how efficiently your software uses memory, processing power, and storage resources. When your software uses resources efficiently, it can run faster and work well on different devices. 3. Reliability Metrics Reliability metrics measure how well your software functions and performs its tasks. These include metrics like uptime, availability, mean time to failure, and mean time to repair. High reliability means your users can trust that your software will do what it should. Mean Time Between Failures (MTBF): MTBF measures the average time between when your software fails or experiences problems. A high MTBF means that your software is more reliable and less likely to have issues that frustrate your users. Defect Density: Defect density measures the number of bugs or problems found in your code compared to the total size of your code. Low defect density means your code has fewer bugs, which helps make your software more reliable. 4. Usability Metrics Usability metrics measure how easy it is for users to use your software. These metrics are task completion time, error rate, satisfaction survey score, and user feedback. When your software is easy to use, people can use it without problems. Task Completion Rate: This metric examines how many users can complete specific tasks using your software. A high task completion rate means your software is easy to use, and your users can get things done without problems. User Satisfaction: User satisfaction measures how happy your users are with your software. Happy users are more likely to keep using your software and recommend it to others, so ensuring they're satisfied with their experience is crucial. 5. Security Metrics Security metrics measure how secure your software is from malicious attacks by hackers. These metrics involve the number of security vulnerabilities, patch deployment rate, and response time to security incidents. Making sure your software is secure helps protect your users' data. Vulnerability Detection: This metric measures how well your software can detect and handle security threats, like hackers trying to break in. Good vulnerability detection helps keep your software and your users' data safe. Security Compliance: Security compliance determines how well your software meets established security standards and guidelines. When your software complies with these standards, it's more likely to be secure and less likely to have security issues. 6. Test Metrics Test metrics measure the effectiveness of your software tests. These include test coverage, pass rate, failure rate, execution time, and defect removal efficiency (DRE). Testing ensures your software works as expected and has fewer bugs. Test Case Pass Rate: This metric measures the percentage of test cases that pass during testing. A high pass rate means your software works well and is less likely to have bugs or issues. Defect Removal Efficiency: Defect removal efficiency measures how effectively you find and fix bugs in your code. High defect removal efficiency means you're good at identifying and resolving problems, which helps make your software more reliable. 7. Project Management Metrics Project management metrics measure the progress and success of your software project. These include time to market, cost per feature, customer satisfaction score, and user engagement. Keeping track of these metrics helps ensure your project is successful. Schedule Variance: Schedule variance shows the difference between your project's planned timeline and how far you've come. This metric will show a negative variance if your project runs behind schedule. Meanwhile, a positive variance means you're ahead of schedule. Keeping track of schedule variance helps you adjust to meet your project deadlines. Cost Variance: Cost variance measures your project's planned budget and actual costs. A positive cost variance means you're under budget, while a negative variance means you're over budget. Keeping an eye on cost variance helps you control your project's budget and make smart choices about using resources. These essential software quality metrics are vital in ensuring your project's success. By keeping track of code quality, performance, reliability, usability, security, testing, and project management metrics, you can ensure your software is well-built, easy to use, and meets the needs of your users. Tracking these metrics will help you identify areas that need improvement, make better decisions throughout the development process, and ultimately create a better product for your users. Remember, the goal is to make software people love using, and these metrics will help you get there! Implementing Software Quality Metrics in Your Project Now that you know the essential software quality metrics, let's discuss how to implement them in your software project. Using these steps can make a project that works well and has a better chance of success. Establishing a Metrics-Driven Culture The first step in implementing software quality metrics is to create a culture where everyone on your team values and understands the importance of these metrics. To do this, you can: Educate your team members about the metrics and why they matter for your project's success. Ensure each team member can access the resources and tools needed to track and analyze the metrics. Make it easy for everyone to talk and work together so they feel good about discussing and using the metrics. Continuous Monitoring and Feedback Loops Once you've established a metrics-driven culture, it's crucial to continuously monitor your project's progress using the software quality metrics. By doing this, you can: Keep an eye on your project's performance, making identifying and addressing issues more manageable. Make adjustments based on the metrics needed to improve your project's quality and stay on track. Create feedback loops within your team, where everyone is encouraged to share their insights and suggestions for improvement based on the metrics. Using Metrics to Drive Project Decisions and Improvements Finally, use the software quality metrics to guide project decisions and drive improvements. Below are a few ways you can do that: Based on the metrics, prioritize areas of your project that need the most attention. For example, suppose your usability metrics show users struggle with a specific feature. In that case, you can focus on improving that feature. Make data-driven decisions using metrics to evaluate options and choose the best action. For example, you may invest more time and resources into improving your software's performance based on the performance metrics. Continuously iterate and improve your project by using the metrics to measure the impact of changes you make. It will help you see what's working and what's not, allowing you to refine and improve your project. Implementing software quality metrics in your project can create a more efficient, effective, and successful software development process. Remember to establish a metrics-driven culture, continuously monitor your progress, and use the metrics to drive decisions and improvements. It helps you create a project that meets users' needs and stands out from the competition. Challenges in Using Software Quality Metrics As helpful as software quality metrics can be in managing your project, there are some challenges you might face when using them. This section will discuss some common pitfalls and how to balance software quality metrics with your project constraints. Common Pitfalls When Using Software Quality Metrics Overemphasis on Certain Metrics: Sometimes, we can focus too much on one or two metrics while ignoring others. It can lead to an unbalanced view of your project's overall quality. To avoid this, make sure you're considering all the essential metrics we discussed earlier rather than just focusing on one or two. Misinterpreting Metrics: It's vital to understand what each metric tells you and not to conclude too quickly. For example, a high code coverage percentage might not necessarily mean your code is well-tested if you haven't considered other factors like the quality of your test cases. Always look at the context and consider multiple metrics before making decisions. Relying Solely on Metrics: Metrics can provide valuable insights, but they shouldn't be the only thing you count on when evaluating your project's quality. It's also essential to consider what your team members, users, and others say to know how well your project is doing. Balancing Software Quality Metrics With Project Constraints Every project has constraints like time, budget, and resources. Balancing software quality metrics with these constraints can be challenging, but it's essential for ensuring your project's success. Here are several tips to help you strike the right balance: Prioritize Metrics Based on Your Project’s Goals: Not all metrics are equally important for every project. Identify the metrics most relevant to your project's goals and focus on those first. Be Realistic About Your Constraints: Understand your project's limitations and set achievable goals based on your available resources, budget, and timeline. It might not be possible or needed to be perfect in every metric for your project to succeed. Make Trade-Offs When Necessary: Sometimes, you might need to make trade-offs between different metrics or aspects of your project. For example, you might prioritize performance improvements over adding new features if your performance metrics show your software is not meeting the desired standards. Be prepared to make these tough decisions based on your project's priorities and constraints. Using software quality metrics effectively in your project can be challenging, but it's essential for ensuring your project's success. Be aware of the common pitfalls, and learn to balance software quality metrics with your project constraints. It will help you make better decisions and create a high-quality product that meets the needs of your users. By understanding these metrics, you can make a better product and have a higher chance of success. Conclusion To wrap up, using software quality metrics is crucial for the success of your project. These metrics help you monitor and improve various aspects of your software, such as code quality, performance, reliability, usability, security, testing, and project management. We encourage you to implement these essential metrics in your projects. Begin by making your team focus on metrics, choosing the best metrics for your project, and using them to make smart choices and improvements.

By Jeffery Thompson

How To Repair Failed Installations of Exchange Cumulative and Security Updates

To patch the Exchange Servers against known threats and fix bugs and vulnerabilities, Microsoft releases Cumulative and Security updates on a regular basis. These updates also provide new features, security patches, and various other fixes. Usually, the installation of these updates goes smoothly if done with proper planning and process. However, sometimes, you may encounter issues during and post installation of these updates. In this article, we will list some common issues that you may encounter when installing CU and SU and the possible solutions to fix them. Common Issues and Errors When Installing Exchange Updates and Their Solutions Below, we have mentioned some common issues that you may encounter when installing Cumulative and Security updates on the Exchange Server, along with their solutions. HTTP 400 Errors When Browsing OWA and ECP After installing the Cumulative Updates (CU), you may notice that both Outlook Web Access (OWA) and Exchange Control Panel (ECP) are not loading, even after entering the right credentials. You may see the following error. HTTP 400 - bad request Cannot serialize context When you open the Exchange Management Shell (EMS), you would get the following message. ErrorCode : -2144108477 TransportMessage : The WS-Management service cannot process the request because the XML is invalid. ErrorRecord : Connecting to remote server srv01.exchange.local failed with the following error message : For more information, see the about_Remote_Troubleshooting Help topic. The issue could occur if you have a user whose name ends with a dollar ($) sign. Solution: To resolve this issue, you can rename the username and remove the dollar ($) sign. Images Are Missing in OWA and ECP After the updates, you may notice that images in Outlook Web Access (OWA) and Exchange Control Panel (ECP) are not showing. It looks like they are missing from the source. It indicates that something went wrong during the installation of the Cumulative Update. Solution: A possible solution is to uninstall the Cumulative Update and re-install it from the command prompt running as Administrator. After that, reboot your server. Blank Page in All Portals This is a most common issue on Exchange Server 2016 and 2013. When you open Exchange Control Panel (ECP) or Outlook Web Access (OWA), you may just see a blank page. When you check the event viewer, you will notice that an event with ID 15021 is recorded. This happens due to some misconfiguration or issues with the SSL bindings on 0.0.0.0 with port 443. The possible causes are misconfiguration, wrong certificate assigned, and incorrect information. Solution: To resolve the issue, you need to check the SSL certificate and restart the Internet Information Services (IIS). Follow the below steps. Open the Internet Information Services (IIS). Open Sites, click on DefaultWebSite, and then click on Bindings. Click on the https443 binding and open it. Confirm that the right certificate is assigned and the certificate is still valid and matches the Exchange Server. Now, restart the services. For this, open PowerShell and run the following command. Restart-Service WAS, W3SVC Note: If you have a separate server for mailbox services, open the IIS from the server and check the certificate being used in the bindings on the Exchange Back End site. Check for the https 444 binding. Error During Update Rollup Installation When installing the update on a server where there is no internet connection, the installation would take a long time to finish. You may also face an error saying: Creating Native images for .Net assemblies. This issue is caused when the installation fails to access the below URL: http://crl.microsoft.com/pki/crl/products/CodeSigPCA.crl This is used to access the Certificate Revocation List on the native image generation (Ngen). As the server is not connected to the internet, each request needs to wait for time out before it can continue. This would take a long time and may time out the entire installation and cause issues. You might also encounter the same issue with the error saying. Installation cannot continue. The Setup Wizard has determined that this Interim Update is incompatible with the current Microsoft Exchange Server 2013 Cumulative Update 23 configuration. Solution: In situations where the internet connection is unavailable, you can disable the Check for Publisher’s Certificate Revocation option. For this, open Internet Explorer and go to Tools > Internet Options > Advanced > Security. After the installation is complete, you can re-enable the option. The other solution is to uninstall the previously installed Interim Update (IU) before installing the Cumulative Update. Upgrade Patch Can’t Be Installed You may get the “Upgrade patch cannot be installed” error if you’re installing the incorrect version of the Cumulative Update or Security Update. Solution: You can visit Microsoft’s website to check and install the correct update. Installation Failed Because Services Did Not Stop During the installation of the update, the setup may fail if the Exchange Services does not stop. This could happen due to the performance of the server, antivirus not having the proper exclusions in place, or any other process hindering the services. Solution: You can check that the Exchange Servers are set to automatic, rename or remove the C:\ExchangeSetupLogs folder, and restart the installation of the update. Restart From Previous Installation Is Pending When running the update, it may fail and display the following error message. Microsoft Exchange Server setup cannot continue because a restart from a previous installation or update is pending. This issue may occur if a previous installation failed or was canceled. Solution: You can run the HealthChecker script to check the health and detect any issues with the installation or configuration. You can also run the SetupAssist script that can assist in resolving the issue. No Mail Flow After Update After installation of the update, you may face a situation where the mail flow stops working. It may happen if, The services didn’t start automatically after the update. The services fail to start after the update. Solution: First, you can try to manually start the services. Then, look at the event viewer. There could be underlying issues with the installation that might have corrupted the configuration or the services’ executables. This would have stopped the services from starting. It is also suggested to check if the server has enough storage to run the services. You can also check that the server is not in maintenance mode and ensure that the services are not set as disabled. To Conclude Above, we have mentioned some common issues that you may encounter when installing Cumulative and Security updates on the Exchange Server. In addition, various issues might occur during the installation of the updates, such as: Hardware failures Sudden loss of power Human errors Conflict with third-party applications These issues may stop the process in the middle of an update, rendering the Exchange Server setup unusable. Since there are multiple points of failure, it is difficult to recover from such situations. In such cases, you can try to re-install the Exchange Server with the recovery mode and then restore the databases. However, if transaction logs or databases are damaged or corrupted, you will not be able to restore the databases. In such a case, you can use specialized tools like Stellar Repair for Exchange. With this tool, you can scan and repair the corrupt databases (EDB files) from any version of the Exchange Server. After repair, you can granularly export the EDB data directly to a live Exchange Server database with automatic mailbox matching. You can also use the application to export EDB data to Office 365, PST, and other file formats.

By Shelly Bhardwaj

The Evolution of Bugs

Programming, regardless of the era, has been riddled with bugs that vary in nature but often remain consistent in their basic problems. Whether we're talking about mobile, desktop, server, or different operating systems and languages, bugs have always been a constant challenge. Here's a dive into the nature of these bugs and how we can tackle them effectively. As a side note, if you like the content of this and the other posts in this series, check out my Debugging book that covers this subject. If you have friends who are learning to code, I'd appreciate a reference to my Java Basics book. If you want to get back to Java after a while, check out my Java 8 to 21 book. Memory Management: The Past and the Present Memory management, with its intricacies and nuances, has always posed unique challenges for developers. Debugging memory issues, in particular, has transformed considerably over the decades. Here's a dive into the world of memory-related bugs and how debugging strategies have evolved. The Classic Challenges: Memory Leaks and Corruption In the days of manual memory management, one of the primary culprits behind application crashes or slowdowns was the dreaded memory leak. This would occur when a program consumes memory but fails to release it back to the system, leading to eventual resource exhaustion. Debugging such leaks was tedious. Developers would pour over code, looking for allocations without corresponding deallocations. Tools like Valgrind or Purify were often employed, which would track memory allocations and highlight potential leaks. They provided valuable insights but came with their own performance overheads. Memory corruption was another notorious issue. When a program writes data outside the boundaries of allocated memory, it corrupts other data structures, leading to unpredictable program behavior. Debugging this required understanding the entire flow of the application and checking each memory access. Enter Garbage Collection: A Mixed Blessing The introduction of garbage collectors (GC) in languages brought in its own set of challenges and advantages. On the bright side, many manual errors were now handled automatically. The system would clean up objects not in use, drastically reducing memory leaks. However, new debugging challenges arose. For instance, in some cases, objects remained in memory because unintentional references prevented the GC from recognizing them as garbage. Detecting these unintentional references became a new form of memory leak debugging. Tools like Java's VisualVM or .NET's Memory Profiler emerged to help developers visualize object references and track down these lurking references. Memory Profiling: The Contemporary Solution Today, one of the most effective methods for debugging memory issues is memory profiling. These profilers provide a holistic view of an application's memory consumption. Developers can see which parts of their program consume the most memory, track allocation, and deallocation rates, and even detect memory leaks. Some profilers can also detect potential concurrency issues, making them invaluable in multi-threaded applications. They help bridge the gap between the manual memory management of the past and the automated, concurrent future. Concurrency: A Double-Edged Sword Concurrency, the art of making software execute multiple tasks in overlapping periods, has transformed how programs are designed and executed. However, with the myriad of benefits it introduces, like improved performance and resource utilization, concurrency also presents unique and often challenging debugging hurdles. Let's delve deeper into the dual nature of concurrency in the context of debugging. The Bright Side: Predictable Threading Managed languages, those with built-in memory management systems, have been a boon to concurrent programming. Languages like Java or C# made threading more approachable and predictable, especially for applications that require simultaneous tasks but not necessarily high-frequency context switches. These languages provide in-built safeguards and structures, helping developers avoid many pitfalls that previously plagued multi-threaded applications. Moreover, tools and paradigms, such as promises in JavaScript, have abstracted away much of the manual overhead of managing concurrency. These tools ensure smoother data flow, handle callbacks, and aid in better structuring asynchronous code, making potential bugs less frequent. The Murky Waters: Multi-Container Concurrency However, as technology progressed, the landscape became more intricate. Now, we're not just looking at threads within a single application. Modern architectures often involve multiple concurrent containers, microservices, or functions, especially in cloud environments, all potentially accessing shared resources. When multiple concurrent entities, perhaps running on separate machines or even data centers, try to manipulate shared data, the debugging complexity escalates. Issues arising from these scenarios are far more challenging than traditional localized threading issues. Tracing a bug may involve traversing logs from multiple systems, understanding inter-service communication, and discerning the sequence of operations across distributed components. Reproducing The Elusive: Threading Bugs Thread-related problems have earned a reputation for being some of the hardest to solve. One of the primary reasons is their often non-deterministic nature. A multi-threaded application may run smoothly most of the time but occasionally produce an error under specific conditions, which can be exceptionally challenging to reproduce. One approach to identifying such elusive issues is logging the current thread and/or stack within potentially problematic code blocks. By observing logs, developers can spot patterns or anomalies that hint at concurrency violations. Furthermore, tools that create "markers" or labels for threads can help in visualizing the sequence of operations across threads, making anomalies more evident. Deadlocks, where two or more threads indefinitely wait for each other to release resources, although tricky, can be more straightforward to debug once identified. Modern debuggers can highlight which threads are stuck, waiting for which resources, and which other threads are holding them. In contrast, livelocks present a more deceptive problem. Threads involved in a livelock are technically operational, but they're caught in a loop of actions that render them effectively unproductive. Debugging this requires meticulous observation, often stepping through each thread's operations to spot a potential loop or repeated resource contention without progress. Race Conditions: The Ever-Present Ghost One of the most notorious concurrency-related bugs is the race condition. It occurs when software's behavior becomes erratic due to the relative timing of events, like two threads trying to modify the same piece of data. Debugging race conditions involves a paradigm shift: one shouldn't view it just as a threading issue but as a state issue. Some effective strategies involve field watchpoints, which trigger alerts when particular fields are accessed or modified, allowing developers to monitor unexpected or premature data changes. The Pervasiveness of State Bugs Software, at its core, represents and manipulates data. This data can represent everything from user preferences and current context to more ephemeral states, like the progress of a download. The correctness of software heavily relies on managing these states accurately and predictably. State bugs, which arise from incorrect management or understanding of this data, are among the most common and treacherous issues developers face. Let's delve deeper into the realm of state bugs and understand why they're so pervasive. What Are State Bugs? State bugs manifest when the software enters an unexpected state, leading to malfunction. This might mean a video player that believes it's playing while paused, an online shopping cart that thinks it's empty when items have been added, or a security system that assumes it's armed when it's not. From Simple Variables to Complex Data Structures One reason state bugs are so widespread is the breadth and depth of data structures involved. It's not just about simple variables. Software systems manage vast, intricate data structures like lists, trees, or graphs. These structures can interact, affecting one another's states. An error in one structure or a misinterpreted interaction between two structures can introduce state inconsistencies. Interactions and Events: Where Timing Matters Software rarely acts in isolation. It responds to user input, system events, network messages, and more. Each of these interactions can change the state of the system. When multiple events occur closely together or in an unexpected order, they can lead to unforeseen state transitions. Consider a web application handling user requests. If two requests to modify a user's profile come almost simultaneously, the end state might depend heavily on the precise ordering and processing time of these requests, leading to potential state bugs. Persistence: When Bugs Linger The state doesn't always reside temporarily in memory. Much of it gets stored persistently, be it in databases, files, or cloud storage. When errors creep into this persistent state, they can be particularly challenging to rectify. They linger, causing repeated issues until detected and addressed. For example, if a software bug erroneously marks an e-commerce product as "out of stock" in the database, it will consistently present that incorrect status to all users until the incorrect state is fixed, even if the bug causing the error has been resolved. Concurrency Compounds State Issues As software becomes more concurrent, managing the state becomes even more of a juggling act. Concurrent processes or threads may try to read or modify shared state simultaneously. Without proper safeguards like locks or semaphores, this can lead to race conditions, where the final state depends on the precise timing of these operations. Tools and Strategies to Combat State Bugs To tackle state bugs, developers have an arsenal of tools and strategies: Unit tests: These ensure individual components handle state transitions as expected. State machine diagrams: Visualizing potential states and transitions can help in identifying problematic or missing transitions. Logging and monitoring: Keeping a close eye on state changes in real-time can offer insights into unexpected transitions or states. Database constraints: Using database-level checks and constraints can act as a final line of defense against incorrect persistent states. Exceptions: The Noisy Neighbor When navigating the labyrinth of software debugging, few things stand out quite as prominently as exceptions. They are, in many ways, like a noisy neighbor in an otherwise quiet neighborhood: impossible to ignore and often disruptive. But just as understanding the reasons behind a neighbor's raucous behavior can lead to a peaceful resolution, diving deep into exceptions can pave the way for a smoother software experience. What Are Exceptions? At their core, exceptions are disruptions in the normal flow of a program. They occur when the software encounters a situation it wasn't expecting or doesn't know how to handle. Examples include attempting to divide by zero, accessing a null reference, or failing to open a file that doesn't exist. The Informative Nature of Exceptions Unlike a silent bug that might cause the software to produce incorrect results without any overt indications, exceptions are typically loud and informative. They often come with a stack trace, pinpointing the exact location in the code where the issue arose. This stack trace acts as a map, guiding developers directly to the problem's epicenter. Causes of Exceptions There's a myriad of reasons why exceptions might occur, but some common culprits include: Input errors: Software often makes assumptions about the kind of input it will receive. When these assumptions are violated, exceptions can arise. For instance, a program expecting a date in the format "MM/DD/YYYY" might throw an exception if given "DD/MM/YYYY" instead. Resource limitations: If the software tries to allocate memory when none is available or opens more files than the system allows, exceptions can be triggered. External system failures: When software depends on external systems, like databases or web services, failures in these systems can lead to exceptions. This could be due to network issues, service downtimes, or unexpected changes in the external systems. Programming errors: These are straightforward mistakes in the code. For instance, trying to access an element beyond the end of a list or forgetting to initialize a variable. Handling Exceptions: A Delicate Balance While it's tempting to wrap every operation in try-catch blocks and suppress exceptions, such a strategy can lead to more significant problems down the road. Silenced exceptions can hide underlying issues that might manifest in more severe ways later. Best practices recommend: Graceful degradation: If a non-essential feature encounters an exception, allow the main functionality to continue working while perhaps disabling or providing alternative functionality for the affected feature. Informative reporting: Rather than displaying technical stack traces to end-users, provide friendly error messages that inform them of the problem and potential solutions or workarounds. Logging: Even if an exception is handled gracefully, it's essential to log it for developers to review later. These logs can be invaluable in identifying patterns, understanding root causes, and improving the software. Retry mechanisms: For transient issues, like a brief network glitch, implementing a retry mechanism can be effective. However, it's crucial to distinguish between transient and persistent errors to avoid endless retries. Proactive Prevention Like most issues in software, prevention is often better than cure. Static code analysis tools, rigorous testing practices, and code reviews can help identify and rectify potential causes of exceptions before the software even reaches the end user. Faults: Beyond the Surface When a software system falters or produces unexpected results, the term "fault" often comes into the conversation. Faults, in a software context, refer to the underlying causes or conditions that lead to an observable malfunction, known as an error. While errors are the outward manifestations we observe and experience, faults are the underlying glitches in the system, hidden beneath layers of code and logic. To understand faults and how to manage them, we need to dive deeper than the superficial symptoms and explore the realm below the surface. What Constitutes a Fault? A fault can be seen as a discrepancy or flaw within the software system, be it in the code, data, or even the software's specification. It's like a broken gear within a clock. You may not immediately see the gear, but you'll notice the clock's hands aren't moving correctly. Similarly, a software fault may remain hidden until specific conditions bring it to the surface as an error. Origins of Faults Design shortcomings: Sometimes, the very blueprint of the software can introduce faults. This might stem from misunderstandings of requirements, inadequate system design, or failure to foresee certain user behaviors or system states. Coding mistakes: These are the more "classic" faults where a developer might introduce bugs due to oversights, misunderstandings, or simply human error. This can range from off-by-one errors of incorrectly initialized variables to complex logic errors. External influences: Software doesn't operate in a vacuum. It interacts with other software, hardware, and the environment. Changes or failures in any of these external components can introduce faults into a system. Concurrency issues: In modern multi-threaded and distributed systems, race conditions, deadlocks, or synchronization issues can introduce faults that are particularly hard to reproduce and diagnose. Detecting and Isolating Faults Unearthing faults requires a combination of techniques: Testing: Rigorous and comprehensive testing, including unit, integration, and system testing, can help identify faults by triggering the conditions under which they manifest as errors. Static analysis: Tools that examine the code without executing it can identify potential faults based on patterns, coding standards, or known problematic constructs. Dynamic analysis: By monitoring the software as it runs, dynamic analysis tools can identify issues like memory leaks or race conditions, pointing to potential faults in the system. Logs and monitoring: Continuous monitoring of the software in production, combined with detailed logging, can offer insights into when and where faults manifest, even if they don't always cause immediate or overt errors. Addressing Faults Correction: This involves fixing the actual code or logic where the fault resides. It's the most direct approach but requires accurate diagnosis. Compensation: In some cases, especially with legacy systems, directly fixing a fault might be too risky or costly. Instead, additional layers or mechanisms might be introduced to counteract or compensate for the fault. Redundancy: In critical systems, redundancy can be used to mask faults. For example, if one component fails due to a fault, a backup can take over, ensuring continuous operation. The Value of Learning From Faults Every fault presents a learning opportunity. By analyzing faults, their origins, and their manifestations, development teams can improve their processes, making future versions of the software more robust and reliable. Feedback loops, where lessons from faults in production inform earlier stages of the development cycle, can be instrumental in creating better software over time. Thread Bugs: Unraveling the Knot In the vast tapestry of software development, threads represent a potent yet intricate tool. While they empower developers to create highly efficient and responsive applications by executing multiple operations simultaneously, they also introduce a class of bugs that can be maddeningly elusive and notoriously hard to reproduce: thread bugs. This is such a difficult problem that some platforms eliminated the concept of threads entirely. This created a performance problem in some cases or shifted the complexity of concurrency to a different area. These are inherent complexities, and while the platform can alleviate some of the difficulties, the core complexity is inherent and unavoidable. A Glimpse into Thread Bugs Thread bugs emerge when multiple threads in an application interfere with each other, leading to unpredictable behavior. Because threads operate concurrently, their relative timing can vary from one run to another, causing issues that might appear sporadically. The Common Culprits Behind Thread Bugs Race conditions: This is perhaps the most notorious type of thread bug. A race condition occurs when the behavior of a piece of software depends on the relative timing of events, such as the order in which threads reach and execute certain sections of code. The outcome of a race can be unpredictable, and tiny changes in the environment can lead to vastly different results. Deadlocks: These occur when two or more threads are unable to proceed with their tasks because they're each waiting for the other to release some resources. It's the software equivalent of a stand-off, where neither side is willing to budge. Starvation: In this scenario, a thread is perpetually denied access to resources and thus can't make progress. While other threads might be operating just fine, the starved thread is left in the lurch, causing parts of the application to become unresponsive or slow. Thread thrashing: This happens when too many threads are competing for the system's resources, causing the system to spend more time switching between threads than actually executing them. It's like having too many chefs in a kitchen, leading to chaos rather than productivity. Diagnosing the Tangle Spotting thread bugs can be quite challenging due to their sporadic nature. However, some tools and strategies can help: Thread sanitizers: These are tools specifically designed to detect thread-related issues in programs. They can identify problems like race conditions and provide insights into where the issues are occurring. Logging: Detailed logging of thread behavior can help identify patterns that lead to problematic conditions. Timestamped logs can be especially useful in reconstructing the sequence of events. Stress testing: By artificially increasing the load on an application, developers can exacerbate thread contention, making thread bugs more apparent. Visualization tools: Some tools can visualize thread interactions, helping developers see where threads might be clashing or waiting on each other. Untangling the Knot Addressing thread bugs often requires a blend of preventive and corrective measures: Mutexes and locks: Using mutexes or locks can ensure that only one thread accesses a critical section of code or resource at a time. However, overusing them can lead to performance bottlenecks, so they should be used judiciously. Thread-safe data structures: Instead of retrofitting thread safety onto existing structures, using inherently thread-safe structures can prevent many thread-related issues. Concurrency libraries: Modern languages often come with libraries designed to handle common concurrency patterns, reducing the likelihood of introducing thread bugs. Code reviews: Given the complexity of multithreaded programming, having multiple eyes review thread-related code can be invaluable in spotting potential issues. Race Conditions: Always a Step Ahead The digital realm, while primarily rooted in binary logic and deterministic processes, is not exempt from its share of unpredictable chaos. One of the primary culprits behind this unpredictability is the race condition, a subtle foe that always seems to be one step ahead, defying the predictable nature we expect from our software. What Exactly Is a Race Condition? A race condition emerges when two or more operations must execute in a sequence or combination to operate correctly, but the system's actual execution order is not guaranteed. The term "race" perfectly encapsulates the problem: these operations are in a race, and the outcome depends on who finishes first. If one operation 'wins' the race in one scenario, the system might work as intended. If another 'wins' in a different run, chaos might ensue. Why Are Race Conditions So Tricky? Sporadic occurrence: One of the defining characteristics of race conditions is that they don't always manifest. Depending on a myriad of factors, such as system load, available resources, or even sheer randomness, the outcome of the race can differ, leading to a bug that's incredibly hard to reproduce consistently. Silent errors: Sometimes, race conditions don't crash the system or produce visible errors. Instead, they might introduce minor inconsistencies—data might be slightly off, a log entry might get missed, or a transaction might not get recorded. Complex interdependencies: Often, race conditions involve multiple parts of a system or even multiple systems. Tracing the interaction that causes the problem can be like finding a needle in a haystack. Guarding Against the Unpredictable While race conditions might seem like unpredictable beasts, various strategies can be employed to tame them: Synchronization mechanisms: Using tools like mutexes, semaphores, or locks can enforce a predictable order of operations. For example, if two threads are racing to access a shared resource, a mutex can ensure that only one gets access at a time. Atomic operations: These are operations that run completely independently of any other operations and are uninterruptible. Once they start, they run straight through to completion without being stopped, altered, or interfered with. Timeouts: For operations that might hang or get stuck due to race conditions, setting a timeout can be a useful fail-safe. If the operation isn't complete within the expected time frame, it's terminated to prevent it from causing further issues. Avoid shared state: By designing systems that minimize shared state or shared resources, the potential for races can be significantly reduced. Testing for Races Given the unpredictable nature of race conditions, traditional debugging techniques often fall short. However: Stress testing: Pushing the system to its limits can increase the likelihood of race conditions manifesting, making them easier to spot. Race detectors: Some tools are designed to detect potential race conditions in code. They can't catch everything, but they can be invaluable in spotting obvious issues. Code reviews: Human eyes are excellent at spotting patterns and potential pitfalls. Regular reviews, especially by those familiar with concurrency issues, can be a strong defense against race conditions. Performance Pitfalls: Monitor Contention and Resource Starvation Performance optimization is at the heart of ensuring that software runs efficiently and meets the expected requirements of end users. However, two of the most overlooked yet impactful performance pitfalls developers face are monitor contention and resource starvation. By understanding and navigating these challenges, developers can significantly enhance software performance. Monitor Contention: A Bottleneck in Disguise Monitor contention occurs when multiple threads attempt to acquire a lock on a shared resource, but only one succeeds, causing the others to wait. This creates a bottleneck as multiple threads are contending for the same lock, slowing down the overall performance. Why It's Problematic Delays and deadlocks: Contention can cause significant delays in multi-threaded applications. Worse, if not managed correctly, it can even lead to deadlocks where threads wait indefinitely. Inefficient resource utilization: When threads are stuck waiting, they aren't doing productive work, leading to wasted computational power. Mitigation Strategies Fine-grained locking: Instead of having a single lock for a large resource, divide the resource and use multiple locks. This reduces the chances of multiple threads waiting for a single lock. Lock-free data structures: These structures are designed to manage concurrent access without locks, thus avoiding contention altogether. Timeouts: Set a limit on how long a thread will wait for a lock. This prevents indefinite waiting and can help in identifying contention issues. Resource Starvation: The Silent Performance Killer Resource starvation arises when a process or thread is perpetually denied the resources it needs to perform its task. While it's waiting, other processes might continue to grab available resources, pushing the starving process further down the queue. The Impact Degraded performance: Starved processes or threads slow down, causing the system's overall performance to dip. Unpredictability: Starvation can make system behavior unpredictable. A process that should typically be completed quickly might take much longer, leading to inconsistencies. Potential system failure: In extreme cases, if essential processes are starved for critical resources, it might lead to system crashes or failures. Solutions to Counteract Starvation Fair allocation algorithms: Implement scheduling algorithms that ensure each process gets a fair share of resources. Resource reservation: Reserve specific resources for critical tasks, ensuring they always have what they need to function. Prioritization: Assign priorities to tasks or processes. While this might seem counterintuitive, ensuring critical tasks get resources first can prevent system-wide failures. However, be cautious, as this can sometimes lead to starvation for lower-priority tasks. The Bigger Picture Both monitor contention and resource starvation can degrade system performance in ways that are often hard to diagnose. A holistic understanding of these issues, paired with proactive monitoring and thoughtful design, can help developers anticipate and mitigate these performance pitfalls. This not only results in faster and more efficient systems but also in a smoother and more predictable user experience. Final Word Bugs, in their many forms, will always be a part of programming. But with a deeper understanding of their nature and the tools at our disposal, we can tackle them more effectively. Remember, every bug unraveled adds to our experience, making us better equipped for future challenges. In previous posts in the blog, I delved into some of the tools and techniques mentioned in this post.

By Shai Almog

CORE

Building Trust in Data: The Critical Role of Data Quality Engineering in the Digital Age

"Bad data costs businesses in the U.S. over $600 billion a year." This staggering estimate by IBM highlights the colossal risks posed by poor data quality, ranging from erroneous analytics to customer dissatisfaction and regulatory non-compliance. Yet despite multimillion-dollar technology investments, data quality remains a persistent pain point. As organizations increasingly become data-centered, establishing trust and accountability in data is no longer optional. This is where the fast-emerging field of Data Quality Engineering (DQE) comes in. DQE provides the technical capabilities and governance to ingest, manage, and analyze quality data that lives up to the maxim “garbage in, garbage out.” This article demystifies DQE and provides business leaders with an actionable guide to leverage it for competitive advantage. Understanding Data Quality Engineering DQE involves the design, implementation, and oversight of integrated data quality controls across the data lifecycle. It brings an engineering rigor to enable reliable data quality measurement, monitoring, and improvement. The key principles underpinning DQE are: Accuracy: Data must represent the true state of analyzed entities and parameters. Completeness: Data must capture entire target populations without gaps or duplication. Consistency: Data must align across various systems and uses without contradiction. Timeliness: Data must be up-to-date and current for effective analysis and decision-making. DQE intersects with data governance to translate these principles into technical practices and organizational policies. Data profiling, quality rules, metadata management, workflow integration, and automation of monitoring/correction comprise DQE’s technical arsenal. Top-down accountability, stewardship programs, and internal data SLAs enforce its governance aspects. With robust DQE, the axiom “quality data in, quality analytics out” rings true. The High Costs of Poor Data Quality Deficient data quality manifests in tangible costs and lost opportunities. Statistically, dirty data costs the average Fortune 1000 company around $8 million per year. But compounding revenue loss, 21% of companies lose customers due to data quality issues. Beyond the financial impact, faulty data leads to dangerous outcomes like medical errors, security breaches, and compliance gaps. “Our greatest data vulnerability is inferior data quality,” warns Pete Lindley, Chief Data Officer at Experian. These damning repercussions underscore why merely investing in data infrastructure is insufficient — the output's utility relies on input quality. The Critical Role of Data Quality Engineering "Data quality doesn't improve on its own. It requires engineering discipline and organizational commitment," explains Diane Giangregorio, VP of Data Governance at Freddie Mac. DQE provides precisely this rigor to detect and mitigate quality risks proactively. Techniques like parsing, standardization, deduplication, and validation enable automating quality checks within data pipelines. This shift left quality control to prevent rather than cure downstream issues. Monitoring metadata like error logs and data lineage provides insights into root causes for earlier remediation. Leveraging machine learning algorithms to automate quality management is an emerging trend. But DQE software alone cannot guarantee success — a focus on people and processes is equally vital. Cross-departmental collaborations between engineers, scientists, and business stakeholders foster a culture of accountability. Leadership, organizational governance, and stewardship programs also drive the behavioral changes essential for quality-centric data usage. DQE Strategies and Best Practices "Data quality does not have a finish line. It needs to become ingrained in how we operate," says Tamara Rasamny, Director of Data Management at Blue Shield of California. Mature DQE implementation involves several leading practices: Profiling and auditing data to establish baselines, KPIs, and business impact of quality gaps. Building quality validation checks into upstream and downstream data processes. This enables real-time monitoring. Leveraging automation, ML, and cloud platforms for scalable remediation across data types and sources. Institutionalizing data certification protocols for suppliers, internal users, and outputs. Instilling quality-oriented design principles in data architecture and system acquisitions. Enforcing strong data governance through policies, protocols, and cross-departmental accountabilities. Developing DQE talent through training and collaborative partnerships across teams. While DQE needs customized applications, these tenets help engrain it across the data value chain. DQE in Action: Success Stories DQE may seem complex to adopt, but leading companies reveal its tangible payoffs: Uber improved map data quality by 11% using automated validation, easing driver operations. BBVA bank accelerated real-time payments by fixing data quality issues causing transaction failures. Target enhanced recommendation engine accuracy by 15% through DQE, improving customer data hygiene. UPS resolves over 60 million inaccurate addresses annually using algorithms to validate, correct, and standardize address data. These examples prove that strategic DQE adoption mitigates data risks, prevents revenue leakage, and enables trusted analytics, delivering a compelling competitive edge. Getting Started With DQE While the DQE journey requires sustained focus, simple first steps can deliver quick wins: Conduct an audit identifying quality gaps, technical debt, and risks tied to bad data. Define business-relevant data quality KPIs like accuracy, completeness, and usability. Identify data quality pain points with the highest business impact and tackle those first. Start implementing automated DQ monitoring and checks within key data pipelines. Build a business case for a larger DQE program based on audit findings and quick win results. Conclusion Data is only useful if it is accurate, timely, and trustworthy. As leaders increasingly rely on data insights for strategy and operations, overlooking data quality risks is no longer an option. However, as this article outlines, Data Quality Engineering provides the essential capabilities to take control of data quality in a scalable, sustainable fashion. Developing a mature DQE practice requires patience but pays long-term dividends in enabling confident decision-making. Companies that embed an engineering mindset and governance for quality will gain an unassailable competitive edge. With data's centrality only set to grow, the time for action on DQE is now.

By Sandeep Rangineni

A Comprehensive Guide to Securing ESXi Hosts: Safeguarding Virtual Infrastructure

ESXi hosts are the backbone of virtualization, serving as the foundation for running virtual machines and managing critical workloads and as such, ensuring the security of ESXi hosts is paramount to protect the entire virtual infrastructure. As virtualization technology continues to evolve, securing the underlying hypervisor becomes crucial for ensuring the safety and integrity of virtualized environments. VMware ESXi, a widely adopted hypervisor, requires comprehensive security measures to protect against potential vulnerabilities and unauthorized access. This article will delve into the various techniques and best practices for securing ESXi hosts, mitigating potential vulnerabilities, and fortifying your virtual environment against threats. Secure Physical Access Securing physical access to ESXi hosts is a critical aspect of overall host security. Physical access to the hosts can potentially lead to unauthorized modifications, tampering, or theft of sensitive data. To ensure the physical security of ESXi hosts, consider implementing the following measures: Secure Location: Place ESXi hosts in a secure location, such as a locked server room or data center. Limit access to authorized personnel only, and maintain strict control over who has physical access to the area. Access Control Systems: Implement robust access control systems to regulate entry into the server room or data center. This can include measures such as key cards, biometric authentication, or combination locks. These systems provide an additional layer of security by ensuring that only authorized individuals can physically access the ESXi hosts. Video Surveillance: Install video surveillance cameras in the server room or data center to monitor and record activities. Video surveillance acts as a deterrent and helps in identifying any unauthorized access or suspicious behavior. Ensure that the cameras cover all critical areas, including the ESXi hosts and their surroundings. Secure Rack Cabinets: Place the ESXi hosts in lockable rack cabinets or enclosures. These cabinets provide physical protection against tampering or unauthorized access. Additionally, ensure that the rack cabinets are securely bolted to the floor or wall to prevent physical theft. Cable Management: Proper cable management not only improves airflow and organization but also helps in maintaining the physical security of the ESXi hosts. Ensure that cables are neatly managed and secured, minimizing the risk of accidental disconnections or unauthorized access through unplugged cables. Asset Tagging: Label and tag the ESXi hosts with unique identifiers or asset tags. This helps in easy identification and inventory management. It also acts as a deterrent to potential theft or unauthorized movement of the hosts. Regular Auditing and Documentation: Maintain a detailed inventory of ESXi hosts, including their physical location, serial numbers, and configuration details. Perform regular audits to verify the physical presence and integrity of the hosts. Keep accurate documentation of access logs, including dates, times, and authorized individuals who accessed the server room or data center. Employee Awareness and Training: Educate employees and personnel about the importance of physical security and the potential risks associated with unauthorized access to ESXi hosts. Conduct regular training sessions to ensure that employees understand and follow physical security protocols. Incident Response Plan: Develop an incident response plan that includes procedures for addressing physical security breaches or suspicious activities. This plan should outline the steps to be taken, including reporting incidents, isolating affected hosts, and engaging appropriate security personnel or law enforcement agencies if necessary. By putting these measures in place, businesses can significantly improve the physical security of their ESXi hosts and reduce the dangers posed by unauthorized physical access. A thorough security framework must integrate physical security measures with more general security procedures applied at the host and virtualization levels. Update and Patch Regularly Keep your ESXi hosts up to date with the latest security patches and updates. Regularly check for vendor-provided patches and apply them promptly to address any known vulnerabilities. To simplify this task and guarantee that security updates are consistently applied, enable automatic updates or set up a patch management procedure. Regularly updating and patching ESXi hosts is a critical aspect of maintaining their security. VMware releases updates and patches to address known vulnerabilities, bugs, and performance problems. Organizations can make sure their ESXi hosts are running on the most recent security updates and fixes by staying up to date. Observe the following guidelines when patching and updating ESXi hosts: Develop a Patch Management Plan: Create a comprehensive patch management plan that outlines the process for updating and patching ESXi hosts. This plan should include a regular schedule for checking for updates, testing patches in a non-production environment, and deploying them to production hosts. Establish roles and responsibilities for the patch management process, ensuring that there is clear accountability for keeping the hosts up to date. Monitor Vendor Notifications and Security Advisories: Stay informed about updates and security advisories released by VMware. Monitor vendor notifications, security bulletins, and mailing lists to receive timely information about patches and vulnerabilities. VMware provides security advisories that highlight critical vulnerabilities and the recommended patches or workarounds. Test Updates and Patches in a Non-Production Environment: Before applying updates and patches to production ESXi hosts, perform thorough testing in a non-production environment. This helps ensure that the updates do not introduce compatibility issues or unintended consequences. Create a test bed that closely resembles the production environment and verify the compatibility and stability of the updates with your specific configurations and workloads. Prioritize and Schedule Updates: Assess the severity and criticality of updates and patches to prioritize their installation. Some patches address critical security vulnerabilities, while others may provide performance improvements or bug fixes. Develop a prioritization scheme that aligns with your organization’s risk tolerance and business requirements. Schedule maintenance windows to minimize disruption and ensure that updates can be applied without impacting critical workloads. Employ Automation and Centralized Management: Utilize automation tools and centralized management solutions to streamline the update and patching process. Tools like VMware vCenter Server provide centralized management capabilities that simplify the deployment of updates across multiple ESXi hosts. Automation helps reduce human error and ensures consistent and timely patching across the infrastructure. Monitor and Verify Update Status: Regularly monitor the update status of ESXi hosts to ensure that patches are applied successfully. Use monitoring tools and dashboards to track the patching progress and verify that all hosts are running the latest versions. Implement alerts or notifications to flag any hosts that have not received updates within the expected timeframe. Maintain Backup and Rollback Plans: Before applying updates and patches, ensure that you have a reliable backup strategy in place. Take snapshots or create backups of the ESXi hosts and associated virtual machines. This allows for easy rollback in case any issues or unexpected behavior arises after the update process. Having a backup strategy mitigates the risk of data loss or system instability. Stay Informed about EOL and Product Lifecycle: Be aware of the end-of-life (EOL) and product lifecycle of ESXi versions you are using. VMware provides guidelines and support timelines for each release. Plan for the timely upgrade or migration to newer versions to ensure continued access to security updates and support. By following these best practices and maintaining a proactive approach to update and patch management, organizations can significantly enhance the security and stability of their ESXi hosts, minimizing the risk of vulnerabilities and exploits. Implement Strong Access Controls To guarantee that only authorized individuals can access and manage the hypervisor environment, strong access controls must be implemented in ESXi hosts. Organizations can prevent unauthorized access, reduce the risk of malicious activities, and safeguard sensitive virtualized resources by enforcing strict access controls. Here are key measures to implement strong access controls in ESXi hosts: Role-Based Access Control (RBAC): Utilize RBAC to define and assign roles with specific privileges and permissions to users and groups. Create roles based on job responsibilities and restrict access rights to only what is necessary for each role. This principle of least privilege ensures that users have appropriate access levels without unnecessary administrative capabilities. Regularly review and update role assignments to align with organizational changes. Secure Password Policies: Enforce strong password policies for ESXi host access. Set password complexity requirements, such as minimum length, character combinations, and expiration periods. Encourage the use of passphrase-based passwords. Implement account lockout mechanisms to protect against brute-force attacks. Consider using password management tools or password vaults to securely store and manage passwords. Two-Factor Authentication (2FA): Implement 2FA to add an extra layer of security to ESXi host access. This requires users to provide a second form of authentication, typically a one-time password or a token, in addition to their regular credentials. 2FA significantly strengthens access controls by reducing the risk of unauthorized access in case of password compromise. Secure Shell (SSH) Access: Limit SSH access to ESXi hosts to authorized administrators only. Disable SSH access when not actively required for administrative tasks. When enabling SSH, restrict access to specific IP addresses or authorized networks. Implement SSH key-based authentication instead of password-based authentication for stronger security. ESXi Shell and Direct Console User Interface (DCUI): Control access to ESXi Shell and DCUI, which provide direct access to the ESXi host’s command line interface. Limit access to these interfaces to authorized administrators only. Disable or restrict access to the ESXi Shell and DCUI when not needed for troubleshooting or maintenance. Audit Logging and Monitoring: Enable auditing and logging features on ESXi hosts to capture and record user activities and events. Regularly review logs for suspicious activities and security incidents. Implement a centralized log management system to collect and analyze logs from multiple ESXi hosts. Real-time monitoring and alerts can help detect and respond to potential security breaches promptly. Secure Management Interfaces: Secure the management interfaces used to access ESXi hosts, such as vSphere Web Client or vSphere Client. Implement secure communication protocols, such as HTTPS, to encrypt data transmitted between clients and hosts. Utilize secure channels, such as VPNs or dedicated management networks, for remote access to ESXi hosts. Regular Access Reviews and Account Management: Perform regular access reviews to ensure that user accounts and privileges are up to date. Disable or remove accounts that are no longer required or associated with inactive users. Implement a formal process for onboarding and offboarding personnel, ensuring that access rights are granted or revoked in a timely manner. Patch Management: Maintain up-to-date patches and security updates for the ESXi hosts. Regularly apply patches to address vulnerabilities and security issues. A secure and well-patched hypervisor environment is fundamental to overall access control and host security. By implementing these access control measures, organizations can significantly strengthen the security of their ESXi hosts, reduce the risk of unauthorized access or misuse, and maintain a secure virtualization environment. It is crucial to regularly review and update access controls to adapt to evolving security requirements and organizational changes. Secure ESXi Management Network Protecting the integrity and confidentiality of administrative access to ESXi hosts requires securing the ESXi management network. The management network offers a means of remotely controlling, maintaining, and configuring ESXi hosts. Strong security measures are put in place to protect against unauthorized access, data breaches, and potential attacks. Here are some essential actions to protect the ESXi management network: Network Segmentation: Isolate the ESXi management network from other networks, such as VM networks or storage networks, by implementing network segmentation. This prevents unauthorized access to the management network from other less secure networks. Use separate physical or virtual network switches and VLANs to separate management traffic from other network traffic. Dedicated Management Network: Consider implementing a dedicated network solely for ESXi management purposes. By segregating management traffic, you minimize the risk of interference or compromise from other network activities. Ensure that this dedicated network is physically and logically isolated from other networks to enhance security. Network Firewalls and Access Control Lists (ACLs): Implement network firewalls and ACLs to restrict access to the ESXi management network. Configure rules that allow only necessary traffic to reach the management network. Limit the source IP addresses or IP ranges that can access the management network. Regularly review and update firewall rules to align with changing requirements and security best practices. Secure Communication Protocols: Utilize secure communication protocols to protect data transmitted over the management network. Enable and enforce Secure Socket Layer (SSL)/Transport Layer Security (TLS) encryption for management interfaces, such as vSphere Web Client or vSphere Client. This ensures that communications between clients and ESXi hosts are encrypted and secure. Avoid using unencrypted protocols like HTTP or Telnet for management purposes. Virtual Private Network (VPN): Require the use of a VPN when accessing the ESXi management network remotely. A VPN establishes an encrypted connection between the remote client and the management network, providing an additional layer of security. This prevents unauthorized access to the management network by requiring users to authenticate before accessing the ESXi hosts. Strong Authentication and Access Control: Implement strong authentication mechanisms for accessing the ESXi management network. Enforce the use of complex passwords, password expiration policies, and account lockout mechanisms. Utilize two-factor authentication (2FA) for an extra layer of security. Restrict access to the management network to authorized administrators only and regularly review and update access control lists. Intrusion Detection and Prevention Systems (IDPS): Deploy IDPS solutions to monitor and detect potential threats or malicious activities targeting the ESXi management network. These systems can detect and alert administrators about unauthorized access attempts, unusual traffic patterns, or other indicators of compromise. Configure the IDPS to provide real-time alerts for prompt response to potential security incidents. Regular Monitoring and Auditing: Implement monitoring and auditing mechanisms to track activities within the ESXi management network. Monitor log files, network traffic, and system events for any signs of unauthorized access or suspicious behavior. Perform regular audits to ensure compliance with security policies and identify any potential security gaps. Firmware and Software Updates: Regularly update the firmware and software of networking equipment, such as switches and routers, used in the ESXi management network. Keep them up to date with the latest security patches and updates to address any vulnerabilities. Organizations can improve the security of the ESXi management network by putting these security measures in place, protecting administrative access to ESXi hosts, and lowering the risk of unauthorized access or data breaches. To respond to new threats and changing security requirements, it is crucial to periodically review and update security controls. Enable Hypervisor-Level Security Features Enhancing the overall security posture of the virtualization environment requires turning on hypervisor-level security features in ESXi hosts, which is a critical step. These features offer additional layers of defense against various threats and vulnerabilities. In ESXi, you can enable the following significant hypervisor-level security features: Secure Boot: Enable Secure Boot, which verifies the integrity and authenticity of the ESXi boot process. This feature ensures that only signed and trusted components are loaded during boot-up, preventing the execution of unauthorized or malicious code. Secure Boot helps protect against bootkits and rootkits. Virtual Trusted Platform Module (vTPM): Enable vTPM, a virtualized version of the Trusted Platform Module. vTPM provides hardware-level security functions, such as secure key storage, cryptographic operations, and integrity measurements for virtual machines. It helps protect sensitive data and ensures the integrity of virtual machine configurations. Virtualization-Based Security (VBS): Enable VBS, a feature that leverages hardware virtualization capabilities to provide additional security boundaries within virtual machines. VBS includes features such as Virtualization-based Protection of Code Integrity (HVCI) and Credential Guard, which enhance the security of guest operating systems by isolating critical processes and protecting against memory attacks. Secure Encrypted Virtualization (SEV): If using AMD processors that support SEV, enable this feature to encrypt virtual machine memory, isolating it from other virtual machines and the hypervisor. SEV provides an additional layer of protection against memory-based attacks and unauthorized access to virtual machine data. ESXi Firewall: Enable the built-in ESXi firewall to control incoming and outgoing network traffic to and from the ESXi host. Configure firewall rules to allow only necessary traffic and block any unauthorized access attempts. Regularly review and update firewall rules to align with security requirements and best practices. Control Flow Integrity (CFI): Enable CFI, a security feature that protects against control-flow hijacking attacks. CFI ensures that the execution flow of the hypervisor and critical components follows predetermined rules, preventing malicious code from diverting program execution. CFI helps mitigate the risk of code exploitation and improves the overall security of the hypervisor. ESXi Secure Boot Mode: Enable Secure Boot Mode in ESXi to ensure that only signed and trusted ESXi components are loaded during boot-up. This feature helps protect against tampering and unauthorized modifications to the hypervisor and its components. MAC Address Spoofing Protection: Enable MAC address spoofing protection to prevent unauthorized manipulation of MAC addresses within virtual machines. This feature helps maintain network integrity and prevents malicious activities that rely on MAC address spoofing. Encrypted vMotion: Enable Encrypted vMotion to encrypt data transferred between ESXi hosts during live migrations. Encrypted vMotion protects against eavesdropping and data interception, ensuring the confidentiality and integrity of virtual machine data during migrations. Hypervisor-Assisted Guest Mitigations (Spectre and Meltdown): Enable the necessary mitigations for Spectre and Meltdown vulnerabilities at the hypervisor level. These mitigations protect guest operating systems against speculative execution-based attacks by isolating sensitive information and preventing unauthorized access. Enabling these hypervisor-level security features in ESXi hosts strengthens the security posture of the virtualization environment, protecting against a wide range of threats and vulnerabilities. Regularly update and patch ESXi hosts to ensure that the latest security enhancements and fixes are in place. Additionally, stay informed about new security features and best practices provided by VMware to further enhance the security of ESXi hosts. Monitor and Audit ESXi Hosts For the virtualization environment to remain secure and stable, monitoring and auditing ESXi hosts is crucial. Organizations can track configuration changes, ensure adherence to security policies, and identify and address potential security incidents by keeping an eye on host activity and conducting routine audits. In order to monitor and audit ESXi hosts, follow these simple instructions: Logging and Log Analysis: Enable and configure logging on ESXi hosts to capture important events, system activities, and security-related information. Configure log settings to capture relevant details for analysis, such as authentication attempts, administrative actions, and system events. Regularly review and analyze logs to identify any suspicious activities, anomalies, or potential security incidents. Centralized Log Management: Implement a centralized log management solution to collect and store log data from multiple ESXi hosts. Centralized logging simplifies log analysis, correlation, and reporting. It enables administrators to identify patterns, detect security breaches, and generate alerts for timely response. Consider using tools like VMware vCenter Log Insight or third-party log management solutions. Real-time Monitoring and Alerts: Utilize monitoring tools that provide real-time visibility into the ESXi host’s performance, health, and security. Monitor key metrics such as CPU usage, memory utilization, network activity, and storage performance. Configure alerts and notifications to promptly notify administrators of any critical events or threshold breaches. Security Information and Event Management (SIEM): Integrate ESXi host logs and events with a SIEM solution to correlate data across the entire infrastructure. SIEM systems help identify patterns and indicators of compromise by aggregating and analyzing log data from multiple sources. They provide a comprehensive view of security events, facilitate incident response, and enable compliance reporting. Configuration Management and Change Tracking: Implement configuration management tools to track and manage changes made to ESXi host configurations. Monitor and track modifications to critical settings, such as user accounts, permissions, network configurations, and security-related parameters. Establish a baseline configuration and compare it with current settings to detect unauthorized changes or misconfigurations. Regular Vulnerability Scanning: Perform regular vulnerability scans on ESXi hosts to identify potential security weaknesses and vulnerabilities. Use reputable vulnerability scanning tools that are specifically designed for virtualized environments. Regular scanning helps identify security gaps, outdated software versions, and configuration issues that could be exploited by attackers. Regular Security Audits: Conduct periodic security audits to assess the overall security posture of ESXi hosts. Audits can include reviewing access controls, user accounts, permissions, and configurations. Verify compliance with security policies, industry standards, and regulatory requirements. Perform penetration testing or vulnerability assessments to identify potential vulnerabilities or weaknesses. User Activity Monitoring: Monitor and audit user activities within the ESXi host environment. Track administrative actions, user logins, privilege escalations, and resource usage. User activity monitoring helps detect any unauthorized or suspicious actions, aiding in incident response and identifying insider threats. Patch and Update Management: Regularly apply patches and updates to ESXi hosts to address security vulnerabilities. Monitor vendor notifications and security advisories to stay informed about the latest patches and security fixes. Implement a patch management process to test and deploy patches in a controlled manner, ensuring minimal disruption to production environments. Compliance Monitoring: Regularly review and validate compliance with security policies, regulations, and industry standards applicable to your organization. This includes standards such as the Payment Card Industry Data Security Standard (PCI DSS) or the General Data Protection Regulation (GDPR). Implement controls and procedures to ensure ongoing compliance and address any identified gaps. By implementing robust monitoring and auditing practices for ESXi hosts, organizations can detect and respond to security incidents promptly, ensure compliance, and proactively maintain the security and stability of the virtualization environment. It is crucial to establish a well-defined monitoring and auditing strategy and regularly review and update these practices to adapt to evolving security threats and organizational requirements. Protect Against Malware and Intrusions Protecting ESXi hosts against malware and intrusions is crucial to maintaining the security and integrity of your virtualization environment. Malware and intrusions can lead to unauthorized access, data breaches, and disruptions to your ESXi hosts and virtual machines. Here are some key measures to help protect your ESXi hosts against malware and intrusions: Use Secure and Verified Sources: Download ESXi software and patches only from trusted sources, such as the official VMware website. Verify the integrity of the downloaded files using cryptographic hash functions provided by the vendor. This ensures that the software has not been tampered with or modified. Keep ESXi Hosts Up to Date: Regularly update ESXi hosts with the latest security patches and updates provided by VMware. Apply patches promptly to address known vulnerabilities and security issues. Keeping your hosts up to date helps protect against known malware and exploits. Harden ESXi Hosts: Implement security hardening practices on ESXi hosts to minimize attack surfaces. Disable unnecessary services and protocols, remove or disable default accounts, and enable strict security configurations. VMware provides a vSphere Security Configuration Guide that offers guidelines for securing ESXi hosts. Use Secure Boot: Enable Secure Boot on ESXi hosts to ensure that only digitally signed and trusted components are loaded during the boot process. Secure Boot helps prevent the execution of unauthorized or malicious code, protecting against bootkits and rootkits. Implement Network Segmentation: Segment your ESXi management network, VM networks, and storage networks using virtual LANs (VLANs) or physical network separation. This helps isolate and contain malware or intrusions, preventing lateral movement within your virtualization environment. Enable Hypervisor-Level Security Features: Leverage the hypervisor-level security features available in ESXi to enhance protection. Features like Secure Encrypted Virtualization (SEV), Virtualization-Based Security (VBS), and Control Flow Integrity (CFI) provide additional layers of protection against malware and code exploits. Install Antivirus/Antimalware Software: Deploy antivirus or antimalware software on your ESXi hosts. Choose a solution specifically designed for virtualized environments and compatible with VMware infrastructure. Regularly update antivirus signatures and perform regular scans of the host file system. Implement Firewall and Access Controls: Configure firewalls and access control lists (ACLs) to control inbound and outbound network traffic to and from your ESXi hosts. Only allow necessary services and protocols, and restrict access to authorized IP addresses or ranges. Regularly review and update firewall rules to align with your security requirements. Monitor and Log Activities: Implement comprehensive monitoring and logging of ESXi host activities. Monitor system logs, event logs, and network traffic for any suspicious activities or indicators of compromise. Set up alerts and notifications to promptly detect and respond to potential security incidents. Educate and Train Administrators: Provide security awareness training to ESXi administrators to educate them about malware threats, social engineering techniques, and best practices for secure administration. Emphasize the importance of following security policies, using strong passwords, and being vigilant against phishing attempts. Regular Security Audits and Assessments: Perform regular security audits and assessments of your ESXi hosts. This includes vulnerability scanning, penetration testing, and security audits to identify potential vulnerabilities and address them proactively. Backup and Disaster Recovery: Implement regular backups of your virtual machines and critical data. Ensure that backups are securely stored and regularly tested for data integrity. Establish a disaster recovery plan to restore your ESXi hosts and virtual machines in case of a malware attack or intrusion. By implementing these measures, you can significantly enhance the security of your ESXi hosts and protect them against malware and intrusions. Regularly review and update your security controls to stay ahead of emerging threats and vulnerabilities in your virtualization environment. Conclusion Protecting your virtual infrastructure from potential threats and unauthorized access requires securing ESXi hosts. You can significantly improve the security posture of your ESXi hosts by adhering to these best practices and putting in place a multi-layered security approach. Remember that a thorough ESXi host security strategy must include regular update maintenance, the implementation of strict access controls, the protection of the management network, and monitoring host activity. To protect your virtual environment, be on the lookout for threats, adapt to them, and continually assess and enhance your security measures. Businesses can reduce risks and keep a secure and resilient virtualization infrastructure by proactively addressing security concerns.

By Aditya Bhuyan

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