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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:38 - 4 May 2024 -
EP110: Top 5 Strategies to Reduce Latency
EP110: Top 5 Strategies to Reduce Latency
Forwarded this email? Subscribe here for moreThis week’s system design refresher:
Top 5 Strategies to Reduce Latency
Load Balancer Realistic Use Cases You May Not Know
Top 4 data sharding algorithms explained
Top 8 C++ Use Cases
Apache Kafka in 100 Seconds
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Top 5 Strategies to Reduce Latency
10 years ago, Amazon found that every 100ms of latency cost them 1% in sales.
That’s a staggering $5.7 billion in today’s terms.
For high-scale user-facing systems, high latency is a big loss of revenue.
Here are the top strategies to reduce latency:
Database Indexing
Caching
Load Balancing
Content Delivery Network
Async Processing
Data Compression
Over to you: What other strategies to reduce latency have you seen?
Load Balancer Realistic Use Cases You May Not Know
Load balancers are inherently dynamic and adaptable, designed to efficiently address multiple purposes and use cases in network traffic and server workload management.
Let's explore some of the use cases:
Failure Handling:
Automatically redirects traffic away from malfunctioning elements to maintain continuous service and reduce service interruptions.Instance Health Checks:
Continuously evaluates the functionality of instances, directing incoming requests exclusively to those that are fully operational and efficient.Platform Specific Routing:
Routes requests from different device types (like mobiles, desktops) to specialized backend systems, providing customized responses based on platform.SSL Termination:
Handles the encryption and decryption of SSL traffic, reducing the processing burden on backend infrastructure.Cross Zone Load Balancing:
Distributes incoming traffic across various geographic or network zones, increasing the system's resilience and capacity for handling large volumes of requests.User Stickiness:
Maintains user session integrity and tailored user interactions by consistently directing requests from specific users to designated backend servers.
Over to you:
Which of these use cases would you consider adding to your network to enhance system reliability and why?Latest articles
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Top 4 Data Sharding Algorithms Explained
We are dealing with massive amounts of data. Often we need to split data into smaller, more manageable pieces, or “shards”. Here are some of the top data sharding algorithms commonly used:
Range-Based Sharding
This involves partitioning data based on a range of values. For example, customer data can be sharded based on alphabetical order of last names, or transaction data can be sharded based on date ranges.Hash-Based Sharding
In this method, a hash function is applied to a shard key chosen from the data (like a customer ID or transaction ID).
This tends to distribute data more evenly across shards compared to range-based sharding. However, we need to choose a proper hash function to avoid hash collision.Consistent Hashing
This is an extension of hash-based sharding that reduces the impact of adding or removing shards. It distributes data more evenly and minimizes the amount of data that needs to be relocated when shards are added or removed.Virtual Bucket Sharding
Data is mapped into virtual buckets, and these buckets are then mapped to physical shards. This two-level mapping allows for more flexible shard management and rebalancing without significant data movement.
Top 8 C++ Use Cases
C++ is a highly versatile programming language that is suitable for a wide range of applications.
Embedded Systems
The language's efficiency and fine control over hardware resources make it excellent for embedded systems development.Game Development
C++ is a staple in the game development industry due to its performance and efficiency.Operating Systems
C++ provides extensive control over system resources and memory, making it ideal for developing operating systems and low-level system utilities.Databases
Many high-performance database systems are implemented in C++ to manage memory efficiently and ensure fast execution of queries.Financial Applications
Web Browsers
C++ is used in the development of web browsers and their components, such as rendering engines.Networking
C++ is often used for developing network devices and simulation tools.Scientific Computing
C++ finds extensive use in scientific computing and engineering applications that require high performance and precise control over computational resources.
Over to you - What did we miss?
Apache Kafka in 100 Seconds
This post is written by guest author Sanaz Zakeri, who is a Senior Software Engineer @Uber.
Apache Kafka is a distributed event streaming platform used for building real-time data processing pipelines and streaming applications. It is highly scalable, fault-tolerant, reliable, and can handle large volumes of data.
In order to understand Kafka, we need to define two terms:
Events: a log of state of something at a specific point in time
Event streams: continuous and unbounded series of events
Kafka can be used as a Messaging in a publish-subscribe model, where producers write event streams, and consumers read the events. This publish-subscribe model enables decoupling of event stream producers and consumers. Also, Kafka can be used as a log aggregation platform, ingesting and storing logs from multiple sources in a durable and fault-tolerant way.
Kafka Components:
Kafka cluster has multiple key components to provide the distributed infrastructure and reliably capture, store, order and provide event streams to client applications.
Brokers:
At the heart of the Kafka cluster lies the brokers which are physical servers that handle event streams. After events are published by producers, the broker makes the events available to consumers. Brokers bring scalability to Kafka as Kafka clusters can span multiple brokers across a variety of infrastructure setup to handle large volumes of events. They also bring fault tolerance since events can be stored and replicated across multiple brokers.
Topics:
Topic is the subject name of where the events are published by producers. Topics can have zero or more consumers listening to them and processing the events.
Partition:
In a topic, data is organized into partitions which store ordered streams of events. Each event within a partition is assigned a unique sequential identifier called offset that represents its position in the partition. Events are appended continually to the partition. A Topics can have one or more partitions. Having more than one partition in a topic enables parallelism as more consumers can read from the topic.
Partitions belonging to a topic can be distributed across separate brokers in the cluster, which brings high data availability and scalability. If one broker fails, the partitions on the remaining brokers can continue to serve data, ensuring fault tolerance.
Producers:
Producers are client applications that write events to Kafka topics as a stream of events.
Consumers:
Consumers are the client applications that subscribe to topics and process or store the events coming to the specific topic. Consumers read events in the order they were received within each partition.
Applications which require real time processing of data will have multiple consumers in a consumer group which can read from partitions on the subscribed topic.
Consumer Groups:
Consumer group is used to organize consumers that are reading a stream of events from one or more topics. Consumer groups enable parallel processing of events and each consumer in the consumer group can read from one partition to enable load balancing on the client application. This functionality not only brings the parallel processing but also brings fault tolerance since if a consumer fails in a consumer group, Partition can be reassigned to another group member.
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 4 May 2024 -
The CFO conundrum
by "McKinsey Highlights" <publishing@email.mckinsey.com> - 11:15 - 4 May 2024 -
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by "Sunny Thakur" <sunny.thakur@uffizio.com> - 08:00 - 3 May 2024 -
Why do bad leaders rise to the top?
The Shortlist
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Curated by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
As the tech buzz becomes ever louder, executives are turning back to basics. In the future, many workers will likely be using AI to help them do the technical parts of their jobs. This is likely to free people up for more collaborative work, which means interpersonal skills may become more valuable to executives than ever. This edition, some expect AI to diminish the relative importance of technical skill. Interpersonal skills, therefore, may become more important to executives than ever. This edition of the CEO Shortlist points to new research on why we need more emotionally intelligent leaders and how we can pick them. We also double-click on deep-cut AI concepts, transforming a centuries-old bank, and more. We hope you enjoy the read.
—Liz and Homayoun
Hitting your stride. The early years of a CFO’s tenure are often about scoping out the challenge and pulling together the core team. Midtenure is when the best finance leaders get bold. Our interviews with eight CFOs shed light on how leaders can rise to the occasion.
Reinvent the finance function—and yourself—with “Faster, smarter, bolder: How midtenure CFOs shift into a higher gear,” by Ankur Agrawal, Cristina Catania, Christian Grube, and John Kelleher.
The never-ending story. Business transformation is more than a one-off; it’s an attempt to build a new company on the fly. Successful transformations set their sights far beyond next year’s financial targets, instead thinking decades—or even centuries—into the future. Frankly, most such transformations don’t work. But those that do, as in the case of 240-year-old US bank BNY Mellon, offer inspiration.
What would American founding father Andrew Mellon do? We don’t know, exactly, but we know what leaders of today’s BNY Mellon did. Hear more from Roman Regelman, former senior executive at BNY Mellon, in “Driving long-term business transformation,” by Kevin Carmody.
Your prompt reply is appreciated. Today’s business leaders are no strangers to AI (especially if they’re staying up to date with our publishing). But quick: what does prompt engineering mean for your existing employees? How is tokenization in payments different from tokenization in large language models? Or what needs to happen for AI to be capable of empathy?
Got AI? Then you’ve likely got questions as well. Fill in the gaps in your understanding with “What’s the future of AI?,” a new package of McKinsey Explainers with insights from Michael Chui, Alex Singla, Kate Smaje, Lareina Yee, and many more.
Hot air rises to the top. According to new research, gender—before abilities, competencies, interests, and personalities—is one of the strongest predictors of whether someone reaches a leadership role. And that’s not all. The men who benefit from antimeritocratic discrimination, says author and psychologist Dr. Tomas Chamorro-Premuzic, often exhibit traits that hamper organizational progress.
Listen to the good doctor weigh in on “Why so many bad bosses still rise to the top,” the latest episode of the McKinsey Talks Talent podcast, featuring Chamorro-Premuzic and McKinsey talent leaders Bryan Hancock and Brooke Weddle.We hope you find these ideas inspiring and helpful. See you next time with four more McKinsey ideas for the CEO and others in the C-suite.
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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:46 - 3 May 2024 -
How can employees go from ‘meh’ to motivated?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
Dissatisfied workers. According to recent McKinsey research, more than half of workers report being relatively dissatisfied with their jobs. That’s a big percentage that strikes at the heart of value creation for organizations that are already facing rising labor costs and declining productivity, McKinsey senior partner Aaron De Smet and coauthors share. Identifying where workers fall along a satisfaction spectrum could help leaders solve the problem.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:05 - 3 May 2024 -
Digital twins, the future of AI, MSMEs, and more: The Daily Read Weekender
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May is here and it’s almost the weekend. Take a breather and dive into the week’s highlights on digital twins, the future of AI, MSMEs, and more.
QUOTE OF THE DAY
chart of the day
Ready to unwind?
—Edited by Joyce Yoo, editor, New York
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by "McKinsey Daily Read" <publishing@email.mckinsey.com> - 12:27 - 3 May 2024 -
A microscope on small businesses: Spotting opportunities to boost productivity
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 02:43 - 2 May 2024 -
Unlocking the Power of SQL Queries for Improved Performance
Unlocking the Power of SQL Queries for Improved Performance
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SQL, or Structured Query Language, is the backbone of modern data management. It enables efficient retrieval, manipulation, and management of data in a Database Management System (DBMS). Each SQL command taps into a complex sequence within a database, building on concepts like the connection pool, query cache, command parser, optimizer, and executor, which we covered in our last issue.
Crafting effective queries is essential. The right SQL can enhance database performance; the wrong one can lead to increased costs and slower responses. In this issue, we focus on strategies such as using the Explain Plan, adding proper indexes, and optimizing commands like COUNT(*) and ORDER BY. We also dive into troubleshooting slow queries.
While MySQL is our primary example, the techniques and strategies discussed are applicable across various database systems. Join us as we refine SQL queries for better performance and cost efficiency.
Explain Plan
In MySQL, the EXPLAIN command, known as EXPLAIN PLAN in systems like Oracle, is a useful tool for analyzing how queries are executed. By adding EXPLAIN before a SELECT statement, MySQL provides information about how it processes the SQL. This output shows the tables involved, operations performed (such as sort, scan, and join), and the indexes used, among other execution details. This tool is particularly useful for optimizing SQL queries, as it helps developers see the query execution plan and identify potential bottlenecks.
When an EXPLAIN statement is executed in MySQL, the database engine simulates the query execution. This simulation generates a detailed report without running the actual query. This report includes several important columns:
id: Identifier for each step in the query execution.
select_type: The type of SELECT operation, like SIMPLE (a basic SELECT without unions or subqueries), SUBQUERY, or UNION.
table: The table involved in a particular part of the query.
type: The join type shows how MySQL joins the tables. Common types include ALL (full table scan), index (index scan), range (index range scan), eq_ref (unique index scan), const/system (constant value optimization).
possible_keys: Potential indexes that might be used.
key: The key (index) chosen by MySQL.
key_len: The length of the chosen key.
ref: Columns or constants used with the key to select rows.
rows: Estimated number of rows MySQL expects to examine when executing the query.
Extra: Additional details, such as the use of temporary tables or filesorts.
Let's explore a practical application of the EXPLAIN command using a database table named orders. Suppose we want to select orders with user_id equal to 100.
SELECT * FROM orders WHERE user_id = 100;To analyze this query with EXPLAIN, we would use:
EXPLAIN SELECT * FROM orders WHERE user_id = 100;The output might look like this:
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by "ByteByteGo" <bytebytego@substack.com> - 11:38 - 2 May 2024 -
New Relic AI monitoring—the industry’s first APM for AI—now generally available
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How BlackLine saved $16 million per year by consolidating toolsConsolidating tools shifted incident culture and created new ways to build code at BlackLine. Here’s how.
Informa runs on New Relic to deliver business value
Informa improves their engineering processes to move beyond performance-based conversations to conversations around how their technology is impacting their business. With observability, Informa is building a culture of continuous improvement.Useful reads
Custom events are a developer's best friendExplore how New Relic custom events can offer deeper insights and enhance operational efficiency for developers.Upcoming Events AWS Summits EMEAWe're thrilled to announce our participation in the upcoming AWS Summits happening in multiple cities across Europe and beyond! Here's where you can catch us.
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by "New Relic" <emeamaketing@newrelic.com> - 05:03 - 2 May 2024 -
What’s the state of European grocery retail in 2024?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
Lower growth. European grocers faced a challenging year in 2023. Consumers tightened their belts amid rising inflation, leading to a drop in volume and significant downtrading. As a result, industry growth was significantly lower than food price inflation. In Europe, food price inflation averaged 12.8% in 2023, while grocery sales grew at a rate of only 8.6%, McKinsey senior partner Franck Laizet and coauthors share. Still, even though macroeconomic uncertainty will likely persist in 2024, McKinsey data shows some signs of hope for 2024.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:30 - 2 May 2024 -
A new tool to drive software performance
Re:think
FRESH TAKES ON BIG IDEAS
ON SOFTWARE EXCELLENCE
Can software developer productivity really be measured?Chandra Gnanasambandam
Companies have long had a difficult time tracking the experience and productivity of their software engineering teams and figuring out improvements. Part of the problem is that writing software code is an inherently creative and collaborative process. Establishing a clear link between the various inputs and outputs (rather than business outcomes) is also challenging.
Yet learning how to measure the maturity of practices that contribute to developer experience and productivity is more critical than ever. Virtually every company today wants to become a software company to one extent or another. There are currently about 25 million to 30 million developers worldwide, a number expected to reach close to 50 million by the end of the decade. Low-code/no-code platforms and the emergence of generative AI (gen AI) are likely to greatly expand the pool of folks who can create applications and build digital solutions.
Today, there are two main measurement systems that the industry uses to provide insight into developer productivity. DORA (short for “DevOps research and assessment”) metrics focus on outcomes, and SPACE (short for “satisfaction/well-being, performance, activity, communication/collaboration, and efficiency/flow”) takes a multidimensional view of productivity. Both systems provide useful insights.
We believe an additional set of metrics can provide deeper insight into the root causes and identify bottlenecks that slow developers down. This system is intended to help create the best environment and experience to improve overall performance and foster innovation. Critically, it is not intended for performance management or oversight of developers but rather to improve their day-to-day experience and flow; indeed, all data is anonymized and not attributable to a specific individual. The approach involves a set of metrics that analyze work at the system, group, and individual level, focusing on a few key areas of the development process.
The first area is the divide in software development between the inner loop, which encompasses the core work that developers do when they are in the “flow,” and the outer loop, which focuses on all other tasks required to ship a product (integration testing, dependency management, setting up environments, et cetera). Outer-loop activity has real value, particularly in activities early in the development life cycle, such as technical discovery and design work or ensuring code meets the bar on quality, security, and compliance. However, our experience has shown that too much time in the outer loop can be a symptom of underlying issues that affect productivity—including manual activities to release code, holding patterns where developers are waiting on another colleague or team, and multiple meetings to manage dependencies. Leading tech companies aim for developers to spend close to 70 percent of their time on inner-loop activity. By tracking how much time developers are spending on the two loops, companies can optimize the use of in-demand talent.“Tracking developer productivity in a more holistic way can shorten the time it takes to launch a product by 30 to 40 percent.”
The next step is applying the Developer Velocity Index, which collects insights directly from developers to identify the factors that most affect developer experience and productivity. While this tool has its limitations, it can help companies gain qualitative insight into their practices, tools, culture, and talent management and surface and correct any potential weaknesses they find.
The third thing the system does is conduct a broad-based contribution analysis that examines how teams are functioning collectively. Working with backlog management tools such as Jira, it plots a contribution distribution curve and identifies opportunities for improvement in the way that teams are set up or operating, such as increasing automation, developing individual skills, and rethinking role distribution. One company, for example, discovered its newest hires were having difficulty becoming productive and responded by reassessing its onboarding, documentation, and mentorship programs.
More than 50 companies across sectors have already implemented this new approach. Early findings are encouraging, including a 30 to 40 percent reduction in the time it takes to launch a product, a 15 to 25 percent improvement in product quality, a 20 percent jump in developer experience scores, and a 60 percent improvement in customer satisfaction ratings. We have found that developers are typically happy when companies put in place a holistic measurement system like this, because it highlights issues they have dealt with and been frustrated by. This approach has also had the effect of strengthening a culture of psychological safety, where all team members feel free to take risks and share ideas without fear of negative repercussions or personal judgment. McKinsey research on Developer Velocity has previously shown that psychological safety can be a leading driver of developer experience and innovation.
As effective as these metrics have proven to be so far, there are some pitfalls to avoid in how they are applied. In addition to not employing the metrics for any kind of performance management, they should not be used to attempt to create “targets” for teams, since they are too blunt an instrument to optimize for (and can incentivize the wrong behaviors). Nor should they be leveraged to compare teams, as each has its distinct way of working. Lastly, for any engineering metric, absolute numbers usually do not help, and it’s better to look at trends.
Still, this type of holistic approach could be even more important in the coming years. There is emerging evidence that gen AI can help boost productivity for software development teams that have already started to make improvements in this area. While results vary greatly depending on the specific task and developers’ years in the field, pilots show that gen AI can help further increase developer productivity by as much as 15 to 25 percent. In particular, complex activities such as code refactoring (migrating or updating legacy code) and code documentation (maintaining detailed records and explanations of the changes made to existing code) enjoy sizable boosts from gen AI. That research has also shown that usage of gen AI can increase overall developer happiness and satisfaction. And even as gen AI’s impact on the developer experience and software innovation grows, one thing that isn’t likely to change is that a happier developer (or any worker) tends to be a more productive developer.Share Chandra Gnanasambandam’s insights
ABOUT THIS AUTHOR
Chandra Gnanasambandam is a senior partner in McKinsey’s Bay Area office.
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John Murnane on canal cargo
Simultaneous slowdowns at the Panama and Suez Canals created supply chain headaches. But some companies might transform a logistical challenge into a strategic advantage.
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by "Cara" <member226@glafamily.com> - 05:40 - 1 May 2024 -
How can US manufacturing and construction address skilled-worker shortages?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
Skilled-labor shortage. The US economy is struggling with a shortage of skilled trades workers such as carpenters and plumbers. This shortage affects the nation’s manufacturing and construction industries and has increased labor costs, which threatens economic growth. For some roles, the rate of churn is so high that it could cost companies more than $5.3 billion every year in talent acquisition and training costs alone, McKinsey partner Brooke Weddle and coauthors share.
—Edited by Jana Zabkova, senior editor, New York
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by "Only McKinsey" <publishing@email.mckinsey.com> - 11:06 - 30 Apr 2024 -
How to Execute End-to-End Tests at Scale
How to Execute End-to-End Tests at Scale
Forwarded this email? Subscribe here for moreRunning E2E tests reliably and efficiently is a critical piece of the puzzle for any software organization.
There are mainly two expectations software teams have when it comes to testing:
Ship as fast as possible without introducing (or reintroducing) bugs
Run tests as cheaply as possible without compromising on quality.
In today’s issue, we are fortunate to host guest author John Gluck, Principal Testing Advocate at QA Wolf. He’ll be sharing insights into QA Wolf’s specialized infrastructure capable of running thousands of concurrent E2E tests in just a few minutes and meeting the expectations of their customers.
QA Wolf is a full-service solution for mid-to-large product teams who want to speed up their QA cycles and reduce the cost of building, running, and maintaining comprehensive regression test coverage.
Also, Mufav Onus of QA Wolf spoke at Kubecon 2024 in Paris about how they automatically resume pods on spot instances after unexpected shutdowns. Take a look.
The Challenge of Running E2E Tests
Running E2E tests efficiently is challenging for any organization. The runners tend to cause resource spikes, which cause tests and applications to behave unpredictably. That’s why it’s fairly common for large product teams to strategically schedule their test runs. As the number of tests and the number of runs increases, the challenges become exponentially more difficult to overcome.
While the largest companies in the world may run 10,000 end-to-end tests each month, and a handful run 100,000, QA Wolf runs more than 2 million. At our scale, to support the number of customers that we do, our infrastructure has to address three major concerns:
Availability - Customers can execute their tests at any time with no restrictions on the number or frequency of parallel runs. The system must be highly available. We can’t use scheduling tricks to solve this.
Speed - Tests need to run fast. DORA recommends 30 minutes as the maximum time for test suite execution, and customers want to follow this principle.
Reliability - Node contention issues, instance hijacking, and test system execution outages (the things in-house test architects deal with on a regular basis) are simply not tolerable when people are paying you to execute their tests.
For better or worse, StackOverflow didn’t have blueprints for the kind of test-running infrastructure we needed to build. Success came from lots of experimentation and constant refinement.
In this post, we discuss the problems we faced and the decisions we made so that we could solve them through experimentation.
The Tech Stack Breakdown
The tech stack To set the stage, we are completely cloud-native and built our infrastructure on the Google Cloud Platform (GCP).
We went with GCP for its GKE (Kubernetes) implementation and cluster autoscaling capabilities, which are critical for handling the demand for test execution nodes. There are similar tools out there, but our engineers also had previous experience with GCP, which helped us get started.
We adopted a GitOps approach so we could run lots of configuration experiments on our infrastructure quickly and safely without disrupting ongoing operations.
Argo CD was a good choice because of its support for GitOps and Kubernetes. A combination of Helm and Argo Workflows helps make the deployment process consistent and organized. We used Argo CD Application Sets and App of Apps patterns which are considered best practices.
For IaC, we chose Pulumi because it’s open source, and unlike Terraform, it doesn’t force developers to adopt another DSL (Domain-Specific Language)
Lastly, we used Typescript to write the tests. Our customers look at the test code written for them, and Typescript makes it easy to understand. We chose Playwright as the test executor and test framework for multiple reasons, such as:
Playwright can handle the complex tests that customers may need to automate.
Simpler APIs and an easier install prevent customers from being locked into our solution.
It’s backed by Microsoft, and more active development is expanding the list of native capabilities.
The Ecosystem
For the infrastructure ecosystem, we went with one VPC and three main application clusters.
Each of the three clusters has a specific role:
The application cluster
The test instance or runner cluster
Operations cluster
The operations cluster is the primary cluster and manages the other two clusters. Argo CD runs within this cluster.
See the diagram below that shows this arrangement.
At the time of startup, the operations cluster creates both the application and runner clusters. It provisions warm nodes on the runner cluster, each containing two pods, and each pod is built on a single container image.
See the diagram below for reference:
This structure is fully expendable. Our developers can tear down the entire system and rebuild it from scratch with the touch of a button, which increases predictability for developers and is also great for supporting disaster recovery.
GKE’s cluster autoscaler scales the warm nodes on the runner cluster up and down based on demand.
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The Customer-Facing Application
The customer-facing application is a specialized IDE where our QA engineers can write, run, and maintain Playwright tests. It has views for managing configuration and third-party integrations with visualization dashboards.
QA Wolf’s Test Editor Writing Tests
The tests built and maintained by our in-house QA engineers are autonomous, isolated, idempotent, and atomic, so they can run predictably in a fully parallelized context.
When a QA engineer saves a test, the application persists the code for the test onto GCS with its corresponding helpers and any associated parsed configuration needed to run it in Playwright. This is the Run Data File for the test. In case you don’t know, GCS is the GCP equivalent of AWS S3.
In the initial implementation, we tried passing the Run Data File as a payload in HTML, but the payload containing the test code for all tests in a run was too large for Kubernetes etcd. To get around this, we took the path of least resistance by writing all the code to a central file and giving the client a reference to the file location to pass back to the application.
The Execution Flow
As mentioned earlier, we orchestrate runs with Argo Workflows because it can run on a Kubernetes cluster without external dependencies.
Customers or QA engineers can use a scheduler in the application or an API call to start a test run. When a test run is triggered, the application gathers the locations of all necessary Run Data Files. It also creates a new database record for each test run, including a unique build number that acts as an identifier for the test run request. The application uses the build number later to associate system logs and video locations.
Lastly, it passes the Run Data file locations list to the Run Director service.
The below diagram depicts the entire execution flow at a high level.
The Run Director
The Run Director is a simple, long-living, horizontally-scalable HTTP service.
When invoked, the Run Director reports the initial test run status to the application via a webhook and the build number. For each location in the list, the Run Director invokes an Argo Workflows template and hydrates it with the Run Data file at that location. By performing both actions simultaneously, individual test runs can be started faster so that all the tests in the run can finish more quickly.
The Argo Workflow then provisions a Kubernetes pod for each test run requested from the available warm nodes. It attaches the code for each test to a volume on a corresponding container on the pod. This approach allows us to use the same container build for every test execution. If there aren’t enough pods for the run on warm nodes, GKE uses cluster autoscaling to meet demand.
Each test runs in its own pod and container, which isolates the tests and makes it easier for the developers to troubleshoot them. Running tests like this also confines resource consumption issues to the node where the specific tests are having trouble.
The test code runs from the container entry point. Argo Workflow drives the provisioning process and starts each container with the help of Kubernetes
The application runs all the tests in headed browsers. This is important because the container is destroyed after the test finishes, and the headed browser makes it possible to capture videos of tests. The videos are an essential debugging tool to know about what happened at the moment, especially in cases where it’s difficult to recreate a particular failure.
Due to the high standard for test authorship and the infrastructure reliability, the primary cause of test failure is when the system-under-test (SUT) is not optimized for testing. It makes sense when you think about it. The slower the SUT, the more the test is required to poll, increasing the demand on the processor running the test. Though we can’t tell the customer how to build their application to improve test performance, we can isolate each test’s resource consumption to prevent it from impacting other tests.
The Flake Detection
We maintain a very high standard of test authorship, which allows us to make certain assumptions.
Since the tests are expected to pass, we can safely assume that a test failure or error is due to an anomaly, such as a temporarily unavailable SUT. The application schedules such failures for automatic retry. It flags any other failure – such as a suspected infrastructure problem – for investigation and doesn’t retry. Argo Workflows will attempt to re-run a failed test three times.
QA Wolf Flake Detection Process If the tests pass on retry, the application resumes as usual and assumes the failure was anomalous. In case all retries fail, the system creates a defect report, and a QA engineer investigates to confirm whether the failure is due to a bug in the application or some other issue.
The Run Shard Clusters
One of the most significant advantages of the Run Director service is the concept of Run Shard Clusters.
The sharding strategy allows us to spread the various test runs across clusters located worldwide. We have a GCP global VPC with a bunch of different subnets in different regions. This makes it possible to provision sharding clusters in different regions that can be accessed privately via the Run Director service.
Shard clusters provide several advantages, such as:
Replicated high availability - If one region goes down, not everything grinds to a halt.
Closer-to-home testing - The ability to run customer tests close to their home region results in a more accurate performance of their applications and systems.
Experimentation - We can experiment with different versions of our Argo Workflows implementation or different run engines without cutting overall traffic to the same version. This also allows us to experiment with cost-saving measures such as spot instances.
Reporting
The QA Wolf Application Dashboard Of course, our customers also want to see test results, so we needed to create a reliable system that allowed them to do so.
Once the test finishes running and retrying (if needed), the Argo Workflow template uploads any run artifacts saved by Playwright back to GCS using the build number. Some of this information will be aggregated and appear on our application’s dashboard. Other pieces of information from these artifacts are displayed at the test level, such as logs and run history.
Run History for a QA Wolf test On the infrastructure side, the Argo Workflow triggers Kubernetes to shut down the container and detach the volume, ensuring that the system doesn’t leave unnecessary resources running. This helps keep down operational costs.
Conclusion
Our unique approach was developed to meet customer needs for speed, availability, and reliability. We are one of the few companies running e2e tests at this scale, so we needed to discover how to create a system to support that through trial and error; therefore, we designed our system to also support fast iteration. Our cost-efficient, full parallel test execution is the backbone of our application and we see it delivering value for our customers on a daily basis.
If you’d like to learn more about QA Wolf’s test run infrastructure or how it can help you ship faster with fewer escapes, visit their website to schedule a demo.
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by "ByteByteGo" <bytebytego@substack.com> - 12:29 - 30 Apr 2024 -
Join me on Thursday for troubleshooting applications and back end performance with APM 360
Hi MD,
It's Liam Hurrell, Manager of Customer Training at New Relic University, here. Are you ready to revolutionise your application monitoring and troubleshooting practices with our latest offering, New Relic APM 360? If so, you can register for the free online workshop that I'll be hosting on Thursday, 2nd May at 10 AM BST/11 AM CEST.
APM 360 allows you to get a unified view of critical telemetry data across your stack and development lifecycle. Prevent issues before they escalate and troubleshoot faster with integrated infrastructure monitoring, error user impact analysis, and distributed tracing. In this workshop, I’ll show you how New Relic APM 360 can help eliminate blind spots with guided workflows.
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by "Liam Hurrell, New Relic" <emeamarketing@newrelic.com> - 05:02 - 30 Apr 2024 -
What motivates gen AI talent and keeps them in their jobs?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
The right skills. When it comes to crafting an effective gen AI talent strategy, organizations have focused mostly on increasing productivity. But to match the right talent to the right jobs, leaders should first know how gen AI is changing the way employees view their work experience, McKinsey senior partner Aaron De Smet and coauthors share. To that end, they recently surveyed about 12,800 workers across 16 sectors. One surprising finding? Heavy users and creators of gen AI overwhelmingly feel they need higher-level cognitive and social–emotional skills to do their jobs.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:12 - 30 Apr 2024 -
Remote is #1 Global Employment Platform, freshly landed in Korea, new webinar alert, and more!
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by "Remote" <hello@remote-comms.com> - 12:02 - 30 Apr 2024 -
Are you sure? A leader’s guide to strategizing during uncertainty
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Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
The ancient Stoic philosophers believed that it was essential to be prepared for downturns—whether contemplating war, shipwrecks, torture, or exile, the “premeditation of the evils and troubles that might lie ahead” was a way to manage life’s inevitable disasters. Modern leaders may be unlikely to endure shipwrecks, but they are beset with uncertainty of all kinds, from geopolitical upheavals to global supply chain shocks. Operating in a near-permanent state of uncertainty may require its acceptance in ways that traditional strategists may not be prepared for. This week, we explore this concept in more detail.
“Imperfectionism sounds like a bad thing, but what we mean is accepting the ambiguity of not having perfect knowledge before making strategic moves,” says investor and McKinsey alumnus Charles Conn in an episode of our Inside the Strategy Room podcast. In a world marked by disruptions, conventional approaches to strategy can “yield either incomplete or misleading results,” he says; instead, dynamic, real-time, and nonlinear actions have a better chance of success. Adopting a few different mindsets can help leaders go with the flow rather than wait for certainty. For example, an “Ever Curious” mindset “starts with an audacious question or a long-term vision” that leaders should encourage their teams to cultivate, Conn suggests. “Your people have fantastic ideas, but there’s nothing more boring than being told to stay in your lane. You need to change incentive structures so that people aren’t penalized for working on the side.”
That’s the percentage of respondents to our March 2024 global economic conditions survey who view geopolitical instability and conflict as the top threat to economic growth. With more than 60 countries holding national elections this year, concerns about political leadership transitions have displaced worries about inflation and supply chain disruptions. Nevertheless, respondents are more hopeful about the global economic outlook than they were in December 2023. The mixed economic picture—for example, unemployment is inching up in some countries—means that leaders may need to be more attuned to uncertainty in the coming months.
The COVID-19 pandemic led to widespread operational disruptions and a heightened sense of uncertainty about the future. “Our normal style of operating tends to lock in assumptions,” says McKinsey senior partner Patrick Finn in an episode of our Inside the Strategy Room podcast. “It is challenging for executives to admit that their direction of travel is wrong because the underlying information has changed.” When faced with information instability, leaders need to recognize that “the solutions to the crisis may have to be invented, not only implemented,” adds partner Mihir Mysore. “You have to go all the way to the basic tenets of the problem you are trying to solve. Many companies are not set up to do that.” Strategies may need to be flexible and include constant testing of assumptions. “In extreme uncertainty, there is no such thing as a forecast; there are only scenarios,” says Finn.
Do you generally expect the worst? You may be on to something. While a positive outlook often contributes to success, thinking negatively does not necessarily lead to defeat—and may help you deal better with uncertainty, according to research. For example, a business leader may deliberately encourage negative thinking among employees, asking them to imagine what could or will go wrong to prevent them from being blindsided during tough times or to manage stakeholder expectations. And high-stakes uncertainty—where you’re waiting for critical decisions or outcomes—can help you build business resilience as well as the strength to face geopolitical tensions.
Lead by managing uncertainty.
– Edited by Rama Ramaswami, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:42 - 29 Apr 2024
