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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 11:09 - 5 Dec 2024 -
Mastering Modern Authentication: Cookies, Sessions, JWT, and PASETO
Mastering Modern Authentication: Cookies, Sessions, JWT, and PASETO
Forwarded this email? Subscribe here for moreAuthentication serves as the first line of defense in ensuring the security of applications and the sensitive data they handle.
Whether it’s a personal banking app, a corporate platform, or an e-commerce website, effective authentication mechanisms are needed to verify the identity of users and safeguard their access to resources.
Authentication ensures that only authorized users gain access to specific data or actions within an application. Without proper authentication, applications are vulnerable to unauthorized access, data breaches, and malicious attacks, potentially resulting in significant financial loss, reputational damage, and privacy violations.
In addition to security, authentication plays a critical role in the user experience. By effectively identifying users, applications can provide personalized services, remember user preferences, and enable functionalities like Single Sign-On (SSO) across platforms.
With evolving threats, implementing secure and efficient authentication is more challenging than ever. Developers must navigate between competing priorities such as:
Security: Ensuring protection against different attack types like session hijacking, token theft, and replay attacks.
Scalability: Supporting millions of users without compromising performance.
User Experience: Maintaining ease of use while applying strong security measures.
To tackle these challenges, developers rely on various authentication mechanisms. In this post, we’ll explore multiple authentication mechanisms used in modern applications and also study their advantages and disadvantages.
Fundamentals of Authentication...
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by "ByteByteGo" <bytebytego@substack.com> - 11:37 - 5 Dec 2024 -
Dive into the data: The State of Observability in Europe report
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What HR experts can do to put employees first
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Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
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Under pressure. Between the COVID-19 pandemic, the Great Resignation, and a volatile business environment, HR professionals are grappling not only with rising employee stress and dissatisfaction but also with their own work-related challenges. That’s according to McKinsey’s Brooke Weddle and Bryan Hancock and Wendy Miller, McKinsey’s chief people officer for North America, who explain that HR often feels caught between caring for employees and meeting organizational expectations. This tension may lead to underdelivering care to employees at a moment when workers’ needs are at an all-time high, Miller reveals.
—Edited by David Weidner, senior editor, Bay Area
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The secrets to e-commerce success
Re:think
FRESH TAKES ON BIG IDEAS
ON E-COMMERCE
E-commerce leaders do things differentlyE-commerce has been around for more than a quarter century, but many companies still have trouble generating real profits from it. Yes, almost every company has a website, an online shopping experience, and some social media presence, but those are really just trappings. The companies that are most successful with e-commerce don’t treat it like a bolt-on application or channel to their regular business (an all-too-familiar outcome); they embed it into the core of their business.
Embedding e-commerce into the core of the business means integrating digital into how companies develop, market, and sell products and services to customers wherever they search and shop. Companies that are successful at this don’t think primarily in terms of how to build experiences around their channels, but instead how to use their channels to create the best—and most profitable—experiences. This thinking leads to a profound commitment to next-generation commerce based on integrating capabilities—technology, talent, strategy, operations, marketing, and sales—into a coherent operating model.
Leading companies (those growing at ten percentage points above the market average) commit to next-gen e-commerce. They are prudent with their spending but cutting costs is not their top priority. Instead, they invest in technology (generative AI is the top priority for 20 percent of leaders, while less than 5 percent of laggards make a similar push), talent, channel integration, and shopping-event days, like Black Friday and Cyber Monday.
Leading companies bring top tech and e-commerce talent in-house. Agencies and vendors will always have a role. But in-house experts who know how to build things—data engineers, software engineers, cloud engineers—give the business the speed and flexibility to innovate at a pace that can keep up with the changes in customer behavior and market dynamics. That’s because successful e-commerce is intensely cross-functional (teams with a range of experts across the business who work together) and end-to-end (functions including R&D, logistics, and customer service that all need to work together to deliver a complete customer experience). “Almost 20 percent of business leaders are planning to spend more than $100 million on e-commerce technology infrastructure, compared with about 8 percent of laggards.”
Bringing in the right talent is a big challenge, because companies tend to fall into a few traps. The first is that they aren’t sure about what they’re trying to achieve so they’re not sure what kind of talent they need. The second is that companies don’t look for the best person for the job but rather the best person for the job who fits the pay band they have in place. Getting good talent is expensive, but companies often focus on the cost without understanding the benefit. Paying for top talent is actually a good deal because these people can deliver better results more quickly, and also attract better talent in turn.
If companies want to bring in top talent, they need to give them cutting-edge technology to work on. Relying just on monolithic e-commerce systems is a sure way to chase away the best talent, who want to work on technologies that can help build their skills. Almost 20 percent of business leaders, in fact, plan to spend more than $100 million on e-commerce technology infrastructure, according to our analysis (compared with about 8 percent of laggards).
In practice, this focus means having a technology architecture that embraces “MACH” development principles (microservices; API-enabled; cloud-native software as a service; and “headless,” where front-end design is decoupled from back-end systems). Open-source technologies meet many of these criteria, providing developers with significant control and flexibility. As AI and gen AI become more important, the critical ability won’t be so much how to build technologies from scratch but how to combine them so they can operate cost effectively at scale.
The power of advanced technologies is starting to bear some real fruit when it comes to e-commerce. One European bank, for example, implemented an AI program to understand why customers were opening accounts. In one hour, the program recommended 80 personalized marketing campaigns to target similar cohorts of potential customers. The team narrowed down the options to 40 campaigns, 38 of which ended up being successful. That kind of success rate is unheard of and a sign of how much value AI can generate in e-commerce when it’s well developed and deployed.
It's easy to become enamored of today’s modern technologies in terms of what it can do for e-commerce, but it’s critical to remember that it’s never just tech. Effectively developing, deploying, and improving tech programs requires companies to rewire how they work. How companies develop meaningful strategies, work across wider swaths of the organization, use their data more effectively, and scale programs to different regions, products, or customers are all critical building blocks for creating value.Share Arun Arora’s insights
ABOUT THIS AUTHOR
Arun Arora is a senior partner in McKinsey’s Paris office.
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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 01:13 - 4 Dec 2024 -
Guest Post Contribution Inquiry
Guest Post Contribution Inquiry
Hi,
I’d like to pitch David Maricich, President and Chief Creative Officer of Maricich Health, as a contributor for Odoo. David is a visionary leader and healthcare entrepreneur who has dedicated over 20 years to consulting on healthcare innovation, branding, marketing, and communications.
David can provide valuable insights into effective healthcare marketing during major transitions, such as mergers and acquisitions, and can share strategies for reaching diverse target markets and enhancing public health campaigns. Additionally, he is keen to discuss the transformative potential of artificial intelligence in healthcare marketing and innovative strategies for unlocking growth potential in healthcare organizations.
Here are possible topics we’d like to contribute:
- Future-Proofing a Legacy: The Role of Innovation in Multi-Generational Companies
- How to Bring Innovation & Change Without Losing Legacy Values in Your Business
- Adapting to Cultural Fluidity: Strategies for Connecting with Evolving Consumer Identities
- How Marketing Leaders Can Drive Transformation in Stubborn & Traditional Enterprises
- Less is More: How Focused, Specialized Positioning Can Unlock Business Growth
Here are some of his writing samples:
- Could Marketing in Schools Help Save America’s Health? - MM&M Online
- Why the Healthcare Ecosystem Matters - DTC Perspectives
Let me know if any of these topics are of interest to Odoo!
P.S. I coordinate the content for Maricich Health and our contributor posts. David will be the one writing the piece, and I will coordinate edits.
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by "Ricardo Gonzalez" <ricardo.g@zupo.co> - 12:51 - 4 Dec 2024 -
My NEW book, Coding Interview Patterns, is now available on Amazon
My NEW book, Coding Interview Patterns, is now available on Amazon
Forwarded this email? Subscribe here for moreMy new book, Coding Interview Patterns, is now available on Amazon!
I’ve been working on this book for 1.5 years with my co-author, Shaun Gunawardane.
We’ve spent countless days for each problem to develop the most intuitive ways to explain the reasoning behind each solution to save you time.
Our goal is to help you understand the underlying patterns of each coding problem, so you don’t have to grind through 100s/1000s of them.
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𝐖𝐡𝐚𝐭’𝐬 𝐢𝐧𝐬𝐢𝐝𝐞?
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by "ByteByteGo" <bytebytego@substack.com> - 11:51 - 4 Dec 2024 -
Quick! Last chance to register!
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Why an inclusive culture is key to doing business
Only McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
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Belonging boosts success. On- and off-site employees alike are increasingly seeking connectivity, purpose, and a sense of belonging at work. Without these elements, disengagement rises, retention drops, and productivity suffers, reveal McKinsey senior partners Brooke Weddle and John Parsons and colleagues. A strong sense of workplace community links to better coordination and problem-solving skills, making it crucial for an organization’s overall success.
—Edited by Querida Anderson, senior editor, New York
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:33 - 4 Dec 2024 -
60 years of technology: What’s evolved, or stayed the same?
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Complementing “The future of technology,” the first issue in our yearlong celebration of McKinsey Quarterly’s 60th birthday, this 250-page downloadable bonus issue mines six decades of Quarterly archives to chronicle how discussions about technology in business have evolved—and how much they’ve stayed the same. We hope you delight in the timeless insights found in these 18 hand-selected classic Quarterly articles—previously available only in archival print issues—and appreciate them for the artifacts of business history that they are.
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 01:05 - 3 Dec 2024 -
How LinkedIn Customizes Its 7 Trillion Message Kafka Ecosystem
How LinkedIn Customizes Its 7 Trillion Message Kafka Ecosystem
Forwarded this email? Subscribe here for moreCut your QA cycles down from hours to minutes with automated testing (Sponsored)
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Disclaimer: The details in this post have been derived from the LinkedIn Engineering Blog. All credit for the technical details goes to the LinkedIn engineering team. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
LinkedIn uses Apache Kafka, an open-source stream processing platform, as a key part of its infrastructure.
Kafka was first developed at LinkedIn and later open-sourced. Many companies now use Kafka, but LinkedIn uses it on an exceptionally large scale.
LinkedIn uses Kafka for multiple tasks like:
Tracking user activity
Exchanging messages
Collecting metrics.
They have over 100 Kafka clusters with more than 4,000 servers (called brokers), handling over 100,000 topics and 7 million partitions. In total, LinkedIn's Kafka system processes more than 7 trillion messages every day.
Operating Kafka at this huge scale creates challenges in terms of scalability and operability. To tackle these issues, LinkedIn maintains its version of Kafka, specifically tailored for their production needs and scale. This includes LinkedIn-specific release branches that contain patches for their production requirements and feature needs.
In this post, we’ll look at how LinkedIn manages its Kafka releases running in production and how it develops new patches to improve Kafka for the community and internal usage.
LinkedIn’s Kafka Ecosystem
Let us first take a high-level look at LinkedIn’s Kafka ecosystem.
LinkedIn's Kafka ecosystem is a crucial part of its technology stack, enabling it to handle an immense volume of messages - around 7 trillion per day.
The ecosystem consists of several key components that work together to ensure smooth operation and scalability. See the diagram below:
Here are the details about the various components:
Kafka Clusters and Brokers
LinkedIn maintains over 100 Kafka clusters
These clusters consist of more than 4000 brokers
The clusters handle over 100,000 topics and 7 million partitions
Applications with Kafka Clients
Various applications within LinkedIn’s software stack use Kafka clients to interact with the Kafka clusters.
These applications use Kafka for tasks like activity tracking, message exchanges, and metrics gathering
REST Proxy
The REST proxy enables non-Java clients to interact with Kafka
It provides a RESTful interface for producing and consuming messages
Schema Registry
The schema registry is used for maintaining Avro schemas
Avro is a data serialization format used by LinkedIn for its messages
The schema registry ensures data consistency and compatibility between producers and consumers
Brooklin
Brooklin is a tool used for mirroring data between Kafka clusters
It enables the replication of data across different data centers or environments
Cruise Control
Cruise Control is a tool used for cluster maintenance and self-healing
It automatically balances partitions across brokers and handles broker failures
Cruise Control helps ensure optimal performance and resource utilization
Pipeline Audit and Usage Monitoring
LinkedIn uses a pipeline completeness audit to ensure data integrity
They also have a usage monitoring tool called “Bean Counter” to track Kafka usage metrics
All these components work together to form LinkedIn’s robust and scalable Kafka ecosystem.
The ecosystem enables LinkedIn to handle the massive volume of real-time data generated by its users and systems while maintaining high performance and reliability.
LinkedIn engineering team continuously improves and customizes its Kafka deployment to meet specific needs. They also contribute many enhancements back to the open-source Apache Kafka project.
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LinkedIn’s Kafka Release Branches
LinkedIn maintains its special versions of Kafka, which are based on the official open-source Apache Kafka releases. These special versions are called LinkedIn Kafka Release Branches.
Each LinkedIn Kafka Release Branch starts from a specific version of Apache Kafka. For example, they might create a branch called “LinkedIn Kafka 2.3.0.x”, which is based on the Apache Kafka 2.3.0 release.
LinkedIn makes changes and adds extra code (called “patches”) to these branches to help Kafka work better for their specific needs. They have two main ways of adding these patches:
Upstream First: This is where they make the change in the official Apache Kafka code first. Then, they bring that change into their LinkedIn branch later. This approach is used for changes that are not super urgent.
LinkedIn First (Hotfix): This is where they make the change in their LinkedIn branch first, to fix an urgent problem quickly. Then, they try to also add this change to the official Apache Kafka code later.
The diagram below shows the approach to managing the releases:
In the LinkedIn Kafka Release Branches, one can find a mix of different types of patches:
Regular Apache Kafka patches from when they created the branch.
Patches that LinkedIn made and added to Apache Kafka first (Upstream First).
Patches for urgent fixes that LinkedIn made in their branch first (LinkedIn First/Hotfix).
Some patches are specific to LinkedIn and are not in the official Apache Kafka code.
When LinkedIn creates a new Kafka Release Branch, it starts from the latest Apache Kafka release. Then, they look at their previous LinkedIn branch and bring over any of their patches that haven’t been added to the official Apache Kafka code yet.
They use special notes in the code changes to keep track of which patches have been added to the official release and which are still just in the LinkedIn version. Also, they regularly check the Apache Kafka code and bring in new changes to keep their branch up to date.
Finally, they perform intensive testing of their new LinkedIn Kafka Release Branch. They test it with real data and usage to ensure that it works well and performs fast, before using it for real work at LinkedIn.
Kafka Development Workflow at LinkedIn
When LinkedIn engineers want to make a change or add a new feature to Kafka, they first have to decide whether to make the change in the official Apache Kafka code (called "upstream-first") or to make the change in LinkedIn's version of Kafka first (called "LinkedIn-first" or "hotfix approach").
Here’s what the decision-making process looks like on a high level:
To make this decision, they think about how urgent the change is.
If it's a change that needs to happen very quickly to fix a problem that's happening in LinkedIn's production systems, they'll usually do it LinkedIn-first. This way, they can get the fix into LinkedIn's version of Kafka and deploy it to their servers as fast as possible.
However, if the change is something that can wait a bit longer, like a week or so, and it's not too big of a change, they'll try to do it upstream first. This means they make the change in the official Apache Kafka code, and then later bring that change into LinkedIn's version.
For new features that have been approved through the Kafka Improvement Proposal (KIP) process, they always try to do these upstream first. This is because new features usually aren't as urgent as fixing production issues, and it's good to contribute new features to the main Apache Kafka project so the wider community can benefit from them.
The diagram below shows the entire flow in more detail.
So in summary, the decision between upstream-first and LinkedIn-first depends on factors like:
The urgency of the change. For example, production fixes are more urgent.
The time it would take to get the change into the official Apache Kafka Code. Quicker is better for upstream first.
Whether it's a new feature or a bug fix. New features usually go upstream first
Patch Examples from LinkedIn
LinkedIn has made several changes (called “patches) to Kafka to help it work better for their specific needs. These patches fall into a few main categories:
Scalability Improvements
LinkedIn has some very large Kafka clusters, with over 140 brokers and millions of data copies in a single cluster.
With clusters this big, they sometimes have problems with the central control server being slow or running out of memory. Also, many times brokers take a long time to start up or shut down.
To fix these problems, LinkedIn made patches to:
Reduce the amount of memory the controller uses, by reusing certain objects and avoiding unnecessary logging.
Speed up broker startup and shutdown, by reducing conflicts when multiple processes try to access the same data. This is also known as lock contention.
Operational Improvements
Sometimes, LinkedIn needs to remove brokers from a cluster and add new brokers. When they remove a broker, they want to make sure all the data on that broker is copied to other brokers first, so no data is lost.
However, this was hard to do, because even while they were trying to move data off a broker, new data was constantly being added to it.
To solve this, they created a new mode called “maintenance mode” for brokers. When a broker is in maintenance mode, no new data is added to it. This makes it much easier to move all the data off the broker before shutting it down.
New Features for Apache Kafka
LinkedIn has added several brand new features to their version of Kafka such as:
Keeping track of how much data each user is producing and consuming, so they can bill them accordingly.
Enforcing a minimum number of data copies when creating new topics, to reduce the risk of data loss if a broker fails.
A new way to reset the position in a topic that a consumer is reading from, to the closest valid position.
The LinkedIn engineering team also contributed many improvements directly to the Apache Kafka project, so everyone can benefit from them.
Some major examples include:
Improving how Kafka handles quotas (limits on resource usage).
Adding ways for Kafka to detect and handle outdated control messages and brokers that have been offline.
Separating the communication channels for control messages and data messages.
Adding a way to limit how far behind the compaction process can fall. For reference, compaction is a process that removes old duplicate data.
Conclusion
To summarize, LinkedIn customizes Kafka heavily to handle the immense scale at which it operates.
It also contributes many improvements upstream while maintaining release branches to rapidly address issues. Their development workflow and release branching are designed to balance urgency with contributions going back to the open-source community.
References:
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