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How does the UK’s arts sector contribute to its economy?
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:08 - 29 Mar 2024 -
Serving a greater purpose, productivity growth, sustainable offerings in e-commerce, and more: The Daily Read Weekender
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The weekend is near, so get ready to catch up on the week’s highlights on revisiting CEO Excellence, productivity growth, the new net-zero reality, and more.
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by "McKinsey Daily Read" <publishing@email.mckinsey.com> - 10:13 - 28 Mar 2024 -
Investing in productivity growth
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by "McKinsey Global Institute" <publishing@email.mckinsey.com> - 12:51 - 28 Mar 2024 -
A Crash Course in IPv4 Addressing
A Crash Course in IPv4 Addressing
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Welcome to our latest issue on IPv4 addressing. In this issue, we'll learn about the basic parts of IPv4 addressing, including its structure, the role of subnet masks, and the meaning of network, broadcast, and host addresses. IP is used to send packets from the source to their final destination, either within the same network or across multiple networks.
So, without further ado, let’s jump right into IPv4 addressing.
IPv4 Address Structure
An IPv4 address is written using dotted decimal notation, but it is actually a 32-bit address. This gives us a total of about 4.29 billion possible addresses. We divide the 32-bit address into four 8-bit sections called octets. Then we convert each octet into a decimal value. This is called dotted decimal notation, and that's how we write IPv4 addresses.
The range of an IPv4 address can be from four 0s (0.0.0.0) in dotted decimal notation to four 255s (255.255.255.255).
IPv4 Address and Subnet Mask
An IPv4 address has two main parts:
A network portion
A host portion
The subnet mask (also called the prefix length) separates the network portion from the host portion of the IPv4 address.
A subnet mask is 32 bits long. It has a group of 1s followed by a group of 0s. The 1s indicate the network portion of the IP address, and the 0s indicate the host portion.
How to Write a Subnet Mask
There are two ways to write a subnet mask:
Dotted Decimal Notation
We can use dotted decimal notation, just like for IPv4 addresses.
Example: 255.255.0.0
Slash Notation or Prefix Length
We can also use a slash notation, which shows the number of 1 bits in the mask.
Example:
/16 (indicating 16 one bits)
172.18.0.0/16
Using the slash notation if more common nowadays.
Finding the IPv4 Network Address Using the Subnet Mask
Let’s say we have a host IPv4 address (192.168.1.100) and a subnet mask (255.255.255.0). How do we find the network address from the host address using the subnet mask?
In binary notation, we perform a bitwise AND operation between the host address and the subnet mask. For each bit position, if both the address and mask bits are 1, the result is 1. Otherwise, the result is 0. The result gives us the network address.
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How 10x Banking proactively detects issues with New Relic
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by "New Relic" <emeamaketing@newrelic.com> - 06:06 - 28 Mar 2024 -
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Announcing the winners of the 2024 Remote Excellence Awards, product updates at Remote, Gusto Global's expansion to India, and more!
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Do you know how to adopt generative AI quickly and safely?
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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
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Safely seizing gen AI’s value. Gen AI presents a once-in-a-generation opportunity for companies. The technology could add up to $4.4 trillion in economic value to the global economy, McKinsey research suggests. While many corporate leaders are determined to capture this value, recognition is growing that gen AI opportunities come with significant risks. By adapting proven risk management strategies, it’s possible to move on gen AI responsibly and with good pace, McKinsey Technology Council chair Lareina Yee and coauthors explain.
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Steps to address risks. Many business leaders are focusing on deciding how to respond to the “inbound” risks related to adopting gen AI, such as IP infringement, malicious use, security threats, and third-party risk. Most organizations will benefit from a focused sprint to learn how gen AI is changing their external environment. Consider four steps enterprises can take to benefit from gen AI while minimizing risk, and visit McKinsey Digital to explore highlights from case studies.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:23 - 28 Mar 2024 -
🇺🇸 Simplify your US expansion with Remote, introducing the US Expansion package.
🇺🇸 Simplify your US expansion with Remote, introducing the US Expansion package.
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Wide Range of GPS tracking software products that are easy to white-label and customize for your business needs
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Maximizing gen AI’s potential for US state services
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
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Gen AI in US states. US states that capture gen AI’s value have the opportunity to enhance residents’ experiences with services that state governments provide. Gen AI can also help states implement operational efficiencies, improve productivity, and modernize legacy IT platforms. However, this new technology also comes with a unique set of unknowns and risks. State leaders thus need to adopt innovative approaches to establish and develop capabilities, say McKinsey senior partners Gayatri Shenai and Tim Ward and their coauthors.
—Edited by Querida Anderson, senior editor, New York
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:36 - 27 Mar 2024 -
Is a net-zero transition still possible?
Intersection
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Progress has been made to reduce global carbon emissions—but not enough. Under the current trajectory, the world won’t achieve net-zero emissions even during this century, according to the authors of a recent McKinsey report. To learn more about what opportunities remain for accelerating decarbonization efforts, check out the latest edition of the Five Fifty.
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 04:16 - 26 Mar 2024 -
How Uber Uses Integrated Redis Cache to Serve 40M Reads/Second?
How Uber Uses Integrated Redis Cache to Serve 40M Reads/Second?
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In 2020, Uber launched their in-house, distributed database named Docstore.
It was built on top of MySQL and was capable of storing tens of petabytes of data while serving tens of millions of requests per second.
Over the years, Docstore was adopted by all business verticals at Uber for building their services. Most of these applications required low latency, higher performance, and scalability from the database, while also supporting higher workloads.
Challenges with Low Latency Database Reads
Every database faces a challenge when dealing with applications that need low-latency read access with a highly scalable design.
Some of these challenges are as follows:
Data retrieval speed from a disk has a threshold. Beyond that, you cannot squeeze out more performance by optimizing an application’s data model and queries to improve latency.
Vertical scaling can take you far but assigning more resources by upgrading to better hosts has limitations. Ultimately, the database engine turns into a bottleneck.
Horizontal scaling by splitting your database into multiple partitions is a promising approach. However, it also gets operationally more complex over time and doesn’t eliminate issues such as hot partitions.
Both vertical and horizontal scaling strategies are costly in the long term. For reference, costs get multiplied 6X to handle three stateful nodes across two regions.
To overcome these challenges, microservices typically make use of caching.
Uber started offering Redis as a distributed caching solution for the various teams. They followed the typical caching design pattern where the service writes to the database and cache while serving reads directly from the cache.
The below diagram shows this pattern:
However, the normal caching pattern where a service takes care of managing the cache has a few problems at the scale of Uber.
Each team has to manage its own Redis cache cluster.
The cache invalidation logic is duplicated across multiple microservices and there are chances of deviation.
Services must maintain cache replication to stay hot in case of region failovers.
The main point is that every team that needed caching had to spend a large amount of effort to build and maintain a custom caching solution.
To avoid this, Uber decided to build an integrated caching solution known as CacheFront.
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Design Goals with CacheFront
While building CacheFront, Uber had a few important design goals in mind:
Reduce the need for vertical or horizontal scaling to support low-latency read requests
Improve the P50 and P99 latencies and stabilize latency spikes
Bring down the resource allocation for the database engine layer
Replace the plethora of custom-built caching solutions created by individual teams for their needs. Instead, move the ownership for maintaining and supporting Redis to the Docstore team.
Make caching transparent from the point of view of the service, allowing teams to just focus on the business logic
Decouple the caching solution from Docstore’s partitioning schema to avoid hot partitions
Support horizontal scalability of the caching layer with cheap host machines and make the entire operation cost-effective
High-Level Architecture with CacheFront
To support these design goals, Uber created its integrated caching solution tied to Docstore.
The below diagram shows the high-level architecture of Docstore along with CacheFront:
As you can see, Docstore’s query engine acts as the entry point for services and is responsible for serving reads and writes to clients.
Therefore, it was the ideal place to integrate the caching layer, allowing the cache to be decoupled from the disk-based storage. The query engine implemented an interface to Redis to store cached data along with mechanisms to invalidate the cache entries.
Handing Cached Reads
CacheFront uses a cache aside or look aside strategy when it comes to reads.
The below steps explain how it works:
The query engine layer receives a read request for one or more rows
The query engine tries to get the rows from Redis and streams the response to the users
Next, it retrieves the remaining rows from the database (if needed)
The query engine asynchronously populates Redis with the rows that are not found in the cache.
Stream the remaining rows to the users.
Refer to the below diagram that explains the process more clearly:
Cache Invalidation with CDC
As you may have heard a million times by now, cache invalidation is one of the two hard things in Computer Science.
One of the simplest cache invalidation strategies is configuring a TTL (Time-to-Live) and letting the cache entries expire once they cross the TTL. While this can work for many cases, most users expect changes to be reflected faster than the TTL. However, lowering the default TTL to a very small value can sink the cache hit rate and reduce its effectiveness.
To make cache invalidation more relevant, Uber leveraged Flux, Docstore’s change data capture and streaming service. Flux works by tailing the MySQL binlog events for each database cluster and publishes the events to a list of consumers. It powers replication, materialized views, data lake ingestions, and data consistency validations among various nodes.
For cache invalidation, a new consumer was created that subscribes to the data events and invalidates/upserts the new rows in Redis.
The below diagram shows the read and write paths with cache invalidation.
There were some key advantages of this approach:
They could make the cache consistent with the database within seconds of the database change as opposed to minutes (depending on the TTL).
Also, using binlogs made sure that uncommitted transactions couldn’t pollute the cache.
However, there were also a couple of issues that had to be ironed out.
1 - Deduplicating Cache Writes
Since writes happen to the cache simultaneously between the read and write paths, it was possible to write a stale row to the cache by overwriting the newest value.
To prevent this, they deduplicated writes based on the timestamp of the row set in MySQL.
This timestamp served as a version number and was parsed from the encoded row value in Redis using the EVAL command.
2 - Stronger Consistency Requirement
Even though cache invalidation using CDC with Flux was faster than relying on TTL, it still provided eventual consistency.
However, some use cases required stronger consistency guarantees such as the reading-own-writes guarantee.
For such cases, they created a dedicated API to the query engine that allowed users to explicitly invalidate the cached rows right after the corresponding writes were completed. By doing so, they didn’t have to wait for the CDC process to complete for the cache to become consistent.
Scale and Resiliency with CacheFront
The basic requirement of CacheFront was ready once they started supporting reads and cache invalidation.
However, Uber also wanted this solution to work at their scale. They also had critical resiliency needs around the entire platform.
To achieve scale and resiliency with CacheFront, they utilized multiple strategies.
Compare cache
Measurements are the key to proving that a system works as expected. The same was the case with CacheFront.
They added a special mode to CacheFront that shadows read requests to cache, allowing them to run a comparison between the data in the cache and the database to verify that both were in sync. Any mismatches such as stale rows are logged as metrics for further analysis.
The below diagram shows a high-level design of the Compare cache system.
Based on the results from this system, Uber found that the cache was 99.99% consistent.
Cache Warming
In a multi-region environment, a cache is only effective if it is always warm. If that’s not the case, a region fail-over can result in cache misses and drastically increase the number of requests to the database.
Since a Docstore instance spawned in two different geographical regions with an active-active deployment, a cold cache meant that you couldn’t scale down the storage engine to save costs since there was a high chance of heavy database load in the case of failover.
To solve this problem, the Uber engineering team used cross-region Redis replication.
However, Docstore also had its own cross-region replication. Since operating both replication setups simultaneously could result in consistent data between the cache and database, they enhanced the Redis cross-region replication by adding a new cache warming mode.
Here’s how the cache warming mode works:
They tail the Redis write stream to replicate keys to the remote region
In the remote region, they don’t directly update the cache. Instead, they issue read requests to the query engine layer for the replicated keys
In the case of a cache miss, the query engine layer reads the data from the database and writes it to the cache. The response stream is discarded.
The below diagram shows this approach in detail:
Replicating keys instead of values makes sure that the data in the cache is consistent with the database in its respective region. Also, it ensures that the same set of cached rows is present in both regions, thereby keeping the cache warm in case of a failover.
Sharding
Some large customers of Docstore within Uber can generate a very large number of read-write requests. It was challenging to cache all of it within a single Redis cluster that’s limited by the maximum number of nodes.
To mitigate this, they allowed a single Docstore instance to map to multiple Redis clusters. This helped avoid a massive surge of requests to the database in case multiple nodes in a single Redis cluster go down.
However, there was still a case where a single Redis cluster going down may create a hot shard on the database. To prevent this, they sharded the Redis cluster using a scheme that was different from the database sharding scheme. This makes sure that the load from a single Redis cluster going down is distributed between multiple database shards.
The below diagram explains this scenario in more detail.
Circuit Breaker
When a Redis node goes down, a get/set request to that node generates an unnecessary latency penalty.
To avoid this penalty, Uber implemented a sliding window circuit breaker to short-circuit such requests. They count the number of errors on each node for a particular bucket of time and compute the number of errors within the sliding window’s width.
See the below diagram to understand the sliding window approach:
The circuit breaker is configured to short-circuit a fraction of the requests to a node based on the error count. Once the threshold is hit, the circuit breaker is tripped and no more requests can be made to the node until the sliding window passes.
Results and Conclusion
Uber’s project of implementing an integrated Redis cache with Docstore was quite successful.
They created a transparent caching solution that was scalable and managed to improve latency, reduce load, and bring down costs.
Here are some stats that show the results:
The P75 latency went down by 75% and the P99.9 latency went down by over 67% while also limiting latency spikes
Cache invalidation using flux and compare cache provided a cache consistency of 99.9%.
Sharding and cache warming made the setup scalable and fault-tolerant, allowing a use case with over 6M reads per second with a 99% cache hit rate to successfully failover to a remote region.
Costs were down significantly as the same use case of 6M reads per second approximately required 60K CPU cores for the storage engine. With CacheFront, they achieved the same results with just 3K Redis cores.
At present, CacheFront supports over 40M requests per second in production and the number is growing every day.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 26 Mar 2024 -
Elevate your log storage strategy with New Relic live archives
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Live archive key features:
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by "New Relic" <emeamarketing@newrelic.com> - 10:13 - 26 Mar 2024 -
What do the world’s greatest CEOs have in common?
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
•
Secrets to success. What separates the world’s best leaders from the rest? To find out, McKinsey senior partners Carolyn Dewar, Scott Keller, and Vikram Malhotra studied thousands of CEOs around the world. Through interviewing 67 top CEOs for their book CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest, they determined the mindsets, approaches, and practices that deliver powerful results. Since March 2022, CEO Excellence has sold more than 160,000 copies and is being translated into 14 languages.
•
Being bold. Dewar, Keller, and Malhotra discovered that top CEOs share six mindsets. One is that excellent CEOs are bold. Since the job comes with enormous responsibilities, executives understandably might want to play it safe. Top-performing CEOs, however, are constantly thinking about pushing performance to the next level. Leading CEOs also focus on doing what only they can do; they don’t let themselves get spread thin. Study six mindsets that elite leaders share and learn more about CEO Excellence.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:35 - 26 Mar 2024 -
Generative AI: A leader’s guide to capturing value fast
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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
By now, the potential economic payoff of generative AI (gen AI) is almost undisputed: our research shows that the technology could add trillions of dollars in value to the global economy. But despite its beneficial impact on a range of functions and industries, gen AI poses considerable challenges and risks that leaders may need to prepare for even as they pilot the technology. Here’s a roundup of our latest thinking on gen AI and its implications—positive and negative—for organizations.
The buzz around gen AI has been increasing, but so has concern about its risks—and that can be a problem for leaders who wish to take advantage of its potential for immediate positive impact. In a recent survey, we found that 63 percent of respondents rated gen AI implementation as a high priority, yet 91 percent did not feel ready to manage the process in a responsible manner. We suggest that leaders address four primary sources of “inbound” risk—threats likely to affect organizations regardless of whether they deploy gen AI. For example, it’s important to establish a governance structure that includes expertise, oversight, and training for end users. “Organizations should develop a set of guiding principles agreed on by the executive team and the board that will guide AI adoption and serve as a guardrail for acceptable use cases,” note McKinsey senior partners Ida Kristensen and Lareina Yee and their colleagues.
That’s McKinsey experts on what it takes for a worker to be truly prepared for gen AI on a day-to-day basis. A successful AI team member may need to have design and collaboration skills, contextual understanding to ensure that the most high-quality answers are generated, and forensic skills to figure out the reasons for common breakdowns. “We see a rapid market emerging for people who have learned these skills over the past year,” observe senior partners Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel. “If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies.”
“What we know from our product development research is that, oftentimes, the problem is you close the funnel too quickly,” says McKinsey partner Michael Chui in an episode of The McKinsey Podcast on how companies can use gen AI to gain competitive advantage. “You don’t continue to look at multiple alternatives.” Chui adds that gen AI enables organizations to consider a variety of creative options, as well as enabling individual knowledge workers to increase their productivity. “You can end up with better products at the end of the day, create whole new categories, create new drugs, create systems that are more sustainable and produce less carbon emissions,” he says. “All the kinds of things we want to do with R&D, we can create a superpower by using these technologies.”
In his play Henry VI, Shakespeare may have joked about doing away with lawyers, but today, gen AI isn’t here to replace the legal profession but rather could help its members thrive. From automating repetitive tasks to helping create business strategy, the powerful new technology can potentially transform the daily work of lawyers, according to McKinsey associate general counsel Ilona Logvinova. “I see this as a tremendously exciting time in our profession,” she says. “This newly emerging automation stack can free up real time that lawyers use on heavily time-consuming work with lower payoff.” For example, gen AI can search for and compile defined terms, find applicable precedents, and take a first cut at editing forms and documents. “With legal tech as the copilot, we have an opportunity for automation and acceleration in the way we work,” notes Logvinova.
Lead by capturing gen AI’s value.
– Edited by Rama Ramaswami, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:09 - 25 Mar 2024 -
How are manufacturing leaders using digital twins?
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
•
Factory of the future. Global manufacturers face intense pressure. Amid challenges such as constraints on material and labor, more industry leaders are turning to factory digital twins—real-time virtual representations of the factory. Their use supports faster, smarter, and more cost-effective decision making. In a McKinsey survey of 75 senior leaders in industrials, 86% say that a digital twin is applicable to their organization, McKinsey partner Kevin Goering and colleagues share.
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Real-time decisions. Factory digital twins simulate outcomes from real-time factory conditions, enabling what-if analyses across production scenarios. Manufacturing leaders already use digital twins to increase efficiency and reduce costs. One industrial company recently employed a factory digital twin to redesign its production schedule. By reducing an assembly plant’s overtime requirements, the company saved 5 to 7% in costs each month. Consider how to get started with digital twins, and visit McKinsey Digital to learn more about AI and tech in business.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:48 - 25 Mar 2024 -
Top 10 reports this quarter
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Our top ten reports this quarter look at the geometry of global trade, generative AI in the pharmaceutical industry, and more. At No. 1, McKinsey’s Kweilin Ellingrud, Lucy Pérez, and Valentina Sartori, in collaboration with the World Economic Forum, explore how closing the women’s health gap could provide a $1 trillion boost to the global economy.
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by "McKinsey Top Ten" <publishing@email.mckinsey.com> - 03:09 - 24 Mar 2024 -
The week in charts
The Week in Charts
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Our McKinsey Chart of the Day series offers a daily chart that helps explain a changing world—as we strive toward sustainable and inclusive growth. In case you missed them, this week’s graphics explored AI in manufacturing, global M&A activity, shoppers’ shift to online channels, corporate deposits in transaction banking, and mobility’s tech trends.
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:50 - 23 Mar 2024 -
EP104: How do Search Engines Work?
EP104: How do Search Engines Work?
Forwarded this email? Subscribe here for moreThis week’s system design refresher:
System Design: Why is Kafka so Popular? (Youtube video)
How do Search Engines Work?
Top 9 Website Performance Metrics You Cannot Ignore
Top 6 Data Management Patterns
Comparing Different API Clients
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System Design: Why is Kafka so Popular?
How do Search Engines Work?
The diagram below shows a high-level walk-through of a search engine.
▶️ Step 1 - Crawling
Web Crawlers scan the internet for web pages. They follow the URL links from one page to another and store URLs in the URL store. The crawlers discover new content, including web pages, images, videos, and files.
▶️ Step 2 - Indexing
Once a web page is crawled, the search engine parses the page and indexes the content found on the page in a database. The content is analyzed and categorized. For example, keywords, site quality, content freshness, and many other factors are assessed to understand what the page is about.
▶️ Step 3 - Ranking
Search engines use complex algorithms to determine the order of search results. These algorithms consider various factors, including keywords, pages' relevance, content quality, user engagement, page load speed, and many others. Some search engines also personalize results based on the user's past search history, location, device, and other personal factors.
▶️ Step 4 - Querying
When a user performs a search, the search engine sifts through its index to provide the most relevant results.Latest articles
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Top 9 Website Performance Metrics You Cannot Ignore
Load Time: This is the time taken by the web browser to download and display the webpage. It’s measured in milliseconds.
Time to First Byte (TTFB): It’s the time taken by the browser to receive the first byte of data from the web server. TTFB is crucial because it indicates the general ability of the server to handle traffic.
Request Count: The number of HTTP requests a browser has to make to fully load the page. The lower this count, the faster a website will feel to the user.
DOMContentLoaded (DCL): This is the time it takes for the full HTML code of a webpage to be loaded. The faster this happens, the faster users can see useful functionality. This time doesn’t include loading CSS and other assets
Time to above-the-fold load: “Above the fold” is the area of a webpage that fits in a browser window without a user having to scroll down. This is the content that is first seen by the user and often dictates whether they’ll continue reading the webpage.
First Contentful Paint (FCP): This is the time at which content first begins to be “painted” by the browser. It can be a text, image, or even background color.
Page Size: This is the total file size of all content and assets that appear on the page. Over the last several years, the page size of websites has been growing constantly. The bigger the size of a webpage, the longer it will take to load
Round Trip Time (RTT): This is the amount of time a round trip takes. A round trip constitutes a request traveling from the browser to the origin server and the response from the server going to the browser. Reducing RTT is one of the key approaches to improving a website’s performance.
Render Blocking Resources: Some resources block other parts of the page from being loaded. It’s important to track the number of such resources. The more render-blocking resources a webpage has, the greater the delay for the browser to load the page.
Over to you - What other website performance metrics do you track?
Reference: Cloudflare DocsHow do we manage data? Here are top 6 data management patterns
Cache Aside
When an application needs to access data, it first checks the cache. If the data is not present (a cache miss), it fetches the data from the data store, stores it in the cache, and then returns the data to the user. This pattern is particularly useful for scenarios where data is read frequently but updated less often.Materialized View
A Materialized View is a database object that contains the results of a query. It is physically stored, meaning the data is actually computed and stored on disk, as opposed to being dynamically generated upon each request. This can significantly speed up query times for complex calculations or aggregations that would otherwise need to be computed on the fly. Materialized views are especially beneficial in data warehousing and business intelligence scenarios where query performance is critical.CQRS
CQRS is an architectural pattern that separates the models for reading and writing data. This means that the data structures used for querying data (reads) are separated from the structures used for updating data (writes). This separation allows for optimization of each operation independently, improving performance, scalability, and security. CQRS can be particularly useful in complex systems where the read and write operations have very different requirements.Event Sourcing
Event Sourcing is a pattern where changes to the application state are stored as a sequence of events. Instead of storing just the current state of data in a domain, Event Sourcing stores a log of all the changes (events) that have occurred over time. This allows the application to reconstruct past states and provides an audit trail of changes. Event Sourcing is beneficial in scenarios requiring complex business transactions, auditability, and the ability to rollback or replay events.Index Table
The Index Table pattern involves creating additional tables in a database that are optimized for specific query operations. These tables act as secondary indexes and are designed to speed up the retrieval of data without requiring a full scan of the primary data store. Index tables are particularly useful in scenarios with large datasets and where certain queries are performed frequently.Sharding
Sharding is a data partitioning pattern where data is divided into smaller, more manageable pieces, or "shards", each of which can be stored on different database servers. This pattern is used to distribute the data across multiple machines to improve scalability and performance. Sharding is particularly effective in high-volume applications, as it allows for horizontal scaling, spreading the load across multiple servers to handle more users and transactions.
Reference: Data Management Patterns by Microsoft
Comparing Different API Clients
Postman is a widely used API lifecycle platform. It emerges as a comprehensive and versatile API client suitable for enterprise-level development. Its support for a wide range of protocols, robust feature set, and strong performance make it a top choice for complex projects. With an intuitive design, collaboration features, and a large community, Postman excels in scenarios requiring extensive functionality and community support.
Insomnia is a powerful API client with extensive features and being completely open-source makes it a good choice for developers seeking flexibility and continuous growth. Insomnia is suited for those who value an open-source environment and an active community.
ReadyAPI, with its simplicity and focus on smaller projects, is an ideal choice for scenarios where a lightweight and responsive tool is preferred. It provides essential features, making it suitable for projects with less complexity. However, it may not be the best fit for larger, more intricate endeavors that require extensive functionality.
ThunderClient, a VS Code plugin, is free and user-friendly, catering to developers who prefer an integrated testing environment. However, it lacks extensive features and community support, crucial for larger or complex projects, rendering it more appropriate for smaller teams with simpler requirements. Additionally, its reliance on Visual Studio Code may restrict its appeal to users who prefer alternative development environments. Experienced users accustomed to feature-rich tools may encounter a learning curve and might find ThunderClient lacking in certain functionalities.
Hoppscotch, a free and open-source tool, focuses on functionality over design, offering a lightweight web version with support for various protocols. While it lacks extensive documentation and community support, it provides a cost-effective solution for developers seeking simplicity.
Over to you: Which API client do you prefer?SPONSOR US
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