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Vegan and gelatin capsules with Custom Options
Hi Info，
At Zhejiang Kangle Capsule Co., Ltd., quality and technological innovation are our top priorities. Our state-of-the-art facility in Shaoxing produces an extensive range of capsules – including models 000#, 00#, 0#, 0#el, 1#, 2#, 3#, 4#, 5# – all manufactured under rigorous quality control.
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Zhejiang Kangle Capsule Co., Ltd.
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by "Peter" <Peter@klcapsule.com> - 05:10 - 20 Jul 2025 -
Invitation to Participate in Tender - #928579 -RFQ-DMT-RFP-25-082 /Supply & Installations for Six Construction Packages
Tender PackageSalām,
On behalf of our client, we are pleased to invite you to submit your best competitive price for the above mentioned project.
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The week in charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:16 - 19 Jul 2025 -
EP172: Top 5 common ways to improve API performance
EP172: Top 5 common ways to improve API performance
Forwarded this email? Subscribe here for moreGet the playbook for debugging and testing Airflow pipelines (Sponsored)
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Top 5 common ways to improve API performance
REST API Vs. GraphQL
ByteByteGo Technical Interview Prep Kit
Tokens vs API Keys
The AWS Tech Stack
5 Data Structures That Make DB Queries Super Fast
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20 System Design Concepts You Must Know - Final Part
Top 5 common ways to improve API performance
Result Pagination:
This method is used to optimize large result sets by streaming them back to the client, enhancing service responsiveness and user experience.Asynchronous Logging:
This approach involves sending logs to a lock-free buffer and returning immediately, rather than dealing with the disk on every call. Logs are periodically flushed to the disk, significantly reducing I/O overhead.Data Caching:
Frequently accessed data can be stored in a cache to speed up retrieval. Clients check the cache before querying the database, with data storage solutions like Redis offering faster access due to in-memory storage.Payload Compression:
To reduce data transmission time, requests and responses can be compressed (e.g., using gzip), making the upload and download processes quicker.Connection Pooling:
This technique involves using a pool of open connections to manage database interaction, which reduces the overhead associated with opening and closing connections each time data needs to be loaded. The pool manages the lifecycle of connections for efficient resource use.
Over to you: What other ways do you use to improve API performance?
REST API Vs. GraphQL
When it comes to API design, REST and GraphQL each have their own strengths and weaknesses.
REST
Uses standard HTTP methods like GET, POST, PUT, DELETE for CRUD operations.
Works well when you need simple, uniform interfaces between separate services/applications.
Caching strategies are straightforward to implement.
The downside is it may require multiple roundtrips to assemble related data from separate endpoints.
GraphQL
Provides a single endpoint for clients to query for precisely the data they need.
Clients specify the exact fields required in nested queries, and the server returns optimized payloads containing just those fields.
Supports Mutations for modifying data and Subscriptions for real-time notifications.
Great for aggregating data from multiple sources and works well with rapidly evolving frontend requirements.
However, it shifts complexity to the client side and can allow abusive queries if not properly safeguarded
Caching strategies can be more complicated than REST.
The best choice between REST and GraphQL depends on the specific requirements of the application and development team. GraphQL is a good fit for complex or frequently changing frontend needs, while REST suits applications where simple and consistent contracts are preferred.
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Tokens vs API Keys
Both tokens (such as JWT) and API keys are used for authentication and authorization, but they serve different purposes. Let’s understand the simplified flow for both.
The Token Flow
End user logs into the frontend web application.
Login credentials are sent to the Identity Service.
On successful authentication, a JWT token is issued and returned.
The frontend makes API calls with the JWT in the Authorization header.
API Gateway intercepts the request and validates the JWT (signature, expiry, and claims).
If valid, the gateway sends a validation response.
The validated request is forwarded to the user-authenticated service.
The service processes the request and interacts with the database to return results.
The API Key Flow
A 3rd party developer registers on the Developer Portal.
The portal issues an API Key.
The key is also stored in a secure key store for later verification.
The developer app sends future API requests with the API Key in the header.
The API Gateway intercepts the request and sends the key to the API Key Validation service.
The validation service verifies the key from the key store and responds.
For valid API keys, the gateway forwards the request to the public API service.
The service processes it and accesses the database as needed.
Over to you: What else will you add to the explanation?
The AWS Tech Stack
Frontend
Static websites are hosted on S3 and served globally via CloudFront for low latency. Other services that support the frontend development include Amplify, Cognito, and Device Farm.API Layer
API Gateway and AppSync expose REST and GraphQL APIs with built-in security and throttling. Other services that work in this area are Lambda, ELB, and CloudFront.Application Layer
This layer hosts business logic. Some services that are important in this layer are Fargate, EKS, Lambda, EventBridge, Step Functions, SNS, and SQS.Media and File Handling
Media is uploaded to S3, transcoded via Elastic Transcoder, and analyzed using Rekognition for moderation. CloudFront signed URLs ensure secure delivery of videos and files to authenticated users.Data Layer
The primary services for this layer are Aurora, DynamoDB, ElastiCache, Neptune, and OpenSearch.Security and Identity
Some AWS services that help in this layer of the stack are IAM, Cognito, WAF, KMS, Secrets Manager, and CloudTrail.Observability and Monitoring
CloudWatch monitors logs, metrics, and alarms. X-Ray provides tracing of request paths. CloudTrail captures API calls. Config ensures compliance, and GuardDuty detects security threats.CI/CD and DevOps
The key services used in this layer are CodeCommit, CodeBuild, CodeDeploy, CodePipeline, CloudFormation, ECR, and SSM.Multi-Region Networking
Route 53 and Global Accelerator ensure fast DNS and global routing. VPC segments the network while NAT and Transit Gateways handle secure traffic flow. AWS Backup provides disaster recovery across regions.
Over to you: Which other service will you add to the list?
5 Data Structures That Make DB Queries Super Fast
Data structures are crucial for database indexes because they determine how efficiently data can be searched, inserted, and retrieved, directly impacting query performance.
B-Tree Index
B-Tree indexes use a balanced tree structure where keys and data pointers exist in internal and leaf nodes. They support efficient range and point queries through ordered traversal.B+ Tree Index
B+ Tree indexes store all data pointers in the leaf nodes, while internal nodes hold only keys to guide the search. Leaf nodes are linked for fast range queries via sequential access.Hash Index
Hash indexes apply a hash function to a search key to directly locate a bucket with pointers to data rows. They are optimized for equality searches but not for range queries.Bitmap Index
Bitmap indexes represent column values using bit arrays for each possible value, allowing fast filtering through bitwise operations. They’re ideal for low-cardinality categorical data.Inverted Index
Inverted indexes map each unique term to a list of row IDs containing that term, enabling fast full-text search.
Over to you: Which other data structure will you add to the list?
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 19 Jul 2025 -
Make your research more efficient: Explore efficient pre-processing solutions
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Create your dream space: discover our innovative wall and floor designs
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Effortless Setup: TikTok’s All-in-One Kit to Launch Your Store in Minutes
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Scaling AI? API management is the key.
AI and APIs go hand-in-hand
AI and APIs go hand-in-hand
Hi Md Abul,
AI adoption is surging, so scaling AI effectively requires strong API management.
Here’s why they go hand-in-hand:
- Centralized AI access – seamlessly integrate AI services across backend systems.
- Enhanced data consumption – transform data for AI compatibility and efficiency.
- Accelerated development – mock endpoints streamline front-end and backend workflows.
- Data-driven insights – track AI usage patterns for smarter decisions.
Keen to find out more? Get our guide below.
AI, APIs, and AI readiness:
The strategic blueprint for enterprise
Discover our free guide on AI, APIs, and AI readiness: The strategic blueprint for enterprise.
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We hope you find this useful!
Yours,
Budha
Tyk, Huckletree 199 Bishopsgate, Broadgate, London, City of London EC2M 3TY, United Kingdom, +44 (0)20 3409 1911
by "Budha from Tyk" <communities@tyk.io> - 04:09 - 18 Jul 2025 -
What’s behind the rise of GLP-1 medications?
On McKinsey Perspectives
by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:41 - 18 Jul 2025 -
A Guide to Database Sharding: Key Strategies
A Guide to Database Sharding: Key Strategies
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A Guide to Database Replication: Key Concepts and Strategies
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As usage grows and features pile on, applications begin generating more data, often by the hour. That’s a healthy sign for the business. But architecturally, it raises a red flag: the database starts showing strain.
The database sits at the core of nearly every system. Reads, writes, and updates funnel through it. However, unlike stateless services, databases are notoriously hard to scale horizontally. CPUs and memory can be upgraded, but at some point, a single instance, no matter how powerful, becomes the bottleneck. Response times degrade, and queries can time out. Replicas fall behind. Suddenly, what worked at 10,000 users breaks at 10 million.
This is where sharding enters the picture.
Sharding splits a large database into smaller, independent chunks called shards. Each shard handles a subset of the data, allowing traffic and storage to scale out across multiple machines instead of piling onto one.
But sharding is a major shift with real consequences. Application logic often needs to adapt. Query patterns change, and joins become harder. Transactions span physical boundaries. There’s overhead in managing routing, rebalancing, and failover.
This article looks at the fundamentals of database sharding. We cover details like why it matters, how it works, and what trade-offs come with it. We’ll walk through common sharding strategies and practical engineering considerations.
What is Sharding?...
Continue reading this post for free in the Substack app
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 17 Jul 2025 -
Problem solving: Test your knowledge
Email 7 of 7
Now that you’ve completed “The McKinsey Publishing Guide to problem solving,” test what you’ve learned with our six-question quiz—one for each email in the guide—starting with the one below. If you get any answers wrong, simply follow the article links to brush up on the material.
Make the most of your data
Which of the following will not be a responsibility of Chief data officers (CDOs) by 2025?
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Plus, don’t forget to check out our other McKinsey Publishing Guides, drawn from our cutting-edge insights on business and tech.
And let us know how we did! Send a note to McKinsey_Guides@mckinsey.com with any feedback about your experience with this guide or suggestions for other guide topics.This email contains information about McKinsey’s research, insights, services, or events. By opening our emails or clicking on links, you agree to our use of cookies and web tracking technology. For more information on how we use and protect your information, please review our privacy policy.
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by "McKinsey Publishing Guides" <publishing@email.mckinsey.com> - 03:04 - 17 Jul 2025 -
Reimagining AI agents as corporate citizens
On 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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Agents of intent. Agentic AI systems have massive potential to create business value but also involve meaningful risks, such as perpetuating biases. According to McKinsey Senior Partner Lari Hämäläinen and coauthors, treating agents as corporate citizens that are accountable, governed, and expected to deliver measurable value can help ensure that AI is managed as human employees are. With this approach, AI agents would be guided by several factors, including defined objectives, company policies, and performance metrics. These standards could ensure that AI agents become strategic workforce assets rather than just tactical tools.
—Edited by Audrey Kim, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:29 - 17 Jul 2025 -
New items about plant based pallet strap
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by "Bajraj Tahedl" <hohlensrikanth290@gmail.com> - 11:40 - 16 Jul 2025 -
Greetings from Beijing TransFar Electronics Group Co.,Ltd
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M:+86-13315672143（WhatsApp）
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by "Charanjit Budnickb" <budnickcharanjit@gmail.com> - 07:33 - 16 Jul 2025
