Nikita Shamgunov’s Post

More use cases are coming up in which you want your DB to be lightweight, fast to deploy, efficient - Neon is perfect for this. For agents particularly, Neon also has some nuggets, like the ability to iterate easily via branches. Both AI and dev can duplicate many scenarios / prompts / configs on their own isolated dataset. Reach out to us at Neon if you’re working on AI agents and tell us what would help you the most, we want to learn more about this use case!

Nice post from Nikita Shamgunov on how Neon Serverless Postgres is an ideal database for AI Agent workloads. It's worth reiterating how the Scale-to-Zero/Autoscaling components have substantial cost savings benefits compared to traditional database services that run 24X7 and don't dynamically adjust resources based on actual usage. Based on our work with customers migrating workloads from traditional services like RDS, SQL Server, MySQL, Google Cloud SQL, Heroku, Digital Ocean, etc the 💰 cost savings can easily be 75% when moved to Neon.

Hi, Nikita Shamgunov I saw some of the integrations for agents, but I focus more on the tools for AI that do need fast access to the DB, and I wasn't able to find a native integration of the DB with LLMs other than RAGs on prompts, Do you guys have any tips on that?

If you want to get better at system design and distributed systems, read these 10 articles: ↓ 1. Observability https://lnkd.in/eP5Sx_Hh 2. Database Isolation Levels https://lnkd.in/efWFskfr 3. The sidecar pattern https://lnkd.in/e53cJhg3 4. Vertical scaling https://lnkd.in/eVctNC5Z 5. How Quora scaled MySQL https://lnkd.in/e7hxRY6n 6. How to design a system for scale https://lnkd.in/eXGm8MBC 7. How to Synchronize Distributed Machines https://lnkd.in/ew2Qq48V 8. Latency, throughput and Bandwidth https://lnkd.in/eVG4jyVQ 9. Database Replication Lag https://lnkd.in/e5GuCYBy 10. Distributed Clocks https://lnkd.in/e32tk-jM

Finding 𝘵𝘩𝘦 approach to manage highly complex infrastructures can really make the difference for data teams. In our latest article, we dive into the somewhat less talked about world of Domain-Specific Languages (DSLs) & explore how they can offer a simpler alternative to custom Terraform scripts, especially when managing complex data environments. 📌 Tl;dr - by abstracting away some of the nitty-gritty, DSLs can help teams stay focused on their core goals without getting bogged down in code. There is a flipside, though. And it’s not about pitting Terraform against DSL—it’s just one of the paths that can help teams work more efficiently. 🚀 Curious to learn more? Check out the full article right there 👇 #Terraform #DSL #InfrastructureAsCode #CloudSolutions #DataPlatform

Explore how to implement a robust pub-sub system using Strawberry Shake 🍓 and GraphQL Subscriptions. Discover why constructor DI 💉is not the best solution when utilizing this framework. By leveraging these technologies, we can build a scalable and efficient system for integrating data from multiple sources in real-time 🚀💡

The observability landscape is evolving rapidly, and today I discovered OpenObserve: Key highlights: - Cost Efficiency: Reduce log storage costs by ~140x compared to Elasticsearch. - OpenTelemetry Support: Full compatibility with OTLP for logs, metrics, and traces. - Real User Monitoring (RUM): Features include performance tracking, error logging, and session replay. Flexible Querying: Query logs and traces with SQL, and metrics using SQL or PromQL. I’m working on integrating this platform into a comprehensive Data and AI stack, enabling seamless reporting of any events within the Data AI stack. If you're curious about how this can enhance your observability, feel free to reach out!

One of the most challenging backend issues I encountered recently involved optimizing a high-load API with complex database queries in a ride-hailing application. Here's a breakdown of the problem and the solution: The Challenge The application had a feature to match riders with nearby drivers in real time based on location. Under peak usage, the API slowed significantly, causing delays in rider-driver matching. This was due to: Inefficient queries: The database queries to find nearby drivers using latitude and longitude were not optimized, leading to performance bottlenecks. Concurrency issues: Simultaneous requests from multiple users were overwhelming the system. Scalability: The system couldn’t handle the surge in traffic during peak hours. Approach and Solution 1. Analyzing the Problem Tools Used: I profiled the API using monitoring tools like New Relic and PostgreSQL EXPLAIN to identify slow queries and bottlenecks. Finding: The primary bottleneck was the computation of the Haversine formula on large datasets in the database, combined with a lack of proper indexing. 2. Optimizing Database Queries Spatial Indexing: I replaced the manual latitude-longitude calculation with a PostGIS extension. PostGIS supports geospatial data types and queries optimized for such use cases. Solution: Converted the driver locations into geospatial points and used PostGIS’s ST_DWithin function to find drivers within a given radius. Impact: Reduced query time by over 70%. 3. Introducing Caching Redis Caching: Implemented Redis to cache frequently queried data, such as the list of active drivers in a city or zone. Impact: This significantly reduced the database load and improved response times. 4. Handling Concurrency Message Queues: Introduced RabbitMQ to queue incoming ride requests and process them asynchronously, ensuring fair distribution of server resources. Impact: Prevented request collisions and ensured a smooth user experience. 5. Real-Time Updates Switched to gRPC for live updates instead of frequent API polling, which reduced unnecessary load on the server. Outcome Performance: Reduced API response times from 2–3 seconds to under 200ms. Scalability: The system handled 3x more traffic during the next peak period without downtime. Efficiency: Database load dropped significantly due to caching and optimized queries. What I Learned Profiling tools are critical for identifying bottlenecks in production systems. Leveraging specialized tools like PostGIS can dramatically improve performance for specific use cases. Asynchronous processing and caching are indispensable for handling high-load systems. Real-time systems need efficient communication protocols to balance performance and user experience. This experience taught me how to approach backend performance issues holistically and solve them incrementally.

Top 5 Common Ways to Improve API Performance 🚀 In today's fast-paced digital world, ensuring that your APIs are performant is crucial for delivering a seamless user experience and maintaining the efficiency of your applications. Here are the top 5 common ways to boost your API performance: 1. Optimize Database Queries 📊 Indexing: Use appropriate indexes to speed up query execution. Query Optimization: Write efficient queries, avoid unnecessary joins, and use pagination for large datasets. Caching Results: Cache frequently requested data to reduce database load. 2. Implement Caching 🗃️ In-Memory Caching: Use tools like Redis or Memcached to store frequently accessed data in memory. HTTP Caching: Use cache headers (e.g., Cache-Control, ETag) to reduce the need for repeated requests to the server. CDN (Content Delivery Network): Distribute static assets globally to reduce latency. 3. Use Asynchronous Processing 🔄 Async/Await: Utilize asynchronous programming to handle I/O-bound operations without blocking the main thread. Background Processing: Offload heavy or time-consuming tasks to background jobs using tools like Celery, RabbitMQ, or AWS SQS. 4. Reduce Payload Size 📦 Compression: Use GZIP or Brotli to compress responses. Selective Data Fetching: Use query parameters to allow clients to request only the necessary data. Minimize Data: Strip unnecessary fields from responses and optimize data structures. 5. Load Balancing and Rate Limiting ⚖️ Load Balancing: Distribute incoming requests across multiple servers to ensure no single server is overwhelmed. Use tools like Nginx, HAProxy, or AWS Elastic Load Balancer. Rate Limiting: Implement rate limiting to protect your API from abuse and ensure fair usage. Use middleware or tools like Kong, API Gateway, or custom implementations. Follow Eleke Great for more deep dives gif cred: Alex Xu #coding #softwareengineering #programming

🚀 𝐃𝐚𝐲 𝟒𝟎 - 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐕𝐨𝐥𝐮𝐦𝐞𝐬: 𝐞𝐦𝐩𝐭𝐲𝐃𝐢𝐫 𝐚𝐧𝐝 𝐡𝐨𝐬𝐭𝐏𝐚𝐭𝐡 🚀 Today, I dove deep into the world of Kubernetes volumes and learned about two key types: emptyDir and hostPath. These volumes provide different functionalities, allowing containers to manage data effectively during their lifecycle. 🔹𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 𝟏. 𝐞𝐦𝐩𝐭𝐲𝐃𝐢𝐫: Used for temporary storage, great for tasks like caching or intermediate data processing. It lasts as long as the Pod runs but is deleted once the Pod is gone. 𝟐. 𝐡𝐨𝐬𝐭𝐏𝐚𝐭𝐡: Maps a directory from the host Node’s filesystem into the container, making it ideal for persistent storage like logs or configuration files that need to survive Pod restarts. 🔹𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬: 1. emptyDir for data processing and session storage. 2.hostPath for storing logs or database files that need to persist across Pod restarts. 🔹𝐓𝐚𝐬𝐤𝐬 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐞𝐝: 1. Created and deployed a Pod with an emptyDir volume, verified file creation, and observed how data behaves during Pod restarts. 2. Set up a hostPath volume to map a directory from the host machine into a Pod, storing data persistently on the Node. Check out my latest newsletter for a deep dive into these concepts! 🚀👇 Let’s keep learning and growing! If you have any questions, drop a comment, and we can discuss.🚀 Happy Learning!😊 #𝟗𝟎𝐃𝐚𝐲𝐬𝐎𝐟𝐃𝐞𝐯𝐎𝐩𝐬 #Kubernetes #DevOps #Containers #CloudComputing Kedar Pattanshetti #TechLearning #emptyDir #hostPath #PersistentStorage #CloudNative #K8s #LearningJourney #DevOpsCommunity Shubham Londhe TrainWithShubham