How to Query MongoDB Records in C# with FilterDefinitionBuilder

✨ A tiny MongoDB trick that saves big headaches Most devs update numbers like this: Fetch → Change → Save. That’s slow, heavy, and risky when many updates happen at once. Instead, use "$inc". 🚀 It directly tells MongoDB: “increase (or decrease) this value by X.” 👉 Perfect for: • subscribersCount • views • scores ✅ Atomic updates ✅ Less DB load ✅ One clean line of code Why fetch when $inc can do it smarter? Have you tried it yet? #mongodb #mongoose #backend

Software engineering is all about finding creative workarounds and sharing them with the community. In my latest article, I show how a small hack, can be a big win—short-lived Redis credentials with Celery https://lnkd.in/ei96MQkj #celery #redis #aws #elastiCache

MongoDB Indexing When working with large datasets, queries can become slow. That’s where indexes come in! 🔹 Without an index → MongoDB scans the entire collection (very slow). 🔹 With an index → MongoDB jumps directly to relevant documents (much faster 🚀). 👉 Key Takeaway: Always identify frequently queried fields and index them for performance. Question for you all: ➡️ Have you ever optimized a query using indexes? What was the performance boost you saw? #NodeJS #MongoDB #BackendDevelopment #SystemDesign #DailyLearning

Monitoring databases doesn’t have to be complicated. I published a blog on Medium on setting up Grafana dashboards for PostgreSQL and Redis. From account setup, adding data sources, to building panels that give you real-time insights. If you’re working with Postgres or Redis and want to analyse and monitor your data, this walkthrough can help you get started quickly. Medium Blog: https://lnkd.in/dRq9Syuy #grafana #redis #postgresql #postgres #dashboard #realtime #Dashboards #Redis

Struggling with slow MongoDB queries? With our Query Profiler, you can quickly see which queries are dragging performance, why they’re slowing down, and how to fix them. From spotting missing indexes to avoiding costly collection scans, it gives you the insights you need to keep MongoDB running fast. Check out our Knowledge Base article to learn more: https://lnkd.in/daCJPzg #MongoDB #Studio3T #DatabasePerformance

No Compass needed! Explore your MongoDB data with SquaredUp. Tools like Compass are really useful GUIs for MongoDB, but SquaredUp's new plugin is the easiest and fastest way to query and visualise your MongoDB data. You can run it securely against a host of MongoDB compatible backends such as AWS DocumentDB, Azure Cosmos DB and Google Firestore See how quickly you can get started in this article on the SquaredUp blog. #MongoDB #CosmosDB #dashboards #NoSQL https://lnkd.in/eRE2umYv

⚡ 𝗗𝗔𝗫 𝗼𝗿 𝗘𝗹𝗮𝘀𝘁𝗶𝗖𝗮𝗰𝗵𝗲 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗗𝘆𝗻𝗮𝗺𝗼𝗗𝗕 𝗮𝗽𝗽 Need microsecond latency for your DynamoDB reads? Here's how to choose: 📌 𝗗𝗔𝗫 𝗪𝗵𝗲𝗻: • DynamoDB-only workloads • Need fully-managed cache invalidation (read-through / write-through cache) • Prioritize operational simplicity 📌 𝗘𝗹𝗮𝘀𝘁𝗶𝗖𝗮𝗰𝗵𝗲 𝗪𝗵𝗲𝗻: • Multiple data sources needed in cache • Want to operate a serverless cache • Cross-region capabilities important • Existing #Valkey / #Redis expertise available #DynamoDB #AWS #NoSQL #Caching #Performance

Proud to showcase my MongoDB Indexing Design Fundamentals from MongoDB. This badge validates my expertise in designing efficient indexes to speed up queries and reduce resource consumption in MongoDB. Having struggled with large index sizes in the past, this course highlighted some excellent strategies. I wanted to share my key takeaways on how to efficiently manage and optimize MongoDB indexes. 1. Embrace Partial Indexes: This is a game-changer. Instead of indexing all documents in a collection, partial indexes only index documents that meet a specific filter. This drastically reduces the index size, saving disk space and memory, without sacrificing performance for your most critical queries. 2. Regularly Review Your Indexes: A large index size can be a sign of a bloated system. I learned the importance of reviewing existing indexes at least once a quarter. This helps you identify and remove redundant or unused indexes that are consuming valuable resources for no good reason. 3. Test with Hidden Indexes: Dropping an index can be risky. That's where hidden indexes come in. This feature allows you to temporarily remove an index from the query planner's consideration. You can safely test the impact on your queries before committing to a permanent deletion. 4. The ESR Strategy for Compound Indexes: When building compound indexes, the Equality, Sort, Range (ESR) strategy is a must. It dictates the optimal field order: place fields for exact matches first, followed by fields for sorting, and finally, fields for range queries. This ensures the index is used as efficiently as possible. 5. Understand Low Cardinality Fields: A query on a low-cardinality field (one with very few unique values) can often return a large number of documents. For this reason, these fields are generally poor candidates for a standalone index. However, they are highly effective when used as the first field in a compound index to quickly narrow down the results for a more selective query. These strategies provide a solid framework for building and maintaining a healthy, high-performing MongoDB database. I'm looking forward to applying these concepts in my work! Another very useful video: Solving the Mystery of Index Use https://lnkd.in/gFy3a3fm #MongoDB #DatabaseOptimization #Indexes #DataManagement #Learnin

🔍 Recently, I was comparing Cosmos DB and MongoDB for a project, and it made me reflect on how these two databases, though often mentioned together, are built for very different needs. 🚀 Cosmos DB vs MongoDB – Which One Should You Choose? Databases are the backbone of modern applications, and picking the right one can make a big difference in scalability and performance. 🔹 Cosmos DB (Azure) • Fully managed, globally distributed NoSQL service • Supports multiple models (document, key-value, graph, columnar) • Five consistency models with 99.999% availability SLA • Built-in autoscale, serverless, and deep Azure integration 🔹 MongoDB • Open-source, document-oriented database (JSON/BSON) • Flexible schema + powerful query language • Can be self-hosted or used via MongoDB Atlas • Strong community and developer ecosystem 🔄 Key takeaway: • Cosmos DB → Best for Azure-native, globally distributed applications with guaranteed SLAs • MongoDB → Best for open-source flexibility and portability; scales globally via Atlas 💡 My view: If you’re heavily invested in Azure, Cosmos DB gives you enterprise-grade scalability and global reach out of the box. If you value flexibility and open-source adoption, MongoDB is often the go-to choice. ⸻ #Azure #CosmosDB #MongoDB #NoSQL #Cloud #Database #developer #technical

## Redis: Key-Value Powerhouse! ⚡️ Redis is a super-fast, in-memory data store, perfect for caching, session management, and more! Think of it like a super-efficient dictionary. **Example:** `SET mykey "Hello"` *(Sets the key "mykey" to the value "Hello")* `GET mykey` *(Retrieves the value of "mykey" - outputs "Hello")* **Why use it?** * **Speed:** In-memory = blazing fast reads/writes. * **Versatile:** Supports strings, lists, sets, hashes, and more. * **Simple:** Easy to learn and use. Boost your app's performance with Redis! #redis #database #caching #performance