Mastering Observability’s Post

#Memgraph's in-memory architecture + #GraphDatabase scaling techniques = Frictionless scaling for complex graph workloads ▪️Horizontally scale read queries ▪️Index to accelerate database scans ▪️Isolated graphs, one instance ▪️Be Failover ready ▪️Vertically scale boost database capacity ▪️Optimize query execution paths How to achieve enterprise-grade scale 👉 https://lnkd.in/gJrGANme

When we built PowerSync using #Postgres logical replication, we ran into some challenges related to making the PowerSync architecture compatible with logical replication assumptions. Conrad Hofmeyr explores some of those challenges and how we solved them.

Is your database architecture prepared for the unexpected? With ProxySQL, build a resilient, reliable infrastructure that withstands challenges. From sudden traffic spikes to unforeseen failures, ProxySQL has you covered. Ready to reinforce your database? Let’s build a resilient future together. #databasearchitecture #ProxySQL #resilience

Is your database architecture prepared for the unexpected? With ProxySQL, build a resilient, reliable infrastructure that withstands challenges. From sudden traffic spikes to unforeseen failures, ProxySQL has you covered. Ready to reinforce your database? Let’s build a resilient future together. #databasearchitecture #ProxySQL #resilience

Key System Design Concepts 1. Load Balancing: Distributes traffic across servers for better performance. 2. Caching: Stores frequently accessed data for faster retrieval. 3. Sharding: Splits databases into smaller pieces to enhance performance. 4. Replication: Copies data across multiple machines for high availability. 5. Message Queues: Enables asynchronous communication between components. 6. Microservices: Breaks applications into independent, manageable services. 7. Monolithic Architecture: Unified codebase that can become complex over time. 8. CAP Theorem: Balances consistency, availability, and partition tolerance in distributed systems. 9. Database Indexing: Speeds up data retrieval through indexes. 10. Rate Limiting: Controls traffic to maintain stability and prevent overload. [Explore More In The Post] Don’t Forget to save this post for later and follow us for more such information ! #System Design #Load Balancing: #Microservices #CAP Theorem:

Is your business ready for the next level of high availability (HA)? Download our whitepaper and discover how EDB Postgres Distributed (PDG) will help businesses like yours achieve 99.999% availability 👉 https://bit.ly/3NqD2Ao Here’s a sneak peek of the content: ✅ Challenges of achieving HA with PostgreSQL. ✅ Overcoming PostgreSQL’s HA challenges with PDG. ✅ Why the PGD Always On architecture is now the top solution for achieving highly available Postgres. #EDB #DigitalTransformation #ITSolutionsProvider #VentureTogether #ITGroupInc #TechEmpowermentWith1TG #LetsVentureITtoGether #1ToGether

Seven strategies to scale database 1. Indexes 2. Sharding (Horizontal Scaling) 3. Vertical scaling 4. Replication(Read replicas) 5. Materialized Views 6. Denormalization 7. Caching #databasescaling #db #data

Pipelining is the programming technique in eXtremeDB that accelerates processing by combining the database system’s vector-based statistical functions into assembly lines of processing data, with the output of one function becoming input for the next, and it cuts all but the mandatory latency. Learn more: https://bit.ly/48DzVy0

Companies of all sizes and across industries are struggling to cope with an explosion of data never before seen in the short history of computing. As applications reach new levels of sophistication and become deeply interconnected, these companies find themselves increasingly overworked, overheated, and at their wits’ end, desperately trying to squeeze just a bit more performance and availability out of their aging database architectures. Enter sharding, a powerful database architecture pattern that offers a solution to these challenges. Sharding scales out databases as data volume and user load grow, providing performance and high availability by spreading a database’s data across multiple servers. Read on for more: https://bit.ly/3MK6TDm

Here are the 8 basic strategies to be followed for scaling a system: 𝐒𝐭𝐚𝐭𝐞𝐥𝐞𝐬𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A stateless architecture maintains no information about the client states; therefore, there are no data dependencies on one server. This makes it easier to scale. 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Expand your server count to distribute workload and enhance system performance. 𝐋𝐨𝐚𝐝 𝐁𝐚𝐥𝐚𝐧𝐜𝐢𝐧𝐠: The incoming requests are spread uniformly to multiple servers with the help of a load balancer. 𝐀𝐮𝐭𝐨-𝐒𝐜𝐚𝐥𝐢𝐧𝐠: With auto-scaling, the resources can be attached or detached according to the traffic demand/requirement of resources in real-time. 𝐂𝐚𝐜𝐡𝐢𝐧𝐠: Outline caching to minimize loads on your database and deal with most of the repetitive requests efficiently at scale. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Copy data across nodes to scale read operations and promote data redundancy. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐬𝐡𝐚𝐫𝐝𝐢𝐧𝐠: Sharding data across numerous instances of a database proficiently scales both read and write operations. 𝐀𝐬𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐨𝐮𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠: Offload resource-intensive tasks to background workers for processing using asynchronous processing to handle several requests concurrently and efficiently. Share your experiences: Which other strategies have worked for you to scale systems? Follow for some amazing content! PC: ByteByteGo #StatelessArchitecture #HorizontalScaling #LoadBalancing #AutoScaling #Caching #DatabaseReplication #DatabaseSharding #AsyncProcessing