tryexponent
Exponent
7Following3736Followers24.8KLikes
Succeed in your tech career đ»Interview prep |đĄCoaching |đQuestions / Guides
Videos
Liked
Drawbacks of NoSQL Databases in Distributed Systems NoSQL databases may be more flexible, but they have their drawbacks. They're designed for distributed use cases, and multiple right shards for the same data partition can support right-heavy systems. However, there's a trade-off: a loss of strong consistency after a right to a shard in a distributed NoSQL cluster. This delay before updates are propagated to other replicas means reading from a replica can result in accessing stale data. #NoSQL #database #distributedsystems #consistency
Advantages of Using NoSQL Databases NoSQL databases are more flexible and simpler to set up because they do not support table relationships. Data is usually stored in documents or as key-value pairs. Plus, they're better at handling unstructured data and can shard data across different data stores for distributed databases. #NoSQL #database #unstructureddata #distributeddatabases
SQL Databases for Structured Data SQL databases are great for structured data, but they have some weaknesses. Tables and columns must be created beforehand, making setup time-consuming. They're also not ideal for unstructured data. Scaling horizontally can be difficult and expensive for write-heavy systems. #SQL #NoSQL #DatabaseScaling #StructuredData #UnstructuredData
Choosing the Right Database for System Design Choosing the correct database is crucial for system design. SQL and no SQL databases have their own strengths and weaknesses. SQL databases are table-based and vertically scalable, while no SQL databases are document key-value graphs or wide column stores and are horizontally scalable. SQL databases allow easy querying on relationships between data, which is important for organizing and structuring data effectively. Plus, they're well-structured and reduce the potential for errors. SQL databases are also acid compliant, meaning transactions are atomic consistent, isolated, and durable. #systemdesign #database #SQL #noSQL
Strategies for Building Reliable AI Systems Using Saga in Microservices Looking to design a reliable system for your AI? Consider using the Saga strategy, especially in a microservice architecture. Succeeding in a distributed transaction can be complicated, especially when things fail halfway through. With Saga, local transactions are grouped and executed sequentially, allowing for predefined countermeasures to be invoked in case of failure. Check out our latest post on AI design and reliability for more strategies like this! #AIDesign #reliability #Sagas #microservices #transactionmanagement
Using Circuit Breakers for Reliable AI Systems Designing reliable AI systems can be a challenge. One strategy is to use circuit breakers to prevent cascading failures. Unlike retry patterns, circuit breakers accept failure and stop applications from repeatedly trying to execute. This saves computing power and helps prevent system overload. Circuit breakers also have a built-in mechanism to test for service restoration and restore access when repairs are complete. #AIDesign #reliability #circuitbreakers #systemdesign
Retry Strategies for Reliable Systems When designing a reliable system, retries can be an effective strategy. But repeat retries may overload the system, causing the thundering herd problem. A solution is introducing randomness to delay intervals so client requests don't synchronize. Sometimes, it's better to fail fast and let users know. Implement a low retry limit and alert users they'll need to try again later. #reliability #userexperience #systemdesign #thunderingherdproblem #retrystrategy
Building for Reliability in Distributed Systems - Building for reliability means added security, error handling, disaster recovery, and countless other contingencies. No matter how good of an engineer you are, things will fail. Most failures in distributed systems come from either hardware errors or application errors. When implementing reliability techniques, focus on mitigating the most important or most likely risks. Assume that failures will happen and design your system to recover gracefully. Include testing strategies and monitoring techniques to benchmark the system regarding requirements. Monitor its health and make changes as needed. #reliability #engineering #disasterrecovery #testing #monitoring
