This is a carefully conceived series that aims to give readers a core grasp of the distributed system in a story-telling way.
This is the first part of a carefully conceived series of 20-30 articles on distributed systems, I hope to take the journey with you to understand the ins and outs of the distributed systems.
This is the second blog of the distributed systems series. Today we look at the intriguing history of how academia and industry, open-source and business get along with each other.
As the data grows rapidly and exponentially, cloud servers often run out of space to store them. Luckily, with distributed file systems like HDFS, we are now cracking the problem of low memory.
Last time we talked about WHERE to store massive data, and this time, HOW. Massive data brings massive costs.
How does data calculation work? Check out how MapReduce help divides the problem into smaller tasks and execute the task!
Part 6 of this series discusses how to save costs through resource scheduling.
Part 7 of this series discusses one of the core problems of distributed systems: scalability.
Part 8 of this series discusses one of the core problems of distributed systems: availability.
Part 9 of this series introduces the replica mechanism for high availability and discusses data consistency.
Part 10 of this series introduces several implementations of distributed transactions as a second preventive solution to data inconsistency.
The application scenarios of the consensus algorithm are very wide. When we jump out of the scenario of data replication that leads to data consistenc.
While performance and availability are very important, slow systems and the ones with low availability are often unacceptable, the weak consistency is also very useful.
Today we will take a look at the distributed trasactions based on Dynamo and Base, and find out what the advantages and disvantages are.
Inconsistency is so protruding, and we have tried every means to solve it. We want high availability under scalability.
60 posts | 47 followers
Follow
