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Inconsistency is so protruding, and we have tried every means to solve it. We want high availability under scalability.
Today we will take a look at the distributed trasactions based on Dynamo and Base, and find out what the advantages and disvantages are.
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.
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.
Distributed transactions can be used to solve consistency problems during data replication, but that's not the whole purpose of them, at least not just that.
The combination of the two characteristics of replication - master-slave and timeliness, is causing data consistency risks.
What makes a system reliable is its high availability and high SLA. Today we look at how distributed service runs stably so that users can continue to benefit from its capabilities.
Learn how to store,read and use massive data including files, key-value, or doc, json on ElasticSearch by partitioning.
Part 6 of this series discusses how to save costs through resource scheduling.
How does data calculation work? Check out how MapReduce help divides the problem into smaller tasks and execute the task!
Last time we talked about WHERE to store massive data, and this time, HOW. Massive data brings massive costs.
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.
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.
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.
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