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In the first phase of the billion Traffic System Architecture series, we started from scratch and covered several aspects of building a large data platform, including:
- How to carry the storage challenge of ten billion data
- How to host a highly fault-tolerant distributed architecture
- How to design a high-performance architecture that can carry ten billion traffic
- How do you design a high-concurrency architecture that can support hundreds of thousands of concurrent queries per second
- How to design a 99.99% high availability architecture for all links
Good! At this point in the evolution of the architecture, is the system invulnerable?
Of course not!
Since ancient times, what can disintegrate an army’s combat effectiveness is not only external impact, but also internal factors.
Similarly, for our billion-level flow system, we have resisted the external impact, and now the test comes from the system itself. First, the scalability of the system poses a serious challenge…
Therefore, in the second stage, we spent a lot of space, divided into the first, middle and second parts, and discussed in detail the pain points and improvements in scalability of the architecture.
Do you still remember the pain points and technical solutions for 2018?
If you forget, don’t worry, follow this article and learn something new. The author hopes that you can learn something new and put some of the technical solutions in the paper into practice in your own company.
Similarly, the review of the scalability scheme is also the foundation for the improvement of the system in other aspects behind, so that when everyone reads the following article, they will not be confused because of the middle knowledge fault…
one
We have divided the discussion on the scalability of 100 million traffic architecture into three parts. In the first part, I came straight to the point and found that the coupling between real-time computing platform and data query platform was serious, which caused many pain points:
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The data query team is passively saddled with high concurrent write pressures that they shouldn’t be
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The online system performance jitter caused by database o&M operations
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The real-time computing platform team lost data due to the bug of its own writing mechanism, and then let the data query team to troubleshoot. Typical blame dumping!
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The real-time computing platform team actually needs to implement the double-write consistency guarantee mechanism by itself, which directly leads to a large amount of code mixed in the code that does not belong to the team’s business logic
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The data query platform has done the operation of dividing database and table, which requires the real-time computing platform team to modify the configuration and test the deployment online together
All in all, these pain points lead to the result that the students of the two teams are bored with each other every day, and they are forced to…
Do you have any ideas about the causes of these systemic pain points? If you forget, poke the link below, first hurry to review a wave of it, know the disease, just good medicine!
Click the link below:
How to Design scalable Architecture for Ten Thousand Concurrent Scenarios (PART 1)?
two
Ok, from the last article, we’ve seen the various pain points caused by system coupling, and it really hurts!
So now is the time to target those pain points. Take a look at the following. Can you remember?
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Similar to traditional Chinese medicine, the first step to solve the problem is to find the cause. In our case, the first step in solving the coupling problem is to clearly delineate the system boundaries.
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The second thing, after we’ve drawn the boundary, is, of course, decoupling. How to decouple: Leverage messaging middleware
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Good! Now that we’ve introduced messaging middleware decoupling, do you remember a pain point from the previous article: when real-time computing platforms have high concurrency writes, data query platforms are burdened with high concurrency writes
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So when we introduce middleware, we can solve this problem by using message middleware to do peak valley filling, remember what peak valley filling is and how to do it?
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After decoupling, we cooperate with database operation and maintenance through manual flow switch, and directly turn off the flow switch in a certain low-peak period by students in our team, and quickly complete database operation and maintenance. That’s one more pain point!
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The benefits go beyond that. For example, when we introduce middleware decoupling, other systems can also subscribe to MQ on demand and subscribe to the data calculated by real-time computing platforms. No more looking at other platforms
In general, decoupling allows teams to do their jobs without being forced to spend time together. Without human intervention, the system runs more smoothly and efficiently.
As for these targeted solutions, I suggest you take a closer look. These are all experiences from real online production. Maybe some of these solutions can help you!
Click the link below:
How to Design scalable Architecture in Ten thousand Concurrent Scenarios (In)?
three
Having talked about the actual implementation of the solution, we come to the next part of the scalability of the billion traffic architecture.
In our discussion in the Extensibility section, we mentioned that one of the benefits of decoupling is the ability to implement a “Pub/Sub” model of messages, where different platforms can subscribe to the same data according to their needs.
In the next part, we will discuss the “Pub/Sub” model based on messaging middleware and explain its implementation at the code level using RabbitMQ as an example.
What is Exchange? What is the default Exchange? How do I bind my queue to Exchange for consumption? Do you remember these? If you forget, just click on the link below and go back!
In general, decoupling allows teams to do their jobs without being forced to spend time together. Without human intervention, the system runs more smoothly and efficiently.
As for these targeted solutions, I suggest you take a closer look. These are all experiences from real online production. Maybe some of these solutions can help you!
Click the link below:
How to Design scalable Architecture in Ten thousand Concurrent Scenarios (Part 2)?
The above is a brief summary about the scalability of multi-million level traffic. The road of reconstruction is endless and interlocking. I hope that through this summary, before we continue on the road, lay a solid foundation, deepen understanding, do not mistakenly cut wood.
If there is any harvest, please help to forward, your encouragement is the biggest power of the author, thank you!
A large wave of micro services, distributed, high concurrency, high availability of original series of articles is on the way
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