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Lambda architecture is a real-time big data processing framework proposed by Nathan Marz, the author of Storm. Marz developed the famous real-time big data processing framework Storm,Lambda architecture during his Twitter work, which is based on years of experience in distributed big data systems. The goal of Lambda architecture is to design an architecture that can meet the key characteristics of real-time big data system, including high fault tolerance, low latency and scalability. Lambda architecture integrates offline computing and real-time computing, integrates a series of architecture principles such as Immunability, read-write separation and complexity isolation, and can integrate various big data components such as Hadoop,Kafka,Storm,Spark,Hbase.

Key features of Lambda architecture

Marz believes that the big data system should have the following key features:

Robust and fault-tolerant (fault tolerance and robustness): for large-scale distributed systems, machines are unreliable and may crash, but the system needs to be robust and behave correctly, even if machine errors are encountered. Apart from machine mistakes, people are more likely to make mistakes. It is inevitable that there will be some Bug in software development, and the system must have sufficient adaptability to the error data written by programs with Bug, so the human operation fault tolerance is more important than machine fault tolerance. For large-scale distributed systems, human and machine errors may occur every day, how to deal with human and machine errors, so that the system can quickly recover from the errors is particularly important.

Low latency reads and updates (low latency): many applications require very high latency for read and write operations and low latency response to updates and queries.

Scalable (horizontal expansion): when the amount of data / load increases, the scalable system maintains performance by adding more machine resources. That is to say, the system needs to be linearly scalable, usually using scale out (by increasing the number of machines) rather than scale up (by enhancing the performance of machines).

General (versatility): the system needs to be able to adapt to a wide range of applications, including financial field, social network, e-commerce data analysis and so on.

Extensible (Extensible): when new functions and features need to be added, scalable systems can add new functions with minimum development cost.

Allows ad hoc queries (convenient query): there is value in the data, and it needs to be able to query the needed data conveniently and quickly.

Minimal maintenance (easy to maintain): if the system wants to be easy to maintain, the key is to control its complexity. The more complex the system is, the easier it is to make mistakes and the more difficult to maintain.

Debuggable (easy to debug): when something goes wrong, the system needs enough information to debug the error and find the source of the problem. The key is to be able to trace back to each data generation point.

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