Open Source Exchanges: How to handle high concurrency and large scale transactions?

Open Source Exchanges: How to handle high concurrency and large scale transactions?

Development Engineer:DexDao123

Exchange's approach to handling high concurrency

A. Load balancing techniques

Load balancing is a technique for distributing workloads across multiple computers or network devices to multiple system resources to maximize throughput, minimize response time, and avoid overload. In an exchange, load balancing can handle high concurrency by distributing traffic to different servers.

The common load balancing techniques include: polling, IP hashing, least-connected, weighted least-connected and so on. Polling is the simplest and most commonly used load balancing technique, which assigns requests to different servers in order to achieve load balancing. IP hashing calculates a hash of the requested IP addresses and then matches the hash result with the server list to decide which server to send the request to. Least Connected, on the other hand, sends requests to the server with the least number of connections to ensure the least load on the server.

B. Distributed architecture

Distributed architecture is the decomposition of an application into smaller, more manageable parts, distributed across multiple servers and connected together through a network. In an exchange, the use of distributed architecture allows for load balancing, high availability, and flexible scaling, which in turn can handle high concurrency and large-scale transactions.

Common distributed architectures include: microservice architecture, containerized architecture, etc. Microservice architecture is to divide an application into multiple microservices, each of which can be independently developed, tested, deployed and extended, and communicate with each other through the network, thus building a distributed system. Containerized architecture is to package the application and its dependent libraries into a container, and then deploy and run the application inside the container, thus improving the portability and scalability of the application.

C. Caching technology

Caching technology is a technique for storing data in memory to improve data access speed and reduce database pressure. In an exchange, caching technology can be used to cache commonly used data, such as transaction pair information, order books, etc., thus reducing the pressure on the database and improving the system's responsiveness and concurrent processing capability.

Common caching technologies include Redis, Memcached, etc. Redis is a memory-based caching database that supports multiple data structures and high concurrent reads and writes, allowing data to be stored in memory for faster data access. memcached is a high-performance distributed in-memory object caching system that stores cached objects on multiple servers and Fast access through hash tables

D. Asynchronous processing techniques

Asynchronous processing technology is an important technique for processing requests in highly concurrent scenarios, which can greatly improve the concurrency and responsiveness of exchange systems. By decoupling the request processing logic from the request initiation logic, asynchronous processing technology allows requests to be processed asynchronously in the background after the request is initiated, without waiting for the processing to complete before returning a response. This allows the system to respond to user requests more quickly and improves system performance.

Common asynchronous processing techniques used in exchanges include message queues and asynchronous IO.

    1. Message Queue

Message queuing is a common asynchronous processing technology that minimizes the coupling between message senders and message receivers, enabling asynchronous request processing and response. In a message queue, the message sender sends the message to the queue, and the message receiver gets the message from the queue and processes it. Since the sending and receiving of messages are asynchronous, message queues can greatly improve the concurrency and reliability of the system.

In an exchange, message queues can be used to asynchronously process user requests, such as order aggregation and transaction record generation. When a user places an order, the order information is sent to the message queue and processed asynchronously in the background. When an order is successfully summarized or a transaction record is generated, the result is then returned to the user.

Commonly used message queue software includes RabbitMQ, Kafka, etc.

    1. Asynchronous IO

Asynchronous IO is another common asynchronous processing technique that improves system concurrency and performance by leaving IO operations to the operating system to be handled asynchronously, allowing applications to perform other tasks while IO operations are waiting.

In an exchange, asynchronous IO can be used to handle operations that interact with the database, such as order queries and transaction record queries. Since the database of an exchange usually needs to handle a large number of query requests, asynchronous IO can improve the efficiency of queries and thus the performance of the entire exchange system.

Commonly used asynchronous IO frameworks include Tornado, Node.js, etc.

E. Database optimization techniques

The database is the core of the exchange, so optimizing the database is the key to improving the performance of the exchange. Common database optimization techniques include index optimization, splitting database and tables, and read/write separation.

Index optimization: Adding indexes to fields that are frequently queried can improve query efficiency. However, too many indexes can affect write performance, so optimization needs to be done according to the actual situation.

Sub-database and sub-table: Spreading data into multiple databases according to certain rules can reduce the pressure on a single database and improve processing capacity.

Read-write separation: By assigning read and write operations to different databases for processing, read and write operations can be avoided to affect each other, thus improving concurrency.

F.Database optimization and splitting database and table

In high concurrency scenarios, the database becomes the bottleneck of system performance, so database optimization and split-table techniques become critical.

I. Database optimization

    1. Index Optimization Add indexes to common query statements to reduce full table scans and improve query efficiency.
    2. SQL statement optimization Reduce useless queries, reduce duplicate queries, rational use of correlation queries, etc.
    3. Database cache Put frequently queried data into the cache to improve query speed.

II. Sub-bank and sub-table

In high concurrency scenarios, splitting the database and splitting the tables is a common approach to solve the database performance bottleneck problem.

    1. Horizontal table splitting Splitting data into multiple tables according to certain rules to reduce the pressure on individual tables and improve the read and write performance of the system.
    2. Vertical Branching Split a large database into multiple smaller databases, each storing only a portion of the data, reducing the burden on a single database.
    3. Horizontal splitting Store different business data in different databases and solve the database performance bottleneck problem by splitting the database.

The above are commonly used database optimization and database splitting techniques. For exchanges dealing with high concurrency and large-scale transactions, reasonable database optimization and database splitting can significantly improve system performance and reliability and ensure smooth transactions.

Development Engineer:DexDao123

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