Go language can be used for scientific calculations and numerical analysis, but it needs to be understood. The advantage lies in concurrency support and performance, which is suitable for parallel algorithms such as distributed solution, Monte Carlo simulation, etc.; community libraries such as gonum and mat64 provide basic numerical calculation functions; hybrid programming can be used to call C/C and Python through Cgo or interface to improve practicality. The limitation is that the ecosystem is not as mature as Python, the visualization and advanced tools are weaker, and some library documents are incomplete. It is recommended to select appropriate scenarios based on Go features and refer to source code examples to use them in depth.

USDC is safer than USDT. 1. In terms of reserve assets and transparency, USDC is composed of cash and US Treasury bonds, and is audited and reported publicly every month by Deloitte, with high transparency; while USDT reserves are complex, audit institutions are not top-level, and have low transparency. 2. In terms of regulation and compliance, USDC is issued by the US company Circle, which holds multiple payment licenses and is actively compliant; USDT is under tension due to historical issues and regulatory relations. 3. In terms of issuance entities, USDC was jointly launched by Circle and Coinbase, with a strong background and a clear compliance image; USDT is closely related to Bitfinex, and its independence is questioned. Therefore, users with high security requirements recommend choosing USDC, and those with high liquidity are required

Understand core components: Producers publish events to Topics, Consumers subscribe and process events, KafkaBroker manages message storage and delivery; 2. Locally build Kafka: Use Docker to quickly start ZooKeeper and Kafka services, expose port 9092; 3. Java integration Kafka: introduce kafka-clients dependencies, or use SpringKafka to improve development efficiency; 4. Write Producer: configure KafkaProducer to send JSON format order events to orders topic; 5. Write Consumer: Subscribe to o through KafkaConsumer

High-frequency trading is one of the most technologically-rich and capital-intensive areas in the virtual currency market. It is a competition about speed, algorithms and cutting-edge technology that ordinary market participants are hard to get involved. Understanding how it works will help us to have a deeper understanding of the complexity and specialization of the current digital asset market. For most people, it is more important to recognize and understand this phenomenon than to try it yourself.

The core of optimizing front-end build time is to reduce redundant work, improve processing efficiency, utilize caches and select efficient tools. 1. Use TreeShaking and code segmentation reasonably to ensure that it is introduced on demand and dynamic import reduces the packaging volume; 2. Reduce unnecessary loader processing, exclude node_modules, upgrade loaders and relax the scope of Babel translation; 3. Use the caching mechanism to speed up repeated construction, enable Webpack cache, CI cache and use offline installation; 4. Upgrade toolchain, such as using Vite, esbuild or Rollup to improve the construction speed, although there is migration cost, it has significant effect.

The equals() and hashCode() methods must be rewrite correctly at the same time, otherwise the hash set (such as HashMap and HashSet) will be invalid; 2. Equals() is used to define the logical equality of objects, and the actual field values need to be compared instead of references; 3. HashCode() returns the object hash code, and it is necessary to ensure that the equal objects have the same hash value; 4. Violating the contract will make it impossible to find the stored object from the collection, because hash search first uses hashCode() to locate the bucket, and then uses equals() to confirm the match; 5. It is recommended to use Objects.equals() and Objects.hash() to implement null safe and consistent logic, and avoid objects used as keys.

Master the core data structure and its applicable scenarios, such as the selection of HashMap and TreeMap, and the expansion mechanism of ArrayList; 2. Practice algorithms from the Java perspective, proficient in double pointer, sliding window, DFS/BFS and other modes and can be clearly implemented; 3. Write clean and robust Java code, pay attention to naming, boundary processing and language features (such as generics and final); 4. Prepare the practical question of "why use Java" and understand the impact of StringBuilder, GC, etc. on performance; maintain practice and clear expression to stand out.

The core difference between ETH and ETC comes from the hard fork after the DAO incident in 2016. 1. ETH chose to recover losses through hard forks, adhere to the concept of pragmatism, and support blockchain variability to cope with crises; 2. ETC adheres to "code is law" and maintains the immutability of the original chain; 3. ETH has a huge and active developer ecosystem and PoS consensus, with high security and wide application; 4. ETC follows the PoW mechanism, has slow development, a small ecosystem, has encountered 51% attacks, and has weak market influence; 5. Both can be traded on mainstream exchanges such as Binance, Ouyi, Huobi, and Gate.io, among which ETH is the second largest crypto asset in the world, with liquidity and recognition far exceeding ETC.