18 Jul 2025

When processing big data, the key to Java performance optimization lies in four aspects: 1. Rationally set JVM memory parameters to avoid frequent GC or resource waste; 2. Reduce the overhead of serialization and deserialization, and choose efficient libraries such as Kryo; 3. Use parallel and concurrency mechanisms to improve processing capabilities, and use thread pools and asynchronous operations reasonably; 4. Choose appropriate data structures and algorithms to reduce memory usage and improve processing speed.

05 Aug 2025

This tutorial explores how to efficiently convert raw data containing a dictionary list into nested attrs data class structures. In response to the common misunderstandings in the field converter parameter in attrs when processing lists, the article recommends using the cattrs library. Through its powerful structure function and clear type prompts, it realizes automatic parsing and instantiation of complex nested data structures, thereby simplifying the code and improving the elegance of data conversion.

16 Jul 2025

WebStreamsAPI is a standard interface for efficient processing of streaming data. It mainly includes three stream types: ReadableStream, WritableStream and TransformStream. It is suitable for large file uploads, real-time audio and video processing and other scenarios. The advantage is that it can process data in chunks, reduce memory usage and improve response speed. When using it, streaming operations are usually combined with FetchAPI or file reading, such as parsing JSON while downloading, or uploading files while reading. Practical applications include file upload optimization, data analysis speedup and real-time audio and video processing. Notes include checking browser compatibility, improving error handling, reasonable control of back pressure and timely shutting down flow to avoid resource leakage

21 Jul 2025

Geospatial data processing is a technology that ordinary people can master. Its core lies in understanding five steps: first, clarify the data source and format, such as Shapefile, GeoJSON, KML, GPX and PostGIS; second, carry out data cleaning and pre-processing, including deduplication, unified projection, complementary attributes and checking coordinate validity; third, analyze and visualize based on business needs, such as heat map, buffer analysis, distance calculation and cluster analysis; fourth, select appropriate output formats for display or sharing, such as GeoJSON, PDF, PNG, Shapefile or CSV; fifth, attach data descriptions to ensure that others can correctly understand and use them. As long as you master these key points and practice them manually, you can be effective

08 Aug 2025

Using a while loop combined with a PHP generator can efficiently process large-scale data sets to avoid memory overflow; 2. The generator returns data one by one through the yield keyword, and only generates values when needed, significantly reducing memory usage; 3. The while loop provides more refined control compared to foreach, supporting early exit, conditional judgment and runtime logic adjustment; 4. This mode is suitable for scenarios such as large file reading, database result streaming processing, such as reading logs line by line or processing user data in batches; 5. Data filtering, conversion and pipeline processing can be achieved through a combination generator to improve code reusability and flexibility; 6. This method has the advantages of low memory consumption, support early termination, and integrated current limiting mechanisms. It is an ideal for handling unlimited or massive data streams.

25 Jul 2025

The core of efficient data processing is to select the right tools and use the right methods. 1. The data input and output should be fast, and binary formats such as Parquet and Feather should be used first. Batch reading will reduce I/O overhead, and add indexes to avoid full table scanning when querying databases. 2. Parallel processing should select multi-process or single-thread according to the task type. Large-scale data can be used to implement distributed computing with Dask or PySpark. 3. Data cleaning requires processing missing values and outliers in advance, unify the field format and delete duplicate data to avoid subsequent errors affecting efficiency. Controlling the details of each link is the key to improving overall performance.