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Streaming API

8 articles · Page 1 of 1
Java Tech Enthusiast
Java Tech Enthusiast
Sep 8, 2026 · Backend Development

Goodbye EasyExcel: Migrate to Apache Fesod for Better Memory Efficiency & Apache Support

This article explains why EasyExcel was archived, introduces FastExcel as a rewrite, details its donation to Apache as Fesod, provides migration steps with code examples, compares performance against alternatives, and recommends Apache Fesod for new and existing projects due to its streaming API, low GC overhead, and Apache Foundation backing.

Apache FesodApache IncubatorEasyExcel
0 likes · 16 min read
Goodbye EasyExcel: Migrate to Apache Fesod for Better Memory Efficiency & Apache Support
Su San Talks Tech
Su San Talks Tech
Sep 8, 2026 · Backend Development

EasyExcel Archived: Migrate to Apache Fesod for Better Performance & Long-Term Support

This article explains why EasyExcel was archived, introduces its successor FastExcel (now Apache Fesod), compares performance and features, provides migration steps with code examples, and recommends Apache Fesod for new and existing projects due to its streaming API, memory efficiency, and Apache Foundation backing.

Apache FesodApache IncubatorEasyExcel
0 likes · 15 min read
EasyExcel Archived: Migrate to Apache Fesod for Better Performance & Long-Term Support
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
Jun 24, 2026 · Backend Development

Ditch Traditional JSON Parsing: Boost Spring Boot API Performance by 30×

A four‑month investigation revealed that Jackson’s default object‑mapper consumed over 60% of CPU time during order‑submission requests, causing 900 ms latency; switching to Jackson’s streaming API reduced average response time from 912 ms to 28 ms, cut GC pauses, and increased throughput eight‑fold, while introducing readability and validation trade‑offs.

JSON ParsingJacksonJava
0 likes · 8 min read
Ditch Traditional JSON Parsing: Boost Spring Boot API Performance by 30×
360 Smart Cloud
360 Smart Cloud
Dec 1, 2025 · Artificial Intelligence

How to Build Real‑Time Streaming Speech Recognition with a Large‑Model API (Go & Python)

This guide explains the background of speech‑to‑text technology, introduces the large‑model streaming speech recognition API, walks through obtaining an API key, and provides detailed Go and Python code for establishing a WebSocket connection, sending full‑client and audio‑only requests, and parsing server responses.

AIStreaming APIgolang
0 likes · 12 min read
How to Build Real‑Time Streaming Speech Recognition with a Large‑Model API (Go & Python)
Fun with Large Models
Fun with Large Models
Nov 17, 2025 · Artificial Intelligence

Building a Multimodal RAG System with LangChain 1.0: Core Architecture and Smart Q&A Development

This article walks through the design and implementation of a multimodal Retrieval‑Augmented Generation (RAG) system using LangChain 1.0, detailing a front‑end/back‑end separated architecture, FastAPI service setup, multimodal data handling, conversation history management, streaming responses, and Postman testing to verify the intelligent Q&A module.

FastAPILangChainMultimodal RAG
0 likes · 15 min read
Building a Multimodal RAG System with LangChain 1.0: Core Architecture and Smart Q&A Development
Lin is Dream
Lin is Dream
Jul 9, 2025 · Backend Development

Mastering Reactive Streaming with Spring WebFlux: Build Real‑Time APIs Like DeepSeek

This article explains how traditional synchronous JSON responses differ from modern streaming responses, introduces reactive programming concepts, and shows step‑by‑step how to implement non‑blocking, high‑throughput APIs using Spring WebFlux, Mono/Flux, SSE, WebSocket, and related protocols.

JavaServer‑Sent EventsSpring WebFlux
0 likes · 10 min read
Mastering Reactive Streaming with Spring WebFlux: Build Real‑Time APIs Like DeepSeek
ITPUB
ITPUB
Jun 26, 2016 · Big Data

How to Combine R with Hadoop for Petabyte-Scale Data Processing

This article explains three practical approaches—Streaming APIs, the Rhipe package, and RHadoop—to integrate R with Hadoop, enabling R to process petabyte-scale datasets, compares their setup complexity, capabilities, and trade‑offs, and highlights key conclusions for choosing the right method.

HadoopRRHadoop
0 likes · 4 min read
How to Combine R with Hadoop for Petabyte-Scale Data Processing