This article walks through using Apache DolphinScheduler 3.2.2 in a lightweight Docker standalone setup to model, create, and run an ODS → ETL → ADS offline workflow via the Open API, demonstrating dependency handling, retries, backfill, and troubleshooting for reliable daily batch processing.
This article systematically explains how large‑language‑model token pricing works, identifies eight high‑consumption usage patterns, presents nine actionable optimization principles, and offers a tiered model‑selection framework so engineering teams can reduce token spend by up to 80% without sacrificing result quality.
This lesson explains why a single database cannot scale for massive e‑commerce data, introduces the three core pillars of distributed storage—sharding, replication, and horizontal scaling—covers batch and stream processing differences with Spark, Hive, Flink and Storm, compares compute‑storage integration versus separation, and shows how Kafka, HDFS, Spark and Flink fit together in a real‑time data platform.
The article breaks down the essential big data components—data ingestion, distributed storage, resource management, batch and stream processing, analytics, search, and cluster operations—using a restaurant metaphor and lists typical tools such as Flume, Kafka, HDFS, Spark, Flink, Elasticsearch, and Kubernetes.
When migrating tens of billions of rows, the article explains how a simple progress‑tracking table and failure‑log table enable automatic checkpoint‑based resumption, stateless execution, and dynamic batch tuning without restarting or rewriting code.
The article demonstrates how to create a custom @BatchProcess annotation combined with AOP to aggregate high‑frequency requests into batches, persist metadata in Redis, and process them efficiently, thereby reducing connection, I/O, and CPU overhead in distributed, high‑concurrency Spring Boot 3.5.0 applications.
The article explains why coupon expiration cannot be handled by a simple scheduled scan and presents a production‑grade architecture that uses a task‑table, four‑plane design, fine‑grained splitting, lease‑based worker coordination, idempotent updates, and observability to reliably expire billions of coupons under peak load.
The bug‑report‑writer v1.1.1 upgrade adds Feishu Base one‑click API writing, cutting single‑bug entry time from two minutes to 10‑15 seconds, boosting overall bug‑submission efficiency by 80% over v1.0 and up to 72‑fold versus manual entry, while supporting batch imports and a six‑dimension quality score.
DSpark, DeepSeek’s new LLM inference framework, combines batch processing, speculative decoding, Eagle‑style draft models and DFlash‑style parallel generation with a lightweight sequential head and hardware‑aware scheduling, delivering 60‑85% speedups while preserving model quality.
The article breaks down three fundamental mechanisms—synchronous RPC, asynchronous MQ, and offline scripts—by comparing their lifecycles, IO models, bottlenecks, and consistency guarantees, and provides a concrete decision framework for choosing the right tool for different business scenarios.
The article details how a sluggish batch‑processing system handling millions of records was redesigned with XXL‑JOB sharding, Redis‑based dynamic task distribution, cursor pagination, and selective transaction scopes, achieving nearly ten‑fold throughput improvement while addressing resource contention, load‑balancing, and reliable result reconciliation.
The author recounts a Python‑based workflow that automatically adds watermarks to foreign‑trade .doc documents, details client validation, unexpected .docx conversion issues, and the iterative debugging steps taken to ensure reliable batch processing.
This article explains why naive for‑loop DB operations fail on large data sets, introduces Spring Batch’s chunk, transaction, retry, skip and restart features, and provides step‑by‑step SpringBoot configurations, code samples for Tasklet and Chunk jobs, database and CSV readers/writers, and manual or scheduled job triggering.
The article explains why generic OCR struggles with structured tables, proposes a partition‑based fixed‑region recognition method using PaddleOCR, provides a complete Python script for batch processing, and demonstrates how this approach consistently achieves over 99% accuracy.
Faced with 30,000 report images containing 300,000 rows of tabular data, the author explains why WPS failed at scale, analyzes the OCR error sources, and shares a Python script that batch‑crops images to boost recognition accuracy before exporting everything to Excel.
The article explains why batch processing tasks often slip out of control, defines a four‑layer observability model covering status, progress, quality and performance, proposes a unified task state machine and event flow, and provides concrete metric, logging, tracing and alerting designs—including Go and Java SDK examples—for reliable production‑level batch job monitoring.
The article explains how to design a production‑grade, unified batch‑task platform on Kubernetes that goes beyond a simple job UI, covering unified abstractions, multi‑tenant governance, state‑machine modeling, scalable scheduling, high‑concurrency handling, observability, security, and a phased roadmap for incremental implementation.
This article explains how to design and operate a scalable, multi‑tenant batch processing platform on Kubernetes using Argo Workflows, covering core concepts, DAG scheduling, concurrency control, controller scaling, artifact handling, event‑driven triggers, and practical best‑practice patterns for production reliability and cost efficiency.
This article explains how Kubernetes Jobs and CronJobs work under the hood, outlines production‑grade design principles such as idempotency, failure modeling, scaling, observability, and security, and provides concrete YAML configurations and Go code examples for building a reliable, high‑throughput batch processing platform.
The article walks through a real production incident caused by a massive IN‑list query, then presents a complete methodology for designing, implementing, and tuning an enterprise‑grade MyBatis persistence layer—including core execution chain, caching strategies, batch processing, read/write splitting, sharding, observability, and deployment best practices.
This case study details how a media platform processed 2,500 short‑series episodes in three days using a serverless AWS pipeline that merged and transcoded videos while reducing total cost by 60% compared with a MediaConvert‑only solution.
The article examines why large database transactions inside API endpoints degrade performance, outlines issues such as data inconsistency, lock contention, undo‑log overhead, and DB pressure, and presents practical optimizations including removing remote RPC calls, using programmatic transactions, batch processing, splitting into smaller transactions, and asynchronous parallel execution with code examples.
This article analyzes JD's end‑to‑end upgrade of its retail coupon search infrastructure, detailing the business drivers, data‑skew challenges, the shift from dual KV and batch pipelines to a unified stream‑batch model built on Apache Doris, and the resulting performance, resource and stability gains across multiple scenarios.
The unofficial notebooklm-py library provides a Python API and CLI that give programmatic access to Google NotebookLM’s hidden capabilities, enabling batch import of PDFs or videos, automated generation of podcasts, mind‑maps, and other outputs, and integration into AI agents for scalable research workflows.
When a Spring Boot application reaches millions of rows, it often suffers from OutOfMemoryErrors, slow queries, and high CPU, but by applying five proven strategies—pagination, streaming, batch processing, indexing, and asynchronous execution—you can halve memory usage and achieve up to ten‑fold speed gains.
This article demonstrates how to configure advanced Ollama API options—including temperature, top‑p, and custom model creation—and shows a complete Python batch‑processing workflow that benchmarks multiple LLMs while measuring latency and success rates.
This article explains why a naïve full‑table UPDATE on massive MySQL tables can cripple replication, explores deep‑pagination and IN‑clause inefficiencies, and presents a batch‑processing strategy using NO_CACHE, FORCE INDEX, and rate‑controlled scripts to perform safe, high‑performance updates.
The author measured six realistic Spring Boot scenarios on PostgreSQL with over 40,000 records, comparing Spring Data JPA and native JDBC, and found JDBC to be 4‑460× faster for batch inserts, updates, deletes and complex statistics, while JPA remains adequate for small‑scale CRUD operations.
This article walks through a series of practical backend techniques—batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelization, indexing, transaction management, pagination, and lock granularity—to dramatically reduce API response times from several seconds to just a few milliseconds.
This guide details how to implement a full‑stack H5 PAG player for the “Use Basketball to Know Me” activity, covering SDK loading, canvas‑based image manipulation (drag, scale, rotate), dynamic layer and text replacement, real‑time preview synchronization, snapshot export, batch synthesis, performance tuning, and fallback strategies.
This article presents a comprehensive, step‑by‑step guide to optimizing Java‑based API interfaces, covering batch processing, asynchronous execution, caching, pre‑processing, pooling, transaction handling, pagination, SQL tuning, lock granularity, and code restructuring, with practical code examples and diagrams.
This article explains how River, a Go job‑queue library, implements delayed execution, priority handling, exponential‑backoff retries, batch inserts, monitoring, and best‑practice patterns, and compares it with other queue solutions to help developers build reliable, high‑performance background processing pipelines.
Learn how Kubernetes Jobs and CronJobs enable one‑off and scheduled batch processing, understand their core concepts, key differences, YAML specifications, typical use cases, advanced configurations, monitoring, logging, and cleanup strategies, and see real‑world examples with complete YAML snippets and command‑line tips.
This article provides a detailed walkthrough of bank core system architecture, covering its evolution, global overview, key modules such as customer information, account types, deposit and loan cores, payment and settlement, general ledger, card management, and batch processing, illustrated with diagrams and real‑world examples.
This article explains the key techniques Kafka uses to maximize throughput, including sequential disk writes with zero‑copy, partition‑based parallelism, batch processing with compression, and configurable asynchronous replication for balancing performance and reliability.
Learn how to create a modular Python script that batch processes Excel files—merging, cleaning, renaming, and more—using pandas and openpyxl, with a clear project structure and extensible design for real-world office automation.
This article explains Redis Pipeline’s core principle of batching commands to reduce network round‑trips, presents benchmark data showing up to 17‑fold speedups, details real‑world use cases such as cache warm‑up, heartbeat reporting, and high‑traffic events, and provides best‑practice guidelines on batch sizing, error handling, cluster constraints, and comparisons with transactions and Lua scripts.
This guide walks a new e‑commerce developer through the evolution of order data synchronization—from naïve full‑table loads, through incremental and batch strategies, cursor‑based pagination, performance tuning, and finally to real‑time CDC with message queues—highlighting pitfalls and practical solutions.
This article explains why batch tasks significantly affect system and database performance, and outlines three key strategies—task simplification, resource and performance tuning, and proactive monitoring with fault recovery—to design and optimize batch jobs effectively in enterprise development.
This article explains why deleting tens of millions of rows is challenging, analyzes transaction, lock, resource, and business impacts, and presents five practical solutions—including batch deletion, table rebuild, partition dropping, online migration, and professional tools—along with best‑practice guidelines and a decision matrix.
This guide outlines core principles and practical solutions for building resilient backend systems, covering fast‑failure handling, automatic retries with exponential back‑off, circuit‑breaker usage, idempotency, batch job strategies, online transaction patterns, and robust message‑queue processing.
This guide explains how Kubernetes Jobs reliably run one‑time batch tasks and how CronJobs extend that capability with cron‑based scheduling, covering core concepts, key YAML fields, practical examples, common use cases, best‑practice recommendations, and operational tips for monitoring and troubleshooting.
This article details a step‑by‑step performance overhaul for importing 60,000+ XML records into MySQL, covering baseline measurements, MySQL batch processing, asynchronous writes with Disruptor, XML parsing optimizations, and tuning MySQL buffers, ultimately reducing total runtime from 300 seconds to just four seconds.
This article explains the core banking accounting system, covering the transaction‑driven entry engine, multidimensional accounting, internal account management, end‑of‑day batch processing, and a complete loan lifecycle example that shows how each step is recorded, reconciled, and reported.
This article explains how merging similar or duplicate requests upstream with Hystrix Collapser, a custom BatchCollapser, and Guava's ConcurrentHashMultiset can dramatically reduce downstream load, improve system throughput, and outlines their implementations, configurations, and ideal use‑cases.
This article explains how Kafka achieves massive throughput by horizontally scaling partitions across brokers, optimizing sequential writes with Linux page cache, employing zero‑copy system calls, and batching messages to reduce I/O and network overhead.
This article explains how Baidu’s Feed real‑time data warehouse was rebuilt using a pure streaming architecture, detailing the limitations of the previous stream‑batch design, the technical solutions—including core/non‑core data separation, metric calculation in streaming, and Parquet storage with Apache Arrow—and the resulting cost reductions, latency improvements, and future roadmap.
This article provides a comprehensive guide to Spring Batch, covering its purpose, supported business scenarios, core components and interfaces, Maven setup, sample job definitions, parallel execution, decision flows, nested jobs, data reading and writing, validation processing, and REST‑based job scheduling, all illustrated with complete code examples.
This article evaluates the efficiency of inserting massive data into MySQL using Java, comparing three strategies—MyBatis without transactions, plain JDBC with and without transactions, and JDBC batch processing—showing that batch processing combined with transactions yields the fastest insertion speed for hundreds of millions of rows.
In high‑concurrency systems, Kafka message backlog occurs when producers outpace consumers, leading to unprocessed messages that threaten stability and real‑time performance, and this article explains the root causes and provides practical producer‑side and consumer‑side optimization techniques to resolve the issue.
This article explores how Apache SeaTunnel addresses modern data integration challenges by providing a high‑performance, distributed, plugin‑based platform that decouples connectors from execution engines, enabling seamless batch and streaming synchronization across heterogeneous sources such as databases, message queues, and data lakes.
This article provides a comprehensive guide to Spring Batch, covering its purpose, architecture, core components such as Job, Step, ItemReader/Writer/Processor, chunk processing, skip strategies, and practical tips for configuring and optimizing batch jobs in Java applications.
This article examines how a ten‑year‑old financial accounting platform transformed from a monolithic design into a cloud‑native, micro‑service architecture that achieved massive scalability, high availability, and 24‑hour real‑time processing through distributed batch scheduling, elastic scaling, and intelligent fault‑tolerance.
This article walks through a Python file‑processing challenge, explains the logic behind selective batch renaming using regular expressions, provides a complete script, and demonstrates the solution with clear code and illustrative screenshots for practical automation.
This comprehensive guide explains why batch processing is essential, walks through typical banking, e‑commerce, logging and medical data scenarios, details Spring Batch's core architecture and components, provides step‑by‑step setup and code examples, and presents a production‑grade bank reconciliation case with monitoring and troubleshooting tips.
This article walks through optimizing a 60,000‑row XML‑to‑MySQL import by profiling the environment, measuring baseline performance, and applying JDBC batch rewriting, write aggregation, and asynchronous writes with LMAX Disruptor, ultimately reducing execution time from 300 seconds to about four seconds while keeping memory usage reasonable.
This article presents a comprehensive, step‑by‑step guide to optimizing API latency by applying batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, code refactoring, pagination, SQL tuning, and fine‑grained locking techniques.
This article explains the need for lightweight batch processing, outlines a layered architecture and robustness strategies, and demonstrates how Spring Batch implements these concepts with clear interfaces, job management, and support for ignore, retry, and restart mechanisms.
This article explores practical performance‑optimization techniques, covering six fundamental time‑for‑space trade‑offs such as indexing, compression, caching, prefetching, peak‑shaving, and batch processing, followed by four advanced methods that boost parallelism like resource draining, horizontal scaling, sharding, and lock‑free programming.
This article summarizes how major tech firms such as Vivo, Shopee, Alibaba, and TikTok adopt Apache Paimon to unify batch and streaming data pipelines, improve latency, reduce costs, and optimize storage, highlighting key challenges, architectural solutions, and real‑world performance gains.
This article presents a comprehensive set of twelve backend optimization strategies—including batch processing, asynchronous execution, caching, pooling, parallelism, indexing, transaction management, and SQL tuning—to dramatically reduce API latency and improve overall system efficiency.
This article explains the necessity of batch processing, presents typical use cases such as daily interest calculation, e‑commerce order archiving, log analysis and medical data migration, then dives deep into Spring Batch's core components, provides step‑by‑step code examples, performance‑tuning tips, production‑grade fault‑tolerance, monitoring solutions and a comprehensive FAQ.
This article breaks down the core techniques that give Kafka its high‑throughput capability, including producer batch settings (batch.size, linger.ms), broker append‑only writes, consumer poll configuration, partition distribution, zero‑copy data transfer, dual‑thread processing, and configurable compression algorithms.
This article compares three request‑collapsing techniques—Hystrix Collapser, a custom BatchCollapser, and Guava's ConcurrentHashMultiset—explaining their designs, configurations, code implementations, and suitable scenarios for reducing downstream load in Java backend systems.
This guide demonstrates how to create a Python script that uses the win32com library to batch convert Word (.doc/.docx) files to PDF on Windows, including code examples, handling of Office/WPS detection, folder management, common pitfalls, and packaging the script into an executable.
At the 2025 DA Data+AI Conference in Shanghai, Tencent Cloud unveiled Setats—a unified stream‑batch‑incremental engine that cuts system costs, delivers second‑level data visibility and real‑time changelog generation, and demonstrates measurable performance gains in automotive IoT analytics while integrating tightly with the WeData platform.
This article examines the evolution of a merchant batch processing system, detailing its transition from a centralized, tightly‑coupled architecture to a configurable, SPI‑based design and finally to a localized task‑reporting model, while discussing code reuse, scheduling strategies, isolation techniques, and performance challenges.
This article explains how to merge concurrent user requests into a single batch SQL query using Java's LinkedBlockingQueue, ScheduledThreadPoolExecutor and CompletableFuture in a Spring Boot application, thereby saving database connections and improving performance under high concurrency.
The Kuaishou‑hosted Apache Hudi Asia technical salon gathered over 230 attendees and featured seven experts from Kuaishou, Meituan, TikTok, Huawei, JD and others, who shared best practices, architecture designs, and performance optimizations for large‑scale data lake applications across AI, BI, and real‑time workloads.
This tutorial explains how to import data into Excel, craft formulas that concatenate cell values into UPDATE or INSERT SQL statements, and drag the formulas down to produce batch SQL commands for efficient database operations.
This article analyzes why traditional Excel import methods crash under massive loads and presents four practical optimization techniques—including streaming parsing, batch inserts, asynchronous processing, and parallel sharding—backed by code samples, configuration tips, and real‑world performance benchmarks for importing millions of rows efficiently.
This article examines the critical role of product reviews for buyers and sellers, outlines the limitations of traditional review processing, and proposes a GenAI‑powered solution—including platform and model choices, batch inference, and semantic search—to efficiently analyze large‑scale e‑commerce feedback.
This article presents a comprehensive set of backend optimization strategies—including batch processing, asynchronous execution, caching, pre‑processing, pooling, parallelism, indexing, transaction management, program refactoring, pagination, SQL tuning, and fine‑grained locking—to dramatically reduce API latency and improve system efficiency.
This article explains how to merge multiple user‑detail requests into a single database query using a blocking queue, scheduled thread pool, and CompletableFuture in Spring Boot, providing code examples, a high‑concurrency test, and discussion of trade‑offs such as added latency and timeout handling.
This article presents a systematic guide to improving API response times by applying batch processing, asynchronous execution, caching, pooling, parallelism, indexing, transaction management, pagination, SQL tuning, and proper lock granularity, supplemented with practical Java code examples and diagrams.
This article explains how to batch multiple user‑info requests on the server side, merge them into a single SQL query using a blocking queue and ScheduledThreadPoolExecutor, and return the results individually, thereby reducing database connection usage and improving performance under high concurrency.
This article explains how to combine multiple simultaneous user requests on the server side using a queue, scheduled thread pool and CompletableFuture in Spring Boot, reducing database connections while handling high concurrency, and discusses implementation details, testing, and potential pitfalls.
This article describes how to dramatically speed up the import of 60,000+ XML records into MySQL by analyzing the original environment, measuring baseline performance, and applying a series of backend optimizations—including MySQL batch mode, rewriteBatchedStatements, multithreaded asynchronous writes with Disruptor, and XML parsing improvements—ultimately cutting the total runtime from five minutes to just a few seconds.
This article walks through a real‑world case of importing over 60,000 XML rows into MySQL, profiling the initial 300‑second runtime, and applying a series of optimizations—including JDBC batch processing, rewriteBatchedStatements, multithreaded asynchronous writes with Disruptor, and MySQL tuning—to achieve a final import time of around 4 seconds while managing memory usage.
This article demonstrates how to build a Python desktop application that batch‑processes cosmetic product images, sends them to Youdao's OCR translation service, and displays the translated text, covering API preparation, request parameters, signature generation, and full source code.
This article presents Bilibili's end‑to‑end exploration of a streaming‑batch unified data pipeline built on Apache Iceberg, detailing the original and iterated architectures for massive user behavior transmission, online AI training, DB synchronization, and dimension‑join, along with performance gains, cost savings, and future plans.
An Nginx‑based data collection gateway began crashing with OOM kills, prompting a detailed investigation that uncovered memory spikes caused by aggressive batch processing, oversized protobuf payloads, and insufficient memory‑pool management, leading to a custom core‑dump solution and several mitigation strategies.
Updating billions of rows in a MySQL table can overwhelm binlog replication and cause deep‑pagination inefficiencies, so this article explains the pitfalls of direct UPDATE, explores limit‑based and IN‑based approaches, and presents a production‑ready batch update strategy using NO_CACHE and forced primary‑key indexing.
This article outlines a series of backend interface performance optimization strategies—including batch processing, asynchronous execution, caching, preprocessing, pooling, parallelization, indexing, transaction management, program structure refactoring, deep pagination, SQL tuning, and lock granularity—to help developers reduce latency and improve system efficiency.
This article explains how to configure XXL‑JOB with a sharding‑broadcast strategy in a SpringBoot application, use ID modulo hashing to partition large tables, implement the task with XxlJobHelper, write MyBatis sharding SQL, and verify correct parallel processing through detailed logs.
The article narrates the author’s 30‑year programming career and shares technical reflections on building business‑agnostic, configurable data platforms, covering batch, streaming, interactive computing, big‑data sharding, Spark, Flink, cloud migration, and the philosophy of software architecture.
This article explains how Kafka attains extremely high throughput by using batch sending, message compression, sequential disk I/O, PageCache, zero‑copy transfers, and memory‑mapped index files, detailing the underlying code paths and trade‑offs that enable millions of messages per second.
This comprehensive guide walks backend architects through six core optimization methods—caching, batch processing, asynchronous handling, data compression, parallelization, and eliminating unnecessary requests—detailing their problem domains, implementation strategies, real‑world scenarios, benefits, and trade‑offs.
This article shares a personal, experience‑driven overview of Apache Paimon, highlighting its design simplicity, key capabilities such as schema evolution, stream‑batch unified processing, primary‑key support, and closed‑loop data handling, while discussing when its features are appropriate for production environments.
This article explains the Lambda architecture—a three‑layer model combining batch and real‑time processing for large‑scale data, outlines its components, advantages, disadvantages, common tools, and compares it with the Kappa alternative while providing practical insights for data engineers.
This article outlines nine practical methods—caching, parallelization, batch processing, data compression, lock‑free design, sharding, request avoidance, pooling, and asynchronous handling—demonstrating how each can be applied to backend services to dramatically reduce latency and improve throughput.
This article explains RocketMQ’s batch processing concepts, walks through the early batch model, the index‑build pipeline refactor, the BatchCQ and AutoBatch models, and presents benchmark results that show dramatic throughput gains and lower latency compared to Kafka.
This article examines the current evolution of data lakes, detailing their overall architecture, batch and real‑time integration methods, Lakehouse core functionalities such as enhanced DML, schema evolution, ACID support, and open‑design principles that enable multi‑cloud deployment and seamless interaction with diverse compute engines.
This article explains how designers and developers can use a short Python script to automatically compress large numbers of high‑resolution images, covering the problem background, required environment, step‑by‑step implementation, and the impressive compression results achieved.
This article presents a complete guide to Hudi-based real-time data lake ingestion, covering overall data integration architecture, batch and streaming ingestion strategies, advanced table design, and practical recommendations for handling challenges such as deduplication, latency, partitioning, and performance optimization.
The article reviews Apache Paimon's graduation to an Apache Top-Level Project, outlines the essential capabilities of modern lakehouse frameworks—including streaming and batch I/O, multi‑engine integration, and advanced features—and discusses the problems they solve and the promising direction of the lakehouse ecosystem.
This article presents a comprehensive performance test of inserting massive amounts of randomly generated person records into MySQL, comparing three strategies—MyBatis lightweight insertion, direct JDBC handling, and JDBC batch processing—both with and without transactions, and concludes that combining batch processing with transactions yields the fastest insertion speed for large‑scale data loads.
This guide explains why batch processing is essential for API tuning and provides step‑by‑step techniques—including bulk database operations, request merging, pagination, parallel execution, caching, and monitoring—backed by concrete Java code samples and SQL queries to help engineers dramatically improve throughput and latency.
This article presents Li Auto’s end‑to‑end data integration journey, detailing the evolution of its data platform, the challenges of heterogeneous sources, and how a unified Flink‑on‑K8s solution with cloud‑native architecture, operator management, monitoring, and checkpointing addresses batch‑stream convergence and future scalability.
This article presents a detailed case study of optimizing a distributed batch processing system by replacing static shard‑key concurrency with a dynamic task‑grabbing mechanism, dramatically reducing execution time from several hours to under fifteen minutes while maintaining stable resource usage.
