Tagged articles

Time Travel

11 articles · Page 1 of 1

Aug 30, 2026 · Artificial Intelligence

Human-in-the-Loop and Time-Travel Debugging: Making AI Graphs Production-Ready

The article explains why autonomous agents need human supervision in critical steps, introduces three HITL scenarios, shows how LangGraph’s interrupt_before/after and update_state enable pause‑and‑review workflows, demonstrates time‑travel debugging and observability with Langfuse, and provides practical design principles and a production‑grade configuration.

AgentLangGraphLangfuse

0 likes · 20 min read

Human-in-the-Loop and Time-Travel Debugging: Making AI Graphs Production-Ready

Xike

Aug 1, 2026 · Big Data

Big Data Series #6: Getting Started with Iceberg Lakehouse – Snapshots, Time Travel, and Writes

This tutorial explains how Apache Iceberg adds snapshot metadata to files on HDFS or object storage, enabling atomic writes, time‑travel queries, and schema evolution, and walks through a Docker‑based setup with a REST catalog, Spark 3.5.3, and hands‑on examples including table creation, data insertion, version queries, and troubleshooting tips.

Apache IcebergData LakeHDFS

0 likes · 16 min read

Big Data Series #6: Getting Started with Iceberg Lakehouse – Snapshots, Time Travel, and Writes

StarRocks

Feb 11, 2026 · Big Data

How StarRocks and Apache Paimon Build a True Lakehouse Native Engine

This article details the deep integration of StarRocks with Apache Paimon, describing the unified architecture, version evolution, performance enhancements, time‑travel queries, native readers/writers, distributed planning, and future roadmap for achieving lakehouse‑native analytics at scale.

Apache PaimonData LakeLakehouse

0 likes · 10 min read

How StarRocks and Apache Paimon Build a True Lakehouse Native Engine

DataFunSummit

Dec 29, 2025 · Databases

Why Graph Lakehouses Matter: Inside Flavius’ Cloud‑Native Architecture

This article explains the need for graph lakehouses, defines the concept, details Flavius’ cloud‑native three‑layer architecture (FE, BE, MS), highlights its core innovations such as resource management, metadata design, time‑travel, integrated graph compute and training, and showcases real‑world industry applications.

Time Travelcloud-nativegraph analytics

0 likes · 17 min read

Why Graph Lakehouses Matter: Inside Flavius’ Cloud‑Native Architecture

Big Data Technology & Architecture

Dec 12, 2024 · Big Data

Understanding Time Travel and Snapshot Retention in Lake Frameworks (Hudi & Paimon)

This article explains how lake frameworks like Hudi and Paimon implement Time Travel by recording older data versions, the snapshot retention policies that limit historical data access, and practical recommendations for managing snapshots and consumption patterns to reduce storage costs in large‑scale data warehouses.

HudiPaimonSnapshot Retention

0 likes · 7 min read

Understanding Time Travel and Snapshot Retention in Lake Frameworks (Hudi & Paimon)

Alibaba Cloud Big Data AI Platform

Apr 16, 2024 · Big Data

MaxCompute’s Integrated Offline & Near‑Real‑Time Architecture: Transaction Table 2.0 Explained

This article explains MaxCompute’s new integrated offline‑and‑near‑real‑time architecture, Transaction Table 2.0, detailing its unified storage and compute design, automatic data governance, schema evolution, upsert and time‑travel capabilities, and how it simplifies complex big‑data pipelines while delivering minute‑level latency and lower costs.

MaxComputeNear Real-TimeSQL

0 likes · 27 min read

MaxCompute’s Integrated Offline & Near‑Real‑Time Architecture: Transaction Table 2.0 Explained

Alibaba Cloud Big Data AI Platform

Aug 30, 2023 · Big Data

How Transaction Table2.0 Cuts Data Deduplication Costs by 98% in MaxCompute

This article explains how Renliji's data warehouse team leveraged MaxCompute's Transaction Table2.0 to dramatically reduce incremental data deduplication costs and execution time, while also introducing efficient small‑file merging, time‑travel queries, and future data‑sync strategies for a high‑growth HR SaaS platform.

Cost OptimizationMaxComputeTime Travel

0 likes · 11 min read

How Transaction Table2.0 Cuts Data Deduplication Costs by 98% in MaxCompute

Tencent Cloud Developer

Aug 23, 2023 · Big Data

WeChat Experiment Platform: Architecture Design and Iceberg Lakehouse Optimization

The WeChat Experiment Platform migrated its 60,000 metric, 200,000 core, 30 PB plus data pipeline to an Iceberg based lakehouse, leveraging three layer metadata, fine grained partitioning, MERGE into writes, time travel snapshots and skew handling UDFs, which cut core time by 69%, saved ~100 PB storage, and reduced latency by up to 70%.

LakehouseMerge IntoMetric Computation

0 likes · 18 min read

WeChat Experiment Platform: Architecture Design and Iceberg Lakehouse Optimization

Baidu Geek Talk

Aug 24, 2022 · Frontend Development

Mastering State Management and Time‑Travel Debugging with san‑store

This article explains why front‑end state management is essential, walks through the flux‑based san‑store architecture, demonstrates core usage and code patterns, and details how san‑devtools implements time‑travel debugging by logging state snapshots, computing diffs, and replaying them to restore previous UI states.

FluxJavaScriptTime Travel

0 likes · 11 min read

Mastering State Management and Time‑Travel Debugging with san‑store

Baidu App Technology

Nov 15, 2021 · Frontend Development

State Management in San: san-store Implementation and Time‑Travel Debugging

The article explains front‑end state management using San’s san‑store, detailing its Flux‑style architecture, action registration, component connection, immutable updates with san‑update, and a time‑travel debugging system that logs state changes, restores snapshots, computes efficient diffs, and discusses its benefits and limitations.

FluxSANTime Travel

0 likes · 12 min read

State Management in San: san-store Implementation and Time‑Travel Debugging

vivo Internet Technology

Aug 25, 2021 · Backend Development

Design and Implementation of a Time‑Travel Module for E‑commerce Promotion Validation

The team built a whitelist‑based time‑travel module that overrides the system clock for selected operators, letting them preview future promotional prices in Vivo’s e‑commerce flow without creating real orders, by propagating an openId context through Dubbo filters and replacing System.currentTimeMillis() with a custom TimeTravelUtil.

BackendJavaTime Travel

0 likes · 12 min read

Design and Implementation of a Time‑Travel Module for E‑commerce Promotion Validation