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.
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.
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.
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.
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.
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%.
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.
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.
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.
