Proactivity, efficiency, professionalism, and empathy are the core values of the Snowball Engineer Team; curiosity, passion, and sharing of technology drive their continuous progress.
The article details Snowball's large‑scale financial knowledge graph, covering its background challenges, two‑layer ontology and data design, data sourcing and pipeline, graph database selection, search and NLP services, domain‑specific pre‑training models, and future industrial considerations.
This article details a technical solution for balancing visual aesthetics and readability in mobile app text rendering, specifically addressing challenges of two-column justification with long English words and numbers by implementing manual hyphenation strategies.
This article outlines Snowball's data warehouse background, compares major modeling approaches such as ER, dimensional, DataVault and Anchor models, describes the current challenges of their dimensional model, and details the OneData methodology—including OneModel, OneID, and OneService—along with its practical implementation, results, and future plans.
This article explains what performance testing is, when to conduct it, typical scenarios, step‑by‑step execution—including requirements, test design, tool selection, script examples with Locust, result analysis, and key metrics such as throughput, response time, P90, and optimal concurrency.
This article details the design, challenges, and solutions of Snowball's unified push platform, covering problem analysis, channel capability construction for Android and iOS, system and data architecture, business operations, and future enhancements to achieve high‑availability, scalable, and reliable push notifications across multiple mobile manufacturers.
This article examines ClickHouse's join mechanisms, detailing the limitations of standard joins, the advantages of Global joins, and optimization strategies such as hash and merge joins, subquery filtering, and memory considerations, illustrated with SQL examples and source‑code analysis.
This article details Snowball's transition from a single‑datacenter setup to a dual‑active, cloud‑native architecture using Apache APISIX, covering background challenges, problem analysis, gateway selection, architectural adjustments, authentication unification, observability enhancements, ZooKeeper integration, and future plans.
This article describes how a mobile development team refactored a card‑creation feature by applying the Open/Closed and Single‑Responsibility principles, introduced a factory pattern with reflection, and ultimately built a fully automated dynamic injection framework using Java Annotation Processing (APT) to eliminate manual code updates.
This article details the Snowball data team's migration from Hive3 on Tez to Spark SQL, covering the motivations, comparative performance tests, encountered compatibility issues, configuration work‑arounds, and future plans for consolidating ETL workloads on Spark.
The article presents Snowball's model feature platform, detailing its motivation, architecture, feature lifecycle management, online engine design, optimization techniques, and the resulting improvements in feature iteration speed, reuse, and system stability for recommendation and search services.
