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The article introduces Archer, a new big‑data warehouse engine built on Iceberg that adds an inverted‑index mechanism using Tantivy to provide full‑text and JSON search, storage‑compute separation, and significant performance gains over traditional Elasticsearch and Iceberg connectors.
This article describes Bilibili’s intelligent optimization project that automatically analyzes historical query workloads to configure multi‑dimensional sorting, various indexes, and pre‑aggregation on Iceberg tables, thereby reducing scan volume by 28% across dozens of tables and improving OLAP query latency.
This article details Xiaomi’s practical deployment of Trino, covering its architectural role, core and extended capabilities, performance comparisons, integration with Iceberg and Spark, operational enhancements, multi‑cluster and ad‑hoc query scenarios, future cloud‑storage plans, and a Q&A session.
Alluxio Edge is a library that runs inside Trino or PrestoDB workers, using local SSD or memory to cache data from cloud storage, which restores data locality, cuts storage egress, and delivers up to ten‑fold IO speed gains and up to ten‑fold query performance improvements in real deployments.
This article details Zhihu's evaluation of PrestoDB and Trino, the integration of Alluxio for query acceleration, the architectural choices and deployment modes, extensive TPC‑DS and production performance tests, encountered challenges, and future optimization directions for their OLAP platform.
This article presents Bilibili's practical implementation of Trino within a lakehouse architecture, focusing on the compute engine placement, stability enhancements, and containerized deployment, while detailing indexing strategies, pre‑computation techniques, Iceberg metadata optimizations, and performance gains for large‑scale analytical queries.
The Venus log platform at iQiyi migrated from an ElasticSearch‑Kibana architecture to an Iceberg‑based data lake with Trino, cutting storage and compute costs by over 70%, boosting stability by 85%, and efficiently supporting billions of daily logs through write‑heavy, low‑query workloads.
This article explains how to develop, package, and deploy custom Trino plugins—illustrated with an access‑control plugin—by using Java SPI, Maven dependencies, implementing the Plugin and SystemAccessControl interfaces, and configuring the plugin in Trino’s configuration files.
This article outlines the challenges of scaling data integration platforms, proposes a comprehensive solution using Apache Seatunnel and Dinky, details the implementation of Trino and Kyuubi JDBC support, and describes the platform's architecture, task publishing workflow, logging, monitoring, resource management, and future enhancements.
Bilibili evolved its log platform from ClickHouse‑based Billions 2.0 to Billions 3.0 lakehouse using Iceberg, HDFS, Trino, retaining ClickHouse for acceleration; this reduces storage cost by over 20%, improves observability, solves the compute‑storage mismatch, adds flexible indexing, and supports complex ETL while staying open‑source.
Comcast’s hybrid‑cloud data platform, built on Trino and Amazon S3, faced challenges such as fragmented data access, costly data copies, and latency, leading the DX team to adopt Alluxio as a unified, cache‑enabled, secure middle‑layer that bridges storage and compute.
This article details iQIYI's adoption of the Iceberg data lake, covering its OLAP architecture, reasons for a lake, Iceberg table format advantages over Hive, platform construction, extensive performance optimizations, and real‑world business use cases such as ad‑flow unification, log analysis, audit, and CDC pipelines.
The article provides a comprehensive walkthrough of Apache Kyuubi 1.6.0, detailing server‑side enhancements such as batch (JAR) task submission, metadata store and unified API/authentication, client‑side improvements to the built‑in JDBC driver and Beeline, as well as engine plugins for Spark, Flink, Trino and Hive, and concludes with the community’s roadmap and statistics.
This article details how Xiaomi integrated the open‑source Kyuubi SQL gateway into its evolving big‑data platform, describing the challenges of multiple SQL services, the architectural redesign for a unified, high‑availability service, performance gains, new features such as engine pooling and Z‑ordering, and future roadmap plans.
Bilibili’s new lake‑house platform, built on Trino and Iceberg, replaces Hive‑based pipelines by ingesting logs and DB data into Iceberg tables, applying advanced sorting, Z‑order/Hilbert clustering, bitmap and bloom indexes, virtual join columns and pre‑aggregation, enabling 70 000 daily queries on 2 PB with average scans of 2 GB and sub‑2‑second response times.
This talk presents the end‑to‑end deployment of Kyuubi as a unified, high‑availability SQL gateway on Xiaomi’s big‑data platform, covering its integration, architecture upgrades, multi‑engine support, performance gains, operational improvements, and future roadmap.