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Bilibili Tech

Jul 15, 2022 · Big Data

Lakehouse Architecture Practice at Bilibili: Query Acceleration and Index Enhancement

Bilibili’s lakehouse architecture merges Iceberg‑based data lake flexibility with data‑warehouse efficiency, using Kafka‑Flink real‑time ingestion, Spark offline loads, Trino queries, Alluxio caching, Z‑Order/Hilbert sorting, and enhanced BloomFilter and bitmap indexes to boost query speed up to tenfold while drastically cutting file reads.

Query OptimizationZ-Order sortingbig data architecture

0 likes · 17 min read

Lakehouse Architecture Practice at Bilibili: Query Acceleration and Index Enhancement

JD Tech

Apr 20, 2021 · Databases

Space-Filling Curves for Efficient Multidimensional Data Storage and Querying

This article introduces space-filling curves such as Z‑ordering, Hilbert, and XZ‑Ordering, explaining their mapping algorithms and how they transform multidimensional spatial data into one‑dimensional indices for efficient storage and querying in key‑value databases, while discussing challenges and practical examples.

Space-filling CurvesXZ-OrderingZ-Ordering

0 likes · 12 min read

Space-Filling Curves for Efficient Multidimensional Data Storage and Querying

Big Data Technology Architecture

Mar 4, 2021 · Big Data

Improving Interactive Analysis on Massive Datasets with Data Clustering and Data Skipping Using Spark and Iceberg

This article explores how data clustering techniques such as linear order, Z‑order, and Hilbert‑curve ordering can be applied in Apache Spark and Apache Iceberg to achieve efficient data skipping on terabyte‑scale tables, dramatically reducing file scans and enabling sub‑second interactive analytics for multi‑dimensional queries.

Data SkippingSparkZ-Order

0 likes · 20 min read

Improving Interactive Analysis on Massive Datasets with Data Clustering and Data Skipping Using Spark and Iceberg