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Lakehouse Research Base
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Lakehouse Research Base

Focused on technical sharing in the data field, covering a tech stack that includes Hadoop, Spark, Flink, Kafka, Fluss, Paimon, Iceberg, StarRocks, ClickHouse, ES, Milvus, and more. Welcome to follow.

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Recent Articles

Latest from Lakehouse Research Base

25 recent articles
Lakehouse Research Base
Lakehouse Research Base
Aug 17, 2026 · Big Data

StarRocks Real-Time Wide Tables: Direct Write vs Paimon Lakehouse Architecture Comparison

This article deeply compares two StarRocks real-time wide table architectures—direct primary key table writes versus Paimon lakehouse external tables—across seven dimensions including query performance, write latency, data reuse, resource isolation, operational complexity, storage cost, and evolution potential, providing banking scenario selection criteria and a recommended Flink+Fluss+Paimon+StarRocks streaming lakehouse architecture.

FlinkLakehousePaimon
0 likes · 21 min read
StarRocks Real-Time Wide Tables: Direct Write vs Paimon Lakehouse Architecture Comparison
Lakehouse Research Base
Lakehouse Research Base
Aug 12, 2026 · Big Data

Apache Fluss Graduates to TLP: Lakestream Unifies Streaming & Lakehouse

Apache Fluss graduates to a top-level project, introducing the Lakestream architecture that unifies real-time streaming and historical lakehouse storage through tiered hot/cold storage, unified metadata, and Union Read, eliminating Lambda architecture complexity and enabling sub-second analytics on a single logical table.

Apache FlussFlinkLakestream
0 likes · 15 min read
Apache Fluss Graduates to TLP: Lakestream Unifies Streaming & Lakehouse
Lakehouse Research Base
Lakehouse Research Base
Jul 14, 2026 · Big Data

Flink + Paimon End-to-End Exactly-Once: Production Implementation Guide

This guide details how to achieve end-to-end exactly-once semantics with Apache Flink and Apache Paimon, covering consistency models, three-layer coordination mechanisms, production-grade configurations, concurrency rules, verification tests, failure injection scenarios, performance trade-offs, and operational SOPs for lakehouse streaming workloads.

Apache FlinkApache PaimonCheckpoint
0 likes · 26 min read
Flink + Paimon End-to-End Exactly-Once: Production Implementation Guide
Lakehouse Research Base
Lakehouse Research Base
Jun 4, 2026 · Big Data

Paimon LSM Storage Model: How Tiered Compaction Powers Lakehouse Read/Write

This article explains Paimon's LSM-based storage model, detailing its four-tier architecture (MemTable, Immutable MemTable, L0, L1+), write/read paths, Merge-On-Read with loser-tree K-way merge, four merge engines, compaction triggers and modes, changelog production, and performance tuning practices for lakehouse workloads.

ChangelogCompactionFlink
0 likes · 22 min read
Paimon LSM Storage Model: How Tiered Compaction Powers Lakehouse Read/Write
Lakehouse Research Base
Lakehouse Research Base
May 27, 2026 · Databases

Paimon Bloom Index Deep Dive: 25x Query Speedup from 38s to 1.5s with Production Best Practices

This article analyzes Paimon Bloom Index internals, demonstrates a real-world 25x query acceleration (38s to 1.5s) on a 400GB partitioned table, explains false positive rates, optimal use cases, and provides production-ready configuration patterns for StarRocks+Paimon lakehouse architectures.

Bloom IndexData EngineeringFalse Positive Rate
0 likes · 19 min read
Paimon Bloom Index Deep Dive: 25x Query Speedup from 38s to 1.5s with Production Best Practices
Lakehouse Research Base
Lakehouse Research Base
Feb 12, 2026 · Big Data

Spark & Flink in Lakehouse: Latency, Accuracy & Cost Trade-offs for Batch-Stream Design

This article analyzes how Spark and Flink should divide responsibilities in lakehouse architectures by evaluating three core dimensions—data latency, computation accuracy, and resource cost—arguing that mature batch-stream integration balances business needs against technical overhead rather than pursuing universal real-time processing.

Batch ProcessingComputation AccuracyData Latency
0 likes · 9 min read
Spark & Flink in Lakehouse: Latency, Accuracy & Cost Trade-offs for Batch-Stream Design
Lakehouse Research Base
Lakehouse Research Base
Jan 29, 2026 · Big Data

Unstructured Data Lake Ingestion: File Body + Metadata Registration Patterns

This article details the industry-standard dual pattern of file body storage and metadata registration for unstructured data lake ingestion, covering a three-layer architecture, batch and real-time implementation steps using Paimon and Flink, AI-specific optimizations like label standardization and format conversion, and key operational considerations for permissions, versioning, and cost control.

AI data preparationFlinkPaimon
0 likes · 11 min read
Unstructured Data Lake Ingestion: File Body + Metadata Registration Patterns