apache data ecosystem

Existing metadata catalogs (e.g., Hive Metastore, Iceberg, Delta Lake) face fundamental limitations in concurrent read/write throughput, strong consistency guarantees, and expressive query capabilities for hyperscale data systems. TreeCat addresses these challenges by introducing the first high-performance, purpose-built metadata catalog engine. It employs a hierarchical data model and a path-based query language; implements MVOCC—a multi-version optimistic concurrency control protocol ensuring serializable isolation; and pioneers an associative scan execution mechanism to accelerate metadata traversal and filtering. Its custom storage format is co-designed for efficient range queries and fine-grained version management. Experimental evaluation demonstrates that TreeCat delivers strict consistency under high concurrency while achieving up to 8.2× higher throughput for range queries compared to state-of-the-art systems, significantly outperforming existing solutions across key metadata management workloads.

Open table formats (OTFs) such as Apache Iceberg and Delta Lake—widely adopted in lakehouse architectures—lack native support for cross-table ACID transactions and crash recovery, leading to non-repeatable reads, stale data, and high operational overhead. This paper presents the first non-intrusive solution that enables multi-table transactions, strong consistency isolation—including for long-running transactions—and fault tolerance on OTFs without modifying their storage formats or computation engines. Our approach introduces log-coordinated lightweight two-phase commit, snapshot version-chain management, atomic metadata writes, and incremental rollback. Evaluation shows minimal performance overhead: only 2.0% on YCSB write workloads and 2.5% on TPC-DS read workloads. The system supports high-concurrency mixed workloads while eliminating dirty reads and non-repeatable reads entirely, thereby ensuring production-grade consistency for cloud-scale analytical data.

To address the challenge of processing multi-source, heterogeneous data in social networks, this paper proposes a unified batch-stream-graph analytics framework built upon the Hadoop-Spark ecosystem. The method systematically integrates Hive (for SQL-based batch processing), HBase (for low-latency key-value lookups), and GraphX (for scalable graph computation) under a single Spark execution layer. It supports three core analytical tasks: user influence assessment, high-frequency term statistics, and community relationship mining. Leveraging HDFS for distributed storage, YARN for resource orchestration, and multi-language APIs, the framework achieves loosely coupled integration of computation and storage. End-to-end experiments on real-world social datasets demonstrate that the hybrid architecture accelerates complex relational analysis by 1.8–3.2× compared to single-component baselines, significantly improving both processing efficiency and system flexibility.

This work proposes OceanBase Mercury, a distributed near real-time analytical processing system built upon OceanBase, designed to deliver enterprise-grade analytical capabilities—including multi-tenancy, high availability, and elastic scalability—for petabyte-scale data. Traditional OLAP systems struggle to simultaneously support real-time transactions and efficient analytics at scale, often suffering from high data redundancy, complex cross-system synchronization, and poor timeliness. Mercury addresses these challenges through three key innovations: an adaptive columnar storage format with hybrid layout optimization, a materialized view differential refresh mechanism that ensures temporal consistency, and a polymorphic vectorized execution engine compatible with three distinct data formats. Experimental results on real-world workloads demonstrate that Mercury achieves 1.3–3.1× faster query latency than specialized OLAP engines while maintaining sub-second response times, effectively balancing analytical depth with operational agility.

Modern distributed applications and cloud environments demand highly scalable, low-latency key-value (KV) stores. Method: This paper conducts a systematic, end-to-end empirical evaluation of three contemporary NoSQL KV databases—Redis, Aerospike, and Dragonfly—using the YCSB benchmark under read-heavy, write-heavy, and balanced workloads. We apply gradient stress testing across 1–32 concurrent clients and quantitatively analyze latency, throughput, and memory overhead. Contribution/Results: Our study is the first to empirically characterize performance trade-offs and scalability bottlenecks of these systems under high concurrency. Results show Dragonfly achieves superior write throughput; Aerospike delivers optimal memory efficiency and stable latency; Redis exhibits the lowest read latency at low concurrency. This work fills a critical gap in comparative, reproducible, fine-grained empirical assessment of mainstream modern KV stores, providing actionable, evidence-based guidance for production-grade technology selection.

Document-oriented NoSQL databases, which typically adopt eventual consistency models, struggle to support highly reliable transaction processing. This work proposes a four-phase transaction management framework that achieves conflict-serializable consistency while preserving system scalability. By integrating transaction lifecycle management, operation classification, pre-execution conflict detection, and an adaptive locking strategy, the approach effectively eliminates deadlocks and significantly reduces both transaction abort rates and latency variance. Experimental results demonstrate that the abort rate decreases from 8.3% to 4.7%, and latency variance is reduced by 34.2%. Under high concurrency, throughput improves by 6.3%–18.4%, with a 15.2% increase observed in a 9-node cluster, accompanied by a 53% reduction in abort rate.

This work proposes a serverless real-time data processing framework that integrates the MapReduce programming model with Function-as-a-Service (FaaS) to address the demand for efficient handling of massive, real-time data streams in modern logistics systems. Built on Kubernetes and Knative, the framework employs an event-driven architecture composed of five loosely coupled services for data ingestion, aggregation, and analysis. It leverages Apache Kafka for event transport, Redis for metadata management, and AWS S3 for durable storage. By innovatively combining MapReduce’s batch-processing semantics with FaaS’s elastic scaling capabilities, the system supports on-demand autoscaling and scale-to-zero functionality. Experimental results demonstrate that the proposed approach achieves low-latency processing while significantly improving resource efficiency, thereby fulfilling the requirements of highly elastic and highly available data pipelines.

Multi-cloud ETL faces significant challenges, including high cross-cloud data movement costs, incompatibility among heterogeneous SQL engines, complex orchestration, and fragmented security policies. This work presents the first systematic evaluation of predicate pushdown optimization in multi-cloud ETL scenarios and proposes a novel协同 strategy that integrates localized pushdown with data federation to offload transformation logic across heterogeneous query engines such as Amazon Redshift and Google BigQuery. By intelligently pushing computation closer to the data sources while leveraging federated querying capabilities, the approach substantially reduces cross-cloud data transfer, thereby lowering end-to-end execution latency and cost. The proposed paradigm offers an efficient and scalable solution for optimizing multi-cloud data integration workflows.

This work addresses the challenge of Spark SQL performance tuning, which is hindered by an enormous configuration space and the prohibitive cost of full-fidelity evaluation, making it difficult to identify high-quality configurations within practical time limits. To overcome this, the authors propose MFTune, a novel approach that introduces a query-level multi-fidelity partitioning mechanism to construct a representative, low-cost performance surrogate using a carefully selected subset of SQL queries with high correlation to overall workload behavior. MFTune further integrates density-based clustering for parameter space compression, tailored transfer learning, and a two-stage warm-start strategy to dramatically enhance tuning efficiency. Experimental results on TPC-H and TPC-DS benchmarks demonstrate that MFTune consistently outperforms five state-of-the-art methods under realistic time constraints, rapidly converging to superior configurations.

This work addresses key challenges in multimodal data analytics, including inefficient I/O, rigid query optimization, and performance degradation caused by resource decoupling. To overcome these limitations, the authors propose a cloud-native OLAP engine that integrates a unified table engine—combining two-layer logical abstraction with physically consistent layout—alongside CrossCache, a cluster-wide shared SSD cache, and NexusFS, a virtual file system enabling efficient local data access. Furthermore, the system incorporates a query optimizer that leverages historical execution traces and AI-driven insights to enhance plan selection. Experimental results demonstrate that the proposed system significantly outperforms existing solutions in multimodal query efficiency, resource utilization, and end-to-end latency, enabling highly effective coordination among analytical, batch, and incremental workloads.