RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems

• URL: http://arxiv.org/abs/2503.04252v1
• Date: Thu, 06 Mar 2025 09:35:20 GMT
• Title: RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems
• Authors: Biao Ouyang, Yingying Zhang, Hanyin Cheng, Yang Shu, Chenjuan Guo, Bin Yang, Qingsong Wen, Lunting Fan, Christian S. Jensen,
• Abstract summary: Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision.<n>This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries.<n>To enable more accurate and detailed diagnoses, we propose the multimodal Ranking for the Root Causes of slow queries (RCRank) framework.
• Score: 38.72325043937881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the continued migration of storage to cloud database systems,the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision. This paper proposes a method capable of both identifying possible root cause types for slow queries and ranking these according to their potential for accelerating slow queries. This enables prioritizing root causes with the highest impact, in turn improving slow-query revision effectiveness. To enable more accurate and detailed diagnoses, we propose the multimodal Ranking for the Root Causes of slow queries (RCRank) framework, which formulates root cause analysis as a multimodal machine learning problem and leverages multimodal information from query statements, execution plans, execution logs, and key performance indicators. To obtain expressive embeddings from its heterogeneous multimodal input, RCRank integrates self-supervised pre-training that enhances cross-modal alignment and task relevance. Next, the framework integrates root-cause-adaptive cross Transformers that enable adaptive fusion of multimodal features with varying characteristics. Finally, the framework offers a unified model that features an impact-aware training objective for identifying and ranking root causes. We report on experiments on real and synthetic datasets, finding that RCRank is capable of consistently outperforming the state-of-the-art methods at root cause identification and ranking according to a range of metrics.

Related papers

• FastLane: Efficient Routed Systems for Late-Interaction Retrieval [58.060096779432094]
  FastLane is a novel retrieval framework that dynamically routes queries to their most informative representations.<n>By bridging late-interaction models with Approximate Nearest Neighbor Search (ANNS), FastLane enables scalable, low-latency retrieval.
  arXiv  Detail & Related papers  (2026-01-10T02:22:01Z)
• Multi-hop Reasoning via Early Knowledge Alignment [68.28168992785896]
  Early Knowledge Alignment (EKA) aims to align Large Language Models with contextually relevant retrieved knowledge.<n>EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency.<n>EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models.
  arXiv  Detail & Related papers  (2025-12-23T08:14:44Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation [55.47971671635531]
  Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA)<n>Retrieval-Augmented Generation (RAG) addresses this limitation by enriching LLMs with external knowledge.<n>Existing systems primarily rely on unstructured documents, while largely overlooking relational databases.
  arXiv  Detail & Related papers  (2025-09-30T22:19:44Z)
• Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference [8.823529310904162]
  Multi-agent systems (MAS) are critical for automating complex tasks, yet their practical deployment is hampered by the challenge of failure attribution.<n>We introduce the first failure attribution framework for MAS grounded in multi-granularity causal inference.
  arXiv  Detail & Related papers  (2025-09-10T15:22:00Z)
• Adaptive Root Cause Localization for Microservice Systems with Multi-Agent Recursion-of-Thought [11.307072056343662]
  We introduce RCLAgent, an adaptive root cause localization method for microservice systems.<n>We show that RCLAgent achieves superior performance by localizing the root cause using only a single request-outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-28T02:34:19Z)
• DeepSieve: Information Sieving via LLM-as-a-Knowledge-Router [57.28685457991806]
  DeepSieve is an agentic RAG framework that incorporates information sieving via LLM-as-a-knowledge-router.<n>Our design emphasizes modularity, transparency, and adaptability, leveraging recent advances in agentic system design.
  arXiv  Detail & Related papers  (2025-07-29T17:55:23Z)
• Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation [39.545788636148025]
  We present the first comprehensive study of position bias in multimodal RAG systems.<n>Our results reveal that multimodal interactions intensify position bias compared to unimodal settings.<n>These findings highlight the need for evidence reordering or debiasing strategies to build more reliable and equitable generation systems.
  arXiv  Detail & Related papers  (2025-05-30T06:48:02Z)
• Learning to Route Queries Across Knowledge Bases for Step-wise Retrieval-Augmented Reasoning [60.84901522792042]
  Multimodal Retrieval-Augmented Generation (MRAG) has shown promise in mitigating hallucinations in Multimodal Large Language Models (MLLMs)<n>We propose R1, a novel MRAG framework that learns to decide when and where to retrieve knowledge based on the evolving reasoning state.<n>R1- can adaptively and effectively leverage diverse KBs, reducing unnecessary retrievals and improving both efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-05-28T08:17:57Z)
• Demystifying and Enhancing the Efficiency of Large Language Model Based Search Agents [9.862334188345791]
  Large Language Model (LLM)-based search agents have shown remarkable capabilities in solving complex tasks.<n>We introduce SearchAgent-X, a high-efficiency inference framework for LLM-based search agents.<n>SearchAgent-X consistently outperforms state-of-the-art systems such as vLLM and HNSW-based retrieval.
  arXiv  Detail & Related papers  (2025-05-17T16:07:01Z)
• TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems [42.50432360919637]
  Large language models (LLMs)-driven root cause analysis (RCA) in cloud-native systems has become a key topic of modern software operations and maintenance.<n>Existing LLM-based approaches face three key challenges: multi-modality input constraint, context window limitation, and dynamic dependence graph.<n>We propose a tool-assisted LLM agent with multi-modality observation data for fine-grained RCA, namely TAMO.
  arXiv  Detail & Related papers  (2025-04-29T06:50:48Z)
• HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generation [11.53083922927901]
  HM-RAG is a novel Hierarchical Multi-agent Multimodal RAG framework. It pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data.
  arXiv  Detail & Related papers  (2025-04-13T06:55:33Z)
• Knowledge-Aware Iterative Retrieval for Multi-Agent Systems [0.0]
  We introduce a novel large language model (LLM)-driven agent framework. It iteratively refines queries and filters contextual evidence by leveraging dynamically evolving knowledge. The proposed system supports both competitive and collaborative sharing of updated context.
  arXiv  Detail & Related papers  (2025-03-17T15:27:02Z)
• MultiConIR: Towards multi-condition Information Retrieval [57.6405602406446]
  We introduce MultiConIR, the first benchmark designed to evaluate retrieval models in multi-condition scenarios. We propose three tasks to assess retrieval and reranking models on multi-condition robustness, monotonic relevance ranking, and query format sensitivity.
  arXiv  Detail & Related papers  (2025-03-11T05:02:03Z)
• Progressive Multimodal Reasoning via Active Retrieval [64.74746997923967]
  Multi-step multimodal reasoning tasks pose significant challenges for large language models (MLLMs)<n>We propose AR-MCTS, a universal framework designed to progressively improve the reasoning capabilities of MLLMs.<n>We show that AR-MCTS can optimize sampling diversity and accuracy, yielding reliable multimodal reasoning.
  arXiv  Detail & Related papers  (2024-12-19T13:25:39Z)
• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [102.31558123570437]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs)<n>We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• Online Multi-modal Root Cause Analysis [61.94987309148539]
  Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
  arXiv  Detail & Related papers  (2024-10-13T21:47:36Z)
• Multi-Head RAG: Solving Multi-Aspect Problems with LLMs [13.638439488923671]
  Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) Existing RAG solutions do not focus on queries that may require fetching multiple documents with substantially different contents. This paper introduces Multi-Head RAG (MRAG), a novel scheme designed to address this gap with a simple yet powerful idea.
  arXiv  Detail & Related papers  (2024-06-07T16:59:38Z)
• Multi-modal Causal Structure Learning and Root Cause Analysis [67.67578590390907]
  We propose Mulan, a unified multi-modal causal structure learning method for root cause localization. We leverage a log-tailored language model to facilitate log representation learning, converting log sequences into time-series data. We also introduce a novel key performance indicator-aware attention mechanism for assessing modality reliability and co-learning a final causal graph.
  arXiv  Detail & Related papers  (2024-02-04T05:50:38Z)
• The PetShop Dataset -- Finding Causes of Performance Issues across Microservices [3.87228935312714]
  This paper introduces a dataset specifically designed for evaluating root cause analyses in microservice-based applications. The dataset encompasses latency, requests, and availability metrics emitted in 5-minute intervals from a distributed application. In addition to normal operation metrics, the dataset includes 68 injected performance issues, which increase latency and reduce availability throughout the system.
  arXiv  Detail & Related papers  (2023-11-08T16:30:12Z)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.