Logs serve as a primary source of information for engineers to diagnose failures in large-scale online service systems. Log parsing, which extracts structured events from massive unstructured log data, is a critical first step for downstream tasks like anomaly detection and failure diagnosis. With advances in large language models (LLMs), leveraging their strong text understanding capabilities has proven effective for accurate log parsing. However, existing LLM-based log parsers all focus on the constant part of logs, ignoring the potential contribution of the variable part to log parsing. This constant-centric strategy brings four key problems. First, inefficient log grouping and sampling with only constant information. Second, a relatively large number of LLM invocations due to constant-based cache, leading to low log parsing accuracy and efficiency. Third, a relatively large number of consumed constant tokens in prompts leads to high LLM invocation costs. At last, these methods only retain placeholders in the results, losing the system visibility brought by variable information in logs.

Microservice systems, characterized by their distributed nature and dynamic environments, pose significant challenges for failure diagnosis. Traditional failure diagnosis methods based on a single source of observability data, such as logs, metrics, or traces, fall short due to their inability to manage the complexity of inter-service communications. Consequently, in recent years, many methods based on multi-source observability data have been proposed, which promise a comprehensive analysis by integrating logs, metrics, traces. However, despite the promising potential of multi-source methods, we find that existing works often overlook a critical question: whether multi-source data is truly necessary for failure diagnosis. To address this, we conduct a systematic evaluation of eleven representative failure diagnosis methods based on multi-source observability data, using three public datasets and our own curated dataset. Our experiments focus on multiple aspects of multi-source datasets and methods. The results indicate that the quality of existing open-source datasets is inconsistent, and not all studied methods consistently perform well. Surprisingly, we find that adding more data sources does not necessarily improve the performance of microservice failure diagnosis in some cases.

Microservice systems are inherently complex and prone to failures, which can significantly impact user experience. Existing diagnostic approaches based on single-modal data such as logs, metrics, or traces cannot comprehensively capture failure patterns. For those multimodal data-based failure diagnosis methods, the dominant modality can overshadow others, hindering low-yield modalities from fully leveraging their characteristics. This paper proposes Medicine, a modal-independent microservice failure diagnosis framework based on multimodal adaptive optimization. It encodes different modalities separately to retain their unique features and employs adaptive optimization to adjust the learning pace between modalities, thereby enhancing overall diagnostic performance. Experimental results demonstrate that Medicine outperforms existing single-modal and multimodal diagnostic approaches on three public datasets, with F1-score improving by 15.72% to 70.84%. Even in cases where individual modal data is missing or of lower quality, Medicine maintains high diagnostic accuracy.