Graphical analysis of computer log files
Communications of the ACM
LISA '02 Proceedings of the 16th USENIX conference on System administration
Towards informatic analysis of syslogs
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
What Supercomputers Say: A Study of Five System Logs
DSN '07 Proceedings of the 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
From dirt to shovels: fully automatic tool generation from ad hoc data
Proceedings of the 35th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Analyzing system logs: a new view of what's important
SYSML'07 Proceedings of the 2nd USENIX workshop on Tackling computer systems problems with machine learning techniques
An automated approach for abstracting execution logs to execution events
Journal of Software Maintenance and Evolution: Research and Practice - Special Issue on Program Comprehension through Dynamic Analysis (PCODA)
Understanding customer problem troubleshooting from storage system logs
FAST '09 Proccedings of the 7th conference on File and storage technologies
Clustering event logs using iterative partitioning
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Logging Approach for Effective Dependability Evaluation of Complex Systems
DEPEND '09 Proceedings of the 2009 Second International Conference on Dependability
One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
SALSA: analyzing logs as state machines
WASL'08 Proceedings of the First USENIX conference on Analysis of system logs
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Application console logs are a ubiquitous tool for diagnosing system failures and anomalies. While several techniques exist to interpret logs, describing and assessing log quality remains relatively unexplored. In this paper, we describe an abstract graphical representation of console logs called the identifier graph and a visualization based on this representation. Our representation breaks logs into message types and identifier fields and shows the interrelation between the two. We describe two applications of this visualization. We apply it to Hadoop logs from two different deployments, showing that we capture important properties of Hadoop's logging as well as relevant differences between the two sites. We also apply our technique to logs from two other systems under development. We show that our representation helps highlight flaws in the underlying application logging.