Elasticsearch, a widely-used search and analytics engine, can crash due to various user-induced pitfalls that often stem from misunderstandings of its architecture and operations. Common issues include mapping explosions caused by excessively dynamic fields leading to memory overutilization, the problem of excessive shards overwhelming system resources, and overly large size parameters in queries that strain internal data structures. Additionally, long-running scripts and overly deep aggregations can monopolize processing threads and memory, respectively, resulting in system unresponsiveness. While some of these issues have been mitigated in newer versions of Elasticsearch, users are advised to implement best practices such as using dedicated clusters, optimizing query parameters, and avoiding ad hoc analytics on production clusters to maintain system integrity and performance. Despite these challenges, Elasticsearch remains a powerful tool, and ongoing improvements aim to enhance its reliability and resource management.