GraphQL's rising popularity has led to the identification of several common anti-patterns in schema design that can impact performance and usability. One prevalent issue is the default nullable fields in GraphQL schemas, which can lead to unexpected null values and frontend errors; this can be addressed by making fields non-nullable based on product needs. Documentation miscommunication can be mitigated by adding descriptive strings in the self-generated docs to clarify field names and types. Another challenge is lengthy arguments in mutations, which can be streamlined using input objects to enhance readability and maintainability. Insufficient mutation responses can necessitate additional queries to update frontend data, but returning modified data within the mutation response itself can resolve this. Invalid inputs can be restricted using custom scalars and enums, ensuring data integrity and reducing redundancy. Circular references pose a risk of infinite depth queries, which can be controlled using utilities like graphql-depth-limit. Finally, massive data responses can crash clients if limits aren't set, so assigning default values for query limits during schema design is crucial. Understanding these anti-patterns and implementing effective solutions is essential for optimizing GraphQL's performance and reliability.