Serverless computing has emerged as a flexible model for deploying applications in multi-tenant environments, where small, isolated functions often share the same host and compete for local resources. This co-location can lead to resource contention, making performance isolation a fundamental challenge, particularly given the variability of workloads, infrastructure, and fine-grained resource sharing. Although existing surveys address performance isolation in cloud systems, they do not account for the unique characteristics of serverless computing, such as cold starts, fine-grained scaling, and function-level isolation. Therefore, in this paper, we provide insights into state-of-the-art methods that deal with the challenges of performance isolation for serverless functions. The selected approaches are evaluated based on multiple criteria, including the technique used to achieve isolation, the virtualization level at which isolation is enforced, the decision-making approach, and the primary isolation technique. We analyze and classify existing performance isolation techniques, organizing them into runtime, provisioning, and hybrid approaches. Building on this classification, we outline performance and reliability engineering mechanisms applicable to serverless computing that isolate functions while addressing serverless-specific challenges. Our findings show that i) Isolation is often treated as a secondary goal, primarily to reduce latency or SLO violations, rather than a primary objective. ii) Existing solutions frequently focus on CPU or memory contention while overlooking other critical shared components, such as the network, and iii) offer limited ways to tune the trade-off between isolation and performance, iv) unpredictability of serverless functions is the main challenge to performance isolation, v) novel metrics are required to monitor and quantify performance interference. Addressing these gaps will require more comprehensive and adaptive hybrids that unify multiple aspects of performance isolation. By consolidating and structuring these techniques in the context of serverless computing, this paper lays the foundation for developing resilient, efficient, and interference-tolerant serverless platforms.