Traditional Named Entity Recognition (NER) models are typically designed for domain-specific datasets and limited to fixed predefined types, resulting in difficulty generalizing to new domains. Recently, prompt-based generative methods attempt to mitigate this constraint by training models jointly on diverse datasets and extract specified entities via prompt instructions. However, due to autoregressive structure, these methods cannot directly model entity span and suffer from slow sequential decoding. To address these issues, we propose a novel Span-based Unified NER framework via contrastive learning (SUNER), which aligns text span and entity type representations in a shared semantic space to extract entities in parallel. Specifically, we first extract mention spans without considering entity types to better generalize across datasets. Then, by leveraging the power of contrastive learning and well-designed entity marker structure, we map candidate spans and their textual type descriptions into the same vector representation space to differentiate entities across domains. Extensive experiments on both supervised and zero/few-shot settings demonstrate that proposed SUNER model achieves better performance and higher efficiency than previous state-of-the-art unified NER models.