  Learnable embedding vector is one of the most important applications in
machine learning, and is widely used in various database-related domains.
However, the high dimensionality of sparse data in recommendation tasks and the
huge volume of corpus in retrieval-related tasks lead to a large memory
consumption of the embedding table, which poses a great challenge to the
training and deployment of models. Recent research has proposed various methods
to compress the embeddings at the cost of a slight decrease in model quality or
the introduction of other overheads. Nevertheless, the relative performance of
these methods remains unclear. Existing experimental comparisons only cover a
subset of these methods and focus on limited metrics. In this paper, we perform
a comprehensive comparative analysis and experimental evaluation of embedding
compression. We introduce a new taxonomy that categorizes these techniques
based on their characteristics and methodologies, and further develop a modular
benchmarking framework that integrates 14 representative methods. Under a
uniform test environment, our benchmark fairly evaluates each approach,
presents their strengths and weaknesses under different memory budgets, and
recommends the best method based on the use case. In addition to providing
useful guidelines, our study also uncovers the limitations of current methods
and suggests potential directions for future research.
