Learned FBF: Learning-Based Functional Bloom Filter for Key-Value Storage

Hayoung Byun, Hyesook Lim

Research output: Contribution to journalArticlepeer-review


As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key--value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used.

Original languageEnglish
JournalIEEE Transactions on Computers
StateAccepted/In press - 2021


  • Data models
  • Data structures
  • deep learning
  • functional Bloom filter
  • Indexes
  • Key-value storage
  • Memory management
  • Neural networks
  • Programming
  • search failure
  • Task analysis


Dive into the research topics of 'Learned FBF: Learning-Based Functional Bloom Filter for Key-Value Storage'. Together they form a unique fingerprint.

Cite this