Streaming Algorithms and Bloom Filters

By John Paul Mueller, Luca Massaron

At the heart of many streaming algorithms are Bloom filters. Created almost 50 years ago by Burton H. Bloom, at a time when computer science was still quite young, the original intent of this algorithm’s creator was to trade space (memory) and/or time (complexity) against what he called allowable errors. His original paper is titled Space/Time Trade-offs in Hash Coding with Allowable Errors.

You may wonder about the space and time that Bloom considers motivators for his algorithm. Imagine that you need to determine whether an element has already appeared in a stream using some previously discussed data structure. Finding something in a stream implies recording and searching are fast, thus a hash table seems an ideal choice. Hash tables simply require adding the elements that you want to record and storing them. Recovering an element from a hash table is fast because the hash table uses easily manipulated values to represent the element, rather than the element itself (which could be quite complex). Yet, storing both elements and an index to those elements has limitations. If a hash table faces more elements than it can handle, such as the elements in a continuous and potentially infinite stream, you’ll end up incurring memory problems at some point.

An essential consideration for Bloom filters is that false positives can occur, but false negatives can’t. For example, a data stream might contain real-time monitoring data for a power plant. When using a Bloom filter, the analysis of the data stream would show that expected readings are probably part of the set of allowed readings, with some errors allowed. However, when an error occurs in the system, the same analysis shows that the readings aren’t part of the set of allowed readings. The false positives are unlikely to cause problems, but the absence of false negatives means that everyone remains safe. Because of the potential for false positives, filters such as the Bloom filter are probabilistic data structures — they don’t provide a certain answer but a probable one.

Hashes, the individual entries in a hash table, are fast because they act like the index of a book. You use a hash function to produce the hash; the input is an element containing complex data, and the output is a simple number that acts as an index to that element. A hash function is deterministic because it produces the same number every time you feed it a specific data input. You use the hash to locate the complex information you need. Bloom filters are helpful because they are a frugal way to record traces of many elements without having to store them away as a hash table does. They work in a simple way and use the following as main ingredients:

  • A bit vector: A list of bit elements, where each bit in the element can have a value of 0 or 1. The list is a long number of bits called m. The greater m is, the better, though there are ways of optimally defining its size.
  • A series of hash functions: Each hash function represents a different value. The hash functions can quickly crunch data and produce uniformly distributed results, which are results equally ranging from the minimum to the maximum output values of the hash.