Introduction to the Probabilistic Data Structures

Introduction

In this tutorial, we will discuss probabilistic data structures in detail. This tutorial will cover the meaning of a Probabilistic Data Structure, its types, and its benefits.

When dealing with large data sets or Big Data, basic data structures that use hashtables or HashSets would not be effective enough. As the data size increases, memory requirements increase with limited time for solving a query which restricts the functionality of deterministic basic data structures.

Probabilistic data structures are approximate data structures that are collections of data structures. They are called so because they do not provide exact values. They help deal with large data sets and resolve queries using less time. The results can be approximate or probabilistic (not accurate) with fewer memory requirements.

The three common Probabilistic data structures are Bloom Filters, HyperLogLog, and Count-Min Sketch.

What is a Probabilistic Data Structure

Probabilistic data structures are used to process large data sets by providing approximate answers with a high degree of correctness. They process queries in real-time while maintaining efficiency and memory. The key highlight of the probabilistic data structure is its complex algorithms that consume less memory with real-time processing.

These data structures are efficient enough to solve the operations of large data sets using union and intersection operations. They ignore collisions and control errors within a certain timeframe. These data structures are used in data analytics, big data, network security, streaming applications, and distributed systems.

They are mostly used in areas like approximate nearest neighbor search, approximate set membership testing, distinct element counting, frequency counting, and more like this

Types of Probabilistic Data Structures

There are three types of Probabilistic Data Structures commonly used to process large data sets while using less memory and constant time.

• Bloom filter

  Bloom filter Probabilistic data structures are used to find missing elements in a data set. It is used for approximate set membership testing. It is a m-bit array initialized to zero. The elements of this array are added by inserting them into its k hash functions, which give the position of the k array and set the values of the array.

  To identify or query particular elements in a set or not, use the K hash function. When the bit position is 0 for a particular element that means that element is not in the set. When the bits position is 1 that means a particular element has the possibility of being present in the set.

• HyperLogLog

  It is a probabilistic/streaming data structure that helps find the number of distinct elements in a set. The data set is large and uses only 1.5KB of memory for counting one billion different elements with 2% accuracy.

  HyperLogLog data structure provides reasonable accuracy with controlled memory consumption.

• Count-Min Sketch

  It is a probabilistic streaming data structure that counts the frequency of elements in a stream. The count-min sketch takes O(k) time to determine the frequency of the elements. It uses the ADD operation to perform union operations. This data structure never results in under-counting the elements but can result in over-counting while providing high accuracy.

Benefits of Probabilistic Data Structures

• Memory efficiency

  With the increasing size of data sets the memory requirement also increases and basic data structures with hash structures use large amounts of memory to process queries. Probabilistic data structures use less memory and time to solve problems in streaming data applications.

• Efficient Query Resolution Time

  Probabilistic data structures provide fast query processing. In advanced streaming applications, the time constraint is the primary requirement, and these data structures assist in solving queries with constant or near-constant complexity.

• Process Large Data Set

  Probabilistic data structures can deal with large data sets using fixed memory and limited time. They are useful for streaming data applications and big data.

• Versatility

  Probabilistic data structures are not limited to certain applications. Instead, they are used in a variety of applications like data analytics, databases, networking, distributed systems, and other areas.

• Controlled error rate

  Probabilistic data structures provide approximate results while avoiding collisions and maintaining accuracy. They do not provide accurate results but the estimated results that they provide are accurate and close to zero error.

Drawbacks of Probabilistic Data Structures

• Complexity

  Probabilistic data structures are not as easy to understand as basic data structures. Their complexity is due to algorithms and mathematics. They take more time to understand, leading to debugging problems.

• Probability of error

  These data structures deal with approximate results and do not provide exact values. Sometimes the approximate values are not useful in exact values.

• Limited Functionality

  The functionality of probabilistic data structures is limited to the problems that accept approximate and near-exact values. They fail to work with problems requiring basic data structures.

Deterministic Data Structures Vs Probabilistic Data Structures

There are some differences between Deterministic and Probabilistic data structures and those differences are as follows:

Conclusion

Probabilistic data structures are useful for large data sets and their requirement increases as data sets grow tremendously. These data structures with their powerful algebraic and mathematical properties are used by Google's Guava, Twitter’s Scala library, and Algebird. The efficiency of probabilistic data structures with reduced memory consumption and time is a significant advantage to resolving queries of large data sets.

Sonal Meenu Singh

Updated on: 18-Aug-2023

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