Implementing Inverted Indexes in ClickHouse for Fast Search (Part 2)

Inverted Indexes in ClickHouse

Table of Contents

Introduction

Inverted indexes are a common technique used in search engines and database systems to quickly search for and retrieve data. In ClickHouse, inverted indexes are implemented using a combination of algorithms and data structures.

At a high level, an inverted index is a mapping from values in a column to the rows that contain those values. For example, if a column contains the values [“apple”, “banana”, “cherry”], the inverted index would map “apple” to the rows that contain “apple”, “banana” to the rows that contain “banana”, and so on.

In ClickHouse, inverted indexes are implemented using a combination of hash tables, bitmaps, and skip lists. Here is an overview of the algorithms and data structures used to implement inverted indexes in ClickHouse:

  1. Hash tables: ClickHouse uses hash tables to efficiently map values in a column to the rows that contain those values. Hash tables provide constant-time lookup of values, which is essential for efficient search operations.
  2. Bitmaps: ClickHouse uses bitmaps to efficiently store and manipulate sets of rows. Bitmaps are essentially arrays of bits, where each bit represents a row. By using bit operations, ClickHouse can efficiently perform set operations such as AND, OR, and XOR, which are used to combine the bitmaps of multiple values.
  3. Skip lists: ClickHouse uses skip lists to efficiently store and retrieve row numbers associated with each value in the inverted index. Skip lists are a type of linked list that use multiple levels of pointers to skip over elements, reducing the number of comparisons required to find a particular element.

Here is a more detailed explanation of how inverted indexes are implemented internally in ClickHouse:

  1. Preprocessing: Before an inverted index can be created, the data in the column must be sorted and partitioned. ClickHouse uses a radix sort algorithm to sort the data and a hash function to partition the data into separate buckets.
  2. Building the inverted index: ClickHouse uses a hash table to map each value in the column to a set of rows that contain that value. For each value in the column, ClickHouse computes a hash value and uses it as an index into the hash table. Each entry in the hash table contains a bitmap that represents the rows that contain that value.
  3. Storing the inverted index: ClickHouse stores the inverted index as a collection of bitmaps and skip lists. Each bitmap represents a set of rows that contain a particular value, and each skip list maps a value to the rows that contain that value.
  4. Querying the inverted index: When a query is executed, ClickHouse first looks up the bitmap associated with the value being searched. ClickHouse then uses bit operations to combine the bitmaps of multiple values, such as OR, AND, and XOR, to efficiently retrieve the set of rows that match the query.

Conclusion

Overall, the combination of hash tables, bitmaps, and skip lists allows ClickHouse to efficiently implement inverted indexes, providing fast and efficient search operations. By using these algorithms and data structures, ClickHouse is able to process large datasets and complex queries quickly and efficiently.

To know more about Inverted Index Search, do read the following article:

About Shiv Iyer 215 Articles
Open Source Database Systems Engineer with a deep understanding of Optimizer Internals, Performance Engineering, Scalability and Data SRE. Shiv currently is the Founder, Investor, Board Member and CEO of multiple Database Systems Infrastructure Operations companies in the Transaction Processing Computing and ColumnStores ecosystem. He is also a frequent speaker in open source software conferences globally.