Comparing B-Tree and LSM Index Structures: Pros, Cons, and Use Cases

Comparing B-Tree and LSM Index Structures: Pros, Cons, and Use Cases

Indexes are crucial in the realm of databases for effective data processing and retrieval. An index is a type of data structure that arranges data to make it easier to search, sort, and retrieve information quickly based on certain criteria, like a single value or a range of values. Two of the most popular index structures are B-Tree and Log-Structured Merge Tree (LSM).

A balanced tree structure known as a B-Tree arranges data in a hierarchical fashion to facilitate effective searching, data insertion, and data deletion. LSM, on the other hand, is a disk-based index structure that employs a different method for storing and retrieving data and is geared for workloads that involve a lot of writing.

We will contrast the B-Tree and LSM index structures in this blog, outlining their benefits and drawbacks while also giving instances of databases that make use of each index structure. We'll also look into CockroachDB's decision to employ a hybrid index structure that combines B-Tree and LSM. CockroachDB is a well-known distributed database. By the time you've finished reading this blog, you'll know more about the advantages and disadvantages of each index structure as well as what to look for when selecting the best index structure for your database system.

B-Tree Index

The balanced tree data structure known as the B-Tree is frequently used to index data in databases. The B-Tree is a type of self-balancing tree, which means that when new data is added or withdrawn, the tree will automatically restructure. Data may be efficiently retrieved using range searches, point queries, and sorting thanks to the B-Tree index, which is created depending on the values of a certain column.


  • B-Tree indexes are excellent for use cases involving a mixture of read and write operations since they are very efficient for both read and write operations.
  • B-Tree indexes are a suitable option for huge datasets since they can manage a lot of keys efficiently.
  • B-Tree indexes are helpful for query optimisation since they may be used to sort data.


  • B-Tree indexes require more space than other types of indexes, which can be a concern for databases with limited storage.
  • B-Tree indexes are not as efficient for write-heavy workloads, as every update to the index requires a disk write operation, making them slower than LSM indexes in such scenarios.
  • B-Tree indexes are not as scalable as other index structures, as the performance of the index structure degrades as the number of keys increases.

Examples of Databases that us B Tree Index

  • MySQL: MySQL uses B-Tree indexes to index columns in tables.
  • PostgreSQL: PostgreSQL uses B-Tree indexes as its default indexing mechanism for all data types.
  • Oracle: Oracle also uses B-Tree indexes as its default indexing mechanism for all data types.

Summarising, B tree Indexes are excellent for workloads that need a lot of reading, sorting data, and situations where a healthy balance of reading and writing is anticipated. They are suited for the majority of use cases and are frequently employed in conventional database management systems.

LSM - Log Structured Merge

Databases frequently employ the alternate index structure known as the Log-Structured Merge Tree (LSM). Data is organised differently by LSM than by B-Tree, and it is retrieved and processed in a different way. LSM structures are made up of two or more layers, each of which stores data in a distinct format to be optimised for a particular kind of query.

LSM indexes are designed for write-intensive workloads, where data is often changed or added and the database needs to be optimised for write operation performance.


  • LSM indexes are highly efficient for write-heavy workloads as they minimize the number of disk writes and provide better performance than B-Tree indexes in such scenarios.
  • LSM indexes require less space than B-Tree indexes as they don't require constant reorganization of data.
  • LSM indexes are more scalable than B-Tree indexes, as they can handle an increasing number of keys efficiently.


  • LSM indexes can be slower than B-Tree indexes for read-heavy workloads or point queries since the index structure requires data to be merged from multiple layers before data can be retrieved.
  • LSM indexes can result in more disk I/O operations during read operations, leading to increased disk usage and potential disk fragmentation.
  • LSM indexes are not well-suited for sorting data, as the merge process can be slow and result in performance degradation.

Examples of Databases that us B Tree Index

  • Cassandra: Cassandra uses LSM indexes as its primary indexing mechanism.
  • RocksDB: RocksDB is a key-value database that uses LSM indexes to store and retrieve data.

Summarising, LSM indexes are ideal for write-heavy workloads, scenarios where disk space is a concern, and situations where scalability is critical. They are commonly used in distributed systems and can provide significant performance improvements over B-Tree indexes in certain use cases.

CockroachDB - Which one does it use?

A distributed SQL database called CockroachDB employs a hybrid index structure that combines LSM and B-Tree indexes to offer the advantages of both index structures. The hybrid index structure is intended to offer read-heavy workloads the efficiency and scalability of B-Tree indexes while still offering the performance advantages of LSM indexes for write-heavy workloads.

A B-Tree index for point queries and short range queries, and an LSM index for big range queries and write-intensive workloads, make up the hybrid index structure of CockroachDB. Smaller data ranges are maintained by the B-Tree index, and wider data ranges are maintained by the LSM index.


  • For use cases that involve a combination of read and write operations, the hybrid index structure in CockroachDB offers the advantages of both B-Tree and LSM indexes.
  • The hybrid index structure is very scalable and effective at handling huge datasets.
  • With its hybrid index structure, CockroachDB is the perfect solution for use scenarios when disc capacity is a concern.


  • The hybrid index structure in CockroachDB can be more complex to manage and maintain than a single index structure.
  • The hybrid index structure can result in higher write latencies compared to an LSM-only index structure for write-heavy workloads.

The CockroachDB hybrid index structure is made to offer the advantages of both B-Tree and LSM indexes, making it suitable for a wide range of use cases. It can effectively handle huge datasets and strikes a fair mix between read scalability and write efficiency. The hybrid index structure in CockroachDB is a great illustration of how a database system can greatly benefit from integrating several index structures.