Apache Cassandra is a highly scalable and distributed NoSQL database that is designed to handle large volumes of data across multiple commodity servers. One of the key features of Cassandra is its ability to automatically manage data distribution, replication, and consistency across a cluster of nodes, providing high availability and fault tolerance.

One important aspect of managing data in Cassandra is compaction. Compaction is the process of merging and removing unnecessary data from SSTables, which are the on-disk storage structures used by Cassandra. Over time, as data is written and deleted, SSTables can become fragmented and inefficient, which can impact performance and consume valuable disk space. Compaction helps to optimize the storage and access of data in Cassandra by merging multiple SSTables into a single, more efficient SSTable.


In Apache Cassandra, an SSTable (Sorted String Table) is the basic on-disk storage structure that holds data for a single partition. An SSTable is an immutable file that contains a sorted set of key-value pairs, where the keys are partition keys and the values are column values. Cassandra stores SSTables on disk and uses them for efficient read and write operations.

SSTables are designed to be compact and efficient. They are sorted by key, which makes them easy to search and retrieve data from. When data is written to Cassandra, it is first written to a commit log, which is an append-only log that records all write operations. Once the write is acknowledged, the data is written to an in-memory data structure called a Memtable. As the Memtable grows, Cassandra flushes it to disk as an SSTable. This process is known as a minor compaction, where multiple small SSTables are merged into a larger SSTable. Cassandra also periodically runs a major compaction, where all SSTables are merged into a single, compacted SSTable.

Consider a simple table that contains two columns - “name” and “age”. Here’s an example of how the data would be stored in an SSTable:

Partition Key: "John"
| Column Name | Column Value |
|    name     |     John     |
|     age     |      35      |

Partition Key: "Jane"
| Column Name | Column Value |
|    name     |     Jane     |
|     age     |      28      |

In this example, the SSTable contains two rows, one for John and one for Jane. Each row contains a partition key and a set of column values. The column names and values are stored as key-value pairs, and are sorted by column name.

SSTables in Cassandra also contain additional metadata to help with efficient reads and writes. For example, each SSTable contains a bloom filter that allows Cassandra to quickly determine whether a key is present in the SSTable or not. Additionally, each SSTable contains an index that allows Cassandra to quickly locate a particular key or range of keys.

SSTables in Cassandra are optimized for efficient reads. When you query Cassandra, it first checks the Memtable for the requested data. If the data is not found in the Memtable, Cassandra searches the SSTables on disk. Since SSTables are sorted by key, Cassandra can quickly locate the data by performing a binary search. Once the data is found, Cassandra reads the entire row from disk and returns the requested data.

SSTables also have built-in support for efficient range queries. Since SSTables are sorted by key, Cassandra can easily retrieve a range of keys by performing a binary search on the first and last keys in the range. Once the range is located, Cassandra reads the corresponding rows from disk and returns the requested data.

One of the benefits of using SSTables in Cassandra is that they are highly resilient to failures. Since SSTables are immutable, once they are written to disk, they cannot be modified. This makes them highly resistant to data corruption and disk failures. In the event of a failure, Cassandra can simply discard the corrupted SSTable and use a replica to restore the data.

In summary, SSTables are the basic on-disk storage structure used by Apache Cassandra to store and retrieve data. They are immutable, compact, and highly optimized for read and write operations. By using SSTables, Cassandra provides a highly scalable and fault-tolerant database solution for large-scale applications.

Compaction Strategies

Cassandra offers several built-in compaction strategies that allow you to control how SSTables are compacted and when they are merged. Let’s take a closer look at some of the most common compaction strategies in Cassandra:

SizeTieredCompactionStrategy SizeTieredCompactionStrategy is the default compaction strategy in Cassandra 2.x and 3.x. It is based on the size of the SSTables and groups SSTables of similar size together to be compacted. This compaction strategy is simple and efficient for write-heavy workloads, but it may not be optimal for read-heavy workloads because data that is frequently accessed may be spread across multiple SSTables, leading to more disk I/O and slower read performance.

LeveledCompactionStrategy LeveledCompactionStrategy is a more advanced compaction strategy that divides SSTables into levels, where each level contains SSTables of a similar size. SSTables are first compacted within their level and then promoted to the next level. This process continues until the highest level, which contains a single SSTable. This strategy is optimal for read-heavy workloads because data that is frequently accessed is more likely to be stored in the highest levels, which leads to faster read performance. However, it can be less efficient for write-heavy workloads because it requires more disk I/O for compaction.

DateTieredCompactionStrategy DateTieredCompactionStrategy is a compaction strategy that is optimized for time-series data. SSTables are grouped by their age, and older SSTables are compacted more frequently than newer SSTables. This strategy is useful for workloads where the most recent data is more important than older data. The base_time_seconds and max_sstable_age_days options control how often SSTables are compacted and how long they are stored.

TimeWindowCompactionStrategy TimeWindowCompactionStrategy is similar to DateTieredCompactionStrategy, but it allows you to specify a time window for data retention. SSTables are compacted based on the time window, and older SSTables are deleted when they are no longer needed. This strategy is useful for workloads where you want to automatically expire old data.


When choosing a compaction strategy in Cassandra, it’s important to consider your specific workload and data access patterns. Each strategy has its own strengths and weaknesses, and the optimal strategy depends on factors such as read/write ratio, data distribution, and retention requirements.

In addition to the built-in compaction strategies, Cassandra also offers options for configuring compaction thresholds, tombstone compaction, and more. These options allow you to fine-tune the compaction process to better suit your needs.

Compaction is a critical aspect of managing data in Apache Cassandra, and understanding how to properly configure and optimize compaction can have a significant impact on your database performance and storage efficiency. By choosing the right compaction strategy and tuning your compaction options