Consistency Levels in Apache Cassandra: Guaranteeing Reliability

Apache Cassandra is a distributed, NoSQL database that is open-source, highly scalable, and available. It is capable of handling humongous amounts of structured, semi-structured, and unstructured data. It is a wide-column store database that stores and organizes data into flexible columns spread across multiple database nodes or servers.

Cassandra heavily relies on data replication across clusters to achieve high availability and reliability. Hence, it becomes essential to ensure that all the replicas of data across multiple nodes have the same value to maintain integrity. This is where the role of consistency comes into play.

This article aims to introduce you to the concept of consistency in Apache Cassandra. But before that let us understand the term data replication.

What is Data Replication? 

Apache Cassandra data replication refers to duplicating data (each row) and storing it on multiple nodes or servers. It also involves replicating ongoing transactions so that all the replicas are up-to-date and synchronized with the source.

This significantly improves data availability and ensures high reliability and fault tolerance. If any of the database nodes fail due to a certain reason, replication ensures that the same data is available on other nodes. With data replication, users can access data related to their tasks seamlessly without interfering with others’ work.

Replication Factor (RF): It is the number of nodes across a cluster that stores the replica of data. For instance, if the replication factor is 2, the copy of each row is present on two different nodes of a cluster. The replication factor is always equal to or less than the number of nodes in a cluster.

Apache Cassandra Replication Strategies

• SimpleStrategy: It is intended for a single data center and one rack topology. The practitioner logic determines the node where the first replica is placed. Other replicas are stored in further nodes clockwise in a ring.

• NetworkTopologyStrategy: It is ideal for multiple data centers and multiple racks. You can use it when you have a cluster deployed across multiple data centers. Plus, you can specify how many replicas you want in each data center. NetworkTopologyStrategy aims to place replicas on different racks, as nodes in the same rack fail simultaneously due to power loss, network issues, etc.

What is Consistency in Apache Cassandra? 

Consistency in Apache Cassandra implies how recent and in sync all replicas of a row of data are. It is tunable or configurable, allowing users to balance availability, consistency, and partition tolerance based on an application’s needs. In most cases, Cassandra depends on eventual consistency.

Apache Cassandra Eventual Consistency: It ensures that the update to data will propagate through the system and eventually be applied to all nodes in a cluster.

A consistency level in Apache Cassandra is the number of Cassandra nodes that must acknowledge read/write operations before considering an operation successful. It is used to determine data consistency versus data availability for transactions during the CQL shell session.

CQL Shell: It is a Python-based command line client for executing CQL (Cassandra Query Language) commands.

Any distributed system can guarantee consistency, availability, and partition tolerance, according to the Apache Cassandra CAM theorem.

• Consistency: Each node in a cluster contains the same data at the same time.
• Availability: Every time, at least one node in a cluster must be available to serve data.
• Partition Tolerance: The system fails rarely.

However, Cassandra primarily prioritizes availability and partition tolerance over consistency. As mentioned above, you can tune consistency with the replication factor and consistency level.

Different Consistency Levels in Apache Cassandra 

It is possible to choose a different consistency level for different operations, such as read and write operations. However, while choosing the consistency level for each operation, you must understand the level’s balance between consistency and availability.

The level of consistency you choose determines the consistency’s strength.

Strong Consistency: R+W>RF

Weak Consistency: R+W<RF

where,

R = Consistency for reads

W = Consistency for writes

RF = Replication factor

Before we jump on to discussing consistency levels, it is important to understand three terms related to read/write transactions.

• Commit log: Every write operation is written to the commit log. It is Cassandra’s crash-recovery mechanism.
• Mem-table: It is a memory-resident data structure where the data is written after the commit log.
• SSTable: The data from the mem-table is flushed into SSTable (a disk file) when its content reaches a threshold value.

Consistency Levels On Write

The consistency level for write operations specifies the number of replica nodes that must respond to write so that it is considered successful and reported the same to the client.

Note: It is important to note that the consistency level (the number of nodes for acknowledgment) and the replication factor (the number of stores having replicas) are different.

For instance, consider the consistency level ONE and RF = 3. It requires only one replica node to acknowledge the successful write operation. Still, Cassandra replicates the data to two other nodes.

The following are the consistency levels available for write operations:

• ONE: Requires acknowledgment from only one replica node. Hence, the write operation is the fastest in this case.
• TWO: Requires acknowledgment from two replica nodes.
• THREE: Requires acknowledgment from three replica nodes.
• QUORUM: Needs acknowledgment from 51% of replica nodes across all data centers.
• LOCAL_QUORUM: Needs acknowledgment from 51% of replica nodes from the same data center as a coordinator. This ensures low latency, as there is no inter-data center communication. 
• ALL: Requires acknowledgment from all replica nodes. As a result, the write operation is the slowest. In addition, even if a single replica node is down, the write operation fails, affecting availability. So, it is not recommended to use this consistency level in production deployment.

Consistency Levels On Read

The consistency level for read operations indicated the number of replica nodes that must respond to the read request before considering it valid for reading.

Here are different consistency levels applicable to read operations:

• ONE: Only one replica node returns data. Consequently, the data retrieval operation is the fastest.
• TWO: Two replica nodes return data.
• THREE: Three replica nodes return data.
• QUORUM: 51% of replica nodes across all data centers respond to the coordinator node to return data to the client.
• LOCAL_QUORUM: 51% of replica nodes within the same data center respond to the coordinator to return data to the client.
• ALL: All replica nodes respond, and the coordinator returns data to the client.

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Conclusion 

Consistency in Apache Cassandra plays a vital role in maintaining data integrity, as data is replicated across multiple nodes in a cluster. Without consistency, different nodes will have different versions of data, resulting in incoherence and potential issues. However, Apache Cassandra gives more importance to availability and partition tolerance. You can manage consistency with the consistency level. It helps you manage a perfect balance between consistency, availability, and partition tolerance.