Understanding Cassandra Consistency Levels: A Deep Dive

Understanding Cassandra Consistency Levels: A Deep Dive. Cassandra is a distributed and highly scalable database designed to handle huge volumes of data. One of Cassandra’s core features is its configurable consistency which allows you to achieve fast read and write speeds alongside high availability. 

Consistency refers to the degree to which all replicas of a data row are synchronized across the distributed network.  It is one of Cassandra’s best practices that enables you to achieve high availability. Cassandra has various consistency levels ranging from ONE to ALL. These levels dictate how many replica nodes must acknowledge a read or write operation for it to be considered successful.

This article discusses the concept of consistency levels in Cassandra. Read on!

How Cassandra Performs Write Operations

Initiating Write Operation at the Coordinator Node

The process begins when a write query is initiated and sent to a coordinator node. This node is typically one of the Cassandra nodes that the client communicates with directly. The coordinator node identifies the partition key from the write query. It also determines which nodes in the cluster act as replicas for this partition.

The node then checks the availability of the required replicas based on the specified consistency level for the write operation. If there are not enough available replicas to meet the consistency level, the operation returns an error.

Request Forwarding to Replicas

Once the coordinator node identifies the partition and the corresponding replica nodes, it forwards the write request to all these replicas. In a multi datacenter cluster, the coordinator node also forwards the write request to coordinators in other datacenters. This ensures that the write is replicated across all geographical locations as required.

Replica Acknowledgment

The replica nodes that are “alive” or available receive the data and store it. Cassandra then waits for acknowledgments from these replicas. Nodes that are currently “dead” or unavailable miss the write operation. However, Cassandra employs various anti-entropy mechanisms to ensure data consistency across the cluster. These mechanisms include:

  • Hinted handoff: Cassandra temporarily stores data for the unavailable replica on another node. This node later passes this data to the intended replica once it becomes available.
  • Read repair: During subsequent read operations, it detects and corrects discrepancies between replicas.
  • Anti-entropy repair: This is a routine background process that reconciles differences across replicas to maintain data consistency.

Client Acknowledgment

Once the coordinator node receives acknowledgments from a sufficient number of replicas as dictated by the consistency level (e.g., ONE, QUORUM), it sends an acknowledgment back to the client, indicating that the write operation has been successfully completed. Basically, the write operation involves careful coordination between the coordinator node and the replicas. There are robust mechanisms to handle node failures and ensure data consistency.

How Cassandra Handles Read Operations in Cassandra

Initiating Read Query to the Coordinator Node

The process starts with a read query initiated and directed to a coordinator node within the Cassandra cluster. Plays a central role in managing the read operation. The coordinator node identifies the partition key included in the query to determine the relevant partition. Uses the key to identify the replica nodes responsible for that partition’s data. If the coordinator node itself is not one of the replicas for the requested data, it forwards the query to the nearest replica node.

Digesting Requests

Cassandra employs a concept known as digest requests during read operations. This involves the coordinator node asking the replica nodes to return a digest (a hash) of the requested data, rather than the data itself. This efficiently verifies the consistency of the data across replicas without transferring the entire data set. 

Data Comparison and Read Repair

The coordinator node then compares the digests returned from the different replicas. Consistent digests indicate that all replicas have the same data. Then, the coordinator retrieves the actual data from the fastest responding replica to fulfill the client’s read request. If the digests are not consistent, it indicates a discrepancy in the data among replicas. The coordinator node performs a process known as read repair. This involves resolving the inconsistencies by updating the out-of-date replicas with the most recent data version. This ensures data consistency across the cluster.

The necessity and frequency of read repairs depend on the chosen consistency level for the read operation. Higher consistency levels (like ALL or QUORUM) require more replicas to respond with consistent data, thereby increasing the likelihood of performing read repairs. Through the use of digest requests and read repairs, Cassandra maintains data accuracy.

Write and Read Consistency Levels in Cassandra

The configurable consistency levels allow users to achieve diverse application requirements. They determine how Cassandra replicates and retrieves data across its distributed architecture. Here is a comprehensive explanation of how each level functions and how to best utilize it for write and read operations:

1. ALL

Write: When using the ALL consistency level for writes, every replica node in the cluster must acknowledge the write operation for it to be successful. This ensures the highest level of data accuracy, as every replica holds the latest data. Ideal for critical data. 

Read: For reads, the ALL level requires all replicas to respond with the requested data. Guarantees that the most recent write is read, reflecting all updates from all replicas. It provides the strongest consistency and upto date data. However, it also leads to high latency and reduced availability, especially if any replica is unresponsive.


Write: QUORUM for write operations requires a majority of the replica nodes across all datacenters to acknowledge the write. This level strikes a balance between strong consistency and availability. Besides, it is less stringent than ALL, yet it ensures that a write is replicated to more than half of the nodes. This makes it a commonly used default setting.

Read: In read operations, QUORUM also demands a response from a majority of the replicas. This level ensures that the data read is consistent with more than half of the replicas. By doing so, it reduces the likelihood of reading stale data. Like writes, it offers a good balance for applications that need a strong consistency guarantee, without major sacrifices in performance.


Write: LOCAL_QUORUM for writes requires acknowledgment from a majority of replicas within the same data center as the coordinator node. Effective in reducing the latency associated with writes in multi-datacenter configurations by confining the quorum requirement to the local datacenter.

Read: For reads, LOCAL_QUORUM works similarly by requiring a majority of replicas within the coordinator’s datacenter to respond. Ideal for read-intensive applications that prioritize local consistency and speed over global data accuracy. Besides, the LOCAL_QUORUM helps avoid delays caused by datacenter intercommunication.


Write (ONE, TWO, THREE): Indicate the minimum number of replicas that must acknowledge a write. ONE ensures the highest availability but the lowest consistency, as it only requires a single acknowledgment. TWO and THREE increase consistency by requiring more replicas to acknowledge. However, this comes at the cost of reduced availability.

Read (ONE, TWO, THREE): Similarly, for read operations, ONE returns the data from the first replica to respond, which can be the fastest but may result in stale data. TWO and THREE require responses from more replicas. In essence, this enhances the likelihood of reading upto date data at the expense of increased latency.


Write: LOCAL_ONE is generally not used for writes as it does not make sense to limit write acknowledgments to the local datacenter while the data is being replicated across all datacenters.

Read: LOCAL_ONE is used for read operations and requires a response from the closest replica in the local datacenter. Offers quick and efficient reads with the potential for stale data. Suitable for non critical information where read speed is more important than accuracy.


Specific to read operations and are used in lightweight transactions. SERIAL ensures that a read reflects the most recent write across the entire cluster, including in-progress transactions. LOCAL_SERIAL confines this to the local datacenter, reducing latency at the cost of global consistency. These levels are crucial for applications relying on strong consistency for operations like conditional updates.

7. ANY

Write: The ANY consistency level for write operations in Cassandra is unique in that it requires acknowledgment from just one replica node. If no replicas are available, it only requires a hint of the replica. This level ensures the highest possible availability for write operations. Ideally, a write succeeds even in extreme scenarios where all designated replicas are down. 

The data is temporarily stored on a live node and later forwarded to the appropriate replicas when they become available. Best, where the ability to write data is critical and cannot be hindered by node failures or network issues. Downside is a significant reduction in consistency. This is because there’s no immediate guarantee that the data is replicated across the cluster.

Read: The ANY consistency level does not apply t0 read operations in Cassandra. For reading data, Cassandra offers other consistency levels like ONE, QUORUM, or ALL. Each of these provides different guarantees about the freshness and accuracy of the data. 

How to Calculate Quorum Levels

Calculating quorum levels helps determine the required number of acknowledgments for read and write operations to be considered successful. This applies under the QUORUM and LOCAL_QUORUM consistency levels. This calculation ensures a majority consensus, which is key to finding a balance between consistency and availability.

The QUORUM level is calculated based on the replication factor (RF) of the data. The replication factor represents the number of replicas across the cluster that store the same data.

The formula for quorum is:

					Quorum = (RF / 2) + 1

Ensures that more than half of the replicas must agree for an operation to be considered successful. Examples:

  1. RF = 3: In a cluster with a replication factor of 3, the quorum would be (3 / 2) + 1 = 2. This means at least 2 replicas must respond for a QUORUM operation to succeed.

2. RF = 5: With a replication factor of 5, the quorum would be (5 / 2) + 1 = 3. At least 3 replicas are needed for successful operations under the QUORUM level.

The formula for Local Quorum is:

					Local Quorum = (RF_local / 2) + 1

RF_local  is the replication factor within the local datacenter. LOCAL_QUORUM ensures that more than half of the replicas in the local datacenter acknowledge a read or write operation. Reduces latency involved in inter-datacenter communication. It also provides strong consistency within the datacenter. 

In clusters with multiple datacenters, it’s important to consider the replication factor in each datacenter independently when calculating LOCAL_QUORUM. The calculated quorum value is always rounded down to the nearest whole number if the result is a decimal.

Strong Consistency

Strong consistency refers to a state where all read operations return the most recent write for a given piece of data. Crucial in scenarios where it’s essential to have the most up-to-date and accurate data. Every read operation retrieves the latest written data. It ensures data accuracy and consistency across all nodes in the cluster. To achieving strong consistency in Cassandra, you have to make specific configurations for read and write operations.

Some of the configurations to achieve strong consistency include Write ALL and Read ONE. This is because all nodes have the latest data after a write operation. In QUORUM levels, strong consistency is achieved when the sum of read (R) and write (W) consistency levels is greater than the replication factor (RF). The formula, R + W > RF, guarantees overlap between read and write operations on at least one node. 

However, implementing strong consistency in Cassandra may have negative impacts. While it guarantees the most upto date data, it can lead to increased latency and reduced availability. For instance, using a write consistency level of ALL may cause write operations to fail if any single node is down. The failure negatively impacts the system’s availability. Therefore, before using strong consistency, you should consider the specific needs of the application. Ideally, a balance between having the most recent data against the potential impact on performance and availability.

Understanding Cassandra Consistency Levels: A Deep Dive Conclusion

The Apache Cassandra architecture provides a flexible framework for managing data consistency across distributed systems. By properly configuring the consistency levels for both read and write operations, you customize Cassandra to suit a wide range of application requirements. The key to leveraging Cassandra effectively lies in balancing the trade offs between data accuracy, system availability, and performance. 

Strong consistency ensures you have up to date data, however, it may impact system responsiveness and availability. Lower consistency levels can enhance performance and availability. However, this comes at the risk of serving stale data. Therefore, you should choose consistency levels that align with the specific needs and goals of the application.

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Dennis Muvaa

Dennis is an expert content writer and SEO strategist in cloud technologies such as AWS, Azure, and GCP. He's also experienced in cybersecurity, big data, and AI.

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