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Consistency levels in Cassandra
Developed to manage massive volumes of data across commodity servers, Apache Cassandra is a distributed, highly scalable NoSQL database management system. Cassandra's configurable consistency is one of its core characteristics, allowing users to combine data consistency with speed and availability. In this post, we will go through the various consistency levels in Cassandra and present examples of how to use them.
A consistency level in Cassandra is the number of replicas that react before providing a response to a user. Cassandra's consistency is configurable, which means that any client may decide how much consistency and availability they want. It is also allocated at the query level and can be modified for various service components. Users can specify several levels of consistency for each operation, both reads and writes. You should grasp the tradeoff between consistency and availability when selecting the consistency level for your business. Cassandra's constancy might be powerful or weak depending on your chosen level.
The strong consistency formula is R+W>RF, where R stands for read consistency, W stands for write consistency, and RF stands for replication factor. Consistency is deemed weak if R+W <RF.
Cassandra offers five consistency levels
Consistency Level ONE − For the read or write operation to be regarded successful, just one node needed to recognize it. This category offers the least amount of consistency guarantee but the most availability and performance. This level is appropriate for applications where data integrity is not crucial, such as tracking user activity.
Example − Administration of User Profiles
Consider a web application that enables users to edit and maintain their profiles. As it is not necessary for all nodes to concur on the user profile data in this situation, we may choose Consistency Level ONE. The application may instantly access user profile information from any node with Consistency Level ONE, enhancing performance and availability.
Consistency Level TWO − For the read or write operation to be regarded as successful, two nodes must recognize it. This level promises greater consistency than Consistency Level ONE while providing adequate availability and performance. This level is appropriate for use cases requiring some amount of data consistency, such as managing user preferences.
Example − A genuine instance of utilizing consistency level 2 (CL_TWO) in Cassandra could be a virtual entertainment stage where clients can post and view content.
Let's say a user writes a post to the Cassandra cluster with a consistency level of 2 and writes it there. Cassandra will send the post data to two nodes in this scenario and wait for an acknowledgement from at least one of them. The user will immediately be able to view the post if the write is successful.
Presently, assume one more client attempts to see the post. The read activity will likewise be started with consistency level 2, and Cassandra will peruse the post information from two hubs and return the latest rendition of the information that it peruses. In the event that the two hubs return various variants of the information, Cassandra will utilize the latest form and dispose of the more seasoned one. As a result, the user will be able to see the most recent post to at least one of the two nodes.
Consistency Level Three − For a read or write operation to be deemed successful at this consistency level, three nodes must confirm it. Even more consistency assurances than Consistency Level Two are provided at this level, albeit at the penalty of decreased availability and performance. For use situations where data consistency is essential, such as financial transactions, this level is appropriate.
Example − A financial trading application where transactions are being processed might be a real-world application of Cassandra's consistency level 3 (CL_THREE).
Let's say a user initiates a transaction, which must be written to the Cassandra cluster at consistency level 3 in order to be successful. Cassandra will write the transaction data to three nodes in this scenario and wait for at least two of them to acknowledge it. By requiring affirmations from no less than two hubs, CL_THREE guarantees that the compose activity is sturdy and that the information is accessible even on account of a hub disappointment or organization segment.
Now, let's say that a different user tries to view the transaction. Cassandra will read the transaction data from three nodes and return the most recent version of the data that it reads as part of the read operation, which will also be initiated with consistency level 3. In the event that the three hubs return various renditions of the information, Cassandra will utilize the latest form and dispose of the more established ones. As a result, the most recent and accurate version of the transaction data will be accessible to the user.
Consistency Level QUORUM − For a read or write operation to be deemed successful, a quorum of nodes (n/2 + 1), where n is the total number of nodes in the cluster, must recognize it. Compared to Consistency Level THREE, this level offers higher consistency assurances and improved availability and performance. This level is appropriate for use cases that demand robust data consistency, for example in inventory management.
Example − Financial transactions in a Banking application
Let's say we have a banking application that enables account transfers for users. To guarantee that the transaction is successfully completed in this instance, we require high data consistency. We can employ Consistency Level QUORUM, which necessitates the consensus of a quorum of nodes in order for a transaction to be deemed successful. We can guarantee great data consistency with Consistency Level QUORUM while preserving good speed and availability.
Consistency Level ALL − In order for a read or write operation to be deemed successful, all cluster nodes must acknowledge it. This level offers the strongest consistency guarantees, but performance and availability are sacrificed. This level is appropriate for use cases when maintaining data integrity, for example, calls for absolute consistency in data.
Example − Let's say we have a medical application that keeps track of patient information. Data integrity is crucial in this situation, and we cannot risk losing or allowing any data to become tainted. By using Consistency Level ALL, we can make sure that before the cluster is deemed successful, all of the nodes in it concur on the data. We can guarantee data integrity with Consistency Level ALL but at the sacrifice of performance and availability.
Conclusion
Data consistency, availability, and performance may be balanced with Cassandra's adjustable consistency levels. Depending on the particular use case and the significance of data consistency, the appropriate consistency level must be chosen. While a greater consistency level can be used to provide robust data consistency for key use cases, a lower consistency level can be utilized for non-critical use cases to improve performance and availability. While selecting a consistency level, it is crucial to comprehend the trade-offs between consistency, availability, and performance. With proper planning, developers may create scalable and dependable distributed applications by taking into account Cassandra's consistency levels.
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