Cassandra DB in Practice

Cassandra is an open-source, NoSQL distributed database capable of handling large volumes of structured and unstructured data. It stores data in the form of columns and allows unlimited column generation to its users. Working according to the peer-to-peer architecture, the Cassandra database ensures extraordinary scalability and protection from unproductive downtimes. Read on to learn how to use Cassandra and its performance in practice.

How Does Cassandra Work?

Cassandra follows a ring-based cluster model that permits the users to connect as many nodes to the cluster as they like. The database supports Masterless replication that enables the nodes to gossip with each other and cover for one another if any of them goes down. In this way, data remains up-to-date and accessible all the time. Apache Cassandra uses a specific language, known as CQL, that can be used to develop tables and generate queries.

As mentioned earlier, the Cassandra database follows a column-oriented model for data storage which brings partition tolerance. Take a look at the following image to know how data is arranged.

Image: Column-oriented Database Model

Here’s an example of how you can make your way around Cassandra DB.

After connecting to the Cassandra server, a keyspace called “training” is created with a replication factor of 3. Next, a table named “movies” is created. Afterward, the columns and their data types are defined.

You can use the keyword “insert” to add data to the table “movies.”

After insertion, you can take a look at all the columns present in the table by using the “select” keyword. A user can also filter the movies by their id.

The update operation can be performed by using the keyword “update” e.g., by changing the year of release to 1996 of the movie id 4. You can delete a row in a table by using the keyword “delete.”

You can take a look at all the keyspaces within a cluster by using a simple command of “DESCRIBE KEYSPACES’.

The above-mentioned steps briefly summarize what sort of measures needs to be taken to function well with Cassandra.

What Makes Apache Cassandra Special?

• Decentralized Architecture; Cassandra does not follow a Master-slave model. It has a decentralized architecture that operates on the peer-to-peer approach. Hence, the system does not get affected if any machine is not working as there is no primary node.
• Quick Sharding; Cassandra is super-fast when it comes to distributing data across multiple data centers. This feature helps its data models achieve tremendous scalability.
• Efficient Fault Detection; Cassandra uses its gossip protocol to identify and assess faults in the system. Gossip history is used to evaluate the communication between different nodes.
• High Scalability; You never lose data with the Cassandra database. If a system gets down partially or a node stops functioning, the data is not lost, and the rest of the system keeps on functioning normally.

Application in Industry

Apache Cassandra is being widely used across different industries to enhance performance and productivity. They are:

· Product catalog purpose (For example; Spotify)

· Fraud Detection (For example; Simility)

· Data storage (For example, transaction data storage by Netflix, eBay, CERN, etc.)

· Generating Recommendations (For example; Eventbrite)

· Messaging (For example; Comcast)

Apache Cassandra DB Best Practices

To make the most of Cassandra, you need to approach the database logically and devise a data model that works while avoiding the common mistakes that many practitioners make. Besides visualizing your Cassandra data model while it’s still in the conceptual stage, you can adopt the following steps to get the maximum out of this database.

Adopt a Structured Approach

With Cassandra, you need to follow a structured approach. What is meant by it is that you should introduce some sort of structure into your data model before you start storing the data in the database. Remember, Cassandra does not entertain relations between the data sets. Not only is the data not stored in an organized fashion, but it is also consumed as a whole.

Moreover, you should try to anticipate the queries for your application. In this way, you can plan beforehand for what data sets need to be fetched together and what sorts of updates will be required.

Capitalize on Distribution

Apache Cassandra has the ability to run on multiple machines. Although one node can be used to centralize the functioning, it can render undone the numerous benefits Cassandra brings if the model is not designed to make the most of its distribution abilities. Being a decentralized database, Cassandra is built to distribute data across multiple data centres. Its distribution capacity ensures increased availability and accessibility of data and is aided by the multiple partition keys and the clustering key that Cassandra contains within its primary key.

Conduct a Load Test

It is always prudent to carry out a load test before you start production through your Cassandra data model. You can start off by setting up an environment that matches the future production scenario. You should also take care to keep the read and write volumes to normal, i.e., how they are going to be in future production. This performance testing lets you assess how the Cassandra data model will do during the job.

Conclusion

Cassandra database presents an unconventional solution to the issues of the modern-world IT sector. It brings immense scalability and speed to the processes like data management, storage, and retrieval. If you have queries regarding the usage of the Cassandra database, feel free to reach out to the Royal Cyber team.

Author bio:

Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and data analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.