Cassandra Tutorial – A Beginner’s Guide to Cassandra

If you want to learn more about Cassandra, you’ll find that the documentation for this database can be a great place to start. Here, you’ll learn about Cassandra’s innate architecture, default configuration, replication, and secondary indexes. You’ll also learn about its unique tunable consistency feature.

Cassandra’s innate architecture

Cassandra uses a rack-like configuration with data in multiple nodes. Each machine has a CPU, memory, and hard disk. Each node is connected to a cluster by a network switch. The machines are also connected by a common power supply. A rack can fail due to a network switch failure or a power outage.

In Cassandra, data is distributed between nodes via a ring-like topology. Each node in the cluster owns a particular range of data. Keys are assigned to nodes based on a consistent hashing algorithm, which allows nodes to be added or removed. Cassandra’s partitioner determines which nodes own a particular partition. There are several partitioner implementations shipped with Cassandra, but developers can also define their partitioner interface. This topic will be covered in greater detail in the next chapter.

Cassandra’s innate design enables it to handle massive amounts of data with ease. It also offers replication across multiple data centers. Using a Cassandra cluster, data from multiple data centers are immediately replicated and can be accessed by applications from any location. Moreover, users can create separate data centers for online transactions, heavy analysis workloads, or a combination of both.

Cassandra’s default configuration

If you have multiple clusters, you can change Cassandra’s default configuration. To do so, add the Cassandra configuration directory to all cluster nodes. After making the changes, restart the cluster. If you are using the snitch, you must enable it to use the new configuration file.

You can change Cassandra’s default configuration to meet your data storage requirements. You can change the replication strategy, data, and column families, as well as the durability of the writes. You can also drop a keyspace and any data it contains. Cassandra will take a snapshot of the keyspace and return an error if the data already exists.

Cassandra is designed for high-volume, distributed computing workloads. The database’s distributed architecture prevents a single point of failure, allowing it to handle large amounts of data in parallel. Data is stored in tombstones and SSTable data files and can span multiple physical locations.

Cassandra’s replication

Replication is an important feature of Cassandra. It helps keep data consistent across all replicas. Even if you have low consistency level data, it will still benefit from replication. This is known as eventual consistency. Depending on your need, you can choose one or more replicas to store your data. If you are not sure which option to choose, refer to the Cassandra docs to learn more.

Replication can help you recover from a server failure and minimize network latency. It allows Cassandra to manage huge amounts of data and provides high availability without a single point of failure. Replication can be configured per keyspace, replication factor, and strategy.

Cassandra’s secondary indexes

Secondary indexes are indexes that are created over column values. For example, if we have a table called users, the primary index is the user ID, and we want to perform inverse queries against users, we would create a secondary index over the email column of the table. Secondary indexes are easy to create and are highly efficient on data sets with low cardinality.

Secondary indexes in Cassandra are very useful since they allow you to search and query data efficiently. These indexes are created by using the CONTAINS keyword, which enables you to search based on keywords and specific terms.

Cassandra’s partitioning algorithm

Partitioning is an important part of Cassandra’s architecture. The partitioning algorithm in Cassandra determines where to store data based on a partition key. Partition keys are stored as tokens. Tokens are out of the range of -263 to +263. A partitioner maps each token to a partition key. This helps the database index data on each node.

Cassandra replicates each partition to multiple nodes. To do this, the coordinator first hashes the partition key, which determines how many nodes are required to complete the operation. Then, the partitions are replicated according to the Replication Strategy.