Practical Guide to Apache Cassandra

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many servers, providing high availability with no single point of failure.

This practical guide will help you understand how to use Cassandra for real-world applications.

Key Points

  • Distributed and Decentralized: No single point of failure.
  • NoSQL database
  • Scalability: Horizontal scaling by adding more nodes.
  • High Availability: Data is replicated across multiple nodes.
  • Fault Tolerance: Can withstand node failures.
  • Tunable Consistency: Allows you to choose the consistency level.
  • Open Source database handled by Apache

Key Concepts in Cassandra


A keyspace is an outermost container for data in Cassandra, similar to a database in relational databases. It defines how data is replicated on nodes.


  • name: Keyspace name.
  • replication: Defines the replication strategy and factor.
  • durable_writes: Ensures data is written to disk.

Replication Strategies:

  • SimpleStrategy: Used for a single data centre.
  • NetworkTopologyStrategy: Used for multiple data centres.

Creating a Keyspace:

CREATE KEYSPACE my_keyspace WITH REPLICATION = { 'class' : 'SimpleStrategy', 'replication_factor' : 1 };

Using a Keyspace:

USE my_keyspace;


A table in Cassandra is a collection of rows. Each row is identified by a unique primary key.


  • name: Table name.
  • columns: Columns in the table.
  • primary key: Unique identifier for rows.

Creating a Table:

    title TEXT,
    author TEXT,
    published_date DATE

Describing a Table:


Data Types

Basic Types: text, int, uuid, boolean, timestamp, etc.
Collection Types: list, set, map.

Basic Operations


INSERT INTO users (user_id, name, email) VALUES (uuid(), 'John Doe', '');

Query Data

SELECT * FROM users WHERE user_id = <some-uuid>;

Update data

UPDATE users SET email = '' WHERE user_id = <some-uuid>;

Delete Data

DELETE FROM users WHERE user_id = <some-uuid>;

Cassandra Architecture

Cassandra is a decentralized multi-node database that physically spans separate locations and uses replication and partitioning to infinitely scale reads and writes.


  • Cassandra is decentralized because no node is superior to other nodes.
  • No concept of master and slave nodes
  • Every node acts in different roles as needed without any central controller.

Cassandra’s decentralized property is what allows it to handle situations easily in case one node becomes unavailable or a new node is added.

Every Node Is a Coordinator

  • Data is replicated to different nodes. If certain data is requested, a request can be processed from any node.
  • This initial request receiver becomes the coordinator node for that request. If other nodes need to be checked to ensure consistency then the coordinator requests the required data from replica nodes.

The coordinator can calculate which node contains the data using a so-called consistent hashing algorithm.

Cassandra Coordinator

The coordinator is responsible for many things, such as request batching, repairing data, or retries for reads and writes.

Data Partitioning

  • Partitioning is a method of splitting and storing a single logical dataset in multiple databases.
  • By distributing the data among multiple machines, a cluster of database systems can store larger datasets and handle additional requests.

How Sharding Works by Jeeyoung Kim

As with many other databases, you store data in Cassandra in a predefined schema. You need to define a table with columns and types for each column.

Additionally, you need to think about the primary key of your table. A primary key is mandatory and ensures data is uniquely identifiable by one or multiple columns.

The concept of primary keys is more complex in Cassandra than in traditional databases like MySQL. In Cassandra, the primary key consists of 2 parts:

  • a mandatory partition key and
  • an optional set of clustering columns.

You will learn more about the partition key and clustering columns in the data modeling section.

For now, let’s focus on the partition key and its impact on data partitioning.

Consider the following table:

Table Users | Legend: p - Partition-Key, c - Clustering Column

country (p) | user_email (c)  | first_name | last_name | age
US          |  | John       | Wick      | 55  
UK          | | Peter      | Clark     | 65  
UK          |   | Bob        | Sandler   | 23 
UK          | | Alice      | Brown     | 26 

Together, the columns user_email and country make up the primary key.

The country column is the partition key (p). The CREATE-statement for the table looks like this:

CREATE TABLE learn_cassandra.users_by_country (
    country text,
    user_email text,
    first_name text,
    last_name text,
    age smallint,
    PRIMARY KEY ((country), user_email)

The first group of the primary key defines the partition key. All other elements of the primary key are clustering columns:

country --> partion key
user_email --> primary key(clustering columns)

In the context of partitioning, the words partition and shard can be used interchangeably.

Cassandra partition key

Partitions are created and filled based on partition key values. They are used to distribute data to different nodes. By distributing data to other nodes, you get scalability. You read and write data to and from different nodes by their partition key.

The distribution of data is a crucial point to understand when designing applications that store data based on partitions. It may take a while to get fully accustomed to this concept, especially if you are used to relational databases.


Consistency levels in Apache Cassandra are a crucial concept that helps balance between availability and consistency in distributed systems. They determine how many nodes in the cluster must acknowledge a read or write operation before it is considered successful. This allows you to fine-tune the trade-off between strong consistency and high availability based on your application’s needs.

Consistency Levels in Cassandra


Consistency levels are configured for both read and write operations. They define the number of replica nodes that must respond for an operation to be successful. The choice of consistency level affects the latency, throughput, and fault tolerance of your Cassandra application.

Types of Consistency Levels

  1. ANY
    • Write: A write operation is considered successful once any node (even if it’s a hinted handoff) acknowledges it.
    • Read: Not applicable.
    • Use Case: Maximum write availability but minimal consistency.
  2. ONE
    • Write: Requires acknowledgment from at least one replica node.
    • Read: Returns the value from the first replica node that responds.
    • Use Case: Low-latency operations where strong consistency is not critical.
  3. TWO
    • Write: Requires acknowledgment from at least two replica nodes.
    • Read: Returns the most recent data from two replicas.
    • Use Case: Better consistency than ONE but still relatively low latency.
  4. THREE
    • Write: Requires acknowledgment from at least three replica nodes.
    • Read: Returns the most recent data from three replicas.
    • Use Case: Higher consistency than TWO but at the cost of higher latency.
    • Write: Requires acknowledgment from a majority (quorum) of replica nodes.
    • Read: Requires reading from a majority of replica nodes and returns the most recent data.
    • Use Case: Balanced consistency and availability. Suitable for many production scenarios.
    • Formula: (replication_factor / 2) + 1
  6. ALL
    • Write: Requires acknowledgment from a majority of replica nodes in the local datacenter.
    • Read: Reads from a majority of replica nodes in the local datacenter.
    • Use Case: Multi-datacenter deployments where you want strong consistency within a datacenter.
cqlsh>  SELECT * FROM learn_cassandra.users_by_country WHERE country='US';

In your cqlsh shell will send a request only to a single Cassandra node by default. This is called a consistency level of one, which enables excellent performance and scalability.

What does strong consistency mean?

In contrast to eventual consistency, strong consistency means only one state of your data can be observed at any time in any location.

For example, when consistency is critical, like in a banking domain, you want to be sure that everything is correct. You would rather accept a decrease in availability and increase of latency to ensure correctness.


Consider a lot of write requests arriving for a single partition. All requests would be sent to a single node with technical limitations such as CPU, memory, and bandwidth. Additionally, you want to handle read and write requests if this node is not available.

That is where the concept of replication comes in. By duplicating data to different nodes, so called replicas, you can serve more data simultaneously from other nodes to improve latency and throughput. It also enables your cluster to perform reads and writes in case a replica is not available.

A replication factor of one means there’s only one copy of each row in the cluster. If the node containing the row goes down, the row cannot be retrieved.

A replication factor of two means two copies of each row, where each copy is on a different node. All replicas are equally important; there is no primary or master replica.

As a general rule, the replication factor should not exceed the number of nodes in the cluster. However, you can increase the replication factor and then add the desired number of nodes later.

Usually, it’s recommended to use a replication factor of 3 for production use cases. It makes sure your data is very unlikely to get lost or become inaccessible because there are three copies available. Also, if data is not consistent between replicas at any point in time, you can ask what information state is held by the majority.

In your local cluster setup, the majority means 2 out of 3 replicas. This allows us to use some powerful query options that you will see in the next section.

Data Modeling

Key Concepts:

  • Keyspace: Similar to a database in relational systems. It contains tables.
  • Table: Collection of rows, similar to a table in relational systems.
  • Partition Key: Determines which node stores the data.
  • Clustering Columns: Defines the order of data within a partition.

Creating a Keyspace

CREATE KEYSPACE mykeyspace WITH replication = {
  'class': 'SimpleStrategy',
  'replication_factor': 3

Creating Table:

USE mykeyspace;

  name TEXT,
  email TEXT

Interesting Points

Why should you start with 3 nodes?

It’s recommended to have at least 3 nodes or more. One reason is, in case you need strong consistency, you need to get confirmed data from at least 2 nodes. Or if 1 node goes down, your cluster would still be available because the 2 remaining nodes are up and running.


Case 1: With 1 node: If there is only one node, if it fails, the system can’t be repaired

case 2: with 2 nodes: If one of the nodes goes out of sync, then we node should be considered as truth

Case 2: with 3 nodes: if one node goes down, we get data from the other 2 nodes. In case you need strong consistency, you can confirm data from at least 2 nodes.


Apache Cassandra is a powerful database suited for applications requiring high availability and scalability. By understanding its setup, configuration, data modelling, and integration, you can leverage its capabilities to build robust, distributed systems. Remember to monitor your cluster, perform regular maintenance, and fine-tune performance settings to keep your Cassandra deployment running smoothly.

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