In recent times, the volume of data collected by most businesses has increased exponentially. This is because interactions today are laboriously dependent upon data-driven insights to make everyday decisions. Given the volume of data being collected today, traditional relational databases can not continue to meet the data storage requirements. This is because of its inability to scale horizontally and manage unstructured data suitably.
As a result, most businesses are migrating to NoSQL Database solutions which are designed to handle large datasets, keeping the Big Data requirements in mind. And, one such NoSQL Database platform is Cassandra.
This article will provide you with an in-depth understanding of the various factors that are driving the big “Apache Cassandra vs MongoDB” debate. This article will talk briefly about Apache Cassandra and its key features. You will also gain a holistic understanding of Data Modeling, Apache Cassandra Data Models (rules and components), and some of the best practices to be followed while working with the Cassandra Data Models.
Read along to find out in-depth information about Apache Cassandra Data Models.
Table of Contents
What is Apache Cassandra?
Apache Cassandra is a free and open-source wide-column NoSQL Database. It uses a Columnar Storage Architecture and can handle large amounts of data across multiple nodes. Each Apache Cassandra node can perform read and write operations. As a result, data can be replicated across multiple nodes to ensure availability when a node failure occurs. If a node fails, the user is redirected to the nearest available node with the data needed. As a result, it can be observed that Apache Cassandra lacks a single point of failure and can thus provide high data availability. This is regarded as one of the most significant benefits of leveraging Apache Cassandra.
Cassandra fetches data by leveraging Cassandra Query Language (CQL), which has a syntax that is very similar to Structured Query Language (SQL). Because of its similarity to SQL, most developers can easily transition to Apache Cassandra.
More information about Apache Cassandra can be found here.
Key Features of Apache Cassandra
Some of the key features of Apache Cassandra are as follows:
1) Data Distribution
Data distribution in Cassandra is simple because it allows you to distribute data wherever you need it, that, too, by replicating data across clusters in multiple data centers known as nodes. However, there is no master because each node can service any request, which proves that each node in a cluster plays the same role.
2) High Scalability
As your needs grow, adding nodes to the Cassandra cluster should be simple and available at any time. It is intended to gradually increase r/w throughput as new machines are added without interfering with other applications. Moreover, Cassandra can grow horizontally as per your needs and across as many geographical sites as possible, rather than vertically.
For fault tolerance, data is automatically stored and replicated in Cassandra. As all nodes are treated equally, if one fails, it is quickly replaced by another one. You can essentially add enough nodes to ensure that you never experience a full-fledged “lights out” scenario.
4) Query Language
Cassandra conceived the Cassandra Query Language (CQL). It has a simple and easy-to-understand interface for interacting with Cassandra. And as Cassandra is a NoSQL database, you can move data horizontally across clusters more easily. It has the potential for massive scalability, and it is not constrained by joins and fixed schemas.
5) Tunable Consistency
Cassandra has two types of consistency: Eventual consistency and Strong consistency. The developer can select any of them based on his requirements. As soon as a “write” is accepted by the cluster, eventual consistency ensures that the client approves it. Strong consistency, on the other hand, ensures that any update is broadcasted to all nodes or machines where the specific data is suitable. You can also use a combination of the two consistency types.
What is Data Modeling?
Data Modelling is the process of analyzing, organizing, and comprehending the data requirements for a product or service. Data modeling establishes the structure in which your data will reside. It specifies how items are labeled and organized, as well as how your data can and will be used.
It is the process of creating a visual representation of a system and the elements it contains, as well as their connectivity and workflow, using various signs and symbols. In other words, it is a blueprint of the data system.
What is Cassandra Data Model?
A Cassandra database is distributed across several machines that work in conjunction. Cassandra assigns data to nodes in the Keyspace, the outermost cluster in a ring cluster. Each node has a replica that takes over in the event of a failure.
Data in Cassandra is modeled around specific queries. The structure and organization of data are determined by data access patterns and application queries, which are then used to design database tables. It implies that all entities involved in a query must be in the same table to allow for very fast data access.
A table in Cassandra may contain one or more entities, depending on the specific needs of the query. Since entities typically have relationships with each other and queries may involve entities with relationships among them, a single entity may be included in multiple tables.
The Cassandra data model offers tunable consistency or the ability for the client application to choose how consistent the requested data must be for any given read or write operation.
The rules to be followed in Cassandra data models are:
- Spread Data Evenly Around the Cluster
Each node in the cluster should have pretty much the same amount of data. Rows are distributed across the cluster using a hash of the partition key, which is the first element of the Primary key. So, to evenly spread data is to select a good Primary key.
- Minimize the Number of Partitions Read
Partitions are collections of rows that all have the same partition key. When you issue a read query, the best way to do it is to minimize the number of partitions. The reason for this is that each partition may be located on a different node. In most cases, the coordinator will need to send separate commands to different nodes for each partition you request. This adds a lot of overhead and increases latency variation. Moreover, due to the way rows are stored, reading from multiple partitions on a single node is more expensive than reading from a single one.
The different components of the Apache Cassandra Data Model are as follows:
- Keyspaces: In its most basic form, a Cassandra Data Model comprises data containers known as Keyspaces. Keyspaces are analogous to a schema in a relational database. A keyspace typically contains a large number of tables.
- Tables: In Cassandra, tables (column families) are defined within Keyspaces. A table contains data in both horizontal and vertical formats, which are referred to as rows and columns. Tables also consist of Primary keys.
- Columns: Columns define the data structure within a table, and each column has a unique type associated with it. Boolean, double, integer, and text are a few of these types.
Working With Apache Cassandra Data Models
Some of the examples that showcase how to work with Cassandra Data Models are as follows:
1) User Lookup
In this case, the major requirement is “you have users and you want to look them up”. First, determine the specific queries required to be able to look up users let’s say by their username or their email address. Try to get detailed information about the user. Further, you can write the query to create a table with either of the lookup methods.
- CREATE TABLE users_by_username (username text PRIMARY KEY, email text, age int)
- CREATE TABLE users_by_email (email text PRIMARY KEY, username text, age int )
Now, you can check the queries according to the two rules of Cassandra Data Models.
- Spreads data evenly: In the above queries, each user gets their partition. So, the data is spread evenly.
- Minimal partitions read: In the above queries, you only have to read one partition; the partitions to be read are minimum.
2) User Groups
In this case, the major requirement is “users are in groups and you want to get all users in a group”. In the process, you first need to determine the queries that are required to get detailed information about every user in a particular group. And here, the order of users isn’t important. Now, you can write a query to create a table and read only one partition. For this, you can use a compound primary key.
In this example, the primary key has two components:
- groupname: It is the partitioning key.
- username: It is the clustering key. This will help provide you with one partition per the partitioning key (groupname).
For fetching groups, you can do the following:
In this way, you can minimize the number of partitions that are read. But, it doesn’t fully satisfy the first goal of evenly spreading data around the cluster.
3) User Groups by Join Date
Continuing with the previous example of groups, suppose now you want to add the newest users in a group, you can use the following query to create a table.
In this case, timeuuid is a timestamp and it is used as the clustering column. Rows within a group (partition) are ordered by the date the user joined the group. This allows us to gather the newest users in a group in the manner as given in the following query:
SELECT * FROM group_join_dates
WHERE groupname =?
ORDER BY joined DESC
Since you’re reading a subset of rows from a single partition, the above query is fairly efficient. However, rather than always using ORDER BY joined DESC, you can simply reverse the clustering order thus making it more effective, as illustrated in the following query:
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The query which will be a bit more efficient is as follows:
SELECT * FROM group_join_dates
WHERE groupname = ?
In this case, if any of the groups becomes too large, it may be difficult to distribute data evenly across the cluster. Partitions are split somewhat randomly in this case, but you can split partitions differently: by a time range. For example, you could split partitions by date as illustrated below:
In this case, you’re using a compound partition key, i.e., the join date.
What are the Components of Cassandra Data Model?
There are several key components of the Cassandra data model:
- Keyspace: A keyspace is a logical container for a set of tables in Cassandra. It is similar to a database in a traditional relational database management system.
- Table: A table in Cassandra is a collection of rows that are organized into columns. Tables can be configured to store data in different ways, such as with a single partition or with multiple partitions across different nodes.
- Column: A column in Cassandra represents a single data element in a table. Columns are organized into families, which are groups of related columns that are stored together.
- Partition key: The partition key is a special column or set of columns that determines how data is distributed across the nodes in a Cassandra cluster. The partition key is used to determine which node a particular piece of data belongs on.
- Clustering columns: Clustering columns are optional columns that are used to further organize data within a partition. They are used to determine the order in which data is stored within a partition.
By using these components, Cassandra is able to store and retrieve data efficiently and scale horizontally to support very large datasets.
What are the DDL and DML statements in Cassandra Data Model?
In the Cassandra data model, DDL (Data Definition Language) statements are used to create, alter, and drop database objects such as keyspaces, tables, and indexes. Some common examples of DDL statements in Cassandra include:
CREATE KEYSPACE: This statement is used to create a new keyspace in Cassandra.
CREATE TABLE: This statement is used to create a new table in a keyspace.
ALTER TABLE: This statement is used to alter the structure of an existing table, such as adding or dropping columns.
DROP TABLE: This statement is used to delete a table from a keyspace.
CREATE INDEX: This statement is used to create a new index on a table, which can improve the performance of queries that filter data based on specific columns.
DML (Data Manipulation Language) statements are used to insert, update, and delete data from Cassandra tables. Some common examples of DML statements in Cassandra include:
INSERT INTO: This statement is used to insert new rows into a table.
UPDATE: This statement is used to modify the values of existing rows in a table.
DELETE: This statement is used to delete rows from a table.
SELECT: This statement is used to retrieve data from a table.
By using a combination of DDL and DML statements, you can create and modify the structure of Cassandra keyspaces and tables, as well as manipulate data stored in them.
What are the Cassandra Data Modeling Tools?
There are several tools available for modeling data in Cassandra. Some of them are as follows:
- Cassandra Query Language (CQL): CQL is a SQL-like language used to create, modify, and query Cassandra tables. It provides a simple interface for users familiar with SQL, and is the primary way to interact with Cassandra.
- Cassandra Data Modeler: Cassandra Data Modeler is a visual tool that allows you to create and modify Cassandra tables, as well as visualize and explore your data. It is available as a standalone application, as well as a plugin for popular IDEs like IntelliJ and Eclipse.
- Cassandra Reaper: Cassandra Reaper is an open-source tool for managing and repairing Cassandra clusters. It includes features for data modeling, including the ability to create and modify tables, as well as migrate data between clusters.
- Cassandra-stress: Cassandra-stress is a tool that is used to test the performance of Cassandra clusters. It can be used to simulate workloads and measure the performance of different data models, as well as identify potential bottlenecks and optimize the design of your Cassandra tables.
- Cassandra Data Modeling and Query Language (CDMQL): This is a tool that helps users design and optimize Cassandra data models. It provides a visual interface for creating and modifying data structures, as well as a query builder for creating CQL queries.
- DataStax Studio: This is a web-based tool that allows users to interact with Cassandra using CQL, as well as visualize data and create and execute queries.
- Cassandra GUI: This is a graphical user interface for Cassandra that allows users to view and modify data structures, as well as execute queries and view query results.
- Hackolade: This is a Cassandra data modeling tool that supports schema design for many NoSQL databases. It supports multiple data types including UDTs and collections and unique CQL concepts such as clustering columns and partition keys. It also lets you capture the database schema with a Chebotko diagram.
- Kashlev Data Modeler: This is a Cassandra data modeling tool that automates the Cassandra data modeling principles described in the Cassandra data model documentation, including schema generation, logical, conceptual, and physical data modeling, and identifying access patterns. It also includes model design patterns.
BONUS: 5 Best Practices to Assess While Working With Cassandra Data Models
The best practices to be kept in mind while working with Cassandra Data Models are as follows:
1) Cassandra isn’t a Relational Database
Cassandra is a NoSQL database, therefore it won’t follow the attributes and features of a Relational database. So, while designing a Cassandra Data Model, some of the points which should be kept in mind are as follows:
- Optimize data distribution around a cluster.
- Follow the denormalization of tables.
- Define how you are planning to access the data tables at the beginning of the data modeling process.
- Sorting in Cassandra can only be done on the clustering columns specified in the primary key.
2) Follow the 3 Data Distribution Goals
Cassandra is a distributed data system. It distributes incoming data into chunks, known as partitions. And distinct partitions are grouped based on a partition key. This partition key distributes the data among the nodes in a cluster.
A good Cassandra Data Model should follow the following data distribution goals.
- It should spread data evenly across the nodes in a cluster.
- It should place limits on the size of a partition.
- It should always minimize the number of partitions a query returns.
3) Understand the importance of the Primary Key
Every table in the Cassandra data structure must have a primary key i.e., a column that contains values that uniquely identify each row in the table. Apart from giving a unique identity, the primary key also determines the structure of the table.
In Cassandra, the primary key comprises two parts. These are:
- Partition Key: The first column or a set of columns in a primary key represents a partition key(s). These keys are associated with a node. Cassandra is structured as a cluster of nodes, with each node holding an equal share of the partition key hashes. The hashed value of the partition key value is an indicator of the position of the partition within the cluster.
- Clustering Key: The name of the column(s) which come after partition key(s) is called clustering key. The default sort order of rows within a partition is determined by the clustering key.
In Cassandra, a primary key is made up of one or more partition keys and zero or more clustering key components.
4) Focus on Query-Centered Design
Always keep in mind that Apache Cassandra isn’t a relational database and so are its functionalities and structure. Hence, you can’t follow the same approach for modeling as you do for relational databases. From the beginning of the data modeling process, aim for query-centered design and define how data tables will be accessed. Since Cassandra does not support derived tables or joins, denormalization is essential in Cassandra’s table design.
5) Conduct Performance Testing
The transition from development to production first requires accurate load testing. For this, you can set up a pre-production environment that corresponds to the product specifications, and create a load that corresponds to your expected read and writes volumes. Run this load test for a few days to ensure that compaction and other background processes can cope with the pace. Use this load test to establish benchmarks, and then save the baseline metrics for future purposes. This way, you’ll be able to compare outputs to benchmarks when adjusting settings or comparing performance under a heavier load.
In this article, you have learned comprehensively about Cassandra Data Models. This article also provided information on Data Modeling, Apache Cassandra Data Models, its rules and components, and some of the best practices to be followed while working with the Cassandra Data Models.
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