Mastering Apache Cassandra 3.x - Third Edition

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By Aaron Ploetz , Tejaswi Malepati , Nishant Neeraj
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  1. Quick Start

About this book

With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features.

Once you’ve covered a brief recap of the basics, you’ll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You’ll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You’ll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apache Spark.

By the end of this book, you’ll be able to analyse big data, and build and manage high-performance databases for your application.

Publication date:
October 2018


Quick Start

Welcome to the world of Apache Cassandra! In this first chapter, we will briefly introduce Cassandra, along with a quick, step-by-step process to get your own single-node cluster up and running. Even if you already have experience working with Cassandra, this chapter will help to provide assurance that you are building everything properly. If this is your first foray into Cassandra, then get ready to take your first steps into a larger world.

In this chapter, we will cover the following topics:

  • Introduction to Cassandra
  • Installation and configuration
  • Starting up and shutting down Cassandra
  • Cassandra Cluster Manager (CCM)

By the end of this chapter, you will have built a single-node cluster of Apache Cassandra. This will be a good exercise to help you start to see some of the configuration and thought that goes into building a larger cluster. As this chapter progresses and the material gets more complex, you will start to connect the dots and understand exactly what is happening between installation, operation, and development.


Introduction to Cassandra

Apache Cassandra is a highly available, distributed, partitioned row store. It is one of the more popular NoSQL databases used by both small and large companies all over the world to store and efficiently retrieve large amounts of data. While there are licensed, proprietary versions available (which include enterprise support), Cassandra is also a top-level project of the Apache Software Foundation, and has deep roots in the open source community. This makes Cassandra a proven and battle-tested approach to scaling high-throughput applications.

High availability

Cassandra's design is premised on the points outlined in the Dynamo: Amazon's Highly Available Key-value Store paper ( Specifically, when you have large networks of interconnected hardware, something is always in a state of failure. In reality, every piece of hardware being in a healthy state is the exception, rather than the rule. Therefore, it is important that a data storage system is able to deal with (and account for) issues such as network or disk failure.

Depending on the Replication Factor (RF) and required consistency level, a Cassandra cluster is capable of sustaining operations with one or two nodes in a failure state. For example, let's assume that a cluster with a single data center has a keyspace configured for a RF of three. This means that the cluster contains three copies of each row of data. If an application queries with a consistency level of one, then it can still function properly with one or two nodes in a down state.


Cassandra is known as a distributed database. A Cassandra cluster is a collection of nodes (individual instances running Cassandra) all working together to serve the same dataset. Nodes can also be grouped together into logical data centers. This is useful for providing data locality for an application or service layer, as well as for working with Cassandra instances that have been deployed in different regions of a public cloud.

Cassandra clusters can scale to suit both expanding disk footprint and higher operational throughput. Essentially, this means that each cluster becomes responsible for a smaller percentage of the total data size. Assuming that the 500 GB disks of a six node cluster (RF of three) start to reach their maximum capacity, then adding three more nodes (for a total of nine) accomplishes the following:

  • Brings the total disk available to the cluster up from 3 TB to 4.5 TB
  • The percentage of data that each node is responsible for drops from 50% down to 33%

Additionally, let's assume that before the expansion of the cluster (from the prior example), the cluster was capable of supporting 5,000 operations per second. Cassandra scales linearly to support operational throughput. After increasing the cluster from six nodes to nine, the cluster should then be expected to support 7,500 operations per second.

Partitioned row store

In Cassandra, rows of data are stored in tables based on the hashed value of the partition key, called a token. Each node in the cluster is assigned multiple token ranges, and rows are stored on nodes that are responsible for their tokens.

Each keyspace (collection of tables) can be assigned a RF. The RF designates how many copies of each row should be stored in each data center. If a keyspace has a RF of three, then each node is assigned primary, secondary, and tertiary token ranges. As data is written, it is written to all of the nodes that are responsible for its token.



To get started with Cassandra quickly, we'll step through a single-node, local installation.

The following are the requirements to run Cassandra locally:

  • A flavor of Linux or macOS
  • A system with between 4 GB and 16 GB of random access memory (RAM)
  • A local installation of the Java Development Kit (JDK) version 8, latest patch
  • A local installation of Python 2.7 (for cqlsh)
  • Your user must have sudo rights to your local system
While you don't need to have sudo rights to run Apache Cassandra, it is required for some of the operating system configurations.
Apache Cassandra 3.11.2 breaks with JDK 1.8.0_161. Make sure to use either an older or newer version of the JDK.

Head to the Apache download site for the Cassandra project (, choose 3.11.2, and select a mirror to download the latest version of Cassandra. When complete, copy the .tar or .gzip file to a location that your user has read and write permissions for. This example will assume that this is going to be the ~/local/ directory:

mkdir ~/local
cd ~/local
cp ~/Downloads/apache-cassandra-3.11.2-bin.tar.gz .

Untar the file to create your cassandra directory:

tar -zxvf apache-cassandra-3.11.2-bin.tar.gz

Some people prefer to rename this directory, like so:

mv apache-cassandra-3.11.2/ cassandra/


At this point, you could start your node with no further configuration. However, it is good to get into the habit of checking and adjusting the properties that are indicated as follows.


It is usually a good idea to rename your cluster. Inside the conf/cassandra.yaml file, specify a new cluster_name property, overwriting the default Test Cluster:

cluster_name: 'PermanentWaves'

The num_tokens property default of 256 has proven to be too high for the newer, 3.x versions of Cassandra. Go ahead and set that to 24:

num_tokens: 24

To enable user security, change the authenticator and authorizer properties (from their defaults) to the following values:

authenticator: PasswordAuthenticator
authorizer: CassandraAuthorizer
Cassandra installs with all security disabled by default. Even if you are not concerned with security on your local system, it makes sense to enable it to get used to working with authentication and authorization from a development perspective.

By default, Cassandra will come up bound to localhost or For your own local development machine, this is probably fine. However, if you want to build a multi-node cluster, you will want to bind to your machine's IP address. For this example, I will use To configure the node to bind to this IP, adjust the listen_address and rpc_address properties:


If you set listen_address and rpc_address, you'll also need to adjust your seed list (defaults to as well:


I will also adjust my endpoint_snitch property to use GossipingPropertyFileSnitch:

endpoint_snitch: GossipingPropertyFileSnitch

In terms of NoSQL databases, Apache Cassandra handles multi-data center awareness better than any other. To configure this, each node must use GossipingPropertyFileSnitch (as previously mentioned in the preceding cassandra.yaml configuration process) and must have its local data center (and rack) settings defined. Therefore, I will set the dc and rack properties in the conf/ file:



Starting Cassandra

To start Cassandra locally, execute the Cassandra script. If no arguments are passed, it will run in the foreground. To have it run in the background, send the -p flag with a destination file for the Process ID (PID):

cd cassandra
bin/cassandra -p

This will store the PID of the Cassandra process in a file named in the local/cassandra directory. Several messages will be dumped to the screen. The node is successfully running when you see this message:

Starting listening for CQL clients on localhost/ (unencrypted).

This can also be verified with the nodetool status command:

bin/nodetool status

Datacenter: ClockworkAngels
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns (effective) Host ID Rack
UN 71.26 KiB 24 100.0% 0edb5efa... R40

Cassandra Cluster Manager

If you want an even faster way to install Cassandra, you can use an open source tool called CCM. CCM installs Cassandra for you, with very minimal configuration. In addition to ease of installation, CCM also allows you to run multiple Cassandra nodes locally.

First, let's clone the CCM repository from GitHub, and cd into the directory:

git clone
cd ccm

Next, we'll run the setup program to install CCM:

sudo ./ install

To verify that my local cluster is working, I'll invoke nodetool status via node1:

ccm node1 status

Datacenter: datacenter1
|/ State=Normal/Leaving/Joining/Moving
-- Address Load Tokens Owns (effective) Host ID Rack
UN 100.56 KiB 1 66.7% 49ecc8dd... rack1
UN 34.81 KiB 1 66.7% 404a8f97... rack1
UN 34.85 KiB 1 66.7% eed33fc5... rack1

To shut down your cluster, go ahead and send the stop command to each node:

ccm stop node1
ccm stop node2
ccm stop node3

Note that CCM requires a working installation of Python 2.7 or later, as well as a few additional libraries (pyYAML, six, ant, and psutil), and local IPs through to be available. Visit for more information.

Using CCM actually changes many of the commands that we will follow in this book. While it is a great tool for quickly spinning up a small cluster for demonstration purposes, it can complicate the process of learning how to use Cassandra.

A quick introduction to the data model

Now that we have a Cassandra cluster running on our local machine, we will demonstrate its use with some quick examples. We will start with cqlsh, and use that as our primary means of working with the Cassandra data model.

Using Cassandra with cqlsh

To start working with Cassandra, let's start the Cassandra Query Language (CQL) shell . The shell interface will allow us to execute CQL commands to define, query, and modify our data. As this is a new cluster and we have turned on authentication and authorization, we will use the default cassandra and cassandra username and password, as follows:

bin/cqlsh -u cassandra -p cassandra

Connected to PermanentWaves at
[cqlsh 5.0.1 | Cassandra 3.11.2 | CQL spec 3.4.4 | Native protocol v4]
Use HELP for help.
[email protected]>

First, let's tighten up security. Let's start by creating a new superuser to work with.

New users can only be created if authentication and authorization are properly set in the cassandra.yaml file:

[email protected]> CREATE ROLE cassdba WITH PASSWORD='flynnLives' AND LOGIN=true and SUPERUSER=true;

Now, set the default cassandra user to something long and indecipherable. You shouldn't need to use it ever again:

[email protected]> ALTER ROLE cassandra WITH PASSWORD='dsfawesomethingdfhdfshdlongandindecipherabledfhdfh';

Then, exit cqlsh using the exit command and log back in as the new cassdba user:

[email protected]> exit
bin/cqlsh -u cassdba -p flynnLives

Connected to PermanentWaves at
[cqlsh 5.0.1 | Cassandra 3.11.2 | CQL spec 3.4.4 | Native protocol v4]
Use HELP for help.
[email protected]>

Now, let's create a new keyspace where we can put our tables, as follows:

[email protected]> CREATE KEYSPACE packt WITH replication =
{'class': 'NetworkTopologyStrategy', 'ClockworkAngels': '1'}
AND durable_writes = true;
For those of you who have used Cassandra before, you might be tempted to build your local keyspaces with SimpleStrategy. SimpleStrategy has no benefits over NetworkTopologyStrategy, and is limited in that it cannot be used in a plural data center environment. Therefore, it is a good idea to get used to using it on your local instance as well.

With the newly created keyspace, let's go ahead and use it:

[email protected]> use packt;
[email protected]:packt>
The cqlsh prompt changes depending on the user and keyspace currently being used.

Now, let's assume that we have a requirement to build a table for video game scores. We will want to keep track of the player by their name, as well as their score and game on which they achieved it. A table to store this data would look something like this:

CREATE TABLE hi_scores (name TEXT, game TEXT, score BIGINT,
PRIMARY KEY (name,game));

Next, we will INSERT data into the table, which will help us understand some of Cassandra's behaviors:

INSERT INTO hi_scores (name, game, score) VALUES ('Dad','Pacman',182330);
INSERT INTO hi_scores (name, game, score) VALUES ('Dad','Burgertime',222000);
INSERT INTO hi_scores (name, game, score) VALUES ('Dad','Frogger',15690);
INSERT INTO hi_scores (name, game, score) VALUES ('Dad','Joust',48150);
INSERT INTO hi_scores (name, game, score) VALUES ('Connor','Pacman',182330);
INSERT INTO hi_scores (name, game, score) VALUES ('Connor','Monkey Kong',15800);
INSERT INTO hi_scores (name, game, score) VALUES ('Connor','Frogger',4220);
INSERT INTO hi_scores (name, game, score) VALUES ('Connor','Joust',48850);
INSERT INTO hi_scores (name, game, score) VALUES ('Avery','Galaga',28880);
INSERT INTO hi_scores (name, game, score) VALUES ('Avery','Burgertime',1200);
INSERT INTO hi_scores (name, game, score) VALUES ('Avery','Frogger',1100);
INSERT INTO hi_scores (name, game, score) VALUES ('Avery','Joust',19520);

Now, let's execute a CQL query to retrieve the scores of the player named Connor:

[email protected]:packt> SELECT * FROM hi_scores WHERE name='Connor';
name | game | score
Connor | Frogger | 4220
Connor | Joust | 48850
Connor | Monkey Kong | 15800
Connor | Pacman | 182330
(4 rows)

That works pretty well. But what if we want to see how all of the players did while playing the Joust game, as follows:

[email protected]:packt> SELECT * FROM hi_scores WHERE game='Joust';

InvalidRequest: Error from server: code=2200 [Invalid query] message="Cannot execute this query as it might involve data filtering and thus may have unpredictable performance. If you want to execute this query despite the performance unpredictability, use ALLOW FILTERING"
As stated in the preceding error message, this query could be solved by adding the ALLOW FILTERING directive. Queries using ALLOW FILTERING are notorious for performing poorly, so it is a good idea to build your data model so that you do not use it.

Evidently, Cassandra has some problems with that query. We'll discuss more about why that is the case later on. But, for now, let's build a table that specifically supports querying high scores by game:

CREATE TABLE hi_scores_by_game (name TEXT, game TEXT, score BIGINT,

Now, we will duplicate our data into our new query table:

INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Dad','Pacman',182330);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Dad','Burgertime',222000);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Dad','Frogger',15690);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Dad','Joust',48150);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Connor','Pacman',182330);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Connor','Monkey Kong',15800);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Connor','Frogger',4220);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Connor','Joust',48850);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Avery','Galaga',28880);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Avery','Burgertime',1200);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Avery','Frogger',1100);
INSERT INTO hi_scores_by_game (name, game, score) VALUES ('Avery','Joust',19520);

Now, let's try to query while filtering on game with our new table:

[email protected]:packt> SELECT * FROM hi_scores_by_game
WHERE game='Joust';
game | score | name
Joust | 48850 | Connor
Joust | 48150 | Dad
Joust | 19520 | Avery
(3 rows)

As mentioned previously, the following chapters will discuss why and when Cassandra only allows certain PRIMARY KEY components to be used in the WHERE clause. The important thing to remember at this point is that in Cassandra, tables and data structures should be modeled according to the queries that they are intended to serve.


Shutting down Cassandra

Before shutting down your cluster instances, there are some additional commands that should be run. Again, with your own, local node(s), these are not terribly necessary. But it is a good idea to get used to running these, should you ever need to properly shut down a production node that may contain data that people actually care about.

First, we will disable gossip. This keeps other nodes from communicating with the node while we are trying to bring it down:

bin/nodetool disablegossip

Next, we will disable the native binary protocol to keep this node from serving client requests:

bin/nodetool disablebinary

Then, we will drain the node. This will prevent it from accepting writes, and force all in-memory data to be written to disk:

bin/nodetool drain

With the node drained, we can kill the PID:

kill 'cat'

We can verify that the node has stopped by tailing the log:

tail logs/system.log

INFO [RMI TCP Connection(2)-] 2018-03-31 17:49:05,789 - Node localhost/ state jump to shutdown
INFO [RMI TCP Connection(4)-] 2018-03-31 17:49:49,492 - Stop listening for CQL clients
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:11,312 - DRAINING: starting drain process
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:11,313 - Paused hints dispatch
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:11,314 - Announcing shutdown
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:11,314 - Node localhost/ state jump to shutdown
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:13,315 - Waiting for messaging service to quiesce
INFO [ACCEPT-localhost/] 2018-03-31 17:50:13,316 - MessagingService has terminated the accept() thread
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:14,764 - Paused hints dispatch
INFO [RMI TCP Connection(6)-] 2018-03-31 17:50:14,861 - DRAINED



In this chapter, we introduced Apache Cassandra and some of its design considerations and components. These aspects were discussed and a high level description of each was given, as well as how each affects things like cluster layout and data storage. Additionally, we built our own local, single-node cluster. CCM was also introduced, with minimal discussion. Some basic commands with Cassandra's nodetool were introduced and put to use.

With a single-node cluster running, the cqlsh tool was introduced. We created a keyspace that will work in a plural data center configuration. The concept of query tables was also introduced, as well as running some simple read and write operations.

In the next chapter, we will take an in-depth look at Cassandra's underlying architecture, and understand what is key to making good decisions about cluster deployment and data modeling. From there, we'll discuss various aspects to help fine-tune a production cluster and its deployment process. That will bring us to monitoring and application development, and put you well on your way to mastering Cassandra!

About the Authors

  • Aaron Ploetz

    Aaron Ploetz is the NoSQL Engineering Lead for Target, where his DevOps team supports Cassandra, MongoDB, and Neo4j. He has been named a DataStax MVP for Apache Cassandra three times and has presented at multiple events, including the DataStax Summit and Data Day Texas. Aaron earned a BS in Management/Computer Systems from the University of Wisconsin-Whitewater, and an MS in Software Engineering from Regis University. He and his wife, Coriene, live with their three children in the Twin Cities area.

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  • Tejaswi Malepati

    Tejaswi Malepati is the Cassandra Tech Lead for Target. He has been instrumental in designing and building custom Cassandra integrations, including a web-based SQL interface and data validation frameworks between Oracle and Cassandra. Tejaswi earned a master's degree in computer science from the University of New Mexico, and a bachelor's degree in electronics and communication from Jawaharlal Nehru Technological University in India. He is passionate about identifying and analyzing data patterns in datasets using R, Python, Spark, Cassandra, and MySQL.

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  • Nishant Neeraj

    Nishant Neeraj is an independent software developer with experience in developing and planning out architectures for massively scalable data storage and data processing systems. Over the years, he has helped to design and implement a wide variety of products and systems for companies, ranging from small start-ups to large multinational companies. Currently, he helps drive WealthEngine's core product to the next level by leveraging a variety of big data technologies.

    Browse publications by this author

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