Cassandra 3.x High Availability: Achieve scalability and high availability without compromising on performance, Second Edition

One of the most significant obstacles that prevents traditional databases from achieving high availability is that they attempt to strongly guarantee the ACID properties:

Database designers most commonly achieve these properties via write masters, locks, elaborate storage area networks, and the like – all of which tend to sacrifice availability. As a result, achieving some semblance of high availability frequently involves bolt-on components, log shipping, leader election, sharding, and other such strategies that attempt to preserve the original design.

The simplest design approach to guarantee ACID properties is to implement a monolithic architecture where all functions reside on a single machine. Since no coordination among nodes is required, the task of enforcing all the system rules is relatively straightforward.

Increasing availability in such architectures typically involves hardware layer improvements, such as RAID arrays, multiple network interfaces, and hot-swappable drives. However, the fact remains that even the most robust database server acts as a single point of failure. This means that if the server fails, the application becomes unavailable. This architecture can be illustrated with the following diagram:

A common means of increasing capacity to handle requests on a monolithic architecture is to move the storage layer to a shared component such as a storage area network (SAN) or network attached storage (NAS). Such devices are usually quite robust, with large numbers of disks and high-speed network interfaces. This approach is shown in a modification of the previous diagram, which depicts two database servers using a single NAS.

You'll notice that while this architecture increases the overall request-handling capacity of the system, it simply moves the single failure point from the database server to the storage layer. As a result, there is no real improvement from an availability perspective.

As distributed systems have become more commonplace, the need for higher capacity distributed databases has grown. Many distributed databases still attempt to maintain ACID guarantees (or in some cases only the consistency aspect, which is the most difficult in a distributed environment), leading to the master-slave architecture.

In this approach, there might be many servers handling requests, but only one server can actually perform writes so as to maintain data in a consistent state. This avoids the scenario where the same data can be modified via concurrent mutation requests to different nodes. The following diagram shows the most basic scenario:

However, we still have not solved the availability problem, as a failure of the write master would lead to application downtime. It also means that writes do not scale well, since they are all directed to a single machine.

Using sharding to scale writes

A variation on the master-slave approach that enables higher write volumes is a technique called sharding, in which the data is partitioned into groups of keys, such that one or more masters can own a known set of keys. For example, a database of user profiles can be partitioned by the last name, such that A-M belongs to one cluster and N-Z belongs to another, as follows:

An astute observer will notice that both master-slave and sharding introduce failure points on the master nodes, and in fact the sharding approach introduces multiple points of failure–one for each master! Additionally, the knowledge of where requests for certain keys go rests with the application layer, and adding shards requires manual shuffling of data to accommodate the modified key ranges.

Some systems employ shard managers as a layer of abstraction between the application and the physical shards. This has the effect of removing the requirement that the application must have knowledge of the partition map. However, it does not obviate the need for shuffling data as the cluster grows.

The reality is that not every transaction in every application requires full ACID guarantees, and ACID properties themselves can be viewed as more of a continuum where a given transaction might require different degrees of each property.

Cassandra's approach to availability takes this continuum into account. In contrast to its relational predecessors–and even most of its NoSQL contemporaries–its original architects considered availability as a key design objective, with the intent to achieve the elusive goal of 100 percent uptime. Cassandra provides numerous knobs that give the user highly granular control of the ACID properties, all with different trade-offs.

The remainder of this chapter offers an introduction to Cassandra's high availability attributes and features, with the rest of the book devoted to help you to make use of these in real-world applications.

Unlike either monolithic or master-slave designs, Cassandra makes use of an entirely peer-to-peer architecture. All nodes in a Cassandra cluster can accept reads and writes, no matter where the data being written or requested actually belongs in the cluster. Internode communication takes place by means of a gossip protocol, which allows all nodes to quickly receive updates without the need for a master coordinator.

This is a powerful design, as it implies that the system itself is both inherently available and massively scalable. Consider the following diagram:

Note that in contrast to the monolithic and master-slave architectures, there are no special nodes. In fact, all nodes are essentially identical and as a result Cassandra has no single point of failure, and therefore no need for complex sharding or leader election. But how does Cassandra avoid sharding?

In this chapter we've discussed Cassandra's approach to availability and why the fundamental design decisions were made. The remainder of this book will build on this foundation. We will cover such topics as: how to configure Cassandra for high availability, design highly available applications on Cassandra, avoid common antipatterns, and handle various failure scenarios.

By the end of this book, you should possess a solid grasp of these concepts and be confident that you've successfully deployed one of the most robust and scalable database platforms available today.

However, we need to take it a step at a time, so in the next few chapters, we will build a deeper understanding of how Cassandra manages data. This foundation will be necessary for the topics covered later in the book. We'll start with a discussion of Cassandra's data placement strategy in the next chapter.