Chapter 1. Introducing Cassandra

If at first the idea is not absurd, then there is no hope for it.

Albert Einstein

Welcome to Cassandra: The Definitive Guide. The aim of this book is to help developers and database administrators understand this important new database, explore how it compares to the relational database management systems we’re used to, and help you put it to work in your own environment.

What’s Wrong with Relational Databases?

If I had asked people what they wanted, they would have said faster horses.

Henry Ford

I ask you to consider a certain model for data, invented by a small team at a company with thousands of employees. It is accessible over a TCP/IP interface and is available from a variety of languages, including Java and web services. This model was difficult at first for all but the most advanced computer scientists to understand, until broader adoption helped make the concepts clearer. Using the database built around this model required learning new terms and thinking about data storage in a different way. But as products sprang up around it, more businesses and government agencies put it to use, in no small part because it was fast—capable of processing thousands of operations a second. The revenue it generated was tremendous.

And then a new model came along.

The new model was threatening, chiefly for two reasons. First, the new model was very different from the old model, which it pointedly controverted. It was threatening because it can be hard to understand something different and new. Ensuing debates can help entrench people stubbornly further in their views—views that might have been largely inherited from the climate in which they learned their craft and the circumstances in which they work. Second, and perhaps more importantly, as a barrier, the new model was threatening because businesses had made considerable investments in the old model and were making lots of money with it. Changing course seemed ridiculous, even impossible.

Of course I’m talking about the Information Management System (IMS) hierarchical database, invented in 1966 at IBM.

IMS was built for use in the Saturn V moon rocket. Its architect was Vern Watts, who dedicated his career to it. Many of us are familiar with IBM’s database DB2. IBM’s wildly popular DB2 database gets its name as the successor to DB1—the product built around the hierarchical data model IMS. IMS was released in 1968, and subsequently enjoyed success in Customer Information Control System (CICS) and other applications. It is still used today.

But in the years following the invention of IMS, the new model, the disruptive model, the threatening model, was the relational database.

In his 1970 paper “A Relational Model of Data for Large Shared Data Banks,” Dr. Edgar F. Codd, also at IBM, advanced his theory of the relational model for data while working at IBM’s San Jose research laboratory. This paper, still available at, became the foundational work for relational database management systems.

Codd’s work was antithetical to the hierarchical structure of IMS. Understanding and working with a relational database required learning new terms that must have sounded very strange indeed to users of IMS. It presented certain advantages over its predecessor, in part because giants are almost always standing on the shoulders of other giants.

While these ideas and their application have evolved in four decades, the relational database still is clearly one of the most successful software applications in history. It’s used in the form of Microsoft Access in sole proprietorships, and in giant multinational corporations with clusters of hundreds of finely tuned instances representing multiterabyte data warehouses. Relational databases store invoices, customer records, product catalogues, accounting ledgers, user authentication schemes—the very world, it might appear. There is no question that the relational database is a key facet of the modern technology and business landscape, and one that will be with us in its various forms for many years to come, as will IMS in its various forms. The relational model presented an alternative to IMS, and each has its uses.

So the short answer to the question, “What’s wrong with relational databases?” is “Nothing.”

There is, however, a rather longer answer that I gently encourage you to consider. This answer takes the long view, which says that every once in a while an idea is born that ostensibly changes things, and engenders a revolution of sorts. And yet, in another way, such revolutions, viewed structurally, are simply history’s business as usual. IMS, RDBMS, NoSQL. The horse, the car, the plane. They each build on prior art, they each attempt to solve certain problems, and so they’re each good at certain things—and less good at others. They each coexist, even now.

So let’s examine for a moment why, at this point, we might consider an alternative to the relational database, just as Codd himself four decades ago looked at the Information Management System and thought that maybe it wasn’t the only legitimate way of organizing information and solving data problems, and that maybe, for certain problems, it might prove fruitful to consider an alternative.

We encounter scalability problems when our relational applications become successful and usage goes up. Joins are inherent in any relatively normalized relational database of even modest size, and joins can be slow. The way that databases gain consistency is typically through the use of transactions, which require locking some portion of the database so it’s not available to other clients. This can become untenable under very heavy loads, as the locks mean that competing users start queuing up, waiting for their turn to read or write the data.

We typically address these problems in one or more of the following ways, sometimes in this order:

  • Throw hardware at the problem by adding more memory, adding faster processors, and upgrading disks. This is known as vertical scaling. This can relieve you for a time.

  • When the problems arise again, the answer appears to be similar: now that one box is maxed out, you add hardware in the form of additional boxes in a database cluster. Now you have the problem of data replication and consistency during regular usage and in failover scenarios. You didn’t have that problem before.

  • Now we need to update the configuration of the database management system. This might mean optimizing the channels the database uses to write to the underlying filesystem. We turn off logging or journaling, which frequently is not a desirable (or, depending on your situation, legal) option.

  • Having put what attention we could into the database system, we turn to our application. We try to improve our indexes. We optimize the queries. But presumably at this scale we weren’t wholly ignorant of index and query optimization, and already had them in pretty good shape. So this becomes a painful process of picking through the data access code to find any opportunities for fine tuning. This might include reducing or reorganizing joins, throwing out resource-intensive features such as XML processing within a stored procedure, and so forth. Of course, presumably we were doing that XML processing for a reason, so if we have to do it somewhere, we move that problem to the application layer, hoping to solve it there and crossing our fingers that we don’t break something else in the meantime.

  • We employ a caching layer. For larger systems, this might include distributed caches such as memcached, EHCache, Oracle Coherence, or other related products. Now we have a consistency problem between updates in the cache and updates in the database, which is exacerbated over a cluster.

  • We turn our attention to the database again and decide that, now that the application is built and we understand the primary query paths, we can duplicate some of the data to make it look more like the queries that access it. This process, called denormalization, is antithetical to the five normal forms that characterize the relational model, and violate Codd’s 12 Commandments for relational data. We remind ourselves that we live in this world, and not in some theoretical cloud, and then undertake to do what we must to make the application start responding at acceptable levels again, even if it’s no longer “pure.”

I imagine that this sounds familiar to you. At web scale, engineers have started to wonder whether this situation isn’t similar to Henry Ford’s assertion that at a certain point, it’s not simply a faster horse that you want. And they’ve done some impressive, interesting work.

We must therefore begin here in recognition that the relational model is simply a model. That is, it’s intended to be a useful way of looking at the world, applicable to certain problems. It does not purport to be exhaustive, closing the case on all other ways of representing data, never again to be examined, leaving no room for alternatives. If we take the long view of history, Dr. Codd’s model was a rather disruptive one in its time. It was new, with strange new vocabulary and terms such as “tuples”—familiar words used in a new and different manner. The relational model was held up to suspicion, and doubtless suffered its vehement detractors. It encountered opposition even in the form of Dr. Codd’s own employer, IBM, which had a very lucrative product set around IMS and didn’t need a young upstart cutting into its pie.

But the relational model now arguably enjoys the best seat in the house within the data world. SQL is widely supported and well understood. It is taught in introductory university courses. There are free databases that come installed and ready to use with a $4.95 monthly web hosting plan. Often the database we end up using is dictated to us by architectural standards within our organization. Even absent such standards, it’s prudent to learn whatever your organization already has for a database platform. Our colleagues in development and infrastructure have considerable hard-won knowledge.

If by nothing more than osmosis—or inertia—we have learned over the years that a relational database is a one-size-fits-all solution.

So perhaps the real question is not, “What’s wrong with relational databases?” but rather, “What problem do you have?”

That is, you want to ensure that your solution matches the problem that you have. There are certain problems that relational databases solve very well.

If massive, elastic scalability is not an issue for you, the trade-offs in relative complexity of a system such as Cassandra may simply not be worth it. No proponent of Cassandra that I know of is asking anyone to throw out everything they’ve learned about relational databases, surrender their years of hard-won knowledge around such systems, and unnecessarily jeopardize their employer’s carefully constructed systems in favor of the flavor of the month.

Relational data has served all of us developers and DBAs well. But the explosion of the Web, and in particular social networks, means a corresponding explosion in the sheer volume of data we must deal with. When Tim Berners-Lee first worked on the Web in the early 1990s, it was for the purpose of exchanging scientific documents between PhDs at a physics laboratory. Now, of course, the Web has become so ubiquitous that it’s used by everyone, from those same scientists to legions of five-year-olds exchanging emoticons about kittens. That means in part that it must support enormous volumes of data; the fact that it does stands as a monument to the ingenious architecture of the Web.

But some of this infrastructure is starting to bend under the weight.

In 1966, a company like IBM was in a position to really make people listen to their innovations. They had the problems, and they had the brain power to solve them. As we enter the second decade of the 21st century, we’re starting to see similar innovations, even from young companies such as Facebook and Twitter.

So perhaps the real question, then, is not “What problem do I have?” but rather, “What kinds of things would I do with data if it wasn’t a problem?” What if you could easily achieve fault tolerance, availability across multiple data centers, consistency that you tune, and massive scalability even to the hundreds of terabytes, all from a client language of your choosing? Perhaps, you say, you don’t need that kind of availability or that level of scalability. And you know best. You’re certainly right, in fact, because if your current database didn’t suit your current database needs, you’d have a nonfunctioning system.

It is not my intention to convince you by clever argument to adopt a non-relational database such as Apache Cassandra. It is only my intention to present what Cassandra can do and how it does it so that you can make an informed decision and get started working with it in practical ways if you find it applies. Only you know what your data needs are. I do not ask you to reconsider your database—unless you’re miserable with your current database, or you can’t scale how you need to already, or your data model isn’t mapping to your application in a way that’s flexible enough for you. I don’t ask you to consider your database, but rather to consider your organization, its dreams for the future, and its emerging problems. Would you collect more information about your business objects if you could?

Don’t ask how to make Cassandra fit into your existing environment. Ask what kinds of data problems you’d like to have instead of the ones you have today. Ask what new kinds of data you would like. What understanding of your organization would you like to have, if only you could enable it?

A Quick Review of Relational Databases

Though you are likely familiar with them, let’s briefly turn our attention to some of the foundational concepts in relational databases. This will give us a basis on which to consider more recent advances in thought around the trade-offs inherent in distributed data systems, especially very large distributed data systems, such as those that are required at web scale.

RDBMS: The Awesome and the Not-So-Much

There are many reasons that the relational database has become so overwhelmingly popular over the last four decades. An important one is the Structured Query Language (SQL), which is feature-rich and uses a simple, declarative syntax. SQL was first officially adopted as an ANSI standard in 1986; since that time it’s gone through several revisions and has also been extended with vendor proprietary syntax such as Microsoft’s T-SQL and Oracle’s PL/SQL to provide additional implementation-specific features.

SQL is powerful for a variety of reasons. It allows the user to represent complex relationships with the data, using statements that form the Data Manipulation Language (DML) to insert, select, update, delete, truncate, and merge data. You can perform a rich variety of operations using functions based on relational algebra to find a maximum or minimum value in a set, for example, or to filter and order results. SQL statements support grouping aggregate values and executing summary functions. SQL provides a means of directly creating, altering, and dropping schema structures at runtime using Data Definition Language (DDL). SQL also allows you to grant and revoke rights for users and groups of users using the same syntax.

SQL is easy to use. The basic syntax can be learned quickly, and conceptually SQL and RDBMS offer a low barrier to entry. Junior developers can become proficient readily, and as is often the case in an industry beset by rapid changes, tight deadlines, and exploding budgets, ease of use can be very important. And it’s not just the syntax that’s easy to use; there are many robust tools that include intuitive graphical interfaces for viewing and working with your database.

In part because it’s a standard, SQL allows you to easily integrate your RDBMS with a wide variety of systems. All you need is a driver for your application language, and you’re off to the races in a very portable way. If you decide to change your application implementation language (or your RDBMS vendor), you can often do that painlessly, assuming you haven’t backed yourself into a corner using lots of proprietary extensions.

Transactions, ACID-ity, and two-phase commit

In addition to the features mentioned already, RDBMS and SQL also support transactions. A database transaction is, as Jim Gray puts it, “a transformation of state” that has the ACID properties (see A key feature of transactions is that they execute virtually at first, allowing the programmer to undo (using ROLLBACK) any changes that may have gone awry during execution; if all has gone well, the transaction can be reliably committed. The debate about support for transactions comes up very quickly as a sore spot in conversations around non-relational data stores, so let’s take a moment to revisit what this really means.

ACID is an acronym for Atomic, Consistent, Isolated, Durable, which are the gauges we can use to assess that a transaction has executed properly and that it was successful:


Atomic means “all or nothing”; that is, when a statement is executed, every update within the transaction must succeed in order to be called successful. There is no partial failure where one update was successful and another related update failed. The common example here is with monetary transfers at an ATM: the transfer requires subtracting money from one account and adding it to another account. This operation cannot be subdivided; they must both succeed.


Consistent means that data moves from one correct state to another correct state, with no possibility that readers could view different values that don’t make sense together. For example, if a transaction attempts to delete a Customer and her Order history, it cannot leave Order rows that reference the deleted customer’s primary key; this is an inconsistent state that would cause errors if someone tried to read those Order records.


Isolated means that transactions executing concurrently will not become entangled with each other; they each execute in their own space. That is, if two different transactions attempt to modify the same data at the same time, then one of them will have to wait for the other to complete.


Once a transaction has succeeded, the changes will not be lost. This doesn’t imply another transaction won’t later modify the same data; it just means that writers can be confident that the changes are available for the next transaction to work with as necessary.

On the surface, these properties seem so obviously desirable as to not even merit conversation. Presumably no one who runs a database would suggest that data updates don’t have to endure for some length of time; that’s the very point of making updates—that they’re there for others to read. However, a more subtle examination might lead us to want to find a way to tune these properties a bit and control them slightly. There is, as they say, no free lunch on the Internet, and once we see how we’re paying for our transactions, we may start to wonder whether there’s an alternative.

Transactions become difficult under heavy load. When you first attempt to horizontally scale a relational database, making it distributed, you must now account for distributed transactions, where the transaction isn’t simply operating inside a single table or a single database, but is spread across multiple systems. In order to continue to honor the ACID properties of transactions, you now need a transaction manager to orchestrate across the multiple nodes.

In order to account for successful completion across multiple hosts, the idea of a two-phase commit (sometimes referred to as “2PC”) is introduced. But then, because two-phase commit locks all associate resources, it is useful only for operations that can complete very quickly. Although it may often be the case that your distributed operations can complete in sub-second time, it is certainly not always the case. Some use cases require coordination between multiple hosts that you may not control yourself. Operations coordinating several different but related activities can take hours to update.

Two-phase commit blocks; that is, clients (“competing consumers”) must wait for a prior transaction to finish before they can access the blocked resource. The protocol will wait for a node to respond, even if it has died. It’s possible to avoid waiting forever in this event, because a timeout can be set that allows the transaction coordinator node to decide that the node isn’t going to respond and that it should abort the transaction. However, an infinite loop is still possible with 2PC; that’s because a node can send a message to the transaction coordinator node agreeing that it’s OK for the coordinator to commit the entire transaction. The node will then wait for the coordinator to send a commit response (or a rollback response if, say, a different node can’t commit); if the coordinator is down in this scenario, that node conceivably will wait forever.

So in order to account for these shortcomings in two-phase commit of distributed transactions, the database world turned to the idea of compensation. Compensation, often used in web services, means in simple terms that the operation is immediately committed, and then in the event that some error is reported, a new operation is invoked to restore proper state.

There are a few basic, well-known patterns for compensatory action that architects frequently have to consider as an alternative to two-phase commit. These include writing off the transaction if it fails, deciding to discard erroneous transactions and reconciling later. Another alternative is to retry failed operations later on notification. In a reservation system or a stock sales ticker, these are not likely to meet your requirements. For other kinds of applications, such as billing or ticketing applications, this can be acceptable.


Gregor Hohpe, a Google architect, wrote a wonderful and often-cited blog entry called “Starbucks Does Not Use Two-Phase Commit.” It shows in real-world terms how difficult it is to scale two-phase commit and highlights some of the alternatives that are mentioned here. Check it out at It’s an easy, fun, and enlightening read.

The problems that 2PC introduces for application developers include loss of availability and higher latency during partial failures. Neither of these is desirable. So once you’ve had the good fortune of being successful enough to necessitate scaling your database past a single machine, you now have to figure out how to handle transactions across multiple machines and still make the ACID properties apply. Whether you have 10 or 100 or 1,000 database machines, atomicity is still required in transactions as if you were working on a single node. But it’s now a much, much bigger pill to swallow.


One often-lauded feature of relational database systems is the rich schemas they afford. You can represent your domain objects in a relational model. A whole industry has sprung up around (expensive) tools such as the CA ERWin Data Modeler to support this effort. In order to create a properly normalized schema, however, you are forced to create tables that don’t exist as business objects in your domain. For example, a schema for a university database might require a Student table and a Course table. But because of the “many-to-many” relationship here (one student can take many courses at the same time, and one course has many students at the same time), you have to create a join table. This pollutes a pristine data model, where we’d prefer to just have students and courses. It also forces us to create more complex SQL statements to join these tables together. The join statements, in turn, can be slow.

Again, in a system of modest size, this isn’t much of a problem. But complex queries and multiple joins can become burdensomely slow once you have a large number of rows in many tables to handle.

Finally, not all schemas map well to the relational model. One type of system that has risen in popularity in the last decade is the complex event processing system, which represents state changes in a very fast stream. It’s often useful to contextualize events at runtime against other events that might be related in order to infer some conclusion to support business decision making. Although event streams could be represented in terms of a relational database, it is an uncomfortable stretch.

And if you’re an application developer, you’ll no doubt be familiar with the many object-relational mapping (ORM) frameworks that have sprung up in recent years to help ease the difficulty in mapping application objects to a relational model. Again, for small systems, ORM can be a relief. But it also introduces new problems of its own, such as extended memory requirements, and it often pollutes the application code with increasingly unwieldy mapping code. Here’s an example of a Java method using Hibernate to “ease the burden” of having to write the SQL code:

  joinColumns = @JoinColumn(name="store_code"))
private Map<String, String> getMap() {
//... etc.

Is it certain that we’ve done anything but move the problem here? Of course, with some systems, such as those that make extensive use of document exchange, as with services or XML-based applications, there are not always clear mappings to a relational database. This exacerbates the problem.

Sharding and shared-nothing architecture

If you can’t split it, you can’t scale it.

Randy Shoup, Distinguished Architect, eBay

Another way to attempt to scale a relational database is to introduce sharding to your architecture. This has been used to good effect at large websites such as eBay, which supports billions of SQL queries a day, and in other Web 2.0 applications. The idea here is that you split the data so that instead of hosting all of it on a single server or replicating all of the data on all of the servers in a cluster, you divide up portions of the data horizontally and host them each separately.

For example, consider a large customer table in a relational database. The least disruptive thing (for the programming staff, anyway) is to vertically scale by adding CPU, adding memory, and getting faster hard drives, but if you continue to be successful and add more customers, at some point (perhaps into the tens of millions of rows), you’ll likely have to start thinking about how you can add more machines. When you do so, do you just copy the data so that all of the machines have it? Or do you instead divide up that single customer table so that each database has only some of the records, with their order preserved? Then, when clients execute queries, they put load only on the machine that has the record they’re looking for, with no load on the other machines.

It seems clear that in order to shard, you need to find a good key by which to order your records. For example, you could divide your customer records across 26 machines, one for each letter of the alphabet, with each hosting only the records for customers whose last names start with that particular letter. It’s likely this is not a good strategy, however—there probably aren’t many last names that begin with “Q” or “Z,” so those machines will sit idle while the “J,” “M,” and “S” machines spike. You could shard according to something numeric, like phone number, “member since” date, or the name of the customer’s state. It all depends on how your specific data is likely to be distributed.

There are three basic strategies for determining shard structure:

Feature-based shard or functional segmentation

This is the approach taken by Randy Shoup, Distinguished Architect at eBay, who in 2006 helped bring their architecture into maturity to support many billions of queries per day. Using this strategy, the data is split not by dividing records in a single table (as in the customer example discussed earlier), but rather by splitting into separate databases the features that don’t overlap with each other very much. For example, at eBay, the users are in one shard, and the items for sale are in another. At Flixster, movie ratings are in one shard and comments are in another. This approach depends on understanding your domain so that you can segment data cleanly.

Key-based sharding

In this approach, you find a key in your data that will evenly distribute it across shards. So instead of simply storing one letter of the alphabet for each server as in the (naive and improper) earlier example, you use a one-way hash on a key data element and distribute data across machines according to the hash. It is common in this strategy to find time-based or numeric keys to hash on.

Lookup table

In this approach, one of the nodes in the cluster acts as a “yellow pages” directory and looks up which node has the data you’re trying to access. This has two obvious disadvantages. The first is that you’ll take a performance hit every time you have to go through the lookup table as an additional hop. The second is that the lookup table not only becomes a bottleneck, but a single point of failure.


To read about how they used data sharding strategies to improve performance at Flixster, see

Sharding can minimize contention depending on your strategy and allows you not just to scale horizontally, but then to scale more precisely, as you can add power to the particular shards that need it.

Sharding could be termed a kind of “shared-nothing” architecture that’s specific to databases. A shared-nothing architecture is one in which there is no centralized (shared) state, but each node in a distributed system is independent, so there is no client contention for shared resources. The term was first coined by Michael Stonebraker at University of California at Berkeley in his 1986 paper “The Case for Shared Nothing.”

Shared Nothing was more recently popularized by Google, which has written systems such as its Bigtable database and its MapReduce implementation that do not share state, and are therefore capable of near-infinite scaling. The Cassandra database is a shared-nothing architecture, as it has no central controller and no notion of master/slave; all of its nodes are the same.


You can read the 1986 paper “The Case for Shared Nothing” online at It’s only a few pages. If you take a look, you’ll see that many of the features of shared-nothing distributed data architecture, such as ease of high availability and the ability to scale to a very large number of machines, are the very things that Cassandra excels at.

MongoDB also provides auto-sharding capabilities to manage failover and node balancing. That many nonrelational databases offer this automatically and out of the box is very handy; creating and maintaining custom data shards by hand is a wicked proposition. It’s good to understand sharding in terms of data architecture in general, but especially in terms of Cassandra more specifically, as it can take an approach similar to key-based sharding to distribute data across nodes, but does so automatically.


In summary, relational databases are very good at solving certain data storage problems, but because of their focus, they also can create problems of their own when it’s time to scale. Then, you often need to find a way to get rid of your joins, which means denormalizing the data, which means maintaining multiple copies of data and seriously disrupting your design, both in the database and in your application. Further, you almost certainly need to find a way around distributed transactions, which will quickly become a bottleneck. These compensatory actions are not directly supported in any but the most expensive RDBMS. And even if you can write such a huge check, you still need to carefully choose partitioning keys to the point where you can never entirely ignore the limitation.

Perhaps more importantly, as we see some of the limitations of RDBMS and consequently some of the strategies that architects have used to mitigate their scaling issues, a picture slowly starts to emerge. It’s a picture that makes some NoSQL solutions seem perhaps less radical and less scary than we may have thought at first, and more like a natural expression and encapsulation of some of the work that was already being done to manage very large databases.

Web Scale

An invention has to make sense in the world in which it is finished, not the world in which it is started.

Ray Kurzweil

Because of some of the inherent design decisions in RDBMS, it is not always as easy to scale as some other, more recent possibilities that take the structure of the Web into consideration. But it’s not only the structure of the Web we need to consider, but also its phenomenal growth, because as more and more data becomes available, we need architectures that allow our organizations to take advantage of this data in near-time to support decision making and to offer new and more powerful features and capabilities to our customers.


It has been said, though it is hard to verify, that the 17th-century English poet John Milton had actually read every published book on the face of the earth. Milton knew many languages (he was even learning Navajo at the time of his death), and given that the total number of published books at that time was in the thousands, this would have been possible. The size of the world’s data stores have grown somewhat since then.

We all know the Web is growing. But let’s take a moment to consider some numbers from the IDC research paper “The Expanding Digital Universe.” (The complete paper is available at

  • YouTube serves 100 million videos every day.

  • Chevron accumulates 2TB of data every day.

  • In 2006, the amount of data on the Internet was approximately 166 exabytes (166EB). In 2010, that number reached nearly 1,000 exabytes. An exabyte is one quintillion bytes, or 1.1 million terabytes. To put this statistic in perspective, 1EB is roughly the equivalent of 50,000 years of DVD-quality video. 166EB is approximately three million times the amount of information contained in all the books ever written.

  • Wal-Mart’s database of customer transactions is reputed to have stored 110 terabytes in 2000, recording tens of millions of transactions per day. By 2004, it had grown to half a petabyte.

  • The movie Avatar required 1PB storage space, or the equivalent of a single MP3 song—if that MP3 were 32 years long (source:

  • As of May 2010, Google was provisioning 100,000 Android phones every day, all of which have Internet access as a foundational service.

  • In 1998, the number of email accounts was approximately 253 million. By 2010, that number is closer to 2 billion.

As you can see, there is great variety to the kinds of data that need to be stored, processed, and queried, and some variety to the businesses that use such data. Consider not only customer data at familiar retailers or suppliers, and not only digital video content, but also the required move to digital television and the explosive growth of email, messaging, mobile phones, RFID, Voice Over IP (VoIP) usage, and more. We now have Blu-ray players that stream movies and music. As we begin departing from physical consumer media storage, the companies that provide that content—and the third-party value-add businesses built around them—will require very scalable data solutions. Consider too that as a typical business application developer or database administrator, we may be used to thinking of relational databases as the center of our universe. You might then be surprised to learn that within corporations, around 80% of data is unstructured.

Or perhaps you think the kind of scale afforded by NoSQL solutions such as Cassandra don’t apply to you. And maybe they don’t. It’s very possible that you simply don’t have a problem that Cassandra can help you with. But I’m not asking you to envision your database and its data as they exist today and figure out ways to migrate to Cassandra. That would be a very difficult exercise, with a payoff that might be hard to see. It’s almost analytic that the database you have today is exactly the right one for your application of today. But if you could incorporate a wider array of rich data sets to help improve your applications, what kinds of qualities would you then be looking for in a database? The question becomes what kind of application would you want to have if durability, elastic scalability, vast storage, and blazing-fast writes weren’t a problem?

In a world now working at web scale and looking to the future, Apache Cassandra might be one part of the answer.

The Cassandra Elevator Pitch

Hollywood screenwriters and software startups are often advised to have their “elevator pitch” ready. This is a summary of exactly what their product is all about—concise, clear, and brief enough to deliver in just a minute or two, in the lucky event that they find themselves sharing an elevator with an executive or agent or investor who might consider funding their project. Cassandra has a compelling story, so let’s boil it down to an elevator pitch that you can present to your manager or colleagues should the occasion arise.

Cassandra in 50 Words or Less

“Apache Cassandra is an open source, distributed, decentralized, elastically scalable, highly available, fault-tolerant, tuneably consistent, column-oriented database that bases its distribution design on Amazon’s Dynamo and its data model on Google’s Bigtable. Created at Facebook, it is now used at some of the most popular sites on the Web.” That’s exactly 50 words.

Of course, if you were to recite that to your boss in the elevator, you’d probably get a blank look in return. So let’s break down the key points in the following sections.

Distributed and Decentralized

Cassandra is distributed, which means that it is capable of running on multiple machines while appearing to users as a unified whole. In fact, there is little point in running a single Cassandra node. Although you can do it, and that’s acceptable for getting up to speed on how it works, you quickly realize that you’ll need multiple machines to really realize any benefit from running Cassandra. Much of its design and code base is specifically engineered toward not only making it work across many different machines, but also for optimizing performance across multiple data center racks, and even for a single Cassandra cluster running across geographically dispersed data centers. You can confidently write data to anywhere in the cluster and Cassandra will get it.

Once you start to scale many other data stores (MySQL, Bigtable), some nodes need to be set up as masters in order to organize other nodes, which are set up as slaves. Cassandra, however, is decentralized, meaning that every node is identical; no Cassandra node performs certain organizing operations distinct from any other node. Instead, Cassandra features a peer-to-peer protocol and uses gossip to maintain and keep in sync a list of nodes that are alive or dead.

The fact that Cassandra is decentralized means that there is no single point of failure. All of the nodes in a Cassandra cluster function exactly the same. This is sometimes referred to as “server symmetry.” Because they are all doing the same thing, by definition there can’t be a special host that is coordinating activities, as with the master/slave setup that you see in MySQL, Bigtable, and so many others.

In many distributed data solutions (such as RDBMS clusters), you set up multiple copies of data on different servers in a process called replication, which copies the data to multiple machines so that they can all serve simultaneous requests and improve performance. Typically this process is not decentralized, as in Cassandra, but is rather performed by defining a master/slave relationship. That is, all of the servers in this kind of cluster don’t function in the same way. You configure your cluster by designating one server as the master and others as slaves. The master acts as the authoritative source of the data, and operates in a unidirectional relationship with the slave nodes, which must synchronize their copies. If the master node fails, the whole database is in jeopardy. The decentralized design is therefore one of the keys to Cassandra’s high availability. Note that while we frequently understand master/slave replication in the RDBMS world, there are NoSQL databases such as MongoDB that follow the master/slave scheme as well.

Decentralization, therefore, has two key advantages: it’s simpler to use than master/slave, and it helps you avoid outages. It can be easier to operate and maintain a decentralized store than a master/slave store because all nodes are the same. That means that you don’t need any special knowledge to scale; setting up 50 nodes isn’t much different from setting up one. There’s next to no configuration required to support it. Moreover, in a master/slave setup, the master can become a single point of failure (SPOF). To avoid this, you often need to add some complexity to the environment in the form of multiple masters. Because all of the replicas in Cassandra are identical, failures of a node won’t disrupt service.

In short, because Cassandra is distributed and decentralized, there is no single point of failure, which supports high availability.

Elastic Scalability

Scalability is an architectural feature of a system that can continue serving a greater number of requests with little degradation in performance. Vertical scaling—simply adding more hardware capacity and memory to your existing machine—is the easiest way to achieve this. Horizontal scaling means adding more machines that have all or some of the data on them so that no one machine has to bear the entire burden of serving requests. But then the software itself must have an internal mechanism for keeping its data in sync with the other nodes in the cluster.

Elastic scalability refers to a special property of horizontal scalability. It means that your cluster can seamlessly scale up and scale back down. To do this, the cluster must be able to accept new nodes that can begin participating by getting a copy of some or all of the data and start serving new user requests without major disruption or reconfiguration of the entire cluster. You don’t have to restart your process. You don’t have to change your application queries. You don’t have to manually rebalance the data yourself. Just add another machine—Cassandra will find it and start sending it work.

Scaling down, of course, means removing some of the processing capacity from your cluster. You might have to do this if you move parts of your application to another platform, or if your application loses users and you need to start selling off hardware. Let’s hope that doesn’t happen. But if it does, you won’t need to upset the entire apple cart to scale back.

High Availability and Fault Tolerance

In general architecture terms, the availability of a system is measured according to its ability to fulfill requests. But computers can experience all manner of failure, from hardware component failure to network disruption to corruption. Any computer is susceptible to these kinds of failure. There are of course very sophisticated (and often prohibitively expensive) computers that can themselves mitigate many of these circumstances, as they include internal hardware redundancies and facilities to send notification of failure events and hot swap components. But anyone can accidentally break an Ethernet cable, and catastrophic events can beset a single data center. So for a system to be highly available, it must typically include multiple networked computers, and the software they’re running must then be capable of operating in a cluster and have some facility for recognizing node failures and failing over requests to another part of the system.

Cassandra is highly available. You can replace failed nodes in the cluster with no downtime, and you can replicate data to multiple data centers to offer improved local performance and prevent downtime if one data center experiences a catastrophe such as fire or flood.

Tuneable Consistency

Consistency essentially means that a read always returns the most recently written value. Consider two customers are attempting to put the same item into their shopping carts on an ecommerce site. If I place the last item in stock into my cart an instant after you do, you should get the item added to your cart, and I should be informed that the item is no longer available for purchase. This is guaranteed to happen when the state of a write is consistent among all nodes that have that data.

But there’s no free lunch, and as we’ll see later, scaling data stores means making certain trade-offs between data consistency, node availability, and partition tolerance. Cassandra is frequently called “eventually consistent,” which is a bit misleading. Out of the box, Cassandra trades some consistency in order to achieve total availability. But Cassandra is more accurately termed “tuneably consistent,” which means it allows you to easily decide the level of consistency you require, in balance with the level of availability.

Let’s take a moment to unpack this, as the term “eventual consistency” has caused some uproar in the industry. Some practitioners hesitate to use a system that is described as “eventually consistent.”

For detractors of eventual consistency, the broad argument goes something like this: eventual consistency is maybe OK for social web applications where data doesn’t really matter. After all, you’re just posting to mom what little Billy ate for breakfast, and if it gets lost, it doesn’t really matter. But the data I have is actually really important, and it’s ridiculous to think that I could allow eventual consistency in my model.

Set aside the fact that all of the most popular web applications (Amazon, Facebook, Google, Twitter) are using this model, and that perhaps there’s something to it. Presumably such data is very important indeed to the companies running these applications, because that data is their primary product, and they are multibillion-dollar companies with billions of users to satisfy in a sharply competitive world. It may be possible to gain guaranteed, immediate, and perfect consistency throughout a highly trafficked system running in parallel on a variety of networks, but if you want clients to get their results sometime this year, it’s a very tricky proposition.

The detractors claim that some Big Data databases such as Cassandra have merely eventual consistency, and that all other distributed systems have strict consistency. As with so many things in the world, however, the reality is not so black and white, and the binary opposition between consistent and not-consistent is not truly reflected in practice. There are instead degrees of consistency, and in the real world they are very susceptible to external circumstance.

Eventual consistency is one of several consistency models available to architects. Let’s take a look at these models so we can understand the trade-offs:

Strict consistency

This is sometimes called sequential consistency, and is the most stringent level of consistency. It requires that any read will always return the most recently written value. That sounds perfect, and it’s exactly what I’m looking for. I’ll take it! However, upon closer examination, what do we find? What precisely is meant by “most recently written”? Most recently to whom? In one single-processor machine, this is no problem to observe, as the sequence of operations is known to the one clock. But in a system executing across a variety of geographically dispersed data centers, it becomes much more slippery. Achieving this implies some sort of global clock that is capable of timestamping all operations, regardless of the location of the data or the user requesting it or how many (possibly disparate) services are required to determine the response.

Causal consistency

This is a slightly weaker form of strict consistency. It does away with the fantasy of the single global clock that can magically synchronize all operations without creating an unbearable bottleneck. Instead of relying on timestamps, causal consistency instead takes a more semantic approach, attempting to determine the cause of events to create some consistency in their order. It means that writes that are potentially related must be read in sequence. If two different, unrelated operations suddenly write to the same field, then those writes are inferred not to be causally related. But if one write occurs after another, we might infer that they are causally related. Causal consistency dictates that causal writes must be read in sequence.

Weak (eventual) consistency

Eventual consistency means on the surface that all updates will propagate throughout all of the replicas in a distributed system, but that this may take some time. Eventually, all replicas will be consistent.

Eventual consistency becomes suddenly very attractive when you consider what is required to achieve stronger forms of consistency.

When considering consistency, availability, and partition tolerance, we can achieve only two of these goals in a given distributed system (we explore the CAP Theorem in the section Brewer’s CAP Theorem). At the center of the problem is data update replication. To achieve a strict consistency, all update operations will be performed synchronously, meaning that they must block, locking all replicas until the operation is complete, and forcing competing clients to wait. A side effect of such a design is that during a failure, some of the data will be entirely unavailable. As Amazon CTO Werner Vogels puts it, “rather than dealing with the uncertainty of the correctness of an answer, the data is made unavailable until it is absolutely certain that it is correct” (“Dynamo: Amazon’s Highly Distributed Key-Value Store”: [], 207).

We could alternatively take an optimistic approach to replication, propagating updates to all replicas in the background in order to avoid blowing up on the client. The difficulty this approach presents is that now we are forced into the situation of detecting and resolving conflicts. A design approach must decide whether to resolve these conflicts at one of two possible times: during reads or during writes. That is, a distributed database designer must choose to make the system either always readable or always writable.

Dynamo and Cassandra choose to be always writable, opting to defer the complexity of reconciliation to read operations, and realize tremendous performance gains. The alternative is to reject updates amidst network and server failures.

In Cassandra, consistency is not an all-or-nothing proposition, so we might more accurately term it “tuneable consistency” because the client can control the number of replicas to block on for all updates. This is done by setting the consistency level against the replication factor.

The replication factor lets you decide how much you want to pay in performance to gain more consistency. You set the replication factor to the number of nodes in the cluster you want the updates to propagate to (remember that an update means any add, update, or delete operation).

The consistency level is a setting that clients must specify on every operation and that allows you to decide how many replicas in the cluster must acknowledge a write operation or respond to a read operation in order to be considered successful. That’s the part where Cassandra has pushed the decision for determining consistency out to the client.

So if you like, you could set the consistency level to a number equal to the replication factor, and gain stronger consistency at the cost of synchronous blocking operations that wait for all nodes to be updated and declare success before returning. This is not often done in practice with Cassandra, however, for reasons that should be clear (it defeats the availability goal, would impact performance, and generally goes against the grain of why you’d want to use Cassandra in the first place). So if the client sets the consistency level to a value less than the replication factor, the update is considered successful even if some nodes are down.

Brewer’s CAP Theorem

In order to understand Cassandra’s design and its label as an “eventually consistent” database, we need to understand the CAP theorem. The CAP theorem is sometimes called Brewer’s theorem after its author, Eric Brewer.

While working at University of California at Berkeley, Eric Brewer posited his CAP theorem in 2000 at the ACM Symposium on the Principles of Distributed Computing. The theorem states that within a large-scale distributed data system, there are three requirements that have a relationship of sliding dependency: Consistency, Availability, and Partition Tolerance.


All database clients will read the same value for the same query, even given concurrent updates.


All database clients will always be able to read and write data.

Partition Tolerance

The database can be split into multiple machines; it can continue functioning in the face of network segmentation breaks.

Brewer’s theorem is that in any given system, you can strongly support only two of the three. This is analogous to the saying you may have heard in software development: “You can have it good, you can have it fast, you can have it cheap: pick two.”

We have to choose between them because of this sliding mutual dependency. The more consistency you demand from your system, for example, the less partition-tolerant you’re likely to be able to make it, unless you make some concessions around availability.

The CAP theorem was formally proved to be true by Seth Gilbert and Nancy Lynch of MIT in 2002. In distributed systems, however, it is very likely that you will have network partitioning, and that at some point, machines will fail and cause others to become unreachable. Packet loss, too, is nearly inevitable. This leads us to the conclusion that a distributed system must do its best to continue operating in the face of network partitions (to be Partition-Tolerant), leaving us with only two real options to choose from: Availability and Consistency.

Figure 1-1 illustrates visually that there is no overlapping segment where all three are obtainable.

CAP Theorem indicates that you can realize only two of these properties at once
Figure 1-1. CAP Theorem indicates that you can realize only two of these properties at once

It might prove useful at this point to see a graphical depiction of where each of the nonrelational data stores we’ll look at falls within the CAP spectrum. The graphic in Figure 1-2 was inspired by a slide in a 2009 talk given by Dwight Merriman, CEO and founder of MongoDB, to the MySQL User Group in New York City (you can watch it online at However, I have modified the placement of some systems based on my research.

Figure 1-2 shows the general focus of some of the different databases we discuss in this chapter. Note that placement of the databases in this chart could change based on configuration. As Stu Hood points out, a distributed MySQL database can count as a consistent system only if you’re using Google’s synchronous replication patches; otherwise, it can only be Available and Partition-Tolerant (AP).

It’s interesting to note that the design of the system around CAP placement is independent of the orientation of the data storage mechanism; for example, the CP edge is populated by graph databases and document-oriented databases alike.

Where different databases appear on the CAP continuum
Figure 1-2. Where different databases appear on the CAP continuum

In this depiction, relational databases are on the line between Consistency and Availability, which means that they can fail in the event of a network failure (including a cable breaking). This is typically achieved by defining a single master server, which could itself go down, or an array of servers that simply don’t have sufficient mechanisms built in to continue functioning in the case of network partitions.

Graph databases such as Neo4J and the set of databases derived at least in part from the design of Google’s Bigtable database (such as MongoDB, HBase, Hypertable, and Redis) all are focused slightly less on Availability and more on ensuring Consistency and Partition Tolerance.


If you’re interested in the properties of other Big Data or NoSQL databases, see this book’s Appendix A.

Finally, the databases derived from Amazon’s Dynamo design include Cassandra, Project Voldemort, CouchDB, and Riak. These are more focused on Availability and Partition-Tolerance. However, this does not mean that they dismiss Consistency as unimportant, any more than Bigtable dismisses Availability. According to the Bigtable paper, the average percentage of server hours that “some data” was unavailable is 0.0047% (section 4), so this is relative, as we’re talking about very robust systems already. If you think of each of these letters (C, A, P) as knobs you can tune to arrive at the system you want, Dynamo derivatives are intended for employment in the many use cases where “eventual consistency” is tolerable and where “eventual” is a matter of milliseconds, read repairs mean that reads will return consistent values, and you can achieve strong consistency if you want to.

So what does it mean in practical terms to support only two of the three facets of CAP?


To primarily support Consistency and Availability means that you’re likely using two-phase commit for distributed transactions. It means that the system will block when a network partition occurs, so it may be that your system is limited to a single data center cluster in an attempt to mitigate this. If your application needs only this level of scale, this is easy to manage and allows you to rely on familiar, simple structures.


To primarily support Consistency and Partition Tolerance, you may try to advance your architecture by setting up data shards in order to scale. Your data will be consistent, but you still run the risk of some data becoming unavailable if nodes fail.


To primarily support Availability and Partition Tolerance, your system may return inaccurate data, but the system will always be available, even in the face of network partitioning. DNS is perhaps the most popular example of a system that is massively scalable, highly available, and partition-tolerant.


Note that this depiction is intended to offer an overview that helps draw distinctions between the broader contours in these systems; it is not strictly precise. For example, it’s not entirely clear where Google’s Bigtable should be placed on such a continuum. The Google paper describes Bigtable as “highly available,” but later goes on to say that if Chubby (the Bigtable persistent lock service) “becomes unavailable for an extended period of time [caused by Chubby outages or network issues], Bigtable becomes unavailable” (section 4). On the matter of data reads, the paper says that “we do not consider the possibility of multiple copies of the same data, possibly in alternate forms due to views or indices.” Finally, the paper indicates that “centralized control and Byzantine fault tolerance are not Bigtable goals” (section 10). Given such variable information, you can see that determining where a database falls on this sliding scale is not an exact science.


Cassandra is frequently referred to as a “column-oriented” database, which is not incorrect. It’s not relational, and it does represent its data structures in sparse multidimensional hashtables. “Sparse” means that for any given row you can have one or more columns, but each row doesn’t need to have all the same columns as other rows like it (as in a relational model). Each row has a unique key, which makes its data accessible. So although it’s not wrong to say that Cassandra is columnar or column-oriented, it might be more helpful to think of it as an indexed, row-oriented store, as we examine more thoroughly in Chapter 3. I list the data orientation as a feature, because there are several data models that are easy to visualize and use in a nonrelational model; it’s a weird mixture of laziness and possibly inviting far more work than necessary to just assume that the relational model is always best, regardless of your application.

Cassandra stores data in what can be thought of for now as a multidimensional hash table. That means you don’t have to decide ahead of time precisely what your data structure must look like, or what fields your records will need. This can be useful if you’re in startup mode and are adding or changing features with some frequency. It is also attractive if you need to support an Agile development methodology and aren’t free to take months for up-front analysis. If your business changes and you later need to add or remove new fields on the fly without disrupting service, go ahead; Cassandra lets you.

That’s not to say that you don’t have to think about your data, though. On the contrary, Cassandra requires a shift in how you think about it. Instead of designing a pristine data model and then designing queries around the model as in RDBMS, you are free to think of your queries first, and then provide the data that answers them.


Cassandra requires you to define an outer container, called a keyspace, that contains column families. The keyspace is essentially just a logical namespace to hold column families and certain configuration properties. The column families are names for associated data and a sort order. Beyond that, the data tables are sparse, so you can just start adding data to it, using the columns that you want; there’s no need to define your columns ahead of time. Instead of modeling data up front using expensive data modeling tools and then writing queries with complex join statements, Cassandra asks you to model the queries you want, and then provide the data around them.

High Performance

Cassandra was designed specifically from the ground up to take full advantage of multiprocessor/multicore machines, and to run across many dozens of these machines housed in multiple data centers. It scales consistently and seamlessly to hundreds of terabytes. Cassandra has been shown to perform exceptionally well under heavy load. It consistently can show very fast throughput for writes per second on a basic commodity workstation. As you add more servers, you can maintain all of Cassandra’s desirable properties without sacrificing performance.

Where Did Cassandra Come From?

The Cassandra data store is an open source Apache project available at Cassandra originated at Facebook in 2007 to solve that company’s inbox search problem, in which they had to deal with large volumes of data in a way that was difficult to scale with traditional methods. Specifically, the team had requirements to handle huge volumes of data in the form of message copies, reverse indices of messages, and many random reads and many simultaneous random writes.

The team was led by Jeff Hammerbacher, with Avinash Lakshman, Karthik Ranganathan, and Facebook engineer on the Search Team Prashant Malik as key engineers. The code was released as an open source Google Code project in July 2008. During its tenure as a Google Code project in 2008, the code was updateable only by Facebook engineers, and little community was built around it as a result. So in March 2009 it was moved to an Apache Incubator project, and on February 17, 2010 it was voted into a top-level project.


A central paper on Cassandra by Facebook’s Lakshman and Malik called “A Decentralized Structured Storage System” is available at:

Cassandra today presents a kind of paradox: it feels new and radical, and yet it’s solidly rooted in many standard, traditional computer science concepts and maxims that successful predecessors have already institutionalized. Cassandra is a realist’s kind of database; it doesn’t depart from the relational model to be a fun art project or experiment for smart developers. It was created specifically to solve a real-world problem that existing tools weren’t able to solve. It acknowledges the limitations of prior methods and faces our new world of big data head-on.

Use Cases for Cassandra

We have now unpacked the elevator pitch and have an understanding of Cassandra’s advantages. Despite Cassandra’s sophisticated design and smart features, it is not the right tool for every job. So in this section let’s take a quick look at what kind of projects Cassandra is a good fit for.

Large Deployments

You probably don’t drive a semi truck to pick up your dry cleaning; semis aren’t well suited for that sort of task. Lots of careful engineering has gone into Cassandra’s high availability, tuneable consistency, peer-to-peer protocol, and seamless scaling, which are its main selling points. None of these qualities is even meaningful in a single-node deployment, let alone allowed to realize its full potential.

There are, however, a wide variety of situations where a single-node relational database is all we may need. So do some measuring. Consider your expected traffic, throughput needs, and SLAs. There are no hard and fast rules here, but if you expect that you can reliably serve traffic with an acceptable level of performance with just a few relational databases, it might be a better choice to do so, simply because RDBMS are easier to run on a single machine and are more familiar.

If you think you’ll need at least several nodes to support your efforts, however, Cassandra might be a good fit. If your application is expected to require dozens of nodes, Cassandra might be a great fit.

Lots of Writes, Statistics, and Analysis

Consider your application from the perspective of the ratio of reads to writes. Cassandra is optimized for excellent throughput on writes.

Many of the early production deployments of Cassandra involve storing user activity updates, social network usage, recommendations/reviews, and application statistics. These are strong use cases for Cassandra because they involve lots of writing with less predictable read operations, and because updates can occur unevenly with sudden spikes. In fact, the ability to handle application workloads that require high performance at significant write volumes with many concurrent client threads is one of the primary features of Cassandra.

According to the project wiki, Cassandra has been used to create a variety of applications, including a windowed time-series store, an inverted index for document searching, and a distributed job priority queue.

Geographical Distribution

Cassandra has out-of-the-box support for geographical distribution of data. You can easily configure Cassandra to replicate data across multiple data centers. If you have a globally deployed application that could see a performance benefit from putting the data near the user, Cassandra could be a great fit.

Evolving Applications

If your application is evolving rapidly and you’re in “startup mode,” Cassandra might be a good fit given its schema-free data model. This makes it easy to keep your database in step with application changes as you rapidly deploy.

Who Is Using Cassandra?

Cassandra is still in its early stages in many ways, not yet seeing its 1.0 release at the time of this writing. There are few easy, graphical tools to help manage it, and the community has not settled on certain key internal and external design questions that have been revisited. But what does it say about the promise, usefulness, and stability of a data store that even in its early stages is being used in production by many large, well-known companies?


It is a logical fallacy, informally called the Bandwagon Fallacy, to argue that just because something is growing in popularity means that it is “true.” Cassandra is without a doubt enjoying skyrocketing growth in popularity, especially over the past year or so. Still, my point here is that the many successful production deployments at a variety of companies for a variety of purposes is sufficient to suggest its usefulness and readiness.

The list of companies using Cassandra is growing. These companies include:

  • Twitter is using Cassandra for analytics. In a much-publicized blog post (at, Twitter’s primary Cassandra engineer, Ryan King, explained that Twitter had decided against using Cassandra as its primary store for tweets, as originally planned, but would instead use it in production for several different things: for real-time analytics, for geolocation and places of interest data, and for data mining over the entire user store.

  • Mahalo uses it for its primary near-time data store.

  • Facebook still uses it for inbox search, though they are using a proprietary fork.

  • Digg uses it for its primary near-time data store.

  • Rackspace uses it for its cloud service, monitoring, and logging.

  • Reddit uses it as a persistent cache.

  • Cloudkick uses it for monitoring statistics and analytics.

  • Ooyala uses it to store and serve near real-time video analytics data.

  • SimpleGeo uses it as the main data store for its real-time location infrastructure.

  • Onespot uses it for a subset of its main data store.

Cassandra is also being used by Cisco and Platform64, and is starting to see use at Comcast and for personalized television streaming to the Web and to mobile devices. There are others. The bottom line is that the uses are real. A wide variety of companies are finding use cases for Cassandra and seeing success with it. As of this writing, the largest known Cassandra installation is at Facebook, where they have more than 150TB of data on more than 100 machines.

Many more companies are currently evaluating Cassandra for production use in different projects, and a services company called Riptano, cofounded by Jonathan Ellis, the Apache Project Chair for Cassandra, was started in April of 2010. As more features are added and better tooling and support options are rolled out, anticipate even broader adoption.


In this chapter, we’ve taken an introductory look at Cassandra’s defining characteristics, history, and major features. We have seen which major companies are using it and what they’re using it for. We also examined a bit of history of the evolution of important contributions to the database field in order to gain a historical view of Cassandra’s value proposition.

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