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Intro to NoSQL database apps, Part 1: Everything you ever wanted to know but were afraid to ask

When a company is small — say, just a few people, not much revenue — it’s natural to keep track of everything in simple spreadsheets or user-friendly databases such as Microsoft Access.  As you grow, you realize that your needs go beyond what Access was designed for, and you start thinking about large commercial databases such as SQL Server, or Oracle, or one of the open source databases such as MySQL.

But eventually, when you get big enough, you discover that even those databases aren’t beefy enough to handle the job.  Maybe it’s the type of data, or the volume of data, or your new cloud-based architecture, but you realize it’s time.

You need to start learning about NoSQL.

Can’t wait to jump right in?  Watch a free crash course on NoSQL and Cassandra.

NoSQL originally meant that literally: “No Structured Query Language”.  These days, though, it actually means “Not Only Structured Query Language”, because a NoSQL database tends to have much broader applications and flexibility than

NoSQL originally meant that literally: “No Structured Query Language”.  These days, though, it actually means “Not Only Structured Query Language”, because a NoSQL database tends to have much broader applications and flexibility than RDBMS.

There are more than 225 different NoSQL databases, including the more well known open source projects such as Cassandra, Redis, and Etcd, cloud-based versions such as Amazon Web Services DynamoDB, and proprietary products such as Oracle NoSQL. They’re all different, but they share various traits.

In this article, we’ll discuss what makes them different from traditional relational databases and why you might want to take advantage of them.

What is NoSQL and how is it different from RDBMS

The most obvious way that a NoSQL database differs from traditional relational databases is in the structure of the data.  An RDBMS consists of tables of data structured according to a schema:

These tables are usually “normalized”, meaning that specific pieces of data appear only once, and they’re linked together by keys.  So if, for example, you wanted to see all of Alice’s skills, you could do it with an SQL query:

SELECT EMPLOYEE.name, SKILLS.skill_name from SKILLS, EMPLOYEES where SKILLS.emp_id = EMPLOYEES.id and EMPLOYEES.name = 'Alice'

This kind of query brings the data from both tables together using a “join”.

Non-relational, or NoSQL databases, are different, in that there is no defined schema. Data is added and defined as it comes in, much as relationships are created and modified on the fly in an object oriented system, rather than being pre-defined in a dictionary.  The structure of the data doesn’t matter.

Well, almost.

In fact, there are four different types of NoSQL databases:

  • Key-value stores:  Key-value stores, such as Redis and etcd, do exactly what it says on the tin; they store a value associated with a key.  This might be something as simple as:
    NewLymeOH = 44047
    StatenIslandNY = 10314
    BostonMA = 02134
    

    Or it might be something more complicated, with parameterized keys such as

    Employee:1:firstname = Nick
    Employee:1:lastname = Chase
    Employee:2:firstname = Buddy
    Employee:2:lastname = Rich
    
  • Wide Column: Wide Column databases, such as Cassandra, are like a cross between a RDBMS and a key-value store, in that they do have tables, and the tables consist of rows, but each row can have different columns:

    Sometimes these database will support a query language similar to the SQL used with relational databases, but not always.

  • Document: Document databases, such as MongoDB, store each entity as a single document within the database. Like Wide Column databases, each document can have a completely different structure, which is often represented as JSON.
  • Graph:  Graph databases, such as Neo4J, concentrate not so much on the elements of the data itself but on the relationships between those elements.  They’re built on the concept of nodes or entities, analogous to a row in a table, properties, or information about each node, and edges, or relationships between nodes. (Ironically, a Graph database is usually persisted using a Key-Value store, but some actually use an RDBMS as their persistence layer.)

But structure isn’t the only difference between SQL and NoSQL databases. One of the most important differences has to do with consistency. RDBMSs are defined by the acronym ACID, an initialism for:

  • Atomicity: Either all operations in a transaction succeed or all of them fail.
  • Consistency:  Consistency means that the database will always be in a working state, with all constraints and triggers satisfied.
  • Isolation:  Another defining property of transactions is that once one begins, none of the changes are visible from outside of that transaction until the transaction is committed.
  • Durability: Once a transaction is committed, the data is saved in such a way that it will not be lost, even if there’s a crash or power failure.

NoSQL databases, on the other hand, are defined by the acronym BASE (because developers love a good pun), which stands for:

  • Basically Available: NoSQL databases are architected to be highly available; with no single point of failure, even if a node goes down, the database will still be
    operational.
  • Soft state:  The state of a NoSQL database can change without affecting the availability of the
  • Eventual consistency:  A NoSQL database can accept a transaction even if it takes time — usually on the order of milliseconds — for all nodes to reach a consistent state.

There’s no need to change over to a NoSQL database all at once; the two types can coexist quite nicely using a paradigm called polyglot persistence. But why would you even want to consider it in the first place?

Why you’d want to use a NoSQL database

There are lots of reasons that you might find yourself thinking about using one for the various NoSQL databases, including scalability and cost, performance, and flexibility.

In a high impact environment, data streams  and feeds can operate too quickly to allow for traditional transaction execution, which requires a commit and flush to the database in order to make them permanent. NoSQL databases, on the other hand, are designed to hold entries in memory and persistent them when storage has had time to catch up, enabling you to be more “run and gun” than an RDBMS.

NoSQL databases are designed so that they can be scaled horizontally, which means that you can start with a single small server and scale up — or down — as you need to, for a true Cloud native architecture. For example, let’s say you know from the beginning that you want high availability, so you start with three small servers to satisfy your requirement for redundancy.

These databases enable the system to continue functioning should one or more nodes go down — unlike RDBMS, where the failure of a single drive or server can bring down the entire application.  This means that if usage begins to outstrip the capacity of your three servers, you can add more, and in general they will be auto-discovered by the cluster and the data populated to the new nodes.

On the other hand, when usage goes down, you can shut down those additional nodes, and because the failure or disappearance of an individual node doesn’t affect the performance of the system, your application keeps on going.

Contrast this with a traditional RDBMS, which (usually) can only scale upwards, meaning that to get better performance, you need a larger machine.  As a result, you spend the vast majority of your time in one of two states: either your machine is too small for the traffic you’re getting and your users are getting a poor experience, or your machine is too big for the traffic you’re getting and you’ve got spare capacity sitting around, wasting money.

As far as performance is concerned, NoSQL databases are designed for very large datasets, and as a result, usually perform faster.  In addition, many, such as Redis, are in-memory databases, which improves performance even more.

Finally, there’s the NoSQL advantage itself. The ability to have a “schemaless” system provides a number of benefits, including:

In systems with huge amounts of data, getting locked into a particular schema can cause enormous problems later on.  Sure, you can always change the schema later, but for large systems this can be extremely dangerous and time consuming before you even consider changes to the application based on that database.

  • NoSQL databases are a good choice when speed and simplicity are more important than the ability to do transactions or immediate consistency.
  • You can store unstructured or differently structured data, because you don’t have to define a schema for every piece of data that goes on. If you have large amounts of unstructured data, such as documents, this means you can store it without alteration, leaving the original data intact.
  • You can create a hierarchy of data that is self-referential, or described by the data itself, enabling complicated structures without complicated planning.

It’s important to remember, however, that not all NoSQL databases are created equal.

Choosing the right NoSQL database

There is one significant drawback to NoSQL databases, however. While RDBMS’s are generally the same — SQL has been fairly standardized for decades, and it’s usually a matter of simply changing drivers to change the database behind your application — NoSQL databases are all different, and it’s important to know what you want before committing to one over another.

There are four major differences between NoSQL databases:

  • Data model: As we discussed earlier, there are four different kinds of NoSQL databases. If you’re primarily dealing with large documents, you’ll be better off with a Document-oriented database such as MongoDB, Couchbase, or CouchDB. If you have simple key-value pairs, obviously a key-value store such as Redis, etcd, or MemcacheDB is your best bet. If your data is very SQL-like, a wide-column database such as Cassandra or HBase should be your focus. Finally, if you are very interested in the relationships between various pieces of data, you’ll want a graph database such as Neo4J.
  • Architecture: While NoSQL databases are generally architected for scalability, they’re not all implemented in the same way. Some, like MongoDB, use the Master/Slave model where a single node acts as the database of record and other nodes assist. Others, like Cassandra or etcd, are masterless systems, in which every node is exactly the same. Depending on how you intend to operate your system, this may matter to you.
  • Data distribution model:  How is the data synchronized? With some NoSQL databases, all nodes are read-write, taking data and replicating it out to all other nodes. This method is an advantage when the application frequently writes to
    the database, as latency can be reduced by sending write operations to the closest node. Others designate a single node to accept write operations, and that node replicates the data to others to speed up reads. This can be beneficial in situations in which the application send makes changes to the database, but when writes are made, you want to make sure they’re captured quickly.
  • API: If you’re coming from the RDBMS world, one thing that might surprise you is the lack of standardization in the ways in which you interact with the data in each database. Some databases require the use of a specific API, where others make a SQL-like query language available, such as Cassandra’s CQL.

In addition, different types of NoSQL databases have different strengths and weaknesses.  For example, key-value stores have high performance and flexibility, but support only low complexity in the data. A graph database can handle high complexity but has only moderate performance.

If you’re thinking that this leads to the functional equivalent of “vendor lockin”, you’re right.  So it is always a good idea to thoroughly investigate before committing, even if that means doing a small proof of concept first.

Next week, we’ll be starting a project demonstrating the use of these NoSQL databases, building, and eventually containerizing, an application built on Cassandra.

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