NoSQL databases are designed to be simple when compared with relational databases, yet they sometimes seem complex to new users. In this article, we’ll explore the NoSQL architecture to help bring you up to speed.
The world of data storage and retrieval can be incredibly simple, or very challenging.
When you leverage the right solution for the amounts and types of data you’re dealing with, then you’ll make life much easier for yourself.
First, you need to know what types of storage options are available to you. In general, you can store and retrieve data using any of the following:
Spreadsheets are best for small datasets. For instance, when you’ve got less than 10,000 records to manage, you can easily store them in rows within a single spreadsheet. Later, you can quickly search that spreadsheet to find the exact data you need.
Relational databases are a step up from spreadsheets. When you’ve got 10k+ records, or when you’ve got multiple spreadsheets with 10k+ records, then it makes sense to use a relational system that is designed to quickly store and retrieve data across multiple systems (spreadsheets).
As you continue to scale up toward big data, and as your datasets become more complex, you eventually reach a point where relational databases become inefficient. To get real-time analytics, and to deal with more sophisticated records, you need a faster, more powerful alternative, and that’s where the NoSQL database comes in.
Now that you have an idea of the uses for different types of systems, let’s explore how data is actually stored within NoSQL.
Non-relational databases are powerful storage systems that emerged in the early 2000s. They’re designed to manage large volumes of unstructured data, return real-time web app analytics, and process big data across the internet of things (IoT).
Within the NoSQL system, there are multiple ways to store and retrieve records that surpass the limitations of relational systems. The primary ways data can be stored in NoSQL include:
Some NoSQL databases work with more than one type of record store. Most non-relational systems use one to three of the above. A select few, such as BangDB, offer queries across all five.
The key-value store is a database system that stores record assets of unique identifiers with an associated (paired) value. This data pairing is referred to as a “key-value pair.” The “key” is the unique identifier. The “value” is the data being identified, or its location.
A major benefit of key-value stores is that they are fast for data retrieval. Where relational systems store data across rows and columns and need to query across the database to return a record, the key-value store is more flexible and only has to search for the key, then return the associated value.
Due to the speed of returns, and their flexibility, key-value stores are particularly useful in certain cases such as:
The ability to minimize reads and writes, and to quickly locate datasets based on unique identifiers makes the key-value store a blazing fast option that outperforms relational databases in almost every way for businesses that deal in retail, advertising, eCommerce, and other web applications.
Another storage option, the document-store, stores data in a semi-structured document. The data can then be ordered with markers.
Information in this data type needs to be encoded in XML, JSON, BSON, or as a YAML file and is never stored in a table (which is why it is unsuitable for relational storage). Instead, complex datasets are contained in a single record.
Retrieval occurs when a key is used to locate the document, and then that document is searched for the information required.
A benefit of the document store is that different types of documents can be contained within a single store, and updates to those documents do not need to be related to the database.
Also, because there are no fields within this store, and therefore, no empty cells for missing records, the document store is incredibly efficient at returning data fast.
Document stores are highly useful when:
Document stores are flexible and easily scalable, and developers can work within them, even without prior knowledge of the system. These benefits make them a worthy tool for web applications, and for sensibly handling big data.
Column-based stores can also be a good NoSQL storage option. It records data in columns, rather than in rows. By storing data in columns, it is contained as a single, ongoing entry. This minimizes the number of disks accessed and avoids pulling in unnecessary memory, which speeds up record retrieval since a query does not need to pass over irrelevant rows to return information. Instead, only the information within the column is queried.
Column stores are most frequently used by companies that deal with large data warehousing setups. The data is structured as a table with columns and rows, and is then stored logically in a column-wise format so irrelevant data does not have to be bypassed before the target data is accessed and returned.
Column store databases are best for:
Column stores can save you time and computing power, especially when you have a lot of information that repeats, such as rows of names, addresses, phone numbers, and any other records that might be stored under individual data points.
For some businesses, relationships and connections between data take priority, so a graph NoSQL storage makes the most sense. Graph stores represent data in graphs instead of tables which makes them highly flexible and easily extendable.
The graph NoSQL storage database returns search results fast and speeds up indexing by representing data as networks of nodes and edges. In a graph store, data is stored in nodes and then connected with relationships in edges which are then grouped by labels.
Graph stores are most useful for things like:
They’re best at querying related datasets, although they may not be as efficient when working with big data which can slow their process down.
A final NoSQL storage option is the time series store which is primarily used for managing datasets that change over time. Time series store captures fixed and dynamic value sets and returns timely analytics.
Imagine a car lot with multiple cars. The time series store might have a fixed value data point for each car and then tracks the dynamic values within each car such as oil levels, tire pressure, etc. alongside a timestamp to allow the end-user to see how these metrics have changed over time.
Time series stores are valuable for:
Time series store is best for businesses where multiple systems require ongoing measurements within individual data points.
As any good developer will tell you, there is no one-size-fits-all solution. Each business is different and has unique storage needs.
When you deal with small volumes of structured data, then individual spreadsheets or relational databases can be a good fit; however, as your need for big data and real-time information increases, you will need to upgrade to a non-relational database.
From there, you will have to decide which storage solutions make the most sense for your business based on what you want to accomplish and the types of data you work with.
If you work with a variety of data types, and you need multiple storage options, then consider BangDB – one of the only NoSQL database providers that offer all of the storage types listed above in a single solution, even for free.