What Is a Columnar Database?

You might be familiar with databases that store records in rows. A columnar database, however, stores data in columns. A columnar database is a form of NoSQL database that stores unstructured data. It can retrieve data faster than a traditional structured row-based database. Databases that store data in columns allow for much faster reads but sacrifice performance on write transactions. Read performance is improved because data is stored grouped by column rather than by rows.

What Is a Columnar Database?

To speed up queries, a columnar database stores data in columns rather than rows. These modern databases are also sometimes called “column-oriented” or “wide-column” stores. As businesses increase the amount of data stored, they could reach terabytes (or more) of data storage that must be retrieved. Columnar databases speed up query processing and are often used for big data or queries for machine learning analytics.

Key Features of Columnar Databases

Improved query performance is one key benefit of columnar databases, but they have several other advantages. Here are a few reasons why you would benefit from switching from row-based databases to columnar databases:

• Data compression: Advanced data compression lowers the amount of storage requirements, which also requires less seek time to find the data on disk. The faster seek times and performance upgrades speed up common calculations (e.g., MIN or SUM).
• Faster analytics speed: Machine learning and analytics software require massive amounts of data, so a columnar database speeds up these applications with faster query processing of large data sets.
• Self-indexing: Administrators used to manual indexes on traditional databases will appreciate the columnar database’s ability to self-index, which also reduces the amount of storage space necessary for data.
• Vectorization: Columnar databases handle multiple data points for advanced analytics and mathematical functions much faster than standard row-based databases.
• Elimination of NULL: Instead of storing NULL values, which takes up storage space, columnar databases do not store missing or NULL values.

Use Cases for Columnar Databases

Columnar databases are most beneficial for data queries where only a few columns are necessary for results. Traditional relational databases have tables that could have several columns for a single row, but columnar databases group data based on columns. If you have a query that only needs a few columns to display results to users, then a columnar database will improve performance of your applications.

A few use cases for columnar databases:

• Business analytics: For many business metrics, you need a few columns to summarize success. A columnar database can better display analytics and machine learning predictions based on these few columns. For example, analytics based on total sales for a product could be well-suited for columnar database storage.
• Security or application monitoring: Data collected from application events (e.g., authentication errors or response times) can be stored in a columnar database and used in analytics for improving performance and stopping any ongoing cyberattacks.
• IoT: IoT sensors for warehouse machinery or healthcare monitoring collect data and store it in specific columns, which can then be used to detect anomalies in machinery or human bioactivity.

Comparison with Row-based Databases

The main difference between a column-based database and a row-based database is the backend storage functionality. A columnar database groups column data together, so queries don’t need to seek out entire rows for each column that must be retrieved. Instead, columns are grouped together for faster retrieval.

Row-based databases group storage of entire rows using indexes, so they’re beneficial when you have transactional queries. For example, if you host a site where users search for their recent purchases, a relational database offers better performance and development strategies. Column-based databases are better suited for big data and analytics. If you need to search millions of records to find purchases and feed results to machine learning algorithms, a column-based database would be better.

Popular Columnar Database Solutions

Several popular columnar databases are available for your development solutions. Each one has its own advantages and disadvantages. Here are a few to consider:

• Snowflake: Snowflake is popular with large data warehouse infrastructure. It can combine multiple data sources together to provide a query engine from one location. Snowflake is mainly used for machine learning and analytics, but it’s known for Snowpipe, which is a continuous data ingestion feature great for real-time output.
• MariaDB: MariaDB is a modified, more scalable version of MySQL, so it’s often used when the current infrastructure works with MySQL. Administrators familiar with MySQL will appreciate the extended JSON query support, and MariaDB supports up to 200,000 concurrent connections. MariaDB uses more extended storage engines including XtraDB, Aria, InnoDB, MariaDB ColumnStore, Memory, Cassandra, and Connect. Use MariaDB when you have high-volume connections and need fast real-time results.
• Redshift: Redshift is an Amazon solution, so it’s often used when an organization has AWS infrastructure. It’s beneficial for businesses working with AWS cloud databases that need to share data with Redshift for machine learning, forecasts, financial predictions, and user dashboards for analytics.
• BigQuery: For Google Cloud Platform (GCP) users, Google offers BigQuery. Like Redshift, administrators with data already stored on the Google platform can take advantage of BigQuery and use the data in GCP to build a silo of data fed to machine learning algorithms. Business intelligence and analytics are commonly used with BigQuery.
• Vertica: Administrators with the goal of integrating Hadoop solutions might find that Vertica is much more convenient than the other columnar databases listed here. Vertica is also beneficial if you want to deploy it on premises.
• SAP HANA: SAP HANA Cloud offers the SAP HANA DPaaS (database platform as a service), and SAP works with its own database for its ERP technology. Developers building JavaScript solutions might appreciate the SAP HANA JavaScript framework with HTML5 to support their ERP projects.
• Cosmos DB: Cosmos DB is a Microsoft Azure solution, so it’s used when administrators already have Azure cloud services. It’s commonly used in Microsoft environments, but it’s beneficial for IoT data collection, retail and marketing, gaming, and social applications in need of predictions and real-time analytics.

Conclusion

If you have large data sets based on a few columns in a relational database, you could improve performance by switching to a columnar database. These databases are perfect for analytics, real-time applications, machine learning, predictive analytics, and other big data applications. Most columnar databases work with big data with terabytes of storage requirements. Everpure provides solutions to store your big data that can be ingested and stored into your columnar database.