Columnar Databases were first introduced in the early 1970s into the field of biology in the form of a software package called TAXIR, short for Taxonomic Information Retrieval. Since then, these Databases have found their significance in various domains. Today, these Columnar Databases have become even more prominent due to the increasing Cloud-based analytics workloads.
Historically, data had been stored in relational tables in a row-wise manner. However, this format becomes a bottleneck when there is a large quantity of data. This is the reason Columnar Databases were introduced. Column-based storage speeds up database query performance by significantly reducing I/O during query execution. The same fundamental feature has been powering Relational Warehouses such as Amazon Redshift and Snowflake.
What is Columnar Database?
Unlike traditional databases, a Columnar Database stores data in columns instead of rows, enhancing efficiency and speed for analytics workflows. This column-based structure reduces storage costs and significantly improves query performance.
For example, when surveying a city, data such as names, addresses, and genders are stored separately in distinct column families, allowing applications to read only the relevant column without scanning all data.
Each column family consists of logically connected columns that are typically fetched or modified together, and new columns can be added dynamically without requiring every row to have a value for each column.
Column-family data stores adopt a denormalized approach for managing sparse data, allowing for improved analytical query performance through techniques like dictionary compression, run-length encoding (RLE), or Huffman-based compression. These methods enable the storage of large multi-terabyte databases in memory, which were previously confined to disk storage.
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A columnar database is that DBMS which is based upon the columnar architecture rather than row-based architecture and hence optimized for analytical workflows, highly efficient in terms of query performances and data storage for filtering and aggregating columns of table operations.
Key Benefits of Columnar Databases
- Faster Query Speeds: Columnar databases retrieve only relevant columns in a query. It therefore bypasses unnecessary data. This is suitable for analytical applications where filtering and aggregations are very frequent.
- Better Compression: Columns have similar data, so there is a possibility of using dictionary compression, run-length encoding (RLE), and Huffman-based compression. These help reduce the cost of storage and large multi-terabyte databases can be stored in memory, thus enabling faster processing.
- Storage Efficiency: Columnar databases are excellent at handling sparse data using a denormalized approach that reduces redundancy and improves storage efficiency. They are great for data warehousing and managing huge amounts of data.
- Scalability: Columnar databases are created to be distributed systems that could be scaled horizontally across the clusters of inexpensive hardware and are thus cost-effective when one requires big data.
Columnar Database vs Row Based Database
1) Storage
Row-wise storage: 1-emp1-d1-2-emp2-d2-3-emp3-d3
As a traditional database stores data serially in rows such as ID, Name, Department, it also leads to poor access times, as similar data is not ordered together.
Column-wise storage: 1-2-3-emp1-emp2-emp3-d1-d2-d3
Columnar databases store data in columns, that is, all the names together. This organization simplifies data extraction and accelerates query performance, especially while dealing with large datasets.
2) Performance
Row Databases: Suitable for OLTP systems where most reading, writing, and updating happen.
Column databases: Optimized for OLAP (Online Analytical Processing) systems; they can often support faster retrievals because, once disk I/O is minimized, compression is also better. This, in turn, can lead to a drastic improvement in query speed.
| Feature | Row Database | Column Database |
| Storage Format | Data stored row-wise | Data stored column-wise |
| Access Speed | Slower for analytics | Faster for similar data retrieval |
| Use Case | OLTP systems (e.g., financial transactions) | OLAP systems (e.g., data analytics) |
| Performance | Optimal for frequent updates | Optimized for fast retrieval |
| Compression | Less efficient | Better compression reduced I/O |
| Query Performance | Slower with large datasets | Faster due to reduced disk I/O |
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Columnar Database Advantages vs Disadvantages
Advantages
- Queries that involve only a few columns.
- Aggregation queries against vast amounts of data.
- Column-wise compression.
Disadvantages
- Incremental data loading.
- Online Transaction Processing (OLTP) usage.
- Queries against only a few rows.
Columnar Database Example
- All of the values in a column are physically grouped together in a columnar database.
- For example, all of the values in column 1 are grouped together, then all of the values in column 2 are grouped together, and so on.
- Because the data is stored in record order, the 100th entry in column 1 and the 100th entry in column 2 both belong to the same input record.
- Individual data elements, such as customer name, can now be accessed in columns as a group rather than row by row.
An example to illustrate the storage of data in columnar Databases:
| Account number | Last name | First name | Purchase (in dollars) |
| 0211 | Warne | Jim | 56.75 |
| 0212 | Down | Jolly | 240.00 |
| 0213 | Bing | Taylor | 120.88 |
In a columnar DBMS, the data would be stored as given below:
- 0211, 0212, 0213;
- Warne, Down, Bing;
- Jim, Joly, Taylor;
- 56.75, 240.00, 120.88
In a row-oriented DBMS, the data would be stored as given below:
- 0211, Warne, Jim, 56.75;
- 0212, Down, Joly, 240.00;
- 0213, Bing, Taylor, 120.88
Use Case of Columnar Database
Big Data Processing, Business Intelligence (BI), and Analytics all benefit from columnar databases. High volumes of data are fed into data stores by IoT (Internet of Things) devices. Since IoT records may have few data pieces, and fresh records arrive in a continuous stream, evaluating the data would be suitable in a big data columnar database. For example, cellular telecommunications rely on the continuous connection between the handset and the towers. In order to discover difficulties with a tower, telecom operators must evaluate that data in near real-time.
Top 6 Columnar Databases
The following are some of the best Open Source Columnar Databases available in the market today:
1) Apache Druid
- Apache Druid is an open-source real-time analytics database designed to run OLAP queries on large data sets at faster rates.
- Apache Druid is mostly used for real-time ingestion (up to millions of records/sec), and fast query performance and high uptime are critical.
- The speed boost to queries on Apache Druid is mainly due to its column-oriented storage.
- On Druid, each column is stored optimized for its particular data type, which supports fast scans and aggregations. Apache Druid is deployed in clusters of hundreds of servers and can offer high data ingestion rates, high retention of trillions of records, and query latencies as low as milliseconds.
2) Apache HBase
- Apache HBase is a column-oriented, distributed NoSQL database that works on the Apache Hadoop framework and is open-source. On top of the Hadoop Distributed File System (HDFS), Apache HBase is a column-oriented Non-Relational Database.
- HBase allows users to group attributes together into Column Families such that the elements of a Column Family are all stored together. With HBase, users must predefine the table schema and specify the Column Families. However, the schema is flexible as new columns can be added to the Column Families as per the application requirements.
3) Apache Kudu
- Apache Kudu is an open-source distributed data storage engine used to run fast analytics on ever-changing data.
- Apache Kudu’s column-based storage allows efficient encoding and compression. Thanks to techniques such as run-length encoding, differential encoding, and vectorized bit-packing, Kudu reads the data at a great pace as it is space-efficient at storing it.
- Using techniques such as lazy data materialization and predicate pushdown, Kudu can perform drill-down and needle-in-a-haystack queries over billions of rows and TBs of data in seconds.
4) MonetDB
- Since 1993, MonetDB has been offering Column-Store solutions for high-performance Data Warehouses for business intelligence services. MonetDB’s storage model is based on vertical fragmentation.
- Its query execution architecture is tuned to modern computers and has a modular software architecture. MonetDB is based on SQL 2003 and has a run-time query optimization feature as well.
5) ClickHouse
- ClickHouse is an open-source OLAP Database Management System that is quick and easy to use. It is column-oriented and allows the generation of analytical reports using SQL queries in real-time.
- With the same available I/O throughput and CPU capacity, ClickHouse processes common analytical queries two to three orders of magnitude faster than traditional row-oriented systems.
- The Column Storage format allows more hot data to be stored in RAM, resulting in faster usual response times.
6) MariaDB Column Store
- MariaDB Column Store is an open-source column-based storage engine that employs a parallel distributed data architecture designed for processing petabytes of data.
- MariaDB is linearly scalable and offers exceptional performance with real-time responses to analytical queries.
- MariaDB leverages the benefits of column-based storage, such as compression, just-in-time projection, and horizontal and vertical partitioning, to deliver tremendous performance when analyzing large data sets.
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Conclusion
In this article, you have gained a basic understanding of column-wise and row-wise databases and have been exposed to various column-based databases. Understand fragmentation and replication in distributed databases with our comprehensive guide on managing data across distributed systems
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FAQs
1. What is the difference between a database and data storage?
The difference between a database and data storage is that databases are more structured and have a defined schema.
2. Is NoSQL a columnar database?
NoSQL is a broad category of databases. Some examples of the different types of databases classified as NoSQL include key-value, document, columnar, and graph databases. Columnar databases are a type of NoSQL, though not all NoSQL databases exist as columnar structures.
3. When should I use a columnar database?
Use a columnar database when you need fast query performance for analytical tasks, especially with large datasets, or when you frequently need to retrieve and analyze specific columns of data rather than entire rows.
4. what are the applications of columnar databases?
Columnar data stores are used in various applications. To name a few: For data warehousing, Big Data analytics and business intelligence, IoT Processing, Transactional systems or OLTP (Online Transactional Processing).
Dharmendra Kumar is a specialist writer in the data industry, known for creating informative and engaging content on data science. He expertly blends his problem-solving skills with his writing, making complex topics accessible and captivating for his audience.
