Columnar Storage Formats: Benefits and Use Cases in Data Engineering

TL;DR

Data storage formats determine how data is organized and stored in a file or database. There are several different formats for data engineers to choose from. While high-level SQL vs. NoSQL database discussions are common, engineers can also consider column-oriented databases.

In columnar storage, data is organized and stored by columns. Each column contains the values of a specific attribute across all records. This format is ideal for analytical and OLAP (Online Analytical Processing) workloads.

Column stores are growing in importance in the current data ecosystem. They are used in data warehousing, business intelligence, analytics, machine learning, IoT data processing, and ad hoc querying.

In this article, we will explain what columnar storage is and how it works. Then, we will delve into its advantages and use cases, so data engineers can understand why it’s a worthy option. We’ve also provided six tips for successfully implementing a column database.

What is Columnar Storage?

Columnar storage (also known as column-oriented or c-store) is a data storage technique that organizes and stores data by columns. It is used for data warehousing and big data analytics, where fast query performance and efficient data compression are essential.

In a columnar database, each column of a table is stored separately, with all values from that column grouped together. This means that individual data elements of a particular attribute, such as “Name” or “Age,” are stored together.

This is in contrast to traditional row-oriented databases, where each row is stored contiguously, including all attributes of that row. A relational database (RDBMS), like MySQL or PostgreSQL, is an example of a row-oriented database. Here, the data is stored and retrieved by entire rows.

But, in columnar databases, like Snowflake or BigQuery, the data is stored and accessed column by column.

To understand how column-oriented databases work, let’s consider an example table with three columns: “Name,” “Age,” and “City.” In traditional databases that use row-oriented storage, the data might be stored like this:

Row1: [“John Smith”, 30, “New York”]

Row2: ["Jane Doe", 25, "Chicago"]

Row3: [“Bob Johnson”, 35, “Miami”]

In a column-oriented database, the same data type would be stored in one column. It could be stored like this:

Name: [“John Smith”, “Jane Doe”, “Bob Johnson”]

Age: [30, 25, 35]

City: [“New York”, “Chicago”, “Miami”]

This storage method makes it easier and faster to retrieve relevant data.

In a simple example, if we wanted to find the average of all the ages, it would be faster in a columnar database, where all the ages are grouped together, compared to other database methodologies.

Column-oriented databases are especially effective when dealing with large datasets and complex analytical queries.

Advantages of Columnar-oriented Databases

There are three key benefits to using column-oriented storage:

Data compression

Column-oriented databases allow for better data compression ratios compared to row-oriented databases. This is because compression algorithms can take advantage of the similar or repetitive values stored in each column.

For example, dictionary encoding can be applied to store unique values in a dictionary, replacing the original values with shorter references. 

Additionally, compression algorithms can exploit patterns and repetitions within a column to achieve higher compression ratios. 

Improved compression leads to more efficient storage and cache utilization, better query performance, cost savings, and, in some cases, increased data security compared to traditional databases.

Query performance

In columnar data storage, the database engine can read and process only the necessary columns, reducing I/O and improving query performance.

Since the relevant values are stored together, analysts can perform aggregations, like sum, average, or count operations, more efficiently. This is particularly beneficial for analytical use cases that involve aggregations and analytics.

In a columnar database, column values enable vectorized processing, where operations are applied to multiple values at once using SIMD (Single Instruction, Multiple Data) instructions. This allows for parallel processing of various values within a column.

Columnar databases also facilitate efficient predicate pushdown. Filters and conditions can be applied directly to the relevant columns, reducing the amount of data that needs to be processed.

These factors can lead to significant performance improvements for OLAP workloads and aggregation queries.

Scalability

Column-oriented databases are highly scalable. They allow data engineers to add more columns to a table without affecting the existing columns. In addition, each column can be independently compressed, indexed, or partitioned to optimize performance and storage. 

This flexibility enables organizations to scale easily to accommodate evolving business requirements.

In modern column stores, like cloud data warehouses, the distributed architecture allows for data partitioning at the column level. This enables parallel processing and horizontal scalability by adding more nodes.

These features enable organizations to manage increasing data volumes and growing query workloads while maintaining performance and efficiency.

Columnar Storage Formats

Columnar storage formats are specific implementations that define how data is organized and stored in a columnar database. These formats optimize storage, compression, and query performance for columnar data. 

Here are three well-known formats:

1. Apache Parquet

Parquet is a popular columnar storage format used in big data processing frameworks like Apache Hadoop and Apache Spark. It offers efficient compression and encoding techniques, enabling high-performance query execution. 

Parquet supports schema evolution, predicate pushdown, and predicate filtering, making it well-suited for analytical workloads.

1. Apache ORC

Apache ORC (Optimized Row Columnar) is a high-performance columnar format for data processing frameworks. It aims to provide efficient storage, compression, and query execution for analytical workloads. 

It provides advanced compression algorithms, predicate pushdown, and lightweight indexes for fast data retrieval. ORC also supports fine-grained type-specific encoding, enabling the processing of complex data types.

Capacitor/ BigQuery

Capacitor is a column-based format specifically designed for BigQuery, a fully-managed cloud data warehouse by Google, to optimize storage and query performance.

It leverages techniques like run-length encoding, dictionary encoding, and bitmap indexes to achieve high compression ratios and efficient query execution. Capacitor also supports nested data structures and schema evolution.

Use Cases for Columnar Storage in Data Engineering

Columnar databases have three main use cases that leverage their specific advantages and optimizations:

Data warehousing and big data analytics

Columnar databases are widely used in data warehousing environments. Since data warehouses store large volumes of data from multiple sources, column-oriented storage provides benefits such as compression, efficient storage utilization, and fast query execution.

This enables modern cloud data warehouses to handle massive datasets and complex queries effectively.

It is also employed in big data analytics platforms and frameworks since it enables massive parallel processing (MPP). 

A columnar database’s ability to selectively access relevant columns and apply compression techniques is particularly advantageous when dealing with large-scale data processing, high-performance analytics, and machine learning tasks.

Machine learning and AI workloads

Machine learning and AI workloads often involve complex data transformations and feature engineering. A column-oriented database’s optimized retrieval and query performance can significantly speed up these operations, allowing for faster model training and experimentation.

In addition, a column store’s ability to selectively process specific columns facilitates feature selection and extraction processes. Machine learning practitioners can easily extract relevant features from columnar datasets.

A c-store that supports schema evolution is particularly useful in machine learning workflows where data sources may evolve over time or when managing multiple versions of training datasets.

Popular columnar formats, like Parquet or ORC, are widely supported by popular machine learning and analytics tools. So, they can be seamlessly integrated with frameworks such as Apache Spark, TensorFlow, or PyTorch, providing a consistent and efficient processing experience.

IoT data processing

Columnar databases can play a significant role in IoT (Internet of Things) data processing, which involves handling and analyzing all the data generated by many connected devices and sensors.

Since IoT data often contains multiple attributes or sensor readings per device, a columnar database can greatly reduce storage requirements by compressing and storing similar data together. 

Moreover, columnar databases that support schema evolution are crucial in dynamic IoT environments, where there are diverse and evolving schemas as new devices or sensors are added to the network.

A column-oriented database also enables faster query performance by minimizing I/O operations and data transfer. This is particularly useful where only a subset of attributes needs to be analyzed or aggregated.

IoT data is often transmitted over networks with limited bandwidth. So, a column database’s compression algorithms are especially beneficial for bandwidth optimization.

These advantages contribute to more efficient and scalable IoT processing workflows, enabling organizations to derive valuable insights and make real-time informed decisions.

Considerations When Implementing Columnar Storage

While a column store can be beneficial, several factors must be considered before implementing them.

When to use and when not to use a columnar database

A column store is ideal for: 

• Analytical workloads: Columnar databases are ideal for OLAP workloads and aggregation queries. It provides fast query performance and efficient data retrieval, making it well-suited for business intelligence, reporting, and data analysis tasks.
• High data volumes: If your data size is significant and you want to minimize storage costs and optimize data retrieval speed, a columnar database can be suitable.
• Selective data access: A columnar database can provide significant performance improvements if your data processing and analysis primarily involves accessing specific columns or attributes rather than the entire dataset.
• Time-series analysis: A columnar database is a good option for time-series analysis, where data is frequently queried and aggregated based on timestamps.
• Data warehousing: Columnar databases are widely used in data warehousing environments where efficient storage and rapid query performance are critical. It supports complex analytics and querying of large volumes of structured and semi-structured data.

A columnar database is not suitable for:

• Transactional workloads: A columnar database is not built for transactional operations that involve frequent updates, inserts, or deletes. It cannot be used for Online Transaction Processing (OLTP) workloads that involve incremental data loading.
• Real-time data ingestion: A columnar database may not be the best choice if you require real-time data ingestion and immediate availability of the latest data. Using a column store may introduce some latency in data ingestion and updates.
• Small datasets: If your dataset is relatively small and query performance is not a significant concern, simpler storage formats can suffice.
• Random access patterns: If your workload involves frequent random access to different rows or records within the dataset, row-based storage formats may perform better.
• Limited storage resources: Although columnar databases provide efficient storage utilization, they may require additional computational resources. Consider the trade-off between storage savings and processing overhead.

Ultimately, choosing a columnar database depends on your requirements, use cases, dataset characteristics, and workload patterns.

Potential challenges and how to overcome them

Several potential challenges may arise when using a columnar database: 

• Write performance: Write operations, like data ingestion and updates, can be slower than row-oriented storage. 

To overcome this challenge, you can employ batch processing or buffering techniques to optimize the write operations. By batching multiple write operations together or buffering data before writing, you can reduce the overhead of individual writes and improve overall write performance.

• Schema evolution: Managing schema evolution can be challenging, especially when dealing with large datasets or complex data structures.

Planning and designing your schema evolution strategy is crucial. Engineers must consider the impact on current data, compatibility with existing queries and analytics, and maintaining data integrity during schema changes.

• Data skew and performance variability: In some cases, specific columns may have data skew, where the distribution of values across the column is uneven. This can result in performance variability, with queries on skewed columns taking longer to execute. 

To mitigate this issue, you can consider data partitioning or indexing techniques. Partitioning the data based on relevant criteria can distribute the workload more evenly, improving query performance. 

Additionally, creating appropriate indexes on columns can further optimize query execution.

• Data updates and deletes: A columnar database may not perform well when it comes to frequent updates of individual records because they may require rewriting entire column chunks.

If your use case involves frequent updates, you can evaluate alternative strategies, such as hybrid storage solutions that combine column-oriented and row-oriented databases or employ specialized techniques like delta encoding or log-structured storage.

• Null values: If your dataset has many null values in certain columns, it can lead to inefficient storage and query performance. Null values take up space in a columnar database and may result in storage waste. 

To address this challenge, you can explore techniques like bitmap encoding or run-length encoding to handle null values efficiently.

6 tips for successful implementation of columnar storage

Here are six tips to successfully implement a columnar database that meets your performance, scalability, and analytical requirements:

• Understand your data and workload: Gain a deep understanding of your data characteristics and the specific analytical workloads. Analyze the query and data access patterns and performance requirements to determine which columns are frequently accessed and should be prioritized.
• Choose the correct format: Evaluate different columnar formats’ features, performance, and compatibility. Consider compression capabilities, schema evolution support, integration with existing tools, and ecosystem support.
• Optimize data organization and compression: Experiment with different compression techniques and configurations to find the best balance between storage efficiency and query performance.
• Plan for schema evolution: If your data schema is likely to evolve, plan for schema evolution in advance. Choose a storage format that supports schema evolution and design strategies for managing schema changes while minimizing disruption to existing processes.
• Leverage indexing: Continuously analyze the query patterns and create appropriate indexes to ensure accurate query planning and execution.
• Monitor and optimize performance: Track query execution times, data ingestion rates, and storage utilization to identify areas for optimization. Regularly review and fine-tune configurations based on evolving data and workload patterns.

Conclusion

A columnar database stores data by columns instead of rows like most traditional row-oriented databases. This storage format offers several benefits for analytical workloads.

Data engineers can create a data ecosystem that uses traditional relational databases for online transaction processing and column stores for OLAP workloads. This hybrid architecture can boost query performance and data compression for their projects.

Implementing columnar databases requires understanding your workload, but when done correctly, it empowers organizations to derive valuable insights from their data for informed decision-making. If you're eager to expand your knowledge, delve into our comprehensive article on Data Partitioning for in-depth insights.

You can learn more about databases and analytics on our blog.