Time-Series Database vs Relational Database: Key Differences

Deciding between a time-series database vs relational database is not just about where to store the data. It is crucial for how your organization handles data – it is about optimizing how you retrieve insights from that data – it is about speed, efficiency, and accuracy.

Imagine the possibilities: streamlined operations, faster decision-making, and a competitive edge in your industry. That is the power of choosing the right database. But to make the most of it, you need to understand the differences between these databases and use their unique strengths to your advantage.

For this purpose, we have put together today’s guide where we will compare time series databases and relational databases. We will keep it simple, breaking down the differences to help you choose the right one for your needs.

What Is A Time-Series Database?

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Time-series databases are specifically designed to store, retrieve, and analyze data points that are indexed by time. These databases are optimized for handling time-stamped or time-series data from sensors, IoT devices, server metrics, financial market data, and any other data that is time-bound. 

Time-series databases provide specialized functions and data structures tailored for time-series data which help in faster inserts, efficient time-based queries, and downsampling/aggregation of data over time intervals.

What Is A Relational Database?

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Relational databases are designed for general-purpose data storage and retrieval. They organize data into tables with rows and columns and use SQL for querying and managing data. These databases use a schema to define the structure of the data to ensure data integrity and relationships between tables through foreign keys. 

Traditional relational databases are ideal for handling structured data with complex relationships between entities. They are widely used in various applications where data integrity, ACID (Atomicity, Consistency, Isolation, Durability) compliance, and complex querying capabilities are crucial, like eCommerce, finance, and traditional business applications.

Time-Series Database vs Relational Database: Understanding The 10 Key Differences

Let's break down the 10 key differences between time-series databases and relational databases to see which one fits the bill for specific data handling needs.

1. Data Structure

1.1. Relational Databases

Relational databases are structured around tables consisting of rows and columns. Each row contains specific data instances or records while columns have defined data types, specifying the kind of information it can store, like integers, strings, dates, or floating-point numbers. 

These databases employ a schema to establish the structure and relationships between different tables. For instance, if you have tables for customers and their orders, a relational database will use primary and foreign keys to link these tables. This ensures data integrity and facilitates queries across related information.

1.2. Time-Series Databases

Time-series databases center their structure primarily around timestamped data. They store sequences of data points collected over time. Instead of tables with various types of data, it focuses on a timestamp as its primary key. These databases efficiently handle time-stamped data entries, like sensor readings, financial market data, or logs. 

Time-series databases do not demand a predefined schema in the same way as relational databases do; instead, they optimize for quickly retrieving and analyzing time-based data. Their architecture prioritizes the chronological arrangement of data points which helps in streamlined retrieval and analysis of time-stamped information.

2. Data Modeling

2.1. Relational Databases

Relational databases rely on a structured data modeling approach. They use schemas that define the tables, columns, relationships, and constraints to organize and maintain the data. The schema outlines the structure of the data that will be stored. It supports complex querying and advanced data analysis with relational algebra operations.

This rigid schema enforces rules about what type of data can be stored in each field and how different tables relate to each other through keys. This modeling approach maintains data integrity and ensures consistency through constraints, keys, and normalization techniques like 1NF, 2NF, 3NF, etc. Queries across related tables are performed using JOIN operations.

2.2. Time-Series Databases

Time-series databases have a more flexible data modeling strategy. It primarily revolves around optimizing storage and query performance for time-stamped data. These databases employ specialized data structures like compressed or indexed representations to handle chronological data efficiently. 

Time-series databases provide schema on write data collection and low latency. This adaptability provides easier scalability and the ability to store various data types without major alterations to the database structure.

3. Querying Capabilities

3.1. Relational Databases

Relational databases are well-suited for complex querying tasks, especially in scenarios involving structured data and online transaction processing (OLTP). They use SQL as the standard interface for querying data. SQL provides a powerful set of commands that allow you to perform various operations like retrieving, updating, inserting, and deleting data. 

These databases support complex queries that involve multiple tables, using JOIN operations to combine data from different sources based on established relationships. You can specify conditions and criteria to filter and sort the data precisely according to their requirements.

3.2. Time-Series Databases

Time-series databases prioritize efficient querying and analysis of time-stamped data. They can handle vast amounts of time-stamped information and are optimized for time-oriented queries. Instead of relying solely on SQL, time-series databases offer specialized query languages tailored specifically for time-series data analysis. 

Time-series databases simplify the process of analyzing trends, patterns, and anomalies within time-series data by providing specialized functions for time-series analytics, like interpolation, downsampling, or calculating moving averages. This allows you to extract valuable insights from large volumes of timestamped data swiftly.

4. Optimization

4.1. Relational Databases

Relational databases are designed with a focus on optimizing the storage and retrieval of structured data with complex relationships, often emphasizing transactional integrity and ACID (Atomicity, Consistency, Isolation, Durability) properties. These databases use indexing, caching, and query optimization techniques to enhance performance. 

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Indexes create sorted structures based on specified columns for faster lookup operations and to improve the speed of data retrieval. Also, caching mechanisms store frequently accessed data in memory which reduces the need to retrieve information from disk storage repeatedly. 

Relational databases use query optimizers that analyze SQL queries and determine the most efficient execution plan, considering factors like data distribution, table sizes, and available indexes.

4.2. Time-Series Databases

Time-series databases excel in managing large volumes of sequential data points and historical information. To optimize the handling of time-series data, these databases implement specialized storage engines and compression techniques for efficiently storing sequences of timestamped entries. 

They organize data in a way that facilitates rapid retrieval and analysis of historical data points, prioritizing the speed of accessing time-oriented information over the complexity of relational structures. Time-series databases employ compression algorithms optimized for time-series data to minimize storage space while maintaining quick access to historical data points.

5. Performance

5.1. Relational Databases

Relational databases are optimized for transactional operations and complex queries involving structured data. These databases excel in handling various types of queries across tables, ensuring data integrity through normalization and enforcing relationships. 

However, when it comes to handling vast amounts of time-stamped or time-series data, their performance might degrade. Queries involving large datasets, especially those with complex JOIN operations across multiple tables or extensive aggregations, can experience latency. 

As the volume of data increases, the performance of relational databases in terms of query speed and responsiveness might suffer unless the database is carefully tuned, indexed, and structured for specific use cases.

5.2. Time-Series Databases

Time-series databases exhibit superior performance when dealing with time-based queries, like retrieving data within specific time ranges, aggregating data over intervals, or analyzing historical trends. Their performance remains consistent even as the volume of time-stamped data grows as they are structured and optimized for this specific use case. 

The uniqueness of time-series databases lies in their optimized storage mechanisms and specialized data structures, like the use of append-only storage and compression techniques. Additionally, they use specific indexing techniques like inverted indexes or bitmap indexes for faster access to time-oriented data.

6. Scalability

6.1. Relational Databases

Relational databases have historically faced challenges in scaling horizontally because of their design focusing on maintaining data integrity and consistency across various tables. As data stored in relational databases grows, scaling horizontally – spreading the database load across multiple servers – presents complexities in maintaining ACID properties and ensuring data coherence. 

While vertical scaling (increasing the resources of a single server) is a common approach to enhance performance, it has limitations regarding how much data a single server can handle.

6.2. Time-Series Databases

Time-series databases, on the other hand, are built with scalability as a primary concern, especially considering the nature of time-series data. Their architecture allows for horizontal scaling by distributing data across nodes or clusters. This way, they can handle larger volumes of data and a higher number of concurrent read and write operations.

7. Data Integrity & Consistency

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7.1. Relational Databases

Relational databases prioritize maintaining data integrity and enforcing consistency across the stored data. They achieve this through various mechanisms like enforcing constraints (e.g., unique, not null), defining relationships using foreign keys, and adhering to normalization principles. 

These databases support ACID properties to ensure that transactions are processed reliably and maintain the consistency of the data. ACID compliance guarantees that transactions are either executed completely or not at all which prevents partial or inconsistent updates to the database.

7.2. Time-Series Databases

Time-series databases focus more on efficiently storing and querying time-stamped data rather than traditional transactional consistency. As a result, they might not prioritize the strict ACID properties as heavily as relational databases do. Instead, time-series databases employ strategies that optimize for high-speed writes and queries on time-series data. 

While some time-series databases do offer strong consistency models, others provide eventual consistency, ensuring that data eventually reaches a consistent state across distributed systems but might momentarily exhibit inconsistencies during rapid write operations.

8. Maintenance & Management

8.1. Relational Databases

Relational databases typically require structured maintenance routines because of their complex schema and relationships. Database administrators (DBAs) perform tasks like schema alterations, index optimizations, and query tuning to ensure optimal performance. Regular backups, updates, and patches are crucial for maintaining data consistency and security.

Also, maintaining historical data in relational databases can be challenging, especially when dealing with vast amounts of archived data. The database size can significantly impact performance and require strategies like partitioning or archiving old data to manage the database size effectively.

8.2. Time-Series Databases

Time-series databases are engineered to simplify maintenance and management. They come with built-in functionalities for automatic deletion or archiving of older data points. This feature reduces the manual intervention required for managing the data lifecycle.

Additionally, because of their specialized architecture, time-series databases are designed to handle high ingestion rates. They use compression techniques and efficient storage formats to reduce the strain on storage systems and make it easier to manage and maintain high volumes of incoming data.

9. Storage Efficiency

9.1. Relational Databases

Relational databases store data in a tabular format which might cause redundant storage of similar information across different tables. The normalization process, while beneficial for maintaining data consistency and reducing data redundancy, can also contribute to increased storage requirements. 

Indexing and other optimization techniques in relational databases also occupy additional storage space for faster query processing.

9.2. Time-Series Databases

Time-series databases typically use techniques like data compression, downsampling, and efficient storage formats to minimize the storage footprint while accommodating vast amounts of time-series data. The focus on sequential data points allows for more streamlined storage as the databases prioritize storing and retrieving data based on timestamps.

Additionally, time-series databases employ strategies like tiered storage where data is stored in different tiers based on its importance or access frequency. This approach optimizes storage utilization by allocating higher-performing storage to frequently accessed data and using more cost-effective storage for less frequently accessed information.

10. Data Retention

10.1. Relational Databases

In relational databases, data retention strategies often depend on specific business requirements or regulatory constraints. Generally, the data in relational databases is retained for long periods, sometimes indefinitely, unless explicitly deleted or archived. Archiving old data or defining retention policies needs manual intervention and scripting to migrate or purge older records. 

Because of the normalized structure and complex relationships, deciding which data to retain and for how long can be challenging. As the volume of data grows, retaining extensive historical data in relational databases causes performance degradation and increased storage costs.

10.2. Time-Series Databases

Time-series databases come equipped with built-in functionalities for implementing data retention policies based on time intervals or specific criteria. They offer features to automatically delete or archive older data points which ensures that the database efficiently manages the lifecycle of time-series data.

Time-series databases also incorporate strategies where data points are aggregated or summarized over time intervals to reduce storage requirements while retaining important information. Some time-series databases provide configurable retention policies that allow you to define rules for retaining or purging data based on customized criteria.

	Relational Databases	Time-Series Databases
Data Structure	Tables with rows and columns; linked data via keys	Sequential time-stamped data as the primary focus
Data Modeling	Structured, rigid schemas for defined relationships	Flexible, easily adaptable to different data types
Querying	SQL-based, complex queries across tables	Specialized languages for time-based data analysis
Optimization	Indexing, caching, tailored for structured data	Specialized storage, compression for time-stamped entries
Performance	Best for structured data; can slow with huge volumes	Consistently high performance, even with increased data
Scalability	Complexities in horizontal scaling	Designed for horizontal scaling; handles large data
Data Integrity	Emphasizes integrity, follows ACID properties	Prioritizes speed, may sacrifice strict consistency
Maintenance	Requires structured upkeep and management	Simplified management with automated data handling
Storage Efficiency	Normalization may increase storage; indexing adds space	Efficient storage and compression for optimized use
Data Retention	Manual, complex retention strategies	Built-in policies for automatic data retention and deletion

Timeplus: A Powerful Streaming Data Analytics Platform

Timeplus offers a versatile solution that seamlessly functions as both a relational database and a time-series database, providing a unified and efficient platform for different data handling needs. Its foundation in streaming database technology allows for efficient operation across both database types.

This integration enables Timeplus to effectively manage complex data structures, adapt its data model for various operations like INSERT, UPDATE, and DELETE through Changelog Streams, and utilize sophisticated querying capabilities. 

These features, combined with the platform's innate flexibility, make it an ideal choice for applications demanding real-time data processing and in-depth data analysis.

Timeplus is designed for speed and efficiency and can handle large volumes of data with low latency. This makes it ideal for applications where timely data processing and analysis are critical. Also, Timeplus supports a wide range of data sources and provides powerful analytic tools, making it a comprehensive solution for diverse data engineering and data science needs.

Let’s take a look at its major features.

A. Timeplus Features Specific To Relational Databases

• Schema management: Timeplus offers dynamic schema management with which you can define and alter the schema of data streams.

• Data transformation: You can dynamically transform data, like scrubbing sensitive fields or deriving new columns, much like transformation capabilities in relational databases.

• SQL engine: Timeplus incorporates a high-performance SQL engine that allows you to interact with streaming data using familiar SQL syntax. This includes complex queries, joins, and aggregations.

• Data ingestion from various sources: It supports data ingestion from multiple sources like Apache Kafka, Amazon S3, and Kinesis, similar to the versatile data input capabilities of relational databases.

• Joins: It supports diverse join operations like stream-to-stream and stream-to-dimension table joins. These functionalities help combine data from different streams or the enrichment of streaming data with static information from dimension tables. 

B. Timeplus Features Specific To Time-Series Databases

• Time series data visualization: With its real-time visualization capabilities, you can observe trends and patterns over time.

• Time-based retention: It supports retention strategies based on data freshness or age, optimizing storage management and ensuring data relevance.

• Time series index: Timeplus uses event time to build an internal index for historical storage which enhances data retrieval and analysis efficiency.

• Windowed stream processing: Timeplus supports windowing functions like tumble, hop, and session, which are essential for segmenting and analyzing data over specific periods.

• Streaming predictive analytics: It enables streaming predictive analytics, a feature highly beneficial for time series data, allowing for forecasting based on incoming data streams.

• Streaming-first architecture: Timeplus is designed with a focus on streaming data, making it adept at handling time series data, which is inherently sequential and time-stamped.

• Real-time analytics: Timeplus processes and analyzes data as it arrives – a standout feature for databases that handle real-time information. This capability provides immediate insights from streaming data.

• High performance on time series data: Using a column-based data format (Timeplus Data Format) and native log storage, it offers high performance in data ingestion and querying which is critical for large-scale, high-velocity data.

• Event time processing: It organizes and analyzes data based on the specific times at which events actually occur. This helps in scenarios where understanding the sequence and timing of events is key to making accurate assessments and decisions.

Conclusion

When choosing between a time-series database vs relational database, the decision is not always black or white. It is about understanding what your data needs and how your applications will interact with it.

Consider the nature of your data – its structure, volume, velocity, and variety. Evaluate the need for real-time analysis, scalability, and the frequency of data updates. Match these aspects with the strengths of each database type.

Timeplus is our cloud-based streaming analytics platform that empowers you to seamlessly ingest, process, and analyze streaming data at scale. With Timeplus, you can get real-time visibility into your data streams so you can identify trends, anomalies, and opportunities as they unfold.

Start your journey today and experience the power of streaming data analytics.