DataBend vs TimescaleDB

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of DataBend and TimescaleDB so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DataBend and TimescaleDB perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

DataBend Overview

DataBend is an open-source, cloud-native data processing and analytics platform designed to provide high-performance, cost-effective, and scalable solutions for big data workloads. The project is driven by a community of developers, researchers, and industry professionals aiming to create a unified data processing platform that combines batch and streaming processing capabilities with advanced analytical features. DataBend’s flexible architecture allows users to build a wide range of applications, from real-time analytics to large-scale data warehousing.

TimescaleDB Overview

TimescaleDB is an open source time series database built on top of PostgreSQL. It was created to address the challenges of managing time series data, such as scalability, query performance, and data retention policies. TimescaleDB was first released in 2017 and has since become a popular choice for storing and analyzing time series data due to its PostgreSQL compatibility, performance optimizations, and flexible data retention policies.

DataBend for Time Series Data

DataBend’s architecture and processing capabilities make it a suitable choice for working with time series data. Its support for both batch and streaming data processing allows users to ingest, store, and analyze time series data at scale. Additionally, DataBend’s integration with Apache Arrow and its powerful query execution framework enable efficient querying and analytics on time series data, making it a versatile choice for applications that require real-time insights and analytics.

TimescaleDB for Time Series Data

TimescaleDB is specifically designed for time series data, making it a natural choice for storing and querying such data. It provides several advantages for time series data management like horizontal scalability, columnar storage, and retention policy support. However, TimescaleDB may not be the best choice for all time series use cases. One example would be if an application requires very high write throughput or real-time analytics, other specialized time series databases like InfluxDB may be more suitable.

DataBend Key Concepts

• DataFusion: DataFusion is a core component of DataBend, providing an extensible query execution framework that supports both SQL and DataFrame-based query APIs.
• Ballista: Ballista is a distributed compute platform within DataBend, built on top of DataFusion, that allows for efficient and scalable execution of large-scale data processing tasks.
• Arrow: DataBend leverages Apache Arrow, an in-memory columnar data format, to enable efficient data exchange between components and optimize query performance.

TimescaleDB Key Concepts

• Hypertable: A hypertable is a distributed table that is partitioned by time and possibly other dimensions, such as device ID or location. It is the primary abstraction for storing time series data in TimescaleDB and is designed to scale horizontally across multiple nodes.
• Chunk: A chunk is a partition of a hypertable, containing a subset of the hypertable’s data. Chunks are created automatically by TimescaleDB based on a specified time interval and can be individually compressed, indexed, and backed up for better performance and data management.
• Distributed Hypertables: For large-scale deployments, TimescaleDB supports distributed hypertables, which partition data across multiple nodes for improved query performance and fault tolerance.

DataBend Architecture

DataBend is built on a cloud-native, distributed architecture that supports both NoSQL and SQL-like querying capabilities. Its modular design allows users to choose and combine components based on their specific use case and requirements. The core components of DataBend’s architecture include DataFusion, Ballista, and the storage layer. DataFusion is responsible for query execution and optimization, while Ballista enables distributed computing for large-scale data processing tasks. The storage layer in DataBend can be configured to work with various storage backends, such as object storage or distributed file systems.

TimescaleDB Architecture

TimescaleDB is an extension built on PostgreSQL, inheriting its relational data model and SQL support. However, TimescaleDB extends PostgreSQL with custom data structures and optimizations for time series data, such as hypertables and chunks.