Prometheus vs TimescaleDB
A detailed comparison
Compare Prometheus and TimescaleDB for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Prometheus and TimescaleDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Prometheus 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.
Prometheus vs TimescaleDB Breakdown
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| Database Model | Time series database |
Time Series Database |
| Architecture | Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes. |
TimescaleDB is built on top of PostgreSQL and inherits its architecture. It extends PostgreSQL with time-series-specific optimizations and functions, allowing it to manage time series data efficiently. It can be deployed as a single node, in a multi-node setup, or in the cloud as a managed service. |
| License | Apache 2.0 |
Timescale License (for TimescaleDB Community Edition); Apache 2.0 (for core PostgreSQL) |
| Use Cases | Monitoring, alerting, observability, system metrics, application metrics |
Monitoring, observability, IoT, real-time analytics, financial market data |
| Scalability | Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers) |
Horizontally scalable through native support for partitioning, replication, and sharding. Offers multi-node capabilities for distributing data and queries across nodes. |
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Prometheus Overview
Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.
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.
Prometheus for Time Series Data
Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.
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.
Prometheus Key Concepts
- Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
- Time Series: A collection of data points for a metric, indexed by timestamp.
- Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
- PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.
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.
Prometheus Architecture
Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.
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.
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Prometheus Features
Pull-based Model
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
PromQL
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Alerting
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
TimescaleDB Features
Partitioning
TimescaleDB automatically partitions time series data tables using hypertables and chunks, which simplifies data management and improves query performance.
Time series focused SQL functions
TimescaleDB provides several specialized SQL functions and operators for time series data application scenarios, such as time_bucket, first, and last, which simplify querying and aggregating time series data.
Query optimization
As mentioned earlier, TimescaleDB extends PostgreSQL’s query planner for writing and querying time series data, including optimizations like time-based indexing and chunk pruning.
Prometheus Use Cases
Infrastructure Monitoring
Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.
Application Performance Monitoring (APM)
Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.
Alerting and Anomaly Detection
Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.
TimescaleDB Use Cases
Monitoring and metrics
TimescaleDB is well-suited for storing and analyzing monitoring and metrics data, such as server performance metrics, application logs, and sensor data. Its hypertable structure and query optimizations make it easy to store, query, and visualize large volumes of time series data.
IoT data storage
TimescaleDB can be used to store and analyze IoT data, such as sensor readings and device status information. Its support for automatic partitioning and specialized SQL interfaces simplifies the management and querying of large-scale IoT datasets.
Financial data
TimescaleDB is suitable for storing and analyzing financial data, such as stock prices, exchange rates, and trading volumes. Its query optimizations and specialized SQL functions make it easy to perform time-based aggregations and analyze trends in financial data.
Prometheus Pricing Model
Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.
TimescaleDB Pricing Model
TimescaleDB is available in two editions: TimescaleDB Open Source and TimescaleDB Cloud. The open-source edition is free to use and can be self-hosted, while the cloud edition is a managed service with a pay-as-you-go pricing model based on storage, compute, and data transfer usage. TimescaleDB Cloud offers various pricing tiers with different levels of resources and features, such as continuous backups and high availability.
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