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 Amazon Timestream for LiveAnalytics and PostgreSQL so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Amazon Timestream for LiveAnalytics and PostgreSQL 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.

Amazon Timestream for LiveAnalytics vs PostgreSQL Breakdown


 
Database Model

Time series database

Relational database

Architecture

Timestream is a fully managed, serverless time series database service that is only available on AWS.

PostgreSQL can be deployed on various platforms, such as on-premises, in virtual machines, or as a managed cloud service like Amazon RDS, Google Cloud SQL, or Azure Database for PostgreSQL.

License

Closed source

PostgreSQL license (similar to MIT or BSD)

Use Cases

Monitoring, observability, IoT, real-time analytics

Web applications, geospatial data, business intelligence, analytics, content management systems, financial applications, scientific applications

Scalability

Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention

Supports vertical scaling, horizontal scaling through partitioning, sharding, and replication using available tools

Looking for the most efficient way to get started?

Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.

Amazon Timestream for LiveAnalytics Overview

Amazon Timestream for LiveAnalytics is a fully managed, serverless time series database service developed by Amazon Web Services (AWS). Launched in 2020, Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it an ideal choice for IoT, monitoring, and analytics applications that require high ingestion rates, efficient storage, and fast querying capabilities. As a part of the AWS ecosystem, Timestream seamlessly integrates with other AWS services, simplifying the process of building and deploying time series applications in the cloud.

PostgreSQL Overview

PostgreSQL, also known as Postgres, is an open-source relational database management system that was first released in 1996. It has a long history of being a robust, reliable, and feature-rich database system, widely used in various industries and applications. PostgreSQL is known for its adherence to the SQL standard and extensibility, which allows users to define their own data types, operators, and functions. It is developed and maintained by a dedicated community of contributors and is available on multiple platforms, including Windows, Linux, and macOS.


Amazon Timestream for LiveAnalytics for Time Series Data

Amazon Timestream for LiveAnalytics is designed specifically for handling time series data, making it a suitable choice for a wide range of applications that require high ingestion rates, efficient storage, and fast querying capabilities. Its dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, allows Timestream to automatically manage data retention and optimize storage costs based on data age and access patterns. Additionally, Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

PostgreSQL for Time Series Data

PostgreSQL can be used for time series data storage and analysis, although it was not specifically designed for this use case. With its rich set of data types, indexing options, and window function support, PostgreSQL can handle time series data. However, Postgres will not be as optimized for time series data as specialized time series databases when it comes to things like data compression, write throughput, and query speed. PostgreSQL also lacks a number of features that are useful for working with time series data like downsampling, retention policies, and custom SQL functions for time series data analysis.


Amazon Timestream for LiveAnalytics Key Concepts

  • Memory Store: In Amazon Timestream for LiveAnalytics, the Memory Store is a component that stores recent, mutable time series data in memory for fast querying and analysis.
  • Magnetic Store: The Magnetic Store in Amazon Timestream for LiveAnalytics is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
  • Time-to-Live (TTL): Amazon Timestream for LiveAnalytics allows users to set a TTL on their time series data, which determines how long data is retained in the Memory Store before being moved to the Magnetic Store or deleted.

PostgreSQL Key Concepts

  • MVCC: Multi-Version Concurrency Control is a technique used by PostgreSQL to allow multiple transactions to be executed concurrently without conflicts or locking.
  • WAL: Write-Ahead Logging is a method used to ensure data durability by logging changes to a journal before they are written to the main data files.
  • TOAST: The Oversized-Attribute Storage Technique is a mechanism for storing large data values in a separate table to reduce the main table’s disk space consumption.


Amazon Timestream for LiveAnalytics Architecture

Amazon Timestream for LiveAnalytics is built on a serverless, distributed architecture that supports SQL-like querying capabilities. Its data model is specifically tailored for time series data, using time-stamped records and a flexible schema that can accommodate varying data granularities and dimensions. The core components of Timestream’s architecture include the Memory Store and the Magnetic Store, which together manage data retention, storage, and querying. The Memory Store is optimized for fast querying of recent data, while the Magnetic Store provides cost-efficient, long-term storage for historical data.

PostgreSQL Architecture

PostgreSQL is a client-server relational database system that uses the SQL language for querying and manipulation. It employs a process-based architecture, with each connection to the database being handled by a separate server process. This architecture provides isolation between different users and sessions. PostgreSQL supports ACID transactions and uses a combination of MVCC, WAL, and other techniques to ensure data consistency, durability, and performance. It also supports various extensions and external modules to enhance its functionality.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

Amazon Timestream for LiveAnalytics Features

Serverless architecture

Amazon Timestream for LiveAnalytics serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.

Dual-tiered storage

Timestream’s dual-tiered storage architecture, consisting of the Memory Store and Magnetic Store, automatically manages data retention and optimizes storage costs based on data age and access patterns.

SQL-like querying

Amazon Timestream for LiveAnalytics supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.

PostgreSQL Features

Extensibility

PostgreSQL allows users to define custom data types, operators, and functions, making it highly adaptable to specific application requirements.

PostgreSQL has built-in support for full-text search, enabling users to perform complex text-based queries and analyses.

Geospatial support

With the PostGIS extension, PostgreSQL can store and manipulate geospatial data, making it suitable for GIS applications.


Amazon Timestream for LiveAnalytics Use Cases

IoT device monitoring

Amazon Timestream for LiveAnalytic’s support for high ingestion rates and efficient storage makes it an ideal choice for monitoring and analyzing data from IoT devices, such as sensors and smart appliances.

Application performance monitoring

Timestream’s fast querying capabilities and ability to handle large volumes of time series data make it suitable for application performance monitoring, allowing users to track and analyze key performance indicators in real-time and identify bottlenecks or issues.

Infrastructure monitoring

Amazon Timestream for LiveAnalytics can be used to monitor and analyze infrastructure metrics, such as CPU utilization, memory usage, and network traffic, enabling organizations to optimize resource utilization, identify potential issues, and maintain a high level of performance for their critical systems.

PostgreSQL Use Cases

Enterprise applications

PostgreSQL is a popular choice for large-scale enterprise applications due to its reliability, performance, and feature set.

GIS applications

With the PostGIS extension, PostgreSQL can be used for storing and analyzing geospatial data in applications like mapping, routing, and geocoding.

OLTP workloads

As a relational database, PostgreSQL is a good fit for pretty much any application that involves transactional workloads.


Amazon Timestream for LiveAnalytics Pricing Model

Amazon Timestream for LiveAnalyticsv offers a pay-as-you-go pricing model based on data ingestion, storage, and query execution. Ingestion costs are determined by the volume of data ingested into Timestream, while storage costs are based on the amount of data stored in the Memory Store and Magnetic Store. Query execution costs are calculated based on the amount of data scanned and processed during query execution. Timestream also offers a free tier for users to explore the service and build proof-of-concept applications without incurring costs.

PostgreSQL Pricing Model

PostgreSQL is open source software, 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 PostgreSQL server. Several cloud-based managed PostgreSQL services, such as Amazon RDS, Google Cloud SQL, and Azure Database for PostgreSQL, offer different pricing models based on factors like storage, computing resources, and support.