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 AWS Timestream and Elasticsearch so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how AWS Timestream and Elasticsearch 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.
AWS Timestream vs Elasticsearch Breakdown
Time series database
Distributed search and analytics engine, document-oriented
Timestream is a fully managed, serverless time series database service that is only available on AWS.
Elasticsearch is built on top of Apache Lucene and uses a RESTful API for communication. It stores data in a flexible JSON document format, and the data is automatically indexed for fast search and retrieval. Elasticsearch can be deployed as a single node, in a cluster configuration, or as a managed cloud service (Elastic Cloud)
Monitoring, observability, IoT, real-time analytics
Full-text search, log and event data analysis, real-time application monitoring, analytics
Serverless and automatically scalable, handling ingestion, storage, and query workload without manual intervention
Horizontally scalable with support for data sharding, replication, and distributed querying
AWS Timestream Overview
AWS Timestream is a fully managed, serverless time series database service developed by Amazon Web Services (AWS). Launched in 2020, Timestream 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.
Elasticsearch is an open-source distributed search and analytics engine built on top of Apache Lucene. It was first released in 2010 and has since become popular for its scalability, near real-time search capabilities, and ease of use. Elasticsearch is designed to handle a wide variety of data types, including structured, unstructured, and time-based data. It is often used in conjunction with other tools from the Elastic Stack, such as Logstash for data ingestion and Kibana for data visualization.
AWS Timestream for Time Series Data
AWS Timestream 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.
Elasticsearch for Time Series Data
Elasticsearch can be used for time series data storage and analysis, thanks to its distributed architecture, near real-time search capabilities, and support for aggregations. However, it might not be as optimized for time series data as dedicated time series databases. Despite this, Elasticsearch is widely used for log and event data storage and analysis which can be considered time series data.
AWS Timestream Key Concepts
- Memory Store: In AWS Timestream, 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 AWS Timestream is responsible for storing historical, immutable time series data on disk for cost-efficient, long-term storage.
- Time-to-Live (TTL): AWS Timestream 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.
Elasticsearch Key Concepts
- Inverted Index: A data structure used by Elasticsearch to enable fast and efficient full-text searches.
- Cluster: A group of Elasticsearch nodes that work together to distribute data and processing tasks.
- Shard: A partition of an Elasticsearch index that allows data to be distributed across multiple nodes for improved performance and fault tolerance.
AWS Timestream Architecture
Timestream 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.
Elasticsearch is a distributed, RESTful search and analytics engine that uses a schema-free JSON document data model. It is built on top of Apache Lucene and provides a high-level API for indexing, searching, and analyzing data. Elasticsearch’s architecture is designed to be horizontally scalable, with data distributed across multiple nodes in a cluster. Data is indexed using inverted indices, which enable fast and efficient full-text searches.
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AWS Timestream Features
AWS Timestream’s serverless architecture eliminates the need for users to manage or provision infrastructure, making it easy to scale and reducing operational overhead.
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.
AWS Timestream supports SQL-like querying and integrates with popular analytics tools, making it easy for users to gain insights from their time series data.
Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.
Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.
Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.
AWS Timestream Use Cases
IoT device monitoring
AWS Timestream’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.
AWS Timestream 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.
Elasticsearch Use Cases
Log and Event Data Analysis
Elasticsearch is widely used for storing and analyzing log and event data, such as web server logs, application logs, and network events, to help identify patterns, troubleshoot issues, and monitor system performance.
Elasticsearch is a popular choice for implementing full-text search functionality in applications, websites, and content management systems due to its powerful search capabilities and flexible data model.
Elasticsearch, in combination with other Elastic Stack components, can be used for security analytics, such as monitoring network traffic, detecting anomalies, and identifying potential threats.
AWS Timestream Pricing Model
AWS Timestream 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.
Elasticsearch Pricing Model
Elasticsearch is open-source software and can be self-hosted without any licensing fees. However, operational costs, such as hardware, hosting, and maintenance, should be considered. Elasticsearch also offers a managed cloud service called Elastic Cloud, which provides various pricing tiers based on factors like storage, computing resources, and support. Elastic Cloud includes additional features and tools, such as Kibana, machine learning, and security features.
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