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 Elasticsearch and TDengine so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Elasticsearch and TDengine 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.
Elasticsearch vs TDengine Breakdown
Distributed search and analytics engine, document-oriented
Time series database
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)
TDengine can be deployed on-premises, in the cloud, or as a hybrid solution, allowing flexibility in deployment and management.
Full-text search, log and event data analysis, real-time application monitoring, analytics
IoT data storage, industrial monitoring, smart energy, smart home, monitoring and observability
Horizontally scalable with support for data sharding, replication, and distributed querying
Linearly scalable with clustering and built-in load balancing
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.
TDengine is a high-performance, open source time series database designed to handle massive amounts of time series data efficiently. It was created by TAOS Data in 2017 and is specifically designed for Internet of Things (IoT), Industrial IoT, and IT infrastructure monitoring use cases. TDengine has a unique hybrid architecture that combines the advantages of both relational and NoSQL databases, providing high performance, easy-to-use SQL-like querying, and flexible data modeling capabilities.
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.
TDengine for Time Series Data
TDengine is designed from the ground up as a time series database, so it will be a good fit for most use cases that heavily involve storing and analyzing time series data.
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.
TDengine Key Concepts
- Super Table: A template for creating multiple tables with the same schema. It’s similar to the concept of table inheritance in some other databases.
- Sub Table: A table created based on a Super Table, inheriting its schema. Sub Tables can have additional tags for categorization and querying purposes.
- Tag: A metadata attribute used to categorize and filter Sub Tables in a Super Table. Tags are indexed and optimized for efficient querying.
- Stable: A synonym for Super Table.
- TSQL: TDengine’s SQL-like query language, designed specifically for time series data manipulation and retrieval.
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.
TDengine uses a hybrid architecture that combines the advantages of relational databases (support for SQL-like querying) and NoSQL databases (scalability and flexibility). It is based on a distributed, columnar storage model and uses a time series data model. TDengine uses data nodes to store data and handle queries. Management nodes coordinate the data nodes and store metadata like schema and cluster information.
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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.
TDengine supports high-speed data ingestion, with the ability to handle millions of data points per second. It supports batch and individual data insertion using TSQL.
TDengine provides a SQL-like query language (TSQL) that allows users to easily query time series data using familiar SQL syntax. It supports various aggregation functions, filtering, and joins.
Data retention and compression
TDengine automatically compresses data to save storage space and provides data retention policies to automatically delete old data.
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.
TDengine Use Cases
IoT data storage and analysis
TDengine is designed to handle massive amounts of time series data generated by IoT devices. Its high-performance ingestion, querying, and storage capabilities make it a suitable choice for IoT data storage and analysis.
Industrial IoT monitoring
TDengine can be used to store and analyze data from industrial IoT sensors and devices, helping organizations monitor equipment performance, detect anomalies, and optimize operations.
TDengine can be used to collect and analyze time series data from IT infrastructure components, such as servers, networks, and applications, facilitating real-time monitoring, alerting, and performance optimization.
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.
TDengine Pricing Model
TDengine is open source and free to use under the AGPLv3 license. TAOS Data also offers commercial licenses and enterprise support options for organizations that require additional features, support, or compliance with specific licensing requirements.
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