InfluxDB vs Elasticsearch
A detailed comparison
Compare InfluxDB and Elasticsearch 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 InfluxDB and Elasticsearch so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how InfluxDB 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.
InfluxDB vs Elasticsearch Breakdown
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| Database Model | Distributed search and analytics engine, document-oriented |
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| Architecture | Cloud native architecture that can be used as a managed cloud service or self-managed on your own hardware locally |
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) |
| License | MIT |
Elastic License |
| Use Cases | Monitoring, observability, IoT, real-time analytics |
Full-text search, log and event data analysis, real-time application monitoring, analytics |
| Scalability | Horizontally scalable with decoupled storage and compute with InfluxDB 3.0 delivers up to 90% reduced storage costs( benchmarks ) |
Horizontally scalable with support for data sharding, replication, and distributed querying |
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InfluxDB Overview
InfluxDB is a high-performance, time series database capable of storing any form of time series data, such as metrics, events, logs and traces. InfluxDB is developed by InfluxData and first released in 2013. InfluxDB is an open source database written in Go, with a focus on performance, scalability, and developer productivity. The database is optimized for handling time series data at scale, making it a popular choice for use cases involving monitoring performance metrics, IoT data, and real-time analytics.
InfluxDB 3.0 is the newest version of InfluxDB, currently available in InfluxDB Cloud Serverless and InfluxDB Cloud Dedicated. Built in Rust, a modern programming language designed for performance, safety, and memory management. InfluxDB also features a decoupled architecture that allows compute and storage to be scaled independently. InfluxDB 3.0 provides query support for both SQL and InfluxQL (custom SQL-like query language with added support for time-based functions).
Elasticsearch Overview
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.
InfluxDB for Time Series Data
InfluxDB is specifically designed for time series data, making it well-suited for applications that involve tracking and analyzing data points over time. It excels in scenarios where data is being written continuously at high volumes while users also require the ability to query that data quickly after ingest for monitoring and real time analytics use cases.
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.
InfluxDB Key Concepts
- Columnar storage: InfluxDB stores data in a column-oriented format, using Parquet for persistent file storage and Apache Arrow as the in-memory representation of data. Columnar storage results in better data compression and faster queries for analytics workloads.
- Data Model: The InfluxDB data model will be familiar to anyone who has worked with other database systems. At the highest level are buckets, which are similar to what other systems call databases. InfluxDB measurements are synonymous with tables. Specific data points for a measurement contain tags and values. Tags are used as part of the primary key for querying data and should be used for identifying information used for filtering during queries. InfluxDB is schemaless so new fields can be added without requiring migrations or modifying a schema.
- Integrations: InfluxDB is built to be flexible and fit into your application’s architecture. One key aspect of this is the many ways InfluxDB makes it easy to read and write data. To start, all database functionality can be accessed via HTTP API or with the InfluxDB CLI. For writing data InfluxDB created Telegraf, a tool that can collect data from hundreds of different sources via plugins and write that data to InfluxDB. Client libraries are also available for the most popular programming languages to allow writing and querying data.
- Decoupled architecture: InfluxDB 3.0 features a decoupled architecture which allows query compute, data ingestion, and storage to be scaled independently. This allows InfluxDB to be fine-tuned for your use case and results in significant cost savings.
- Query Languages: InfluxDB can be queried using standard SQL or InfluxQL, an SQL dialect with a number of specialized functions useful for working with time series data.
- Retention Policies: InfluxDB allows you to define retention policies that determine how long data is stored before being automatically deleted. This is useful for managing the storage of high volume 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.
InfluxDB Architecture
At a high level, InfluxDB’s architecture is designed to optimize storage and query performance for time series data. The exact architecture of InfluxDB will vary slightly depending on the version and how you deploy InfluxDB.
InfluxDB 3.0’s architecture can be broken down into four key components that operate almost independently from each other, allowing for InfluxDB to be extremely flexible in terms of configuration. These components are are data ingest, data querying, data compaction, and garbage collection. Data is written via the ingesters with millisecond latency. This data can be queried almost immediately by the data queriers while in the background the compactor takes the newly written data files and combines them into larger files that will be sent to object storage. The garbage collector is responsible for data retention and space reclamations by scheduling soft and hard deletion of data.
They key part of InfluxDB’s architecture is the separation of the ingest and query components, which allows each to be scaled independently depending on the current write and query workload. The querier being able to seamlessly pull in recently written data from the ingesters as well as from object storage allows data to be stored cheaply without increasing query latency.
Elasticsearch Architecture
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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InfluxDB Features
High-performance storage and querying
InfluxDB is optimized for time series data, providing high-performance storage and querying capabilities. In terms of storage InfluxDB is able to scale effortlessly due to its decoupled architecture. Object storage is used to persist data and query nodes can be scaled independently to improve query performance and capacity.
Compared to previous versions of InfluxDB, the newly released InfluxDB 3.0 compresses data 4.5x more effectively and queries are 2.5-45x faster depending on the type of query.
Retention policies
InfluxDB allows users to define retention policies that automatically delete data points after a specified duration. This feature helps manage data storage costs and ensures that only relevant data is retained.
Data compression
InfluxDB’s storage engine automatically compacts data on disk, reducing storage requirements and improving query performance. With InfluxDB 3.0 data is stored using the Parquet file format to get even higher compression ratios on time series data.
Horizontal scaling and clustering
InfluxDB supports horizontal scaling and clustering, allowing users to distribute data across multiple nodes for increased performance and fault tolerance.
Data tiering
InfluxDB 3.0 is able to seamlessly move data from cheap object storage into faster storage for low latency queries without expensive SSD or high amounts of RAM utilization. This allows users to store data for longer at higher frequencies while still saving in storage costs.
Elasticsearch Features
Full-Text Search
Elasticsearch provides powerful full-text search capabilities with support for complex queries, scoring, and relevance ranking.
Scalability
Elasticsearch’s distributed architecture enables horizontal scalability, allowing it to handle large volumes of data and high query loads.
Aggregations
Elasticsearch supports various aggregation operations, such as sum, average, and percentiles, which are useful for analyzing and summarizing data.
InfluxDB Use Cases
Monitoring and alerting
InfluxDB is widely used for monitoring and alerting purposes, as it can efficiently store and process time series data generated by various systems, applications, and devices. With its high-performance query engine and integration with visualization tools like Grafana, users can create real-time dashboards and set up alerts based on specific conditions or thresholds.
IoT data storage and analysis
Due to its high write and query performance, InfluxDB is an ideal choice for storing and analyzing IoT data generated by sensors, devices, and applications. Users can leverage InfluxDB’s scalability and retention policies to manage large volumes of time series data, and use its powerful query languages to gain insights into the IoT ecosystem.
Real-time analytics
InfluxDB’s performance and flexibility make it suitable for real-time analytics use cases, such as tracking user behavior, monitoring application performance, and analyzing financial data. With its support for InfluxQL and SQL, users can perform complex data analysis and aggregation in real-time, enabling them to make data-driven decisions.
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.
Full-Text Search
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.
Security Analytics
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.
InfluxDB Pricing Model
InfluxDB offers several pricing options, including a free open source version, a cloud-based offering, and an enterprise edition for on-premises deployment:
- InfluxDB Cloud Serverless: InfluxDB Cloud Serverless is a managed, cloud-based offering with a pay-as-you-go pricing model. It provides additional features, such as monitoring, alerting, and data visualization. InfluxDB Cloud is available across all major cloud providers.
- InfluxDB Cloud Dedicated - This is a managed cloud solution that provides an isolated InfluxDB instance on dedicated hardware for use cases that require isolation or benefit from being able to specify and fine-tune hardware configuration.
- InfluxDB Enterprise: On-prem solution with enterprise features for security and support for clustering and other horizontal scaling options.
- InfluxDB Open Source: The open source version of InfluxDB is free to use and provides the core functionality of the database.
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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