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 InfluxDB and QuestDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how InfluxDB and QuestDB 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 QuestDB Breakdown
Time series database
Time series database
Cloud native architecture that can be used as a managed cloud service or self-managed on your own hardware locally
QuestDB is designed for horizontal scaling, enabling you to distribute data and queries across multiple nodes for increased performance and availability. It can be deployed on-premises, in the cloud, or as a hybrid solution, depending on your infrastructure needs and preferences.
Monitoring, observability, IoT, real-time analytics
Monitoring, observability, IoT, Real-time analytics, Financial services, High-frequency trading
Horizontally scalable with decoupled storage and compute with InfluxDB 3.0
High-performance with support for horizontal scaling and multi-threading
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).
QuestDB is an open-source relational column-oriented database designed specifically for time series and event data. It combines high-performance ingestion capabilities with SQL analytics, making it a powerful tool for managing and analyzing large volumes of time-based data. QuestDB addresses the challenges of handling high throughput and provides a simple way to analyze ingested data through SQL queries. It is well-suited for use cases such as financial market data and application metrics.
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.
QuestDB for Time Series Data
QuestDB excels in managing and analyzing time series data. With its high-performance ingestion capabilities, it can handle high data throughput, making it suitable for real-time data ingestion scenarios. QuestDB’s SQL extensions for time series enable users to perform real-time analytics and gain valuable insights from their time-stamped data. Whether it’s financial market data or application metrics, QuestDB simplifies the process of ingesting and analyzing time series data through its fast SQL queries and operational simplicity.
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.
QuestDB Key Concepts
- Time Series: QuestDB focuses on time series data, which represents data points indexed by time. It is optimized for storing and processing time-stamped data efficiently.
- Column-Oriented: QuestDB employs a column-oriented storage format, where data is organized and stored column by column rather than row by row. This format enables efficient compression and faster query performance.
- SQL Extensions: QuestDB extends the SQL language with functionalities specifically tailored for time series data. These extensions facilitate real-time analytics and allow users to leverage familiar SQL constructs for querying time-based data.
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.
QuestDB follows a hybrid architecture that combines features of columnar and row-based databases. It leverages a column-oriented storage format for efficient compression and query performance while retaining the ability to handle relational data with SQL capabilities. QuestDB supports both SQL and NoSQL-like functionalities, providing users with flexibility in their data modeling and querying approaches. The database consists of multiple components, including the ingestion engine, storage engine, and query engine, working together to ensure high-performance data ingestion and retrieval.
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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.
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.
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.
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.
QuestDB is optimized for high throughput ingestion, allowing users to efficiently ingest large volumes of time series data at high speeds.
Fast SQL Queries
QuestDB provides fast SQL queries for analyzing time series data. It extends the SQL language with time series-specific functionalities to assist with real-time analytics.
QuestDB aims to provide a user-friendly experience with operational simplicity. It supports schema-agnostic ingestion using popular protocols such as InfluxDB line protocol and PostgreSQL wire protocol. Additionally, a REST API is available for bulk imports and exports, simplifying data management tasks.
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.
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.
QuestDB Use Cases
Financial Market Data
QuestDB is well-suited for managing and analyzing financial market data. Its high-performance ingestion and fast SQL queries enable efficient processing and analysis of large volumes of market data in real time.
QuestDB can be used for collecting and analyzing application metrics. Its ability to handle high data throughput and provide real-time analytics capabilities makes it suitable for monitoring and analyzing performance metrics, logs, and other application-related data.
IoT Data Analysis
QuestDB’s high-performance ingestion and time series analytics capabilities make it a valuable tool for analyzing IoT sensor data.
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.
QuestDB Pricing Model
QuestDB is an open-source project released under the Apache 2 License. It is freely available for usage and does not require any licensing fees. Users can access the source code on GitHub and deploy QuestDB on their own infrastructure without incurring direct costs. QuestDB also offers a managed cloud service.
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