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 Apache Druid and VictoriaMetrics so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Apache Druid and VictoriaMetrics 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.

Apache Druid vs VictoriaMetrics Breakdown


 
Database Model

Columnar database

Time series database

Architecture

Druid can be deployed on-premises, in the cloud, or using a managed service

VictoriaMetrics can be deployed as a single-node instance for small-scale applications or as a clustered setup for large-scale applications, offering horizontal scalability and replication.

License

Apache 2.0

Apache 2.0

Use Cases

Real-time analytics, OLAP, time series data, event-driven data, log analytics, ad tech, user behavior analytics

Monitoring, observability, IoT, real-time analytics, DevOps, application performance monitoring

Scalability

Horizontally scalable, supports distributed architectures for high availability and performance

Horizontally scalable, supports clustering and replication for high availability and performance

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Apache Druid Overview

Apache Druid is an open-source, real-time analytics database designed for high-performance querying and data ingestion. Originally developed by Metamarkets in 2011 and later donated to the Apache Software Foundation in 2018, Druid has gained popularity for its ability to handle large volumes of data with low latency. With a unique architecture that combines elements of time series databases, search systems, and columnar storage, Druid is particularly well-suited for use cases involving event-driven data and interactive analytics.

VictoriaMetrics Overview

VictoriaMetrics is an open source time series database developed by the company VictoriaMetrics. The database aims to assist individuals and organizations in addressing their big data challenges by providing state-of-the-art monitoring and observability solutions. VictoriaMetrics is designed to be a fast, cost-effective, and scalable monitoring solution and time series database.


Apache Druid for Time Series Data

Apache Druid is designed for real time analytics and can be a good fit for working with time series data that needs to be analyzed quickly after being written. Druid also offers integrations for storing historical data in cheaper object storage so historical time series data can also be analyzed using Druid.

VictoriaMetrics for Time Series Data

VictoriaMetrics is designed for time series data, making it a solid choice for applications that involve the storage and analysis of time-stamped data. It provides high-performance storage and retrieval capabilities, enabling efficient handling of large volumes of time series data.


Apache Druid Key Concepts

  • Data Ingestion: The process of importing data into Druid from various sources, such as streaming or batch data sources.
  • Segments: The smallest unit of data storage in Druid, segments are immutable, partitioned, and compressed.
  • Data Rollup: The process of aggregating raw data during ingestion to reduce storage requirements and improve query performance.
  • Nodes: Druid’s architecture consists of different types of nodes, including Historical, Broker, Coordinator, and MiddleManager/Overlord, each with specific responsibilities.
  • Indexing Service: Druid’s indexing service manages the process of ingesting data, creating segments, and publishing them to deep storage.

VictoriaMetrics Key Concepts

  • Time Series: VictoriaMetrics stores data in the form of time series, which are sequences of data points indexed by time.
  • Metric: A metric represents a specific measurement or observation that is tracked over time.
  • Tag: Tags are key-value pairs associated with a time series and are used for filtering and grouping data.
  • Field: Fields contain the actual data values associated with a time series.
  • Query Language: VictoriaMetrics supports its own query language, which allows users to retrieve and analyze time series data based on specific criteria.


Apache Druid Architecture

Apache Druid is a powerful distributed data store designed for real-time analytics on large datasets. Within its architecture, several core components play pivotal roles in ensuring its efficiency and scalability. Here is an overview of the core components that power Apache Druid.

  • Historical Nodes are fundamental to Druid’s data-serving capabilities. Their primary responsibility is to serve stored data to queries. To achieve this, they load segments from deep storage, retain them in memory, and then cater to the queries on these segments. When considering deployment and management, these nodes are typically stationed on machines endowed with significant memory and CPU resources. Their scalability is evident as they can be expanded horizontally simply by incorporating more nodes.
  • Broker Nodes act as the gatekeepers for incoming queries. Their main function is to channel these queries to the appropriate historical nodes or real-time nodes. Intriguingly, they are stateless, which means they can be scaled out to accommodate an increase in query concurrency.
  • Coordinator Nodes have a managerial role, overseeing the data distribution across historical nodes. Their decisions on which segments to load or drop are based on specific configurable rules. In terms of deployment, a Druid setup usually requires just one active coordinator node, with a backup node on standby for failover scenarios.
  • Overlord Nodes dictate the assignment of ingestion tasks, directing them to either middle manager or indexer nodes. Their deployment mirrors that of the coordinator nodes, with typically one active overlord and a backup for redundancy.
  • MiddleManager and Indexer Nodes are the workhorses of data ingestion in Druid. While MiddleManagers initiate short-lived tasks for data ingestion, indexers are designed for long-lived tasks. Given their intensive operations, these nodes demand high CPU and memory resources. Their scalability is flexible, allowing horizontal expansion based on the volume of data ingestion.
  • Deep Storage is a component that serves as Druid’s persistent storage unit. Druid integrates with various blob storage solutions like HDFS, S3, and Google Cloud Storage.
  • Metadata Storage is the repository for crucial metadata about segments, tasks, and configurations. Druid is compatible with popular databases for this purpose, including MySQL, PostgreSQL, and Derby.

VictoriaMetrics Architecture

VictoriaMetrics is available in two forms: Single-server-VictoriaMetrics and VictoriaMetrics Cluster. The Single-server-VictoriaMetrics is an all-in-one binary that is easy to use and maintain. It vertically scales well and can handle millions of metrics per second. On the other hand, VictoriaMetrics Cluster consists of components that allow for building horizontally scalable clusters, enabling high availability and scalability in demanding environments. The architecture of VictoriaMetrics enables users to choose the deployment option that best suits their needs and scale their database infrastructure as required.

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Apache Druid Features

Data Ingestion

Apache Druid supports both real-time and batch data ingestion, allowing it to process data from various sources like Kafka, Hadoop, or local files. With built-in support for data partitioning, replication, and roll-up, Druid ensures high availability and efficient storage.

Scalability and Performance

Druid is designed to scale horizontally, providing support for large-scale deployments with minimal performance degradation. Its unique architecture allows for fast and efficient querying, making it suitable for use cases requiring low-latency analytics.

Columnar Storage

Druid stores data in a columnar format, enabling better compression and faster query performance compared to row-based storage systems. Columnar storage also allows Druid to optimize queries by only accessing relevant columns.

Time-optimized Indexing

Druid’s indexing service creates segments with time-based partitioning, optimizing data storage and retrieval for time-series data. This feature significantly improves query performance for time-based queries. Data Rollups

Druid’s data rollup feature aggregates raw data during ingestion, reducing storage requirements and improving query performance. This feature is particularly beneficial for use cases involving high-cardinality data or large volumes of similar data points.

VictoriaMetrics Features

High performance

VictoriaMetrics is optimized for high-performance storage and retrieval of time series data. It can efficiently handle millions of metrics per second and offers fast query execution for real-time analysis.

Scalability

The architecture of VictoriaMetrics allows for both vertical and horizontal scalability, enabling users to scale their monitoring and time series database infrastructure as their data volume and demand grow.

Cost-effectiveness

VictoriaMetrics provides a cost-effective solution for managing time series data. Its efficient storage and query capabilities contribute to minimizing operational costs while maintaining high performance.


Apache Druid Use Cases

Geospatial Analysis

Apache Druid provides support for geospatial data and queries, making it suitable for use cases that involve location-based data, such as tracking the movement of assets, analyzing user locations, or monitoring the distribution of events. Its ability to efficiently process large volumes of geospatial data enables users to gain insights and make data-driven decisions based on location information.

Machine Learning and AI

Druid’s high-performance data processing capabilities can be leveraged for preprocessing and feature extraction in machine learning and AI workflows. Its support for real-time data ingestion and low-latency querying make it suitable for use cases that require real-time predictions or insights, such as recommendation systems or predictive maintenance.

Real-Time Analytics

Apache Druid’s low-latency querying and real-time data ingestion capabilities make it an ideal solution for real-time analytics use cases, such as monitoring application performance, user behavior, or business metrics.

VictoriaMetrics Use Cases

Monitoring and Observability

VictoriaMetrics is widely used for monitoring and observability purposes, allowing organizations to collect, store, and analyze metrics and performance data from various systems and applications. It provides the necessary tools and capabilities to track and visualize key performance indicators, troubleshoot issues, and gain insights into system behavior.

IoT Data Management

VictoriaMetrics is suitable for handling large volumes of time series data generated by IoT devices. It can efficiently store and process sensor data, enabling real-time monitoring and analysis of IoT ecosystems. VictoriaMetrics allows for tracking and analyzing data from factories, manufacturing plants, satellites, and other IoT devices.

Capacity Planning

VictoriaMetrics enables retrospective analysis and forecasting of metrics for capacity planning purposes. It allows organizations to analyze historical data, identify patterns and trends, and make informed decisions about resource allocation and future capacity requirements.


Apache Druid Pricing Model

Apache Druid is an open source project, and as such, it can be self-hosted at no licensing cost. However, organizations that choose to self-host Druid will incur expenses related to infrastructure, management, and support when deploying and operating Druid in their environment. These costs will depend on the organization’s specific requirements and the chosen infrastructure, whether it’s on-premises or cloud-based.

For those who prefer a managed solution, there are cloud services available that offer Apache Druid as a managed service, such as Imply Cloud. With managed services, the provider handles infrastructure, management, and support, simplifying the deployment and operation of Druid. Pricing for these managed services will vary depending on the provider and the selected service tier, which may include factors such as data storage, query capacity, and data ingestion rates.

VictoriaMetrics Pricing Model

VictoriaMetrics is an open source project, which means it is available for free usage and doesn’t require any licensing fees. Users can download the binary releases, Docker images, or source code to set up and deploy VictoriaMetrics without incurring any direct costs. VictoriaMetrics also has paid offerings for on-prem Enterprise products and managed VictoriaMetrics instances.