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 Prometheus so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Druid and Prometheus 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 Prometheus Breakdown
Time series database
Druid can be deployed on-premises, in the cloud, or using a managed service
Prometheus uses a pull-based model where it scrapes metrics from configured targets at given intervals. It stores time series data in a custom, efficient, local storage format, and supports multi-dimensional data collection, querying, and alerting. It can be deployed as a single binary on a server or on a container platform like Kubernetes.
Real-time analytics, OLAP, time series data, event-driven data, log analytics, ad tech, user behavior analytics
Monitoring, alerting, observability, system metrics, application metrics
Horizontally scalable, supports distributed architectures for high availability and performance
Prometheus is designed for reliability and can scale vertically (single node with increased resources) or through federation (hierarchical setup where Prometheus servers scrape metrics from other Prometheus servers)
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.
Prometheus is an open-source monitoring and alerting toolkit initially developed at SoundCloud in 2012. It has since become a widely adopted monitoring solution and a part of the Cloud Native Computing Foundation (CNCF) project. Prometheus focuses on providing real-time insights and alerts for containerized and microservices-based environments. Its primary use case is monitoring infrastructure and applications, with an emphasis on reliability and scalability.
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.
Prometheus for Time Series Data
Prometheus is specifically designed for time series data, as its primary focus is on monitoring and alerting based on the state of infrastructure and applications. It uses a pull-based model, where the Prometheus server scrapes metrics from the target systems at regular intervals. This model is suitable for monitoring dynamic environments, as it allows for automatic discovery and monitoring of new instances. However, Prometheus is not intended as a general-purpose time series database and might not be the best choice for high cardinality or long-term data storage.
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.
Prometheus Key Concepts
- Metric: A numeric representation of a particular aspect of a system, such as CPU usage or memory consumption.
- Time Series: A collection of data points for a metric, indexed by timestamp.
- Label: A key-value pair that provides metadata and context for a metric, enabling more granular querying and aggregation.
- PromQL: Prometheus uses its own query language called PromQL (Prometheus Query Language) for querying time series data and generating alerts.
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.
Prometheus is a single-server, standalone monitoring system that uses a pull-based approach to collect metrics from target systems. It stores time series data in a custom, highly compressed, on-disk format, optimized for fast querying and low resource usage. The architecture of Prometheus is modular and extensible, with components like exporters, service discovery mechanisms, and integrations with other monitoring systems. As a non-distributed system, it lacks built-in clustering or horizontal scalability, but it supports federation, allowing multiple Prometheus servers to share and aggregate data.
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Apache Druid Features
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.
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.
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.
Prometheus collects metrics by actively scraping targets, enabling automatic discovery and monitoring of dynamic environments.
The powerful Prometheus Query Language allows for expressive and flexible querying of time series data.
Prometheus supports alerting based on user-defined rules and integrates with various alert management and notification systems.
Apache Druid Use Cases
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.
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.
Prometheus Use Cases
Prometheus is widely used for monitoring the health and performance of containerized and microservices-based infrastructure, including Kubernetes and Docker environments.
Application Performance Monitoring (APM)
Prometheus can collect custom application metrics using client libraries and monitor application performance in real-time.
Alerting and Anomaly Detection
Prometheus enables organizations to set up alerts based on specific thresholds or conditions, helping them identify and respond to potential issues or anomalies quickly.
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.
Prometheus Pricing Model
Prometheus is an open-source project, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed Prometheus server. Additionally, several cloud-based managed Prometheus services, such as Grafana Cloud and Weave Cloud, offer different pricing models based on factors like data retention, query rate, and support.
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