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 M3 so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Druid and M3 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 M3 Breakdown
Time series database
Druid can be deployed on-premises, in the cloud, or using a managed service
The M3 stack can be deployed on-premises or in the cloud, using containerization technologies like Kubernetes or as a managed service on platforms like AWS or GCP
Real-time analytics, OLAP, time series data, event-driven data, log analytics, ad tech, user behavior analytics
Monitoring, observability, IoT, Real-time analytics, large-scale metrics processing
Horizontally scalable, supports distributed architectures for high availability and performance
Horizontally scalable, designed for high availability and large-scale deployments
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.
M3 is a distributed time series database written entirely in Go. It is designed to collect a high volume of monitoring time series data, distribute storage in a horizontally scalable manner, and efficiently leverage hardware resources. M3 was initially developed by Uber as a scalable remote storage backend for Prometheus and Graphite and later open-sourced for broader use.
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.
M3 for Time Series Data
M3 is specifically designed for time-series data. It is a distributed and scalable time-series database optimized for handling large volumes of high-resolution data points, making it an ideal solution for storing, querying, and analyzing time-series data.
M3’s architecture focuses on providing fast and efficient querying capabilities, as well as high ingestion rates, which are essential for working with time-series data. Its horizontal scalability and high availability ensure that it can handle the demands of large-scale deployments and maintain performance as data volumes grow.
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.
M3 Key Concepts
- Time Series Compression: M3 has the ability to compress time series data, resulting in significant memory and disk savings. It uses two compression algorithms, M3TSZ and protobuf encoding, to achieve efficient data compression.
- Sharding: M3 uses virtual shards that are assigned to physical nodes. Timeseries keys are hashed to a fixed set of virtual shards, making horizontal scaling and node management seamless.
- Consistency Levels: M3 provides variable consistency levels for read and write operations, as well as cluster connection operations. Write consistency levels include One (success of a single node), Majority (success of the majority of nodes), and All (success of all nodes). Read consistency level is One, which corresponds to reading from a single nod
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.
M3 is designed to be horizontally scalable and handle high data throughput. It uses fileset files as the primary unit of long-term storage, storing compressed streams of time series values. These files are flushed to disk after a block time window becomes unreachable. M3 has a commit log, equivalent to the commit log or write-ahead-log in other databases, which ensures data integrity. Client Peer streaming is responsible for fetching blocks from peers for bootstrapping purposes. M3 also implements caching policies to optimize efficient reads by determining which flushed blocks are kept in memory.
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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.
M3 uses a commit log to ensure data integrity, providing durability for write operations.
M3’s client peer streaming fetches data blocks from peers for bootstrapping purposes, optimizing data retrieval and distribution.
M3 implements various caching policies to efficiently manage memory usage, keeping frequently accessed data blocks in memory for faster reads.
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.
M3 Use Cases
Monitoring and Observability
M3 is particularly suitable for large-scale monitoring and observability tasks, as it can store and manage massive volumes of time-series data generated by infrastructure, applications, and microservices. Organizations can use M3 to analyze, visualize, and detect anomalies in the metrics collected from various sources, enabling them to identify potential issues and optimize their systems.
IoT and Sensor Data
M3 can be used to store and process the vast amounts of time-series data generated by IoT devices and sensors. By handling data from millions of devices and sensors, M3 can provide organizations with valuable insights into the performance, usage patterns, and potential issues of their connected devices. This information can be used for optimization, predictive maintenance, and improving the overall efficiency of IoT systems.
Financial Data Analysis
Financial organizations can use M3 to store and analyze time-series data related to stocks, bonds, commodities, and other financial instruments. By providing fast and efficient querying capabilities, M3 can help analysts and traders make more informed decisions based on historical trends, current market conditions, and potential future developments.
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.
M3 Pricing Model
M3 is an open source database and can be used freely, although you will have to account for the cost of managing your infrastructure and the hardware used to run M3. Chronosphere is the co-maintainer of M3 along with Uber and also offers a hosted observability that uses M3 as the backend storage layer.
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