Apache Druid vs QuestDB

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 QuestDB so you can quickly see how they compare against each other.

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

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.

QuestDB Overview

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.

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.

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.

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.

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.

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.

QuestDB Architecture

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.