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 Apache Pinot so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Druid and Apache Pinot 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 Apache Pinot Breakdown
Druid can be deployed on-premises, in the cloud, or using a managed service
Pinot can be deployed on-premises, in the cloud, or using a managed service
Real-time analytics, OLAP, time series data, event-driven data, log analytics, ad tech, user behavior analytics
Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics
Horizontally scalable, supports distributed architectures for high availability and performance
Horizontally scalable, supports distributed architectures for high availability and performance
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.
Apache Pinot Overview
Apache Pinot is a real-time distributed OLAP datastore, designed to answer complex analytical queries with low latency. It was initially developed at LinkedIn and later open-sourced in 2015. Pinot is well-suited for handling large-scale data and real-time analytics, providing near-instantaneous responses to complex queries on large datasets. It is used by several large organizations, such as LinkedIn, Microsoft, and Uber.
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.
Apache Pinot for Time Series Data
Apache Pinot is a solid choice for working with time series data due to its columnar storage and real-time ingestion capabilities. Pinot’s ability to ingest data from streams like Apache Kafka ensures that time series data can be analyzed as it is being generated, in addition to having options for bulk ingesting 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.
Apache Pinot Key Concepts
- Segment: A segment is the basic unit of data storage in Pinot. It is a columnar storage format that contains a subset of the table’s data.
- Table: A table in Pinot is a collection of segments.
- Controller: The controller manages the metadata and orchestrates data ingestion, query execution, and cluster management.
- Broker: The broker is responsible for receiving queries, routing them to the appropriate servers, and returning the results to the client.
- Server: The server stores segments and processes queries on those segments.
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.
Apache Pinot Architecture
Pinot is a distributed, columnar datastore that uses a hybrid data model, combining features of both NoSQL and SQL databases. Its architecture consists of three main components: Controller, Broker, and Server. The Controller manages metadata and cluster operations, while Brokers handle query routing and Servers store and process data. Pinot’s columnar storage format enables efficient compression and quick query processing.
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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.
Apache Pinot Features
Pinot supports real-time data ingestion from Kafka and other streaming sources, allowing for up-to-date analytics.
Pinot’s distributed architecture and partitioning capabilities enable horizontal scaling to handle large datasets and high query loads.
Low-latency Query Processing
Pinot’s columnar storage format and various performance optimizations allow for near-instantaneous responses to complex queries.
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.
Apache Pinot Use Cases
Pinot is designed to support real-time analytics, making it suitable for use cases that require up-to-date insights on large-scale data, such as monitoring and alerting systems, fraud detection, and recommendation engines.
Ad Tech and User Analytics
Apache Pinot is often used in the advertising technology and user analytics space, where low-latency, high-concurrency analytics are crucial for understanding user behavior, optimizing ad campaigns, and personalizing user experiences.
Anomaly Detection and Monitoring
Pinot’s real-time analytics capabilities make it suitable for anomaly detection and monitoring use cases, enabling users to identify unusual patterns or trends in their data and take corrective action as needed.
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.
Apache Pinot Pricing Model
As an open-source project, Apache Pinot is free to use. However, organizations may incur costs related to hardware, infrastructure, and support when deploying and managing a Pinot cluster. There are no specific pricing options or deployment models tied to Apache Pinot itself.
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