Apache Pinot vs QuestDB
A detailed comparison
Compare Apache Pinot and QuestDB for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Pinot and QuestDB so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Pinot 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 Pinot vs QuestDB Breakdown
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 |
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| Database Model | Columnar database |
Time series database |
| Architecture | Pinot can be deployed on-premises, in the cloud, or using a managed service |
QuestDB is designed for horizontal scaling, enabling you to distribute data and queries across multiple nodes for increased performance and availability. It can be deployed on-premises, in the cloud, or as a hybrid solution, depending on your infrastructure needs and preferences. |
| License | Apache 2.0 |
Apache 2.0 |
| Use Cases | Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics |
Monitoring, observability, IoT, Real-time analytics, Financial services, High-frequency trading |
| Scalability | Horizontally scalable, supports distributed architectures for high availability and performance |
High-performance with support for horizontal scaling and multi-threading |
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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.
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 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.
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 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.
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 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.
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.
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Apache Pinot Features
Real-time Ingestion
Pinot supports real-time data ingestion from Kafka and other streaming sources, allowing for up-to-date analytics.
Scalability
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.
QuestDB Features
High-Performance Ingestion
QuestDB is optimized for high throughput ingestion, allowing users to efficiently ingest large volumes of time series data at high speeds.
Fast SQL Queries
QuestDB provides fast SQL queries for analyzing time series data. It extends the SQL language with time series-specific functionalities to assist with real-time analytics.
Operational Simplicity
QuestDB aims to provide a user-friendly experience with operational simplicity. It supports schema-agnostic ingestion using popular protocols such as InfluxDB line protocol and PostgreSQL wire protocol. Additionally, a REST API is available for bulk imports and exports, simplifying data management tasks.
Apache Pinot Use Cases
Real-time Analytics
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.
QuestDB Use Cases
Financial Market Data
QuestDB is well-suited for managing and analyzing financial market data. Its high-performance ingestion and fast SQL queries enable efficient processing and analysis of large volumes of market data in real time.
Application Metrics
QuestDB can be used for collecting and analyzing application metrics. Its ability to handle high data throughput and provide real-time analytics capabilities makes it suitable for monitoring and analyzing performance metrics, logs, and other application-related data.
IoT Data Analysis
QuestDB’s high-performance ingestion and time series analytics capabilities make it a valuable tool for analyzing IoT sensor data.
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.
QuestDB Pricing Model
QuestDB is an open-source project released under the Apache 2 License. It is freely available for usage and does not require any licensing fees. Users can access the source code on GitHub and deploy QuestDB on their own infrastructure without incurring direct costs. QuestDB also offers a managed cloud service.
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