DuckDB vs Apache Pinot

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 DuckDB and Apache Pinot so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how DuckDB 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.

DuckDB Overview

DuckDB is an in-process SQL OLAP (Online Analytical Processing) database management system. It is designed to be simple, fast, and feature-rich. DuckDB can be used for processing and analyzing tabular datasets, such as CSV or Parquet files. It provides a rich SQL dialect with support for transactions, persistence, extensive SQL queries, and direct querying of Parquet and CSV files. DuckDB is built with a vectorized engine that is optimized for analytics and supports parallel query processing. It is designed to be easy to install and use, with no external dependencies and support for multiple programming languages.

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.

DuckDB for Time Series Data

DuckDB can be used effectively with time series data. It supports processing and analyzing tabular datasets, which can include time series data stored in CSV or Parquet files. With its optimized analytics engine and support for complex SQL queries, DuckDB can perform aggregations, joins, and other time series analysis operations efficiently. However, it’s important to note that DuckDB is not specifically designed for time series data management and may not have specialized features tailored for time series analysis like some dedicated time series databases.

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.

DuckDB Key Concepts

• In-process: DuckDB operates in-process, meaning it runs within the same process as the application using it, without the need for a separate server.
• OLAP: DuckDB is an OLAP database, which means it is optimized for analytical query processing.
• Vectorized engine: DuckDB utilizes a vectorized engine that operates on batches of data, improving query performance.
• Transactions: DuckDB supports transactional operations, ensuring the atomicity, consistency, isolation, and durability (ACID) properties of data operations.
• SQL dialect: DuckDB provides a rich SQL dialect with advanced features such as arbitrary and nested correlated subqueries, window functions, collations, and support for complex types like arrays and structs

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.

DuckDB Architecture

DuckDB follows an in-process architecture, running within the same process as the application. It is a relational table-oriented database management system that supports SQL queries for producing analytical results. DuckDB is built using C++11 and is designed to have no external dependencies. It can be compiled as a single file, making it easy to install and integrate into applications.

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.