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 Doris 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 Doris 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 Doris vs Apache Pinot Breakdown


 
Database Model

Data warehouse

Columnar database

Architecture

Doris can be deployed on-premises or in the cloud and is compatible with various data formats such as Parquet, ORC, and JSON.

Pinot can be deployed on-premises, in the cloud, or using a managed service

License

Apache 2.0

Apache 2.0

Use Cases

Interactive analytics, data warehousing, real-time data analysis, reporting, dashboarding

Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics

Scalability

Horizontally scalable with distributed storage and compute

Horizontally scalable, supports distributed architectures for high availability and performance

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Apache Doris Overview

Apache Doris is an MPP-based interactive SQL data warehousing system designed for reporting and analysis. It is known for its high performance, real-time analytics capabilities, and ease of use. Apache Doris integrates technologies from Google Mesa and Apache Impala. Unlike other SQL-on-Hadoop systems, Doris is designed to be a simple and tightly coupled system that does not rely on external dependencies. It aims to provide a streamlined and efficient solution for data warehousing and 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 Doris for Time Series Data

Apache Doris can be effectively used with time series data for real-time analytics and reporting. With its high performance and sub-second response time, Doris can handle massive amounts of time-stamped data and provide timely query results. It supports both high-concurrent point query scenarios and high-throughput complex analysis scenarios, making it suitable for analyzing time series data with varying levels of complexity.

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 Doris Key Concepts

  • MPP (Massively Parallel Processing): Apache Doris leverages MPP architecture, which allows it to distribute data processing across multiple nodes, enabling parallel execution and scalability.
  • SQL: Apache Doris supports SQL as the query language, providing a familiar and powerful interface for data analysis and reporting.
  • Point Query: Point query refers to retrieving a specific data point or a small subset of data from the database.
  • Complex Analysis: Apache Doris can handle complex analysis scenarios that involve processing large volumes of data and performing advanced computations and aggregations.

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 Doris Architecture

Apache Doris is based on MPP architecture, which enables it to distribute data and processing across multiple nodes for parallel execution. It is a standalone system and does not depend on other systems or frameworks. Apache Doris combines the technology of Google Mesa and Apache Impala to provide a simple and tightly coupled system for data warehousing and analytics. It leverages SQL as the query language and supports efficient data processing and query optimization techniques to ensure high performance and scalability.

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 Doris Features

High Performance

Apache Doris is designed for high-performance data analytics, delivering sub-second query response times even with massive amounts of data.

Real-Time Analytics

Apache Doris enables real-time data analysis, allowing users to gain insights and make informed decisions based on up-to-date information.

Scalability

Apache Doris can scale horizontally by adding more nodes to the cluster, allowing for increased data storage and processing capacity.

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.


Apache Doris Use Cases

Real-Time Analytics

Apache Doris is well-suited for real-time analytics scenarios where timely insights and analysis of large volumes of data are crucial. It enables businesses to monitor and analyze real-time data streams, make data-driven decisions, and detect patterns or anomalies in real time.

Reporting and Business Intelligence

Apache Doris can be used for generating reports and conducting business intelligence activities. It supports fast and efficient querying of data, allowing users to extract meaningful insights and visualize data for reporting and analysis purposes.

Data Warehousing

Apache Doris is suitable for building data warehousing solutions that require high-performance analytics and querying capabilities. It provides a scalable and efficient platform for storing, managing, and analyzing large volumes of data for reporting and decision-making.

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.


Apache Doris Pricing Model

As an open-source project, Apache Doris is freely available for usage and does not require any licensing fees. Users can download the source code and set up Apache Doris on their own infrastructure without incurring any direct costs. However, it’s important to consider the operational costs associated with hosting and maintaining the database infrastructure.

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.