Apache Pinot vs PostgreSQL
A detailed comparison
Compare Apache Pinot and PostgreSQL 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 PostgreSQL so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how Apache Pinot and PostgreSQL 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 PostgreSQL Breakdown
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 |
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| Database Model | Columnar database |
Relational database |
| Architecture | Pinot can be deployed on-premises, in the cloud, or using a managed service |
PostgreSQL can be deployed on various platforms, such as on-premises, in virtual machines, or as a managed cloud service like Amazon RDS, Google Cloud SQL, or Azure Database for PostgreSQL. |
| License | Apache 2.0 |
PostgreSQL license (similar to MIT or BSD) |
| Use Cases | Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics |
Web applications, geospatial data, business intelligence, analytics, content management systems, financial applications, scientific applications |
| Scalability | Horizontally scalable, supports distributed architectures for high availability and performance |
Supports vertical scaling, horizontal scaling through partitioning, sharding, and replication using available tools |
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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.
PostgreSQL Overview
PostgreSQL, also known as Postgres, is an open-source relational database management system that was first released in 1996. It has a long history of being a robust, reliable, and feature-rich database system, widely used in various industries and applications. PostgreSQL is known for its adherence to the SQL standard and extensibility, which allows users to define their own data types, operators, and functions. It is developed and maintained by a dedicated community of contributors and is available on multiple platforms, including Windows, Linux, and macOS.
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.
PostgreSQL for Time Series Data
PostgreSQL can be used for time series data storage and analysis, although it was not specifically designed for this use case. With its rich set of data types, indexing options, and window function support, PostgreSQL can handle time series data. However, Postgres will not be as optimized for time series data as specialized time series databases when it comes to things like data compression, write throughput, and query speed. PostgreSQL also lacks a number of features that are useful for working with time series data like downsampling, retention policies, and custom SQL functions for time series data analysis.
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.
PostgreSQL Key Concepts
- MVCC: Multi-Version Concurrency Control is a technique used by PostgreSQL to allow multiple transactions to be executed concurrently without conflicts or locking.
- WAL: Write-Ahead Logging is a method used to ensure data durability by logging changes to a journal before they are written to the main data files.
- TOAST: The Oversized-Attribute Storage Technique is a mechanism for storing large data values in a separate table to reduce the main table’s disk space consumption.
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.
PostgreSQL Architecture
PostgreSQL is a client-server relational database system that uses the SQL language for querying and manipulation. It employs a process-based architecture, with each connection to the database being handled by a separate server process. This architecture provides isolation between different users and sessions. PostgreSQL supports ACID transactions and uses a combination of MVCC, WAL, and other techniques to ensure data consistency, durability, and performance. It also supports various extensions and external modules to enhance its functionality.
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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.
PostgreSQL Features
Extensibility
PostgreSQL allows users to define custom data types, operators, and functions, making it highly adaptable to specific application requirements.
Full-text search
PostgreSQL has built-in support for full-text search, enabling users to perform complex text-based queries and analyses.
Geospatial support
With the PostGIS extension, PostgreSQL can store and manipulate geospatial data, making it suitable for GIS applications.
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.
PostgreSQL Use Cases
Enterprise applications
PostgreSQL is a popular choice for large-scale enterprise applications due to its reliability, performance, and feature set.
GIS applications
With the PostGIS extension, PostgreSQL can be used for storing and analyzing geospatial data in applications like mapping, routing, and geocoding.
OLTP workloads
As a relational database, PostgreSQL is a good fit for pretty much any application that involves transactional workloads.
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.
PostgreSQL Pricing Model
PostgreSQL is open source software, and there are no licensing fees associated with its use. However, costs can arise from hardware, hosting, and operational expenses when deploying a self-managed PostgreSQL server. Several cloud-based managed PostgreSQL services, such as Amazon RDS, Google Cloud SQL, and Azure Database for PostgreSQL, offer different pricing models based on factors like storage, computing resources, and support.
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