Comparison / Apache Cassandra vs Apache Pinot

Apache Cassandra vs Apache Pinot

A detailed comparison

Compare Apache Cassandra and Apache Pinot for time series and OLAP workloads

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 Cassandra 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 Cassandra 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 Cassandra vs Apache Pinot Breakdown


Database Model	Distributed wide-column database	Columnar database
Architecture	Apache Cassandra follows a masterless, peer-to-peer architecture, where each node in the cluster is functionally the same and communicates with other nodes using a gossip protocol. Data is distributed across nodes in the cluster using consistent hashing, and Cassandra supports tunable consistency levels for read and write operations. It can be deployed on-premises, in the cloud, or as a managed service	Pinot can be deployed on-premises, in the cloud, or using a managed service
License	Apache 2.0	Apache 2.0
Use Cases	High write throughput applications, time series data, messaging systems, recommendation engines, IoT	Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics
Scalability	Horizontally scalable with support for data partitioning, replication, and linear scalability as nodes are added	Horizontally scalable, supports distributed architectures for high availability and performance

Apache Cassandra Overview

Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.

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 Cassandra for Time Series Data

Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.

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 Cassandra Key Concepts

Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.

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 Cassandra Architecture

Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.

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.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

Download now

Apache Cassandra Features

Linear Scalability

Cassandra can scale horizontally, adding nodes to the cluster to accommodate growing workloads and maintain consistent performance.

High Availability

With no single point of failure and support for data replication, Cassandra ensures data is always accessible, even in the event of node failures.

Tunable Consistency

Users can balance between data consistency and performance by adjusting consistency levels based on their application’s requirements.

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 Cassandra Use Cases

Cassandra’s high availability and low-latency make it suitable for messaging and social media applications that require fast, consistent access to user data.

IoT and Distributed Systems

With its ability to handle large amounts of data across distributed nodes, Cassandra is an excellent choice for IoT applications and other distributed systems that generate massive data streams.

E-commerce

Cassandra is a good fit for E-commerce use cases because it has the ability to support things like real-time inventory status and it’s architecture also allows for reduced latency by allowing region specific data to be closer to users.

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 Cassandra Pricing Model

Apache Cassandra is an open-source project, 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 Cassandra cluster. Additionally, several managed Cassandra services, such as DataStax Astra and Amazon Keyspaces, offer different pricing models based on factors like data storage, request throughput, and support.

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.

Get started with InfluxDB for free

InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.

Get Started Now