Exploring Apache Pinot

Published On: 6 March 2024.By Sanket Agarwal.

Exploring Apache Pinot

Introduction

Apache Pinot stands out as a specialised, real-time distributed OLAP data-store designed for swift analytics, catering to high-volume, low-latency queries effortlessly handling data ingestion from diverse sources such as Apache Kafka, Amazon S3, and others. It excels in delivering ultra-low latency analytics even with substantial throughput, functioning as a columnar data store equipped with smart indexing and limitless scalability. Its proficiency in user-facing analytics and varied use cases ensures instant data availability with latencies below a second.

Tailored specifically for user-facing real-time analytics, Apache Pinot caters to personalised end-user analytics, swiftly processing queries at a rate reaching hundreds of thousands per second. It priorities data freshness, accommodating high-velocity, multi-source data while upholding reliability, scalability, and cost-efficiency. Recognised and trusted by industry giants like LinkedIn, Uber, and Walmart, Apache Pinot has become integral to modern business analytics.

Furthermore, it allows organisations to concentrate on application development and data modelling while streamlining back-end administration, ensuring a smooth operational experience.

Meticulous attention is essential in the capacity planning for a Pinot cluster, which includes vital components like Controller, Zookeeper, Broker, Server, and optional Minion nodes. This planning directly impacts performance, reliability, and cost-effectiveness.

As real-time analytics continue to evolve, Apache Pinot leads the way, adapting to intricate use cases like user-facing analytics, personalisation, anomaly detection, and root cause analysis. It has risen to meet the heightened standards for query latency, throughput, data freshness, and flexibility, reshaping the landscape of modern analytical systems.

Apache Pinot Storage Model

Apache Pinot’s storage model revolves around key components: Segments, Tables, Tenants, and Clusters. Segments act as distributed shards of data across nodes, accommodating the continuous growth of tables over time. Tables in Pinot function similarly to traditional databases, holding columns and rows that are queried through SQL. Tables are uniquely configured, enabling diverse settings like indexing strategies and partitioning. Tenants enable multi-tenancy, allowing logical namespace segregation, crucial for isolating applications and teams. Clusters, comprising tenants, form the foundation of Pinot’s architecture, with scalability achieved by adding nodes linearly.

Apache Pinot Components

An Apache Pinot cluster encompasses various distributed system components:

Controller: Manages consistency and routing within the cluster, maintaining metadata, allocating resources, and serving as the system’s HTTP gateway for administrative tasks. It ensures synchronised states among components and is embedded with Helix for optimised functionality.
Broker: Acts as an intermediary between clients and servers, receiving and directing queries to appropriate servers, then consolidating responses for clients. It utilises Helix for query optimisation and accuracy.

Server: Hosts and serves data segments, catering to both real-time and offline data processing. Real-time servers handle high-throughput streams continuously, while offline servers manage segments for query processing.

Minion (optional): Executes background tasks, such as data purging for compliance purposes. It alleviates intensive tasks from other components, reducing the impact on query latency.

The Controller orchestrates consistency and routing, handling state changes and resource allocations aided by Helix, while also serving as the HTTP gateway for administrative purposes. The Broker receives and directs queries to Apache Pinot servers, coordinating query execution and consolidating responses. Servers host segments allocated across nodes and function in real-time or offline modes, managed by Helix for operational state changes. The Minion executes tasks like data purging for compliance, optimising segments and building additional indices without affecting server query latency.

Understanding these components is crucial for operators monitoring system usage or managing cluster deployments. It enables effective cluster management and issue debugging, ensuring optimal system performance.

Conclusion

In the realm of real-time analytics, Apache Pinot stands as a game-changer for data management. Renowned for its lightning-fast analytics and ability to handle diverse data sources like Apache Kafka and Amazon S3, it’s trusted by industry giants such as LinkedIn, Uber, and Walmart, redefining modern business analytics.

Game-changer in real-time analytics: Positions Pinot as a leader in the field.
Trusted by industry giants: Adds credibility by mentioning established users.
Seamless data processing: Emphasises efficient architecture and configuration.
Adaptability for diverse needs: Outlines its flexibility for various data and use cases.
Innovative storage optimisation: Highlights efficient resource utilisation.
Sets the standard for evolving needs: Positions Pinot as future-proof.
Transformative, efficient, and scalable: Emphasises its impact on data analytics.

Continuously evolving, Apache Pinot sets the standard for user-facing analytics, anomaly detection, and root cause analysis, reshaping real-time analytical systems. Embracing Apache Pinot means adopting a transformative, efficient, and scalable approach to data analytics—a crucial step in today’s data-driven world.

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