Apache Pinot vs OpenTSDB

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

The primary purpose of this article is to compare how Apache Pinot and OpenTSDB 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 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.

OpenTSDB Overview

OpenTSDB (Open Time Series Database) is an open-source, distributed, and scalable time series database built on top of Apache HBase, a NoSQL database. OpenTSDB was designed to address the growing need for storing and processing large volumes of time series data generated by various sources, such as IoT devices, sensors, and monitoring systems. It was initially developed by StumbleUpon in 2010 and later became an independent project with an active community of contributors.

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.

OpenTSDB for Time Series Data

OpenTSDB is designed for time series data storage and analysis, making it an ideal choice for managing large scale time series datasets. Its architecture enables high write and query performance, and it can handle millions of data points per second with minimal resource consumption. OpenTSDB’s flexible querying capabilities allow users to perform complex analysis on time series data efficiently.

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.

OpenTSDB Key Concepts

• Data Point: A single measurement in time consisting of a timestamp, metric, value, and associated tags.
• Metric: A named value that represents a specific aspect of a system, such as CPU usage or temperature.
• Tags: Key-value pairs associated with data points that provide metadata and help categorize and query the data.

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.

OpenTSDB Architecture

OpenTSDB is built on top of Apache HBase, a distributed and scalable NoSQL database, and relies on its architecture for data storage and management. OpenTSDB stores time series data in HBase tables, with data points organized by metric, timestamp, and tags. The database uses a schema-less data model, which allows for flexibility when adding new metrics and tags. The OpenTSDB architecture also supports horizontal scaling by distributing data across multiple HBase nodes.