Table of Contents

  1. Introduction
  2. Core Features of Chronon
  3. Architecture and Components
  4. Use Cases and Benefits
  5. Getting Started with Chronon
  6. Conclusion

Airbnb announced the open-sourcing of Chronon, their machine learning feature platform designed to streamline the transformation of raw data into ML-ready features. This blog post will summarize the key points and features of Chronon as discussed in various InfoQ articles and presentations.

author: Varant Zanoyan

Introduction

Chronon is Airbnb’s solution to address the complexities of managing and serving a vast number of features used in machine learning models. By transforming raw data into features suitable for both training and inference, Chronon aims to relieve ML practitioners from the time-consuming tasks of data management, allowing them to focus more on modeling.

Core Features of Chronon

Chronon allows ML engineers to define features once, which can then be used for both offline training and online inference. It distinguishes between batch features, which are computed daily, and streaming features, which provide real-time updates. This dual capability ensures that the platform can handle various ML tasks efficiently.

Architecture and Components

Chronon is built on a robust architecture that integrates several open-source technologies:

  • Kafka: Used for real-time data streaming.
  • Spark/Spark Streaming: For data processing and transformation.
  • Hive: For data warehousing and offline storage.
  • Airflow: For orchestrating workflows and managing data pipelines.

Key Components

  • GroupBy: SQL-like operation for aggregating data.
  • Join: SQL operation to combine data from different sources.
  • StagingQuery: Supports complex SQL queries for data transformation.
  • Feature Views: Windows, buckets, and time-based aggregations for real-time data handling.

Chronon Architecture

Use Cases and Benefits

Chronon addresses several challenges in feature engineering:

  • Data Pipeline Complexity: Simplifies the creation and management of data pipelines.
  • Consistency in Feature Calculation: Ensures that feature computation is consistent across training and inference.
  • Latency and Scalability: Optimized for low-latency feature serving and scalable data processing.

Chronon supports advanced feature computations such as feature derivations, chaining, and external/contextual features. This flexibility allows ML engineers to implement sophisticated feature engineering workflows efficiently.

Getting Started with Chronon

Airbnb provides a quickstart guide that includes an example implementation of a model using Chronon’s API. The API supports Java, Scala, and Python clients, making it accessible for a wide range of ML practitioners.

Example Usage

from chronon import GroupBy, Join, StagingQuery

# Define a GroupBy operation
grouped_data = GroupBy(data, key="user_id")

# Join with another dataset
joined_data = Join(grouped_data, other_data, on="user_id")

# Execute a StagingQuery
result = StagingQuery(joined_data, query="SELECT * FROM joined_data WHERE ...")

Conclusion

By open-sourcing Chronon, Airbnb has provided the ML community with a powerful tool to simplify feature engineering and streamline the transformation of raw data into useful features. This initiative not only enhances productivity but also fosters innovation by allowing ML practitioners to focus more on modeling and less on data management.

For more detailed insights, you can read the full article on InfoQ: Airbnb Open-Sources Chronon.