Advantages of Metadata Indexing and Asynchronous Indexing in Apache Hudi

The amount of data being generated today is growing at an exponential rate. Managing large-scale data workloads can be a challenge, especially when it comes to processing and querying the data in a timely and efficient manner. This is where metadata indexing and asynchronous indexing come in handy. In this blog post, we'll explore the advantages of these two features in the context of Hudi, an open-source data management framework.

Apache Hudi is a popular data management framework that enables data ingestion and storage in real-time. It supports efficient data management by providing various indexing mechanisms that help optimize query performance and reduce storage costs. Metadata indexing is one such mechanism that enables Hudi to quickly access metadata information such as file paths, partition values, and column statistics. In this blog, we will focus on the COLUMN_STATS index, its advantages, and how it helps improve query performance.

What is Metadata Indexing?

Metadata indexing is a mechanism used in Apache Hudi to store and manage metadata information related to datasets. Metadata indexing enables Hudi to quickly access metadata information such as file paths, partition values, and column statistics. This information is used to optimize query performance and reduce storage costs by enabling Hudi to skip reading unnecessary data.

Hudi supports various types of indexing mechanisms, including Bloom filters, simple indexes, and composite indexes. Each indexing mechanism has its advantages and is used to optimize query performance based on specific use cases.

What are Advantages?Meta Data Indexing ?

1. Improved query performance: Metadata indexing enables data management systems to quickly access metadata information, such as column statistics and file paths, which helps optimize query performance by skipping unnecessary data and reducing disk I/O.
2. Reduced storage costs: By enabling data management systems to skip reading unnecessary data, metadata indexing can help reduce storage costs by requiring less storage space than storing all data.
3. Real-time data analysis: Metadata indexing can help provide real-time data analysis by providing statistics such as min, max, and count of values for a particular column. This enables users to make more informed decisions on data management and analysis.
4. Scalability: Metadata indexing can be scaled horizontally to support large datasets, making it a useful feature for big data systems.
5. Flexibility: Metadata indexing can be implemented using various techniques, such as Bloom filters, simple indexes, and composite indexes, giving users the flexibility to choose the indexing mechanism that best fits their use case.
6. Improved data quality: Metadata indexing can provide statistics on data quality, such as the number of null values in a column, which can help identify data issues and improve data quality.

Why Asynchronous Indexing in Hudi ?

Asynchronous indexing in Hudi refers to the process of indexing data in a non-blocking manner. This means that other processes can continue to run while indexing is taking place. Async indexing is particularly useful in large-scale data workloads where indexing can take a long time and block other processes.

1. Asynchronous indexing in Apache Hudi allows metadata and index updates to be processed separately from the main data write operation.
2. It improves write performance by reducing the time needed to complete the main write operation.
3. Asynchronous indexing ensures consistency and data integrity by processing metadata and index updates separately from the main write operation, preventing data loss if the main write operation fails.
4. Asynchronous indexing is scalable and can be scaled independently from the main write operation, allowing for improved scalability of the overall system.
5. Asynchronous indexing provides flexibility in terms of indexing mechanisms used, supporting various indexing mechanisms, including Bloom filters, simple indexes, and composite indexes.
6. Asynchronous indexing allows for better resource allocation in the system by enabling resources to be allocated separately for the main write operation and indexing updates.
7. Asynchronous indexing is particularly useful for large datasets and high-velocity data streams.

Advantages of COLUMN_STATS Index

COLUMN_STATS is a metadata index feature in Apache Hudi that provides several advantages, including:

1. Improved query performance: COLUMN_STATS metadata index enables Hudi to retrieve only the necessary data columns needed for a query, thereby improving query performance. With COLUMN_STATS, the metadata index can provide statistics for a subset of columns, which helps Hudi to skip reading unnecessary data.
2. Efficient storage: The metadata index can be stored in a highly compressed format, requiring significantly less storage space than the actual data. This can reduce storage costs and improve disk I/O.
3. Real-time data analysis: The COLUMN_STATS metadata index can help provide real-time data analysis by providing statistics such as min, max, and count of values for a particular column. This enables users to make more informed decisions on data management and analysis.
4. Scalability: The metadata index can be scaled horizontally to support large datasets, making it a useful feature for big data systems.

Overall, COLUMN_STATS metadata index is a valuable feature in Hudi that can improve query performance, reduce storage costs, enable real-time data analysis, and support scalability for large datasets.

Recently, the latest release of Hudi introduced a new feature called Multi-Modal Index, which is a new way to index data that enables faster data queries and searching. Multi-Modal Index combines two indexing techniques: bloom filters and inverted indices. Bloom filters are memory-efficient data structures that allow for quick filtering of data based on certain properties, while inverted indices are more space-intensive but provide faster search performance. Multi-Modal Index combines these two techniques to provide a more efficient and effective way of indexing data. Moreover, Multi-Modal Index supports multiple data modalities such as structured, semi-structured, and unstructured data, which enables efficient indexing across multiple data formats. Multi-Modal Index is a powerful tool that can improve indexing performance and efficiency in large-scale data workloads.

Conclusion

In summary, metadata indexing and asynchronous indexing are powerful tools in Hudi that enable faster data processing, querying, and searching, and can help ensure data quality and consistency over time. Metadata indexing allows for faster data queries and searching, faster data processing, and easier management of data quality and consistency. Asynchronous indexing allows for parallel processing, greater scalability, and non-blocking indexing, which improves system efficiency and performance.

In large-scale data workloads, metadata indexing and asynchronous indexing are essential features for achieving optimal performance and scalability. Hudi provides these features and more, making it an excellent choice for managing large-scale data workloads in a variety of industries and applications.

References

https://hudi.apache.org/docs/metadata_indexing

https://www.onehouse.ai/blog/asynchronous-indexing-using-hudi

https://github.com/apache/hudi/blob/master/rfc/rfc-45/rfc-45.md