Implementing Keyword Search in Hudi: Building Inverted Indexes with Record Level Index, Metadata Indexing and Point Lookups | Text search on data lake
In the world of big data, efficient and scalable search functionality is crucial for deriving meaningful insights. Apache Hudi, with its advanced data management capabilities, offers a powerful solution for building keyword search functionalities on large-scale data lakes. This blog delves into the process of implementing keyword search using Apache Hudi, focusing on building inverted indexes, scaling for terabytes of text data, and leveraging record-level indexing, metadata indexing, and point lookups
Video Guides : <To be added soon>
Understanding Apache Hudi
Apache Hudi (Hadoop Upserts Deletes and Incrementals) is an open-source data management framework used to simplify incremental data processing and data pipeline development. Hudi provides support for managing large-scale data lakes on top of distributed storage systems like Amazon S3, HDFS, and Azure Blob Storage. Its primary features include support for upserts, incremental data processing, and a variety of indexing mechanisms to optimize data retrieval and queries.
Building the Foundation: The Bronze Documents Table
To implement keyword search, we first need a foundational table, bronze_documents, which will store the raw text data along with document IDs. This table will act as the base layer, holding the unprocessed textual data.
Building the Inverted Index
An inverted index is a data structure used to map content to its location within a dataset, making it a powerful tool for search functionalities. It allows for efficient keyword searches by maintaining a mapping between keywords and the documents they appear in.
To create an inverted index, we first tokenize the text, remove stop words, hash the keywords, and then build the index.
Define UDf in spark to create inverted index
Leveraging Record-Level Indexing and Metadata Indexing
Record-level indexing in Hudi allows for efficient point lookups and fast retrieval of specific records based on a unique identifier. This feature, combined with metadata indexing, enhances query performance and scalability, particularly when dealing with large datasets.
To enable these features, we configure Hudi options accordingly when writing data to the tables.
Performing Keyword Search
With the inverted index in place, performing keyword searches becomes straightforward. We hash the search keywords, look up the corresponding document IDs in the inverted index, and then fetch the relevant documents from the bronze_documents table.
Example: Searching with a Sentence
To search using a sentence, we split the sentence into individual words, remove stop words, hash the keywords, and perform a lookup in the inverted index to fetch the relevant documents.
Lookup are fast since we are using record level indexing.
User types
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We remove stop words and grab the keyword and they respective hash values
Next Part we Query inverted index and grab appropriate Documents IDS the lookup is fast due to Record level index (Record Level Indexing in Apache Hudi Delivers 70% Faster Point Lookups) https://www.dhirubhai.net/pulse/record-level-indexing-apache-hudi-delivers-70-faster-point-shah-hlite/
From this I know documents 4 and 2 have this keywords. Now I can query main index with that primary key and fetch the documents
Output
Lookup are blazing fast since we have used record level index and hash to determine documents via inverted index and then directly querying documents store in hudi
Labs :
https://soumilshah1995.blogspot.com/2024/07/implementing-keyword-search-in-apache.html
This is a great starting point for anyone looking to perform text or keyword searches on data lakes. Feel free to improve and expand upon it, using this as a foundation. You can also incorporate ranking and filtering to return records based on relevance. I hope you find it helpful and enjoy working with it.
Conclusion
By leveraging Apache Hudi's advanced indexing capabilities, we can build efficient and scalable keyword search functionalities for large-scale data lakes. The combination of inverted indexing, record-level indexing, and metadata indexing ensures fast lookups and high query performance, making it a robust solution for handling terabytes of text data. With these techniques, you can enhance your data processing workflows and enable powerful search capabilities in your big data environment.
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