SILO Input Format Migration: A Step-by-Step Guide

Aug 5, 2025 by Felix Dubois 50 views

Migrate to New Silo Input Format: A Comprehensive Guide

Hey guys! So, there's been a big update to the SILO input format, and we need to make sure our ingest process is up to speed. This guide will walk you through the changes and how to adapt to them. Let's dive in!

Understanding the SILO Input Format Migration

The SILO (Sequence Information for Latent Observations) input format has undergone a significant transformation, as detailed in this pull request. This change impacts how data is structured and ingested, particularly for tools like ingest. The core of the migration involves restructuring the JSON objects within the NDJSON files. Previously, metadata and sequence information were nested under separate keys. Now, the new format flattens this structure, making it more streamlined and easier to work with. This migration is crucial for maintaining data integrity and compatibility with the latest SILO updates. Key changes include the consolidation of metadata and sequence data into a single-level JSON structure. This means that fields like age, country, date, and sequence data are now directly accessible at the top level of the JSON object, simplifying data access and processing. Understanding these changes is the first step in ensuring a smooth transition and maintaining the efficiency of our data ingestion pipelines. We need to make sure our systems are updated to handle this new format so that everything runs smoothly. The move aims to improve data handling and streamline processes, which will ultimately benefit our workflows.

Old vs. New SILO Input Format: A Detailed Comparison

Let's break down the specifics by comparing the old and new SILO input formats side-by-side. This will give you a clear picture of the changes and what you need to adjust in your processes. Previously, the SILO input format structured data with nested objects. Metadata, nucleotide insertions, aligned nucleotide sequences, unaligned nucleotide sequences, amino acid insertions, and aligned amino acid sequences were grouped under their respective keys. This meant that to access a specific piece of metadata, such as age or country, you would need to navigate through the metadata object. Similarly, sequence information was nested under keys like alignedNucleotideSequences and unalignedNucleotideSequences. Here's an example of the old format:

{
  "metadata": {
    "age": 4,
    "country": "Switzerland",
    "date": "2021-03-18",
    "division": "Basel-Land",
    "accession": "1234",
    "pango_lineage": "B.1.1.7",
    "qc_value": 0.98,
    "region": "Europe"
  },
  "nucleotideInsertions": {
    "main": []
  },
  "alignedNucleotideSequences": {
    "main": "..."
  },
  "unalignedNucleotideSequences": {
    "main": "..."
  },
  "aminoAcidInsertions": {},
  "alignedAminoAcidSequences": {}
}

In contrast, the new SILO input format adopts a flattened structure. All the essential fields, including metadata and sequence information, are now at the top level of the JSON object. This simplifies data access and makes the structure more intuitive. The main object now directly contains the sequence and insertions. Here’s what the new format looks like:

{
  "age": 4,
  "country": "Switzerland",
  "date": "2021-03-18",
  "division": "Basel-Land",
  "accession": "1234",
  "main": {
    "insertions": ["123:C"],
    "sequence": "..."
  },
  "pango_lineage": "B.1.1.7",
  "qc_value": 0.98,
  "region": "Europe",
  "unaligned_main": "..."
}

The key differences lie in the removal of nested objects for metadata and sequence data. The new format directly includes fields like age, country, date, and sequence information at the top level. The sequence data is now encapsulated within the main object, which includes both insertions and the sequence itself. This streamlined structure reduces the complexity of parsing and accessing data, making it more efficient to work with. This change is designed to make our data handling processes smoother and more efficient.

Adapting Ingest to Produce the New Format

The primary goal is to modify the ingest process to directly produce the new SILO input format. Currently, there's a script available in the LAPIS-SILO repository that can transform data from the old format to the new one. However, this is a post-processing step, and we want to eliminate this extra stage by generating the new format directly. To adapt ingest, we need to modify the data serialization logic. This involves updating the code that constructs the JSON objects to match the new structure. Instead of creating nested metadata and sequence objects, the code should create a flat structure with all fields at the top level. This will require changes in how the data is extracted, transformed, and loaded into the JSON structure. The key steps involve identifying the sections of the ingest code that handle data serialization and modifying them to reflect the new format. This includes mapping the existing data fields to their new locations and ensuring that the output JSON objects conform to the new schema. Additionally, any unit tests or integration tests should be updated to validate the new format. This will ensure that the changes are correct and that the ingest process produces valid data. By directly producing the new format, we can avoid the overhead of the transformation script and streamline our data processing pipeline. This will not only save time but also reduce the potential for errors during the transformation process. We want to make sure our ingest process is as efficient and reliable as possible.

Practical Steps for Migration

To ensure a smooth migration to the new SILO input format, we need a structured approach. Here are the practical steps you should follow:

Analyze the Existing Ingest Process: Start by thoroughly reviewing the current ingest codebase. Identify the sections responsible for data extraction, transformation, and serialization into the old NDJSON format. Pay close attention to how metadata and sequence information are currently handled. Understanding the existing process is crucial for making targeted changes. This involves tracing the data flow from the input source to the final JSON output. Look for the functions or classes that create the JSON objects and how the various fields are populated. Documenting these steps will provide a clear roadmap for the necessary modifications.
Modify Data Serialization Logic: This is the core of the migration. Update the code to construct JSON objects in the new flattened format. Instead of nesting metadata and sequence data, create a single-level structure. Ensure that all required fields are correctly mapped to their new locations. This step requires careful attention to detail. Each field from the old format must be correctly placed in the new format. Pay special attention to the main object, which now contains both insertions and the sequence. Use the new format examples as a reference to ensure accuracy.
Implement Unit Tests: Write unit tests to verify that the modified ingest process produces the correct output. These tests should cover various scenarios, including different data types and edge cases. Thorough testing is essential to catch any errors early in the development process. Unit tests should focus on individual functions or components of the ingest process. Mock data can be used to simulate different input conditions and verify that the output JSON objects conform to the new format.
Update Integration Tests: In addition to unit tests, update integration tests to ensure that the entire data pipeline works seamlessly with the new format. These tests should simulate the end-to-end process, from data ingestion to storage and retrieval. Integration tests validate that the different components of the system work together correctly. This includes testing the interaction between the ingest process and any downstream systems that consume the data. Ensure that the data is correctly ingested, stored, and retrieved in the new format.
Test with Real Data: Once the unit and integration tests pass, test the modified ingest process with real-world data. This will help identify any issues that may not have been apparent during testing with synthetic data. Real data testing is crucial for identifying edge cases and performance bottlenecks. Use a representative sample of your production data to ensure that the ingest process can handle the volume and complexity of real-world scenarios. Monitor the performance of the ingest process and address any issues that arise.
Monitor and Refine: After deploying the changes, closely monitor the performance of the ingest process. Look for any errors or performance issues. Continuously refine the process based on feedback and observations. Ongoing monitoring is essential for maintaining the health and efficiency of the data pipeline. Set up alerts to notify you of any errors or performance issues. Regularly review the logs and metrics to identify areas for improvement. This iterative approach will ensure that the ingest process remains robust and efficient.

By following these steps, we can ensure a smooth and successful migration to the new SILO input format. This structured approach minimizes the risk of errors and ensures that our data pipeline remains reliable and efficient. Remember, thorough testing and monitoring are key to a successful migration. Let's make sure we get this right!

Utilizing the Legacy NDJSON Transformer (If Necessary)

While our primary goal is to directly produce the new format, there might be scenarios where you need to use the legacy NDJSON transformer. This tool, located in the LAPIS-SILO repository, can convert data from the old format to the new format. This can be useful for processing existing data or for troubleshooting purposes. The legacy NDJSON transformer provides a temporary solution for converting data between the old and new formats. It's a command-line tool that takes an NDJSON file in the old format as input and produces an NDJSON file in the new format as output. While we aim to eliminate the need for this tool by directly producing the new format, it can be valuable in certain situations. For example, if you have a backlog of data in the old format that needs to be processed, the transformer can be used to convert it to the new format. Similarly, if you encounter issues with the new format, you can use the transformer to convert data back to the old format for debugging purposes. To use the transformer, you'll need to have Python installed and the necessary dependencies. The repository should provide instructions on how to install and run the tool. The basic usage involves specifying the input and output file paths. However, keep in mind that using the transformer adds an extra step to the data processing pipeline and should be considered a temporary measure. Our long-term goal is to directly produce the new format within the ingest process to streamline our workflows. This tool is a helpful fallback, but we should aim to remove this dependency once our ingest process is fully updated.

Conclusion

Alright guys, migrating to the new SILO input format is a crucial step for keeping our data pipelines efficient and up-to-date. By understanding the changes, adapting our ingest process, and following a structured migration plan, we can make this transition smoothly. Remember to test thoroughly and monitor the process to ensure everything works as expected. Let's tackle this together and keep our data flowing seamlessly! We've got this! The new format is designed to make our lives easier in the long run, so let's embrace the change and make the most of it. By following the steps outlined in this guide, we can ensure a successful migration and continue to deliver high-quality data for our projects. This change will improve our workflows and make our data handling processes more efficient. Let's get started and make this happen! Remember, if you have any questions or run into any issues, don't hesitate to reach out to the team. We're all in this together!