At its core, data indexing is the process of transforming raw data into a format that's optimized for retrieval. Unlike an arbitrary application that may generate new source-of-truth data, indexing pipelines process existing data in various ways while maintaining trackability back to the original source. This intrinsic nature - being a derivative rather than source of truth - creates unique challenges and requirements.
Characteristics of a Good Indexing Pipeline
A well-designed indexing pipeline should possess several key traits:
1. Ease of Building
People should be able to build a new indexing pipeline without mastering techniques such as database manipulation / access, streaming processing, parallelization, fault recovery, etc. In addition, transformation components (a.k.a. operations) should be easily composable and reusable across different pipelines.
2. Maintainability
The pipeline should be easy to understand, modify, and debug. Complex transformation logic should be manageable without becoming a maintenance burden.
On the other hand, indexing pipeline is a stateful system, so besides the transformation logic, it's also important to expose clear status of the pipeline states, e.g. statistics of the number of data entries, their freshness, and how a specific piece of derived data tracks back to the original source.
3. Cost-Effectiveness
Data transformation (with necessary tracking of relationships between data) should be done efficiently without excessive computational or storage costs. Moreover, existing computations should be reused whenever possible. For example, 1% of document change, or a chunking strategy changes that only affects 1% chunks, shouldn't need to entail rerunning the expensive embedding model over the entire dataset.
4. Indexing Freshness
For many applications, the source of truth for indexing is consistently updated, so it's important to make sure the indexing pipeline is also updated accordingly in a timely manner.
Common Challenges in Indexing Pipelines
Incremental Updates Is Challenging
The ability to process only new or changed data rather than reprocessing everything is crucial for both cost efficiency and indexing freshness. This becomes especially important as your data grows.
To make incremental update work, we need to carefully track the state of the pipeline, to decide which portion of the data need to be reprocessed, and make sure states derived from old versions are fully deleted or replaced. It's challenging to make things right while considering various complexities, like fan-in / fan-out in transformations, out-of-order processing, recovery after early termination, etc.
Upgradability Often Overlooked
Many implementations focus on the initial setup but neglect how the pipeline will evolve. When requirements change or new processing steps need to be added, the system should adapt without requiring a complete rebuild.
Traditional pipeline implementations often struggle with changes to the processing steps. Adding or modifying steps typically requires reprocessing all data, which can be extremely expensive and invovles manual process.
The Deterministic Logic Trap
Many systems require deterministic processing logic - meaning the same input should always produce the same output. This becomes problematic when:
- Entry deletion needs to be handled
- Processing logic naturally evolves
- Keys generated in previous runs don't match current runs, leading to data leaks
CocoIndex approaches indexing pipelines with a fundamentally different mental model - similar to how React revolutionized UI development compared to vanilla JavaScript. Instead of focusing on the mechanics of data processing, users can concentrate on their business logic and desired state.
CocoIndex open sourced under Apache 2.0.
https://github.com/cocoindex-io/cocoindex
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