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The term Iceberg Blob refers to a structure used within Apache Iceberg, a data management platform for large-scale datasets. In Iceberg, blobs are associated with the Puffin file format, which is designed to hold additional statistics and indexes to optimize data queries. These blobs are arbitrary pieces of information used to enhance query performance by storing auxiliary data like sketches.

Storage of Additional Statistics:

Iceberg Blob allows the storage of metadata like distinct value counts (NDV), which helps query optimizers make efficient decisions without needing to scan all files or data partitions.

Incremental Updates:

Iceberg Blob allows incremental updates to metadata, meaning that calculations (e.g., distinct counts) do not need to be repeated from scratch. This significantly speeds up operations by reusing precomputed information.

Optimized Query Planning:

The use of blobs in Puffin helps query engines by providing important metrics, which improves data skipping and partition filtering, leading to faster and more efficient query execution.

Performance for Complex Queries:

Complexity of Implementation: Implementing Iceberg Blobs requires a solid understanding of both the Iceberg table format and sketch algorithms. This additional layer of complexity can be a barrier for teams that aren't deeply familiar with these technologies

Approximation in Queries:

Since blobs store sketches that offer approximate results (e.g., for distinct counts or quantiles), the trade-off between speed and accuracy may not be acceptable in all scenarios. For applications requiring exact results, this approximation can be a limitation

Storage Overhead:

Although Puffin optimizes queries by storing metadata in blobs, the blobs themselves (especially when storing large sketches) can add significant storage overhead. This could become problematic in scenarios where storage efficiency is critical.