Crate delta_arrow_reader

Expand description

§Delta Arrow Reader

Delta Lake in. Arrow batches out. No Spark required.

Delta Arrow Reader is a read-only Rust library that streams Delta Lake data as Arrow batches, with optional SQL through DataFusion.

The Delta Arrow Reader documentation has guided examples and design details.

§When to use it

Delta Arrow Reader is meant for Rust services, command-line tools, and data pipelines that read Delta Lake tables. It is a good fit when:

You need to read a large table without holding all of it in memory.
You want to process each batch as soon as it arrives.
Your application already works with Arrow data.
You want to run SQL through DataFusion.

§Why not…

Delta Arrow Reader has one job: read Delta tables and stream Arrow batches. The alternatives below do much more, and their Delta paths carry that extra weight.

§Spark or Trino

Spark is where Delta Lake grew up, and Trino is a proven distributed query engine. If a cluster is already part of your system, either can fit well. If you only need a small, single-node read service, neither does. You would still carry a JVM, a full query runtime, and the operational machinery of a distributed system just to stream Arrow batches.

§The “read everything” engines

DuckDB, Polars, and Daft aim to be one engine for many formats. For Delta reads, the results were poor. DuckDB took 5.8-14.6 times as long as Delta Arrow Reader, and Polars took 1.7-20 times as long. Daft managed only the text projection out of four workloads; it took 2.1 times as long and rejected the deletion-vector tables. All three also used more memory in every comparable run. See the benchmark setup and complete results.

§delta-rs

delta-rs is the closest alternative, but it also covers the full Delta lifecycle, including writes. Delta Arrow Reader narrows that scope to asynchronous reads, bounded memory, Arrow streaming, and efficient deletion vectors. Across the two projection workloads, Delta Arrow Reader ranged from roughly even with delta-rs to finishing 24% sooner. On deletion-vector tables, delta-rs took three times as long to return one live row and seven times as long to scan the full table.

That gap matters because Databricks now recommends deletion vectors for most tables and is rolling out automatic enablement for new tables.

§Installation

Add the reader, Tokio, and the futures utilities used by the example:

cargo add delta-arrow-reader futures-util
cargo add tokio --features macros,rt-multi-thread

For DataFusion, follow the DataFusion installation instructions to add the matching dependencies.

§Read a table

Load a table and consume its batches from asynchronous code:

use delta_arrow_reader::DeltaTableBuilder;
use futures_util::TryStreamExt;

let table = DeltaTableBuilder::new("/tmp/example-delta-table")
    .load_table()
    .await?;
let mut batches = table.scan().build().await?.into_stream();

while let Some(batch) = batches.try_next().await? {
    println!("rows={}", batch.num_rows());
}

Loading a table selects the latest or requested version and reads its schema. By default, the reader evaluates Delta scan metadata, which it uses to choose files, each time it builds a scan. For repeated queries against the same loaded table, eager scan-metadata initialization caches that metadata in memory when the table loads. Later scans can reuse the cache through either the streaming API or DataFusion. Each scan still reads its Parquet data separately when it runs.

The streaming reader quickstart shows how to select columns, filter rows, limit results, and inspect metrics.

§Query with DataFusion

Enable the datafusion feature to query a Delta table through a DataFusion SessionContext. Registration gives an already loaded table a name in DataFusion. It does not change when the reader evaluates scan metadata, and Parquet data is read only when DataFusion executes a query.

The DataFusion quickstart walks through registration and a first SQL query. It also shows how to reuse scan metadata across SQL queries.

§Scope

The reader can load the latest or a selected table snapshot. It supports column selection, row filters, result limits, deletion vectors, bounded read scheduling, and optional DataFusion integration.

It does not write Delta tables, manage transactions, create a Tokio runtime, or provide Delta Funnel orchestration, reporting, or Python APIs.

§Documentation

§Development

For local checks and documentation setup, see the development guide.

Modules§

datafusion: Optional DataFusion table-provider and registration surface.
guides: Guides for getting started and understanding how the reader works.

Structs§

DeltaBatchStream: Pull-driven stream of finalized logical Arrow batches from one Delta scan.
DeltaProtocol: Protocol metadata captured from one immutable Delta snapshot.
DeltaScan: One immutable, single-use streaming Delta scan plan.
DeltaScanBuilder: Configures one single-use streaming Delta scan.
DeltaScanExecutionOptions: Bounded execution settings for one Delta scan.
DeltaScanMetrics: Shared live metrics for one Delta scan.
DeltaScanMetricsSnapshot: Immutable point-in-time metrics for one Delta scan.
DeltaTable: One immutable loaded Delta table snapshot.
DeltaTableBuilder: Configures and loads one immutable Delta table snapshot.
DeltaTableSnapshot: Loaded Delta snapshot metadata awaiting logical Arrow schema conversion.

Enums§

DeltaComparison: Comparison operation in a Delta predicate.
DeltaPredicate: Query-engine-neutral Delta predicate.
DeltaReaderError: Redacted failure returned by reader APIs.
DeltaReaderPhase: Reader operation phase associated with an error.
DeltaScalar: Non-null scalar value in a Delta predicate.
DeltaSnapshotSelection: Delta snapshot selected for a table load.
ParquetReaderBackend: Backend used to read Parquet data files.

Constants§

VERSION: The crate version.

Type Aliases§

DeltaStorageOptions: Storage options forwarded to Delta object-store construction.