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Spark DataSource backed by a DataFusion TableProvider over ADBC

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#112 0 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Clearly specified
Activity status
Stale
Tech stack
java, python, spark

Research direction

Start with the implementation referenced in #111, then compare it with this issue's stated scope: the adbc-datafusion DataSourceV2, pushdowns, partitioned reads, executor connection pooling, and PySpark coverage. Run the end-to-end coverage mentioned in the issue and verify that each listed capability is represented and working.

Written by the indexing model from the issue text.

Description

Is your feature request related to a problem or challenge?

Spark users want to read data from a DataFusion TableProvider as a native Spark DataSourceV2. Today there is no first-class path; options are either a bespoke per-operation JNI surface (more native surface to maintain) or copying data out of process.

Describe the solution you'd like

A Spark DataSourceV2 connector that places the native boundary at a standard ADBC driver. Spark talks to the upstream arrow-adbc Java driver manager (adbc-core + adbc-driver-jni), which loads a native DataFusion ADBC cdylib and returns arrow-java ArrowReaders consumed zero-copy as ArrowColumnVectors on the cluster-provided Arrow. This reuses the upstream ADBC bindings rather than reproducing them.

Scope:

  • adbc-datafusion format registered as a DataSourceV2; schema probed on the driver.
  • Projection / filter / limit pushdown via Substrait, with a SQL fallback.
  • Multi-partition reads (executePartitioned / readPartition) and a target_partitions option.
  • Per-executor connection pool to amortize driver/database setup across task slots.
  • An example DataFusion ADBC driver cdylib plus end-to-end (PySpark) coverage.

Describe alternatives you've considered

A plain-C scan ABI + hand-written JNI shim (discussed on #103 / #104). The ADBC approach reuses standard, separately-reviewed bindings and a stable driver contract instead.

Additional context

Implemented in #111.

Dominant language
Java
Stars
32
Forks
12
PR merge metrics
No merged PRs in 30d

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