andygrove opened a new pull request, #2762:
URL: https://github.com/apache/datafusion-ballista/pull/2762

   ## Which issue does this PR close?
   
   N/A
   
   ## Rationale for this change
   
   `benchmarks/cluster` compares Ballista with Spark, Comet and Trino the same 
way for each engine. [DataFusion 
Distributed](https://github.com/datafusion-contrib/datafusion-distributed) is 
the other distributed engine built on DataFusion, and it is compared with 
Ballista in the docs, but there was no way to reproduce those numbers with 
these scripts.
   
   ## What changes are included in this PR?
   
   - `run.py --engine datafusion-distributed`: a coordinator, N workers behind 
a headless Service, and a runner Job, with the same executor count, CPUs and 
memory as the other engines. TPC-H only.
   - `datafusion-distributed/run_queries.py`: the runner (standard library 
only). It registers the tables with `CREATE EXTERNAL TABLE` (partition columns 
declared when `tpch_partitioned` is set), checks them, runs each query once 
untimed and then 3 times, and checks row counts against 
`tpch_expected_rows.py`. It shares the table definitions with Trino.
   - `docker/datafusion-distributed.Dockerfile` builds the `worker` binary from 
the datafusion-distributed repository at `DFD_REF`, after applying 
`datafusion-distributed/worker-memory-limit.patch`. Upstream's worker has an 
unbounded memory pool; the patch reads a pool size and spill directory from the 
environment so that each pod gets the same kind of limit as a Ballista executor 
(a `FairSpillPool` of 70% of the container, with spilling). `run.py` stops if 
the coordinator does not log its pool.
   - Each query's distributed plan, with metrics, is saved next to the results 
rather than in them.
   - Docs: a configuration page next to the Trino one, and the README.
   
   ## Are there any user-facing changes?
   
   No, benchmark scripts only.
   


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