feat(benchmarks): add dict_encoding_sweep harness

Introduces `dict_encoding_sweep`, a focused benchmark driver that compares
`Dictionary<Int32, Utf8>` against `Utf8View` for the same operator across a
cardinality sweep. For each cell it generates two in-memory tables sharing
identical values, runs the same SQL against each, and reports median / min
/ p25 / p75 runtimes to CSV so the dict vs view crossover is directly
visible.

The harness covers scan, filter, project, aggregation, sort, join, limit,
repartition, coalesce_batches, coalesce_partitions, and spm groups. The
README section documents operator groups, flags, and CSV schema so other
contributors can reproduce the numbers without touching the harness.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Author: Rich-T-kid

benchmarks/README.md

@@ -1144,3 +1144,80 @@ adjacent chunks at RG boundaries.
 ./bench.sh run sort_pushdown_inexact
 ./bench.sh run sort_pushdown_inexact_overlap
 ```
+
+## Dictionary vs Utf8View Encoding Sweep
+
+`dict_encoding_sweep` compares `Dictionary<Int32, Utf8>` against `Utf8View` for
+the same operator across a cardinality sweep. For each cell it generates two
+in-memory tables (one per encoding) that share values, SQL text, and seed; it
+then interleaves warmup + timed iterations of both encodings so cache and
+thermal state stay symmetric. Median / min / p25 / p75 runtimes (nanoseconds)
+are appended to a CSV.
+
+The intent is to make it easy for other engineers to reproduce where dictionary
+encoding wins, where it loses, and at which NDV it crosses over, without
+touching the harness itself.
+
+### Operator groups
+
+Pass `--group` to pick one of:
+
+| Group | Queries |
+|-------|---------|
+| `scan` | `scan_k`, `scan_all`, `scan_count_star`, `scan_count_k` |
+| `filter` | `filter_eq`, `filter_ne`, `filter_in10`, `filter_like_prefix`, `filter_gt`, `filter_1pct_group` |
+| `project` | `proj_length`, `proj_substr`, `proj_upper`, `proj_concat`, `proj_case_like` |
+| `aggregation` | `agg_count_distinct_k`, `agg_group_count_k`, `agg_group_sum_v_k`, `agg_group_count_kw`, `agg_having_k` |
+| `sort` | `sort_k`, `sort_k_desc`, `sort_k_limit_1000`, `sort_kw`, `sort_v_by_k` |
+| `join` | `join_inner`, `join_inner_project`, `join_left`, `join_semi`, `join_anti_half` |
+| `limit` | `limit_k_100`, `limit_k_1k`, `limit_k_10k`, `limit_all_1k`, `limit_offset_50k` |
+| `repartition` | `repart_group_k`, `repart_hash_join`, `repart_project_k` |
+| `coalesce_batches` | `cb_filter_eq`, `cb_filter_project`, `cb_filter_group` |
+| `coalesce_partitions` | `cp_scan_k`, `cp_count_star`, `cp_sum_v` |
+| `spm` | `spm_sort_k`, `spm_sort_k_limit`, `spm_sort_v_by_k` |
+
+Narrow to one query with `--query <id>`.
+
+### Running
+
+```bash
+# build release binary (one time)
+cargo build --release --bin dict_encoding_sweep -p datafusion-benchmarks
+
+# single cell: filter group, 1M rows, sweep over NDVs
+./target/release/dict_encoding_sweep \
+  --group filter --rows 1000000 \
+  --ndvs 10,100,500,1000,4000,8192 \
+  --iters 15 --warmups 3 \
+  --output /tmp/dict_sweep/filter_1m.csv
+```
+
+Useful flags:
+
+| Flag | Default | Notes |
+|------|---------|-------|
+| `--group` | required | Operator group (see table above). |
+| `--query` | all | Restrict to a single query id within the group. |
+| `--rows` | required | Rows in the generated table. |
+| `--ndvs` | required | Comma-separated distinct-value counts to sweep. |
+| `--width` | `16` | Fixed byte width per value. |
+| `--nulls` | `0.0` | Null fraction in column `k`. |
+| `--batch-size` | `8192` | Session + record-batch size. |
+| `--target-partitions` | `1` | Set `>1` to force `RepartitionExec` nodes. |
+| `--dict-chunk` | `500000` | Rows per shared dictionary values-array chunk. |
+| `--iters` | `15` | Timed iterations per cell per encoding. |
+| `--warmups` | `3` | Untimed warmup iterations per cell per encoding. |
+
+### CSV schema
+
+The output CSV uses one row per `(encoding, cell)`. Columns:
+
+```
+git_sha, group, query_id, encoding, rows, ndv, width_bytes, null_rate,
+batch_size, dict_chunk_rows, process_id,
+median_ns, min_ns, p25_ns, p75_ns, iters, note
+```
+
+Pair the two rows for the same cell by `(group, query_id, rows, ndv,
+width_bytes, null_rate, batch_size, dict_chunk_rows)` and divide
+`dict.median_ns / view.median_ns` to get the per-cell speedup.