Feature Request: Support configuring chunk size when reading Parquet files from S3 via read_parquet

### Description

---

**Description:**
When using `pg_duckdb` to read Parquet files stored on S3, such as:

```sql
explain analyse
select
*
from
(
select
r ['scansn'] :: character varying(11) as mcbsno,
r ['subpanelno'] as blockno,
r ['pn'] as partno,
r ['componentid'],
r ['supplierpn'] as mfg_pn,
r ['lotcode'] as lotno,
r ['datecode'],
r ['location'] :: text as reference,
r ['moduleno'],
r ['slotno'],
r ['feederid'],
r ['headid'],
r ['holderno'],
r ['nozzleid'],
r ['machinetype'],
r ['cdt'],
r ['qty']
from
read_parquet(
array ['s3://table-data/smt-componentTrace-days/2025/05/02/2025-05-02.5334d6025622.parquet','s3://table-data/smt-componentTrace-days/2025/05/02/2025-05-02.d6cca87eb27d.parquet','s3://table-data/smt-componentTrace-days/2025/05/03/2025-05-03.7da713e1d8ae.parquet','s3://table-data/smt-componentTrace-days/2025/05/03/2025-05-03.9813e438417b.parquet','s3://table-data/smt-componentTrace-days/2025/05/05/2025-05-05.094fc8111e01.parquet']
) r
) r
where
r.mcbsno in (
'U155GT5541',
'V354RT1027',
'V354RT1052',
'9J54RT0176',
'V354RT0929',
'V354RT0719',
'V354RT1098',
'V354RT1097',
'9J54RT0146',
'BL54RT0202',
'HF54RTA3HE',
'V354RT1099',
'HF54RTA3TW',
'V354RT0760',
'V354RT0932',
'9J54RT0146',
'9J54RT0151',
'HF54RTA39B',
'9J54RT0210',
'V354RT1086',
'9J54RT0151',
'HF54RTA3N0',
'V354RT0932',
'V354RT0788'
)
and string_to_array(r.reference, ',') && array ['BAT4','C961','COMPONENT1','GOIDFINGER','HEATSINK','J371','J372','J373','J374','J375','J376','J377','LS2','MB2_SL1M','PC500','PU128','PU97','R2234','R718','SCERW','SCREW','STUD','SW_PWR']

/*
"Custom Scan (DuckDBScan) (cost=0.00..0.00 rows=0 width=0) (actual time=1186.999..1187.075 rows=1 loops=1)"
" DuckDB Execution Plan: "

"┌─────────────────────────────────────┐"
"│┌───────────────────────────────────┐│"
"││ Query Profiling Information ││"
"│└───────────────────────────────────┘│"
"└─────────────────────────────────────┘"
"EXPLAIN ANALYZE SELECT mcbsno, blockno, partno, r, mfg_pn, lotno, r_1 AS r, reference, r_2 AS r, r_3 AS r, r_4 AS r, r_5 AS r, r_6 AS r, r_7 AS r, r_8 AS r, r_9 AS r, r_10 AS r FROM (SELECT (r_1.scansn)::character varying(11) AS mcbsno, r_1.subpanelno AS blockno, r_1.pn AS partno, r_1.componentid AS r, r_1.supplierpn AS mfg_pn, r_1.lotcode AS lotno, r_1.datecode AS r, (r_1.location)::text AS reference, r_1.moduleno AS r, r_1.slotno AS r, r_1.feederid AS r, r_1.headid AS r, r_1.holderno AS r, r_1.nozzleid AS r, r_1.machinetype AS r, r_1.cdt AS r, r_1.qty AS r FROM system.main.read_parquet(VARIADIC ARRAY['s3://table-data/smt-componentTrace-days/2025/05/02/2025-05-02.5334d6025622.parquet'::text, 's3://table-data/smt-componentTrace-days/2025/05/02/2025-05-02.d6cca87eb27d.parquet'::text, 's3://table-data/smt-componentTrace-days/2025/05/03/2025-05-03.7da713e1d8ae.parquet'::text, 's3://table-data/smt-componentTrace-days/2025/05/03/2025-05-03.9813e438417b.parquet'::text, 's3://table-data/smt-componentTrace-days/2025/05/05/2025-05-05.094fc8111e01.parquet'::text]) r_1) r(mcbsno, blockno, partno, r, mfg_pn, lotno, r_1, reference, r_2, r_3, r_4, r_5, r_6, r_7, r_8, r_9, r_10) WHERE (((mcbsno)::text = ANY ((ARRAY['U155GT5541'::character varying, 'V354RT1027'::character varying, 'V354RT1052'::character varying, '9J54RT0176'::character varying, 'V354RT0929'::character varying, 'V354RT0719'::character varying, 'V354RT1098'::character varying, 'V354RT1097'::character varying, '9J54RT0146'::character varying, 'BL54RT0202'::character varying, 'HF54RTA3HE'::character varying, 'V354RT1099'::character varying, 'HF54RTA3TW'::character varying, 'V354RT0760'::character varying, 'V354RT0932'::character varying, '9J54RT0146'::character varying, '9J54RT0151'::character varying, 'HF54RTA39B'::character varying, '9J54RT0210'::character varying, 'V354RT1086'::character varying, '9J54RT0151'::character varying, 'HF54RTA3N0'::character varying, 'V354RT0932'::character varying, 'V354RT0788'::character varying])::text[])) AND (string_to_array(reference, ','::text) && ARRAY['BAT4'::text, 'C961'::text, 'COMPONENT1'::text, 'GOIDFINGER'::text, 'HEATSINK'::text, 'J371'::text, 'J372'::text, 'J373'::text, 'J374'::text, 'J375'::text, 'J376'::text, 'J377'::text, 'LS2'::text, 'MB2_SL1M'::text, 'PC500'::text, 'PU128'::text, 'PU97'::text, 'R2234'::text, 'R718'::text, 'SCERW'::text, 'SCREW'::text, 'STUD'::text, 'SW_PWR'::text]))"
"┌─────────────────────────────────────┐"
"│┌───────────────────────────────────┐│"
"││ HTTPFS HTTP Stats ││"
"││ ││"
"││ in: 2.3 MiB ││"
"││ out: 0 bytes ││"
"││ #HEAD: 5 ││"
"││ #GET: 316 ││"
"││ #PUT: 0 ││"
"││ #POST: 0 ││"
"│└───────────────────────────────────┘│"
"└─────────────────────────────────────┘"
"┌────────────────────────────────────────────────┐"
"│┌──────────────────────────────────────────────┐│"
"││ Total Time: 1.14s ││"
"│└──────────────────────────────────────────────┘│"
"└────────────────────────────────────────────────┘"
"┌───────────────────────────┐"
"│ QUERY │"
"└─────────────┬─────────────┘"
"┌─────────────┴─────────────┐"
"│ EXPLAIN_ANALYZE │"
"│ ──────────────────── │"
"│ 0 Rows │"
"│ (0.00s) │"
"└─────────────┬─────────────┘"
"┌─────────────┴─────────────┐"
"│ HASH_JOIN │"
"│ ──────────────────── │"
"│ Join Type: SEMI │"
"│ │"
"│ Conditions: ├──────────────┐"
"│ mcbsno = #0 │ │"
"│ │ │"
"│ 52 Rows │ │"
"│ (0.00s) │ │"
"└─────────────┬─────────────┘ │"
"┌─────────────┴─────────────┐┌─────────────┴─────────────┐"
"│ FILTER ││ PROJECTION │"
"│ ──────────────────── ││ ──────────────────── │"
"│ (string_to_array(reference││ UNNEST(list_value(CAST( │"
"│ , ',') && ['BAT4', 'C961',││ 'U155GT5541' AS VARCHAR), │"
"│ 'COMPONENT1', 'GOIDFINGER││ CAST('V354RT1027' AS │"
"│ ', 'HEATSINK', 'J371', ││ VARCHAR), CAST( │"
"│ 'J372', 'J373', 'J374', ││ 'V354RT1052' AS VARCHAR), │"
"│ 'J375', 'J376', 'J377', ││ CAST('9J54RT0176' AS │"
"│ 'LS2', 'MB2_SL1M', 'PC500'││ VARCHAR), CAST( │"
"│ , 'PU128', 'PU97', 'R2234'││ 'V354RT0929' AS VARCHAR), │"
"│ , 'R718', 'SCERW', 'SCREW'││ CAST('V354RT0719' AS │"
"│ , 'STUD', 'SW_PWR']) ││ VARCHAR), CAST( │"
"│ ││ 'V354RT1098' AS VARCHAR), │"
"│ ││ CAST('V354RT1097' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ '9J54RT0146' AS VARCHAR), │"
"│ ││ CAST('BL54RT0202' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ 'HF54RTA3HE' AS VARCHAR), │"
"│ ││ CAST('V354RT1099' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ 'HF54RTA3TW' AS VARCHAR), │"
"│ ││ CAST('V354RT0760' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ 'V354RT0932' AS VARCHAR), │"
"│ ││ CAST('9J54RT0146' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ '9J54RT0151' AS VARCHAR), │"
"│ ││ CAST('HF54RTA39B' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ '9J54RT0210' AS VARCHAR), │"
"│ ││ CAST('V354RT1086' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ '9J54RT0151' AS VARCHAR), │"
"│ ││ CAST('HF54RTA3N0' AS │"
"│ ││ VARCHAR), CAST( │"
"│ ││ 'V354RT0932' AS VARCHAR), │"
"│ ││ CAST('V354RT0788' AS │"
"│ ││ VARCHAR))) │"
"│ ││ │"
"│ 4499 Rows ││ 24 Rows │"
"│ (1.55s) ││ (0.00s) │"
"└─────────────┬─────────────┘└─────────────┬─────────────┘"
"┌─────────────┴─────────────┐┌─────────────┴─────────────┐"
"│ TABLE_SCAN ││ UNNEST │"
"│ ──────────────────── ││ ──────────────────── │"
"│ Function: ││ │"
"│ READ_PARQUET ││ │"
"│ ││ │"
"│ Projections: ││ │"
"│ ScanSN ││ │"
"│ SubPanelNo ││ │"
"│ PN ││ │"
"│ ComponentId ││ │"
"│ SupplierPN ││ │"
"│ LotCode ││ │"
"│ DateCode ││ │"
"│ Location ││ │"
"│ ModuleNo ││ │"
"│ SlotNo ││ │"
"│ FeederId ││ │"
"│ HeadId ││ │"
"│ HolderNo ││ │"
"│ NozzleId ││ │"
"│ MachineType ││ │"
"│ Cdt ││ │"
"│ Qty ││ │"
"│ ││ │"
"│ 1292070 Rows ││ 24 Rows │"
"│ (5.41s) ││ (0.00s) │"
"└───────────────────────────┘└─────────────┬─────────────┘"
" ┌─────────────┴─────────────┐"
" │ DUMMY_SCAN │"
" │ ──────────────────── │"
" │ 1 Rows │"
" │ (0.00s) │"
" └───────────────────────────┘"

"Planning Time: 89.990 ms"
"Execution Time: 1268.680 ms"
*/
```

we observed that reading just 5 Parquet files can result in over 300 HTTP requests being sent to S3. This is because the current default request granularity is very fine (around 1KB per request), which leads to excessive I/O overhead and latency, especially when working with cloud object storage.

We’d like to request the ability to control the read request size, so that instead of reading in 1KB chunks, the engine could fetch data in larger blocks—for example, 100KB or 1MB per request.

This feature would help reduce the number of round-trip requests to S3 and significantly improve performance and efficiency in real-world usage.

**Expected Behavior**:

We would like to see support for:

- A configurable request size parameter (e.g., read_parquet(..., s3_request_size='1MB'))
- Or automatic batching that increases the read block size to reduce request count when reading remote Parquet files

Start with the provided PostgreSQL query using read_parquet on S3 and its EXPLAIN ANALYZE output, which reports 316 GET requests for five files. Identify the read-request sizing path and define completion as exposing a configurable larger request size while preserving correct Parquet reads and reducing excessive S3 requests.

Written by the indexing model from the issue text.