perf(parquet): minor tweaks for iceberg write improvement

[hamilton-earthscope]

Overview

In apache/iceberg-go#643 a benchmark test suite is introduced to measure write performance to iceberg tables. This PR fixes a few hot spots in the parquet writer that improve write performance to iceberg tables across a variety of scenarios. These improvements should be broadly beneficial to anyone using arrow-go to write parquet.

Optimizations

1. Zstd Encoder Pooling

Implemented sync.Pool for zstd compression encoders in arrow-go/parquet/compress/zstd.go. Previously, every parquet file write allocated a new 96MB encoder, causing a lot of memory bloat and GC churn. Now encoders are pooled and reused across writes.

2. Column Path Caching

Pre-compute column path strings (e.g., "data.field.subfield") in arrow-go/parquet/schema/column.go during Column creation instead of rebuilding via strings.Join() on every access. Eliminates CPU overhead, especially effective for nested schemas.

Performance Results

These results are measured using the test suite linked above. No new performance benchmarks are added to arrow-go.

The metrics in the table below indicate 1. the write speed measured in records per second and 2. the volume of memory allocated during the test (not the number of allocations which these changes don't really affect)

Benchmark	Before	After	Improvement
Simple 100K	734K rec/sec, 3.9 GB	2.5M rec/sec, 155 MB	3.5x faster, 96% less memory
Simple 2.5M	3.9M rec/sec, 9.3 GB	4.3M rec/sec, 1.9 GB	1.1x faster, 79% less memory
ListPrimitive 100K	496K rec/sec, 4.6 GB	1.3M rec/sec, 317 MB	2.7x faster, 93% less memory
ListPrimitive 2.5M	1.5M rec/sec, 14.2 GB	2.0M rec/sec, 3.3 GB	1.3x faster, 76% less memory
ListStruct 100K	377K rec/sec, 5.7 GB	927K rec/sec, 565 MB	2.4x faster, 90% less memory
ListStruct 2.5M	772K rec/sec, 19.7 GB	1.1M rec/sec, 5.0 GB	1.4x faster, 74% less memory
MapPrimitive 100K	446K rec/sec, 5.6 GB	995K rec/sec, 511 MB	2.2x faster, 91% less memory
MapPrimitive 2.5M	1.1M rec/sec, 18.0 GB	1.3M rec/sec, 4.7 GB	1.2x faster, 74% less memory
1000 partitions	35K rec/sec, 85.9 GB	341K rec/sec, 576 MB	9.6x faster, 99% less memory

Key improvements:

• Small batches (100K): 2.2-3.5x throughput, 90-96% memory reduction
• Large batches (2.5M): 1.1-1.4x throughput, 74-79% memory reduction
• High partition counts see dramatic gains (9.6x faster for 1000 partitions)

[zeroshade]

Thanks for this! Looks great with massive benefits. Will merge once the CI finishes (assuming no issues)