[IcebergIO] Cache destination Row keys in AssignDestinationsAndPartitions

[atognolas]

Summary

• Add a Map<String, Row> keyCache in AssignDestinationsAndPartitions.AssignDoFn to reuse Row key objects across millions of input rows
• Only N distinct (destination, partition) combinations exist (e.g. 400 for a 400-bucket table), but every row currently allocates a new Row with schema validation overhead
• After the first N rows, all subsequent lookups are cache hits

Motivation

Observed during production benchmarks at scale (39M users, 400 partitions, 99-column schema). Row allocation + schema validation dominated bundle processing time unnecessarily.

Test plan

• Existing AssignDestinationsAndPartitionsTest passes
• Run IcebergIO integration tests
• Verify via profiling that Row allocation drops after initial cache warm-up

🤖 Generated with Claude Code

[gemini-code-assist]

Summary of Changes

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This pull request optimizes the IcebergIO connector by caching destination and partition Row keys. By reusing these objects instead of creating new ones for every input row, the change reduces memory pressure and CPU usage during bundle processing, particularly in large-scale scenarios with many partitions.

Highlights

• Performance Optimization: Introduced a key cache in AssignDoFn to reuse Row objects for destination and partition combinations, significantly reducing object allocation overhead.
• Resource Management: Implemented a Map-based cache that stores Row keys, preventing redundant schema validation and object creation for recurring partition paths.

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[gemini-code-assist]

Code Review

This pull request introduces a cache (keyCache) in AssignDestinationsAndPartitions to avoid recreating Row objects for every element. The reviewer suggests using a nested map structure (Map<String, Map<String, Row>>) instead of a flat map with a concatenated string key to prevent unnecessary string allocations on cache hits, which can significantly improve performance when processing millions of rows.

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[gemini-code-assist]

Using a single flat map with a concatenated string key (tableIdentifier + "|" + partitionPath) requires allocating a new String object for every single input element, even on cache hits. Since this DoFn processes millions of rows, this overhead can be avoided by using a nested map structure: Map<String, Map<String, Row>>.

Suggested change

	private transient @MonotonicNonNull Map<String, Row> keyCache;
	private transient @MonotonicNonNull Map<String, Map<String, Row>> keyCache;

[gemini-code-assist]

Instead of concatenating tableIdentifier and partitionPath into a new String on every element, use a nested map lookup. This avoids any object allocation on cache hits, which is the common case.

      Map<String, Map<String, Row>> cache = checkStateNotNull(keyCache);
      Map<String, Row> tableCache = cache.get(tableIdentifier);
      if (tableCache == null) {
        tableCache = new HashMap<>();
        cache.put(tableIdentifier, tableCache);
      }
      Row destAndPartition = tableCache.get(partitionPath);
      if (destAndPartition == null) {
        destAndPartition =
            Row.withSchema(OUTPUT_SCHEMA).addValues(tableIdentifier, partitionPath).build();
        tableCache.put(partitionPath, destAndPartition);
      }

[atognolas]

@ahmedabu98 — Would you mind reviewing this? These are a set of IcebergIO fixes validated on production-scale benchmarks (39M users, 400 partitions, 99-column schema). The four PRs are independent and can be reviewed/merged in any order.

[ahmedabu98]

I actually think we're okay without this PR. Creating Rows is cheap and we already have done all the work to get partitionPath at this point, so there's not much to optimize

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