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What are the limitations of the REORG command with respect to large datasets?

Spark/Big Datamedium0.5 min read

**REORG** (Delta): OPTIMIZE + Z-order. Compacts files and clusters by columns. **Limitations on Large Data**: (1) **Resource-intensive**—full scan and rewrite; OOM on very large partitions. (2) **Single job per table**—no parallelism across partitions in some implementations....

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
PWC
Key Concepts Tested
partition

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Expert Answer
108 words

REORG (Delta): OPTIMIZE + Z-order. Compacts files and clusters by columns.

Limitations on Large Data: (1) Resource-intensive—full scan and rewrite; OOM on very large partitions. (2) Single job per table—no parallelism across partitions in some implementations. (3) Z-order diminishing returns—high cardinality columns benefit less. (4) Blocks concurrent writes—during REORG, writers may conflict.

Why It Fails: 100GB partition with default executor memory = spill or OOM. Z-order on GUID column = no benefit.

Scalability Trade-offs: Run REORG per partition; off-peak. Use OPTIMIZE alone for compaction without Z-order when Z-order cost exceeds benefit.

Cost Implications: REORG is expensive; run weekly or when small-file count exceeds threshold. Monitor duration and cost.

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