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Sqoop Incremental Import?

Spark/Big Dataeasy0.6 min readPremium

**Why Incremental**: Full table scans on 100M+ row RDBMS tables cause locks, replication lag, and hour-long loads. Incremental captures only delta—minutes instead of hours. **Modes**: (1) **append**—check column (e.g., id) for new rows; append-only tables. (2)...

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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
Altimetrik
Key Concepts Tested
sql

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Expert Answer
129 wordsIncludes code

Why Incremental: Full table scans on 100M+ row RDBMS tables cause locks, replication lag, and hour-long loads. Incremental captures only delta—minutes instead of hours.

Modes: (1) append—check column (e.g., id) for new rows; append-only tables. (2) lastmodified—timestamp column; captures updates and inserts; use --merge-key to upsert.

Architectural Logic: Store --last-value in a control table or file; next run reads it. Without idempotent storage, reruns duplicate data.

Scalability Trade-offs: --split-by must be indexed; unindexed column causes full scan per mapper. --num-mappers > source connection limit causes connection exhaustion.

Cost Implications: Reduces source DB load 80–95% for large tables; enables hourly vs. daily sync. Trade-off: complexity of merge logic for lastmodified.

sqoop import --connect jdbc:mysql://host/db --table orders \
  --incremental lastmodified --check-column last_updated \
  --last-value '2024-01-01' --merge-key id --target-dir /data/orders

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