Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Option A (import-all-tables with filter)**: ```bash sqoop import-all-tables --connect jdbc:mysql://host:3306/mydb \ --username user -P --warehouse-dir /user/hadoop/import \ --exclude-tables audit_log,temp --num-mappers 4 ``` **Option B (table-by-table, recommended for...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Meesho. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow, sql) will help you answer variations of this question confidently.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example. The expert answer includes a code example that demonstrates the implementation pattern.
Option A (import-all-tables with filter):
sqoop import-all-tables --connect jdbc:mysql://host:3306/mydb \
--username user -P --warehouse-dir /user/hadoop/import \
--exclude-tables audit_log,temp --num-mappers 4
Option B (table-by-table, recommended for prod):
for t in table1 table2 table3 table4 table5; do
sqoop import --connect jdbc:mysql://host/mydb --table $t \
--target-dir /user/hadoop/import/$t --num-mappers 4 \
--split-by id
# Use primary key or indexed column for split-by
done
Why Option B: Each table can have its own --split-by (indexed column), --num-mappers, and incremental logic. Failure in one doesn't block others.
Scalability Trade-offs: Parallelize 5 imports in Airflow; each gets 4 mappers. Source connection limit = 5 * 4 = 20; ensure MySQL max_connections > 20.
Cost Implications: Sequential vs. parallel affects wall-clock time and cluster cost. Compress with --compress to reduce HDFS storage.
Pro-Move: 'We use --direct for MySQL to bypass JDBC; 2x faster for large tables.' Red Flag: --split-by on non-indexed column—full table scan per mapper.
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According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.