Hive interview questions

1. A.an optional per-column probabilistic index in ORC files that lets Hive skip stripes which cannot contain a searched value
2. B.a Hive engine mode that processes batches of rows in columnar form to improve CPU efficiency on ORC data
3. C.a table where Hive owns the data, so DROP deletes the underlying files
4. D.the Hive execution engine that runs queries as a directed acyclic graph of tasks, avoiding the multi-stage disk writes of classic MapReduce

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1. A.schema-on-read
2. B.Vectorized execution
3. C.Hive managed table
4. D.Cost-based optimizer

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1. A.Hive managed table — a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes
2. B.Hive managed table — a table where Hive owns the data, so DROP deletes the underlying files
3. C.Hive managed table — the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes
4. D.Hive managed table — the Hive optimizer (backed by Apache Calcite) that uses table and column statistics to reorder joins and choose efficient plans

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1. A.a table where Hive owns only metadata, so DROP keeps the data
2. B.a Hive join optimization that joins corresponding buckets of two bucketed tables in memory, avoiding a full shuffle
3. C.the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult
4. D.the Hive background process that merges base and delta files of transactional tables, with minor compaction merging deltas and major compaction rewriting bases

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1. A.Dynamic partition insert
2. B.Major compaction
3. C.Vectorized execution
4. D.Hive external table

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1. A.Hive external table — applying a schema at query time rather than at write time
2. B.Hive external table — a Hive join optimization that joins corresponding buckets of two bucketed tables in memory, avoiding a full shuffle
3. C.Hive external table — an optional per-column probabilistic index in ORC files that lets Hive skip stripes which cannot contain a searched value
4. D.Hive external table — a table where Hive owns only metadata, so DROP keeps the data

Answer + AI explanation with Pro

1. A.the Hive background process that merges base and delta files of transactional tables, with minor compaction merging deltas and major compaction rewriting bases
2. B.splitting data into directories by column value for partition pruning
3. C.applying a schema at query time rather than at write time
4. D.an optional per-column probabilistic index in ORC files that lets Hive skip stripes which cannot contain a searched value

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1. A.LLAP
2. B.Hive partitioning
3. C.Hive managed table
4. D.Dynamic partition insert

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1. A.Hive partitioning — splitting data into directories by column value for partition pruning
2. B.Hive partitioning — a Hive write mode that creates partitions automatically from column values in the data being inserted
3. C.Hive partitioning — the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult
4. D.Hive partitioning — a Hive join optimization that joins corresponding buckets of two bucketed tables in memory, avoiding a full shuffle

Answer + AI explanation with Pro

1. A.the Hive query optimizer that uses table statistics through Apache Calcite to pick join orders and plans
2. B.the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult
3. C.hashing a column into a fixed number of files within a partition
4. D.a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes

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1. A.Metastore
2. B.Partition pruning
3. C.ORC bloom filter
4. D.Hive bucketing

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1. A.Hive bucketing — a table where Hive owns the data, so DROP deletes the underlying files
2. B.Hive bucketing — hashing a column into a fixed number of files within a partition
3. C.Hive bucketing — a Hive engine mode that processes batches of rows in columnar form to improve CPU efficiency on ORC data
4. D.Hive bucketing — the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes

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1. A.a Hive write mode that creates partitions automatically from column values in the data being inserted
2. B.applying a schema at query time rather than at write time
3. C.a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes
4. D.the Hive optimization that reads only the partition directories matching a query's filter on partition columns

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1. A.ACID tables
2. B.Hive bucketing
3. C.schema-on-read
4. D.ORC bloom filter

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1. A.schema-on-read — a Hive ACID operation that merges several delta files into a single delta file without rewriting base files
2. B.schema-on-read — applying a schema at query time rather than at write time
3. C.schema-on-read — the Hive feature processing batches of about 1024 rows per operation instead of one row at a time to cut CPU overhead
4. D.schema-on-read — the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes

Answer + AI explanation with Pro

1. A.a Hive engine mode that processes batches of rows in columnar form to improve CPU efficiency on ORC data
2. B.the Hive query optimizer that uses table statistics through Apache Calcite to pick join orders and plans
3. C.a Hive join optimization that joins corresponding buckets of two bucketed tables in memory, avoiding a full shuffle
4. D.the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult

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1. A.Cost-based optimizer
2. B.LLAP
3. C.Hive bucketing
4. D.Metastore

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1. A.Metastore — the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult
2. B.Metastore — a Hive join optimization that joins corresponding buckets of two bucketed tables in memory, avoiding a full shuffle
3. C.Metastore — a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes
4. D.Metastore — the Hive Live Long and Process service that keeps daemons and cached data resident in memory for low-latency interactive queries

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1. A.the Hive Live Long and Process service that keeps daemons and cached data resident in memory for low-latency interactive queries
2. B.applying a schema at query time rather than at write time
3. C.Hive transactional tables, stored as ORC, that support row-level insert, update, and delete with snapshot isolation
4. D.the Hive service that stores table schemas, partitions, and storage locations in a relational database for query engines to consult

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1. A.vectorized query execution
2. B.Partition pruning
3. C.Minor compaction
4. D.ACID tables

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1. A.ACID tables — a Hive ACID operation that merges several delta files into a single delta file without rewriting base files
2. B.ACID tables — Hive transactional tables, stored as ORC, that support row-level insert, update, and delete with snapshot isolation
3. C.ACID tables — the Hive feature processing batches of about 1024 rows per operation instead of one row at a time to cut CPU overhead
4. D.ACID tables — a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes

Answer + AI explanation with Pro

1. A.Hive transactional tables, stored as ORC, that support row-level insert, update, and delete with snapshot isolation
2. B.the Hive Live Long and Process service that keeps daemons and cached data resident in memory for low-latency interactive queries
3. C.a Hive write mode that creates partitions automatically from column values in the data being inserted
4. D.the Hive execution engine that runs queries as a directed acyclic graph of tasks, avoiding the multi-stage disk writes of classic MapReduce

Answer + AI explanation with Pro

1. A.Hive partitioning
2. B.Major compaction
3. C.ACID tables
4. D.LLAP

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1. A.LLAP — hashing a column into a fixed number of files within a partition
2. B.LLAP — splitting data into directories by column value for partition pruning
3. C.LLAP — the Hive Live Long and Process service that keeps daemons and cached data resident in memory for low-latency interactive queries
4. D.LLAP — a Hive engine mode that processes batches of rows in columnar form to improve CPU efficiency on ORC data

Answer + AI explanation with Pro

1. A.the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes
2. B.a Hive write mode that creates partitions automatically from column values in the data being inserted
3. C.splitting data into directories by column value for partition pruning
4. D.the Hive execution engine that runs queries as a directed acyclic graph of tasks, avoiding the multi-stage disk writes of classic MapReduce

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1. A.ACID compaction
2. B.bucketed map join
3. C.Hive cost-based optimizer
4. D.Delta and base files

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1. A.Delta and base files — the Hive background process that merges base and delta files of transactional tables, with minor compaction merging deltas and major compaction rewriting bases
2. B.Delta and base files — the Hive optimization that reads only the partition directories matching a query's filter on partition columns
3. C.Delta and base files — the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes
4. D.Delta and base files — Hive transactional tables, stored as ORC, that support row-level insert, update, and delete with snapshot isolation

Answer + AI explanation with Pro

1. A.a Hive ACID operation that merges base and delta files into new base files, applying all updates and deletes
2. B.a Hive ACID operation that merges several delta files into a single delta file without rewriting base files
3. C.the Hive query optimizer that uses table statistics through Apache Calcite to pick join orders and plans
4. D.applying a schema at query time rather than at write time

Answer + AI explanation with Pro

1. A.Dynamic partition insert
2. B.Minor compaction
3. C.Vectorized execution
4. D.bucketed map join

Answer + AI explanation with Pro

1. A.Minor compaction — the Hive ACID layout where base files hold compacted data and delta files record subsequent inserts and deletes
2. B.Minor compaction — a Hive ACID operation that merges several delta files into a single delta file without rewriting base files
3. C.Minor compaction — the Hive feature processing batches of about 1024 rows per operation instead of one row at a time to cut CPU overhead
4. D.Minor compaction — splitting data into directories by column value for partition pruning

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