HBase & NoSQL interview questions

1. A.a group of columns in HBase stored together and configured as a unit for compression and versioning
2. B.an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys
3. C.the unit of horizontal partitioning in HBase, a contiguous range of row keys served by one RegionServer
4. D.a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra

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1. A.Hotspotting
2. B.column family
3. C.Wide-column store
4. D.Region

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1. A.Region — the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
2. B.Region — the primary, sorted identifier of a row in HBase that determines its region placement and access path
3. C.Region — the unit of horizontal partitioning in HBase, a contiguous range of row keys served by one RegionServer
4. D.Region — the HBase grouping of columns physically stored together in the same files, the unit at which compression and TTL are configured

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1. A.the Cassandra model where each query chooses a consistency level such as QUORUM to trade latency against strength of consistency
2. B.a group of columns in HBase stored together and configured as a unit for compression and versioning
3. C.the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
4. D.the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk

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1. A.Cassandra tunable consistency
2. B.Wide-column store
3. C.Column family
4. D.Row key

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1. A.Column family — a group of columns in HBase stored together and configured as a unit for compression and versioning
2. B.Column family — the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
3. C.Column family — the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
4. D.Column family — the unit of horizontal partitioning in HBase, a contiguous range of row keys served by one RegionServer

Answer + AI explanation with Pro

1. A.the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk
2. B.the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
3. C.the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra
4. D.the HBase row-key technique of prefixing keys with a computed value to spread sequential writes across regions and avoid hotspotting

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1. A.MemStore
2. B.Row key
3. C.column family
4. D.LSM tree

Answer + AI explanation with Pro

1. A.MemStore — the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk
2. B.MemStore — the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra
3. C.MemStore — an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys
4. D.MemStore — the HBase grouping of columns physically stored together in the same files, the unit at which compression and TTL are configured

Answer + AI explanation with Pro

1. A.a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra
2. B.the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra
3. C.the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk
4. D.the primary, sorted identifier of a row in HBase that determines its region placement and access path

Answer + AI explanation with Pro

1. A.column family
2. B.MemStore
3. C.LSM tree
4. D.Column family

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1. A.LSM tree — an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys
2. B.LSM tree — the Cassandra model where each query chooses a consistency level such as QUORUM to trade latency against strength of consistency
3. C.LSM tree — the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
4. D.LSM tree — the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra

Answer + AI explanation with Pro

1. A.the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
2. B.the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
3. C.the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk
4. D.an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys

Answer + AI explanation with Pro

1. A.Region
2. B.Write-ahead log
3. C.MemStore
4. D.Wide-column store

Answer + AI explanation with Pro

1. A.Write-ahead log — the HBase grouping of columns physically stored together in the same files, the unit at which compression and TTL are configured
2. B.Write-ahead log — the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
3. C.Write-ahead log — the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
4. D.Write-ahead log — a marker written in Cassandra or HBase to record a delete, removed only after compaction past its grace period

Answer + AI explanation with Pro

1. A.the primary, sorted identifier of a row in HBase that determines its region placement and access path
2. B.the HBase process that merges all store files of a region into one, applying deletes and expiring TTL versions to reclaim space
3. C.a marker written in Cassandra or HBase to record a delete, removed only after compaction past its grace period
4. D.the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash

Answer + AI explanation with Pro

1. A.Row key
2. B.Write-ahead log
3. C.Tombstone
4. D.salting

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1. A.Tombstone — a marker written in Cassandra or HBase to record a delete, removed only after compaction past its grace period
2. B.Tombstone — the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
3. C.Tombstone — the Cassandra model where each query chooses a consistency level such as QUORUM to trade latency against strength of consistency
4. D.Tombstone — a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra

Answer + AI explanation with Pro

1. A.the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra
2. B.the primary, sorted identifier of a row in HBase that determines its region placement and access path
3. C.the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
4. D.an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys

Answer + AI explanation with Pro

1. A.MemStore
2. B.major compaction
3. C.Hotspotting
4. D.Region split

Answer + AI explanation with Pro

1. A.Hotspotting — an HBase problem where monotonically increasing row keys send all writes to one region, fixed by salting or hashing keys
2. B.Hotspotting — the primary, sorted identifier of a row in HBase that determines its region placement and access path
3. C.Hotspotting — the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
4. D.Hotspotting — a group of columns in HBase stored together and configured as a unit for compression and versioning

Answer + AI explanation with Pro

1. A.the primary, sorted identifier of a row in HBase that determines its region placement and access path
2. B.a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra
3. C.the Cassandra model where each query chooses a consistency level such as QUORUM to trade latency against strength of consistency
4. D.the HBase row-key technique of prefixing keys with a computed value to spread sequential writes across regions and avoid hotspotting

Answer + AI explanation with Pro

1. A.Wide-column store
2. B.Hotspotting
3. C.Cassandra tunable consistency
4. D.Region split

Answer + AI explanation with Pro

1. A.Cassandra tunable consistency — the log-structured merge-tree design where writes go to memory and sequential files, later compacted, used by HBase and Cassandra
2. B.Cassandra tunable consistency — the Cassandra model where each query chooses a consistency level such as QUORUM to trade latency against strength of consistency
3. C.Cassandra tunable consistency — the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
4. D.Cassandra tunable consistency — a group of columns in HBase stored together and configured as a unit for compression and versioning

Answer + AI explanation with Pro

1. A.a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra
2. B.the HBase process that divides a region in two when it grows past a size threshold, distributing load across RegionServers
3. C.the HBase grouping of columns physically stored together in the same files, the unit at which compression and TTL are configured
4. D.the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash

Answer + AI explanation with Pro

1. A.Hotspotting
2. B.Wide-column store
3. C.region split
4. D.LSM tree

Answer + AI explanation with Pro

1. A.Wide-column store — the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
2. B.Wide-column store — a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra
3. C.Wide-column store — the HBase process that merges all store files of a region into one, applying deletes and expiring TTL versions to reclaim space
4. D.Wide-column store — the in-memory write buffer in HBase that accumulates edits before they are flushed to an HFile on disk

Answer + AI explanation with Pro

1. A.the HBase operation that divides an oversized region into two so load can be spread across RegionServers
2. B.the primary, sorted identifier of a row in HBase that determines its region placement and access path
3. C.a NoSQL model where rows can have many sparse columns grouped into families, as in HBase and Cassandra
4. D.the HBase grouping of columns physically stored together in the same files, the unit at which compression and TTL are configured

Answer + AI explanation with Pro

1. A.Row key
2. B.Region split
3. C.bloom filter
4. D.major compaction

Answer + AI explanation with Pro

1. A.Region split — the HBase per-store probabilistic index that lets a read skip store files which cannot contain the requested row key
2. B.Region split — the HBase row-key technique of prefixing keys with a computed value to spread sequential writes across regions and avoid hotspotting
3. C.Region split — the durable log HBase appends edits to before applying them in memory so data survives a RegionServer crash
4. D.Region split — the HBase operation that divides an oversized region into two so load can be spread across RegionServers

Answer + AI explanation with Pro