HDFS interview questions

1. A.the architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond a single NameNode's capacity
2. B.128 MB
3. C.a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
4. D.too many files exhausting NameNode memory (~150 bytes each)

Answer + AI explanation with Pro

1. A.NameNode high availability
2. B.HDFS default block size
3. C.short-circuit read
4. D.Erasure coding

Answer + AI explanation with Pro

1. A.HDFS default block size — 128 MB
2. B.HDFS default block size — the HDFS process where a standby or secondary NameNode merges the edit log into the fsimage to bound recovery time
3. C.HDFS default block size — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
4. D.HDFS default block size — an HDFS setup with active and standby NameNodes sharing edits via a JournalNode quorum to remove the single point of failure

Answer + AI explanation with Pro

1. A.the HDFS setup running active and standby NameNodes sharing edits through a JournalNode quorum to remove the single point of failure
2. B.the HDFS process where a standby or secondary NameNode merges the edit log into the fsimage to bound recovery time
3. C.3
4. D.the HDFS storage policy that protects data with parity blocks instead of full replicas, cutting storage overhead from 200 percent to about 50 percent

Answer + AI explanation with Pro

1. A.HDFS default replication factor
2. B.HDFS default block size
3. C.Hadoop 3 erasure coding
4. D.small-files problem

Answer + AI explanation with Pro

1. A.HDFS default replication factor — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
2. B.HDFS default replication factor — the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication
3. C.HDFS default replication factor — the HDFS setup running active and standby NameNodes sharing edits through a JournalNode quorum to remove the single point of failure
4. D.HDFS default replication factor — 3

Answer + AI explanation with Pro

1. A.an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
2. B.the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication
3. C.the HDFS service storing filesystem metadata in memory
4. D.3

Answer + AI explanation with Pro

1. A.Erasure coding
2. B.JournalNode
3. C.NameNode
4. D.short-circuit read

Answer + AI explanation with Pro

1. A.NameNode — the HDFS storage policy that protects data with parity blocks instead of full replicas, cutting storage overhead from 200 percent to about 50 percent
2. B.NameNode — the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth
3. C.NameNode — a feature reducing the 3x replication storage overhead
4. D.NameNode — the HDFS service storing filesystem metadata in memory

Answer + AI explanation with Pro

1. A.the HDFS service storing the actual data blocks
2. B.an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
3. C.the architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond a single NameNode's capacity
4. D.the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth

Answer + AI explanation with Pro

1. A.JournalNode
2. B.small-files problem
3. C.ViewFS
4. D.DataNode

Answer + AI explanation with Pro

1. A.DataNode — the HDFS subsystem that requires a majority of JournalNodes to acknowledge each edit before the active NameNode commits it
2. B.DataNode — the HDFS service storing the actual data blocks
3. C.DataNode — a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
4. D.DataNode — 3

Answer + AI explanation with Pro

1. A.the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth
2. B.too many files exhausting NameNode memory (~150 bytes each)
3. C.a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
4. D.the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication

Answer + AI explanation with Pro

1. A.small-files problem
2. B.JournalNode
3. C.Checkpointing
4. D.erasure coding

Answer + AI explanation with Pro

1. A.small-files problem — too many files exhausting NameNode memory (~150 bytes each)
2. B.small-files problem — the architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond a single NameNode's capacity
3. C.small-files problem — the HDFS optimization letting a client on the same node read block files directly from local disk, bypassing the DataNode process
4. D.small-files problem — an architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond one NameNode

Answer + AI explanation with Pro

1. A.the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth
2. B.a feature reducing the 3x replication storage overhead
3. C.the HDFS storage policy that protects data with parity blocks instead of full replicas, cutting storage overhead from 200 percent to about 50 percent
4. D.128 MB

Answer + AI explanation with Pro

1. A.HDFS default replication factor
2. B.erasure coding
3. C.Hadoop 3 erasure coding
4. D.small-files problem

Answer + AI explanation with Pro

1. A.Hadoop 3 erasure coding — a feature reducing the 3x replication storage overhead
2. B.Hadoop 3 erasure coding — a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
3. C.Hadoop 3 erasure coding — the HDFS tool that redistributes blocks across DataNodes to even out disk utilization after nodes are added or fill unevenly
4. D.Hadoop 3 erasure coding — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one

Answer + AI explanation with Pro

1. A.an architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond one NameNode
2. B.the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication
3. C.3
4. D.a feature reducing the 3x replication storage overhead

Answer + AI explanation with Pro

1. A.DataNode
2. B.erasure coding
3. C.Block
4. D.Checkpointing

Answer + AI explanation with Pro

1. A.Block — 3
2. B.Block — the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication
3. C.Block — the HDFS storage policy that protects data with parity blocks instead of full replicas, cutting storage overhead from 200 percent to about 50 percent
4. D.Block — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one

Answer + AI explanation with Pro

1. A.a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
2. B.the HDFS subsystem that requires a majority of JournalNodes to acknowledge each edit before the active NameNode commits it
3. C.the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth
4. D.an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one

Answer + AI explanation with Pro

1. A.Rack awareness
2. B.erasure coding
3. C.balancer
4. D.small-files problem

Answer + AI explanation with Pro

1. A.Rack awareness — the fixed-size unit, 128 MB by default, into which HDFS splits files for distributed storage and replication
2. B.Rack awareness — the HDFS policy that places block replicas across racks to balance fault tolerance against cross-rack bandwidth
3. C.Rack awareness — an HDFS setup with active and standby NameNodes sharing edits via a JournalNode quorum to remove the single point of failure
4. D.Rack awareness — an architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond one NameNode

Answer + AI explanation with Pro

1. A.3
2. B.an HDFS setup with active and standby NameNodes sharing edits via a JournalNode quorum to remove the single point of failure
3. C.the HDFS setup running active and standby NameNodes sharing edits through a JournalNode quorum to remove the single point of failure
4. D.an architecture using multiple independent NameNodes, each managing a namespace volume, to scale metadata beyond one NameNode

Answer + AI explanation with Pro

1. A.NameNode high availability
2. B.Quorum Journal Manager
3. C.balancer
4. D.NameNode

Answer + AI explanation with Pro

1. A.NameNode high availability — 3
2. B.NameNode high availability — the HDFS service storing filesystem metadata in memory
3. C.NameNode high availability — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
4. D.NameNode high availability — an HDFS setup with active and standby NameNodes sharing edits via a JournalNode quorum to remove the single point of failure

Answer + AI explanation with Pro

1. A.the HDFS setup running active and standby NameNodes sharing edits through a JournalNode quorum to remove the single point of failure
2. B.128 MB
3. C.an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
4. D.an HDFS setup with active and standby NameNodes sharing edits via a JournalNode quorum to remove the single point of failure

Answer + AI explanation with Pro

1. A.HDFS default block size
2. B.HDFS default replication factor
3. C.JournalNode
4. D.short-circuit read

Answer + AI explanation with Pro

1. A.JournalNode — an HDFS daemon that stores the shared edit log so the standby NameNode stays in sync with the active one
2. B.JournalNode — an HDFS storage strategy that uses parity blocks instead of full replication to cut storage overhead while tolerating failures
3. C.JournalNode — a client-side mount table that presents multiple federated HDFS namespaces as a single unified file system view
4. D.JournalNode — 128 MB

Answer + AI explanation with Pro