HBase Interview Questions

Dear readers, these HBase Interview Questions have been designed specially to get you acquainted with the nature of questions you may encounter during your interview for the subject of HBase. As per my experience good interviewers hardly plan to ask any particular question during your interview, normally questions start with some basic concept of the subject and later they continue based on further discussion and what you answer:
There are 5 atomic commands which carry out different operations by Hbase.
Get, Put, Delete, Scan and Increment.
A connection to Hbase is established through Hbase Shell which is a Java API.
The Master server assigns regions to region servers and handles load balancing in the cluster.
The zookeeper maintains configuration information, provides distributed synchronization, and also maintains the communication between clients and region servers.
In Hbase a table is disabled to allow it to be modified or change its settings. .When a table is disabled it cannot be accessed through the scan command.
Hbase > is_disabled “table name”
hbase > disable_all 'p.*'
The command will disable all the table starting with the letter p
Filters are used to get specific data form a Hbase table rather than all the records.
They are of the following types.
Column Value Filter
Column Value comparators
KeyValue Metadata filters.
RowKey filters.
Hbase does not have in-built authentication/permission mechanism
Hbase does not have in-built authentication/permission mechanism
The indexes can be created only on a key column, but in RDBMS it can be done in any column.
The indexes can be created only on a key column, but in RDBMS it can be done in any column.
With one HMaster node there is a  single point of failure.
With one HMaster node there is a  single point of failure.
The catalog tables in Hbase maintain the metadata information. They are named as −ROOT− and .META. The −ROOT− table stores information about location of .META> table and the .META> table holds information about all regions and their locations.
Hbase runs on top of Hadoop which is a distributed system. Haddop can only scale uo as and when required by adding more machines on the fly. So Hbase is a scale out process.
In Hbase the client does not write directly into the HFile. The client first writes to WAL(Write Access Log), which then is accessed by Memstore. The Memstore Flushes the data into permanent  memory from time to time.
As more and more data is written to Hbase, many HFiles get created. Compaction is the process of merging these HFiles to one file and after the merged file is created successfully, discard the old file.
There are two types of compaction. Major and Minor compaction. In minor compaction, the adjacent small HFiles are merged to create a single HFile without removing the  deleted HFiles. Files to be merged are chosen randomly.
In Major compaction, all the HFiles of a column are emerged and a single HFiles is created. The delted HFiles are discarded and it is generally triggered manually.
The Delete column command deletes all versions of a column but the delete family deletes all columns of a particular family.
A cell in Hbase is the smallest unit of a Hbase table which holds a piece of data in the form of a tuple{row,column,version}
This class is used to store  information about a column family such as the number of versions, compression settings, etc. It is used as input when creating a table or adding a column.
The lower bound of versions indicates the minimum number of versions to be stored in Hbase for a column. For example If the value is set to 3 then three latest version wil be maintained and the older ones will be removed.
TTL is a data retention technique using which the version of a cell can be preserved till a specific time period.Once that timestamp is reached the specific version will be removed.
Hbase does not support table jons. But using a mapreduce job we can specify join queries to retrieve data from multiple Hbase tables.
Each row in Hbase is identified by a unique byte of array called row key.
The data in Hbase can be accessed in two ways.
Using the rowkey and table scan for a range of row key values.
Using the rowkey and table scan for a range of row key values.
Using mapreduce in a batch manner.
Using mapreduce in a batch manner.
They are − (i) Short and Wide (ii) Tall and Thin
The short and wide table design is considered when there is
There is a small number of columns
There is a small number of columns
There is a large number of rows
There is a large number of rows
 The tall and thin table design is considered when there is
There is a large number of columns
There is a large number of columns
There is a small number of rows
There is a small number of rows
hbase > alter 'tablename', {NAME => 'ColFamily', VERSIONS => 4}
hbase > alter 'tablename', {NAME => 'colFamily', METHOD => 'delete'}
This command deletes the column family form the table.
 
Hbase > disable ‘tablename’
Hbase > alter ‘tablename’ {NAME => ‘oldcolfamily’,NAME=>’newcolfamily’}
Habse > enable ‘tablename’
 
scan 'tablename', {LIMIT=>10,
STARTROW=>"start_row",
STOPROW=>"stop_row"}
major_compact 'tablename'
Run a major compaction on the table.
There are two main steps to do a data bulk load in Hbase.
Generate Hbase data file(StoreFile) using a custom mapreduce job) from the data source. The StoreFile is created in Hbase internal format which can be efficiently loaded.
Generate Hbase data file(StoreFile) using a custom mapreduce job) from the data source. The StoreFile is created in Hbase internal format which can be efficiently loaded.
The prepared file is imported using another tool like comletebulkload to import data into a running cluster. Each file gets loaded to one specific region.
The prepared file is imported using another tool like comletebulkload to import data into a running cluster. Each file gets loaded to one specific region.
Hbase uses a feature called region replication. In this feature  for each region of a table, there will be multiple replicas that are opened in different RegionServers. The Load Balancer ensures that the region replicas are not co-hosted in the same region servers.
The Hmaster is the Master server responsible for monitoring all RegionServer instances in the cluster and it is the interface for all metadata changes. In a distributed cluster, it runs on the Namenode.
HRegionServer is the RegionServer implementation. It is responsible for serving and managing regions. In a distributed cluster, a RegionServer runs on a DataNode.
HBase provides two different BlockCache implementations: the default on-heap LruBlockCache and the BucketCache, which is (usually) off-heap.
The Write Ahead Log (WAL) records all changes to data in HBase, to file-based storage. if a RegionServer crashes or becomes unavailable before the MemStore is flushed, the WAL ensures that the changes to the data can be replayed.
With a single WAL per RegionServer, the RegionServer must write to the WAL serially, because HDFS files must be sequential. This causes the WAL to be a performance bottleneck.
When a region is edited, the edits in the WAL file which belong to that region need to be replayed. Therefore, edits in the WAL file must be grouped by region so that particular sets can be replayed to regenerate the data in a particular region. The process of grouping the WAL edits by region is called log splitting.
WAL can be disabled to improve performance bottleneck.
This is done by calling the Hbase client field Mutation.writeToWAL(false).
The manual region splitting is done we have an unexpected hotspot in your table because of many clients querying the same table.
A Habse Store hosts a MemStore and 0 or more StoreFiles (HFiles). A Store corresponds to a column family for a table for a given region.
The HFile in Habse which stores the Actual data(not metadata) is designed after the SSTable file of BigTable.
Tables in HBase are initially created with one region by default. Then for bulk imports, all clients will write to the same region until it is large enough to split and become distributed across the cluster. So empty regions are created to make this process faster.
Hotspotting is asituation when a large amount of client traffic is directed at one node, or only a few nodes, of a cluster. This traffic may represent reads, writes, or other operations. This traffic overwhelms the single machine responsible for hosting that region, causing performance degradation and potentially leading to region unavailability.
Hotspotting can be avoided or minimized by distributing the rowkeys across multiple regions. The different techniques to do this is salting and Hashing.
In Hbase values are always freighted with their coordinates; as a cell value passes through the system, it’ll be accompanied by its row, column name, and timestamp. If the rows and column names are large, especially compared to the size of the cell value, then indices that are kept on HBase storefiles (StoreFile (HFile)) to facilitate random access may end up occupying large chunks of the HBase allotted RAM than the data itself  because the cell value coordinates are large.
Rowkeys are scoped to ColumnFamilies. The same rowkey could exist in each ColumnFamily that exists in a table without collision.
The Hbase:meta tables stores details of region in the system in the following format.
info:regioninfo (serialized HRegionInfo instance for this region)
info:server (server:port of the RegionServer containing this region)
info:serverstartcode (start-time of the RegionServer process containing this region)
A Namespace is a logical grouping of tables . It is similar to a database object in a Relational database system.
The complete list of columns in a column family can be obtained only querying all the rows for that column family.
The records fetched form Hbase are always sorted in the order of rowkey-> column Family-> column qualifier-> tiestamp.
Further you can go through your past assignments you have done with the subject and make sure you are able to speak confidently on them. If you are fresher then interviewer does not expect you will answer very complex questions, rather you have to make your basics concepts very strong.
Second it really doesn't matter much if you could not answer few questions but it matters that whatever you answered, you must have answered with confidence. So just feel confident during your interview. We at tutorialspoint wish you best luck to have a good interviewer and all the very best for your future endeavor. Cheers :-)
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