Hadoop HBase Schema and Versioning

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Schema Creation:-

• H BASE schema can be created or updated with H Base Shell by using H Base Admin in the java APZ
• Tables must be disabled when making column family modifications.

Example:-

Configuration config= H Base configuration. Crete();

H Base Admin admin = new H Base Admin (conf);

String Table=”my table”;

Admin. disableTable(table);

HColumn Descriptor cf1 = new H column Descriptor(‘Hadoop’);

Admin. Add column(table, cf1); = adding new column family

H Column Descriptor cf2 = new H column Descriptor(‘Hadoop’);

Admin. Modify column(table, cf2);= Modifying existing column family.

Admin. Enable table (table);

When changes are made to either Table of column families (Ex: region size, block size), these changes take effect the next time whenever there is a major compaction and then stored files get re- written.

H Base Versioning:-

Maximum number of versions:-

• The Maximum number of row versions to store is configured per column family via H column Descriptor.
• The default value for max versions is 3
• As discussed previously, H Base does not over write row values, but rather stores different values per row by time.
• Excess versions are removed during major compactions.

Minimum number of versions:-

• Like max no. of row versions, minimum no. of row versions are configured per column family via H Column Descriptor.
• The default value for minimum versions is 0, which means the feather is disabled.
• The minimum no. of row versions parameter is used together with the time-to-live parameter and can be combined with the no. of row versions parameters to allow configurations such as keep the last T minutes worth of data, almost N versions, but keep atleast M versions around where M is the value for minimum number of row version, M

Map Reduce Integration with H Base:-

• Map Reduce mean for only parallel processing
• Table input format is the class.
• When you want to work with map Reduce programming with H Base, H Base will either act as source/sink which means that map reduce will either take the data from H Base Regions or map reduce will produce the results on top of H Base Regions after processing the data.

Frequently asked Hadoop Interview Questions

Preparation:-

• To run a map reduce job that needs classes from libraries not shipped with hadoop or the map Reduce framework
• We need to make those libraries available before the job is executed.
• We have two choices for static preparation of all task nodes supplying everything needed along with the job.
• For static preparation, it is useful to permanently install its JAR file locally on the task tracker machines and those machines will run the map reduce tasks.
• This is done by the doing the following :

1. Copy the JAR files into a common location on all nodes.
2. Add the JAR files with full location into the hadoop –env -sh configuration file, into the Hadoop- class path variable.

HBase as Source:-

• When HBase act as source for Map Reduce programming, it will take all the split information from the below class org. apache. Hadoop. H base. Map reduce. table input format.

Hbase as sink:-

• In this case, the input data can be taken either from HDFS or H Base, but the final output after processing the data will get stored on top of H Base regions for doing the same. It will use the below class.
• Org. apache. hadoop. hbase.map reduce. Table out put format
• For mapper business logic, these is a class called extends table mapper.
• For reducer business logic, class extends tuple reducer.
• To load the bulk data or if H Base shell is not working , we give below commands :

H Base Admin(class)
H Base configuration conf=new H Base configuration(Admin,””);
Config. create H table(‘Gopal’);
Column Descriptor.

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