Question : MapR-DB stores structured data as a __________ 1. nested series of arrays 2. nested series of maps 3. nested series of lists 4. nested series of sets 5. nested series of linkedsets http://www.training4exam.com/hbase-inroduction-part-1
Correct Answer : 2 Explanation: Like Apache HBase, MapR-DB stores structured data as a nested series of maps. Each map consists of a set of key-value pairs, where the value can be the key in another map. Keys are kept in strict lexicographical order: 1, 10, and 113 come before 2, 20, and 213.
Question : In MapR-DB , what is the maximum supported size of a row key
1. 128 Bytes
2. 1 KB
3. 64 KB
4. 1 MB
Correct Answer : 3 Explanation: Key: Keys identify the rows in a table. In MapR-DB, the maximum supported size of a row key is 64 KB. However, the recommended practice is to keep it lower than a few hundred bytes.
Question : In MapR-DB , please order the descending order of granularity, the elements of a table.
1. Key 2. Column family 3. Timestamp 4. Row 5. Column 6. Value
1. 1,3,4,5,6,2
2. 1,4,2,5,3,6
3. 3,6,4,5,2,1
4. 3,6,4,2,5,1
Correct Answer : 2 Explanation: In descending order of granularity, the elements of a table are: Key: Keys identify the rows in a table. In MapR-DB, the maximum supported size of a row key is 64 KB. However, the recommended practice is to keep it lower than a few hundred bytes. Row: Rows span one or more column families and columns. In MapR-DB, the maximum supported size of a row is 2 GB. However, the recommended practice is to keep the size under 2 MB. In general, MapR-DB performs better with many small rows, rather than with fewer very large rows. Column family: A column family is a key associated with a set of columns. Specify this association according to your individual use case, creating sets of columns. A column family can contain an arbitrary number of columns. MapR-DB tables support up to 64 column families. Column: Columns are keys that are associated with a series of timestamps that define when the value in that column was updated. Timestamp: The timestamp in a column specifies a particular data write to that column. Value: The data written to that column at the specific timestamp.
1. When data volume is huge e.g. TB to PB 2. When High throughput is needed e.g. 1000s queries per second 3. When there is a need of Higher Cache 4. When Data is Sparse 5. All of the above