Querying:
Key-Value collections are ubiquitous in documents and object storage. Their querying is handled natively as per the data store. This translates to a query popularly described in SQL language over relational store as a join where the key-values can be considered a table with columns as key and value pair. The desired keys to include in the predicate can be put in a separate temporary table holding just the keys of interest and a join can be performed between the two based on the match between the keys.
Without the analogy of the join, the key-value collections will require standard query operators like where clause which may test for a match against a set of keys. This is rather expensive compared to the join because we do this with a large list of key-values and possibly repeated iterations over the entire list for matches against one or more keys in the provided set.
Any match for a key regardless of whether it is in a join or against a list requires efficient traversal over all the keys. This is only possible if the keys are already arranged in a sorted order and the keys are efficiently search using the techniques of divide and conquer where the range that does not have the key can be ignored and the search repeated on the remaining range. This technique of binary search is an efficient algorithm that only works when the large list of data is arranged. A useful data structure for this purpose is the B-plus tree that allows a set of keys to be stored or treated as a unit while organizing the units in a tree like manner.
Most key-value collections are scoped. They are not necessarily in a large global list. Such key-values become scoped to the document or the object. The document may be in one of two forms – Json and Xml. The Json format has its own query language referred to as jmesPath and the Xml also support path-based queries. When the key-values are scoped, they can be efficiently searched by an application using standard query operators without requiring the use of paths inherent to a document format as Json or Xml.
However, there are no query languages specific to objects because the notion of object is one of encapsulation and opacity as opposed to documents that are open. However, objects are not limited to binary images or files. They can be text based and often the benefit of using object storage as opposed to a document store is their availability over the web protocols. For example, object store supports S3 control and data path that is universally recognized as a web accessible storage. Document store on the other hand provide easy programmability options to conventional SQL statements and their usage is popular for the syntax like insertMany() and find(). Stream queries are supported in both document and object stores with the help of cursor.
#codingexercise
when we find the kth element spiralwise in a square matrix, we can skip the elements rather than walk them.
Key-Value collections are ubiquitous in documents and object storage. Their querying is handled natively as per the data store. This translates to a query popularly described in SQL language over relational store as a join where the key-values can be considered a table with columns as key and value pair. The desired keys to include in the predicate can be put in a separate temporary table holding just the keys of interest and a join can be performed between the two based on the match between the keys.
Without the analogy of the join, the key-value collections will require standard query operators like where clause which may test for a match against a set of keys. This is rather expensive compared to the join because we do this with a large list of key-values and possibly repeated iterations over the entire list for matches against one or more keys in the provided set.
Any match for a key regardless of whether it is in a join or against a list requires efficient traversal over all the keys. This is only possible if the keys are already arranged in a sorted order and the keys are efficiently search using the techniques of divide and conquer where the range that does not have the key can be ignored and the search repeated on the remaining range. This technique of binary search is an efficient algorithm that only works when the large list of data is arranged. A useful data structure for this purpose is the B-plus tree that allows a set of keys to be stored or treated as a unit while organizing the units in a tree like manner.
Most key-value collections are scoped. They are not necessarily in a large global list. Such key-values become scoped to the document or the object. The document may be in one of two forms – Json and Xml. The Json format has its own query language referred to as jmesPath and the Xml also support path-based queries. When the key-values are scoped, they can be efficiently searched by an application using standard query operators without requiring the use of paths inherent to a document format as Json or Xml.
However, there are no query languages specific to objects because the notion of object is one of encapsulation and opacity as opposed to documents that are open. However, objects are not limited to binary images or files. They can be text based and often the benefit of using object storage as opposed to a document store is their availability over the web protocols. For example, object store supports S3 control and data path that is universally recognized as a web accessible storage. Document store on the other hand provide easy programmability options to conventional SQL statements and their usage is popular for the syntax like insertMany() and find(). Stream queries are supported in both document and object stores with the help of cursor.
#codingexercise
when we find the kth element spiralwise in a square matrix, we can skip the elements rather than walk them.
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