1
NoSQL Technologies(Unit II)
syllabus
Unit 2: Querying, Indexing, and Data Management in NoSQL Databases Querying
NoSQL Stores: Similarities Between SQL and MongoDB Query Features,
Accessing Data from Column-Oriented Databases Like HBase, Querying 02 Page
17 of 48 Redis Data Stores Indexing and Ordering Data Sets: Essential
Concepts Behind a Database Index, Indexing and Ordering in MongoDB, ouchDB
and Apache Cassandra Managing Transactions and Data Integrity: RDBMS and
ACID, Distributed ACID Systems, Upholding CAP, Consistency Implementations
Using NoSQL in The Cloud: Google App Engine Data Store, Amazon SimpleDB
Querying NoSQL Stores:
Similarities between SQL and MongoDB query features.
SQL (Structured Query Language) and MongoDB Query Language are both used for
database querying, but they have significant differences due to the nature of the databases
they are designed for (relational databases for SQL and NoSQL document-oriented databases
for MongoDB). However, there are some similarities and features that can be compared:
Querying Language:
SQL: Uses a standardized language (SQL) for querying relational databases. SQL queries are
written in a declarative language.
MongoDB Query Language: Uses a JSON-like syntax for querying MongoDB. Queries are
written in a document-oriented, flexible, and expressive language.
CRUD Operations:
Both SQL and MongoDB support CRUD (Create, Read, Update, Delete) operations.
SQL uses statements like INSERT, SELECT, UPDATE, and DELETE.
MongoDB uses methods like insert, find, update, and remove.
Filtering:
Both SQL and MongoDB allow for filtering records based on specified criteria.
In SQL, the WHERE clause is commonly used for filtering.
In MongoDB, the find method supports querying based on criteria expressed as a JSON
document.
Sorting:
Both databases support sorting of query results.
1
, 2
SQL uses the ORDER BY clause for sorting.
MongoDB uses the sort method to specify sorting criteria.
Indexing:
Both SQL and MongoDB support indexing to improve query performance.
SQL databases use indexes on columns, and you can create various types of indexes (e.g., B-
tree, hash) depending on the database system.
MongoDB supports indexes on fields within documents.
Aggregation:
Both SQL and MongoDB provide aggregation capabilities to perform computations on data.
SQL uses the GROUP BY clause along with aggregate functions like SUM, AVG, COUNT,
etc.
MongoDB uses the aggregation pipeline framework, which consists of stages like $match,
$group, $project, etc.
Joins:
SQL supports joins to combine data from multiple tables.
MongoDB, being a NoSQL database, does not support traditional joins. Instead, it encourages
denormalization and the use of embedded documents.
Transactions:
SQL databases traditionally support transactions with features like ACID properties
(Atomicity, Consistency, Isolation, Durability).
MongoDB introduced multi-document transactions in recent versions to support similar
transactional capabilities.
Data Integrity:
Both SQL and MongoDB allow you to enforce data integrity through constraints or
validation rules.
SQL uses features like primary keys, foreign keys, and check constraints.
MongoDB provides validation rules that can be applied at the collection level.
Accessing Data from Column-Oriented Databases Like HBase, Querying
Column-oriented databases, exemplified by Apache HBase, provide a robust solution for
handling vast and distributed datasets efficiently. Understanding how to access and query
data in HBase is crucial for deriving meaningful insights from large-scale, sparse datasets.
Data Model:
2
, 3
HBase follows a distributed data model where information is organized into tables, rows, and
columns.
Tables are distributed across a cluster, and each row is uniquely identified by a row key.
Java API:
Interaction with HBase is primarily facilitated through its Java API.
The Java API allows developers to perform Create, Read, Update, and Delete (CRUD)
operations on the data stored in HBase.
// Example of reading data using Java API
Configuration config = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("your_table_name"));
Get get = new Get(Bytes.toBytes("your_row_key"));
Result result = table.get(get);
// Process the result
HBase Shell:
HBase provides an interactive shell that allows users to execute basic commands.
The shell is particularly useful for quick data scans and inspections.
# Example of scanning a table using HBase Shell
scan 'your_table_name'
Client Libraries:
Various client libraries are available for different programming languages, providing
flexibility in implementation.
Python (e.g., HappyBase), Ruby, and other languages also offer ways to interact with HBase.
Querying:
Unlike traditional relational databases, HBase doesn't employ a query language like SQL.
Schema design is critical, and data retrieval is optimized for key-based lookups and range
scans.
Filters:
HBase supports filters to refine data retrieval based on specific conditions.
Filters can be applied when scanning tables, allowing for more targeted queries.3
// Example of scanning with a filter in Java API
3
NoSQL Technologies(Unit II)
syllabus
Unit 2: Querying, Indexing, and Data Management in NoSQL Databases Querying
NoSQL Stores: Similarities Between SQL and MongoDB Query Features,
Accessing Data from Column-Oriented Databases Like HBase, Querying 02 Page
17 of 48 Redis Data Stores Indexing and Ordering Data Sets: Essential
Concepts Behind a Database Index, Indexing and Ordering in MongoDB, ouchDB
and Apache Cassandra Managing Transactions and Data Integrity: RDBMS and
ACID, Distributed ACID Systems, Upholding CAP, Consistency Implementations
Using NoSQL in The Cloud: Google App Engine Data Store, Amazon SimpleDB
Querying NoSQL Stores:
Similarities between SQL and MongoDB query features.
SQL (Structured Query Language) and MongoDB Query Language are both used for
database querying, but they have significant differences due to the nature of the databases
they are designed for (relational databases for SQL and NoSQL document-oriented databases
for MongoDB). However, there are some similarities and features that can be compared:
Querying Language:
SQL: Uses a standardized language (SQL) for querying relational databases. SQL queries are
written in a declarative language.
MongoDB Query Language: Uses a JSON-like syntax for querying MongoDB. Queries are
written in a document-oriented, flexible, and expressive language.
CRUD Operations:
Both SQL and MongoDB support CRUD (Create, Read, Update, Delete) operations.
SQL uses statements like INSERT, SELECT, UPDATE, and DELETE.
MongoDB uses methods like insert, find, update, and remove.
Filtering:
Both SQL and MongoDB allow for filtering records based on specified criteria.
In SQL, the WHERE clause is commonly used for filtering.
In MongoDB, the find method supports querying based on criteria expressed as a JSON
document.
Sorting:
Both databases support sorting of query results.
1
, 2
SQL uses the ORDER BY clause for sorting.
MongoDB uses the sort method to specify sorting criteria.
Indexing:
Both SQL and MongoDB support indexing to improve query performance.
SQL databases use indexes on columns, and you can create various types of indexes (e.g., B-
tree, hash) depending on the database system.
MongoDB supports indexes on fields within documents.
Aggregation:
Both SQL and MongoDB provide aggregation capabilities to perform computations on data.
SQL uses the GROUP BY clause along with aggregate functions like SUM, AVG, COUNT,
etc.
MongoDB uses the aggregation pipeline framework, which consists of stages like $match,
$group, $project, etc.
Joins:
SQL supports joins to combine data from multiple tables.
MongoDB, being a NoSQL database, does not support traditional joins. Instead, it encourages
denormalization and the use of embedded documents.
Transactions:
SQL databases traditionally support transactions with features like ACID properties
(Atomicity, Consistency, Isolation, Durability).
MongoDB introduced multi-document transactions in recent versions to support similar
transactional capabilities.
Data Integrity:
Both SQL and MongoDB allow you to enforce data integrity through constraints or
validation rules.
SQL uses features like primary keys, foreign keys, and check constraints.
MongoDB provides validation rules that can be applied at the collection level.
Accessing Data from Column-Oriented Databases Like HBase, Querying
Column-oriented databases, exemplified by Apache HBase, provide a robust solution for
handling vast and distributed datasets efficiently. Understanding how to access and query
data in HBase is crucial for deriving meaningful insights from large-scale, sparse datasets.
Data Model:
2
, 3
HBase follows a distributed data model where information is organized into tables, rows, and
columns.
Tables are distributed across a cluster, and each row is uniquely identified by a row key.
Java API:
Interaction with HBase is primarily facilitated through its Java API.
The Java API allows developers to perform Create, Read, Update, and Delete (CRUD)
operations on the data stored in HBase.
// Example of reading data using Java API
Configuration config = HBaseConfiguration.create();
Connection connection = ConnectionFactory.createConnection(config);
Table table = connection.getTable(TableName.valueOf("your_table_name"));
Get get = new Get(Bytes.toBytes("your_row_key"));
Result result = table.get(get);
// Process the result
HBase Shell:
HBase provides an interactive shell that allows users to execute basic commands.
The shell is particularly useful for quick data scans and inspections.
# Example of scanning a table using HBase Shell
scan 'your_table_name'
Client Libraries:
Various client libraries are available for different programming languages, providing
flexibility in implementation.
Python (e.g., HappyBase), Ruby, and other languages also offer ways to interact with HBase.
Querying:
Unlike traditional relational databases, HBase doesn't employ a query language like SQL.
Schema design is critical, and data retrieval is optimized for key-based lookups and range
scans.
Filters:
HBase supports filters to refine data retrieval based on specific conditions.
Filters can be applied when scanning tables, allowing for more targeted queries.3
// Example of scanning with a filter in Java API
3
