24 - NoSQL Databases

24 - NoSQL Databases#

Pros and Cons of Relational Databases#

Relational Positives#

  • Can represent relationships in data

  • Easy to understand relational model/SQL

  • Disk-oriented storage

  • Indexing structures

  • Consistent values in database (transactions)

Relational Negatives#

  • RDBS can be strict and complex

    • Want more freedom and/or simplicity

  • Overhead of object to relational mapping

    • Want to store as is

  • Cannot always partition/distribute from a single database server

    • Want to distribute data

  • RDBS limited in throughput

    • Want higher throughput

  • Must scale up (expensive servers)

    • Want to scale out (wide - cheap servers)

  • RDBS providers were slow to move to the cloud

    • Everyone wants to use the cloud

Data Today#

Structured Data#

Information in databases

  • Data organized into chunks, similar entities grouped together

  • Descriptions for entities in groups (same format, length, etc.)

Semi-structured Data#

Data has a certain structure, but not all items are identical

  • Similar entities are grouped together (may have different attributes)

  • Schema may be mixed in with data values

  • Self-describing data, e.g. XML

  • May be displayed as a graph

Unstructured Data#

Data can be of any type, may have no format or sequence

  • Cannot be represented by any type of schema

    • HTML web pages

    • Video, sound, images

NoSQL#

NoSQL is rejection of the relational model. It can stand for “No to SQL” or “Not only SQL.”

There are 4 main types of NoSQL databases:

  • Column stores (BigTable, Hbase, Cassandra)

  • Key-value stores (DynamoDB, Voldemort)

  • Document stores (MongoDB, CouchDB)

  • Graph-based stores (Neo4j)

Column vs Row Stores#

Row Storage#

  • A relational table is serialized as rows that are appended and flushed to disk

  • Whole datasets can be R/W with a I/O operation

  • Good locality of access on disk

  • Operations on columns are expensive since extra data must be read

../../_images/row_storage.jpg

Column Storage#

  • Serializes tables by appending columns and flushing to disk

  • Operations on columns are fast and cheap

  • Operations on rows are expensive since extra data must be read

  • Good for aggregations

../../_images/column_storage.jpg

Column Storage with Locality Groups#

  • Like column storage, but groups columns expected to be accessed together

  • Store groups together and physically separated from other column groups

    • Google’s BigTable

../../_images/column_storage_with_locality_groups.jpg

Column Stores#

  • Stores data as tables

    • Advantages for data warehouses, customer relationship management (CRM) systems

    • More efficient for:

      • Aggregates

      • Update rows in same column

      • Easier to compress

Keys#

  • Most NoSQL databases utilize the concept of keys

  • In column store - called key or row key

  • Row keys are strings that must have the property that they can be lexicographically ordered

  • Each column/column family data stored along with key

HBase Column Store#

  • HBase is an open-source, distributed, versioned, non-relational, column-oriented data store

  • It is an Apache project whose goal is to provide storage for the Hadoop Distributed Computing

  • Data is logically organized into tables, rows, and columns

  • Based on Google’s BigTable

  • Each column family is stored in a separate file

  • Different sets of column families may have different properties and access patterns

  • Tables have one primary index: the row key

    • Tables sorted by row key

  • HBase treats everything as bites

Basic CRUD Operations#

  • Creating a table: create <tablename>, <column family>, <column family>, ...

  • Inserting data: put <tablename>, <rowid>, <column family>:<column qualifier>, <value>

  • Reading data (all in a table): scan <tablename>

  • Retrieve data (one item): get <tablename>, <rowid>

Examples#

Creating a table called EMPLOYEE with three column families: Name, Address, and Details

  • create 'EMPLOYEE', 'Name', 'Address', 'Details'

Inserting data

  • put 'EMPLOYEE', 'row1', 'Name:Fname', 'John'

  • put 'EMPLOYEE', 'row1', 'Name:Lname', 'Smith'

  • put 'EMPLOYEE', 'row1', 'Name:Nickname', 'Johnny'

  • put 'EMPLOYEE', 'row1', 'Details:Job', 'Engineer'

  • put 'EMPLOYEE', 'row1', 'Details:Review', 'Good'

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