Databases in 30 minutes.It might just end up taking 40!

WhoamiAthira MukundanSolution Consultant @Sahajathira_tech@yahoo.in

Let’s start at the beginning - RDBMS.SQL has ruled for 2 decades!

1)Store persistent data allowing applications to grab the bits they need through queries

2)Application Integration ensuring all apps have consistent up-to-date data

3)Mostly standard, use SQL

4)Transactions

5)Reporting

The power of RDBMS - ACID TransactionsTransaction : reliable units of work that allow correct recovery from failures and keep a database consistent even in cases of system failure, when execution stops (completely or partially) and many operations upon a database remain uncompleted, with unclear status.

1)Atomicity: All or none

2)Consistency: any transaction will bring the database from one valid state to another

3)Isolation: ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed sequentially

4)Durability: ensures that once a transaction has been committed, it will remain so, even in the event of power loss, crashes, or errors.

DB-Engines Ranking

Where would RDBMS fail - 1)Rise of unstructured Data

2)Difficult to scale horizontally.

3)Sharding causes operational problems

4)Satisfying ACID is a hinderance for scaling

5)Impedance mismatch

Enter NoSQLA scale-out, shared-nothing architecture designed to run on a cluster

A non-locking concurrency control mechanism so real-time reads won’t conflict with writes

Scalable replication and distribution

Don’t have a fixed schema, allowing you to store any data in any record

Reduce Development Drag

CAP TheoremWhen partition occurs -

1) CA: Single site cluster, therefore all nodes are always in contact. When a partition occurs, the system blocks.

2) CP: Some data is not accessible but the rest is still consistent and accurate.

3) AP: System is available under partitioning but some of the data returned may be inaccurate

Data Model Based Categories in NoSql1)Key-Value

2)Document

3)Columnar

4)Graph

Key-Value DatabasesDesigned for storing, retrieving, and managing associative arrays, a data structure more commonly known today as a dictionary or hash

Advantages : fast look ups

Disadvantages: schema less

eg) Dynamo db, Redis

Document DatabasesDesigned for storing, retrieving and managing document-oriented information, also known as semi-structured data.

Advantages: Incomplete Data Tolerant

Disadvantages: Query performance, no structured query syntax

eg) mongodb, couchDB

Columnar DatabasesStores data tables as columns rather than as rows

Advantages : precisely access the data to answer a query rather than scanning and discarding unwanted data in rows. Query performance is often increased as a result, particularly in very large data sets.

Disadvantages: very long time to insert individual records into a table and even longer for updates

eg) Cassandra, HBase

Graph DatabasesUses graph structures for semantic queries with nodes, edges and properties to represent

and store data.

The relationships allow data in the store to be linked together directly, and in many cases retrieved with one operation.

Advantage: Graph algorithms - Shortest path, connectedness etc

Disadvantage: Not easy to cluster, traverse the whole graph to get the result

eg) Neo4j

How to choose a DB-Think about -

the CAP theorem.

the data model

ACID/BASE (Basically Available Solid State Eventual Consistency)

Development effort

What and When.

source:https://martinfowler.com/articles/nosql-intro-original.pdf