No SQL Cloud Computing Databases with Scalable capabilities

No. SQL & Cloud Computing Databases with Scalable capabilities Presented by Mike Trischetta

No. SQL & Cloud Computing

Introduction � Scalability ◦ Vertical �Improve one resource ◦ Horizontal �Add more resources �Flexible based on needs � No. SQL and Scaling ◦ Easier to partition ◦ More loosely specified ◦ Reduced overhead

Cloud Computing Desirable Properties Transaction Processing Database Scalability

Cloud Computing � Desirable ◦ ◦ properties Data Consistency Availability Predictable performance Scalable & High Performance Storage � Data structures ◦ Must handle large, mixed structures

Transaction Processing in the Cloud � ACID ◦ ◦ ◦ properties Atomicity Consistency Isolation Durability Strong Consistency � Not essential for all use cases

Transaction Processing in the Cloud � CAP properties ◦ Consistency: whenever data is written, all calls to the DB will yield the same version of that data ◦ Availability: Each operation terminates in an intended response ◦ Partition tolerance: The database can still operate when parts of it are completely inaccessible or Eventual Consistency � CAP theorem � Weak ◦ For any system sharing data, it is impossible to guarantee each of the three CAP properties. ◦ Designer must prioritize C, A, or P

Database Scalability � RDBMS ◦ Data replication for consistency ◦ Grids � No. SQL ◦ Use data partitioning ◦ Concurrent computation � ACID ◦ Never guaranteed with No. SQL ◦ Always guaranteed with RDBMS �Can & do still scale vertically �Concurrency still possible � DBMS choice depends on use case

No. SQL Databases Data Models Querying Transactions Physical Data Storage

No. SQL Databases � “Not only SQL” / post-relational � Data Model ◦ Distributed Hash Tables (DHT) ◦ key value couples hashed into buckets ◦ Horizontal Row partitioning �No join, aggregation, order, or nesting operations – must be done client-side �Allows parallel operations

Data Models � JSON format ◦ Key ~ attributes ◦ Document ~ tuples � Big Hash Tables

Querying � Executed via key hashing � Restricted SQL commands � Additional operations ◦ ◦ ◦ get(key) put(key, value) execute(key, operations, parameters) Variations in complexity as per DBMS Usually return tuples

Transactions � ACID not possible ◦ Caveat: some systems do allow it � Over time, reach consistency ◦ Weak/Eventual consistency

Physical Data Storage � Spread over multiple nodes ◦ Tablets ◦ Rows split between nodes ◦ Tablet = table name + end key � Hierarchical � File-based

Conclusion � DBMS choice depends on use cases � No. SQL ◦ Increased speed for large networks ◦ Flexible horizontal scaling ◦ Cheaper than legacy systems � RDBMS ◦ Retains ACID properties ◦ Flexible vertical scaling ◦ Can become expensive to upgrade/maintain

Thank you Questions?