Databases with Scalable capabilities Presented by Mike Trischetta

 Scalability ◦ Vertical  Improve one resource ◦ Horizontal  Add more resources  Flexible based on needs  NoSQL and Scaling ◦ Easier to partition ◦ More loosely specified ◦ Reduced overhead

Desirable Properties Transaction Processing Database Scalability

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

 ACID properties ◦ Atomicity ◦ Consistency ◦ Isolation ◦ Durability ◦ Strong Consistency  Not essential for all use cases

 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  Weak or Eventual Consistency  CAP theorem ◦ For any system sharing data, it is impossible to guarantee each of the three CAP properties. ◦ Designer must prioritize C, A, or P

 RDBMS ◦ Data replication for consistency ◦ Grids  NoSQL ◦ Use data partitioning ◦ Concurrent computation  ACID ◦ Never guaranteed with NoSQL ◦ Always guaranteed with RDBMS  Can & do still scale vertically  Concurrency still possible  DBMS choice depends on use case

Data Models Querying Transactions Physical Data Storage

 “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

 JSON format ◦ Key ~ attributes ◦ Document ~ tuples  Big Hash Tables

 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

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

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

 DBMS choice depends on use cases  NoSQL ◦ 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

Questions?