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Datastore PPT.pptx
This document provides an overview of different types of databases and how to choose the right one. It discusses relational databases like MySQL and SQL Server which use tables and relationships. NoSQL databases like Cassandra and MongoDB are discussed as being schema-less and storing data together. Evented databases like Kafka are covered as focusing on transient data through events and producers/consumers. Key factors in choosing a database are also outlined like scalability, querying capabilities, use cases and storage structure.
Datastore PPT.pptx
- 1. How to choosethe right Database
- 2. HELLO! I am JatinChuglani Product Manager, Zeta Ex-infoworks, Amazon LinkedIn Blog 2
- 3. Who is thisPPT for? ▪ Anyone trying to make a decision about the storage architecture for their application ▪ Students, Engineers, Architects 3
- 4. What is aDatabase?
- 5. What is aDatabase? A database is an organized collection of data stored and accessed electronically 5
- 6. Importance of theright database 6 ▪ Databases are a core part of any application architecture ▪ Database architecture will determine ▫ How many users can be served by the application ▫ Time it takes to respond to a request ▫ How much downtime would there for the system ▪ Migrating Databases is an expensive/time-consuming operation
- 7. Prep work beforethe Decision
- 8. Questions to askyourself ▪ Who are my users? ▪ What are my users’ short term and long term goals and requirements? ▪ Which languages and tools should I be using to meet those requirements? ▪ How would I grow my team as per my users and their requirements? 8
- 9. Decision Framework 9 Maintenance ● Maturity ●Maintainability ● Community Support Functional Reqs ● Query Patterns ● Evolution ● Language Support Scale ● Scalability ● Latency ● Reliability ● Consistency
- 10.
- 11. In Focus Today RelationalDBs ▪ MySQL ▪ SQL Server ▪ Oracle NoSQL DBs ▪ Cassandra ▪ MongoDB ▪ Neo4j Evented Datastores ▪ Kafka ▪ Azure EventHubs 11
- 12. In Focus Today ▪Storage structure ▫ How is the data logically stored ▪ Querying support ▫ Ways the data be accessed ▪ Scalability ▫ How to account for future growth ▪ Use cases ▫ Where to use these databases 12
- 13. Relational Databases Relational DBs ▪MySQL ▪ SQL Server ▪ Oracle
- 14. Relational Databases Overview ▪“SQL” (Structured Query Language) Databases ▪ Tables can have (Foreign Key) relationships with each other ▪ Allow creation of tables with Fixed Structure ▪ Powerful Query interface with SQL ▪ Can be vertically scaled easily, horizontal scaling requires more effort 14
- 15. Storage Structure ▪ ForeignKey - Primary Key Relationship ▪ Columns are assigned a datatype ▪ Columns can have constraints 15
- 16. Querying Capabilities ▪ SQL ▫Joins! ▫ Subqueries ▫ Filter, etc. ▪ Object Relational Mapper (ORM) ▪ Mature Language Support 16
- 17. Scalability ▪ Vertical Scaling(Bigger Machines) ▪ Horizontal Scaling ▫ Replicas (same data in multiple machines) ▫ Sharding (distributing data) ▫ Joins could suffer 17
- 18. Use Cases ▪ RelationalQuery Requirements ▪ Flexibility in Query Patterns ▫ Data Analytics ▪ Column Constraints Requirements ▫ Transaction Processing ▪ Small to Large Scale* ▫ Boutique Ecommerce Website 18 *With Sharding and Replicas
- 19. NoSQL Databases NoSQL DBs ▪Cassandra ▪ MongoDB ▪ Neo4j
- 20. NoSQL Databases Overview ▪“NoSQL” is a loaded term, which refers to any database without SQL ▪ Started getting popular in late 2000s ▪ Schema is not pre-defined, “Schemaless” or “Schema-on- Read” ▪ Instead of breaking data into multiple units (or tables), the data is typically stored together as one. 20
- 21. Storage Structure* Wide ColumnDBs ▪ Tables, Rows, columns ▪ “Column Families” are stored together ▪ No Relationships ▪ No/limited joins ▪ e.g. Cassandra Document DBs ▪ JSON like storage structure ▪ Data stored in one nested structure called “Documents” ▪ E.g. MongoDB 21 *There are other NoSQL stores like key-value (Redis), GraphDB(Neo4j) and search engines (Elastic), we will skip those in today’s conversation
- 22. Scalability ▪ Vertical Scaling(Bigger Machines) ▪ Highly Horizontal Scalable ▫ Tables/documents are self contained ▫ Load Balancer/router layer 22
- 23. Querying Capabilities ▪ CustomQuery Languages ▫ Range from SQL-like (CQL/Cassandra) to very different (JavaScript/MongoDB) ▪ REST API Support ▪ Querying primarily dependent on IDs ▪ Programming Language Support mileage may vary 23
- 24. Use Cases ▪ FixedAccess Pattern, cases where everything always comes together ▪ High Scale and Low latency requirements ▫ Gaming servers ▪ Evolving Data ▫ Product Catalogs 24
- 25. Evented Datastores Evented DBs ▪Kafka ▪ Azure EventHubs
- 26. Evented Datastores Overview ▪Also, called “Messaging Queues” ▪ Storage of “Events” ▪ Event = self- contained,immutable object with timestamp ▪ “Producers” and “Consumers” of events ▪ Used in conjunction with SQL/NoSQL stores 26
- 27. Difference from ConventionalDatabases ▪ Focus on transient data, data usually deleted after sometime ▪ Query tools mostly focused on offsets (message number) ▪ No support for secondary indexes / other search capabilities 27
- 28. Storage Structure ▪ Datacould be in multiple formats ▫ JSON ▫ Text ▫ Binary ▪ Schema constraints can sometimes be added 28
- 29. Querying Capabilities ▪ Consumerscan read/write data using offsets, i.e., message number ▪ Some high level functions ▫ Data manipulations ▫ Aggregation over small periods of time 29
- 30. Scalability ▪ Vertical Scaling(Bigger Machines) ▪ Horizontally Scalable ▫ Partitioning 30
- 31. Use Cases ▪ AsynchronousSystems ▫ Order Processing ▪ Stream Processing ▫ Fraud Detection ▪ Keep systems in sync ▫ Change Data Capture 31
- 32.