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Introduction to MongoDB
- 2. Types of Databases What is NoSQL? Document databases?
- 3. What is NoSQL? NoSQL databases are storing information not in a tabular(excel like format) as traditional RDBMS NoSQL = “non relational”
- 5. Document databases? Document database RDBMS MongoDB documentation: RDBMS to MongoDB Migration Guide
- 7. Schemaless Created dynamically at first use
- 8. Schemaless
- 11. Schemaless model references • Modeling One-to-Few • One-to-Many • One-to-Squillions • Denormalizing from Many -> One • Denormalizing from One -> Many • Denormalizing With “One-To-Squillions” Relationships MongoDB documentation: 6 Rules of Thumb for MongoDB Schema Design: Part 1 6 Rules of Thumb for MongoDB Schema Design: Part 2 6 Rules of Thumb for MongoDB Schema Design: Part 3 BSON attaches significance to order when doing searches. The order of keys within embedded objects matters in MongoDB, { firstname: "Phil", surname: "factor" } does not match { { surname: "factor", firstname: "Phil" }
- 14. Indexes
- 19. Aggregations MongoDB documentation: • Aggregation Pipeline Stages • Aggregation Pipeline Operators • Mongo db aggregation guide Aggregation Framework
- 20. Queries & Aggregations Query Translator - Convert MySQL queries to MongoDB syntax
- 21. Binary data MongoDB documentation: GridFS GridFS specification for storing and retrieving files that exceed the BSON-document size limit of 16 MB GridFS
- 22. Binary data MongoDB documentation: GridFS GridFS specification for storing and retrieving files that exceed the BSON-document size limit of 16 MB GridFS
- 23. CLI tools
- 24. Considerations The SQL trap Materializing query results Schemaless disasters
- 25. The SQL trap Bad documents design will require application side joins If you have normalized your information you will suffer problems with • Referential integrity • Race conditions by lack of transactions • Multiple ”find” queries to retrieve information IMPORTANT: up to MongoDB 4.0 the operations are atomic on document level
- 26. The SQL trap • Referential integrity • Race conditions by lack of transactions IMPORTANT: up to MongoDB 4.0 the operations are atomic on document level
- 27. Materializing query results Be careful when you materialize your query results You should be using the cursor where you expect large query result or you can: • easily fill up the memory • degrade server performance when the Garbage collector kicks in • block the server requests
- 28. Schemaless disasters When no schema is enforced the data validation is left for the developer to take care Mistakes can easily happen when you store or query your data in the wrong format Example: If you store ZIP code as integer (0123) your queries with the string (“0123”) will fail. Even harder will be if you mix string and integer ZIP codes in your document
- 30. Robo 3T free version • How to install • MacOS • brew cask install robo-3t • Windows • Go to robomongo.org
- 31. NoSQLBooster trial • How to install • https://nosqlbooster.com/downloads
- 32. MongoDB decision criteria Do I need data consistency? Am I working with large data sets? Embed or reference documents? Prone to NoSQL injections? (mongod-config-file.conf “javascriptEnabled:false”) What is the cardinality of the relationship? Do you need to access the object on the “N” side separately, or only in the context of the parent object? What is the ratio of updates to reads for a particular field?
- 33. Additional resources • MongoDB Multi-Document ACID Transactions are GA • RDBMS to MongoDB Migration Guide • MongoDB’s Drive to Multi-Document Transactions • MongoDB Drops ACID • Partial Indexes • 14 Things I Wish I’d Known When Starting with MongoDB • MongoDB: To Use Schemas or Not? • Top 5 syntactic weirdnesses to be aware of in MongoDB