1. Some notes on NoSQL, in particular MongoDB Bettina Berendt (with thanks to Matthijs van Leeuwen for some of the slides) 8 December 2015

2. Overview: NoSQL ‘Not only SQL’ No tables, but storage of, e.g. – Collections of documents (e.g. JSON, XML) – Key-value pairs – Columns of values – Graphs – Objects – … 2

3. NoSQL Advantages – horizontally scalable (as opposed to vertically) – No static schema or data model – Cheaper in maintenance Disadvantages – Possibilities can be very system-specific  no universal query language – Often, some coding is necessary – Fewer a/o weaker theoretical guarantees 3

4. Example systems: NoSQL 4

5. MongoDB, the most popular system for document stores (see https://en.wikipedia.org/wiki/MongoDB and references there)https://en.wikipedia.org/wiki/MongoDB MongoDB is “schema-free“!

6. Understanding the MongoDB / NoSQL notion of “document“ – Good example of what computer scientists call “semi- structured data“ (see previous week) – But actually fairly structured in comparison to e.g. a textual document: MongoDB‘s format is called BSON, a binary form of JSON See https://en.wikipedia.org/wiki/JSON, https://en.wikipedia.org/wiki/BSONhttps://en.wikipedia.org/wiki/JSON https://en.wikipedia.org/wiki/BSON – Note: JSON can be thought of as an alternative to XML, as described for example on the – certainly not disinterested - http://www.json.org/xml.html, but not the type of XML you often see for annotating texts, as for example in the Letters of 1916 projecthttp://www.json.org/xml.html 6

7. INSERT a row (SQL)  insert a document (MongoDB) db.inventory.insert( { item: "ABC1", details: { model: "14Q3", manufacturer: "XYZ Company" }, stock: [ { size: "S", qty: 25 }, { size: "M", qty: 50 } ], category: "clothing" } ) 7

8. SELECT (SQL)  find documents (MongoDB) db.inventory.find( { type: { $in: [ 'food', 'snacks' ] } } ) db.inventory.find( { type: 'food', price: { $lt: 9.95 } } ) 8

9. SELECT and SORT 9

10. UPDATE (SQL)  update documents (MongoDB) db.inventory.update( { item: "MNO2" }, { $set: { category: "apparel", details: { model: "14Q3", manufacturer: "XYZ" } }, $currentDate: { lastModified: true } } ) 10

11. Other useful constructs...... such as GROUP BY are also available (see Wikipedia description)... And python interfaces exist. 11

12. Setting indexes in MongoDB: Usage (1): BSON structure Given the following document in the users collection { “_id“ : ObjectID(...), “name“ : “Alice“, “age“ : 27 “score“ : 25 } the following command creates an index on the score field: db.users.createIndex ( { “score“ : 1 } )

13. Usage (2)

14. SELECT and SORT (shown with reference to an index) 14

15. Importance for DHers? – Certainly growing, but probably not necessary for everyone My personal rule of thumb: – Very useful if you know the query you have (for example because you have worked it out on a small data sample, with SQL, python, or whatever), and you need to process LOTS of data – Less useful for very exploratory analysis, since there you may need a universal query language. 15