1. WTT 2014 - Workshop de Tendências Tecnológicas 2014

2. Agenda
NoSQL Concepts
MongoDB Concepts
MongoDB Demos

3. NoSQL databases? Alternative to traditional relational DBMS
“NoSQL” = “No SQL” =
Not using traditional relational DBMS
“No SQL”  Don’t use SQL language
Alternative to traditional relational DBMS
Flexible schema
Quicker/cheaper to set up
Massive scalability
Relaxed consistency  higher performance & availability
No declarative query language  more programming
Relaxed consistency  fewer guarantees

4. NoSQL Systems Document Stores Data model: (key, document) pairs
Map Reduce Framework Originally from Google, open source Hadoop
Key-Values-Stores Google BigTable, Amazon Dynamo, Cassandra, HBase, …
Document Stores
Data model: (key, document) pairs
Document: JSON, XML, other semistructured formats
CouchDB, MongoDB, SimpleDB, …
Graph Database Systems Neo4j, FlockDB, Pregel, …
Big-Table Implementation Teradata, Exadata, GreenPlum, MonetDB, …
Map Reduce Framework
Originally from Google, open source Hadoop
No data model, data stored in files
User provides specific functions as Map Reduce
Key-Values-Stores
Data model: (key, value) pairs
Some allow (non-uniform) columns within value
Document Stores
Data model: (key, document) pairs
Document: JSON, XML, other semistructured formats
Graph Database Systems
Data model: nodes and edges
Single-step versus “path expressions” versus full recursion
Big-Table Implementation
Data model: RDBM
Column stores x Row stores

5. ACID versus BASE
ACID  Atomicity, Consistency, Isolation, Durability  Traditional Databases
CAP
Strong Consistency + High Availability + Partition-tolerance
The CAP-Theorem postulates that only two of the three different aspects of scaling out are can be achieved fully at the same time.
Many of the NOSQL
 BASE Basically Available, Soft-state, Eventually consistent 
ACID (Atomicity, Consistency, Isolation, Durability)  Traditional Databases
Scaling out of ACID-compliant systems has shown to be a problem. Conflicts are arising between the different aspects of high availability in distributed systems that are not fully solvable.  
CAP
Strong Consistency: all clients see the same version of the data, even on updates to the dataset - e. g. by means of the two-phase commit protocol (XA transactions), and ACID,
High Availability: all clients can always find at least one copy of the requested data, even if some of the machines in a cluster is down,
Partition-tolerance: the total system keeps its characteristic even when being deployed on different servers, transparent to the client.
The CAP-Theorem postulates that only two of the three different aspects of scaling out are can be achieved fully at the same time.
Many of the NOSQL databases above all have loosened up the requirements on Consistency in order to achieve better Availability and Partitioning. This resulted in systems know as BASE(Basically Available, Soft-state, Eventually consistent)

6. Quiz. NoSQL Applications ?
[ ] Web Log Analysis URL, timestamp, number of accesses
[ ] Social-network graphs user1, user2, Find friends of a friends
[ ] Wikipedia Pages Large collections, structured and unstructured data
[ ] Twitter messages unstructured data
[ ] Blog maintenance unstructured data
[ ] Account credits and debts

7. MongoDB
MongoDB (from "humongous") is an open-source document database, and the leading NoSQL database. Written in C++, MongoDB features
Document-Oriented Storage
Querying
Full Index Support
Replication & High Availability
Auto-Sharding
Map/Reduce
Geospatial support
Text Search

8. DB-Engines Ranking

9. SQL to MongoDB Mapping Chart

10. JSON x SQL x BSON
JSON
JavaScript Object Notation. A human-readable, plain text format for expressing structured data with support in many programming languages.
BSON
A serialization format used to store documents and make remote procedure calls in MongoDB. “BSON” is a portmanteau of the words “binary” and “JSON”.
SQL Schema Statements
MongoDB Schema Statements
CREATE TABLE users (
Id INT NOT NULL AUTO_INCREMENT,
user_id Varchar(30),
age Number,
status char(1),
PRIMARY KEY (id) )
db.users.insert( { user_id: "abc123", age: 55, status: "A" } )

11. JSON Document Model var p = { ‘_id’: ‘3432’,
‘author’: DBRef(‘User’, 2),
‘title’: ‘Introduction to MongoDB’,
‘body’: ‘MongoDB is an open sources.. ‘,
‘timestamp’: Date(’ ’),
‘tags’: [‘MongoDB’, ‘NoSQL’],
‘comments’: [{‘author’: DBRef(‘User’, 4),
‘date’: Date(’ ’),
‘text’: ‘Did you see.. ‘,
‘upvotes’: 7, … ] }
> db.posts.save(p);

12. Indexes Create Index on any field in the document
// 1 means ascending, -1 means descending
> db.posts.ensureIndex({‘author’: 1});
//Index Nested Documents
> db.posts.ensureIndex(‘comments.author’: 1);
// Index on tags
> db.posts.ensureIndex({‘tags’: 1});
// Geo-spatial Index
> db.posts.ensureIndex({‘author.location’: ‘2d’});

13. Queries? // find posts which has ‘MongoDB’ tag.
> db.posts.find({tags: ‘MongoDB’});
// find posts by author’s comments.
> db.posts.find({‘comments.author’: DBRef(‘User’,2)}).count();
// find posts written after 31st March.
> db.posts.find({‘timestamp’: {‘gte’: Date(’ ’)}});
// find posts written by authors around [22, 42]
> db.posts.find({‘author.location’: {‘near’:[22, 42]});
$gt, $lt, $gte, $lte, $ne, $all, $in, $nin, count, limit, skip, group, etc…

14. Updates? Atomic Operations
db.posts.update({_id: ‘3432’},
{‘title’: ‘Introduction to MongoDB (updated)’,
‘text’: ‘Updated text’,
${addToSet: {‘tags’: ‘webinar’}});
$set, $unset
MongoDB does not support TRANSACTIONS !
$push, $pull, $pop, $addToSet
$inc, $decr, many more…

15. Some Cool features, but not in this lab 
• Geo-spatial Indexes for Geo-spatial queries.
$near, $within_distance, Bound queries (circle, box)
• Map/Reduce
GROUP BY in SQL, map/reduce in MongoDB.
• GridFS
Stores Large Binary Files.

16. Demo: my bond girls database 

17. Demo: mongodb install
C:\mongodb\bin\mongod.exe --dbpath d:\test\mongodb\data

18. Demo: mongodb as a Service

19. Demo: pymongo

20. Demo: mongolab create a account

21. Demo: after a Collection, create a document

22. Demo: find( ) with Python

23. Demo: find( ) with mongo shell

24. Demo: load a .CSV collection with mongo shell

25. Demo: results of load a .CSV collection

26. Demo: find( ) a document with mongo shell

27. Demo: find( ) a document with Python

28. Demo: find( ) select a field

29. References The little Mongodb Book by Karl Seguin
Mongodb
Mongodb-as-a-Service
Pymongo, mongodb drive for Python
Easy Install, to install pymongo for Windows
Also
Pymongo driver
JSON examples
Account name MackLabMongoDB
Username Rogerio <- USE ESTE
Password xuxixaxax8
database dbmacklab1rogerio
user rogerio
password rogerio

30. Conclusion: When to use MongoDB
Schema-less
Writes
db.createCollection(‘logs’, {capped: true, size: })
NO TRANSACTIONS
Data Processing ~ MapReduce
Geospatial
Full Text Search
NoSQL + tools and maturity

31. WTT 2014 - Workshop de Tendências Tecnológicas 2014
Prof. Dr. Rogério de Oliveira