This document provides a gentle, friendly overview of MongoDB. It discusses that MongoDB is a document-oriented, non-relational database that uses JSON-like documents with dynamic schemas. It also covers MongoDB's query language, indexing, data storage and querying, drivers like PyMongo, and how MongoDB provides horizontal scalability through sharding and replication. The document concludes by noting some common use cases for MongoDB and areas it is less suited for.

1. A gentle, friendly overview
Antonio Pintus
CRS4, 08/09/2011
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2. NOSQL /1
• MongoDB belongs to the NoSQL databases family:
• non-relational
• document-oriented
• no prefixed, rigid, database schemas
• no joins
• horizontal scalability
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3. NOSQL /2
• NoSQL DB family includes several DB types:
• document/oriented: mongoDB, CouchDB, ...
• Key Value / Tuple Store: Redis, ...
• Graph databases: Neo4j, ...
• ...
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4. MongoDB
• Performant: C++ • document-based queries
• Schema-free • Map/Reduce
• Full index support • GridFS
• No transactions •a JavaScript interactive shell
• Scalable: replication +
sharding
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5. SCHEMA-FREE
• Schema-free collections = NO TABLES!
•A Mongo deployment (server) holds a set of databases
•A database holds a set of collections
•A collection holds a set of documents
•A document is a set of fields: key-value pair (BSON)
•A key is a name (string), a value is a basic type like
string, integer, float, timestamp, binary, etc.,a document,
or an array of values
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6. DATA FORMAT
• document/oriented
• stores JSON-style documents: BSON (Binary JSON):
• JSON + other data types. E.g., Date type and a BinData
type.
• Can reference other documents
• lightweight, traversable, efficient
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7. BSON
{! "_id" : ObjectId("4dcec9a0af391a0d53000003"),
! "servicetype" : "sensor",
! "description" : "it’s only rock’n’roll but I like it",
! "policy" : "PUBLIC",
! "owner" : "User001",
! "date_created" : "2011-05-02 17:11:28.874086",
! "shortname" : "SampleSensor",
! "content-type" : "text/plain",
! "icon" : "http://myserver.com/images/sens.png"
} 7

8. COLLECTIONS
• More or less, same concept as “table” but dynamic, schema-
free
• collection of BSON documents
• documents can have heterogeneous data structure in the
same collection
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9. QUERIES
• query by documents
• Examples (using the interactive shell):
• db.mycollection.find( {"policy" : "PUBLIC"} );
• db.mycollection.findOne({"policy" : "PUBLIC", “owner”:”User001”});
• db.mycollection.find({"policy" : "PUBLIC", “owner”:”User001”}).limit(2);
• db.mycollection.find( {"policy" : "PUBLIC"}, {“shortname”:1} );
• db.mycollection.find({"counter": {$gt:2}});
• conditional ops: <, <=, >, >=, $and, $in, $or,
$nor, ...
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10. INDEXES
• Full index support: index on any attribute (including multiple)
• increase query performance
• indexes are implemented as “B-Tree” indexes
• data overhead for inserts and deletes, don’t abuse!
• db.mycollection.ensureIndex( {"servicetype" : 1} );
• db.mycollection.ensureIndex( {"servicetype" : 1, “owner”:-1} );
• db.mycollection.getIndexes()
• db.system.indexes.find()
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11. INSERTS
• Simplicity
• db.mycollection.insert({“a”:”abc”,...})
• var doc = {“name”:”mongodb”,...};
• db.mycollection.insert(doc);
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12. UPDATES
1. replace entire document
2. atomic, in-place updates
• db.collection.update( criteria, objNew, upsert, multi )
• criteria: the query
• objNew: updated object or $ operators (e.g., $inc, $set) which manipulate the object
• upsert: if the record(s) do not exist, insert one.
• multi: if all documents matching criteria should be updated
• db.collection.save(...): single object update with upsert
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13. UPDATES /2
• atomic, in-place updates = highly efficient
• provides special operators
• db.mycollection.update( { “shortname”:"Arduino" }, { $inc: { n : 1 } } );
• db.mycollection.update( { “shortname”:"Arduino" }, { $set: { “shortname” :
“OldArduino” } } );
• other atomic ops: $unset, $push, $pushAll, $addToSet, $pop,
$pull, $rename, ...
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14. Mongo DISTRIBUTION
• Mac, Linux, Solaris, Win
• mongod: database server.
• By default, port=27017, store path=/data/db.
• Override with --dbpath, --port command options
• mongo: interactive JavaScript shell
• mongos: sharding controller server
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15. MISCELLANEOUS: REST
• mongod provides a basic REST interface
• launch it with --rest option: default port=28017
• http://localhost:28017/mydb/mycollection/
• http://localhost:28017/mydb/mycollection/?filter_shortname=Arduino
• http://localhost:28017/mydb/mycollection/?filter_shortname=Arduino
• http://localhost:28017/mydb/mycollection/?
filter_shortname=Arduino&limit=10
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16. GOOD FOR
• event logging
• high performance small read/writes
• Web: real-time inserts, updates, and queries. Auto-sharding
(scalability) and replication are provided.
• Real-time stats/analytics
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17. LESS GOOD FOR
• Systems with heavy transactional nature
• Traditional Business Intelligence
• (obviously) System and problems requiring SQL
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18. SHARDING /1
• Horizontal scalability: MongoDB auto-sharding
• partitioning by keys
• auto-balancing
• easy addition of new servers
• no single points-of-failure
• automatic failover/replica-sets
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19. SHARDING /2
mongod mongod mongod
mongod mongod ... mongod Shards
mongod mongod mongod
Config servers
mongod
mongos mongos ...
mongod
mongod Client
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20. DRIVERS
• C# and .NET • Python, Ruby, Delphi
• C, C++ • Scala
• Erlang, Perl • Clojure
• Haskell • Go, Objective C
• Java, Javascript • Smalltalk
• PHP • ...
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21. PyMongo
• Recommended MongoDB driver for the Python language
• An easy way to install it (Mac, Linux):
• easy_install pymongo
• easy_install -U pymongo
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22. QUICK-START: INSERT
• (obviously) mongod must be running ;-)
import pymongo
from pymongo import Connection
conn = Connection() # default localhost:27017; conn=Connection('myhost',9999)
db = conn['test_db'] # gets the database
test_coll = db['testcoll'] # gets the desired collection
doc = {"name":"slides.txt", "author":"Antonio", "type":"text", "tags":
["mongodb", "python", "slides"]} # a dict
test_coll.insert(doc) # inserts document into the collection
• lazycreation: collections and databases are created when the
first document is inserted into them
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23. QUICK-START: QUERY
res = test_coll.find_one() # gets one document
query = {"author":"Antonio"} # a query document
res = test_coll.find_one(query) # searches for one document
for doc in test_coll.find(query): # using Cursors on multiple docs
print doc
...
test_coll.count() # counts the docs in the collection
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24. NOT COVERED (HERE)
• GridFS: binary data storage is limited to 16MB in DB, so
GridFS transparently splits large files among multiple
documents
• MapReduce: batch processing of data and aggregation
operations
• GeoSpatial Indexing: two-dimensional indexing for
location-based queries (e.g., retrieve the n closest restaurants
to my location)
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25. IN PRODUCTION (some...)
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27. Paraimpu LOVES MongoDB
• MongoDB powers Paraimpu, our Social Web of Things tool
• great data heterogeneity
• real-time thousands, small data inserts/queries
• performances
• horizontal scalability
• easy of use, development is funny!
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28. REFERENCES
• http://www.mongodb.org/
• http://www.mongodb.org/display/DOCS/Manual
• http://www.mongodb.org/display/DOCS/Slides+and+Video
• pymongo: http://api.mongodb.org/python/
• Paraimpu: http://paraimpu.crs4.it
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29. THANK YOU
Antonio Pintus
email: pintux@crs4.it
twitter: @apintux
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