distilled mongo db by boris trofimov

distilled

Boris Trofimov Team Lead@Sigma Ukraine

@b0ris_1btrofimoff@gmail.com

Agenda● Part 1. Why NoSQL

– SQL benefints and critics

– NoSQL challange● Part 2. MongoDB

– Overview

– Console and query example

– Java Integration

– Data consistancy

– Scaling

– Tips

Part 1. Why NoSQL

Relational DBMS Benefits

● Simplicity● Uniform representation● Runtime schema modifications

SELECT DISTINCT p.LastName, p.FirstName FROM Person.Person AS p JOIN HumanResources.Employee AS e ON e.BusinessEntityID = p.BusinessEntityID WHERE 5000.00 IN (SELECT Bonus FROM Sales.SalesPerson AS sp WHERE e.BusinessEntityID = sp.BusinessEntityID);

Strong schema definition

Strong consistency

SQL features like Foreign and Primary Keys, Unique fields

ACID (atomicity, consistency, isolation, durability) transactions

Business transactions ~ system transactions

RDBMS Criticism

Big gap between domain and relational model

Performance Issues

JOINS Minimization Choosing right transaction strategy Query Optimization

Consistency costs too much

Normalization Impact Performance issues

Schema migration issuesConsistency issues

Reinventing bicycle

Involving external tools like DBDeploy

Scaling options

Consistency issues

Poor scaling options

SQL Opposition

● Object Databases by OMG● ORM● ?

No SQL Yes

● Transactionaless in usual understanding

● Schemaless, no migration

● Closer to domain

● Focused on aggregates

● Trully scalable

NoSQL Umbrella

Key-Value Databases

Column-Family Databases

Document-oriented Databases

Graph-oriented Databases

Aggregate oriented Databases

● Document databases implement idea of Aggregate oriented database.

● Aggregate is a storage atom● Aggregate oriented databsaes are closer to application

domain.● Ensures atomic operations with aggregate● Aggregate might be replicated or sharded efficiently● Major question: to embed or not to embed

Relations vs Aggregates

// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

// in orders{"id":99,"customerId":1,"orderItems":[ { "productId":47, "price": 444.45, "productName": "iPhone 5" } ],"shippingAddress":[{"city":"Moscow"}]"orderPayment":[ { "ccinfo":"1000-1000-1000-1000", "txnId":"abelif879rft", "billingAddress": {"city": "Moscow"} } ],}

Relational Model Document Model

Part 2. MongoDB

MongoDB Basics

MongoDB is document-oriented and DBMS

MongoDB is Client-Server DBMS

Mongo DB = Collections + Indexes

JSON/JavaScript is major language to access

Collections

Simple creating (during first insert).

Two documents from the same collection might be completly different

NameDocuments

IndexesIndexes

Document

{ "fullName" : "Fedor Buhankin", "course" : 5, "univercity" : "ONPU", "faculty" : "IKS", "_id" : { "$oid" : "5071c043cc93742e0d0e9cc7" } "homeAddress" : "Ukraine, Odessa 23/34", "averageAssessment" : 5, "subjects" : [ "math", "literature", "drawing", "psychology" ] }

Identifier (_id)

Body i JSON (Internally BSON)

● Any part of the ducument can be indexed● Max document size is 16M

● Major bricks: scalar value, map and list

MongoDB Console

Query Examples

// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

// in orders{"id":99,"customerId":1,"orderItems":[ { "productId":47, "price": 444.45, "productName": "iPhone 5" } ],"shippingAddress":[{"city":"Moscow"}],"orderPayment":[ { "ccinfo":"1000-1000-1000-1000", "txnId":"abelif879rft", "billingAddress": {"city": "Moscow"} } ]}

SELECT * FROM ORDERS;

db.orders.find()

Simple Select

SELECT * FROM ORDERS WHERE customerId = 1;

db.orders.find( {"customerId":1} )

Simple Condition// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT * FROM orders WHERE customerId > 1

db.orders.find({ "customerId" : { $gt: 1 } } );

Simple Comparison// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT * FROM orders WHERE customerId = 1 AND orderDate is not NULL

db.orders.find( { customerId:1, orderDate : { $exists : true } } );

AND Condition// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT * FROM orders WHERE customerId = 100 OR orderDate is not NULL

db.orders.find( { $or:[ {customerId:100}, {orderDate : { $exists : false }} ] } );

OR Condition// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT orderId, orderDateFROM orders WHERE customerId = 1

db.orders.find({customerId:1},{orderId:1,orderDate:1})

Select fields// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT * FROM OrdersWHERE Orders.id IN (

SELECT id FROM orderItem WHERE productName LIKE '%iPhone%')

db.orders.find( {"orderItems.productName":/.*iPhone.*/} )

Inner select// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

SELECT * FROM orders WHERE orderDate is NULL

db.orders.find( { orderDate : { $exists : false } } );

NULL checks// in customers{"id":1,"name":"Medvedev","billingAddress":[{"city":"Moscow"}]}

More examples

• db.orders.sort().skip(20).limit(10)

• db.orders.count({ "orderItems.price" : { $gt: 444 })

• db.orders.find( { orderItems: { "productId":47, "price": 444.45, "productName": "iPhone 5" } } );

• db.orders.find()._addSpecial( "$comment" , "this is tagged query" )

Queries between collections

● Remember, MongoDB = no JOINs

● 1 approach: Perform multiple queries (lazy loading)● 2 approach: use MapReduce framework● 3 approach: use Aggregation Framework

Map Reduce Framework● Is used to perform complex grouping with collection

documents● Is able to manipulate over multiple collections● Uses MapReduce pattern● Use JavaScript language● Support sharded environment● The result is similar to materialized views

Map Reduce Concept

......

Launch mapFor every elem

Launch reduce

mapmap

reducereduce cc

f map : A→ B f reduce : B[ ]→C

Implement MAP functionImplement MAP function

Implement REDUCE functionImplement REDUCE function

Execute MAP func:Mark each document

with specific color

Execute MAP func:Mark each document

with specific color

Execute REDUCE func:Merge each colored set

into single element

Execute REDUCE func:Merge each colored set

into single element

REDUCE

Output

Collection X

How it works

Take amount of orders for each customer

db.cutomers_orders.remove(); mapUsers = function() { emit( this.customerId, {count: 1, this.customerId} );}; reduce = function(key, values) { var result = {count: 0, customerId:key}; values.forEach(function(value) { result.count += value.count; }); return result; }; db.customers.mapReduce(mapUsers, reduce, {"out": {"replace""cutomers_orders"}});

Output: [ {count:123, customerId:1}, {count:33, customerId:2} ]

Aggregation andAggregation Framework

● Simplify most used mapreduce operarions like group by criteria

● Restriction on pipeline size is 16MB● Support sharded environment (Aggregation

Framework only)

Indexes

● Anything might be indexed● Indexes improve performance● Implementation uses B-trees

Access via API

Mongo m = new Mongo();// orMongo m = new Mongo( "localhost" );// orMongo m = new Mongo( "localhost" , 27017 );// or, to connect to a replica set, supply a seed list of membersMongo m = new Mongo(Arrays.asList(new ServerAddress("localhost", 27017), new ServerAddress("localhost", 27018), new ServerAddress("localhost", 27019)))DB db = m.getDB( "mydb" );

DBCollection coll = db.getCollection("customers");

ArrayList list = new ArrayList(); list.add(new BasicDBObject("city", "Odessa")); BasicDBObject doc= new BasicDBObject(); doc.put("name", "Kaktus"); doc.put("billingAddress", list); coll.insert(doc);

Use Official MongoDB Java Driver (just include mongo.jar)

Closer to Domain model● Morphia http://code.google.com/p/morphia/● Spring Data for MongoDB

http://www.springsource.org/spring-data/mongodb

Major features:● Type-safe POJO centric model● Annotations based mapping behavior● Good performance● DAO templates● Simple criterias

Example with Morphia@Entity("Customers")class Customer { @Id ObjectId id; // auto-generated, if not set (see ObjectId) @Indexed String name; // value types are automatically persisted List<Address> billingAddress; // by default fields are @Embedded Key<Customer> bestFriend; //referenceto external document @Reference List<Customer> partners = new ArrayList<Customer>(); //refs are stored and loaded automatically // ... getters and setters

//Lifecycle methods -- Pre/PostLoad, Pre/PostPersist... @PostLoad void postLoad(DBObject dbObj) { ... }}

Datastore ds = new Morphia(new Mongo()).createDatastore("tempDB")morphia.map(Customer.class); Key<Customer> newCustomer = ds.save(new Customer("Kaktus",...)); Customer customer = ds.find(Customer.class).field("name").equal("Medvedev").get();

To embed or not to embed● Separate collections are good if you need

to select individual documents, need more control over querying, or have huge documents.

● Embedded documents are good when you want the entire document, size of the document is predicted. Embedded documents provide perfect performance.

Schema migration● Schemaless● Main focus is how the aplication will behave when

new field will has been added● Incremental migration technque (version field)

Use Cases : – removing field– renaming fields– refactoring aggregate

Data Consistency● Transactional consistency

– domain design should take into account aggregate atomicity

● Replication consistency– Take into account Inconsistency window (sticky sessions)

● Eventual consistency● Accept CAP theorem

– it is impossible for a distributed computer system to simultaneously provide all three of the following guarantees: consistency, availability and partition tolerance.

Scaling

Scaling options

● Autosharding● Master-Slave replication● Replica Set clusterization● Sharding + Replica Set

Sharding● MongoDB supports autosharding● Just specify shard key and pattern● Sharding increases writes● Major way for scaling the system

Master-Slave replication● One master, many slaves● Slaves might be hidden or can be used to read● Master-Slave increase

reades and provides

reliability

Replica Set clusterization● The replica set automatically elects a primary (master)● Master shares the same state between all replicas

● Limitation (limit: 12 nodes)● WriteConcern option

● Benefits:– Failover and Reliability

– Distributing read load

– maintance without downtime

Sharding + ReplicaSet

● Allows to build huge scalable failover database

MongoDB Criticism

● Dataloss reports on heavy-write configurations● Atomic operatons over multiple documents

When not to use

● Heavy cross-document atomic operations● Queries against varying aggregate structure

Tips● Do not use autoincrement ids● Small names are are preffered● By default DAO methods are async● Think twise on collection design● Use atomic modifications for a document

Out of scope

● MapReduce options● Indexes● Capped collections

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