12. LAB 1 : HELLO OPENSHIFT
1. Install OpenShift Eclipse plugin
2. Create new application with JBoss EAP and MongoDB
cartridges.
1.
Sign up for OpenShift(if not already)
2.
Create domain name or namespace
3.
Upload SSH keys to OpenShift
4.
Fill application creation wizard
5.
Finish
3. Show OpenShift Explorer View and Server’s View
4. Make a change in index.html
1.
2.
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Commit the change using Git Staging view
Publish the change
13. LAB 2 : HOT DEPLOYMENT
1. Right click on your project and then go to
OpenShift > Configure Markers
2. Choose Hot Deploy marker
3. Commit to git repository
4. Go to servers view and publish your changes.
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15. LAB 3 : REST INTRODUCTION
1. HTTP used right
2. Defines set of RESTful constraints
1.
Everything is a resource
1.
2.
Eg. Post , Tweet , User , etc.
Every resource has an identifier
1. Eg. http://api/twitter.com/tweets/1000011111
3.
Resource can have multiple representations
1. JSON , XML , YAML , etc.
4.
All resources expose a uniform interface
1. GET , POST , PUT , DELETE
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16. LAB 3 JAX-RS INTRODUCTION
1. Java API for REST Services
2. POJO based
3. Annotation heavy
4. HTTP Centric
5. Format independent
6. Container independent
7. Included with Java EE 6
In this workshop we will be talking about JAX-RS 1.1
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17. LAB 3 : SET UP JAX RS
1. Update to Java 7
2. Delete web.xml
3. Update Maven war plugin to 2.3
4. Create JAX-RS configuration class.
5. Write PingResource
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20. CDI
1. CDI stands for Context and Dependency Injection
2. CDI simplifies and sanitizes the API for DI and AOP
like JPA did for ORM -- CDI Tutorial by Rick Hightower
3. Type safe approach to Dependency Injection
4. Strong typing and loose coupling
5. To configure CDI add beans.xml to
1.
WEB-INF of WAR
2.
META-INF of JAR
6. Beans can be injected at method , field , or constructor.
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23. WHAT IS MONGODB
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Open Source NoSQL document datastore
–
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JSON style documents
Schema-less
–
Each document is heterogeneous, and may have completely
unique structure compared to other documents
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Rich query language
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Rich documents
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Easy to get running
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Fast and horizontally scalable
Geospatial indexing
25. SOME QUERIES
// Find all the jobs with skill as mongodb
db.jobs.find({"skills":"mongodb"})
// Find all the jobs with python as skill and
near to given location
db.jobs.find({"lngLat":{$near :
[139.69 , 35.68]},
"skills":"python"})
// Find all the python or mongodb jobs
near to given location
db.jobs.find({"lngLat":{$near :
[139.69 , 35.68]},
"skills":{$in :
["mongodb","python"]}})
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26. LAB 4 : ENABLE CDI AND
MONGODB CONFIGURATION
1. Create beans.xml in src/main/WEB-INF folder
2. Create ApplicationScoped bean for configuring
MongoDB
3. PingResource writes a document to dummy collection
4. Open Eclipse Remote System Explorer perspective
and connect to gear.
Right click on project
1.
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Team > Reset > Remote Tracking > upstream/lab4
27. LAB 5 : IMPLEMENT CREATE
AND FIND ALL STATUS
1. Create Status domain class
2. Create converter for Status to DBObject and vice versa
3. Create StatusResource with create and find all
endpoints.
4. Create ApplicationDao with create and findAll methods.
5. Create Twitter Bootstrap and Backbonejs front end for
create and find all functionalities.
Right click on project
-
27
Team > Reset > Remote Tracking > upstream/lab5
28. LAB 6 : IMPLEMENT NEAR AND
GEONEAR FEATURES
1. Create findNear and findGeoNear methods in
ApplicationDao
2. Create near and geonear search REST endpoints in
StatusResource
3. Implement backbone views for search endpoints
4. Create Index
1.
db.statuses.ensureIndex({“location”:”2dsphere”})
Right click on project
-
28
Team > Reset > Remote Tracking > upstream/lab6
33. GEOSPATIAL INDEXING
BASICS
What is it for?
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l
l
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Find all the MongoDB jobs near me – Proximity Queries
Find all the MongoDB jobs within Bangalore – Bounded
Queries
Find all the MongoDB job at this location – Exact Queries
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Supports only two dimensional indexes.
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You can only have one geospatial index per collection.
By default, 2d geospatial indexes assume longitude
and latitude have boundaries of -180 inclusive and 180
non-inclusive (i.e. [-180, 180))
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34. HOW TO MAKE IT WORK
1) Put your coordinates into an array
{ loc : [ 50 , 30 ] } //SUGGESTED OPTION
{ loc : { x : 50 , y : 30 } }
{ loc : { foo : 50 , y : 30 } }
1) { loc : { lon : 40.739037, lat: 73.992964 } }
2) Make a 2d index
db.places.ensureIndex( { loc : "2d" } )
3)
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If you use latitude and longitude as your coordinate system, always
store longitude first. MongoDB’s 2d spherical index operators only
recognize [ longitude, latitude] ordering.
