NoSQL Tel Aviv Meetup#1: NoSQL Data Modeling
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This document discusses NoSQL data modeling and provides examples of different data modeling approaches for non-relational databases, including document, columnar, graph, and relational models. It covers topics like the role of data modeling, different data domains, schema approaches, normalization vs denormalization, embedding data, and using multiple data models or a "polyglot persistence" approach. Examples are given of one-to-one, one-to-many, and many-to-many relationships and how they can be modeled in a document database.
![IdoFriedman.yml
Name: Ido Friedman,
Past:”SQL Server consultant,Instructor,Team Leader”
Present:”Data engineer and Architect,
Elasticsearch,CouchBase,MongoDB,Python”,…]
WorkPlace:Perion
WhenNotWorking:@Sea](https://image.slidesharecdn.com/nosqldatamodeling1-151012210705-lva1-app6891/85/NoSQL-Tel-Aviv-Meetup-1-NoSQL-Data-Modeling-2-320.jpg)
![Relations example
1 to Many
City : Person
1 to 1
Employee: Resume
{
_id:101,
Name:Jason Voorhees,
Age: 99
Resume_ID:1004
}
{
_id:1004,
Jobs:[Cook]
Education:[Knifery]
Hobbies:[Murder]
Employee_id:101
}
{
"name" : "Dam Square, Amsterdam",
"location" : {
"type" : "polygon",
"coordinates" : [[
[ 4.89218, 52.37356 ],
[ 4.89205, 52.37276 ], ….…
]]}
}
{
_id:101,
Name:Jason Voorhees,
Age: 99
Resume_ID:1004
}
Many to Many
Student : Teacher
{
_id:101,
Name:Jason Voorhees,
Age: 99
Resume_ID:1004
Courses:[“Chainsaw 101”,”Axing”],
Teachers:[101]
}
{
_id:101,
Name: “Freddy Krueger”,
Age: 60
Resume_ID:1004
Skills: [{Skill:}]
}](https://image.slidesharecdn.com/nosqldatamodeling1-151012210705-lva1-app6891/85/NoSQL-Tel-Aviv-Meetup-1-NoSQL-Data-Modeling-12-320.jpg)
NoSQL Tel Aviv Meetup#1: NoSQL Data Modeling
- 2. IdoFriedman.yml Name: Ido Friedman, Past:”SQL Server consultant,Instructor,Team Leader” Present:”Data engineer and Architect, Elasticsearch,CouchBase,MongoDB,Python”,…] WorkPlace:Perion WhenNotWorking:@Sea
- 3. Let’s talk • What is the role of data modeling • What does data modeling effect
- 4. Data models Document Columnar Graph Relational New SQL* And more.. Document
- 5. Data Domains On line Batch Real time analytics Micro batch Streaming
- 6. Schema and structure Schema free Structured Unstruc tured Semi Structured • Who needs schemas? • Schema description
- 7. Normalization/De-Normalization • Born to reserve storage and keep data integrity (RI) • Resolve data joining issues • Performance aspects Is it still relevant???
- 8. Normalization example {"_index": "user_profiles", "_type": "properties", "_id": "25467834901804247006200168495554902214", "_version": 4, "_score": 1, "_source": { "app_package_id": 4495665825523018, "device_id": "b94b29c3-f03f-4e43-a646-53708e025779", "group_id": 876, "customer_user_id": "", "customer_device_id": "b2f5fbfb-5d05-01e9-32e9-a8b332e9a8b3", "advertise_id": "", "device_os": "android", "device_os_comparable_version": "00000004.00000002.00000002.00000017", "device_os_full_name": "android 4.2.2.17", "android_id": "c93334e4e246b83c", "manufacturer": "OPPO", "model": "R1001", "screen_width": 480, "screen_height": 800, "device_language": "vi", "cpu": "", "is_rooted": 1, "is_jailbroken": 0, "vendor": "perion", "app_installation_time": 1435173851000, "push_allowed": 1, "operator": "Beeline VN", "mcc": "452", "mnc": "07", "mac_address": "", "created_at": 1435174685000, "registered_in_desktop": "", "short_country_code": "VN", "updated_at": 1435259712000, "app_package_name": "com.gingersoftware.android.keyboard", "app_version_type": "" } } "manufacturer": "OPPO", {"_index": "events-2015-06-04", "_type": "events", "_id": "AU4-zg5nOG4dkiMGKrCx", "_version": 1, "_score": 1, "_source": { "numeric_value_unit": "key", "event_type_name": "custom", "text_value": "", "event_date": 1433440149000, "numeric_value": 13, "quantity": 0, "event_name": "Saved Tap", "numeric_value_name": "taps saved", "app_package_id": 1433440149000, "api_key": “asasa1w121", "device_id": "c5eb1fe0-8a77-41ef-9f79-ed7ed69d32e6" } } "event_name": "Saved Tap", "event_name": "SVD T", "event_name": "Saved TP", "event_name": "Saved",
- 9. Constraints • No BIG Brother • Data can't be verified once it leaves the application
- 10. Transactions • Atomicity • Locking • Rollback
- 11. Relations type • One to One • One to Many • Many to Many
- 12. Relations example 1 to Many City : Person 1 to 1 Employee: Resume { _id:101, Name:Jason Voorhees, Age: 99 Resume_ID:1004 } { _id:1004, Jobs:[Cook] Education:[Knifery] Hobbies:[Murder] Employee_id:101 } { "name" : "Dam Square, Amsterdam", "location" : { "type" : "polygon", "coordinates" : [[ [ 4.89218, 52.37356 ], [ 4.89205, 52.37276 ], ….… ]]} } { _id:101, Name:Jason Voorhees, Age: 99 Resume_ID:1004 } Many to Many Student : Teacher { _id:101, Name:Jason Voorhees, Age: 99 Resume_ID:1004 Courses:[“Chainsaw 101”,”Axing”], Teachers:[101] } { _id:101, Name: “Freddy Krueger”, Age: 60 Resume_ID:1004 Skills: [{Skill:}] }
- 13. Embedding • Embed • Known doc size • Data is highly related • No joins • Don’t Embed • Very large data sets • Data is updated rapidly
- 14. Doesn’t fit …. • Use the data model that most fits your needs • Don’t be afraid of Polyglot Persistence
- 15. Polyglot Persistence • Data usage patterns • Readers vs. Writers • Online vs. Batch • Concurrency • Issues • Data freshness • Data consistency • System Coupling
- 16. Questions?