In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.

1. www.arangodb.com
Polyglot Persistence
&
Multi-Model Databases
London Java Community
28-10-2014
Michael Hackstein
@mchacki

2. Michael Hackstein
‣ ArangoDB Core Team
‣ Web Frontend
‣ Graph visualisation
‣ Graph features
!
!
‣ Host of cologne.js
!
!
‣ Master’s Degree
(spec. Databases and
Information Systems)
2

3. The Single Model era is over
3
Relational World

4. The Multi Model era begins
‣ Normally based on key-value stores (each document still
4
NoSQL World
Documents - JSON
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
{
“type“: "pants",
“waist": 32,
“length”: 34,
“color": "blue",
“material”: “cotton"
}
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
has a unique key)
‣ Allow to save documents with logical similarity in
“collections”
‣ Treat data records as attribute-structured documents
(data is no more opaque)
‣ Often allow querying and indexing document attributes
‣ Focussed on m-to-n relations between entities
‣ Stores property graphs: entities and edges can have
‣ Easily query paths of variable length
K => V
K => V
K => V
K => V
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
attributes
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
K => V
Graphs
Key Value
{
“type“: "television",
“diagonal screen size": 46,
“hdmi inputs": 3,
“wall mountable": true,
“built-in digital tuner": true,
“dynamic contrast ratio”: “50,000:1”,
Resolution”: “1920x1080”
}
‣ Map value data to unique string keys (identifiers)
‣ Treat data as opaque (data has no schema)
‣ Can implement scaling and partitioning easily

5. An e-commerce system in Relational World
5
Sales-History
Shopping-Cart
Recommendations Customer
Product-Catalog

6. Polyglot Persistence
6
Shopping Cart
Product Catalog
MongoDB
Reporting
RDBMS
Financial Data
RDBMS
Recommendations
Neo4J
Analytics
Cassandra
Product Catalog
Document
Source: Martin Fowler, http://martinfowler.com/articles/nosql-intro.pdf
Riak
User activity log
Cassandra
User Sessions
Redis
Shopping Cart
KeyValue
Reporting
RDBMS
Financial Data
RDBMS
Recommendations
Graph
Analytics
Column
User activity log
Column
User Sessions
KeyValue

7. Single Model Databases
7
Sales-History Recommendations
Customer
{
“userID": 239178239,
“productID”: 128623883,
“number": 5,
“price”: 12.20,
}
DocumentStore GraphStore DocumentStore
{
“userID": 239178239,
“productID”: 128623883,
“number": 5,
“price”: 12.20,
}
{
“Name": "Smith",
“lastLogin”: “2012-11-01",
“Visits": 121,
“shipping address”: “abc”,
“shipping address”: “def”
}
{
“Name": "Meyer",
“lastLogin”: “2012-11-21",
“Visits": 20,
“shipping address”: “xyz”,
}
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
Shopping-Cart Product-Catalog
423453453
4328, “shirt”, “L”, 1, 12.99
6378, “sweater”, “M”, 2, 37.95
3245, “sweater”, “blue”, 1, 99.95
3245, “pants”, “32/34”, “black”, 1, 99.95
=>
874365563
5463, “shirt”, “S”, 1, 9.99
6378, “sweater”, “M”, 2, 37.95
3245, “pants”, “32/34”, “black”, 1, 99.95
=>
{
“type“: "pants",
“waist": 32,
“length”: 34,
“color": "blue",
“material”: “cotton"
}
{
“type“: "television",
“diagonal screen size": 46,
“hdmi inputs": 3,
“wall mountable": true,
“built-in digital tuner": true,
“dynamic contrast ratio”: “50,000:1”,
Resolution”: “1920x1080”
}
KeyValueStore DocumentStore

8. Benefits
‣ Natural mapping of data
into DB
‣ DB optimized for the data
format
‣ Queries are tailored for
your data format
‣ Focus on writing business
logic
8
& Overhead
‣Data has to be stored
redundantly and has to be
kept in sync
‣Several technologies
involved
‣Administration effort is
huge

9. Solution: Multi Model Database
‣ Can natively store several kinds of data models:
‣ Key-value pairs
‣ Documents
‣ Graphs
‣ Delivers query mechanisms for all data models
9

10. Polyglot Persistence Revisited
Financial Data
ArangoDB
Recommendations
ArangoDB
10
User Sessions
KeyValue
Shopping Cart
KeyValue
Product Catalog
Document
Reporting
RDBMS
Financial Data
RDBMS
Recommendations
Graph
Analytics
Column
User activity log
Column
Shopping Cart
ArangoDB
Product Catalog
ArangoDB
Reporting
RDBMS
Source: Martin Fowler, http://martinfowler.com/articles/nosql-intro.pdf
Analytics
Cassandra
User activity log
Cassandra
User Sessions
ArangoDB

11. Use Case: Multi-Model-Databases
11
Sales-History Recommendations
Customer
{
“userID": 239178239,
“productID”: 128623883,
“number": 5,
“price”: 12.20,
}
DocumentStore GraphStore DocumentStore
{
“userID": 239178239,
“productID”: 128623883,
“number": 5,
“price”: 12.20,
}
{
“Name": "Smith",
“lastLogin”: “2012-11-01",
“Visits": 121,
“shipping address”: “abc”,
“shipping address”: “def”
}
{
“Name": "Meyer",
“lastLogin”: “2012-11-21",
“Visits": 20,
“shipping address”: “xyz”,
}
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
{
“type": "sweater",
“color": "blue",
“size": “M”,
“material”: “wool”,
“form”: “turtleneck"
}
Shopping-Cart Product-Catalog
423453453
4328, “shirt”, “L”, 1, 12.99
6378, “sweater”, “M”, 2, 37.95
3245, “sweater”, “blue”, 1, 99.95
3245, “pants”, “32/34”, “black”, 1, 99.95
=>
874365563
5463, “shirt”, “S”, 1, 9.99
6378, “sweater”, “M”, 2, 37.95
3245, “pants”, “32/34”, “black”, 1, 99.95
=>
{
“type“: "pants",
“waist": 32,
“length”: 34,
“color": "blue",
“material”: “cotton"
}
{
“type“: "television",
“diagonal screen size": 46,
“hdmi inputs": 3,
“wall mountable": true,
“built-in digital tuner": true,
“dynamic contrast ratio”: “50,000:1”,
Resolution”: “1920x1080”
}
KeyValueStore DocumentStore

12. My four favorite features of
‣ AQL offering joins & traversals
‣ ACID including Multi Collection Transactions
‣ MULTI-MODEL stores graphs and documents
12
‣ FOXX extend the API and adapt it to your needs

13. AQL
‣Document Query:
FOR user IN users FILTER user.active == true
FOR game IN games FILTER game.player == user._id
RETURN {
username: user.name,
score: game.score
}
‣ Modify Documents:
FOR u IN users FILTER u.status == 'not active'
UPDATE u WITH { active: false } IN users
!
‣ Graph Traversal:
RETURN GRAPH_TRAVERSAL(
"underground_plan", „stations/main_station",
"outbound", {minDepth: 2, maxDepth: 5}
)
13

14. ACID - Transactions
‣ Invoke a transaction:
db._executeTransaction({
collections: {
write: ["users", "products"],
read: "recommendations"
},
action: function() {
// all operations go here
!
}
});
14
throw "failure"; // Triggers rollback

15. Benefits & Overhead
‣ Native mapping of data into
DB
‣ DB optimized
‣ Queries are tailored for
your data format
‣ Focus on writing business
logic
15
‣Data has to be stored
redundantly and has to be
kept in sync
‣Several technologies
‣Administration effort is
‣One technology involved

16. Foxx
‣ Add your own customized and versioned REST-API on top of
ArangoDB in JavaScript
‣ Include as a web service in Rails, Node.js etc.
‣ Use as storage for Web-frameworks like AngularJS, EmberJS, Backbone etc.
‣ Built-in authentication using OAuth2.0 or HTTP-Basic Auth
‣ Operations are encapsulated in the database
‣ low network traffic, direct data access
‣ increases data privacy
➡Multi-device setups
➡Microservices
/
(~(
) ) /_/
( _-----_(@ @)
( /
/|/--| V
" " " "
16

17. ‣ open source and free (Apache 2 license)
‣ sharding & replication
‣ JavaScript throughout (V8 built into server)
‣ drivers for a wide range of languages
‣ web frontend
‣ good & complete documentation
‣ professional as well as community support
17
An overview of other features

18. Join our growing community
18
.. working on the geo index, the full text search and
many APIs: Ruby, Python, PHP, Java, D, Javascript, ...

19. Thank you
!
!
!
‣ Further questions?
‣ Follow me on twitter/github: @mchacki
‣ Write me a mail: mchacki@arangodb.com
‣ Join or google group: https://groups.google.com/forum/#!forum/arangodb
19