Using MongoDB
to build a fast and scalable Content Repository

Some Context

What we Do and What Problems We Try to Solve

Nuxeo

​ we provide a Platform that developers can use to
build highly customized Content Applications

we provide components, and the tools to assemble them

everything we do is open source


 

various  customers - various use cases 




 

Track game builds

Electronic Flight Bags

Central repository for Models

Food industry PLM

https://github.com/nuxeo

DOCUMENT REPOSITORY

SQL based Repository - VCS

KEY Limitations of the SQL approach

  • Impedance issue
    • storing Documents in tables is not easy
    • requires Caching and Lazy loading 
       
  • Scalability
    • Document repository can become very large (versions, workflows ...) 
    • ​scaling out SQL DB is very complex (and never transparent)
       
  • ​Concurrency model
    • Heavy write is an issue (Quotas, Inheritance)
    • ​​Hard to maintain good Read & Write performances 

Need a different
storage model !

No SQL

Integrating MongoDB

Inside nuxeo-dbs storage adapter

FROM SQL to MongoDB Storage

Document base Storage & Mongodb

Storing Nuxeo Documents in MongoDB

{  
   "ecm:id":"52a7352b-041e-49ed-8676-328ce90cc103",
   "ecm:primaryType":"MyFile",
   "ecm:majorVersion":NumberLong(2),
   "ecm:minorVersion":NumberLong(0),
   "dc:title":"My Document",
   "dc:contributors":[ "bob", "pete", "mary" ],
   "dc:created":   ISODate("2014-07-03T12:15:07+0200"), 
...
   "cust:primaryAddress":{  
      "street":"1 rue René Clair", "zip":"75018", "city":"Paris", "country":"France"},
   "files:files":[  
      {  "name":"doc.txt", "length":1234, "mime-type":"plain/text",
         "data":"0111fefdc8b14738067e54f30e568115"
      },
      {  
         "name":"doc.pdf", "length":29344, "mime-type":"application/pdf",
         "data":"20f42df3221d61cb3e6ab8916b248216"
      }
   ],
   "ecm:acp":[  
      {  
         name:"local",
         acl:[ { "grant":false, "perm":"Write", "user":"bob"},
               { "grant":true,  "perm":"Read", "user":"members" } ]
      }]
...
}
  • 40+ fields by default
    • ​depends on config
  • 18 indexes

hIERARCHY & Security

  • Parent-child relationship
     
  • Recursion optimized through                                                      array
      
            
                ecm:parentId
            
        
            
                ecm:ancestorIds
            
        
{ ... "ecm:parentId" : "3d7efffe-e36b-44bd-8d2e-d8a70c233e9d", 
      "ecm:ancestorIds" : [ "00000000-0000-0000-0000-000000000000", 
                            "4f5c0e28-86cf-47b3-8269-2db2d8055848", 
                            "3d7efffe-e36b-44bd-8d2e-d8a70c233e9d" ] ...}
  • Generic ACP stored in ecm:acp field


     
  • Precomputed Read ACLs to avoid post-filtering on search
ecm:racl: ["Management", "Supervisors", "bob"]
{... "ecm:acp":[ {  
              name:"local",
              acl:[ { "grant":false, "perm":"Write", "user":"bob"},
                    { "grant":true,  "perm":"Read", "user":"members" } ]}] ...}

SEARCH

db.default.find({
   $and: [
   {"dc:title": { $in: ["Workspaces", "Sections"] } },
   {"ecm:racl": {"$in": ["bob", "members", "Everyone"]}}
   ]
 }
)
SELECT * FROM Document WHERE dc:title = 'Sections' OR dc:title = 'Workspaces'

Storing Blobs

About Consistency

  • Atomic Document Operations are safe
  • Large batch updates is not so much of an issue
     

     
  • Multi-documents transactions are an issue
    • ex: Workflows

Find a way to mitigate  consistency issues

Transactions can not span across multiple documents

Mitigating consistency issues

  • Transient State Manager 
    • Run all operations in Memory
    • Populate an Undo Log
  • Recover partial Transaction Management
    • Commit / Rollback model
       
  • "Read uncommited" isolation
    • Need to flush transient state for queries
    • "uncommited" changes are visible to others

Hybrid Storage Architecture

​

  • MongoDB

    • store structure & streams in a BASE way
       

  • elasticsearch

    • provide powerful and scalable queries
       

  • ​SQL DB

    • store structures in an ACID way

Storage does not impact application : this can be a deployment choice!

A tomic C onsistent
I solated D urable

B asic  A vailability
S oft state  
E ventually consistent

depends on Availability & Performances requirements 

Nuxeo + MongoDB

Supercharge your Content Repository

No SQL with Mongodb

  • No Impedance issue
    • One Nuxeo Document = One MongoDB Document
       
  • No Scalability issue  for CRUD
    • ​native distributed architecture allows scale out
       
  • ​ No Concurrency performance issue 
    • ​ ​Document Level "Transactions" 
       
  • No  application level cache is needed
    • No need to manage invalidations

Is Nuxeo Fast
with mongodb ?

Nighly CI Benchmarks

Low level read  
(fast re-indexing with elasticsearch)

  • 3,500 documents/s using SQL backend

  • 10, 000 documents/s using MongoDB  

Raw Java API performances

Single Server
6 core HT 3.5Ghz
126 GB RAM
std hdd

about 3 times faster

NIGHLY CI BENCHMARKS

Read via REST API (misc Read APIs)  

  • 900 docs/s using SQL backend
  • 1,300 docs/s using MongoDB (+40%)
  • 1,370 docs/s using MongoDB/wiredTiger  (+50%)  

Raw Http API performances

Single Server
6 core HT 3.5Ghz
126 GB RAM
std hdd

​ Update via REST API

  • ​ 540 docs/s using SQL backend
  • 910 docs/s using MongoDB (+70%) 
  • 990 docs/s using MongoDB/wiredTiger (+80%) 

Storage is no longer the bottleneck

HUGE Repository - Heavy loading

SQL DB Collapse  / MongoDB handles the volume

Massive number
of documents

Automatic versioning

Write intensive
Daily Imports

Benchmarking Mass Import

SQL

with tunning

commodity hardware

SQL

7x faster

Benchmarking Read + Write

Read & Write Operations
are competing

Write Operations
are not blocked

C4.xlarge (nuxeo)
C4.2Xlarge (DB)

SQL

Data LOADING Overflow

Processing on large Document sets are an issue on SQL

Side effects of impedance miss match

Ex: Process 100,000 documents​

  • 750 documents/s with SQL backend (cold cache)

  • 9,500 documents/s with MongoDB / mmapv1: x13

  • 11,500 documents/s with MongoDB / wiredTiger:  x15

lazy loading

cache trashing

Benchmarking Scale out

  • 1 Nuxeo node + 1 MongoDB node

    • ​ 1900 docs/s

    • MongoDB CPU is the bottleneck (800%)
       

  • ​2 Nuxeo nodes + 1 MongoDB node

    • ​ 1850 docs/s

    • MongoDB CPU is the bottleneck (800%)
       

  • ​2 Nuxeo nodes + 2 MongoDB nodes

    • ​ 3400 docs/s when using read preferences

Use massive read operations and queries.

Document repository
is Fast
with MongoDB

So, Yes !

Example use case ?

VOD repository

  • Requirements:
    • store videos
    • manage meta-data & availability
    • manage workflows
    • generate thumbs & conversions
  • Very Large Objects:
    • lots of meta-data  (dublincore, ADI, ratings ...) 
  • Massive daily updates
    • updates on rights and availability
  • Need to track all changes
    • prove what was the availability for a given date ​ 

Real life project choosing Nuxeo with MongoDB backend

good use case  for MongoDB
want to use MongoDB
 

lots of data + lots of updates

Any Questions ?

Thank You !

https://github.com/nuxeo

http://www.nuxeo.com/careers/