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Why I chose
   mongodb
for guardian.co.uk
              Mat Wall
Lead Software Architect, guardian.co.uk
“It is not the strongest of the species that
survives, nor the most intelligent. It is the one
       that is most adaptable to change.”
Early Period

         circa ’95

The “Lash It Together” era
Early Period (95, the “Lash It Together” era)


 Perl, CGI, apache

  Experimental
Manual processes
Bespoke software

 RDBMS, scripts
  & static files
Mid Period

      circa ’00

The “Vendor CMS” era
Mid Period: 2000s (The “Vendor CMS era”)


 Vignette / AOLserver
 TCL, Apache, Oracle

  Platform for online
       publishing

Initially scales well with
acceleration in delivery
        of features
Mid Period: 2000s (The “Vendor CMS era”)


 Surprise! Vendor’s CMS
doesn’t do what we want!

 Mish-mash in templates:
 HTML, JavaScript, TCL,
      SQL, PL-SQL

No model in app tier, only
in RDBMS schema created
    in Oracle Designer
Mid Period: 2000s (The “Vendor CMS era”)
Mid Period: 2000s (The “Vendor CMS era”)
Mid Period: 2000s (The “Vendor CMS era”)



After a few years, very
   difficult to extend

  Database schema
becomes fixed due to
  dependencies in
     templates
Mid Period: 2000s (The “Vendor CMS era”)




If you can’t change the
        system:
Modern Period

       circa ’05-09

The “J2EE Monolithic” era
Q con london2011-matthewwall-whyichosemongodbforguardiancouk
Web server       Web server         Web server



  I bring you NEWS!!!
App server      App server          App server




                 Oracle


         CMS                  Data feeds
Web server         Web server         Web server
              Modern java app
  I bring you NEWS!!!
App server      App server            App server
           Spring / Hibernate

                 DDD / TDD

             Strong Oracle in java
                    model

 Database abstracted away with ORM
         CMS                    Data feeds
Problems
Each release involves schema upgrade

Schema upgrade = downtime for journalists
Complexity still increasing:

               300+ tables,
  10,000 lines of hibernate XML config
1,000 domain objects mapped to database
   70,000 lines of domain object code
      Very tight binding to database
ORM not really masking complexity:

 Database has strong influence on domain model: many
domain objects made more complex mapping joins in
                      RDBMS

Complex hibernate features used, interceptors, proxies

               Complex caching strategy
                Lots of optimisations

                    And:
We still hand code complex queries in SQL!
Load becoming an issue

RDBMS difficult to scale
Partial NoSQL

       circa ’09-10

The “Sticking Plaster” era
Introduce yet more caching to patch up load problems

 Decouple applications from database by building APIs

Power APIs using alternative, more scalable technologies

         APIs used to scale out database reads

               Writes still go to RDBMs
Core
                             Api
   Web servers

                            Solr/API
    App server
                            Solr/API
Memcached (20Gb)
                            Solr/API

     rdbms         Solr
                            Solr/API

      M/Q                   Solr/API

     CMS                  Cloud, EC2
Content API
Mutualised news! Apache Solr
 Read API delivered using

            Hosted in EC2

   Document oriented search engine

  Loose schema: records, fields, facets

    Scales well for read operations
Related content from Solr




           Introduction of memcached
Mutualised news!
We’ve solved our load problem (for now)

                 but

       Increased our complexity
Mutualised news!
      We now have 3 models!

           RDBMS tables

            Java Objects

             JSON API
Mutualised news!
Mutualised news!
Mutualised news!
MutualisedAPI is very simple
           JSON news!

Multiple domain concepts expressed in single document

     Can be designed in forwardly extensible way

What if the JSON API was our primary model?
Full NoSQL

    in development

The “It’s the future!” era
The first project: Identity

Current login/registration system still in TCL/PL-SQL

          3M+ users in relational database

          Very complex schema + PL-SQL

               New system required

      Can we migrate from Oracle to NoSql?
Database selection


      Simple keystore. Too simple?



     Huge scalability. Do we need it?
        Schema design difficult.


    Simple to use, can execute similar
           queries to RDBMs
MongoDB

 Mutualised news! database
     Document oriented
       Stores parsed JSON documents

        Can express complex queries

      Can be flexible about consistency

Malleable schema: can easily change at runtime

    Can work at both large & small scales
MongoDB concepts


Mutualised news!
    RDBMS           MongoDB
     Table          Collection

      Row        JSON Document

     Index            Index

      Join      Embedding & Linking

    Partition         Shard
Flexible Schema


Mutualised news!
Flexible Schema


Mutualised news!
Flexible Schema


Mutualised news!
Can easily represent different classes of tag as
                 documents

    Both documents can be inserted into
             same collection

    Far simpler than equivalent hibernate
       mapped subclass configuration
Flexible Schema

        Simple to query:
Mutualised news!
Flexible Schema

              Simple to query:
Mutualised news!
            Query operators:
  $ne, $nin, $all, $exists, $gt, $lt, $gte ...
Modifying the schema


Mutualised news!
Modifying the schema


Mutualised news!
Modifying the schema


Mutualised news!
Schema upgrades


     Mutualised news!
   Schema can be upgraded simply by upgrading the
                 application version

Application must deal with differing document versions

           Can become complex over time
Schema upgrades


      Mutualised news! by:
           This can be mitigated

        Adding a “version” key to each document

Updating the version each time the application modifies a
                       document

Using MapReduce capability to forcibly migrate documents
            from older versions if required
Mongodb architecture




                     mongod


                  Single node
Durability only possible in upcoming 1.8 release
    (databse fsync from buffer every min)
Mongodb architecture
         master                      replicas
          mongod                      mongod

                                      mongod

    Replica set                       mongod

                                      mongod



 Can choose to read &
                             Can choose to run reads
write from master for full
                             on slaves to scale reads
       consistency
Mongodb architecture
        master                          replicas
        Durability achieved (<1.8) via replication
         mongod                          mongod
         Reads can be scaled out onto replicas
                                       mongod
                (eventual consistency)
    Replica set                        mongod
                 All writes to master
                                         mongod

    If master fails, new master nominated by election
 Can choose to read &         Can choose to accept dirty
write from drivers handle most cluster complexity scale
        DB master for full     reads from slaves to
       consistency                       reads
Mongodb architecture


Aggregator                         mongos


consistent     shard      shard             shard     shard
 (master)

               replica   replica            replica   replica
inconsistent
  (replica)    replica   replica            replica   replica

               replica   replica            replica   replica
Mongodb architecture

                Writes scaled by sharding
Aggregator                            mongos
                Shards populated by ranges
consistent    shard      shard     shard                 shard
 (master) mongos queries appropriate shard(s)

              Shards automatically balanced
               replica   replica     replica             replica
inconsistent
  (replica)     replica    replica    replica    replica
         Developers (essentially) unaware of shards
                replica     replica            replica   replica
Mongodb durability


Relies (pre 1.8) on replication for durability
1.8 features optional journaling & redo logs

 Database users need to be cluster aware,
         each query can specify:

  No error checking / write confirmation
       Write confirmed on master
   Write replicated to N slave servers
Old Idenity system
 Hundreds of tables & stored procedures
Mutualised news!

       New Identity model

     User                     List
       Fields      Text
       Dates     Date/Time
      Statuses    Boolean
Q con london2011-matthewwall-whyichosemongodbforguardiancouk
Very simple domain objects




   Simple, flexible objects
    No hibernate session
Very simple domain objects




Flexible schema embraced in domain object design
Very simple domain objects




Using casbah scala drivers = significant reduction in LOC
                 vs SQL implementation
Build API that can support both backends


    Registration app         guardian.co.uk




                       API



         MongoDB                       Oracle
Build API that can support both backends


    Registration app         guardian.co.uk




                       API             This bit is hard!


         MongoDB                       Oracle
Migrate using API & decommision


 Registration app         guardian.co.uk




                    API



      MongoDB
Add new stuff!


    Registration app           guardian.co.uk




                         API



MongoDB                Solr?                    Redis?
MongoDB

Simple, flexible schema with similar query & indexing to
                        RDBMS
              Great at small or large scale
            Easy for developers to get going
         Commercial support available (10Gen)
        One day may power all of guardian.co.uk

No transactions / joins: developers must cater for this

Produces a net reduction in lines of code / complexity
Shameless plug




        We’re hiring:

http://www.careersatgnl.co.uk

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Q con london2011-matthewwall-whyichosemongodbforguardiancouk

  • 1. Why I chose mongodb for guardian.co.uk Mat Wall Lead Software Architect, guardian.co.uk
  • 2. “It is not the strongest of the species that survives, nor the most intelligent. It is the one that is most adaptable to change.”
  • 3. Early Period circa ’95 The “Lash It Together” era
  • 4. Early Period (95, the “Lash It Together” era) Perl, CGI, apache Experimental Manual processes Bespoke software RDBMS, scripts & static files
  • 5. Mid Period circa ’00 The “Vendor CMS” era
  • 6. Mid Period: 2000s (The “Vendor CMS era”) Vignette / AOLserver TCL, Apache, Oracle Platform for online publishing Initially scales well with acceleration in delivery of features
  • 7. Mid Period: 2000s (The “Vendor CMS era”) Surprise! Vendor’s CMS doesn’t do what we want! Mish-mash in templates: HTML, JavaScript, TCL, SQL, PL-SQL No model in app tier, only in RDBMS schema created in Oracle Designer
  • 8. Mid Period: 2000s (The “Vendor CMS era”)
  • 9. Mid Period: 2000s (The “Vendor CMS era”)
  • 10. Mid Period: 2000s (The “Vendor CMS era”) After a few years, very difficult to extend Database schema becomes fixed due to dependencies in templates
  • 11. Mid Period: 2000s (The “Vendor CMS era”) If you can’t change the system:
  • 12. Modern Period circa ’05-09 The “J2EE Monolithic” era
  • 14. Web server Web server Web server I bring you NEWS!!! App server App server App server Oracle CMS Data feeds
  • 15. Web server Web server Web server Modern java app I bring you NEWS!!! App server App server App server Spring / Hibernate DDD / TDD Strong Oracle in java model Database abstracted away with ORM CMS Data feeds
  • 17. Each release involves schema upgrade Schema upgrade = downtime for journalists
  • 18. Complexity still increasing: 300+ tables, 10,000 lines of hibernate XML config 1,000 domain objects mapped to database 70,000 lines of domain object code Very tight binding to database
  • 19. ORM not really masking complexity: Database has strong influence on domain model: many domain objects made more complex mapping joins in RDBMS Complex hibernate features used, interceptors, proxies Complex caching strategy Lots of optimisations And: We still hand code complex queries in SQL!
  • 20. Load becoming an issue RDBMS difficult to scale
  • 21. Partial NoSQL circa ’09-10 The “Sticking Plaster” era
  • 22. Introduce yet more caching to patch up load problems Decouple applications from database by building APIs Power APIs using alternative, more scalable technologies APIs used to scale out database reads Writes still go to RDBMs
  • 23. Core Api Web servers Solr/API App server Solr/API Memcached (20Gb) Solr/API rdbms Solr Solr/API M/Q Solr/API CMS Cloud, EC2
  • 24. Content API Mutualised news! Apache Solr Read API delivered using Hosted in EC2 Document oriented search engine Loose schema: records, fields, facets Scales well for read operations
  • 25. Related content from Solr Introduction of memcached
  • 26. Mutualised news! We’ve solved our load problem (for now) but Increased our complexity
  • 27. Mutualised news! We now have 3 models! RDBMS tables Java Objects JSON API
  • 31. MutualisedAPI is very simple JSON news! Multiple domain concepts expressed in single document Can be designed in forwardly extensible way What if the JSON API was our primary model?
  • 32. Full NoSQL in development The “It’s the future!” era
  • 33. The first project: Identity Current login/registration system still in TCL/PL-SQL 3M+ users in relational database Very complex schema + PL-SQL New system required Can we migrate from Oracle to NoSql?
  • 34. Database selection Simple keystore. Too simple? Huge scalability. Do we need it? Schema design difficult. Simple to use, can execute similar queries to RDBMs
  • 35. MongoDB Mutualised news! database Document oriented Stores parsed JSON documents Can express complex queries Can be flexible about consistency Malleable schema: can easily change at runtime Can work at both large & small scales
  • 36. MongoDB concepts Mutualised news! RDBMS MongoDB Table Collection Row JSON Document Index Index Join Embedding & Linking Partition Shard
  • 39. Flexible Schema Mutualised news! Can easily represent different classes of tag as documents Both documents can be inserted into same collection Far simpler than equivalent hibernate mapped subclass configuration
  • 40. Flexible Schema Simple to query: Mutualised news!
  • 41. Flexible Schema Simple to query: Mutualised news! Query operators: $ne, $nin, $all, $exists, $gt, $lt, $gte ...
  • 45. Schema upgrades Mutualised news! Schema can be upgraded simply by upgrading the application version Application must deal with differing document versions Can become complex over time
  • 46. Schema upgrades Mutualised news! by: This can be mitigated Adding a “version” key to each document Updating the version each time the application modifies a document Using MapReduce capability to forcibly migrate documents from older versions if required
  • 47. Mongodb architecture mongod Single node Durability only possible in upcoming 1.8 release (databse fsync from buffer every min)
  • 48. Mongodb architecture master replicas mongod mongod mongod Replica set mongod mongod Can choose to read & Can choose to run reads write from master for full on slaves to scale reads consistency
  • 49. Mongodb architecture master replicas Durability achieved (<1.8) via replication mongod mongod Reads can be scaled out onto replicas mongod (eventual consistency) Replica set mongod All writes to master mongod If master fails, new master nominated by election Can choose to read & Can choose to accept dirty write from drivers handle most cluster complexity scale DB master for full reads from slaves to consistency reads
  • 50. Mongodb architecture Aggregator mongos consistent shard shard shard shard (master) replica replica replica replica inconsistent (replica) replica replica replica replica replica replica replica replica
  • 51. Mongodb architecture Writes scaled by sharding Aggregator mongos Shards populated by ranges consistent shard shard shard shard (master) mongos queries appropriate shard(s) Shards automatically balanced replica replica replica replica inconsistent (replica) replica replica replica replica Developers (essentially) unaware of shards replica replica replica replica
  • 52. Mongodb durability Relies (pre 1.8) on replication for durability 1.8 features optional journaling & redo logs Database users need to be cluster aware, each query can specify: No error checking / write confirmation Write confirmed on master Write replicated to N slave servers
  • 53. Old Idenity system Hundreds of tables & stored procedures Mutualised news! New Identity model User List Fields Text Dates Date/Time Statuses Boolean
  • 55. Very simple domain objects Simple, flexible objects No hibernate session
  • 56. Very simple domain objects Flexible schema embraced in domain object design
  • 57. Very simple domain objects Using casbah scala drivers = significant reduction in LOC vs SQL implementation
  • 58. Build API that can support both backends Registration app guardian.co.uk API MongoDB Oracle
  • 59. Build API that can support both backends Registration app guardian.co.uk API This bit is hard! MongoDB Oracle
  • 60. Migrate using API & decommision Registration app guardian.co.uk API MongoDB
  • 61. Add new stuff! Registration app guardian.co.uk API MongoDB Solr? Redis?
  • 62. MongoDB Simple, flexible schema with similar query & indexing to RDBMS Great at small or large scale Easy for developers to get going Commercial support available (10Gen) One day may power all of guardian.co.uk No transactions / joins: developers must cater for this Produces a net reduction in lines of code / complexity
  • 63. Shameless plug We’re hiring: http://www.careersatgnl.co.uk