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If NoSQL is your answer, you
are probably asking the wrong
          question.
Hi, my name is
Lukas Kahwe
     Smith
and I am not a
     troll
@lsmith
@lsmith77
SQL
For the conference we're
looking for someone who can
 be a part of the 'Databases -
 short talks' presentations to
give a lightning talk about SQL.
SQL
NoSQL
No
SQL?
Not Only
 SQL?
Time for some pseudo math
Given that most relational
 databases have an SQL
        interface
RDBMS - SQL = NoSQL ?
NoSQL + SQL = RDBMS ?
Key-Value-Store + Complex Queries
          = NoSQL ?
I am about to blow your mind
I have created a database API
      for unstructured
hierarchical documents
on top of an RDBMS with
     an SQL-like interface
And I wasn’t even the first
    person to do it ..
Can we agree to stop using
    the term NoSQL ?
Using NewSQL is not any
        better!
Instead talk about RDBMS,
 Doc-Stores, Key-Value-
Stores, Graph-Databases ..
There is clearly a growing
number of people considering
 alternatives to RDBMS
Actually Key-Value-Stores
 have been popular for many
        years already
So clearly the current trend is
 not just about sharding
unstructured documents in
  an elastically scaling
            cluster
Its also not about getting rid of
SQL, after all the CAP theorem
    doesn’t talk about what
   query languages to use
RDBMS - relational model = ?
We still love RDBMS for what their good for
   Storing and retrieving
structured relational small to
    large data sets with high
consistency and reliability
And yes we also still love SQL
 Running ad-hoc search
  queries and schema
updates even if we are not
    rocket scientists

http://2012.nosql-matters.org/cgn/slides/#olaf_bachmann
And while JSON queries might
be easier parsed by computers
  SQL is definitely easy to
     parse for humans
So what do we really worry
         about .. ?
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries,
CAP Theorem, Column
Oriented, Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
ACID, Asynchronous, Atomic
Updates, BigData, Binaries, CAP
Theorem, Column Oriented,
Elastic Scaling, Eventual
Consistency, Failover, FullText
Search, Graphs, Low Latency, In
Memory, MapReduce, Master-
Master, Sharding, Smart Clients
RDBMS vendors got stuck
  as a result of their own
          success
Innovators Dilemma
“The two biggest issues that stand out to me are
 that current leading relational databases never
   solved scale out very well, and online
  operations are too expensive. [..] The
innovation hasn't been in the language, but in the
     design of database engines themselves. “
                Brian Aker (Drizzle, previously MySQL)




  http://blog.krow.net/2013/03/mysql-vs-nosql-vs-postgres-vs-sql.html
The good news is that
RDBMS vendors have been
       unstuck
However its hard to move on
    from a monolithic
 architecture, Drizzle is a
  radical attempt at trying it
In many ways its easier to
  start from scratch
But lets look at some
examples of things that have
  happened in the RDBMS
            world
VoltDB, ScaleBase and NuoDB
 promise elastic scaling on
top of an RDBMS with SQL
    and ACID* compliance
        http://voltdb.com/tao-volt/five-principles.php
             http://www.scalebase.com/solution/
  http://www.nuodb.com/explore/sql-cloud-database-how-it-works/
Both PostgreSQL and
MySQL have native support
     for JSON (and XML)
PostgreSQL performs reads
  on par with MongoDB,
 especially for larger data sets

http://www.slideshare.net/stormdb_cloud_database/postgres-xc-askeyvaluestorevsmongodb
          http://jathanism-event-notes.readthedocs.org/en/latest/scale11x/talks/
                            postgres_as_a_schemaless_db.html
MySQL Server and MySQL
Cluster allow by passing SQL
via the Memcache protocol

http://blog.ulf-wendel.de/downloads/nosql_in_mysql.pdf
MySQL and PostgreSQL
    both have forks providing
   Multi-Master replication

http://www.enterprisedb.com/products-services-training/products-overview/xdb-
                        replication-server-multi-master
            http://www.codership.com/content/using-galera-cluster
OQGRAPH Engine to store
  graphs in Maria DB


 http://openquery.com/products/graph-engine
MySQLND plugins for
client side query caching,
   transaction aware load
         balancing
             http://php.net/mysqlnd_qc
  http://php.net/manual/en/book.mysqlnd-ms.php
Conclusion
SQL is not a scalability enemy
SQL is not a scalability enemy
How big is your data ..
      really?
Operational experience only
 comes over time, having
something solid that just
       works matters
There can be innovation in
   the RDBMS world
Using an RDBMS is fine
So is using Doc-Stores, Key-
 Value-Stores, Graph DBs
Using NoSQL is not fine
Just pick the right tool for
          the job ..
Duh!
Fin

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If NoSQL is your answer, you are probably asking the wrong question.

  • 1. If NoSQL is your answer, you are probably asking the wrong question.
  • 2. Hi, my name is Lukas Kahwe Smith
  • 3. and I am not a troll
  • 4.
  • 7. SQL
  • 8. For the conference we're looking for someone who can be a part of the 'Databases - short talks' presentations to give a lightning talk about SQL.
  • 9. SQL
  • 10. NoSQL
  • 13. Time for some pseudo math
  • 14. Given that most relational databases have an SQL interface
  • 15. RDBMS - SQL = NoSQL ?
  • 16. NoSQL + SQL = RDBMS ?
  • 17. Key-Value-Store + Complex Queries = NoSQL ?
  • 18. I am about to blow your mind
  • 19. I have created a database API for unstructured hierarchical documents on top of an RDBMS with an SQL-like interface
  • 20. And I wasn’t even the first person to do it ..
  • 21. Can we agree to stop using the term NoSQL ?
  • 22. Using NewSQL is not any better!
  • 23. Instead talk about RDBMS, Doc-Stores, Key-Value- Stores, Graph-Databases ..
  • 24. There is clearly a growing number of people considering alternatives to RDBMS
  • 25. Actually Key-Value-Stores have been popular for many years already
  • 26. So clearly the current trend is not just about sharding unstructured documents in an elastically scaling cluster
  • 27. Its also not about getting rid of SQL, after all the CAP theorem doesn’t talk about what query languages to use
  • 28. RDBMS - relational model = ?
  • 29. We still love RDBMS for what their good for Storing and retrieving structured relational small to large data sets with high consistency and reliability
  • 30. And yes we also still love SQL Running ad-hoc search queries and schema updates even if we are not rocket scientists http://2012.nosql-matters.org/cgn/slides/#olaf_bachmann
  • 31. And while JSON queries might be easier parsed by computers SQL is definitely easy to parse for humans
  • 32. So what do we really worry about .. ?
  • 33. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 34. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 35. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 36. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 37. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 38. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 39. ACID, Asynchronous, Atomic Updates, BigData, Binaries, CAP Theorem, Column Oriented, Elastic Scaling, Eventual Consistency, Failover, FullText Search, Graphs, Low Latency, In Memory, MapReduce, Master- Master, Sharding, Smart Clients
  • 40. RDBMS vendors got stuck as a result of their own success
  • 42. “The two biggest issues that stand out to me are that current leading relational databases never solved scale out very well, and online operations are too expensive. [..] The innovation hasn't been in the language, but in the design of database engines themselves. “ Brian Aker (Drizzle, previously MySQL) http://blog.krow.net/2013/03/mysql-vs-nosql-vs-postgres-vs-sql.html
  • 43. The good news is that RDBMS vendors have been unstuck
  • 44. However its hard to move on from a monolithic architecture, Drizzle is a radical attempt at trying it
  • 45. In many ways its easier to start from scratch
  • 46. But lets look at some examples of things that have happened in the RDBMS world
  • 47. VoltDB, ScaleBase and NuoDB promise elastic scaling on top of an RDBMS with SQL and ACID* compliance http://voltdb.com/tao-volt/five-principles.php http://www.scalebase.com/solution/ http://www.nuodb.com/explore/sql-cloud-database-how-it-works/
  • 48. Both PostgreSQL and MySQL have native support for JSON (and XML)
  • 49. PostgreSQL performs reads on par with MongoDB, especially for larger data sets http://www.slideshare.net/stormdb_cloud_database/postgres-xc-askeyvaluestorevsmongodb http://jathanism-event-notes.readthedocs.org/en/latest/scale11x/talks/ postgres_as_a_schemaless_db.html
  • 50. MySQL Server and MySQL Cluster allow by passing SQL via the Memcache protocol http://blog.ulf-wendel.de/downloads/nosql_in_mysql.pdf
  • 51. MySQL and PostgreSQL both have forks providing Multi-Master replication http://www.enterprisedb.com/products-services-training/products-overview/xdb- replication-server-multi-master http://www.codership.com/content/using-galera-cluster
  • 52. OQGRAPH Engine to store graphs in Maria DB http://openquery.com/products/graph-engine
  • 53. MySQLND plugins for client side query caching, transaction aware load balancing http://php.net/mysqlnd_qc http://php.net/manual/en/book.mysqlnd-ms.php
  • 55. SQL is not a scalability enemy
  • 56. SQL is not a scalability enemy
  • 57. How big is your data .. really?
  • 58. Operational experience only comes over time, having something solid that just works matters
  • 59. There can be innovation in the RDBMS world
  • 60. Using an RDBMS is fine
  • 61. So is using Doc-Stores, Key- Value-Stores, Graph DBs
  • 62. Using NoSQL is not fine
  • 63. Just pick the right tool for the job ..
  • 64. Duh!
  • 65. Fin