Difference between scaling horizontally and vertically for databases

Question

I have come across many NoSQL databases and SQL databases  There are varying parameters to measure the strength and weaknesses of these databases and scalability is one of them  What is the difference between horizontally and vertically scaling these databases

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SQL databases like Oracle  db2 also support Horizontal scaling through Shared disk cluster  For example Oracle RAC   IBM DB2 purescale or Sybase ASE Cluster edition  New node can be added to Oracle RAC system or DB2 purescale system to achieve horizontal scaling   But the approach is different from noSQL databases  like mongodb  CouchDB or IBM Cloudant  is that the data sharding is not part of Horizontal scaling   In noSQL databases data is shraded during horizontal scaling

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Scaling horizontally     gt  Thousands of minions will do the work together for you  Scaling vertically     gt  One big hulk will do all the work for you

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Adding lots of load balancers creates extra overhead and latency and that is the drawback for scaling out horizontally in nosql databases  It is like the question why people say RPC is not recommended since it is not robust   I think in a real system we should use both sql and nosql databases to utilize both multicore and cloud computing capabilities of today s systems    On the other hand  complex transactional queries has high performance if sql databases such as oracle being used  NoSql could be used for bigdata and horizontal scalability by sharding

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You have a company and there is only 1 worker but you got 1 new project at that time you hire new candidate -- this is horizontal scaling  where new candidate is new machines and project is new traffic calls to your api s   Where as 1 project with an IIT NIT guy handling all request to your api traffic  If any time more request to your api s then fire him and replacing him with a high IQ NIT IIT guy -- this is vertical scaling

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Horizontal scaling means that you scale by adding more machines into your pool of resources whereas Vertical scaling means that you scale by adding more power  CPU  RAM  to an existing machine  An easy way to remember this is to think of a machine on a server rack  we add more machines across the horizontal direction and add more resources to a machine in the vertical direction                              In the database world  horizontal-scaling is often based on the partitioning of the data i e  each node contains only part of the data  in vertical-scaling the data resides on a single node and scaling is done through multi-core i e  spreading the load between the CPU and RAM resources of that machine  With horizontal-scaling it is often easier to scale dynamically by adding more machines into the existing pool - Vertical-scaling is often limited to the capacity of a single machine  scaling beyond that capacity often involves downtime and comes with an upper limit  Good examples of horizontal scaling are Cassandra  MongoDB  Google Cloud Spanner    and a good example of vertical scaling is MySQL - Amazon RDS  The cloud version of MySQL   It provides an easy way to scale vertically by switching from small to bigger machines  This process often involves downtime  In-Memory Data Grids such as GigaSpaces XAP  Coherence etc   are often optimized for both horizontal and vertical scaling simply because they re not bound to disk  Horizontal-scaling through partitioning and vertical-scaling through multi-core support  You can read more on this subject in my earlier posts  Scale-out vs Scale-up and The Common Principles Behind the NOSQL Alternatives

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There is an additional architecture that wasn t mentioned - SQL-based database services that enable horizontal scaling without the complexity of manual sharding  These services do the sharding in the background  so they enable you to run a traditional SQL database and scale out like you would with NoSQL engines like MongoDB or CouchDB  Two services I am familiar with are EnterpriseDB for PostgreSQL and Xeround for MySQL  I saw an in-depth post by Xeround which explains why scale-out on SQL databases is difficult and how they do it differently - treat this with a grain of salt as it is a vendor post  Also check out Wikipedia s Cloud Database entry  there is a nice explanation of SQL vs  NoSQL and service vs  self-hosted  a list of vendors and scaling options for each combination

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Yes scaling horizontally means adding more machines  but it also implies that the machines are equal in the cluster  MySQL can scale horizontally in terms of Reading data  through the use of replicas  but once it reaches capacity of the server mem disk  you have to begin sharding data across servers  This becomes increasingly more complex  Often keeping data consistent across replicas is a problem as replication rates are often too slow to keep up with data change rates    Couchbase is also a fantastic NoSQL Horizontal Scaling database  used in many commercial high availability applications and games and arguably the highest performer in the category  It partitions data automatically across cluster  adding nodes is simple  and you can use commodity hardware  cheaper vm instances  using Large instead of High Mem  High Disk machines at AWS for instance   It is built off the Membase  Memcached  but adds persistence  Also  in the case of Couchbase  every node can do reads and writes  and are equals in the cluster  with only failover replication  not full dataset replication across all servers like in mySQL    Performance-wise  you can see an excellent Cisco benchmark  http   blog couchbase com understanding-performance-benchmark-published-cisco-and-solarflare-using-couchbase-server  Here is a great blog post about Couchbase Architecture  http   horicky blogspot com 2012 07 couchbase-architecture html

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The accepted answer is spot on the basic definition of horizontal vs vertical scaling  But unlike the common belief that horizontal scaling of databases is only possible with Cassandra  MongoDB  etc I would like to add that horizontal scaling is also very much possible with any traditional RDMS  that too without using any third party solutions   I know of many companies  specially SaaS based companies that do this  This is done using simple application logic  You basically take a set of users and divide them over multiple DB servers  So for example  you would typically have a  meta  database table that would store clients  DB server connection strings  etc and a table that stores client server mapping   Then simply direct requests from each client to the DB server they are mapped to    Now some may say this is akin to horizontal partitioning and not  true  horizontal scaling and they will be right in some ways  But the end result is that you have scaled your DB over multiple Db servers   The only difference between the two approaches to horizontal scaling is that one approach  MongoDB  etc  the scaling is done by the DB software itself  In that sense you are  buying  the scaling  In the other approach  for RDBMS horizontal scaling   the scaling is built by application code logic

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Let s start with the need for scaling that is increasing resources so that your system can now handle more requests than it earlier could    When you realise your system is getting slow and is unable to handle the current number of requests  you need to scale the system    This provides you with two options  Either you increase the resources in the server which you are using currently  i e  increase the amount of RAM  CPU  GPU and other resources  This is known as vertical scaling    Vertical scaling is typically costly   It does not make the system fault tolerant  i e if you are scaling application running with single server  if that server goes down  your system will go down   Also the amount of threads remains the same in vertical scaling   Vertical scaling may require your system to go down for a moment when process takes place  Increasing resources on a server requires a restart and put your system down    Another solution to this problem is increasing the amount of servers present in the system  This solution is highly used in the tech industry   This will eventually decrease the request per second rate in each server   If you need to scale the system  just add another server  and you are done  You would not be required to restart the system   Number of threads in each system decreases leading to high throughput  To segregate the requests  equally to each of the application server  you need to add load balancer which would act as reverse proxy to the web servers  This whole system can be called as a single cluster   Your system may contain a large number of requests which would require more amount of clusters like this    Hope you get the whole concept of introducing scaling to the system

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Traditional relational databases were designed as client server database systems  They can be scaled horizontally but the process to do so tends to be complex and error prone  NewSQL databases like NuoDB are memory-centric distributed database systems designed to scale out horizontally while maintaining the SQL ACID properties of traditional RDBMS   For more information on NuoDB  read their technical white paper

[database] Difference between scaling horizontally and vertically for databases

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