High Scalability - High Scalability - 35+ Use Cases for Choosing Your Next NoSQL Database

High Scalability - High Scalability - 35+ Use Cases for Choosing Your Next NoSQL Database

We've asked What The Heck Are You Actually Using NoSQL For?. We've asked 101 Questions To Ask When Considering A NoSQL Database. We've even had a webinar What Should I Do? Choosing SQL, NoSQL or Both for Scalable Web Applications.

Now we get to the point of considering use cases and which systems might be appropriate for those use cases.

What Are Your Options?

First, let's cover what are the various data models. These have been adapted from Emil Eifrem andNoSQL databases.

Document Databases

• Lineage: Inspired by Lotus Notes.
• Data model: Collections of documents, which contain key-value collections.
• Example: CouchDB, MongoDB
• Good at: Natural data modeling. Programmer friendly. Rapid development. Web friendly, CRUD.

Graph Databases

• Lineage: Euler and graph theory.
• Data model: Nodes & relationships, both which can hold key-value pairs
• Example: AllegroGraph, InfoGrid, Neo4j
• Good at: Rock complicated graph problems. Fast.

Relational Databases

• Lineage: E. F. Codd in A Relational Model of Data for Large Shared Data Banks
• Data Model: a set of relations
• Example: VoltDB, Clustrix, MySQL
• Good at: High performing, scalable OLTP. SQL access. Materialized views. Transactions matter. Programmer friendly transactions.

Object Oriented Databases

• Lineage: Graph Database Research
• Data Model: Objects
• Example: Objectivity, Gemstone

Key-Value Stores

• Lineage: Amazon's Dynamo paper and Distributed HashTables.
• Data model: A global collection of KV pairs.
• Example: Membase, Riak
• Good at: Handles size well. Processing a constant stream of small reads and writes. Fast. Programmer friendly.

BigTable Clones

• Lineage: Google's BigTable paper.
• Data model: Column family, i.e. a tabular model where each row at least in theory can have an individual configuration of columns.
• Example: HBase, Hypertable, Cassandra
• Goog at: Handles size well. Stream massive write loads. High availability. Multiple-data centers. MapReduce.

Data Structure Servers

• Lineage: ?
• Example: Redis
• Data model: Operations over dictionaries, lists, sets and string values.
• Good at: Quirky stuff you never thought of using a database for before.

Grid Databases

• Lineage: Data Grid and Tuple Space research.
• Data Model: Space Based Architecture
• Example: GigaSpaces, Coherence
• Good at: High performance and scalable transaction processing.

What Should Your Application Use?

• Key point is to rethink how your application could work differently in terms of the different data models and the different products. Right data model for the right problem. Right product for the right problem.
• To see what models might help your application take a look at What The Heck Are You Actually Using NoSQL For? In this article I tried to pull together a lot of unconventional use cases of the different qualities and features developers have used in building systems.
• Match what you need to do with these use cases. From there you can backtrack to the products you may want to include in your architecture. NoSQL, SQL, it doesn't matter.
• Look at Data Model + Product Features + Your Situation. Products have such different feature sets it's almost impossible to recommend by pure data model alone.
• Which option is best is determined by your priorities.

If Your Application Needs...

• complex transactions because you can't afford to lose data or if you would like a simple transaction programming model then look at a Relational or Grid database.
  • Example: an inventory system that might want full ACID. I was very unhappy when I bought a product and they said later they were out of stock. I did not want a compensated transaction. I wanted my item!
• to scale then NoSQL or SQL can work. Look for systems that support scale-out, partitioning, live addition and removal of machines, load balancing, automatic sharding and rebalancing, and fault tolerance.
• to always be able to write to a database because you need high availability then look at Bigtable Clones which feature eventual consistency.
• to handle lots of small continuous reads and writes, that may be volatile, then look at Document or Key-value or databases offering fast in-memory access. Also consider SSD.
• to implement social network operations then you first may want a Graph database or second, a database like Riak that supports relationships. An in- memory relational database with simple SQL joins might suffice for small data sets. Redis' set and list operations could work too.

If Your Application Needs...

• to operate over a wide variety of access patterns and data types then look at a Document database, they generally are flexible and perform well.
• powerful offline reporting with large datasets then look at Hadoop first and second, products that support MapReduce. Supporting MapReduce isn't the same as being good at it.
• to span multiple data-centers then look at Bigtable Clones and other products that offer a distributed option that can handle the long latencies and are partition tolerant.
• to build CRUD apps then look at a Document database, they make it easy to access complex data without joins.
• built-in search then look at Riak.
• to operate on data structures like lists, sets, queues, publish-subscribe then look at Redis. Useful for distributed locking, capped logs, and a lot more.
• programmer friendliness in the form of programmer friendly data types like JSON, HTTP, REST, Javascript then first look at Document databases and then Key-value Databases.

If Your Application Needs...

• transactions combined with materialized views for real-time data feeds then look at VoltDB. Great for data-rollups and time windowing.
• enterprise level support and SLAs then look for a product that makes a point of catering to that market. Membase is an example.
• to log continuous streams of data that may have no consistency guarantees necessary at all then look at Bigtable Clones because they generally work on distributed file systems that can handle a lot of writes.
• to be as simple as possible to operate then look for a hosted or PaaS solution because they will do all the work for you.
• to be sold to enterprise customers then consider a Relational Database because they are used to relational technology.
• to dynamically build relationships between objects that have dynamic properties then consider a Graph Database because often they will not require a schema and models can be built incrementally through programming.
• to support large media then look storage services like S3. NoSQL systems tend not to handle large BLOBS, though MongoDB has a file service.

If Your Application Needs...

• to bulk upload lots of data quickly and efficiently then look for a product supports that scenario. Most will not because they don't support bulk operations.
• an easier upgrade path then use a fluid schema system like a Document Database or a Key-value Database because it supports optional fields, adding fields, and field deletions without the need to build an entire schema migration framework.
• to implement integrity constraints then pick a database that support SQL DDL, implement them in stored procedures, or implement them in application code.
• a very deep join depth the use a Graph Database because they support blisteringly fast navigation between entities.
• to move behavior close to the data so the data doesn't have to be moved over the network then look at stored procedures of one kind or another. These can be found in Relational, Grid, Document, and even Key-value databases.

If Your Application Needs...

• to cache or store BLOB data then look at a Key-value store. Caching can for bits of web pages, or to save complex objects that were expensive to join in a relational database, to reduce latency, and so on.
• a proven track record like not corrupting data and just generally working then pick an established product and when you hit scaling (or other issues) use on of the common workarounds (scale-up, tuning, memcached, sharding, denormalization, etc).
• fluid data types because your data isn't tabular in nature, or requires a flexible number of columns, or has a complex structure, or varies by user (or whatever), then look at Document, Key-value, and Bigtable Clone databases. Each has a lot of flexibility in their data types.
• other business units to run quick relational queries so you don't have to reimplement everything then use a database that supports SQL.
• to operate in the cloud and automatically take full advantage of cloud features then we may not be there yet.

If Your Application Needs...

• support for secondary indexes so you can look up data by different keys then look at relational databases and Cassandra's new secondary index support.
• creates an ever-growing set of data (really BigData) that rarely gets accessed then look at Bigtable Clone which will spread the data over a distributed file system.
• to integrate with other services then check if the database provides some sort of write-behind syncing feature so you can capture database changes and feed them into other systems to ensure consistency.
• fault tolerance check how durable writes are in the face power failures, partitions, and other failure scenarios.
• to push the technological envelope in a direction nobody seems to be going then build it yourself because that's what it takes to be great sometimes.
• to work on a mobile platform then look at CouchDB/Mobile couchbase.

Which Is Better?

• Moving for a 25% improvement is probably not a reason to go NoSQL.
• Benchmark relevancy depends on the use case. Does it match your situation(s)?
• Are you a startup that needs to release a product as soon as possible and you are playing around with ideas? Both SQL and NoSQL can make an argument.
• Performance may be equal on one box, but what happens when you need N?
• Everything has problems, if you look at Amazon forums it's EBS is slow, or my instances won't reply, etc. For GAE it's the datastore is slow or X. Every product which people are using will have problems. Are you OK with the problems of the system you've selected?

How Facebook Changed Technology in One Day

How Facebook Changed Technology in One Day: "
The biggest deal about Facebook’s open compute project isn’t the project, it’s the wave of innovation this can bring forward at the systems level — which will affect everyone from the chipmakers to the giant systems vendors and data center operators. At its event Thursday, Facebook unveiled the Open Compute Project, which essentially open sources the systems layer that sits between the standard components inside a server and the hypervisor orchestration layer (which itself has been open sourced by the Open Stack project).

There are two things I took away from this: (1) by tying the servers and the data center together in a holistic unit, the data center has now officially become computer and (2) the big iron providers have just had the rug pulled out from under them. They will need to shift their business to this data-center centric viewpoint or they will lose out in the very area where their business is growing fastest.

These server systems, which have historically been built by IBM, HP, Dell and now, even Cisco have already begun on a path to consolidation and players such as HP have already been preparing for this future of the data center as a computer by purchasing EYP, a data center design firm. In this vision of the data center as the computer, the server becomes a component, and now, after Facebook’s announcement, it becomes a commodity component of sorts.

Yes, people will still buy servers from Dell, HP and the like, but as more and more people move to on-demand computing either at the infrastructure level as provided by Amazon’s EC2 or at the platform level as provided by an internal cloud or a public PaaS, the older hardware designed for legacy enterprise applications stopS being a growth business. They become mainframes. They’ll still be in the bowels of the building, but it’s not where the newest applications will be built. So the big iron vendors must learn to play a new game for these customers, and it’s a game that Dell is likely the best equipped to play.

HP's server built on Open Compute specs

Dell doesn’t fret over stripped-down commodity servers — it saw the demand and built out an entire business to sell them called Dell Center Solutions. At the event, Forrest Norrod VP and General Manager of Sever Platforms with Dell, wasn’t worried about the loss to his business from Facebook’s Open Compute efforts, simply saying that Dell will now provide end-to-end solutions and innovation on top of those types of designs. Both HP and Dell were showing off variations of some of the stripped-down, vanity-free Facebook servers and each had borrowed different elements from Facebook while emphasizing how customizable their options were.

In talking to the sales people manning the server prototypes it occurred to me that for webscale customers such as Facebook, it makes sense to put in the time and effort to build your own house, while at the other end there are those who buy EC2 instances or dedicated hosting, which is like renting an apartment. There’s no customization there, a point Jonathan Heiliger the VP of Technical Operations at Facebook, made at the event. However, gear on offer from Cisco, IBM, Dell or HP is like getting a McMansion — there’s some customization, but there are only a few basic models to choose from.

I think in the short-term the market for McMansions is giant as enterprises test out the cloud, but want trusted performance and vendors, but the aspiration for many will be to build their own custom homes. And What Facebook has done is make the custom architecture cheaper to build and run, which in turn makes it easier for other players to come behind and adopt that for better apartments inspired more by the custom homes rather than McMansions.

At the systems level, the news is horribly disruptive, but for the chip companies the pressure is now on. Unlike code, which can be tweaked in a matter of hours or days, hardware has to be built. And if we’re talking about constructing a data center, we’re talking about a construction process that lasts months if not years. So how fast can an open source hardware design iterate? Pat Patla, GM and VP of the server division at AMD, explained that development cycles for hardware and systems have been gradually compressing from 24 months seven or eight years ago to 18 months in the last few years. “Now, lots of folks are comfortable in 12-month development cycles and [Facebook's news] should help more folks get there, but silicon doesn’t move that fast and now there is a very high pressure on the silicon.”

Intel's Jason Waxman (left) and Rackspace's Graham Weston

And for those who think this doesn’t change much, I’ll close with Graham Weston, the Chairman of Rackspace ( s rax) who said that Rackspace was planning its next decade of data center projects and had been working on how to build out the right, efficient infrastructure for the last few years. But after it talked to Facebook and saw its 38 percent savings achieved in running the servers he said of the Rackspace plan, “We threw it out.” Now it plans to adapt Facebook’s ideas for its own use, and perhaps build on them.

And that’s the biggest news of all from today’s announcement. Once something is shared with the community, everyone can take part in adding innovation on top of innovation. Today, Facebook has achieved a power usage effectiveness rating of 1.07 which is down from and industry average of 1.5, but now that Facebook has shared, how long until someone can reach .75, or zero? Sure, this is disruptive for the big iron vendors, but it’s also disruptive to the old, slow way of improving our computing infrastructure. So let’s see what’s next.

Related content from GigaOM Pro (subscription req’d):

• Big Data, ARM and Legal Troubles Transformed Infrastructure in Q4
• Infrastructure Overview, Q2 2010
• In Q4, Data Centers, Not the Cloud, Were the Big Story

---

"

Introduction to Architecting Systems for Scale

Introduction to Architecting Systems for Scale: "
Few computer science or software development programs
attempt to teach the building blocks of scalable systems.
Instead, system architecture is usually picked up on the job by
working through the pain of a growing product
or by working with engineers who have already learned
through that suffering process.

In this post I'll attempt to document some of the
scalability architecture lessons I've learned while working on
systems at Yahoo! and Digg.

• Load Balancing: Scalability & Redundancy
  • Smart Clients
  • Hardware Load Balancers
  • Software Load Balancers
  
  
• Caching
  • Application Versus Database Caching
  • In Memory Caches
  • Content Distribution Networks
  • Cache Invalidation
  
  
• Off-Line Processing
  • Message Queues
  • Scheduling Periodic Tasks
  • Map-Reduce
  
  
• Platform Layer

I've attempted to maintain a color convention for diagrams in this
post:

• green represents an external request from an external
  client (an HTTP request from a browser, etc),
• blue represents your code running in some container
  (a Django app running on mod_wsgi,
  a Python script listening to RabbitMQ, etc), and
• red represents a piece of infrastructure (MySQL, Redis, RabbitMQ, etc).

Load Balancing: Scalability & Redundancy

The ideal system increases capacity linearly with adding hardware.
In such a system, if you have one machine and add another, your capacity would double.
If you had three and you add another, your capacity would increase by 33%.
Let's call this horizontal scalability.

On the failure side, an ideal system isn't disrupted by the loss of a server.
Losing a server should simply decrease system capacity by the same amount it increased
overall capacity when it was added. Let's call this redundancy.

Both horizontal scalability and redundancy are usually achieved via load balancing.

(This article won't address vertical scalability,
as it is usually an undesirable property for a large system, as there is inevitably a point
where it becomes cheaper to add capacity in the form on additional machines rather than
additional resources of one machine, and redundancy and vertical scaling can be
at odds with one-another.)



Load balancing is the process of spreading requests across multiple resources according
to some metric (random, round-robin, random with weighting for machine capacity, etc)
and their current status (available for requests, not responding, elevated error rate, etc).

Load needs to be balanced between user requests and your web servers,
but must also be balanced at every stage to achieve full scalability and redundancy
for your system. A moderately large system may balance load at three layers: from the

• user to your web servers, from your
• web servers to an internal platform layer, and from your
• internal platform layer to your database.

There are a number of ways to implement load balancing in your setup.

Smart Clients

Adding load-balancing functionality into your database (cache, service, etc) client
is usually an attractive solution for the developer. Is it attractive because it is
the simplest solution? Usually, no. Is it seductive because it is the most robust? Sadly, no.
Is it alluring because it'll be easy to reuse? Tragically, no.

Developers lean towards
smart clients because they are developers, and so they are used to writing software to
solve their problems, and smart clients are software.

With that caveat in mind, what is a smart client? It is a client which takes a pool
of service hosts and balances load across them, detects downed hosts and avoids
sending requests their way (they also have to detect recovered hosts, deal with adding
new hosts, etc, making them fun to get working decently and a terror to get working correctly).

Hardware Load Balancers

The most expensive--but very high performance--solution to load balancing is to buy a dedicated hardware
load balancer (something like a Citrix NetScaler). While they can solve a remarkable range of problems,
hardware solutions are remarkably expensive, and they are also 'non-trivial' to configure.

As such, generally even large companies with substantial budgets will often avoid using dedicated hardware for
all their load-balancing needs; instead they use them only as the first point of contact from user
requests to their infrastructure, and use other mechanisms (smart clients or the hybrid approach discussed
in the next section) for load-balancing for traffic within their network.

Software Load Balancers

If you want to avoid the pain of creating a smart client,
and purchasing dedicated hardware is excessive,
then the universe has been kind enough to provide a hybrid approach: software load-balancers.

HAProxy is a great example of this approach.
It runs locally on each of your boxes, and each service you want to load-balance
has a locally bound port. For example, you might have your platform machines accessible
via localhost:9000, your database read-pool at localhost:9001 and your database
write-pool at localhost:9002. HAProxy manages healthchecks and will remove and return
machines to those pools according to your configuration, as well as balancing across all
the machines in those pools as well.

For most systems, I'd recommend starting with a software load balancer and moving to
smart clients or hardware load balancing only with deliberate need.

Caching

Load balancing helps you scale horizontally across an ever-increasing
number of servers, but caching will enable you to make vastly better
use of the resources you already have, as well as making otherwise
unattainable product requirements feasible.

Caching consists of: precalculating results (e.g. the number of visits from each referring domain for the previous day),
pre-generating expensive indexes (e.g. suggested stories based on a user's click history), and
storing copies of frequently accessed data in a faster backend (e.g. Memcache instead of PostgreSQL.

In practice, caching is important earlier in the development process than load-balancing,
and starting with a consistent caching strategy will save you time later on.
It also ensures you don't optimize
access patterns which can't be replicated with your caching mechanism or access patterns where performance becomes
unimportant after the addition of caching
(I've found that many heavily optimized Cassandra applications
are a challenge to cleanly add caching to if/when the database's caching strategy can't
be applied to your access patterns, as the datamodel is generally inconsistent between the Cassandra
and your cache).

Application Versus Database Caching

There are two primary approaches to caching: application caching and
database caching (most systems rely heavily on both).



Application caching requires explicit integration in the application code itself.
Usually it will check if a value is in the cache; if not, retrieve the value from
the database; then write that value into the cache (this value is especially
common if you are using a cache which observes the least recently used caching algorithm).
The code typically looks like (specifically this is a read-through cache, as it
reads the value from the database into the cache if it is missing from the cache):

key = "user.%s" % user_id
user_blob = memcache.get(key)
if user_blob is None:
user = mysql.query("SELECT * FROM users WHERE user_id=\"%s\"", user_id)
if user:
memcache.set(key, json.dumps(user))
return user
else:
return json.loads(user_blob)

The other side of the coin is database caching.



When you flip your database on, you're going to get some level
of default configuration which will provide some degree of caching and performance.
Those initial settings will be optimized for a generic usecase,
and by tweaking them to your system's access patterns you can generally
squeeze a great deal of performance improvement.

The beauty of database caching is that your application code gets faster 'for
free', and a talented DBA or operational engineer can uncover
quite a bit of performance without your code changing a whit
(my colleague Rob Coli spent some time recently optimizing our configuration for Cassandra
row caches, and was succcessful to the extent that he spent a week harassing us with graphs
showing the I/O load dropping dramatically and request latencies
improving substantially as well).

In Memory Caches

The most potent--in terms of raw performance--caches you'll encounter are those which store
their entire set of data in memory. Memcached and
Redis are both examples of in-memory caches (caveat: Redis can be configured
to store some data to disk).
This is because accesses to RAM are orders of magnitude
faster than those to disk.

On the other hand, you'll generally have far less RAM available than disk space, so you'll
need a strategy for only keeping the hot subset of your data in your memory cache. The most
straightforward strategy is least recently used, and is employed by Memcache (and Redis as of 2.2 can
be configured to employ it as well). LRU works by evicting less commonly used data in preference of
more frequently used data, and is almost always an appropriate caching strategy.

Content Distribution Networks

A particular kind of cache (some might argue with this usage of the
term, but I find it fitting) which comes into play for sites serving large amounts
of static media is the content distribution network.



CDNs take the burden of serving static media off of your application servers (which are
typically optimzed for serving dynamic pages rather than static media), and
provide geographic distribution. Overall, your static assets will load more quickly
and with less strain on your servers (but a new strain of business expense).

In a typical CDN setup, a request will first ask your CDN for a piece of static media, the
CDN will serve that content if it has it locally available (HTTP headers are used for configuring
how the CDN caches a given piece of content). If it isn't available, the CDN will query your
servers for the file and then cache it locally and serve it to the requesting user (in this
configuration they are acting as a read-through cache).

If your site isn't yet large enough to merit its own CDN, you can ease a future transition
by serving your static media off a separate subdomain (e.g. static.example.com) using
a lightweight HTTP server like Nginx, and cutover the DNS from
your servers to a CDN at a later date.

Cache Invalidation

While caching is fantastic, it does require you to maintain consistency between your
caches and the source of truth (i.e. your database), at risk of truly bizarre applicaiton behavior.

Solving this problem is known as cache invalidation.

If you're dealing with a single datacenter, it tends to be a straightforward
problem, but it's easy to introduce errors if you have multiple codepaths writing to your
database and cache (which is almost always going to happen if you don't go into writing the application
with a caching strategy already in mind). At a high level, the solution is: each time a value changes,
write the new value into the cache (this is called a write-through cache)
or simply delete the current value from the cache and allow a read-through cache to populate it later
(choosing between read and write through caches depends on your application's details,
but generally I prefer write-through caches as they reduce likelihood of a stampede
on your backend database).

Invalidation becomes meaningfully more challenging for scenarios involving fuzzy queries (e.g if you
are trying to add application level caching in-front of a full-text search engine like
SOLR), or modifications to unknown number of elements
(e.g. deleting all objects created more than a week ago).

In those scenarios you have to consider relying fully on database caching, adding aggressive
expirations to the cached data, or reworking your application's logic to avoid the issue
(e.g. instead of DELETE FROM a WHERE..., retrieve all the items which match the criteria,
invalidate the corresponding cache rows and then delete the rows by their primary key explicitly).

Off-Line Processing

As a system grows more complex, it is almost always necessary to perform processing
which can't be performed in-line with a client's request either because it is creates
unacceptable latency (e.g. you want to want to propagate a user's action across a
social graph) or it because it needs to occur periodically (e.g. want to create daily rollups
of analytics).

Message Queues

For processing you'd like to perform inline with a request but is too slow,
the easiest solution is to create a message queue (for example, RabbitMQ).
Message queues allow your web applications to quickly publish messages to the queue,
and have other consumers processes perform the processing outside the scope and timeline
of the client request.

Dividing work between off-line work handled by a consumer and in-line work done by
the web application depends entirely on the interface you are exposing to your users.
Generally you'll either:

1. perform almost no work in the consumer (merely scheduling a task)
   and inform your user that the task will occur offline, usually with a polling mechanism
   to update the interface once the task is complete
   (for example, provisioning a new VM on Slicehost follows this pattern), or
2. perform enough work in-line to make it appear to the user that the task has completed,
   and tie up hanging ends afterwards (posting a message on Twitter or Facebook likely
   follow this pattern by updating the tweet/message in your timeline but updating
   your followers' timelines out of band; it's simple isn't feasible to update all
   the followers for a Scobleizer
   in real-time).

Message queues have another benefit, which is that they allow you
to create a separate machine pool for performing off-line processing
rather than burdening your web application servers. This allows you
to target increases in resources to your current performance or throughput bottleneck
rather than uniformly increasing resources across the bottleneck and non-bottleneck
systems.

Scheduling Periodic Tasks

Almost all large systems require daily or hourly tasks,
but unfortunately this seems to still be a problem waiting
for a widely accepted solution which easily supports redundancy.
In the meantime you're probably still stuck with cron,
but you could use the cronjobs to publish messages to a consumer,
which would mean that the cron machine is only responsible for scheduling
rather than needing to perform all the processing.

Does anyone know of recognized tools which solve this problem?
I've seen many homebrew systems, but nothing clean and reusable.
Sure, you can store the cronjobs in a Puppet
config for a machine, which makes recovering from losing that machine
easy, but it would still require a manual recovery, which is probably
acceptable but not quite perfect.

Map-Reduce

If your large scale application is dealing with a large quantity of data,
at some point you're likely to add support for map-reduce,
probably using Hadoop, and maybe
Hive or HBase.



Adding a map-reduce layer makes it possible to perform data and/or processing
intensive operations in a reasonable amount of time. You might use it for calculating
suggested users in a social graph, or for generating analytics reports.

For sufficiently small systems you can often get away with adhoc queries
on a SQL database, but that approach may not scale up trivially once the
quantity of data stored or write-load requires sharding your database,
and will usually require dedicated slaves for the purpose of performing
these queries (at which point, maybe you'd rather use a system designed
for analyzing large quantities of data, rather than fighting your database).

Platform Layer

Most applications start out with a web application communicating
directly with a database. This approach tends to be sufficient
for most applications, but there are some compelling reasons
for adding a platform layer, such that your web applications communicate
with a platform layer which in turn communicates with your databases.



First, separating the platform and web application allow you to scale
the pieces independently. If you add a new API, you can add platform servers
without adding unnecessary capacity for your web application tier.
(Generally, specializing your servers' role opens up an additional level
of configuration optimization which isn't available for general purpose
machines; your database machine will usually have a high I/O load and will
benefit from a solid-state drive, but your well-configured application server probably
isn't reading from disk at all during normal operation, but might benefit from
more CPU.)

Second, adding a platform layer can be a way to reuse your infrastructure for multiple
products or interfaces (a web application, an API, an iPhone app, etc) without writing
too much redundant boilerplate code for dealing with caches, databases, etc.

Third, a sometimes underappreciated aspect of platform layers is that they
make it easier to scale an organization. At their best, a platform exposes a crisp product-agnostic
interface which masks implementation details.
If done well, this allows multiple independent teams to develop utilizing the platform's
capabilities, as well as another team implementing/optimizing the platform itself.

---

I had intended to go into moderate detail on handling multiple data-centers, but
that topic truly deserves its own post, so I'll only mention that cache
invalidation and data replication/consistency become rather interesting problems
at that stage.

I'm sure I've made some controversial statements in this post,
which I hope the dear reader will argue with such that we can
both learn a bit. Thanks for reading!
"