Update 2015-05-01-graphql-introduction.md

_posts/2015-05-01-graphql-introduction.md

@@ -90,7 +90,7 @@ Because of multiple round-trips and over-fetching, applications built in the RES
 Many applications have no formalized client-server contract. Product developers access server capabilities through *ad hoc* endpoints and write custom code to fetch the data they need. Servers define procedures, and they return data. This approach has the virtue of simplicity, but can often become untenable as systems age.
 
 * These systems typically define a custom endpoint per view. For systems with a wide surface area this can quickly grow into a maintenance nightmare of orphaned endpoints, inconsistent tooling, and massive server code duplication. Disciplined engineering organizations can mitigate these issues with great engineering practices, high quality abstractions, and custom tooling. However, given our experience we believe that custom endpoints tend to lead to entropic server codebases.
-* Much like REST, the payloads of custom endpoints grow monotonically (even with mitigation from versioning systems) as the server evolves. Deployed clients cannot break, and, with rapid release cycles and backwards compatibility guarantees, distributed applications will have large numbers of extant versions. Under these constraints it is difficult remove data from a custom endpoint.
+* Much like REST, the payloads of custom endpoints grow monotonically (even with mitigation from versioning systems) as the server evolves. Deployed clients cannot break, and, with rapid release cycles and backwards compatibility guarantees, distributed applications will have large numbers of extant versions. Under these constraints it is difficult to remove data from a custom endpoint.
 * Custom endpoints tend to – for a client developer – create a clunky, multi-language, multi-environment development process. No matter if the data has been accessed before in a different view, they are required to first change the custom endpoint, then deploy that code to a server accessible from a mobile device, and only then change the client to utilize that data. In GraphQL  – unless the data in the view is completely new to the entire system  – a product developer adds a field to a GraphQL query and the work on the client continues unabated.
 * Much like REST, most systems with custom endpoints do not have a formalized type system, which eliminates the possibility for the tools and guarantees that introspective type systems can provide. Some custom-endpoint-driven systems do use a strongly typed serialization scheme, such as Protocol Buffers, Thrift, or XML. Those do allow for direct parsing of responses into typed classes and eliminating boilerplate shuffling from JSON into handwritten classes. These systems are as not as expressive and flexible as GraphQL, and the other downsides of *ad hoc* endpoints remain.