GraphQL
GraphQL is a query language and runtime for interfaces (APIs) that lets a client request exactly the data it needs through a single endpoint – no more and no less. In an ERP context, GraphQL connects the ERP system with shops, apps and third-party systems while cutting out unnecessary data transfer.
GraphQL is a query language for interfaces (APIs) published in 2015 by Facebook (now Meta), together with its server-side runtime. What makes it special: a calling program (client) describes in its request exactly which fields it wants back, and the server returns precisely that structure – through a single endpoint rather than many fixed addresses as with classic web APIs. This way the client, not the server, decides how much and which data flows.
In the ERP and e-commerce world, GraphQL is one of several techniques for connecting an inventory management or ERP system to online shops, apps, portals and third-party systems. Compared with the widespread REST style, GraphQL promises above all that an app can fetch several related objects in a single query – for example an order together with its customer, line items and stock levels – without making multiple requests or dragging along superfluous fields. That makes GraphQL especially attractive for data-hungry frontends and mobile applications.
At a glance
- GraphQL = query language + runtime for APIs; the client determines the response structure
- A single endpoint instead of many URLs as with REST
- A schema with types authoritatively defines which data and fields exist
- No over-fetching / under-fetching: you get back exactly what you asked for
- An alternative or complement to REST API, SOAP and webhooks for ERP integration
How GraphQL works: one schema, one endpoint
The heart of every GraphQL interface is the schema. In its own type language it describes which objects exist (such as customer, order, article), which fields those objects have and how they relate to each other. The schema is a binding contract between server and client: every request is validated against this schema, and tools can automatically generate documentation and autocompletion from it. For developers, a GraphQL API therefore often feels like a self-explanatory map of the available data.
Unlike REST, a client does not address many different URLs but sends all requests to a single endpoint. The request contains a query that lists exactly the desired fields – for example, "for this order give me the order number, the customer name and all line items with article number and quantity." The server responds with a JSON object whose structure matches the request. If a field is missing from the request, it does not appear in the response either.
Query, mutation and subscription
GraphQL has three operation types. A query reads data (comparable to a GET in REST). A mutation changes data – so it creates a customer, adjusts a stock level or cancels an order, for instance. A subscription, finally, subscribes to events: the server actively sends the client a message as soon as something changes, for example when a new order comes in. GraphQL thus covers both the classic question-and-answer pattern and real-time notifications.
GraphQL vs. REST: the key difference
The most important difference between GraphQL and the REST API concerns the question of who determines the response. With REST, the server defines a fixed address with a fixed response structure for each resource; the client gets whatever that address delivers. In practice this often leads to two problems: over-fetching, when an address returns significantly more fields than needed, and under-fetching, when several addresses have to be queried one after another for a single view. GraphQL solves both, because the client assembles the desired fields itself and receives related objects in a single query.
In return, GraphQL shifts complexity onto the server and into caching. REST benefits from the fact that every address is easy to cache via the standard mechanisms of the web (HTTP caching, status codes); with GraphQL and its single endpoint and arbitrary queries, this is more involved. In addition, a nested GraphQL query can in theory request a great deal at once, which can strain servers. In practice, GraphQL is therefore not a "better REST" but an alternative with a different profile – many systems offer both, and the choice depends on the use case.
GraphQL in the ERP system
For ERP integration, GraphQL is especially interesting wherever a frontend needs a lot of linked data in compact form. A customer portal, a field-service app or a custom-built shop frontend (headless commerce) can fetch the order header, line items, customer master data and current availabilities with a single query, instead of bundling several REST calls. This reduces the amount of data transferred and the number of round trips between app and ERP – a noticeable advantage on mobile devices and with large volumes of objects.
Not every ERP offers a GraphQL interface; many still rely primarily on REST APIs, SOAP or EDI, partly complemented by webhooks for real-time events. Anyone selecting a system should therefore not treat GraphQL as an end in itself, but ask whether the existing interface covers the required objects for reading and writing and is well documented. Large e-commerce platforms – such as Shopify – have made GraphQL the preferred API access, which is why connections to such channels frequently run over GraphQL in practice.
When GraphQL pays off for a connection
As a rule of thumb, GraphQL pays off when a tailored frontend needs a lot of nested data and the data volume or response time is critical. For simple, clearly bounded server-to-server processes – such as the nightly reconciliation of documents with financial accounting – a lean REST API or a fixed, well-defined data format often remains the more pragmatic choice. What matters is not how modern the technology is, but how well it fits the process.
Benefits, limits and typical pitfalls
GraphQL's strengths lie in flexibility and precision: clients receive exactly the data they need, the schema serves as living documentation, and new fields can be added without breaking existing queries – one reason why GraphQL rarely needs hard version numbers like "v2." For teams building rapidly changing interfaces, this considerably shortens coordination between frontend and backend.
The limits are just as real. Caching is more involved than with REST, protecting against overly complex or deeply nested queries requires additional safeguards (such as depth and cost limits), and for very simple interfaces GraphQL is over-engineered. Error handling also differs: GraphQL often returns HTTP status 200 and reports errors in the response body, which monitoring concepts have to adapt to. Anyone introducing GraphQL should plan for these points from the outset rather than retrofitting them later.
Example
Example: field-service app of a B2B wholesaler
A mid-sized wholesaler equips its field-service team with a tablet app that shows open orders, contact details and current stock levels during customer visits. Via a classic REST API, the app would have to query several addresses one after another per customer: one for the master data, one for the orders, one each for the article availabilities. On a poor mobile connection this takes noticeably longer and transfers many fields that no one sees.
With a GraphQL interface, the app makes a single query that requests exactly the customer name, open order numbers, line items and the current availability per article. The ERP returns precisely this package. The app loads faster, uses less data volume, and when the team later wants to add a field like "last delivery date," it is enough to include that field in the query – without having to rebuild the interface.
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