The document summarizes an experiment comparing the performance of GraphQL and REST APIs when querying data from MongoDB and ElasticSearch databases. The key findings are: 1) Optimizing queries between the client and application server had less impact than expected, with GraphQL responses being smaller but not significantly faster. 2) Optimizing queries between the application server and databases showed more significant performance gains, with projections in GraphQL and optimized database queries improving speeds. 3) GraphQL's ability to optimize queries from client to data storage is an advantage, but it should not be chosen primarily for performance reasons alone. Further testing is needed to evaluate performance for specific use cases.

1. Optimising response
times with GraphQL
an experiment with MongoDB and ElasticSearch
(These slides were adapted to be suitable offline)

2. Intuitions: GraphQL faster
• less requests
• smaller responses

3. Reality Eats
Intuitions
for Breakfast

4. Not obvious
• Client has to send more info
• query contains list of fields
• -> biggest requests payloads
• Server has more work to do
• query validation
• customise response

5. Do not
guess it,
test it

6. –A client
“I want the id and the versions of all products”

7. 30000 50000 100000
MongoDB ES
Perf of REST endpoints

8. query($offset: Int!, $limit: Int!) {
products(offset: $offset, limit: $limit) {
results {
id
version
}
}
}
In GraphiQL
GaphQL query

9. Client
App
Server
Data
storage
Optimisation between client
and application server

10. 30000 50000 100000
MongoDB ES MongoDB GraphQL ES GraphQL

11. • smallest responses -> better performances
• difference not as important as expected

12. Client
App
Server
Data
storage
Optimisation between application
server and data storage

13. select * from products;
select id, version from products;
Optimise DB queries

14. http://sangria-graphql.org/learn/#projections

15. In Mongo
db
.getCollection('products')
.find({})
db
.getCollection('products')
.find({}, {_id: 1, version: 1})

16. 30000 50000 100000
Mongo Mongo GraphQL Mongo GraphQL projections

17. In Elasticsearch
{
"_source": true,
"query" : […]
}
{
"_source": [ "id", "version"],
"query" : […]
}

18. 30000 50000 100000
ES ES GraphQL ES GraphQL projections

19. 30000 50000 100000
Mongo ES
Mongo GraphQL projections ES GraphQL projections

20. • Optimising database queries have an important
impact on performances
• GraphQL allows to optimise from client to data
storage

21. • Tested use-case intentionally picked to be
favorable to GraphQL (test assumption)
• Test yourself for your use-case
• Should not choose GraphQL primary for
performances but for features

22. Further optimisations
• Pre-computed queries
• client sends only values of variables
• queries already validated
• Binary serialisation format