A principled approach to GraphQL query cost analysis

Cha, Alan; Wittern, Erik; Baudart, Guillaume; Davis, James N.; Mandel, Louis; Laredo, Jim

doi:10.1145/3368089.3409670

Cited by 11 publications

(33 citation statements)

References 9 publications

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“…A significant downside for providers when shifting control to clients is the risk of overly complex queries, which are expensive and lead to overloaded servers and/or databases. Even small GraphQL queries can yield excessively large responses [8,11]. Empirical work shows that on many public GraphQL APIs, a linear increase in query size can cause an exponential increase in result size due to the nested nature of queries [27].…”

Section: Introductionmentioning

confidence: 99%

“…However, this requires the server to offer probing facilities, which could themselves strain resources. In contrast, Cha et al propose a static query cost analysis that does not depend on dynamic information from the server, but only provides upper bounds on cost [8]. This approach has been incorporated into IBM API Connect [14], a commercial API management product.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we propose a machine-learning (ML) solution that predicts query costs based on experience generated over multiple user-server communication sessions. Our solution extracts features from query code by combining approaches from natural-language processing, graph neural networks, as well as symbolic features including ones from static compiler analysis (such as the cost estimate in [8]). It then builds separate regressors for each set of features and combines the component models into a stacking ensemble.…”

Section: Introductionmentioning

confidence: 99%

“…To reflect the cost of the response, Cha et al introduced the type complexity [8]: the sums of the fields present in either the query or the response, weighted by a configuration associated to the type of each field. For instance, with a configuration where the weight of a scalar type is 0 and the weight of all other types is 1, the type complexity of the response in Figure 2 with 13 "licenses" and 5 "languages" is 23 (= 13 licenses + 1 repository + 1 issue connection + 2 issues + 1 language connection + 5 languages).…”

Section: Introductionmentioning

confidence: 99%

“…In our example, the length of the lists issues and languages are bounded by the argument first, as dictated by the connection model [24]. Cha et al [8] use this information to statically compute an upper bound on the response size from the query. While this upper bound is as tight as possible, it can grossly differ from the actual cost.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning GraphQL Query Cost

Mavroudeas

Baudart

Cha³

et al. 2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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GraphQL is a query language for APIs and a runtime for executing those queries, fetching the requested data from existing microservices, REST APIs, databases, or other sources. Its expressiveness and its flexibility have made it an attractive candidate for API providers in many industries, especially through the web. A major drawback to blindly servicing a client's query in GraphQL is that the cost of a query can be unexpectedly large, creating computation and resource overload for the provider, and API rate-limit overages and infrastructure overload for the client. To mitigate these drawbacks, it is necessary to efficiently estimate the cost of a query before executing it. Estimating query cost is challenging, because GraphQL queries have a nested structure, GraphQL APIs follow different design conventions, and the underlying data sources are hidden. Estimates based on worst-case static query analysis have had limited success because they tend to grossly overestimate cost. We propose a machine-learning approach to efficiently and accurately estimate the query cost. We also demonstrate the power of this approach by testing it on query-response data from publicly available commercial APIs. Our framework is efficient and predicts query costs with high accuracy, consistently outperforming the static analysis by a large margin.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning GraphQL Query Cost

Mavroudeas

Baudart

Cha³

et al. 2021

2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Self Cite

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GraphQL response data volume prediction based on Code2Vec and AutoML

Zhang,

Wei,

Feng

2024

IJWIS

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Purpose GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement. Design/methodology/approach This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model. Findings Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively. Originality/value This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

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