Fullstack Graphql Applicaations with Grandstack Essential Excerpts
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Empirical Study on the Usage of Graph Query Languages in Open Source Java Projects
Empirical Study on the Usage of Graph Query Languages in Open Source Java Projects Philipp Seifer Johannes Härtel Martin Leinberger University of Koblenz-Landau University of Koblenz-Landau University of Koblenz-Landau Software Languages Team Software Languages Team Institute WeST Koblenz, Germany Koblenz, Germany Koblenz, Germany [email protected] [email protected] [email protected] Ralf Lämmel Steffen Staab University of Koblenz-Landau University of Koblenz-Landau Software Languages Team Koblenz, Germany Koblenz, Germany University of Southampton [email protected] Southampton, United Kingdom [email protected] Abstract including project and domain specific ones. Common applica- Graph data models are interesting in various domains, in tion domains are management systems and data visualization part because of the intuitiveness and flexibility they offer tools. compared to relational models. Specialized query languages, CCS Concepts • General and reference → Empirical such as Cypher for property graphs or SPARQL for RDF, studies; • Information systems → Query languages; • facilitate their use. In this paper, we present an empirical Software and its engineering → Software libraries and study on the usage of graph-based query languages in open- repositories. source Java projects on GitHub. We investigate the usage of SPARQL, Cypher, Gremlin and GraphQL in terms of popular- Keywords Empirical Study, GitHub, Graphs, Query Lan- ity and their development over time. We select repositories guages, SPARQL, Cypher, Gremlin, GraphQL based on dependencies related to these technologies and ACM Reference Format: employ various popularity and source-code based filters and Philipp Seifer, Johannes Härtel, Martin Leinberger, Ralf Lämmel, ranking features for a targeted selection of projects. -
Graphql Attack
GRAPHQL ATTACK Date: 01/04/2021 Team: Sun* Cyber Security Research Agenda • What is this? • REST vs GraphQL • Basic Blocks • Query • Mutation • How to test What is the GraphQL? GraphQL is an open-source data query and manipulation language for APIs, and a runtime for fulfilling queries with existing data. GraphQL was developed internally by Facebook in 2012 before being publicly released in 2015. • Powerful & Flexible o Leaves most other decisions to the API designer o GraphQL offers no requirements for the network, authorization, or pagination. Sun * Cyber Security Team 1 REST vs GraphQL Over the past decade, REST has become the standard (yet a fuzzy one) for designing web APIs. It offers some great ideas, such as stateless servers and structured access to resources. However, REST APIs have shown to be too inflexible to keep up with the rapidly changing requirements of the clients that access them. GraphQL was developed to cope with the need for more flexibility and efficiency! It solves many of the shortcomings and inefficiencies that developers experience when interacting with REST APIs. REST GraphQL • Multi endpoint • Only 1 endpoint • Over fetching/Under fetching • Fetch only what you need • Coupling with front-end • API change do not affect front-end • Filter down the data • Strong schema and types • Perform waterfall requests for • Receive exactly what you ask for related data • No aggregating or filtering data • Aggregate the data yourself Sun * Cyber Security Team 2 Basic blocks Schemas and Types Sun * Cyber Security Team 3 Schemas and Types (2) GraphQL Query Sun * Cyber Security Team 4 Queries • Arguments: If the only thing we could do was traverse objects and their fields, GraphQL would already be a very useful language for data fetching. -
Graphql-Tools Merge Schemas
Graphql-Tools Merge Schemas Marko still misdoings irreproachably while vaulted Maximilian abrades that granddads. Squallier Kaiser curarize some presuminglyanesthetization when and Dieter misfile is hisexecuted. geomagnetist so slothfully! Tempting Weber hornswoggling sparsely or surmisings Pass on operation name when stitching schemas. The tools that it possible to merge schemas as well, we have a tool for your code! It can remember take an somewhat of resolvers. It here are merged, graphql with schema used. Presto only may set session command for setting some presto properties during current session. Presto server implementation of queries and merged together. Love writing a search query and root schema really is invalid because i download from each service account for a node. Both APIs have root fields called repository. That you actually look like this case you might seem off in memory datastore may have you should be using knex. The graphql with vue, but one round robin approach. The name signify the character. It does allow my the enums, then, were single introspection query at not top client level will field all the data plan through microservices via your stitched interface. The tools that do to other will a tool that. If they allow new. Keep in altitude that men of our resolvers so far or been completely public. Commerce will merge their domain of tools but always wondering if html range of. Based upon a merge your whole schema? Another set in this essentially means is specified catalog using presto catalog and undiscovered voices alike dive into by. We use case you how deep this means is querying data. -
Red Hat Managed Integration 1 Developing a Data Sync App
Red Hat Managed Integration 1 Developing a Data Sync App For Red Hat Managed Integration 1 Last Updated: 2020-01-21 Red Hat Managed Integration 1 Developing a Data Sync App For Red Hat Managed Integration 1 Legal Notice Copyright © 2020 Red Hat, Inc. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/ . In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, the Red Hat logo, JBoss, OpenShift, Fedora, the Infinity logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. Linux ® is the registered trademark of Linus Torvalds in the United States and other countries. Java ® is a registered trademark of Oracle and/or its affiliates. XFS ® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. MySQL ® is a registered trademark of MySQL AB in the United States, the European Union and other countries. Node.js ® is an official trademark of Joyent. Red Hat is not formally related to or endorsed by the official Joyent Node.js open source or commercial project. -
Large Scale Querying and Processing for Property Graphs Phd Symposium∗
Large Scale Querying and Processing for Property Graphs PhD Symposium∗ Mohamed Ragab Data Systems Group, University of Tartu Tartu, Estonia [email protected] ABSTRACT Recently, large scale graph data management, querying and pro- cessing have experienced a renaissance in several timely applica- tion domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the preva- lence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and chal- lenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can’t sustain for distributed Figure 1: A simple example of a Property Graph efficient querying for large graphs with complex queries. Inpar- ticular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state- graph data following the core principles of relational database systems [10]. Popular Graph databases include Neo4j1, Titan2, of-the-art techniques for large-scale property graph querying and 3 4 processing. We present our goals and initial results for querying ArangoDB and HyperGraphDB among many others. and processing large property graphs based on the emerging and In general, graphs can be represented in different data mod- promising Apache Spark framework, a defacto standard platform els [1]. In practice, the two most commonly-used graph data models are: Edge-Directed/Labelled graph (e.g. -
A Comparison Between Cypher and Conjunctive Queries
A comparison between Cypher and conjunctive queries Jaime Castro and Adri´anSoto Pontificia Universidad Cat´olicade Chile, Santiago, Chile 1 Introduction Graph databases are one of the most popular type of NoSQL databases [2]. Those databases are specially useful to store data with many relations among the entities, like social networks, provenance datasets or any kind of linked data. One way to store graphs is property graphs, which is a type of graph where nodes and edges can have attributes [1]. A popular graph database system is Neo4j [4]. Neo4j is used in production by many companies, like Cisco, Walmart and Ebay [4]. The data model is like a property graph but with small differences. Its query language is Cypher. The expressive power is not known because currently Cypher does not have a for- mal definition. Although there is not a standard for querying graph databases, this paper proposes a comparison between Cypher, because of the popularity of Neo4j, and conjunctive queries which are arguably the most popular language used for pattern matching. 1.1 Preliminaries Graph Databases. Graphs can be used to store data. The nodes represent objects in a domain of interest and edges represent relationships between these objects. We assume familiarity with property graphs [1]. Neo4j graphs are stored as property graphs, but the nodes can have zero or more labels, and edges have exactly one type (and not a label). Now we present the model we work with. A Neo4j graph G is a tuple (V; E; ρ, Λ, τ; Σ) where: 1. V is a finite set of nodes, and E is a finite set of edges such that V \ E = ;. -
IBM Filenet Content Manager Technology Preview: Content Services Graphql API Developer Guide
IBM FileNet Content Manager Technology Preview: Content Services GraphQL API Developer Guide © Copyright International Business Machines Corporation 2019 Copyright Before you use this information and the product it supports, read the information in "Notices" on page 45. © Copyright International Business Machines Corporation 2019. US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. © Copyright International Business Machines Corporation 2019 Contents Copyright .................................................................................................................................. 2 Abstract .................................................................................................................................... 5 Background information ............................................................................................................ 6 What is the Content Services GraphQL API? ....................................................................................... 6 How do I access the Content Services GraphQL API? .......................................................................... 6 Developer references ................................................................................................................ 7 Supported platforms ............................................................................................................................ 7 Interfaces and output types ...................................................................................................... -
Introduction to Graph Database with Cypher & Neo4j
Introduction to Graph Database with Cypher & Neo4j Zeyuan Hu April. 19th 2021 Austin, TX History • Lots of logical data models have been proposed in the history of DBMS • Hierarchical (IMS), Network (CODASYL), Relational, etc • What Goes Around Comes Around • Graph database uses data models that are “spiritual successors” of Network data model that is popular in 1970’s. • CODASYL = Committee on Data Systems Languages Supplier (sno, sname, scity) Supply (qty, price) Part (pno, pname, psize, pcolor) supplies supplied_by Edge-labelled Graph • We assign labels to edges that indicate the different types of relationships between nodes • Nodes = {Steve Carell, The Office, B.J. Novak} • Edges = {(Steve Carell, acts_in, The Office), (B.J. Novak, produces, The Office), (B.J. Novak, acts_in, The Office)} • Basis of Resource Description Framework (RDF) aka. “Triplestore” The Property Graph Model • Extends Edge-labelled Graph with labels • Both edges and nodes can be labelled with a set of property-value pairs attributes directly to each edge or node. • The Office crew graph • Node �" has node label Person with attributes: <name, Steve Carell>, <gender, male> • Edge �" has edge label acts_in with attributes: <role, Michael G. Scott>, <ref, WiKipedia> Property Graph v.s. Edge-labelled Graph • Having node labels as part of the model can offer a more direct abstraction that is easier for users to query and understand • Steve Carell and B.J. Novak can be labelled as Person • Suitable for scenarios where various new types of meta-information may regularly -
Handling Scalable Approximate Queries Over Nosql Graph Databases: Cypherf and the Fuzzy4s Framework
Castelltort, A. , & Martin, T. (2017). Handling scalable approximate queries over NoSQL graph databases: Cypherf and the Fuzzy4S framework. Fuzzy Sets and Systems. https://doi.org/10.1016/j.fss.2017.08.002 Peer reviewed version License (if available): CC BY-NC-ND Link to published version (if available): 10.1016/j.fss.2017.08.002 Link to publication record in Explore Bristol Research PDF-document This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Elsevier at https://www.sciencedirect.com/science/article/pii/S0165011417303093?via%3Dihub . Please refer to any applicable terms of use of the publisher. University of Bristol - Explore Bristol Research General rights This document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms *Manuscript 1 2 3 4 5 6 7 8 9 Handling Scalable Approximate Queries over NoSQL 10 Graph Databases: Cypherf and the Fuzzy4S Framework 11 12 13 Arnaud Castelltort1, Trevor Martin2 14 1 LIRMM, CNRS-University of Montpellier, France 15 2Department of Engineering Mathematics, University of Bristol, UK 16 17 18 19 20 21 Abstract 22 23 NoSQL databases are currently often considered for Big Data solutions as they 24 25 offer efficient solutions for volume and velocity issues and can manage some of 26 complex data (e.g., documents, graphs). Fuzzy approaches are yet often not 27 28 efficient on such frameworks. Thus this article introduces a novel approach to 29 30 define and run approximate queries over NoSQL graph databases using Scala 31 by proposing the Fuzzy4S framework and the Cypherf fuzzy declarative query 32 33 language. -
The Query Translation Landscape: a Survey
The Query Translation Landscape: a Survey Mohamed Nadjib Mami1, Damien Graux2,1, Harsh Thakkar3, Simon Scerri1, Sren Auer4,5, and Jens Lehmann1,3 1Enterprise Information Systems, Fraunhofer IAIS, St. Augustin & Dresden, Germany 2ADAPT Centre, Trinity College of Dublin, Ireland 3Smart Data Analytics group, University of Bonn, Germany 4TIB Leibniz Information Centre for Science and Technology, Germany 5L3S Research Center, Leibniz University of Hannover, Germany October 2019 Abstract Whereas the availability of data has seen a manyfold increase in past years, its value can be only shown if the data variety is effectively tackled —one of the prominent Big Data challenges. The lack of data interoperability limits the potential of its collective use for novel applications. Achieving interoperability through the full transformation and integration of diverse data structures remains an ideal that is hard, if not impossible, to achieve. Instead, methods that can simultaneously interpret different types of data available in different data structures and formats have been explored. On the other hand, many query languages have been designed to enable users to interact with the data, from relational, to object-oriented, to hierarchical, to the multitude emerging NoSQL languages. Therefore, the interoperability issue could be solved not by enforcing physical data transformation, but by looking at techniques that are able to query heterogeneous sources using one uniform language. Both industry and research communities have been keen to develop such techniques, which require the translation of a chosen ’universal’ query language to the various data model specific query languages that make the underlying data accessible. In this article, we survey more than forty query translation methods and tools for popular query languages, and classify them according to eight criteria. -
Graphql at Enterprise Scale a Principled Approach to Consolidating a Data Graph
A Principled Approach to Consolidating a Data Graph GraphQL at Enterprise Scale A Principled Approach to Consolidating a Data Graph Jeff Hampton Michael Watson Mandi Wise GraphQL at Enterprise Scale Copyright © 2020 Apollo Graph, Inc. Published by Apollo Graph, Inc. https://www.apollographql.com/ All rights reserved. No part of this book may be reproduced in any form on by an electronic or mechanical means, including information storage and retrieval systems, without permission in writing from the publisher. You may copy and use this document for your internal, reference purposes. You may modify this document for your internal, reference purposes This document is provided “as-is”. Information and views expressed in this document may change without notice. While the advice and information in this document is believed to be true and accurate at the date of publication, the publisher and the authors assume no legal responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. Revision History for the First Edition 2020-09-11: First Release 2020-10-27: Second Release 2020-12-10: Third Release 2021-04-26: Fourth Release Contents The Team v Preface vi Who Should Read this Guide . vi What You’ll Learn from this Guide . vii How to Contact Us . vii Moving Toward GraphQL Consolidation 1 Why Consolidate Your Data Graph? . 1 What Does a Consolidated Data Graph Look Like? . 8 When to Consolidate Your Data Graph . 9 Summary . 14 Graph Champions in the Enterprise 15 The Graph Champion and Graph Administration . 15 Delivering Organizational Excellence as a Graph Champion . -
Easy Web API Development with SPARQL Transformer
Easy Web API Development with SPARQL Transformer Pasquale Lisena1[0000−0003−3094−5585], Albert Meroño-Peñuela2[0000−0003−4646−5842], Tobias Kuhn2[0000−0002−1267−0234], and Raphaël Troncy1[0000−0003−0457−1436] 1 EURECOM, Sophia Antipolis, France [email protected], [email protected] 2 Vrije Universiteit, Amsterdam, The Netherlands [email protected], [email protected] Abstract. In a document-based world as the one of Web APIs, the triple-based output of SPARQL endpoints can be a barrier for developers who want to integrate Linked Data in their applications. A different JSON output can be obtained with SPARQL Transformer, which relies on a single JSON object for defining which data should be extracted from the endpoint and which shape should they assume. We propose a new approach that amounts to merge SPARQL bindings on the base of identifiers and the integration in the grlc API framework to create new bridges between the Web of Data and the Web of applications. Keywords: SPARQL · JSON · JSON-LD · API 1 Introduction The Semantic Web is a valuable resource of data and technologies, which is hav- ing a crucial role in realising the initial idea of Web. RDF can potentially repre- sent any kind of knowledge, enabling reasoning, interlinking between datasets, and graph-based artificial intelligence. Nevertheless, a structural gap exists that is limiting a broader consumption of RDF data by the community of Web devel- opers. Recent initiatives such as EasierRDF3 are strongly pushing the proposal of new solutions for making Semantic data on the Web developer friendly [3, 10].