diff --git a/ChangeHistory.md b/ChangeHistory.md index 555c134f..d088b239 100755 --- a/ChangeHistory.md +++ b/ChangeHistory.md @@ -2,6 +2,38 @@ Starting with the v275 release, this file will contain a record of the major changes and updates made in each release. +## Future Release 279 change history (Date TBD) + +Here is a link to the [Git diffs](https://github.com/krlawrence/graph/compare/V278-3.3.1...master) between this release and the prior one. + +The examples in this release have been tested against Apache TinkerPop at the 3.3.1, 3.3.2 and at the newly released 3.3.3 level. They have also been tested against JanusGraph 0.2 (where appropriate) which supports TinkerPop at the 3.2.6 level. + +Major new features in this release +- Significantly expanded and improved the `subgraph` section. +- Added examples to the `where` `by` section. +- Added more examples of `select` keywords `last`, `first` and `mixed`. +- Added to the section that discusses the `union` step. +- Improved the section dedicated to the `match` step. +- Improved coverage of text comparison predicates. +- Improved the `Working with IDs` section. +- Added examples to the `introducing Path` section. +- Added a new section covering use of `withRemote` with a Gremlin Server. +- Expanded coverage of custom ID values. +- Added a new section showing an example of a `path` step used inside a `groupCount` step. +- Added a new section showing how to deduce a graph's schema. +- Added more examples to Chapter 5. +- Corrected output in Section 3.3.1 to reflect correct version of air-routes. Issue #87. +- Expanded coverage of the results from a `Gremlin Server`. +- Sample code and data improvements + - Added new script `regression.groovy` to check that the book examples work with the most recent Tinkerpop versions. + - Added new sample `add-aircraft.Groovy`. + - Added new sample data `aircraft.csv`. + - Added new sample `RemoteClient.java` + - Added new sample `ListAirports.java`. + - Expanded `CreateGraph.java` sample. + - Moved `edges.csv` to `sample-data` folder. + - Provided updated versions of the air-routes data and demo apps. +- Miscellaneous issues resolved and other more minor corrections. ## Release 278 change history (March 28th 2018) diff --git a/README.md b/README.md index 2480578c..ae8992f9 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ This repository is the new home for the source materials, sample code and exampl To read the latest snapshot of the book right away in a browser (HTML format) click [here](http://kelvinlawrence.net/book/Gremlin-Graph-Guide.html) or for a PDF version click [here](http://kelvinlawrence.net/book/Gremlin-Graph-Guide.pdf). You will find other formats including MOBI, EPUB and XML (Docbook) in the [releases](https://github.com/krlawrence/graph/releases) section. Formal releases will be published about once a month assuming there is enough new material. If you want to see the absolute latest updates you can always browse the Asciidoc source file (Gremlin-Graph-Guide.adoc) in the /book folder. The PDF version is currently the "official" version, simply meaning it looks the best! **LATEST NEWS:** -[Feb-11-2018] Revision 277 was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. +[Mar-28-2018] Revision 278 (TP 3.3.1) was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. ## Releases and change history @@ -107,7 +107,8 @@ Chapter 8 - FURTHER READING - We finish up by providing several links to useful web sites where you can find tools and documentation for many of the technologies covered in this document. -**NEWS ARCHIVE:** +**NEWS ARCHIVE:** +[Feb-11-2018] Revision 277 was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. [Jan-12-2018] Revision 276 was just published in all formats. Many updates to book and samples. [Jan-12-2018] Based on feedback I have decided to rename the book "Practical Gremlin" (see issue #29) [Jan-06-2018] Several new Java samples have been added to the sample-code directory and others improved. diff --git a/book/Gremlin-Graph-Guide.adoc b/book/Gremlin-Graph-Guide.adoc index 08888b2a..d983567d 100755 --- a/book/Gremlin-Graph-Guide.adoc +++ b/book/Gremlin-Graph-Guide.adoc @@ -1,12 +1,11 @@ PRACTICAL GREMLIN: An Apache TinkerPop Tutorial =============================================== Kelvin R. Lawrence -//v278-preview, Mar 28, 2018 -v278 (TP 3.3.1), Mar 28, 2018 -// Wed Mar 28, 2018 09:06:24 CDT +//v279-preview, May 28, 2018 +v279 (TP 3.3.3), May 29, 2018 +// Mon May 28, 2018 12:37:16 CDT //:Author: Kelvin R. Lawrence //:Email: gfxman@yahoo.com -//:Date: Mar 28 2018 :Numbered: :source-highlighter: pygments :pygments-style: paraiso-dark @@ -19,11 +18,13 @@ v278 (TP 3.3.1), Mar 28, 2018 //:pygments-style: vim :toc: :toclevels: 4 -//:revision: 278-preview -:revision: 278 (TP 3.3.1) +:revision: 279 +//:revision: 278 (TP 3.3.1) :doctype: book :icons: font //:pdf-page-size: Letter +:draftdate: May 29th 2018 +:tpvercheck: 3.3.3 // NOTE1: I updated the paraiso-dark style so that source code with a style of text // has a white foreground color. The default was unreadable, @@ -104,7 +105,7 @@ remains a lot of fun. Kelvin R. Lawrence + First draft: October 5th, 2017 + -Current draft: March 28th, 2018 + +Current draft: {draftdate} + Providing feedback ~~~~~~~~~~~~~~~~~~ @@ -142,7 +143,7 @@ What is this book about? ~~~~~~~~~~~~~~~~~~~~~~~~ This book introduces the Apache TinkerPop 3 'Gremlin' graph query and traversal -language via real examples featuring real world graph data. That data along with +language via real examples featuring real-world graph data. That data along with sample code and example applications is available for download from the GitHub project as well as many other items. The graph, 'air-routes.graphml', is a model of the world airline route network between 3,373 airports including 43,400 routes. The @@ -151,9 +152,7 @@ the Gremlin console running with a TinkerGraph. How to set that environment up i covered in the <> section below. NOTE: The examples in this book have now been updated and tested using Apache -TinkerPop version 3.3 which introduced a few breaking changes. If you find any I -missed please let me know! The examples have also been tested using the 3.3.1 -release. +TinkerPop release {tpvercheck}. TinkerGraph is an 'in-memory' graph, meaning nothing gets saved to disk automatically. It is shipped as part of the Apache TinkerPop 3 download. The @@ -197,7 +196,7 @@ Chapter 4 - BEYOND BASIC QUERIES:: some slightly more advanced topics in this chapter. Chapter 5 - MISCELLANEOUS QUERIES AND THE RESULTS THEY GENERATE:: - In Chapter five I focus on using what has been covered in the prior Chapters to write - queries that have a more real world feel. I present a lot more examples of the + queries that have a more real-world feel. I present a lot more examples of the output from running queries in this Chapter. I also start to discuss topics such as analyzing distances, route distribution and writing geospatial queries. Chapter 6 - MOVING BEYOND THE CONSOLE AND TINKERGRAPH:: @@ -222,11 +221,11 @@ Introducing the book sources, sample programs and data All work related to this project is being done in the open at GitHub. A list of where to find the key components is provided below. The examples in this book make use of a -sample graph called 'air-routes.graphml' which contains a graph based on the World +sample graph called 'air-routes.graphml' which contains a graph based on the world airline route network between over 3,370 airports. The sample graph data, quite a bit of sample code and some larger demo applications can all be found at the same GitHub location that hosts the book manuscript. You will also find releases of the -the book in various formats (HTML,PDF, DocBook/XML, MOBI and EPUB) at the same GitHub +the book in various formats (HTML, PDF, DocBook/XML, MOBI and EPUB) at the same GitHub location. The sample programs include stand alone Java, Groovy and Ruby examples as well as many examples that can be run from the Gremlin Console. There are some differences between using Gremlin from a stand alone program and from the Gremlin @@ -288,7 +287,7 @@ This book is mainly intended to be a tutorial in working with graph databases an related technology using the Gremlin query language. However, it is worth spending just a few moments to summarize why it is important to understand what a graph database is, what some good use cases for graphs are and why you should care in a -World that is already full of all kinds of SQL and NoSQL databases. In this book +world that is already full of all kinds of SQL and NoSQL databases. In this book we are going to be discussing 'directed property graphs'. At the conceptual level these types of graphs are quite simple to understand. You have three basic building blocks. Vertices (often referred to as nodes), edges and properties. Vertices @@ -345,10 +344,10 @@ very nature, the air routes network used as the basis for all of the examples in this book being a perfect example of such a situation. Those of you that looked at graphs as part of a computer science course are correct -if your reaction was "haven't graphs been around for ages?" Indeed, Leonard Euler is -credited with demonstrating the first graph problem and inventing the whole concept -of "Graph Theory" all the way back in 1763 when he investigated the now famous -"Seven Bridges of Konigsberg" problem. +if your reaction was "Surely graphs have been around for ages, why is this considered +new?". Indeed, Leonard Euler is credited with demonstrating the first graph problem +and inventing the whole concept of "Graph Theory" all the way back in 1763 when he +investigated the now famous "Seven Bridges of Koenigsberg" problem. If you want to read a bit more about graph theory and its present-day application, you can find a lot of good information online. Here's a Wikipedia link to get you @@ -463,7 +462,7 @@ Any such TinkerPop enabled graph databases can be accessed using the Gremlin que language and corresponding API. We can also use the TinkerPop API to write client code in languages like Java that can talk to a TinkerPop enabled graph. For most of this book we will be working within the Gremlin console with a local graph. However -in Section 6 we will take a look at Gremlin Server and some other TinkerPop 3 enabled +in Chapter 6 we will take a look at Gremlin Server and some other TinkerPop 3 enabled environments. Most of Apache Tinkerpop has been developed using Java 8 but there are also bindings available for many other programming languages such as Groovy and Python. Parts of TinkerPop are themselves developed in Groovy, most notably the @@ -836,16 +835,16 @@ Introducing the air-routes graph ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Along with this book I have provided what is, in big data terms,a very small, but -nonetheless real World, graph that is written in GraphML, a standard XML format for +nonetheless real-world graph that is written in GraphML, a standard XML format for describing graphs that can be used to move graphs between applications. The graph, -'air-routes.graphml' is a model I built of the World airline route network that is +'air-routes.graphml' is a model I built of the world airline route network that is fairly accurate. NOTE: The 'air-routes.graphml' file can be downloded from the 'sample-data' folder located in the GitHub repository at the following URL: https://github.com/krlawrence/graph/tree/master/sample-data -Of course, in the real World, routes are added and deleted by airlines all the time +Of course, in the real world, routes are added and deleted by airlines all the time so please don't use this graph to plan your next vacation or business trip! However, as a learning tool I hope you will find it useful and easy to relate to. If you feel so inclined you can load the file into a text editor and examine how it is laid out. @@ -854,7 +853,7 @@ serialization formats. Two common ones are GraphML and GraphSON. The latter is JSON format that is defined by Apache TinkerPop and heavily used in that environment. GraphML is very widely recognized by TinkerPop and many other tools as well such as Gephi, a popular open source tool for visualizing graph data. A lot of graph -ingestion algorithms also sill use comma separated values (CSV) format files. +ingestion algorithms also still use comma separated values (CSV) format files. We will briefly look at loading and saving graph data in Sections 2 and 4. We take a much deeper look at different ways to work with graph data stored in text format @@ -1114,11 +1113,12 @@ commands shown below, available in the '/sample-data' directory. https://github.com/krlawrence/graph/tree/master/sample-data These commands create an in-memory TinkerGraph which will use LONG values for the -vertex and edge IDs. TinkerPop 3 introduced the concept of a 'traversal' so as part -of loading a 'graph' we also setup a graph traversal source called 'g' which we will -then refer to in our subsequent queries of the graph. The 'max-iteration' option -tells the Gremlin console the maximum number of lines of output that we ever want to -see in return from a query. The default, if this is not specified, is 100. +vertex, edge and vertex property IDs. TinkerPop 3 introduced the concept of a +'traversal' so as part of loading a 'graph' we also setup a graph traversal source +object called 'g' which we will then refer to in our subsequent queries of the graph. +The 'max-iteration' option tells the Gremlin console the maximum number of lines of +output that we ever want to see in return from a query. The default, if this is not +specified, is 100. TIP: You can use the 'max-iteration' setting to control how much output the Gremlin Console displays. @@ -1134,6 +1134,7 @@ with minor modifications. conf = new BaseConfiguration() conf.setProperty("gremlin.tinkergraph.vertexIdManager","LONG") conf.setProperty("gremlin.tinkergraph.edgeIdManager","LONG") +conf.setProperty("gremlin.tinkergraph.vertexPropertyIdManager","LONG");[] graph = TinkerGraph.open(conf) graph.io(graphml()).readGraph('air-routes.graphml') g=graph.traversal() @@ -1212,15 +1213,15 @@ for cases where a particular item in a graph is not backed by an index. One exam of this is vertex and edge 'labels' which are typically not indexed but are sometimes very useful items to specify at the start of a query. As most of the examples in this book are intended to work just fine with only a basic TinkerGraph the subject of indexes -is not covered in detail until Section 6 "<>" . However, as TinkerGraph does +is not covered in detail until Chapter 6 "<>" . However, as TinkerGraph does have some indexing capability I have also included some discussion of it in the -"<>" section. In Section 6 where I start to look at additional +"<>" section. In Chapter 6 where I start to look at additional technologies such as JanusGraph I have included a more in depth discussion of indexing as part of that coverage. You should always refer to the specific documentation for the graph system you are using to decide what you need to do about creating an index and schema for your graph. We will explain what TinkerGraph is in the next section. I won't be discussing the creation of an explicit schema again until -Section 6. When working with TinkerGraph there is no need to define a schema ahead of +Chapter 6. When working with TinkerGraph there is no need to define a schema ahead of time. The types of each property are derived at creation time. This is a really convenient feature and allows us to get productive and do some experimenting really quickly. @@ -1448,7 +1449,7 @@ You can refer to the official Apache TinkerPop documentation for full details on of the graph traversal steps that are used in this tutorial. With this tutorial I have not tried to teach every possible usage of every Gremlin step and method, rather, I have tried to provide a good and approachable foundation in writing many -different types of Gremlin query using an interesting and real world graph. +different types of Gremlin query using an interesting and real-world graph. NOTE: The latest TinkerPop 3 documentation is always available at this URL: http://tinkerpop.apache.org/docs/current/reference/ @@ -1815,10 +1816,8 @@ connected to each other. |otherV | The vertex that was not the vertex we came from. |============================================================================== -Note that the steps labelled with an '*' can optionally take the -name of an edge label as a parameter. If omitted, all relevant edges will be -traversed. - +Note that the steps labelled with an '*' can optionally take the name of one or more +edge labels as a parameter. If omitted, all relevant edges will be traversed. Some simple graph traversal examples ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -1997,6 +1996,37 @@ representing the number of runways the second airport has. [LCY,BHD,1] ---- +It is also possible to use a traversal inside of a 'by' modulator. This allows us to +do things like combine multiple values together as part of a path result. The example +below finds five routes that start in Austin and creates a path result containing the +airport code and city name for both the source and destination airports. + +[source,groovy] +---- +g.V(3).out().limit(5).path().by(values('code','city').fold()) + +[[AUS,Austin],[YYZ,Toronto]] +[[AUS,Austin],[LHR,London]] +[[AUS,Austin],[FRA,Frankfurt]] +[[AUS,Austin],[MEX,Mexico City]] +[[AUS,Austin],[PIT,Pittsburgh]] +---- + +To demonstrate that just about any arbitrary traversal can be placed inside the 'by' +modulator here is one more example that counts the number of outgoing routes for the +source and destination airports as part of generating the 'path' result. + +[source,groovy] +---- +g.V(3).out().limit(5).path().by(out().count().fold()) + +[[59],[181]] +[[59],[191]] +[[59],[272]] +[[59],[105]] +[[59],[54]] +---- + In Apache TinkerPop version 3.2.5 the ability to limit what is returned by the 'path' step using 'from' and 'to' modulators was added. This enables us to not return the entire path of a traversal but instead to be more selective. @@ -2067,16 +2097,16 @@ As you can see the results are the same as before. [source,groovy] ---- -EWR,YYZ] -EWR,YVR] -EWR,LHR] -EWR,CDG] -EWR,FRA] -EWR,NRT] -EWR,DEL] -EWR,DUB] -EWR,HKG] -EWR,PEK] +[EWR,YYZ] +[EWR,YVR] +[EWR,LHR] +[EWR,CDG] +[EWR,FRA] +[EWR,NRT] +[EWR,DEL] +[EWR,DUB] +[EWR,HKG] +[EWR,PEK] ---- Obviously there are a lot of ways that 'from' and 'to' can be used. By way of one @@ -2197,8 +2227,8 @@ false [[aselproj]] -Using 'as', 'select' and 'project' to name and refer back to prior steps -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Using 'as', 'select' and 'project' to refer to traversal steps +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Sometimes it is useful to be able to remember a point of a traversal by giving it a name (label) and refer to it later on in the same query. This ability was more @@ -2266,7 +2296,9 @@ the 'select' and 'path' steps used a lot throughout this book. [source,groovy] ---- -g.V().has('code','DFW').out().has('region','US-CA').path().by('code') +g.V().has('code','DFW').out(). + has('region','US-CA'). + path().by('code') ---- Which would produce the following results. Notice that this time the results do not @@ -2298,7 +2330,8 @@ the traversal/query as shown below. [source,groovy] ---- g.V().has('type','airport').limit(10).as('a','b','c'). - select('a','b','c').by('code').by('region').by(out().count()) + select('a','b','c'). + by('code').by('region').by(out().count()) ---- In the most recent releases of TinkerPop you can also use the new 'project' step and @@ -2308,8 +2341,9 @@ of 'as' and 'select'. [source,groovy] ---- -g.V().has('type','airport').limit(10).project('a','b','c'). - by('code').by('region').by(out().count()) +g.V().has('type','airport').limit(10). + project('a','b','c'). + by('code').by('region').by(out().count()) ---- This query, and the prior query, would return the following results. @@ -2335,8 +2369,9 @@ descriptive variable names. [source,groovy] ---- - g.V().has('type','airport').limit(10).project('IATA','Region','Routes'). - by('code').by('region').by(out().count()) + g.V().has('type','airport').limit(10). + project('IATA','Region','Routes'). + by('code').by('region').by(out().count()) ---- When we run the modified query, here is the output we get. @@ -2355,9 +2390,14 @@ When we run the modified query, here is the output we get. [IATA:IAD,Region:US-VA,Routes:136] ---- +[[multias]] +Using multiple 'as' steps with the same label +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + It is actually possible using an 'as' step to give more than one part of a traversal -the same label. In the example below, the label '+++'+++a+++'+++' is used twice but -you will notice that when the label is selected only the last item added is returned. +the same label (name). In the example below, the label '+++'+++a+++'+++' is used +twice but you will notice that when the label is selected only the last item added is +returned. [source,groovy] ---- @@ -2385,8 +2425,49 @@ g.V(1).as('a').V(2).as('a').select(all,'a') [v[1],v[2]] ---- -Keywords such as these are documented in the "<>" section later on in -the book. +Here is another example of a query that labels two different parts of a traversal +with the same '+++'+++a+++'+++' label. As you can see from the results, only the +second one is used because of the 'last' keyword that is provided on the 'select' +step. + +[source,groovy] +---- +g.V().has('code','AUS').as('a'). + out().as('a').limit(10). + select(last,'a').by('code').fold() + +[YYZ,LHR,FRA,MEX,PIT,PDX,CLT,CUN,MEM,CVG] +---- + +Here is the same query but using the 'first' keyword this time as part of the +'select' step. + +[source,groovy] +---- +g.V().has('code','AUS').as('a'). + out().as('a').limit(10). + select(first,'a').by('code').fold() + +[AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS] +---- + +Note that when the same name is used to label a step, the data structure created by +Gremlin is essentially a List. As such, the 'by' modulator cannot be used when the +'all' keyword is used on the 'select' step. To get the values of each element in the +list we can use an 'unfold' step as shown below. + +[source,groovy] +---- +g.V().has('code','AUS').as('a'). + out().as('a').limit(10). + select(all,'a').unfold().values('code').fold() + +[AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS,AUS, + YYZ,LHR,FRA,MEX,PIT,PDX,CLT,CUN,MEM,CVG] +---- + +Keywords such as 'all', 'first' and 'last' are discussed further in the +"<>" section later on in the book. [[exedge]] Examining the edge between two vertices @@ -2395,7 +2476,7 @@ Examining the edge between two vertices Sometimes, it is the edge between two vertices that we are interested in examining and not the vertices themselves. Typically this is because we want to look at one or more properties associated with that edge. By way of an example, let's imagine we -anted to know how many miles the flight is between Miami (MIA) and Dallas Fort Worth +wanted to know how many miles the flight is between Miami (MIA) and Dallas Fort Worth (DFW). In our air routes graph, the distances between vertices are stored using a property called 'dist' on any edge that has a 'route' label. We can use the 'outE' and 'inV' steps to find the edge connecting Miami and Dallas. We can also use the @@ -2782,7 +2863,7 @@ g.V().has('code','AUS').valueMap(true,'region') TIP: If you only need the keys and values for specific properties to be returned it is recommended to pass the names of those properties as parameters to the 'valueMap' step so it does not return a lot more data than you need. Think of this as the -difference, in the SQL World, between selecting just the columns you are interested +difference, in the SQL world, between selecting just the columns you are interested in from a table rather than doing a 'SELECT *'. As shown above, you can specify which properties you want returned by supplying their @@ -2861,7 +2942,7 @@ println "The AUS airport is located in " + aus['city'][0] The AUS airport is located in Austin ---- -NOTE: Because properties are stored as arrays of values. Even if there is only one +NOTE: Properties are stored as arrays of values. Even if there is only one property value for the given key, we still have to add the '[0]' when referencing it otherwise the whole array will be returned if we just used 'aus[+++'city'+++]'. We will explore why property values are stored in this way in the "<>" @@ -3046,21 +3127,23 @@ println s Working with IDs ~~~~~~~~~~~~~~~~ -Every vertex and every edge in a graph has a unique ID that can be used to reference -them individually or as a group. Beware that the IDs you provide when loading a graph -from a GraphML or GraphSON file may not in many cases end up being the IDs that the -back-end graph store actually uses as it builds up your graph. Tinkergraph for -example will preserve user provided IDs but most systems like JanusGraph or -IBM-Graph generate their own IDs. The same is true when you add vertices and edges -using a graph traversal or using the TinkerPop API. This is a long winded way of -saying that you should not depend on the IDs in your GraphML or GraphSON file that -you just loaded remaining unchanged once the data has been loaded into the graph -store. When you add new a vertex or edge to your graph, the graph system will -automatically generate a new, unique ID for it. If you need to figure out the ID for -a vertex or an edge you can always get it from a query of the graph itself! +Every vertex, every edge and even every property in a graph has a unique ID that can +be used to reference it individually or as part of a group. Beware that the IDs you +provide when loading a graph from a GraphML or GraphSON file may not in many cases +end up being the IDs that the back-end graph store actually uses as it builds up your +graph. Tinkergraph for example will preserve user provided IDs but many graph +databases such as JanusGraph generate their own IDs. The same is true when you add +vertices and edges using a graph traversal or using the TinkerPop API. This is a long +winded way of saying that you should not depend on the IDs in your GraphML or +GraphSON file that you just loaded remaining unchanged once the data has been loaded +into the graph store. When you add a new vertex or edge to your graph using a +traversal, the graph system will automatically generate a new, unique ID for it. If +you need to figure out the ID for a vertex or an edge you can always get it from a +query of the graph itself. TIP: Don't rely on the graph preserving the ID values you provide. Write code that -can query the graph itself for ID values. +can query the graph itself for ID values. How IDs are managed will be graph database +implementation dependent. Especially when dealing with large graphs, because using IDs is typically very efficient, you will find that many of your queries will involve collecting one or @@ -3142,8 +3225,13 @@ a=[3,6,8,15] g.V(a).values('code') ---- + +If the graph database that you are using supports it you can set the ID of a +vertex at the time you create it. How that can be done is explained in the +"<>" section. + Every property in the graph also has an ID as we shall explore in the "<>" -section. +section a bit later on. [[lab]] @@ -3195,7 +3283,7 @@ g.V().where(label().is(eq('airport'))).count() // Or using the label key word g.V().has(label,'airport').count() -// But you would perhaps use the hasLabel() method in this case instead +// But you would perhaps use the hasLabel() method in this case instead g.V().hasLabel('airport').count() // How many non airport vertices are there? @@ -3228,6 +3316,15 @@ vertices that have the 'airport' label for a code of "'SYD'". g.V().has('airport','code','SYD') ---- +It is also possible to specify more than one label in the same step as shown below. +In general, whenever a step can be provided a label, more than one may also be +provided. + +[source,groovy] +---- +g.E().hasLabel('route','contains') +---- + [[local]] Using the 'local' step to make sure we get the result we intended ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -3260,7 +3357,7 @@ is essentially the same as dividing the count by one. So how do we fix this? The answer is to use the 'local' step. What we really want to do is to create, in essence, a collection of values, where each value is the route count for just one airport. Having done that, we want to divide the sum of all of -these numbers by the number of members ,airports in this case, int the collection. +these numbers by the number of members, airports in this case, into the collection. Take a look at the modified query below. @@ -3421,11 +3518,11 @@ Testing values and ranges of values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ We have already seen some ways of testing whether a value is within a certain range. -Gremlin provides a number of different steps that we can use to do range testing. The -list below provides a summary of the available steps. We will see each of these in use -throughout this book. +Gremlin provides a number of different predicates that we can use to do range +testing. The list below provides a summary of the available predicates. We will see +each of these in use throughout this book. -.Steps that test values or ranges of values +.Predicates that test values or ranges of values [cols="^1,4"] |============================================================================== |eq | Equal to @@ -3605,7 +3702,7 @@ via Phoenix (PHX) or Los Angeles (LAX). Lastly this query uses both within and without to modify the previous query to just airports within the United States or Canada as Austin now has a direct flight to -London in England we probably don't want to go that way if we are headed to Vegas! +London in England. We probably don't want to go that way if we are headed to Vegas! [source,groovy] ---- @@ -3632,6 +3729,100 @@ g.V().hasId(within([1,2,3])) You will find more examples of these types of queries in the next section. +One thing that may not be obvious is that when using string values with the 'between' +predicate the values do not have to specify exact matches. Take a look at the query +below. This will find any airports in cities whose names start with '"Dal"' as it +looks for strings between '"Dal"' and '"Dam"' in an inclusive/exclusive fashion. The rest +of the characters following '"Dal"' in the strings being tested are ignored. Note +that this is a case sensitive comparison. In other words '"Dal"' and '"dal"' are +different strings in this context. + +TIP: The 'between' predicate can be used to simulate a string 'startsWith' method. + +As discussed more in the "<>" section, Gremlin does not currently support +any methods for applying regular expressions or even more basic text analysis +operators to strings. This use of the 'between' predicate can at least be used to +simulate a 'startsWith' type of operator. + +[source,groovy] +---- +g.V().hasLabel('airport'). + has('city',between('Dal','Dam')). + values('city') +---- + +Here are the results from running the query. As you can see every city name starts +with the characters '"Dal"'. + +[source,groovy] +---- +Dallas +Dallas +Dalaman +Dalian +Dalcahue +Dalat +Dalanzadgad +---- + +You will notice from the results above that '"Dallas"' appears twice as there are two +airports with that city name. We could add a 'dedup' step to our query to only return +unique matches. + +[source,groovy] +---- +g.V().hasLabel('airport'). + has('city',between('Dal','Dam')). + values('city').dedup() +---- + +Here are the modified results. + +[source,groovy] +---- +Dallas +Dalaman +Dalian +Dalcahue +Dalat +Dalanzadgad +---- + +Here is one more example where the range of values being compared is expanded a +little. This query will find any cities that start with '"Dal"' through '"Dar"'. + +[source,groovy] +---- +g.V().hasLabel('airport'). + has('city',between('Dal','Dat')). + values('city').order().dedup() +---- + +As you can see this time, more cities met our search criteria. + +[source,groovy] +---- +Dalaman +Dalanzadgad +Dalat +Dalcahue +Dalian +Dallas +Damascus +Dandong +Dangriga +Daocheng +Daqing Shi +Dar es Salaam +Daru +Darwin +---- + +While Gremlin does not currently provide any advanced text searching capabilities, +graph systems such as JanusGraph do offer such capabilities. Those features are +discussed in the "<>" section. + + [[winout]] Refining flight routes analysis using 'not', 'neq', 'within' and 'without' ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -3670,7 +3861,7 @@ g.V().has('airport','code','AUS'). out().has('code','SYD').path().by('code') ---- -We could also have written the prior query this way replacing 'and' with 'without' +We could also have written the prior query this way replacing 'and' with 'without'. This approach feels a lot cleaner and it is easy to add more airports to the 'without' test as needed. We will look more at steps like 'and' in the "<>" section that is coming up soon. @@ -3828,7 +4019,7 @@ believe that the 'sample(1)' call is doing something similar to this g.V().hasLabel('airport').coin(0.01).limit(1) ---- -Look at the code below. Even if I run that simple experiment many times always gives +Look at the code below. Even if I run that simple experiment many times it always gives results similar to these. [source,groovy] @@ -3897,7 +4088,7 @@ approach appears to be a lot more evenly distributed across all of the possible Note that this approach only works unmodified with the air-routes graph loaded into a TinkerGraph. This is because we know that the TinkerGraph implementation honors user -provided IDs and that in the air routes graph, airport IDs begin at one and are +provided IDs and that in the air-routes graph, airport IDs begin at one and are sequential with no gaps. However, you could easily modify this approach to work with other graphs without relying on knowing that the index values are sequential. For example you could extract all of the IDs into a list and then select one randomly @@ -3906,12 +4097,12 @@ from that list. It is likely that this apparent lack of randomness is more specific to TinkerGraph and the fact that it will respect user provided ID values whereas other graph systems will probably store vertices in a more random order to begin with. Indeed when I ran -these queries on Janus graph the 'sample' step did indeed yield a better selection of +these queries on Janus graph the 'sample' step did yield a better selection of airports from across the graph. If the airport IDs were not all known to be in a sequential order one after the other, we could create a list of all the airport IDs and then select one at random by -doing do something like this if we wanted to use our 'Math.random' technique. +doing something like this if we wanted to use our 'Math.random' technique. [source,groovy] ---- @@ -4150,9 +4341,9 @@ a better approach but this suffices as an example of a bigger 'or' expression. g.V().hasLabel('airport').or(has('region','US-TX'), has('region','US-LA'), has('region','US-AZ'), - has('region','US-OK')) - .order().by('region',incr) - .valueMap().select('code','region') + has('region','US-OK')). + order().by('region',incr). + valueMap().select('code','region') ---- Using 'within' the example above could be written like this so always keep in mind @@ -4477,11 +4668,15 @@ Using 'where' and 'by' to filter results A new capability was added in the Tinkerpop 3.2.4 release that allows a 'where' step to be followed with a 'by' modulator. This makes writing certain types of queries a -lot easier than it was before. The query below starts at the Austin airport and finds -all the airports that you can fly to from there. A 'where' 'by' step is then used to -filter the results to just those airports that have the same number of runways that -Austin has. What is really nice about this is that we do not have to know ahead of -time how many runways Austin itself has as that is handled for us by the query. +lot easier than it was before. Hopefully by now, this capability is supported in many +TinkerPop enabled graph stores but it is always a good idea to verify the version of +TinkerPop supported before starting to design queries. + +The query below starts at the Austin airport and finds all the airports that you can +fly to from there. A 'where' 'by' step is then used to filter the results to just +those airports that have the same number of runways that Austin has. What is really +nice about this is that we do not have to know ahead of time how many runways Austin +itself has as that is handled for us by the query. [source,groovy] ---- @@ -4543,6 +4738,54 @@ when run. [code:[SAN],runways:[1]] ---- +It is also possible to compare two different properties by adding a second 'by' +modulator. This is useful when vertex properties have different key names but may +contain the same values. The query below is definitely contrived, you could achieve +the same thing in a more simple way, but is does demonstrate two 'by' modulators +being used. The 'country' property of an airport vertex is compared with the 'code' +property of a 'country' vertex. The query first finds any airport vertex with a +'city' property containing the string 'London'. Next any connecting 'country' +vertices (ones connected by contains edges) are found. The 'where' test compares the +country code value of the two vertices. Lastly a 'select' is used to pick the +results that we want to return. + +[source,groovy] +---- +g.V().has('airport','city','London').as('a','r'). + in('contains').as('b'). + where('a',eq('b')).by('country').by('code'). + select('a','r','b').by('code').by('region') +---- + +When the query is run we get back all the airports with a city name of 'London' along +with their region code and country code. + +[source,groovy] +---- +[a:LHR,r:GB-ENG,b:UK] +[a:LGW,r:GB-ENG,b:UK] +[a:LCY,r:GB-ENG,b:UK] +[a:STN,r:GB-ENG,b:UK] +[a:LTN,r:GB-ENG,b:UK] +[a:YXU,r:CA-ON,b:CA] +---- + +As I mentioned the above query was used just as an example. In reality the following +query that does not use any 'where' steps would have sufficed in this case. + +[source,groovy] +---- +g.V().has(airport','city','London'). + valueMap('code','region','country') + +[country:[UK],code:[LHR],region:[GB-ENG]] +[country:[UK],code:[LGW],region:[GB-ENG]] +[country:[UK],code:[LCY],region:[GB-ENG]] +[country:[UK],code:[STN],region:[GB-ENG]] +[country:[UK],code:[LTN],region:[GB-ENG]] +[country:[CA],code:[YXU],region:[CA-ON]] +---- + Let's imagine that we want to write a query to find all airports that have the same region code as the French airport of Nice. Let's assume for now that we do not know what the region code is so we cannot just write a simple query to find all airports @@ -4757,10 +5000,10 @@ g.V().hasLabel('airport').limit(10).choose(values('runways')). Using 'match' to do pattern matching ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -'Match' was added in TinkerPop 3 and allows a more declarative style of pattern based -query to be expressed in Gremlin. 'Match' can be a bit hard to master but once you -figure it out it can be a very powerful way of traversing a graph looking for -specific patterns. +The 'match' step was added in TinkerPop 3 and allows a more declarative style of +pattern based query to be expressed using Gremlin. 'Match' can be a bit hard to +master but once you figure it out it can be a very powerful way of traversing a graph +looking for specific patterns. Below is an example that uses 'match' to look for airline route patterns where there is a flight from one airport to another but no return flight back to the original @@ -4772,11 +5015,15 @@ not need to stop on the way back because of the trailing wind. [source,groovy] ---- -// Find any cases of where you can fly from JFK non stop to a place you cannot get back from -// there non stop.This query should return LCY, as the return flight stops in DUB to refuel. -g.V().has('code','JFK').match(__.as('s').out().as('d'), - __.not(__.as('d').out().as('s'))). - select('s','d').by('code') +// Find any cases of where you can fly from JFK non stop +// to a place you cannot get back from non stop. This query +// should return LCY, as the return flight stops in Dublin +// to refuel. + +g.V().has('code','JFK'). + match(__.as('s').out().as('d'), + __.not(__.as('d').out().as('s'))). + select('s','d').by('code') [s:JFK,d:LCY] ---- @@ -4790,9 +5037,10 @@ for and Gremlin does the rest. [source,groovy] ---- // Same as above but from any airport in the graph. -g.V().hasLabel('airport').match(__.as('s').out().as('d'), - __.not(__.as('d').out().as('s'))). - select('s','d').by('code') +g.V().hasLabel('airport'). + match(__.as('s').out().as('d'), + __.not(__.as('d').out().as('s'))). + select('s','d').by('code') ---- If you were to run the query you would find that there are in fact over 200 places in @@ -4802,9 +5050,10 @@ find out just how many there are. [source,groovy] ---- // How many occurrences of the pattern in the graph are there? -g.V().hasLabel('airport').match(__.as('s').out().as('d'), - __.not(__.as('d').out().as('s'))). - select('s','d').by('code').count() +g.V().hasLabel('airport'). + match(__.as('s').out().as('d'), + __.not(__.as('d').out().as('s'))). + count() 238 ---- @@ -4819,10 +5068,11 @@ form A->B->A. [source,groovy] ---- -g.V().hasLabel('airport').match(__.as('a').out().as('b') - ,__.as('b').out().where(neq('a')).as('c') - ,__.not(__.as('a').out().as('c'))). - select('a','b','c').by('code').limit(10) +g.V().hasLabel('airport'). + match(__.as('a').out().as('b') + ,__.as('b').out().where(neq('a')).as('c') + ,__.not(__.as('a').out().as('c'))). + select('a','b','c').by('code').limit(10) ---- @@ -4844,12 +5094,14 @@ query. [a:ATL,b:TRI,c:PIE] ---- -Here is another example of using 'match' along with a 'where'. This query starts out -by looking at how many runways Austin has and then looks at every airport that you -can fly to from Austin and then looks at how many runways those airports have. Only -airports with the same number as Austin are returned. While this is a bit of a -contrived query, it does show the power of 'match' and also illustrates using values -calculated in one part of a query later on in that same query. +Here is another example of using 'match' along with a 'where'. This is actually a +different way of writing a query we saw earlier. This query starts out by looking at +how many runways Austin has and then looks at every airport that you can fly to from +Austin and then looks at how many runways those airports have. Only airports with the +same number as Austin are returned. Using a 'match' step for this task is overkill. +However, it does show the basic constructs used by the 'match' step and again +illustrates using values calculated in one part of a query later on in that same +query. [source,groovy] ---- @@ -4860,18 +5112,49 @@ g.V().has('code','AUS'). select('outa').valueMap().select('code','runways') ---- -As I mentioned, the example above is a bit contrived and it could actually be done -without using a 'match' step at all and just using a 'where' step as shown below. -Hopefully, however, these examples give you an idea of what 'match' can do. +As I mentioned, the example above is not the best way to write this query and it can +be done without using a 'match' step at all and just using a 'where' step as shown in +the three examples below. Each one is simpler than its predecessor. + +One way we could choose to write this query is using multiple select steps. This is +also not a very efficient solution but does work. [source,groovy] ---- g.V().has('code','AUS').as('aus').values('runways').as('ausr'). select('aus').out().as('outa').values('runways').as('outr'). - where('ausr',eq('outr')).select('outa').valueMap().select('code','runways') + where('ausr',eq('outr')). + select('outa').valueMap().select('code','runways') +---- +A better way than either of the prior two combines a 'filter' step with the 'where' +and 'select' steps. + +[source,groovy] +---- +g.V().has('code','AUS').as('a').out().as('b'). + filter(select('a','b').by('runways').where('a',eq('b'))). + valueMap('code','runways') ---- +As mentioned in the "<>" section, this query can be simplified further using +a 'where' step and a 'by' modulator. This capability was introduced in the TinkerPop +3.2.4 release. + +[source,groovy] +---- +g.V().has('code','AUS').as('a').out(). + where(eq('a')).by('runways').valueMap('code','runways') +---- + +So, while there is often a simpler way to write a query that avoids using the 'match' +step, for some queries, especially in more complex cases, it provides a useful and +powerful way to express in a more declarative way, a set of criteria that must be +met. However, before I resort to using a 'match' step I always think carefully about +other ways that I could write the query that might be simpler. I do this because the +syntax of the 'match' step can be tricky to get right without a fair bit of trial and +error in my experience. + [[union]] Using 'union' to combine query results ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -5040,7 +5323,8 @@ AUS from the UK or that leave AUS and arrive in Mexico. // Flights to AUS from the UK or from AUS to Mexico g.V().has('code','AUS'). union(__.in().has('country','UK'), - out().has('country','MX')).path().by('code') + out().has('country','MX')). + path().by('code') ---- @@ -5063,7 +5347,8 @@ codes, all airports in the respective cities are considered. ---- g.V().has('city','London').has('region','GB-ENG'). union(out('route').has('city','Paris'), - out('route').has('city','Berlin')).path().by('code') + out('route').has('city','Berlin')). + path().by('code') ---- Here are the results from running the query. Note that routes from five different @@ -5084,17 +5369,52 @@ London airports were found. [LTN,SXF] ---- -As mentioned above, sometimes, especially for fairly simple queries, there are -alternatives to using 'union'. The previous query could have been written as follows -using the 'within' method. +As mentioned previously, sometimes, especially for fairly simple queries, there are +alternatives to using 'union'. Indeed, it is actually not necessary to use a 'union' +step to achieve the prior result. The re-written version of the query below will +return the same results as the version that uses a 'union' step. This time a simple +'has' step featuring a 'within' predicate is used instead. [source,groovy] ---- -// The above query could have been written without using union() as follows g.V().has('city','London').has('region','GB-ENG'). - out().has('city',within('Paris','Berlin')).path().by('code') + out('route').has('city',within('Berlin','Paris')). + path().by('code') ---- +The previous two queries used a 'path' step to essentially show each individual +route. We can adjust the version of the query that uses a 'union' step just a little +bit and instead turn the result into a series of lists where the first item in the +list is the origin airport and the remaining items in the list are the places you can +fly to from there within our criteria. The double underscore '"__"' is used in the +way that 'identity' could have been used to refer to the incoming vertex. The 'union' +step is wrapped in a 'local' step so that each 'union' is individually folded. If the +'local' step was omitted all of the results would be folded into a single list. In +this case, the 'union' step makes writing the query relatively easy and this is +probably a good example of where the 'union' step should be used. Namely, when you +want to combine multiple traversal results. + +[source,groovy] +---- +g.V().has('city','London').has('region','GB-ENG'). + local(union(__.values('code'), + out('route').has('city','Paris').values('code'), + out('route').has('city','Berlin').values('code')). + fold()) +---- + +Running the amended query shows us the results in perhaps a more useful form. + +[source,groovy] +---- +[LHR,ORY,CDG,TXL] +[LGW,CDG,SXF] +[LCY,ORY,CDG] +[STN,TXL,SXF] +[LTN,CDG,SXF] +---- + + [[unionthree]] Using 'union' to combine more complex traversal results ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -5263,7 +5583,7 @@ those places there are. ---- g.V().has('code','AUS').out().out().dedup().count() -865 +871 ---- There is however a problem with this query. The 865 places is going to include (some @@ -5278,7 +5598,7 @@ query below g.V().has('code','AUS').out().aggregate('nonstop'). out().where(without('nonstop')).dedup().count() -806 +812 ---- After the first 'out' step. all of the vertices that were found are stored in a @@ -5287,7 +5607,7 @@ collection I chose to call 'nonstop'. Then, after the second 'out' we can add a nonstop collection". We still do the 'dedup' step as otherwise we will still end up counting a lot of the remaining airports more than once. -Notice that 806 is precisely 59 less than the 865 number returned by the previous +Notice that 812 is precisely 59 less than the 871 number returned by the previous query which shows the places you can get to non stop were all correctly removed from the second query. This also tells us there are no flights from Austin to places that you cannot get to from anywhere else! @@ -7592,9 +7912,9 @@ PIT Using 'inject' to specify new vertex ID values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -If the graph database you are using supports user provided ID values, you can use an 'inject' -step as one way to specify what you want the ID value of a new vertex to be. For -example consider the example below. +If the graph database you are using supports user provided ID values, you can use an +'inject' step as one way to specify what you want the ID value of a new vertex to be. +For example consider the example below. [source,groovy] ---- @@ -7613,8 +7933,42 @@ v[99997] v[99998] ---- -Note that if you try to create a vertex using an ID that already exists the operation -will fail. +I chose to show use of 'inject' as it provides an interesting example. However, it is +not required to create new IDs in this way. Both of the examples below are also valid +ways to do the same thing. The first example just uses a literal value. + +[source,groovy] +---- +g.addV().property(id,99999L) + +v[99999] +---- + +Alternatively we could pass in a variable. + +[source,groovy] +---- +n=99999L; +g.addV().property(id,n) + +v[99999] +---- + +NOTE: Remember that these methods of specifying an ID value will only work if the +graph database that you are using allows you to specify your own ID values. This +varies by graph database implementation and you should check the documentation for +the system you are using before assuming that you can create your own custom ID +values. + +Even if the graph database that you are using does support user provided ID values +you should check to see what data types can be used for them. All of the examples +above used LONG values. However, as one example, some graph databases that do allow +you to specify custom IDs only support String values. So the key thing is to check +the documentation before you start building your graph. + +Even if a graph database does support custom ID values, if you try to create a vertex +using an ID that already exists the operation will fail. The example below shows what +happens when we try to add a vertex using an ID that already exists to a TinkerGraph. [source,groovy] ---- @@ -8639,6 +8993,111 @@ So as mentioned above, the meta property provides a way to attach metadata to an property. This enables a number of important use cases including being able to attach a date or ACL information to individual properties. +[[deduce]] +Deducing the schema of a graph using queries +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Sometimes, you may find yourself working with a graph while being unsure of its data +model or schema. Using some simple Gremlin queries we can quite easily figure out the +major elements that a graph contains. This technique should only be used if the +graph database you are using does not provide an explicit API for working with the +schema of a graph. First of all, we can figure out the vertex labels that are in use +as shown below. The 'dedup' step insures that we get a list of unique label names +back. + +[source,groovy] +---- +g.V().label().dedup() + +version +airport +country +continent +---- + +Similarly, we can find out the names of the edge labels in the graph. + +[source,groovy] +---- +g.E().label().dedup() + +route +contains +---- + +Now that we know the label names it is very easy to get the names of the property +keys for a vertex with a given label. The query below will display the names of the +property keys found in an 'airport' vertex. + +[source,groovy] +---- +g.V().hasLabel('airport').limit(1).next().keys() + +country +code +longest +city +elev +icao +lon +type +region +runways +lat +desc +---- + +You could easily put the previous query inside a simple loop if you wanted to iterate +through each of the vertex labels that were discovered. As with vertices we can also +query edges to discover their key names. The query below finds the property key names +for 'route' edges. + +[source,groovy] +---- +g.E().hasLabel('route').limit(1).next().keys() + +dist +---- + +Lastly now that we know the label names and we know how to find out the property key +names, we can also figure out the types associated with each key. The code below +creates an array called 'pkeys' containing all of the property key names for an +'airport' vertex. Having done that, the code iterates through the list in a simple +loop to find the type for each key. The code as shown is intended to be run inside +the Gremlin Console. + +[source,groovy] +---- +pkeys=g.V().hasLabel('airport').limit(1).next().keys() + +pkeys.each { + printf("%10s : %s\n" , it, + g.V().hasLabel('airport').limit(1). + values(it).next().class)};[] +---- + +When run, we get back a nicely formatted table showing the key names and their types. + +[source,groovy] +---- + country : class java.lang.String + code : class java.lang.String + longest : class java.lang.Integer + city : class java.lang.String + elev : class java.lang.Integer + icao : class java.lang.String + lon : class java.lang.Double + type : class java.lang.String + region : class java.lang.String + runways : class java.lang.Integer + lat : class java.lang.Double + desc : class java.lang.String +---- + +Later, in the "<>" section, we will look at how JanusGraph allows us to +define an explicit schema and also to query the schema using its Graph Management +API. + [[collrev]] Collections revisited ~~~~~~~~~~~~~~~~~~~~~ @@ -9445,7 +9904,7 @@ the values are subtracted *from* the sack's initialization value. ---- g.V().has('code','AUS').sack(assign).by('runways'). V().has('code','SAF').out(). - sack(mult).by('runways').sack().fold() + sack(minus).by('runways').sack().fold() [-5,-2,-1,-4] ---- @@ -9855,7 +10314,34 @@ closure to do what you want is easier than figuring out the pure Gremlin way to but if at all possible using just Gremlin steps is still the recommended path to take. Lambda functions in general are discouraged. -Finally, here is another example that uses a 'filter' step combined with a closure to +Gremlin currently does not have any regular expression support built in to the +language. However, as we will explore later in the "<>" section, JanusGraph +does provide some support. When working with a graph, such as TinkerGraph, that has +no built in regular expression support you can use a 'filter' step combined with a +Lambda as shown in the following two examples. The examples take advantage of methods +provided by the Java String class. + +[source,groovy] +---- +g.V().hasLabel('airport'). + filter{ it.get().value('city').startsWith('Dal')}. + values('city') +---- + +When the query is run, all the airport city names matching the pattern are found. + +[source,groovy] +---- +Dallas +Dallas +Dalaman +Dalian +Dalcahue +Dalat +Dalanzadgad +---- + +The next example that uses a 'filter' step combined with a closure to find any airport vertex that has a description containing the letter '"F"' followed by a period, as in '"F."'. While this makes an interesting example, there are other ways to achieve this result. One such way would be to used a mixed index and special text @@ -9873,6 +10359,9 @@ g.V()hasLabel('airport').as('a').values('desc'). [EUX,F. D. Roosevelt Airport] ---- +You will see more examples of how to use Lambda expressions with the 'filter' step in +the "<>" section. + [[mapstep]] Introducing the 'Map' step ^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -10498,7 +10987,10 @@ connected by routes to the vertices in the previous level. [source,groovy] ---- //Generate a tree the AUS vertex and it's neighbors and their neighbors - t=g.V().has('code','AUS').repeat(out()).times(2).tree().by('code').next() +tree = g.V().has('code','AUS'). + repeat(out()). + times(2). + tree().by('code').next() ---- The object returned to our variable 't' will be an instance of the @@ -10508,82 +11000,194 @@ provides a set of methods that can be used when working with a Tree. [source,groovy] ---- // Look at part of the tree directly -t['AUS']['DFW'] +tree['AUS']['DFW'] // You can also use the TinkerPop Tree API to work with the tree -t.getLeafObjects() -t.getObjectsAtDepth(1) -t.getObjectsAtDepth(1) -t.getObjectsAtDepth(2) +tree.getLeafObjects() +tree.getObjectsAtDepth(1) +tree.getObjectsAtDepth(1) +tree.getObjectsAtDepth(2) ---- We will see the Tree API used again in the "<>" section later on. -[[sub]] +[[subgraph]] Creating a sub graph ~~~~~~~~~~~~~~~~~~~~ -You can create a sub graph which is a subset of the vertices and edges in the main -graph. Once created, to work with a sub graph, you create a traversal specific to -that new graph and distinct from the trversal being used to process the main graph. -Subgraphs are created using the 'subgraph' traversal step. Note that 'subgraph' takes -edges (not vertices) as input and returns both those edges and the vertices that they -connect with. +Using Gremlin, you can create a subgraph which is a subset of the vertices and edges +in a larger graph you are working with. Once created, to work with a sub graph, you +create a traversal source object specific to that new graph and distinct from the one +being used to process the main graph. Subgraphs are created using the 'subgraph' +traversal step. Note that 'subgraph' works with edges (not vertices) and adds both +those edges and the vertices that they connect with to the new subgraph being +created. + +TIP: You can find all of the sample data in the book's GitHub repository. +https://github.com/krlawrence/graph/tree/master/sample-data + +Let's start with a simple example. One of the sample data sets shipped with this book +is a small version of the main 'air-routes' graph called 'air-routes-small.graphml'. +It contains routes between just the first 46 airports in the full graph. We can quite +easily write a query to generate the subgraph representing the flights between those +airports by extracting the edges and vertices from the full graph. Take a look at the +query below. First we find all the vertices that have an ID between 1 and 46 +inclusive. Then we find all of their outgoing edges but filter out any that do not +also end up at an airport within the same ID range of 1 through 46. Lastly we use a +'subgraph' step to add those edges and vertices to a new subgraph. Note that the +'subgraph' step has to be given a label. In this case I just used '+++'+++a+++'+++'. +This allows us, should we need to, to add to a new subgraph from more that one part +of a traversal. In this case we have no more to add so a 'cap' step is used to +complete the creating of the 'subgraph'. The variable 'subg' will now contain a +reference to the newly created graph. [source,groovy] ---- -// Create a subgraph of all vertices and edges directly connected to the AUS vertex -// Note that using bothE() means we get incoming and outgoing edges. -subg = g.V().has('code','AUS').bothE().subgraph('subGraph').cap('subGraph').next() +subg=g.V(1..46).outE(). + filter(inV().hasId(within(1L..46L))). + subgraph('a').cap('a').next() +---- -// As above but only keep the outgoing edges in the subgraph. -subg = g.V().has('code','AUS').outE().subgraph('subGraph').cap('subGraph').next() +Note that when we run the query, Gremlin shows is that we created a new TinkerGraph +containing 46 vertices and 1326 edges. + +[source,groovy] +---- +tinkergraph[vertices:46 edges:1326] +---- -// Create a subgraph starting at AUS but this time going out two levels. We achieve this -// using a repeat() method to go out from Austin 2 times. -subg = g.V().has('code','AUS').repeat(__.bothE().subgraph('subGraph').outV()).times(2) - .cap('subGraph').next() +Now that the subgraph is created, we need to create a traversal source object for it +so that we can issue queries against it. Gremlin shows us details of the new +traversal source object once it has been created. -// Get a traversal object so that we can traverse the newly created sub graph -sgt = subg.traversal(standard()) +[source,groovy] +---- +sgt = subg.traversal() -// Which airports ended up in the sub graph? -sgt.V().hasLabel('airport').values('code') +graphtraversalsource[tinkergraph[vertices:46 edges:1326], standard] ---- -In this second example we create a subgraph just of airports and routes that -are inside Europe. Effectively we just made the Europe only version of the air -routes main graph. At first glance, this query looks a bit overwhelming but if -you read it slowly and look at each step you should be able to make sense of what -it is doing. It is a bit more sophisticated that the previous examples in that as -well as extracting the routes and airports into the subgraph it also extracts all -of the relevant countries as well. +Now that we have a traversal source object for our newly created subgraph we can run +some queries against it. + +[source,groovy] +---- +// What airports are in the subgraph? +sgt.V().values('code').fold() + +[ATL,ANC,AUS,BNA,BOS,BWI,DCA,DFW,FLL,IAD,IAH,JFK,LAX,LGA,MCO,MIA,MSP,ORD,PBI,PHX,RDU,SEA,SFO,SJC,TPA,SAN,LGB,SNA,SLC,LAS,DEN,HPN,SAT,MSY,EWR,CID,HNL,HOU,ELP,SJU,CLE,OAK,TUS,SAF,PHL,DTW] + +// How many of the 46 airports can you fly to from LAX? +sgt.V().has('code','LAX').out().count() + +40 +---- + +Here are some more examples of working with subgraphs. The query below will create a +subgraph of all vertices and edges directly connected to the Austin (AUS) vertex. Note +that using 'bothE' means we get incoming and outgoing edges. In these examples the +more meaningful label 'subGraph' is used to label the subgraph being created. + +[source,groovy] +---- +subg = g.V().has('code','AUS').bothE(). + subgraph('subGraph').cap('subGraph').next() +---- + +If we only wanted the outgoing routes from Austin we could change the query to just +use an 'outE' step instead. + +[source,groovy] +---- +subg = g.V().has('code','AUS').outE('route'). + subgraph('subGraph').cap('subGraph').next() +---- + +The next example is a little more sophisticated. It will create a subgraph starting +with the Austin vertex but this time going out two hops. We achieve this using a +'repeat' step. + +[source,groovy] +---- +subg = g.V().has('code','AUS'). + repeat(bothE().subgraph('subGraph').outV()).times(2). + cap('subGraph').next() + +[tinkergraph[vertices:1294 edges:11336] +---- + +As before we can now work with the newly created subgraph. + +[source,groovy] +---- +// Get a traversal source object so that we can traverse +// the newly created sub graph. +sgt = subg.traversal() + +// What sort of vertices ended up in the subgraph? +sgt.V().groupCount().by(label) + +[continent:2,country:5,airport:1287] +---- + +Here is a more complicated example. The query will create a subgraph just of airports +and routes that are inside Europe. Effectively this will make the Europe only version +of the air routes main graph. At first glance, this query looks a bit overwhelming +but if you read it slowly and look at each step you should be able to make sense of +what it is doing. It is quite a bit more sophisticated than the previous examples in +that as well as extracting the routes and airports into the subgraph it also extracts +all of the relevant countries and continents as well.Note that this query also uses +multiple 'subgraph' steps. [source,groovy] ---- // Create a sub graph only of airports in Europe and routes between those airports -subgraph = g.V().hasLabel('continent').has('code','EU') - .outE('contains').subgraph('eu-air-routes').inV().as('a') - .inE('contains').subgraph('eu-air-routes') - .outV().hasLabel('country') - .select('a').outE().as('r').inV().hasLabel('airport').in().hasLabel('continent') - .has('code','EU').select('r').subgraph('eu-air-routes') - .cap('eu-air-routes').next() -// Create a traversal object for the subgraph -sg = subgraph.traversal() +subg = g.V().hasLabel('continent').has('code','EU'). + outE('contains').subgraph('eu-air-routes').inV().as('a'). + inE('contains').subgraph('eu-air-routes'). + outV().hasLabel('country'). + select('a').outE().as('r'). + inV().hasLabel('airport').in().hasLabel('continent'). + has('code','EU').select('r').subgraph('eu-air-routes'). + cap('eu-air-routes').next() + +tinkergraph[vertices:630 edges:13665] +---- + +As before we can now work with the newly created subgraph. + +[source,groovy] +---- +// Create a traversal source object for the subgraph +sgt = subg.traversal() // How many routes are there in the subgraph? -sg.E().hasLabel('route').count() +sgt.E().hasLabel('route').count() + +12499 -// How many airports? -sg.V().hasLabel('airport').count() +// What sort of vertices ended up in the subgraph? +sgt.V().groupCount().by(label) + +[continent:1,country:46,airport:583] +---- + +The following query uses the new Europe only subgraph to find out where we can +get to from London Heathrow (LHR) within Europe. + +[source,groovy] +---- +sgt.V().has('code','LHR').out().values('code').fold() -// Now I can query the subgraph to find out where I can get to from LHR within -// the subgraph. -sg.V().has('code','LHR').out().values('code').join(',') +[FAO,JMK,FCO,JTR,KBP,AMS,RJK,TLS,PRG,BCN,LED,MAD,OPO,VIE,ZRH,GVA,LCG,BRU,MUC,MAN,INN,CGN,INV,GOT,BLL,VCE,KRK,SNN,MJV,OSL,MPL,ARN,EDI,PUY,GLA,BDS,DME,SVO,ORY,NCE,MXP,ATH,ZAG,BUD,BIO,IBZ,WAW,MLA,SOF,BEG,IST,HAM,STR,PSA,BLQ,NTE,CPH,LUX,DUS,TXL,LIS,GIB,KEF,PMI,AGP,LBA,ABZ,NCL,BSL,SVG,BGO,TLL,ORK,VKO,SPU,BHD,HAJ,LIN,LYS,MRS,OTP,CDG,RTM,FRA,HEL,DUB] ---- +The ability to create a subgraph from a larger graph is a very powerful feature that +Gremlin provides for us. If you have a large graph but only want to work with a part +of it it is nice to be able to create a subgraph from it and perhaps even work with +that subgraph locally in memory while running some queries. + [[graphmlandjsonintro]] Working with GraphML and GraphSON ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -10653,7 +11257,7 @@ enclosing 'vertices' object. NOTE: The GraphSON file generated will not be very human readable without doing some pretty printing on it using something like the Python json tool - ('python -m json.tool mygraph.json') or using a text editor that can beautify + ('python -m json.tool my-graph.json') or using a text editor that can beautify JSON. @@ -10663,7 +11267,7 @@ GraphSON. [source,groovy] ---- // Save the graph as unwrapped JSON -graph.io(graphson()).writeGraph("graph.json") +graph.io(graphson()).writeGraph("my-graph.json") ---- The following will create a file containing the 'wrapped adjacency list' form of @@ -10677,7 +11281,7 @@ fos = new FileOutputStream("my-graph.json") GraphSONWriter.build().wrapAdjacencyList(true).create().writeGraph(fos,graph) ---- -TIP: If you are ingesting large amounts of data into a TinkerPop 3 enables graph, the +TIP: If you are ingesting large amounts of data into a TinkerPop 3 enabled graph, the unwrapped flavor of GraphSON is probably a much better choice than GraphML or wrapped GraphSON. Using this format you can stream data into a graph one vertex at a time rather than having to try and send the entire graph as a potentially huge JSON file @@ -11693,6 +12297,53 @@ is that three of the top five busiest single runway airports are in England. [apt:XIY,city:Xianyang,routes:105] ---- +[[runwaycountproject]] +Another way of counting runways +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Let's assume we wanted to count the runways in the graph and for each number of +runways produce a simple map result where the runway number and total count of +airports having that number of runways are each meaningfully labeled. One way we +could do that is to use a 'groupCount' step to calculate the distribution of runways +and then use a 'project' step to produce the nicely labeled result. That is what the +query below does. Notice that an 'unfold' step is used so that the results of +the 'groupCount' which itself produces a map, can be further processed. + +[source,groovy] +---- +g.V().hasLabel('airport'). + groupCount().by('runways'). + unfold(). + project('runways','count').by(keys).by(values) +---- + +When run we get a nice map back showing us the number of runways and for each the +total count. Each value has a meaningful key name of 'runways' and 'count' +respectively. + +[source,groovy] +---- +[runways:1,count:2316] +[runways:2,count:762] +[runways:3,count:225] +[runways:4,count:51] +[runways:5,count:14] +[runways:6,count:4] +[runways:7,count:1] +[runways:8,count:1] +---- + +Notice that if the unfold step had not been used, a very different result would have +been generated. + +[source,groovy] +---- +g.V().hasLabel('airport'). + groupCount().by('runways'). + project('runways','count').by(keys).by(values) + +[runways:[1,2,3,4,5,6,7,8],count:[2316,762,225,51,14,4,1,1]] +---- [[canadamost]] Airports with the most routes in Canada ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -12372,7 +13023,7 @@ g.V().hasLabel('airport'). When we run our query again we get the results below. Looking at the data sorted this way helps some new interesting facts stand out. The most interesting thing -we can immediately spot is that there are 15 airports that currently have no +we can immediately spot is that there are 16 airports that currently have no outgoing routes at all! [source,groovy] @@ -12925,19 +13576,23 @@ Here are the results of running the query. [source,groovy] ---- -Istanbul -Helsinki +London Paris Frankfurt -London -Birmingham +Helsinki +Rome +Amsterdam +Madrid Vienna Zurich -Amsterdam -Moscow Brussels Munich -Rome +Stockholm +Moscow +Milan +Istanbul +Copenhagen +Birmingham ---- [[aggrroutes]] @@ -13173,34 +13828,37 @@ between airport pairs. [source,groovy] ---- -// All outes longer than 8,000 miles +// All routes longer than 8,000 miles g.V().as('src').outE('route'). has('dist',gt(8000)).inV().as('dest'). select('src','dest').by('code') ---- -Here is what our query produces. +Here is what our query produces. As before I have arranged the output in columns to +aid readability. [source,groovy] ---- -[src:ATL,dest:JNB] [src:SIN,dest:SFO] -[src:DFW,dest:SYD] [src:DXB,dest:DFW] -[src:DFW,dest:DXB] [src:DXB,dest:IAH] -[src:DFW,dest:HKG] [src:DXB,dest:LAX] -[src:DFW,dest:AUH] [src:DXB,dest:SFO] -[src:IAH,dest:DXB] [src:DXB,dest:AKL] -[src:IAH,dest:DOH] [src:HKG,dest:DFW] -[src:JFK,dest:HKG] [src:HKG,dest:JFK] -[src:LAX,dest:DXB] [src:HKG,dest:EWR] -[src:LAX,dest:DOH] [src:AKL,dest:DXB] -[src:LAX,dest:AUH] [src:DOH,dest:IAH] -[src:LAX,dest:JED] [src:DOH,dest:LAX] -[src:LAX,dest:RUH] [src:JNB,dest:ATL] -[src:SFO,dest:SIN] [src:AUH,dest:DFW] -[src:SFO,dest:DXB] [src:AUH,dest:LAX] -[src:SFO,dest:AUH] [src:AUH,dest:SFO] -[src:EWR,dest:HKG] [src:JED,dest:LAX] -[src:SYD,dest:DFW] [src:RUH,dest:LAX] +[src:ATL,dest:JNB] [src:DXB,dest:IAH] +[src:DFW,dest:SYD] [src:DXB,dest:LAX] +[src:DFW,dest:DXB] [src:DXB,dest:SFO] +[src:DFW,dest:HKG] [src:DXB,dest:AKL] +[src:DFW,dest:AUH] [src:HKG,dest:DFW] +[src:IAH,dest:DXB] [src:HKG,dest:JFK] +[src:IAH,dest:DOH] [src:HKG,dest:EWR] +[src:JFK,dest:HKG] [src:AKL,dest:DXB] +[src:LAX,dest:DOH] [src:AKL,dest:DOH] +[src:LAX,dest:AUH] [src:DOH,dest:IAH] +[src:LAX,dest:JED] [src:DOH,dest:LAX] +[src:LAX,dest:RUH] [src:DOH,dest:AKL] +[src:LAX,dest:DXB] [src:JNB,dest:ATL] +[src:SFO,dest:SIN] [src:MEX,dest:CAN] +[src:SFO,dest:DXB] [src:AUH,dest:DFW] +[src:SFO,dest:AUH] [src:AUH,dest:LAX] +[src:EWR,dest:HKG] [src:AUH,dest:SFO] +[src:SYD,dest:DFW] [src:JED,dest:LAX] +[src:SIN,dest:SFO] [src:RUH,dest:LAX] +[src:DXB,dest:DFW] [src:CAN,dest:MEX] ---- Now let's improve the query by including the distance of each route in the query @@ -13208,7 +13866,8 @@ results. [source,groovy] ---- -// Find routes longer than 8,000 miles, include the distance in returned values. +// Find routes longer than 8,000 miles. +// Include the distance in returned values. g.V().as('src'). outE().has('dist',gt(8000)).as('e'). inV().as('dest'). @@ -13219,24 +13878,26 @@ Here is the modified output showing the distance between the airports. [source,groovy] ---- -[src:ATL,e:8434,dest:JNB] [src:SIN,e:8433,dest:SFO] -[src:DFW,e:8574,dest:SYD] [src:DXB,e:8022,dest:DFW] -[src:DFW,e:8022,dest:DXB] [src:DXB,e:8150,dest:IAH] -[src:DFW,e:8105,dest:HKG] [src:DXB,e:8321,dest:LAX] -[src:DFW,e:8053,dest:AUH] [src:DXB,e:8085,dest:SFO] -[src:IAH,e:8150,dest:DXB] [src:DXB,e:8818,dest:AKL] -[src:IAH,e:8030,dest:DOH] [src:HKG,e:8105,dest:DFW] -[src:JFK,e:8054,dest:HKG] [src:HKG,e:8054,dest:JFK] -[src:LAX,e:8321,dest:DXB] [src:HKG,e:8047,dest:EWR] -[src:LAX,e:8287,dest:DOH] [src:AKL,e:8818,dest:DXB] -[src:LAX,e:8372,dest:AUH] [src:DOH,e:8030,dest:IAH] -[src:LAX,e:8314,dest:JED] [src:DOH,e:8287,dest:LAX] -[src:LAX,e:8246,dest:RUH] [src:JNB,e:8434,dest:ATL] -[src:SFO,e:8433,dest:SIN] [src:AUH,e:8053,dest:DFW] -[src:SFO,e:8085,dest:DXB] [src:AUH,e:8372,dest:LAX] -[src:SFO,e:8139,dest:AUH] [src:AUH,e:8139,dest:SFO] -[src:EWR,e:8047,dest:HKG] [src:JED,e:8314,dest:LAX] -[src:SYD,e:8574,dest:DFW] [src:RUH,e:8246,dest:LAX] +[src:ATL,e:8434,dest:JNB] [src:DXB,e:8150,dest:IAH] +[src:DFW,e:8574,dest:SYD] [src:DXB,e:8321,dest:LAX] +[src:DFW,e:8022,dest:DXB] [src:DXB,e:8085,dest:SFO] +[src:DFW,e:8105,dest:HKG] [src:DXB,e:8818,dest:AKL] +[src:DFW,e:8053,dest:AUH] [src:HKG,e:8105,dest:DFW] +[src:IAH,e:8150,dest:DXB] [src:HKG,e:8054,dest:JFK] +[src:IAH,e:8030,dest:DOH] [src:HKG,e:8047,dest:EWR] +[src:JFK,e:8054,dest:HKG] [src:AKL,e:8818,dest:DXB] +[src:LAX,e:8287,dest:DOH] [src:AKL,e:9025,dest:DOH] +[src:LAX,e:8372,dest:AUH] [src:DOH,e:8030,dest:IAH] +[src:LAX,e:8314,dest:JED] [src:DOH,e:8287,dest:LAX] +[src:LAX,e:8246,dest:RUH] [src:DOH,e:9025,dest:AKL] +[src:LAX,e:8321,dest:DXB] [src:JNB,e:8434,dest:ATL] +[src:SFO,e:8433,dest:SIN] [src:MEX,e:8754,dest:CAN] +[src:SFO,e:8085,dest:DXB] [src:AUH,e:8053,dest:DFW] +[src:SFO,e:8139,dest:AUH] [src:AUH,e:8372,dest:LAX] +[src:EWR,e:8047,dest:HKG] [src:AUH,e:8139,dest:SFO] +[src:SYD,e:8574,dest:DFW] [src:JED,e:8314,dest:LAX] +[src:SIN,e:8433,dest:SFO] [src:RUH,e:8246,dest:LAX] +[src:DXB,e:8022,dest:DFW] [src:CAN,e:8754,dest:MEX] ---- Next let's simplify things a bit. While using 'as' and 'select' gets the job done, @@ -13256,15 +13917,17 @@ The output indeed now looks more readable. [source,groovy] ---- -[ATL,8434,JNB] [LAX,8287,DOH] [SIN,8433,SFO] [AKL,8818,DXB] -[DFW,8574,SYD] [LAX,8372,AUH] [DXB,8022,DFW] [DOH,8030,IAH] -[DFW,8022,DXB] [LAX,8314,JED] [DXB,8150,IAH] [DOH,8287,LAX] -[DFW,8105,HKG] [LAX,8246,RUH] [DXB,8321,LAX] [JNB,8434,ATL] -[DFW,8053,AUH] [SFO,8433,SIN] [DXB,8085,SFO] [AUH,8053,DFW] -[IAH,8150,DXB] [SFO,8085,DXB] [DXB,8818,AKL] [AUH,8372,LAX] -[IAH,8030,DOH] [SFO,8139,AUH] [HKG,8105,DFW] [AUH,8139,SFO] -[JFK,8054,HKG] [EWR,8047,HKG] [HKG,8054,JFK] [JED,8314,LAX] -[LAX,8321,DXB] [SYD,8574,DFW] [HKG,8047,EWR] [RUH,8246,LAX] +[ATL,8434,JNB] [LAX,8314,JED] [DXB,8150,IAH] [DOH,8287,LAX] +[DFW,8574,SYD] [LAX,8246,RUH] [DXB,8321,LAX] [DOH,9025,AKL] +[DFW,8022,DXB] [LAX,8321,DXB] [DXB,8085,SFO] [JNB,8434,ATL] +[DFW,8105,HKG] [SFO,8433,SIN] [DXB,8818,AKL] [MEX,8754,CAN] +[DFW,8053,AUH] [SFO,8085,DXB] [HKG,8105,DFW] [AUH,8053,DFW] +[IAH,8150,DXB] [SFO,8139,AUH] [HKG,8054,JFK] [AUH,8372,LAX] +[IAH,8030,DOH] [EWR,8047,HKG] [HKG,8047,EWR] [AUH,8139,SFO] +[JFK,8054,HKG] [SYD,8574,DFW] [AKL,8818,DXB] [JED,8314,LAX] +[LAX,8287,DOH] [SIN,8433,SFO] [AKL,9025,DOH] [RUH,8246,LAX] +[LAX,8372,AUH] [DXB,8022,DFW] [DOH,8030,IAH] [CAN,8754,MEX] + ---- Our query is looking pretty good but it would be nice to not report the same route @@ -13282,7 +13945,8 @@ shown below to apply this filter. [source,groovy] ---- -// We could avoid returning both directions of travel by refining our query as follows +// We could avoid returning both directions of +// travel by refining our query as follows. g.V().as('a').outE().has('dist',gt(8000)). inV().as('b'). filter(select('a','b').by('code').where('a', lt('b'))). @@ -13295,15 +13959,16 @@ true for all the other 'return journey' routes. [source,groovy] ---- -[ATL,8434,JNB] [DXB,8085,SFO] -[DFW,8574,SYD] [HKG,8054,JFK] -[DFW,8022,DXB] [AKL,8818,DXB] +[ATL,8434,JNB] [HKG,8054,JFK] +[DFW,8574,SYD] [AKL,8818,DXB] +[DFW,8022,DXB] [AKL,9025,DOH] [DFW,8105,HKG] [DOH,8030,IAH] [LAX,8246,RUH] [DOH,8287,LAX] [SFO,8433,SIN] [AUH,8053,DFW] [EWR,8047,HKG] [AUH,8372,LAX] [DXB,8150,IAH] [AUH,8139,SFO] [DXB,8321,LAX] [JED,8314,LAX] +[DXB,8085,SFO] [CAN,8754,MEX] ---- Lastly, now that we have the routes we want, let's tweak the query so that the routes @@ -13312,7 +13977,7 @@ step after finding the routes we are interested in. [source,groovy] ---- -// As above but sorted by route lengths +// As above but sorted by route lengths. g.V().as('a').outE().has('dist',gt(8000)). order().by('dist',decr). inV().as('b'). @@ -13324,29 +13989,30 @@ Here are the results again, this time sorted by route distance in descending ord [source,groovy] ---- +[AKL,9025,DOH] [LAX,8246,RUH] [AKL,8818,DXB] [DXB,8150,IAH] -[DFW,8574,SYD] [AUH,8139,SFO] -[ATL,8434,JNB] [DFW,8105,HKG] -[SFO,8433,SIN] [DXB,8085,SFO] -[AUH,8372,LAX] [HKG,8054,JFK] -[DXB,8321,LAX] [AUH,8053,DFW] -[JED,8314,LAX] [EWR,8047,HKG] -[DOH,8287,LAX] [DOH,8030,IAH] -[LAX,8246,RUH] [DFW,8022,DXB] +[CAN,8754,MEX] [AUH,8139,SFO] +[DFW,8574,SYD] [DFW,8105,HKG] +[ATL,8434,JNB] [DXB,8085,SFO] +[SFO,8433,SIN] [HKG,8054,JFK] +[AUH,8372,LAX] [AUH,8053,DFW] +[DXB,8321,LAX] [EWR,8047,HKG] +[JED,8314,LAX] [DOH,8030,IAH] +[DOH,8287,LAX] [DFW,8022,DXB] ---- -TinkerPop 3.2.3 added additional capability to the 'where' step that allows it to be -followed by a 'by' modulator. This allows us, should we so desire, to simplify our -query a bit more as follows. +As part of the TinkerPop 3.2.3 release an additional capability was added to the +'where' step that allows it to be followed by a 'by' modulator. This allows us, +should we so desire, to simplify our query a bit more as follows. [source,groovy] ---- //Query changed to take advantage of the where().by() construct g.V().as('s'). - outE().has('dist',gt(8000)). - order().by('dist',decr).inV().as('f'). - where('f',lt('s')).by('code'). - path().by('code').by('dist') + outE().has('dist',gt(8000)). + order().by('dist',decr).inV().as('f'). + where('f',lt('s')).by('code'). + path().by('code').by('dist') ---- As you can see, we got the same results from our modified query. Well, almost the same @@ -13356,18 +14022,19 @@ prior case. This is because we compared the values in the opposite order! [source,groovy] ---- +[DOH,9025,AKL] [RUH,8246,LAX] [DXB,8818,AKL] [IAH,8150,DXB] -[SYD,8574,DFW] [SFO,8139,AUH] -[JNB,8434,ATL] [HKG,8105,DFW] -[SIN,8433,SFO] [SFO,8085,DXB] -[LAX,8372,AUH] [JFK,8054,HKG] -[LAX,8321,DXB] [DFW,8053,AUH] -[LAX,8314,JED] [HKG,8047,EWR] -[LAX,8287,DOH] [IAH,8030,DOH] -[RUH,8246,LAX] [DXB,8022,DFW] +[MEX,8754,CAN] [SFO,8139,AUH] +[SYD,8574,DFW] [HKG,8105,DFW] +[JNB,8434,ATL] [SFO,8085,DXB] +[SIN,8433,SFO] [JFK,8054,HKG] +[LAX,8372,AUH] [DFW,8053,AUH] +[LAX,8321,DXB] [HKG,8047,EWR] +[LAX,8314,JED] [IAH,8030,DOH] +[LAX,8287,DOH] [DXB,8022,DFW] ---- -As an interesting sidenote, and this would be true for the queries above as well, if +As an interesting side note, and this would be true for the queries above as well, if we replace the 'lt' with a 'gt' we will get the routes returned in the reverse order. @@ -13380,20 +14047,21 @@ g.V().as('s'). path().by('code').by('dist') ---- -Using the prior query the first result was '[DXB,8818,AKL]'. As you will -see below, our first result is now '[AKL,8818,DXB]'. +Using the prior query the first result was '[DOH,9025,AKL]'. As you will +see below, our first result is now '[AKL,9025,DOH]'. [source,groovy] ---- +[AKL,9025,DOH] [LAX,8246,RUH] [AKL,8818,DXB] [DXB,8150,IAH] -[DFW,8574,SYD] [AUH,8139,SFO] -[ATL,8434,JNB] [DFW,8105,HKG] -[SFO,8433,SIN] [DXB,8085,SFO] -[AUH,8372,LAX] [HKG,8054,JFK] -[DXB,8321,LAX] [AUH,8053,DFW] -[JED,8314,LAX] [EWR,8047,HKG] -[DOH,8287,LAX] [DOH,8030,IAH] -[LAX,8246,RUH] [DFW,8022,DXB] +[CAN,8754,MEX] [AUH,8139,SFO] +[DFW,8574,SYD] [DFW,8105,HKG] +[ATL,8434,JNB] [DXB,8085,SFO] +[SFO,8433,SIN] [HKG,8054,JFK] +[AUH,8372,LAX] [AUH,8053,DFW] +[DXB,8321,LAX] [EWR,8047,HKG] +[JED,8314,LAX] [DOH,8030,IAH] +[DOH,8287,LAX] [DFW,8022,DXB] ---- Finding the 20 longest routes in the graph @@ -13401,12 +14069,12 @@ Finding the 20 longest routes in the graph We could write the query from the previous section a different way looking at edges first and using the 'project' step instead of using the 'as' and 'select' steps. -While in general it is not recommended to start a query with 'g.E()' as there -are typically a lot more edges than vertices in a graph this does illustrate a useful +While in general it is not recommended to start a query with 'g.E()' as there are +typically a lot more edges than vertices in a graph this does illustrate a useful pattern. Also, just to change things a bit, this time we just look for the 20 longest -routes rather than looking for routes longer the 8,000 miles. We again filter the -results to only show the route in one direction. Note that the limit step is passed -a parameter of 40 rather than 20 as we know we will be filtering out the same route in +routes rather than looking for routes longer than 8,000 miles. We again filter the +results to only show the route in one direction. Note that the limit step is passed a +parameter of 40 rather than 20 as we know we will be filtering out the same route in the return direction so we will actually only get 20 routes back. [source,groovy] @@ -13424,15 +14092,50 @@ Here are the results from running the query. [source,groovy] ---- -[a:AKL,b:8818,c:DXB] [a:DXB,b:8150,c:IAH] -[a:CAN,b:8754,c:MEX] [a:AUH,b:8139,c:SFO] -[a:ATL,b:8434,c:JNB] [a:DFW,b:8105,c:HKG] -[a:SFO,b:8433,c:SIN] [a:DXB,b:8085,c:SFO] -[a:AUH,b:8372,c:LAX] [a:HKG,b:8054,c:JFK] -[a:DXB,b:8321,c:LAX] [a:AUH,b:8053,c:DFW] -[a:JED,b:8314,c:LAX] [a:EWR,b:8047,c:HKG] -[a:DOH,b:8287,c:LAX] [a:DOH,b:8030,c:IAH] -[a:LAX,b:8246,c:RUH] [a:DFW,b:8022,c:DXB] +[a:AKL,b:9025,c:DOH] [a:LAX,b:8246,c:RUH] +[a:AKL,b:8818,c:DXB] [a:DXB,b:8150,c:IAH] +[a:CAN,b:8754,c:MEX] [a:AUH,b:8139,c:SFO] +[a:DFW,b:8574,c:SYD] [a:DFW,b:8105,c:HKG] +[a:ATL,b:8434,c:JNB] [a:DXB,b:8085,c:SFO] +[a:SFO,b:8433,c:SIN] [a:HKG,b:8054,c:JFK] +[a:AUH,b:8372,c:LAX] [a:AUH,b:8053,c:DFW] +[a:DXB,b:8321,c:LAX] [a:EWR,b:8047,c:HKG] +[a:JED,b:8314,c:LAX] [a:DOH,b:8030,c:IAH] +[a:DOH,b:8287,c:LAX] [a:DFW,b:8022,c:DXB] +---- + +For completeness, here is the query rewritten slightly to again start with airport +vertices rather than with all 'route' edges but using 'as' and 'select' steps rather +than using a 'path' step as used in the previous section. A 'where' step followed by +a 'by' modulator is still used in this case. So once again, it is clear there are +often many ways to get the results that you are looking for. The key is to think +about which form of a query will be the most effective for the data that you are +working with. + +[source,groovy] +---- +g.V().hasLabel('airport').as('a'). + outE().as('b'). + order().by('dist',decr).limit(40). + inV().as('c'). + where('a',lt('c')).by('code'). + select('a','b','c').by('code').by('dist') +---- + +When the query is run the results look like the ones from the prior query. + +[source,groovy] +---- +[a:AKL,b:9025,c:DOH] [a:LAX,b:8246,c:RUH] +[a:AKL,b:8818,c:DXB] [a:DXB,b:8150,c:IAH] +[a:CAN,b:8754,c:MEX] [a:AUH,b:8139,c:SFO] +[a:DFW,b:8574,c:SYD] [a:DFW,b:8105,c:HKG] +[a:ATL,b:8434,c:JNB] [a:DXB,b:8085,c:SFO] +[a:SFO,b:8433,c:SIN] [a:HKG,b:8054,c:JFK] +[a:AUH,b:8372,c:LAX] [a:AUH,b:8053,c:DFW] +[a:DXB,b:8321,c:LAX] [a:EWR,b:8047,c:HKG] +[a:JED,b:8314,c:LAX] [a:DOH,b:8030,c:IAH] +[a:DOH,b:8287,c:LAX] [a:DFW,b:8022,c:DXB] ---- [[aggrunion]] @@ -13506,9 +14209,8 @@ there are a lot of them! [source,groovy] ---- // Routes Between 100 and 200 miles in length -g.V().as('src'). - outE().has('dist',within(100..200)). - inV().as('dest'). +g.V().outE().has('dist',within(100..200)). + inV(). path().by('code').by('dist'). count() @@ -14726,7 +15428,44 @@ you may need to write. Doing it this way has the advantage that you can also che that each part of the query is working the way you intend it to before you add more parts to it. +[[groupcountpath]] +Using 'groupCount' with 'path' to find duplicate edges +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The prior example found duplicate edges by working with a map created using 'select' +and 'groupCount' steps. It is also possible to use a 'path' step inside a +'groupCount' step to achieve a similar result. + +First of all let's create a duplicate edge between Austin (AUS) and Cancun (CUN). + +[source,groovy] +---- +g.V().has('code','AUS').addE('route').to(V().has('code','CUN')) + +e[53791][3-route->180] +---- + +We can now use a 'groupCount' step containing a 'path' step to look for duplicate +edges originating in Austin. I used a 'limit' step just to reduce the amount of +output a bit. +[source,groovy] +---- +g.V().has('code','AUS').out(). + groupCount().by(path().by('code')).limit(local,5) +---- + +As you can see from the results below the route between AUS and CUN has a count of 2 +associated with it. + +[source,groovy] +---- +[[AUS,CUN]:2,[AUS,MDW]:1,[AUS,MIA]:1,[AUS,DFW]:1,[AUS,BWI]:1] +---- + +Note that this technique does not replace the full query shown in the prior section. +It is more intended to show that you can place a 'path' step inside of a 'groupCount' +step and achieve some useful results. [[flr]] @@ -14741,7 +15480,7 @@ Gremlin. On the good side it is powerful and some things that should be easy are easy. On the bad side, some things that appear easy are in fact very hard to get right especially if you want to build a single query to do a job rather than break it up into a set of smaller programmatic steps. Let's look at an example of this with a -real world query. +real-world query. The query we want to build is to find the route(s) in the graph that are of the maximum length between any two airports. It turns out that this simple looking query @@ -15327,16 +16066,20 @@ defined in the 'P' class. Not all the methods defined are shown below. |between | P.between | has("runways",P.between(2,5)) |============================================================================== -If a traversal path has multiple values assiciated with a single label, such as '"x"' -then you can use the 'first', 'last' and 'all' statics that are defined as part of -the 'Pop' Enum. +If a traversal path has multiple values associated with a single label, such as '"x"' +then you can use the 'first', 'last' , 'all' and 'mixed' statics that are defined as +part of the 'Pop' Enum. As the name suggest, 'first' returns the first item in a +collection. Specifying 'last' returns the last item and 'all' returns all of the +items in a collection. Specifying 'mixed' will return a 'List' if the collection has +more than one item. Otherwise an 'Object' will be returned. -.First, last and all +.First, last, all and mixed [cols="1,1,3"] |============================================================================== |first | Pop.first | select(Pop.first,"x") |last | Pop.last | select(Pop.last,"x") |all | Pop.all | select(Pop.all,"x") +|mixed | Pop.mixed | select(Pop.mixed,"x") |============================================================================== When working with a 'sack' the operators like 'sum' and 'assign' are defined in the @@ -16160,12 +16903,12 @@ NOTE: When working with JanusGraph you must use the version of the Gremlin Conso that is packaged as part of the JanusGraph download. As mentioned above, JanusGraph is not a stand alone service, it is a set of Java -classes that a process to invoke it. The Gremlin Console can play that role. After -you have unzipped JanusGraph you will find the gremlin.sh and gremlin.bat scripts in -the bin directory under the JanusGraph parent directory. Once you have started the -Gremlin Console you can, as we will explore in the next few sections, tell Janus -Graph about the environment you will be operating in in terms of back end store and -index. +classes that still need to be invoked by a calling process. The Gremlin Console can +play that role. After you have unzipped JanusGraph you will find the gremlin.sh and +gremlin.bat scripts in the bin directory under the JanusGraph parent directory. Once +you have started the Gremlin Console you can, as we will explore in the next few +sections, tell Janus Graph about the environment you will be operating in in terms of +back end store and index. [[janusinmemory]] Using JanusGraph with the 'inmemory' option @@ -16501,7 +17244,7 @@ Using the JanusGraph Management API it is possible to specify that a property ca accept as list of values. This can be done by specifying a cardinality of 'LIST' when the property key is created. Unless we explicitly do this, whenever a property is created the cardinality will default to 'SINGLE'. The code below can be run from a -Gremlin Console connected to a JanusGraph instance. A property key called 'myList' is +Gremlin Console connected to a JanusGraph instance. A property key called 'mylist' is created that can accept 'String' values'. Before the key is created, its cardinality is specified as 'LIST'. Always remember to 'commit' the management transaction when you are done making changes. @@ -16521,7 +17264,7 @@ Note that the previous steps could be chained together as shown below. [source,groovy] ---- mgmt = graph.openManagement() -mgmt.makePropertyKey('myList').dataType(String.class).cardinality(LIST).make() +mgmt.makePropertyKey('mylist').dataType(String.class).cardinality(LIST).make() mgmt.commit() ---- @@ -16539,7 +17282,7 @@ LIST mgmt.commit() ---- -We can now create a new vertex and add some values using our new 'myList' property. +We can now create a new vertex and add some values using our new 'mylist' property. Note that as our cardinality is 'LIST' and not 'SET' that we can have duplicate values associated with our new property. @@ -18539,7 +19282,7 @@ of this in a moment. As with Gremlin Server, YAML files can be used to configure a remote connection from the Gremlin Console. The Gremlin Console as well as the version that comes bundled with JanusGraph includes a set of YAML files that can be used as-is or edited as -needed. In order to connect The Gremlin Console to the Gremlin Server that we just +needed. In order to connect the Gremlin Console to the Gremlin Server that we just configured, the file 'remote.yaml' can be used. I had to make one change, as shown below. I commented out the 'serializer' line and replaced it with a slightly modified version. I had to do this because of the version issues I mentioned above. At time of @@ -18668,23 +19411,11 @@ Gremlin Server. sh> curl "http://localhost:8182?gremlin=g.V().has('code','AUS').valueMap()" ---- -The following example shows how to send the same query using an HTTP POST. The Apache -TinkerPop documentation states that this is the recommended way to send queries over -HTTP to a Gremlin Server. Note how in this case we are sending the query packaged as -JSON and that we have to escape the quote characters. +In response to the HTTP GET request the server sends back the result packaged as JSON +as follows. I have formatted the output in a way that makes it easier to read. What +was actually returned did not have any line breaks in it at all and was quite hard to +read.. -[source,console] ----- -sh> curl -X POST -d "{\"gremlin\":\"g.V().has('code','AUS')\"}" \ - "http://localhost:8182" ----- - -Both of the queries above will cause the following response to be sent back to us -from the Gremlin Server. Notice how the query results are packaged as JSON. I have -formatted the output in a way that makes it easier to read. What was actually -returned did not have any line breaks in it at all and was quite hard to read.. - -//[source,json]] [source,groovy] ---- {"requestId":"a8ad654a-a5a3-4bb9-8474-69aca3c3db1e", @@ -18702,8 +19433,181 @@ returned did not have any line breaks in it at all and was quite hard to read.. "lat":[30.1944999694824], "desc":["Austin Bergstrom International Airport"]}], "meta":{}}} + +---- + +The following example shows how to send the same query, but with the 'valueMap' step +removed, using an HTTP POST. The Apache TinkerPop documentation states that using +POST is the recommended way to send queries over HTTP to a Gremlin Server. Note how +in this case we are sending the query packaged as JSON and that we have to escape the +quote characters. + +[source,console] +---- +sh> curl -X POST -d "{\"gremlin\":\"g.V().has('code','AUS')\"}" \ + "http://localhost:8182" +---- + +As with the prior query, the HTTP POST form of the query also returns the result +packaged as JSON. However, in this case, because I left off the 'valueMap' step, the +JSON includes additional information in the form of the ID values and labels for the +vertex and its properties. This is because the result represents a vertex this time +rather than a map. I have again formatted the output in a way that is easier +to read. + + +//[source,json]] +[source,groovy] +---- +{"requestId":"64c757b8-27a6-4509-a54c-ea35ba517667", + "status":{"message":"","code":200,"attributes":{}}, + "result":{ + "data":[ + {"id":12352, + "label":"airport", + "type":"vertex", + "properties": + {"country":[{"id":"8p4-9j4-8p1", "value":"US"}], + "code":[{"id":"93c-9j4-5j9", "value":"AUS"}], + "longest":[{"id":"9hk-9j4-mx1", "value":12250}], + "city":[{"id":"9vs-9j4-7wl", "value":"Austin"}], + "elev":[{"id":"aa0-9j4-but", "value":542}], + "icao":[{"id":"ao8-9j4-6bp", "value":"KAUS"}], + "lon":[{"id":"b2g-9j4-dfp", "value":-97.6698989868164}], + "type":[{"id":"bgo-9j4-745", "value":"airport"}], + "region":[{"id":"buw-9j4-9hh", "value":"US-TX"}], + "runways":[{"id":"c94-9j4-b2d", "value":2}], + "lat":[{"id":"cnc-9j4-cn9", "value":30.1944999694824}], + "desc":[{"id":"d1k-9j4-a9x", + "value":"Austin Bergstrom International Airport"}]}}], + "meta":{}}} + +---- + +I have included examples of the different types of JSON result that you are likely to +have to process in the "<>" section that is coming up soon. + +[[javagsclient]] +Connecting to a Gremlin Server from Java using 'withRemote' +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +While it is perfectly possible to work directly with the JSON returned from a Gremlin +Server it is often more desirable to have the results placed directly into variables +of the appropriate type. It the appropriate Grelmin language driver exists for the +programming language that you are using, this is quite easy to setup. In this section +we will look at connecting to a Gremlin Server from a Java application and taking +advantage of the 'withRemote' capability that TinkerPop provides. + +TIP: The source code for the example shown in this section comes from the +'RemoteClient.java' sample located at +https://github.com/krlawrence/graph/tree/master/sample-code. + +Let's look at the small application in sections. First of all it is necessary to +import the required classes that we will need to make the connection to the server +and retrieve the query results. + +[source,groovy] +---- +import org.apache.tinkerpop.gremlin.driver.Cluster; +import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource; +import org.apache.tinkerpop.gremlin.structure.util.empty.EmptyGraph; +import org.apache.tinkerpop.gremlin.driver.remote.DriverRemoteConnection; +import org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV1d0; +import java.util.Map; +import java.util.List; +import java.util.ArrayList; +---- + +We can now define a small Java class that we will call 'RemoteClient' and setup the +connection to the Gremlin Server. This is done by first of all creating a +'Cluster.Builder' instance that will be used to describe the server we are connecting +to and the protocol we want to use. It is important that these settings match what +the Gremlin Server is configured to use. For this simple example we are just using +'localhost' as the host name but the name of any Gremlin Server that you have access +to can be used instead. The default Gremlin Server port of '8182' is specified and the +'GyroMessageServializerV1d0' serialization format is selected. Again, this needs to +match both the protocol and the version of the protocol that your Gremlin Server is +supporting. + +[source,groovy] +---- +public class RemoteClient +{ + public static void main( String[] args ) + { + Cluster.Builder builder = Cluster.build(); + builder.addContactPoint("localhost"); + builder.port(8182); + builder.serializer(new GryoMessageSerializerV1d0()); +---- + +Once the Cluster.Builder instance has been setup we can use it to create our +'Cluster' instance. + +[source,groovy] +---- + Cluster cluster = builder.create(); ---- +Lastly, we need to setup a GraphTraversalSource object for the Gremlin Server hosted +graph that we will be working with. The TinkerPop documentation recommends that an +instance of an 'EmptyGraph' is used when creating the traversal. Having done that, +the 'withRemote' method can be called to establish the remote connection. Note that +the cluster instance that we just created is passed in as a parameter. While this +looks a little complicated it is really not a lot different than when we connect to a +local graph using the Gremlin Console. The only difference is that by setting up the +remote connection this way, when we start to issue queries against the graph, rather +than getting JSON objects back, the results will automatically be serialized into +Java variables for us. This makes our code a lot easier to write and essentially is +the same code from this point onwards that would also work with a local graph that we +are directly connected to. + +[source,groovy] +---- + GraphTraversalSource g = + EmptyGraph.instance().traversal(). + withRemote(DriverRemoteConnection.using(cluster)); +---- + +We can now use our new graph traversal source object to issue a Gremlin query. The +results will be placed directly into the 'List' called 'vmaps'. The query finds the +first 10 airports with a region code of 'GB-ENG' which is short for Great Britain +- England. + +[source,groovy] +---- + List > vmaps = + g.V().has("airport","region","GB-ENG").limit(10).valueMap().toList(); + + System.out.println("\n\nThe following airports were found\n"); + for (Map m : vmaps) + { + ArrayList code = (ArrayList) m.get("code"); + ArrayList desc = (ArrayList) m.get("desc"); + System.out.println(code.get(0) + " , " + desc.get(0)); + } + + cluster.close(); + } +} +---- + +When the Java application is compiled and run the output should look similar to that +shown below. + +[source,groovy] +---- +LEQ , Land's End Airport +LGW , London Gatwick +MAN , Manchester Airport +LHR , London Heathrow +LCY , London City Airport +STN , London Stansted Airport +EMA , East Midlands Airport +LPL , Liverpool John Lennon Airport +LBA , Leeds Bradford Airport +NCL , Newcastle Airport +---- [[rubyclient]] Connecting to a Gremlin Server from Ruby @@ -18783,7 +19687,7 @@ g.V().as('a').outE().has('dist',gt(8000)). path().by('code').by('dist') ---- -When we run this query using the Gremlin console with TinkerGraph we get get +When we run this query using the Gremlin console with TinkerGraph we get back results that have been to a degree 'pretty printed' by the Console as shown below. @@ -18841,7 +19745,7 @@ exact same query as the one we used above. "meta":{}}} ---- -What is being returned is useful in some cases, for examples we can see the +What is being returned is useful in some cases, for example we can see the 'a' and 'b' labels that we used in our query but in this case all we really wanted was the last part with the airport codes and the distances. We could decide to write code to process this JSON as-is (probably using a JSON helper @@ -18858,7 +19762,7 @@ g.V().as('a').outE().has('dist',gt(8000)). path().by('code').by('dist').toList().toString() ---- -If we were to send this modified form of the query to our Gremlin Server, we shoulkd +If we were to send this modified form of the query to our Gremlin Server, we should get back something that looks a lot more like the result we got back when working with the Gremlin Console. As shown below, it is certainly a bit easier to process in your application now. However, this is still not an ideal result as what we now have @@ -19458,12 +20362,6 @@ g.V().has('region','US-OK').values('code').fold() ---- -Using Gremlin within a Python notebook -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -To be written - - [[serialize]] COMMON GRAPH SERIALIZATION FORMATS ---------------------------------- @@ -19473,7 +20371,7 @@ have provided a brief overview of a few of them. As you will see there are a num of ways you can represent graph data using simple CSV files. There are also XML format, JSON formats and many more. Of these, the one that still seems to be supported across most tools and platforms is GraphML. Not all of the features offered -by Apache TinkerPop can be expressed using GrapphML however. So let's take a look at +by Apache TinkerPop can be expressed using GraphML however. So let's take a look at some of the more commonly used formats. [[csv]] diff --git a/book/README.md b/book/README.md index 4ccdbe31..c8a29a41 100644 --- a/book/README.md +++ b/book/README.md @@ -2,7 +2,7 @@ This folder contains the AsciiDoc source file used to produce the HTML, XML (DOC **LATEST NEWS:** -[Feb-11-2018] Revision 277 was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. +[Mar-28-2018] Revision 278 (TP 3.3.1) was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. ## Releases and change history @@ -28,7 +28,8 @@ The XML (DOCBOOK) file is generated using Asciidoctor and can be viewed on Linux Please note that the DOCBOOK, EPUB and MOBI format files currently do not have all of the nice source code highlighting and colors that the HTML and PDF versions have. -**ARCHIVED NEWS:** +**ARCHIVED NEWS:** +[Feb-11-2018] Revision 277 was just published in all formats. See [change history](https://github.com/krlawrence/graph/blob/master/ChangeHistory.md) for details. [Jan-12-2018] Revision 276 was just plublished in all formats. Many updates to book and samples. [Jan-12-2018] Based on feedback I have decided to rename the book "Practical Gremlin" (see issue #29) [Jan-03-2018] Revision 275 was just published in all formats. Lots of updates to book and sample code. diff --git a/demos/air-routes-latest-visjs.html b/demos/air-routes-latest-visjs.html new file mode 100755 index 00000000..91e1cbf1 --- /dev/null +++ b/demos/air-routes-latest-visjs.html @@ -0,0 +1,48838 @@ + + + +World air route map (graph) + + + + + + + + + + + +
+

+

+ +		 +
+
+ + + + diff --git a/demos/air-routes-small-latest-visjs.html b/demos/air-routes-small-latest-visjs.html new file mode 100755 index 00000000..2b8cdcf6 --- /dev/null +++ b/demos/air-routes-small-latest-visjs.html @@ -0,0 +1,1504 @@ + + + +World air route map (graph) + + + + + + + + + + + +
+

+

+ +		 +
+
+ + + + diff --git a/images/README.md b/images/README.md index ba161b76..7cb9b237 100644 --- a/images/README.md +++ b/images/README.md @@ -1 +1 @@ -This folder contains images used by the book. +This folder contains images used by other parts of this project. diff --git a/sample-code/CreateGraph.java b/sample-code/CreateGraph.java index 684fdfb4..3b723a0b 100755 --- a/sample-code/CreateGraph.java +++ b/sample-code/CreateGraph.java @@ -71,23 +71,42 @@ public static void main(String[] args) { System.out.println(((List)(m.get("code"))).get(0) + " " + m.get(T.id) + " " + m.get(T.label)); } + System.out.println(); - // Display the routes in the graph we just created + // Display the routes in the graph we just created. + // Each path will include the vertex code values and the edge. List paths = new ArrayList(); - paths = g.V().out().path().by("code").toList(); + paths = g.V().outE().inV().path().by("code").by().toList(); for (Path p : paths) { System.out.println(p.toString()); } + // Count how many vertices and edges we just created. + // Using groupCount is overkill when we only have one label + // but typically you will have more so this is a useful technique + // to be aware of. + System.out.println("\nWe just created"); + List verts = g.V().groupCount().by(T.label).toList(); + System.out.println(((Map)verts.get(0)).get("airport") + " airports"); + List edges = g.E().groupCount().by(T.label).toList(); + System.out.println(((Map)edges.get(0)).get("route") + " routes"); + + // Note that we could also use the following code for a simple + // case where we are only interested in specific labels. + Long nv = g.V().hasLabel("airport").count().next(); + Long ne = g.E().hasLabel("route").count().next(); + System.out.println("The graph has " + nv + " airports and " + ne + " routes"); + + // Save the graph we just created as GraphML (XML) or GraphSON (JSON) try { // If you want to save the graph as GraphML uncomment the next line - tg.io(IoCore.graphml()).writeGraph("mygraph.graphml"); + tg.io(IoCore.graphml()).writeGraph("mygraph.graphml"); // If you want to save the graph as JSON uncomment the next line //tg.io(IoCore.graphson()).writeGraph("mygraph.json"); diff --git a/sample-code/ListAirports.java b/sample-code/ListAirports.java new file mode 100755 index 00000000..b332b6d1 --- /dev/null +++ b/sample-code/ListAirports.java @@ -0,0 +1,143 @@ +// ListAirports.java +// +// Another simple example of using TinkerGraph with Java +// +// This example does the following: +// 1. Creates an empty TinkerGraph instance +// 2. Loads the air-routes.graphml file +// 3. Displays information about selected airports. +// +import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource; +import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__; +import org.apache.tinkerpop.gremlin.process.traversal.Path; +import org.apache.tinkerpop.gremlin.process.traversal.*; +import org.apache.tinkerpop.gremlin.structure.Edge; +import org.apache.tinkerpop.gremlin.structure.Vertex; +import org.apache.tinkerpop.gremlin.structure.io.IoCore; +import org.apache.tinkerpop.gremlin.structure.T; +import org.apache.tinkerpop.gremlin.structure.Column; +import org.apache.tinkerpop.gremlin.tinkergraph.structure.*; +import org.apache.tinkerpop.gremlin.util.Gremlin; +import java.io.IOException; +import java.util.List; +import java.util.ArrayList; +import java.util.Map; +import java.util.HashMap; +import org.apache.commons.configuration.BaseConfiguration; + +public class ListAirports +{ + private TinkerGraph tg; + private GraphTraversalSource g; + + // ------------------------------------------------------------- + // Try to create a new graph and load the specified GraphML file + // ------------------------------------------------------------- + public boolean loadGraph(String name) + { + // Make sure index ID values are set as LONG values. + // If this is not done, when we try to sort results by vertex + // ID later it will not sort the way you would expect it to. + + BaseConfiguration conf = new BaseConfiguration(); + conf.setProperty("gremlin.tinkergraph.vertexIdManager","LONG"); + conf.setProperty("gremlin.tinkergraph.edgeIdManager","LONG"); + conf.setProperty("gremlin.tinkergraph.vertexPropertyIdManager","LONG"); + + // Create a new instance that uses this configuration. + tg = TinkerGraph.open(conf) ; + + // Load the graph and time how long it takes. + System.out.println("Loading " + name); + long t1 = System.currentTimeMillis(); + System.out.println(t1); + + try + { + tg.io(IoCore.graphml()).readGraph(name); + } + catch( IOException e ) + { + System.out.println("ERROR - GraphML file not found or invalid."); + return false; + } + + long t2 = System.currentTimeMillis(); + System.out.println(t2 + "(" + (t2-t1) +")"); + System.out.println("Graph loaded\n"); + g = tg.traversal(); + return true; + } + + // ---------------------------------------------- + // Display some information about the selected + // number of airports. A value of -1 means select + // all airports. + // ---------------------------------------------- + public void listAirports(int max) + { + if (max < -1 ) return; + + // Try to find the requested number of airports. + // Note the use of the "__." and "Order" prefixes. + List vlist = + g.V().hasLabel("airport"). + order().by(__.id(),Order.incr). + limit(max). + toList(); + + Long id; // Vertex ID + String iata; // 3 character IATA code. + String icao; // 4 character ICAO code. + String city; // City the airport is in. + String desc; // Airport description. + String ctry; // 2 character country code. + String rgn; // 5 or 6 character Region code + + for (Vertex v : vlist) + { + id = (Long)v.id(); + iata = (String)v.values("code").next(); + icao = (String)v.values("icao").next(); + city = (String)v.values("city").next(); + desc = (String)v.values("desc").next(); + ctry = (String)v.values("country").next(); + rgn = (String)v.values("region").next(); + + System.out.format("%5d %3s %4s %2s %6s %15s %-50s\n", + id,iata,icao,ctry,rgn,city,desc); + } + } + + // --------------------------------------- + // Try to load a graph and run a few tests + // --------------------------------------- + public static void main(String[] args) + { + int required = 10; + boolean failed = false; + + try + { + if (args.length > 0) required = Integer.parseInt(args[0]); + } + catch (Exception e) + { + failed = true; + } + + if (failed || required < -1) + { + System.out.println("Argument should be -1, 0 or any positive integer"); + System.exit(1); + } + + ListAirports lapt = new ListAirports(); + + if ( lapt.loadGraph("air-routes.graphml")) + { + lapt.listAirports(required); + } + + } +} diff --git a/sample-code/README.md b/sample-code/README.md index 8ca61ed2..11380be0 100644 --- a/sample-code/README.md +++ b/sample-code/README.md @@ -4,3 +4,5 @@ This folder contains code snippets and samples referenced by the book as well as Note that files with a ".groovy" extension but with all lowercase names are intended to be launched from within the Gremlin Console. Files with a ".groovy" extenstion and capitalized names, such as TinkerGraphTest.groovy, are stand alone groovy applications that should be run from outside of the console using the groovy runtime. Files with a ".java" extension are stand alone Java applications that need to be compiled and run using a Java JDK. + +Some of the samples use data from CSV files that can be found in the *'/sample-data'* folder. diff --git a/sample-code/RemoteClient.java b/sample-code/RemoteClient.java new file mode 100755 index 00000000..697df572 --- /dev/null +++ b/sample-code/RemoteClient.java @@ -0,0 +1,64 @@ +// RemoteClient.java +// +// Simple example of using GremlinServer from a Java client +// +// This example does the following: +// 1. Configure a new Cluster object +// 2. Use that cluster to connect to a Gremlin Server +// 3. Run a few queries against graph + +import org.apache.tinkerpop.gremlin.driver.Cluster; +import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource; +import org.apache.tinkerpop.gremlin.structure.util.empty.EmptyGraph; +import org.apache.tinkerpop.gremlin.driver.remote.DriverRemoteConnection; +import org.apache.tinkerpop.gremlin.driver.ser.GryoMessageSerializerV1d0; +import java.util.Map; +import java.util.List; +import java.util.ArrayList; + +public class RemoteClient +{ + public static void main( String[] args ) + { + Cluster.Builder builder = Cluster.build(); + builder.addContactPoint("localhost"); + builder.port(8182); + builder.serializer(new GryoMessageSerializerV1d0()); + + Cluster cluster = builder.create(); + + GraphTraversalSource g = + EmptyGraph.instance().traversal(). + withRemote(DriverRemoteConnection.using(cluster)); + + List > vmaps = + g.V().has("airport","region","GB-ENG").limit(10).valueMap().toList(); + + System.out.println("\n\nThe following airports were found\n"); + for (Map m : vmaps) + { + ArrayList code = (ArrayList) m.get("code"); + ArrayList desc = (ArrayList) m.get("desc"); + System.out.println(code.get(0) + " , " + desc.get(0)); + } + + cluster.close(); + } +} + +// The output should look something like this + +/* +The following airports were found + +LEQ , Land's End Airport +LGW , London Gatwick +MAN , Manchester Airport +LHR , London Heathrow +LCY , London City Airport +STN , London Stansted Airport +EMA , East Midlands Airport +LPL , Liverpool John Lennon Airport +LBA , Leeds Bradford Airport +NCL , Newcastle Airport +*/ diff --git a/sample-code/add-aircraft.groovy b/sample-code/add-aircraft.groovy new file mode 100755 index 00000000..0928298d --- /dev/null +++ b/sample-code/add-aircraft.groovy @@ -0,0 +1,64 @@ +;[] // add-aircraft.groovy +;[] // +;[] // This script is intended to be run within the Gremlin Console. +;[] // +;[] // This script will try to read data from a CSV file (aircraft.csv) and add some +;[] // aircraft information to the graph. +;[] // +;[] // It is assumed, but not essential, that the air-routes graph data +;[] // has already been loaded. The first line of the CSV file contains the column +;[] // headers. This will be used as the names of the property keys. A vertex will be +;[] // created for each aircraft type found in the data. The script will +;[] // also create a vertex for each aircraft manufacturer found in the data and +;[] // create edges that connect the manufacturers to the planes they make. The main +;[] // Purpose of this script is to show a CSV file being used and to show queries +;[] // that also create vertices and edges without prior knowledge of exactly what +;[] // data will be loaded from the CSV data. +;[] // +;[] // The ";[]" notation is used to prevent unwanted output from the Gremlin +;[] // Console. + +;[] // Open the CSV file setup a reader +fis = new FileInputStream("aircraft.csv");[] + +reader = new BufferedReader(new InputStreamReader(fis));[] + +;[] // The first line of the file is assumed to be the column headers that +;[] // will be used as property keys. +pkeys = reader.readLine().toLowerCase().split(",");[] + +;[] // Create a vertex for each aircraft type found in the CSV data +reader.eachLine{line -> def values = line.split(","); + g.addV("aircraft"). + property(pkeys[0],values[0]). + property(pkeys[1],values[1]). + property(pkeys[2],values[2]). + property(pkeys[3],values[3]).iterate() } + +;[] // Display the vertices that were just created +newverts = g.V().hasLabel("aircraft").valueMap(true).toList();[] +newverts.each{println it};[] + +;[] // Create a "maker" vertex for each unique plane maker we find +g.V().hasLabel("aircraft").values("maker").dedup().as('a'). + addV('maker').property('name',select('a')).iterate();[] + +;[] // Verify that our new vertices look good +newverts = g.V().hasLabel("maker").valueMap(true).toList();[] +newverts.each{println it};[] + +;[] // Add edges between the maker and the planes they make +g.V().hasLabel('maker').as('m'). + V().hasLabel('aircraft').as('a'). + where('m',eq('a')).by('name').by('maker'). + addE('makes').from('m').to('a').iterate();[] + +;[] // Verify that our new edges look good +g.V().hasLabel('maker'). + order().by('name'). + outE('makes').inV(). + path().by('name').by(label).by('desc') + +;[] // Review how the graph demographic looks now +g.V().groupCount().by(label) +g.E().groupCount().by(label) diff --git a/sample-code/graph-stats.groovy b/sample-code/graph-stats.groovy index 750a22f8..55d97996 100755 --- a/sample-code/graph-stats.groovy +++ b/sample-code/graph-stats.groovy @@ -1,8 +1,15 @@ +;[] // graph-stats.groovy +;[] // +;[] // This script is intended to be run within the Gremlin Console. +;[] // ;[] // Small Groovy script that can be used from within the Gremlin console after ;[] // the air-routes graph has been loaded that will provide statistics about the -;[] // graph. The use of ;[] is just to stop the console printing additional return values. -;[] // This script demonstrates different ways of doing similar things +;[] // graph. This script demonstrates different ways of doing similar things +;[] // ;[] // From the console use :load graph-stats.groovy to run it. +;[] // +;[] // The ";[]" notation is used to prevent unwanted output from the Gremlin +;[] // Console. println "\n\nA few statistics about the air-routes graph";[] println "===========================================";[] diff --git a/sample-code/regression.groovy b/sample-code/regression.groovy new file mode 100755 index 00000000..9aabbe1b --- /dev/null +++ b/sample-code/regression.groovy @@ -0,0 +1,971 @@ +;[] // Regression tests designed to be run from the Gremlin Console with the +;[] // air routes graph loaded. This script is tuned for the version of the graphml +;[] // file shipped with the book (0.77). The Gremlin version should be 3.3.1 or +;[] // higher. This script is intended to be run each time a new Apache TinkerPop +;[] // version is released to make sure the examples in the book are still working. +;[] // This script will be added to as time permits to increase coverage. +;[] // This script assumes the 'graph' and 'g' variables have been defined. + +println "\n\n-------------";[] +println "Running tests";[] +println "-------------";[] + +n = 0;[] + +def status(msg,ctr) { + printf("%4d : %s\n",ctr,msg); + return ctr+1; } ;[] + +;[] //------------------------------------------------------------------------- + +n=status("Checking Gremlin version",n);[] +ver=Gremlin.version();[] +println "Reported version is $ver";[] + +assert ver[0]=='3' && ver[2]=='3' && ver[4].toInteger() >= 1;[] + +n=status( "Checking air-routes version",n);[] +ver=g.V().hasLabel('version').values('code').next();[] +println "Reported version is $ver";[] +assert ver=='0.77';[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking simple traversals with 'outE' and 'inV'",n);[] +d=g.V().has('code','DFW'). + outE().as('a').inV(). + has('code','AUS'). + select('a').values('dist').next();[] +assert d == 190;[] + +n=status( "Checking traversal of form 'has().out().has()'",n);[] +c=g.V().has('code','DEN').out(). + has('country','MX').count().next();[] + +assert c == 7;[] + +n=status( "Checking traversal 'has().out().as()' and 'select'",n);[] +a=g.V().has('code','DEL').out().as('a').in("contains"). + has('code','EU').select('a').by('city').toList();[] + +assert a.size() == 17;[] +assert a.contains('Helsinki');[] +assert a.sort().subList(0,3).flatten() == ['Amsterdam','Birmingham','Brussels'];[] + +n=status( "Checking edge dist property 'within(100..200)'",n);[] +c=g.V().outE().has('dist',within(100..200)).count().next();[] + +assert c == 3029;[] + +c=g.V().has('airport','country','US'). + outE().has('dist',within(100..200)). + inV().has('country','US'). + count().next();[] + +assert c == 583;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'hasNext' and 'tryNext'",n);[] +assert g.V().has('code','AUS').out('route').hasNext();[] +assert g.V().has('code','AUS').out('route').next();[] +assert g.V(3).out('route').tryNext();[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'optional' when test fails",n);[] +s=g.V().has('code','AUS').optional(out().has('code','SYD')).values('city').next();[] +assert s == 'Austin';[] + +n=status( "Checking 'optional' when test matches",n);[] +s=g.V().has('code','DFW').optional(out().has('code','SYD')).values('city').next();[] +assert s == 'Sydney';[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking simple 'has' steps",n);[] +c=g.V().has('region','US-TX').has('longest',gte(12000)).count().next();[] +assert c == 6;[] + + +n=status( "Checking 'hasLabel' and 'count'",n);[] +c=g.V().hasLabel('airport').count().next();[] +assert c == 3374;[] + +n=status( "Checking 'has('region','US-CA')' and 'count'" ,n);[] +c=g.V().has('region','US-CA').count().next();[] +assert c==29;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'order' and 'path'",n);[] +a=g.V().has('airport','code','LCY').outE().inV(). + order().by('code').path().by('code').by('dist').by('code').toList();[] +assert a[0][0]=='LCY';[] +assert a[0][1]==404;[] +assert a[0][2]=='ABZ';[] +assert a[0] instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.MutablePath;[] + +n=status( "Checking 'path'",n);[] +p=g.V().has('code','AUS').outE().inV(). + has('code','MEX'). + path().by('code').by('dist').next();[] + +assert p instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.MutablePath;[] +assert p.isSimple();[] +assert p.head() == 'MEX';[] +assert p[1] == 748;[] + +n=status( "Checking 'path' with 'count(local)'",n);[] +c=g.V().has('code','AUS').out().out().out().limit(1). + path().count(local).next();[] +assert c == 4;[] + +n=status( "Checking 'cyclicPath'",n);[] +p=g.V().has('code','AUS').repeat(out()).until(cyclicPath()). + limit(1).path().by('code').next();[] +assert !p.isSimple();[] +assert p[0] == p[-1];[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'group'",n);[] +m=g.V().hasLabel('continent').group().by('code').by(out().count()).next();[] +assert m instanceof java.util.HashMap;[] +assert m['EU']==583;[] + +n=status( "Checking 'group' for London airports",n);[] +m=g.V().hasLabel('continent').group().by('code').by(out().count()).next();[] +m = g.V().has('region','GB-ENG').has('city','London'). + group().by('code').by(out().count()).next();[] +assert m['LCY']==42;[] +assert m['LHR']==191;[] +assert m['LTN']==104;[] +assert m['STN']==186;[] +assert m['LGW']==200;[] + +n=status( "Checking 'groupCount'",n);[] +m=g.V().hasLabel('airport').groupCount().by('runways').next();[] +assert m[1] == 2316;[] + +n=status( "Checking 'groupCount' and 'select'",n);[] +m=g.V().hasLabel('airport'). + groupCount().by('country').select('FR','GR','BE').next();[] +assert m['GR']==39;[] + +n=status( "Checking 'group' and 'order' using 'by(keys,incr)'",n);[] +m=g.V().has('airport','country','US'). + group().by('code').by('runways'). + order(local).by(keys,incr).next();[] + +assert m.size()==579;[] +assert m['ABE'][0]==2;[] +assert m['DFW'][0]==7;[] + +n=status( "Checking 'group' with 'count(local)'",n);[] +c=g.V().hasLabel('airport').limit(5). + group().by('code').by('city'). + count(local).next();[] +assert c == 5;[] + +n=status( "Checking 'group' with 'unfold' and 'select(keys)'",n);[] +a=g.V().has('airport','country','IE'). + group().by('code').by('runways').select(keys). + unfold().order().fold().next();[] +assert a.size() == 7;[] +assert a[0] == 'CFN';[] +assert a == ['CFN','DUB','KIR','NOC','ORK','SNN','WAT'];[] + +n=status( "Checking nested 'group' steps",n);[] +a=g.V().hasLabel("airport").limit(5). + group(). + by('code'). + by(out("route").limit(5). + group(). + by('code'). + by(out("route").count())). + select('ANC'). + select('IAH','LAX').next();[] +assert a['IAH'] == 192;[] +assert a ['LAX'] == 195;[] + + +n=status( "Checking 'order' with 'valueMap' and 'select'",n);[] +a=g.V().hasLabel('airport'). + order().by('longest',decr).valueMap(). + select('code','longest').limit(10).toList();[] + +assert a[0]['code'][0] == 'BPX';[] +assert a[0]['longest'][0] == 18045;[] + +n=status( "Checking 'group' with 'select(values)'",n);[] +a=g.V().has('code','DFW').project('dfw','route_count'). + by().by(outE().count()).select(values).next();[] + +assert a[0] == g.V().has('code','DFW').next();[] +assert a[1] == 221;[] + +n=status( "Checking 'order by('lobgest',decr)' with 'valueMap' and 'select''",n);[] +a=g.V().hasLabel('airport'). + order().by('longest',decr).valueMap(). + select('code','longest').limit(10).toList();[] + +assert a.size() == 10;[] +assert a[0]['longest'][0] == 18045;[] +assert a[9]['longest'][0] == 15092;[] +;[] //------------------------------------------------------------------------- +n=status( "Checking 'project'",n);[] +a=g.V().has('airport','region','GB-ENG'). + project('IATA','Routes'). + by('code').by(out().count()). + order().by(select('Routes'),decr).toList();[] + +assert a[0]['IATA'] == 'LGW';[] +assert a[0]['Routes'] == 200;[] +assert a.size==27;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'where' with 'by'",n);[] +c=g.V().has('airport','code','AUS').as('a'). + out().where(eq('a')).by('runways').count().next();[] +assert c==9;[] + +n=status( "Checking 'where' with 'is', 'gt' and inline 'and'",n) ;[] +c=g.V().has('airport','code','AUS').out(). + where(values('runways').is(gt(4).and(neq(6)))). + valueMap('code','runways').count().next();[] +assert c==6;[] + +n=status( "Checking 'where' with 'is', 'gt' and inline 'or'",n) ;[] +c=g.V().has('airport','code','AUS').out(). + where(values('runways').is(gt(6)).or().values('runways').is(4)). + valueMap('code','runways').count().next();[] + +assert c==24;[] + +n=status( "Checking 'where' with 'count(local)'",n) ;[] +c=g.V().hasLabel('airport'). + where(out('route').count().is(gt(180))). + values('code').fold().count(local).next();[] + +assert c == 25;[] + +n=status( "Checking 'filter' and a 'where' with two parameters",n) ;[] +c=g.V().has('code','AUS').as('a').out().as('b'). + filter(select('a','b').by('runways').where('a',eq('b'))). + valueMap('code','runways').count().next();[] + +assert c==9;[] + +n=status( "Checking 'where' with two 'by' steps and 3 parameter 'select'",n);[] +c=g.V().has('airport','city','London').as('a','r'). + in('contains').as('b'). + where('a',eq('b')).by('country').by('code'). + select('a','r','b').by('code').by('region').count().next();[] + +assert c==6;[] + +n=status( "Checking 'where('a',lt('b'))'",n);[] +a=g.V().has('region','GB-ENG').order().by('code').as('a'). + out().has('region','GB-ENG').as('b'). + where('a',lt('b')).by('code'). + path().by('code').toList();[] + +assert a.size() == 22;[] +assert a[0][0] == 'BHX';[] +assert a[0][1] == 'NCL';[] +assert a[-1][0] == 'NCL';[] +assert a[-1][1] == 'SOU';[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking two V steps and 'select' inside a 'local' step",n);[] +c=g.V().has('code','NCE').values('region').as('r'). + V().hasLabel('airport').as('a'). + values('region').where(eq('r')).by(). + local(select('a').values('city','code','region').fold()). + count().next();[] +assert c==4;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'choose' with two choices",n);[] +a= g.V().hasLabel('airport').has('country','US').limit(15). + choose(values('code').is(within('AUS','DFW')), + values('city'), + values('region')).order().toList();[] + +assert a[0]=='Austin';[] +assert a[1]=='Dallas';[] +assert a[2]=='US-AK';[] + +n=status( "Checking 'choose' with three 'options'",n);[] +a=g.V().hasLabel('airport'). + choose(values('code')). + option('DFW',values('icao')). + option('AUS',values('region')). + option('LAX',values('code')).fold().next();[] + +assert a.sort() == ['KDFW','LAX','US-TX'];[] + +n=status( "Checking 'choose' as a side effect with labels",n);[] +a=g.V().hasLabel('airport'). + choose(has('runways',4), groupCount('a').by(constant('four'))). + choose(has('runways',lte(2)), groupCount('a').by(constant('low'))). + choose(has('runways',gte(6)), groupCount('a').by(constant('high'))). + choose(has('country','FR'), groupCount('a').by(constant('France'))). + groupCount('a').by(constant('total')).cap('a').next();[] +assert a['total'] == 3374;[] +assert a['low'] == 3078;[] +assert a['four'] == 51;[] +assert a['France'] == 58;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'match'",n);[] +c= g.V().hasLabel('airport'). + match(__.as('s').out().as('d'), + __.not(__.as('d').out().as('s'))). + select('s','d').by('code').count().next();[] +assert c==238;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'union'",n);[] +a=g.V().has('city','London').has('region','GB-ENG'). + local(union(__.values('code'), + out('route').has('city','Paris').values('code'), + out('route').has('city','Berlin').values('code')). + fold()).toList();[] + +assert a.flatten() - ['LHR','ORY','CDG','TXL','LGW','LCY','STN','LTN','SXF'] == [];[] + +n=status( "Checking 'union' with 'identity' and 'constant'",n);[] +a=g.V().has('airport','code','LAX'). + union(identity(),constant(1)).toList();[] +assert a[0] == g.V().has('airport','code','LAX').next();[] +assert a[1] == 1;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'math' addition",n);[] +d=g.V().has('airport','code','DFW').as('a'). + out().has('code','SFO').as('b'). + math('a + b').by('runways').next();[] +assert d==11.0;[] + +n=status( "Checking 'math' sine",n);[] +x=g.inject(60*(Math.PI/180)).math('sin(_)').next();[] +assert x == 0.8660254037844386;[] + +n=status( "Checking 'max' on 'values('longest')",n);[] +c=g.V().hasLabel('airport').values('longest').max().next();[] +assert c == 18045;[] + +n=status( "Checking 'min' on 'values('longest')",n);[] +c=g.V().hasLabel('airport').values('longest').min().next();[] +assert c == 1300;[] + +n=status( "Checking 'mean' on 'values('longest')",n);[] +c=g.V().hasLabel('airport').values('longest').mean().next();[] +assert c == 7570.862477771191;[] + +n=status( "Checking 'sum' on 'values('longest')",n);[] +c=g.V().hasLabel('airport').values('longest').sum().next();[] +assert c == 25544090;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'has' with 'eq'",n);[] +c=g.V().has('runways',eq(3)).count().next();[] +assert c == 225;[] + +n=status( "Checking 'has' with 'neq'",n);[] +c=g.V().has('runways',neq(1)).count().next();[] +assert c == 1058;[] + +n=status( "Checking 'has' with 'gt'",n);[] +c=g.V().has('runways',gt(3)).count().next();[] +assert c == 71;[] + +n=status( "Checking 'has' with 'gte'",n);[] +c=g.V().has('runways',gte(3)).count().next();[] +assert c == 296;[] + +n=status( "Checking 'has' with 'lt'",n);[] +c=g.V().has('runways',lt(3)).count().next();[] +assert c == 3078;[] + +n=status( "Checking 'has' with 'lte'",n);[] +c=g.V().has('runways',lte(3)).count().next();[] +assert c == 3303;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'without('DFW','LAX')'",n);[] +a=g.V().has('airport','code','AUS'). + out().has('code',without('DFW','LAX')). + out().has('code','SYD').path().by('code').toList();[] +assert a.size() == 1;[] +assert a[0].flatten() == ['AUS','SFO','SYD'];[] + + +n=status( "Checking 'aggregate' and 'without'",n);[] +c=g.V().has('code','AUS').out().aggregate('nonstop'). + out().where(without('nonstop')).dedup().count().next();[] +assert c==812;[] + +n=status( "Checking 'within(3..6)'",n);[] +c=g.V().has('runways',within(3..6)).values('code','runways').count().next();[] +assert c == 588;[] + +n=status( "Checking 'without(3..6)'",n);[] +c=g.V().has('runways',without(3..6)).values('code','runways').count().next();[] +assert c == 6160;[] + +n=status( "Checking 'without(3..6)' with 'fold' and 'count(local)'",n);[] +c=g.V().has('runways',without(3..6)).values('code','runways').fold().count(local).next();[] +assert c == 6160;[] + +n=status( "Checking 'within(1,2,3)'",n);[] +c=g.V().has('runways',within(1,2,3)).values('code','runways').count().next();[] +assert c == 6606;[] + +n=status( "Checking 'without(1,2,3)'",n);[] +c=g.V().has('runways',without(1,2,3)).values('code','runways').count().next();[] +assert c == 142;[] + +n=status( "Checking 'within(('DFW','DAL','IAH','HOU','SAT')'",n);[] +a=g.V().has('airport','code','AUS'). + out().has('code',within('DFW','DAL','IAH','HOU','SAT')). + out().has('code','LAS').path().by('code').toList();[] +assert a.size() == 4;[] + +n=status( "Checking 'witout(('DFW','DAL','IAH','HOU','SAT')'",n);[] +a=g.V().has('airport','code','AUS'). + out().has('code',without('DFW','DAL','IAH','HOU','SAT')). + out().has('code','LAS').path().by('code').toList();[] +assert a.size() == 48;[] + +n=status( "Checking 'hasId(within(1..46)'",n);[] +c=g.V().hasId(within(1..46)).out().hasId(between(1,47)).count().next();[] +assert c == 1326;[] + +n=status( "Checking 'between(5,8)'",n);[] +c=g.V().has('runways',between(5,8)).values('code','runways').fold().count(local).next();[] +assert c == 38;[] + +n=status( "Checking 'between('Dal','Dam')'",n);[] +a=g.V().hasLabel('airport'). + has('city',between('Dal','Dam')). + values('city').order().dedup().toList();[] +assert a.size() == 6;[] +assert a[0] == 'Dalaman';[] +assert a[5] == 'Dallas';[] + +n=status( "Checking 'inside(3,6)'",n);[] +c=g.V().has('runways',inside(3,6)).values('code','runways').count().next();[] +assert c == 130;[] + +n=status( "Checking 'outside(-50,77)'",n);[] +c=g.V().has('lat',outside(-50,77)).order().by('lat',incr).count().next();[] +assert c == 10;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'range(local,3,5)'",n);[] +a=g.V().has('airport','country','IE'). + group().by('code').by('runways'). + order(local).by(keys).select(keys). + range(local,3,5).next();[] +assert a.size() == 2;[] +assert a.contains('NOC');[] +assert a.contains('ORK');[] + +n=status( "Checking 'range(local,0,-1)'",n);[] +a=g.V().has('airport','country','IE'). + group().by('code').by('runways'). + order(local).by(keys).select(keys). + range(local,0,-1).next();[] +assert a.size() == 7;[] +assert a.toString() == "[CFN, DUB, KIR, NOC, ORK, SNN, WAT]";[] + +n=status( "Checking 'skip(local)'",n);[] +a=g.V().has('airport','country','IE'). + group().by('code').by('runways'). + order(local).by(keys).select(keys). + skip(local,3).next();[] +assert a.size() == 4;[] +assert a.toString() == "[NOC, ORK, SNN, WAT]";[] + +n=status( "Checking 'limit(3)'",n);[] +a=g.V().has('country','IE'). + order().by('code').limit(3). + values('code').fold().next();[] +assert a.size() == 3;[] +assert a == ['CFN','DUB','KIR'];[] + +n=status( "Checking 'tail(2)'",n);[] +a=g.V().has('country','IE'). + order().by('code').tail(2). + values('code').fold().next();[] +assert a.size() == 2;[] +assert a == ['SNN','WAT'];[] + +n=status( "Checking 'skip(5)'",n);[] +a=g.V().has('country','IE'). + order().by('code').skip(5). + values('code').fold().next();[] +assert a.size() == 2;[] +assert a == ['SNN','WAT'];[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking boolean 'or' operator",n);[] +c=g.V().hasLabel('airport'). + or(has('region','US-TX'), + has('region','US-LA'), + has('region','US-AZ'), + has('region','US-OK')). + order().by('region',incr). + valueMap().select('code','region').count().next();[] +assert c == 48;[] + +n=status( "Checking boolean 'and' operator",n);[] +c=g.V().hasLabel('airport'). + and(has('region','US-TX'), + has('longest',gte(12000))). + values('code').count().next();[] +assert c == 6;[] + +a=g.V().has('airport','code','AUS'). + out().and(has('code',neq('DFW')),has('code',neq('LAX'))). + out().has('code','SYD').path().by('code').toList();[] +assert a.size() == 1;[] +assert a[0].flatten() == ['AUS','SFO','SYD'];[] + +n=status( "Checking 'is(gt(3).and(lt(6))'",n);[] +c=g.V().hasLabel('airport'). + where(values('runways').is(gt(3).and(lt(6)))).count().next();[] +assert c == 65;[] + +c2=g.V().hasLabel('airport').has('runways',between(4,6)).count().next();[] +assert c == c2;[] + +c=status( "Checking 'is(lt(3).and(gt(6))'",n);[] +c=g.V().hasLabel('airport'). + where(values('runways').is(lt(3).or(gt(6)))).count().next();[] +assert c == 3080;[] + +n=status( "Checking boolean 'not' operator",n);[] +c=g.V().not(inE()).count().next();[] +assert c == 245;[] + +c=g.V().not(bothE()).count().next();[] +assert c == 7;[] + +c=g.V().not(hasLabel('airport')).count().next();[] +assert c == 245;[] + +n=status( "Checking 'hasNot('runways')'",n);[] +c=g.V().hasNot('runways').count().next();[] +assert c == 245;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'coalesce' where first param is false",n);[] +s=g.V().has('code','AUS'). + coalesce(out().has('code','SYD'),identity()).values('city').next();[] +assert s=='Austin';[] + +n=status( "Checking 'coalesce' where first param is true",n);[] +s=g.V().has('code','AUS'). + coalesce(out().has('code','DFW'),identity()).values('city').next();[] +assert s=='Dallas';[] + +n=status( "Checking 'coalesce' with a 'constant' for secon param",n);[] +s=g.V().has('code','AUS'). + coalesce(out().has('code','SYD'),constant('no route')).next();[] +assert s == 'no route';[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'withSideEffect', 'store' and a Set",n);[] +s=g.withSideEffect('s', [] as Set). + V().hasLabel('airport').values('runways'). + store('s').cap('s').order(local).next();[] +assert s instanceof java.util.ArrayList;[] +assert s.size == 8;[] +assert s == [1,2,3,4,5,6,7,8];[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'BulkSet' from 'store'",n);[] +s=g.V().has('region','US-MA').store('r').by('runways').cap('r').next();[] +assert s instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.BulkSet;[] +assert s.size() == 7;[] +assert s.flatten().size() == 4;[] +assert s.uniqueSize() == 4;[] +assert s.contains(6);[] +assert s.asBulk()[2] == 3;[] +assert s.asBulk()[3] == 2;[] + +n=status( "Checking 'BulkSet' from 'aggregate'",n);[] +s=g.V().has('airport','country','IE').aggregate('ireland').cap('ireland').next();[] +assert s instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.BulkSet;[] +assert s.size() == 7;[] +assert s.uniqueSize() == 7;[] + +n=status( "Checking 'BulkSet' using 'aggregate' with a 'by' modulator",n);[] +a=g.V().has('airport','country','IE'). + aggregate('ireland').by('runways').cap('ireland').next();[] +assert a.size() == 7;[] +assert a.uniqueSize() == 3;[] +assert a.get(1) == 3;[] +assert a.get(2) == 3;[] +assert a.get(4) == 0;[] +assert a.get(5) == 1;[] + +a=g.V().has('region','US-TX').aggregate('c').by('code').cap('c').next();[] +assert a.size() == 26;[] +assert a.uniqueSize() == 26;[] +assert a.sort()[0] == 'ABI';[] + +;[] //------------------------------------------------------------------------- + +n=status( "Checking 'dedup(local)'",n);[] +a=g.V().has('country','US').values('region'). + order().fold().dedup(local).next();[] +assert a.size() == 51;[] +assert a[1] == 'US-AL';[] +assert a[50] == 'US-WY';[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking property keys",n);[] +a=g.V().has('airport','code','DFW').properties().key().fold().next();[] +assert a.size() == 12;[] +assert a == ['country','code','longest','city','elev','icao','lon','type','region','runways','lat','desc'];[] + +n=status( "Checking property cardinality of LIST",n);[] +g.addV('test').property('a',1).property('a',2).property('a',3).iterate();[] +g.V().hasLabel('test').property(list,'a',4).iterate();[] +a=g.V().hasLabel('test').valueMap().select('a').next();[] +assert a.getClass() == java.util.ArrayList;[] +assert a == [1,2,3,4];[] +g.V().hasLabel('test').drop().iterate();[] + +n=status( "Checking property cardinality of SET",n);[] +g.addV('test').property(set,'s',1).iterate();[] +g.V().hasLabel('test').property(set,'s',2).property(set,'s',1).iterate();[] +g.V().hasLabel('test').property(set,'s',2).property(set,'s',4).iterate();[] +s=g.V().hasLabel('test').valueMap().select('s').next();[] +assert s.sort() == [1,2,4];[] +g.V().hasLabel('test').drop().iterate();[] + +n=status( "Checking meta properties",n);[] +g.V().has('code','AUS'). + properties().hasValue('AUS'). + property('date','6/6/2017').iterate();[] +mp=g.V(3).properties('code').properties('date').next();[] +assert mp instanceof org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerProperty;[] +assert mp.value() == '6/6/2017';[] +g.V(3).properties('code').properties('date').drop().iterate();[] +mp=g.V(3).properties('code').properties().fold().next();[] +assert mp==[];[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking vertex and edge creation",n);[] +graph2=TinkerGraph.open();[] +g2=graph2.traversal();[] +g2.addV('root').property('data',9).as('root'). + addV('node').property('data',5).as('b'). + addV('node').property('data',2).as('c'). + addV('node').property('data',11).as('d'). + addV('node').property('data',15).as('e'). + addV('node').property('data',10).as('f'). + addV('node').property('data',1).as('g'). + addV('node').property('data',8).as('h'). + addV('node').property('data',22).as('i'). + addV('node').property('data',16).as('j'). + addE('left').from('root').to('b'). + addE('left').from('b').to('c'). + addE('right').from('root').to('d'). + addE('right').from('d').to('e'). + addE('right').from('e').to('i'). + addE('left').from('i').to('j'). + addE('left').from('d').to('f'). + addE('right').from('b').to('h'). + addE('left').from('c').to('g').iterate();[] + +cv = g2.V().count().next();[] +assert cv == 10;[] + +ce = g2.E().count().next();[] +assert ce == 9;[] + +n=status( "Checking vertex label creation using a traversal",n);[] +v=g.addV(V().has('code','AUS').label()).property('code','XYZ').next();[] +assert v.label == 'airport';[] +g.V(v).drop(); + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'repeat until'",n);[] +c=g2.V().hasLabel('root'). + repeat(out('right')). + until(out('right').count().is(0)). + values('data').next();[] + +assert c == 22;[] + +n=status( "Checking 'repeat until not'",n);[] +c=g2.V().hasLabel('root'). + repeat(out('left')). + until(__.not(out('left'))). + values('data').next();[] + +assert c == 1;[] + +n=status( "Checking 'repeat until not' with 'path'",n);[] +a=g2.V().hasLabel('root'). + repeat(out('left')). + until(__.not(out('left'))). + path().by('data').next().toList();[] + +assert a.flatten() == [9,5,2,1];[] + +n=status( "Checking 'repeat' with 'unfold'",n);[] +a=g.V().has('airport','region','US-CA'). + order().by('city').limit(5). + group().by('code').by('city'). + select(values). + repeat(unfold()). + until(count(local).is(1)). + unfold().toList();[] + +assert a[1] == 'Bakersfield';[] +assert a[4] == 'Burbank';[] + +n=status( "Checking 'repeat' with 'emit()'",n);[] +p= g.V().has('airport','code','AUS'). + repeat(out()).emit().times(3).has('code','MIA'). + limit(5).path().by('code').next();[] + + +n=status( "Checking 'repeat' with 'emit(has(...))'",n);[] +c=g.V(3).repeat(out()).times(3). + emit(has('region','GB-ENG')). + has('region','GB-ENG'). + path().by('city').count().next();[] + +assert c == 8502;[] + +assert p instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.MutablePath;[] +assert p.isSimple();[] +assert p[0] == 'AUS';[] +assert p.head() == 'MIA';[] +assert p.size() == 2;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking vertex and implicit edge deletion",n);[] + +g2.V().has('data',15).drop().iterate();[] +cv = g2.V().count().next();[] +assert cv == 9;[] + +ce = g2.E().count().next();[] +assert ce == 7;[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'sack' with 'assign' using a 'constant'",n);[] +a=g.V().sack(assign).by(constant(1)). + has('code','SAF'). + out().values('runways'). + sack(sum).sack().fold().next();[] +assert a.flatten() == [8,5,4,7];[] + +n=status( "Checking 'sack' with 'assign' using a property value'",n);[] +a=g.V().has('code','AUS'). + sack(assign).by('runways'). + V().has('code','SAF'). + out(). + sack(mult).by('runways'). + sack().fold().next();[] +assert a.flatten() == [14,8,6,12];[] + +n=status( "Checking 'sack' with 'addAll'",n);[] +a=g.withSack([]). + V().has('code','SAF'). + out().values('runways').fold(). + sack(addAll).sack().next();[] +assert a.flatten() == [7,4,3,6];[] + +n=status( "Checking 'sack' with zero base and 'sum'",n);[] +a=g.withSack(0). + V().has('code','AUS'). + outE(). + sack(sum).by('dist'). + inV(). + outE(). + sack(sum).by('dist'). + inV().has('code','LHR'). + sack(). + order().by(incr).limit(10). + path(). + by('code'). + by('dist'). + by('code'). + by('dist'). + by('code'). + by().toList();[] +assert a[0][5] == 4893;[] + +n=status( "Checking 'sack' with 'assign' using a 'constant' and 'min'",n);[] +a=g.V().sack(assign).by(constant(400)).has('code','SAF'). + outE().sack(min).by('dist').sack().fold().next();[] +assert a.size == 4;[] +assert a == [400,400,369,303];[] + +n=status( "Checking 'sack' with 'assign' using a 'constant' and 'max'",n);[] +a=g.V().sack(assign).by(constant(400)).has('code','SAF'). + outE().sack(max).by('dist').sack().fold().next();[] +assert a.size == 4;[] +assert a ==[549,708,400,400];[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking side effects and lambdas",n);[] +c = 0;[] +n=status( "Checking 'filter { c += 1 }'",n);[] +g.V().has('region','US-OR').sideEffect{ c += 1 }.values('code').fold().next();[] +assert c == 7;[] + +n=status( "Checking 'filter with property check in lambda'",n);[] +c=g.V().hasLabel('airport'). + filter{it.get().property('city').value() =="London"}. + count().next();[] +assert c == 6;[] + +n=status( "Checking 'filter with a 'contains()' in a lambda'",n);[] +c=g.V().hasLabel('airport').as('a').values('desc'). + filter{it.toString().contains('F.')}.select('a'). + local(values('code','desc').fold()).count().next();[] +assert c == 4;[] + +n=status( "Checking 'filter with regex in a lambda'",n);[] +c=g.V().has('airport','type','airport'). + filter{it.get().property('city').value ==~/Dallas|Austin/}. + values('code').count().next();[] +assert c == 3;[] + +n=status( "Checking 'filter with more complex regex in a lambda'",n);[] +c=g.V().has('airport','type','airport'). + filter{it.get().property('city').value()==~/^Dal\w*/}. + values('city').count().next();[] +assert c == 7;[] + +n=status( "Checking 'map' and a lambda",n);[] +a=g.V().has('airport','region','GB-ENG'). + map{it.get().value('code')+" "+it.get().value('city')}.toList();[] + +assert a.sort()[0] == 'BHX Birmingham';[] + +n=status( "Checking 'map' and a multi statement lambda",n);[] +a=g.V().hasLabel('airport').limit(10). + map{a=1;b=2;c=a+b;it.get().id() + c}.fold().next();[] + +assert a == [4,5,6,7,8,9,10,11,12,13];[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'map' containing a traversal",n);[] +a=g.V().hasLabel('airport').has('region','US-NM'). + order().by('code'). + map(properties('city').group().by(key()).by(value())).toList();[] + +assert a.size() == 9;[] +assert a[0]['city'] == 'Albuquerque';[] +assert a[5]['city'] == 'Los Alamos';[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking custom predicates",n);[] +a = g.V().hasLabel('airport').values('longest').mean().next();[] +f = {x,y -> x > y*2};[] +c=g.V().hasLabel('airport').has('longest',test(f,a)).values('code').count().next();[] +assert c == 9;[] + +bp = new java.util.function.BiPredicate() { + boolean test(String val, String pattern) { + return val ==~ pattern }};[] +regex = {new P(bp, it)};[] +c= g.V().has('desc', regex(/^Dal.*/)).count().next();[] +assert c == 5;[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'tree' of routes from AUS",n);[] +tree=g.V().has('code','AUS'). + repeat(out()). + times(2).tree().by('code').next();[] + +assert tree instanceof org.apache.tinkerpop.gremlin.process.traversal.step.util.Tree;[] +items = tree.getObjectsAtDepth(1).toList();[] +assert items.size() == 1;[] +items = tree.getObjectsAtDepth(2).toList();[] +assert items.size() == 59;[] +items = tree.getObjectsAtDepth(3).toList();[] +assert items.size() == 5942;[] +assert tree['AUS']['DFW'].size() == 221;[] + +;[] //------------------------------------------------------------------------- +n=status( "Checking 'subgraph' of first 46 airports.",n);[] + +subg=g.V(1..46).outE(). + filter(inV().hasId(within(1L..46L))). + subgraph('a').cap('a').next();[] + +sgt = subg.traversal();[] + +assert sgt.E().count().next() == 1326;[] +assert sgt.V().count().next() == 46;[] +c = sgt.V().has('code','LAX').out().count().next();[] +assert c == 40;[] + + +n=status( "Checking 'subgraph' of European airports.",n);[] +subg = g.V().hasLabel('continent').has('code','EU'). + outE('contains').subgraph('eu-air-routes').inV().as('a'). + inE('contains').subgraph('eu-air-routes'). + outV().hasLabel('country'). + select('a').outE().as('r'). + inV().hasLabel('airport').in().hasLabel('continent'). + has('code','EU').select('r').subgraph('eu-air-routes'). + cap('eu-air-routes').next();[] + +sgt = subg.traversal();[] + +assert sgt.E().hasLabel('route').count().next() == 12499;[] +assert sgt.E().count().next() == 13665;[] +assert sgt.V().count().next() == 630;[] +m = sgt.V().groupCount().by(label).next();[] +assert m['airport'] == 583;[] +assert m['country'] == 46;[] +assert m['continent'] == 1;[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking classes and types",n);[] +assert graph instanceof org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerGraph;[] + +cls = g.V().has('airport','code','DFW').next();[] +assert cls instanceof org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerVertex;[] + +edge = g.V().has('airport','code','DFW').outE().limit(1).next();[] +assert edge instanceof org.apache.tinkerpop.gremlin.tinkergraph.structure.TinkerEdge;[] + +cls = g.V().has('code','LHR').valueMap().next();[] +assert cls instanceof java.util.HashMap;[] + +assert Pop.values() == [first,last,all,mixed];[] + +assert Operator.values() == [sum,minus,mult,div,min,max,assign,and,or,addAll,sumLong];[] + +assert T.values() == [label,id,key,value];[] + +assert Column.values() == [keys,values];[] + +assert Scope.values() == [global,local];[] + + +;[] //------------------------------------------------------------------------- +n=status( "Checking user defined Groovy methods",n);[] +def dist(g,from,to) { + d=g.V().has('code',from).outE().as('a').inV().has('code',to) + .select('a').values('dist').next() + return d };[] +assert dist(g,'AUS','LHR') == 4901;[] + + diff --git a/sample-data/README-air-routes-latest.txt b/sample-data/README-air-routes-latest.txt new file mode 100755 index 00000000..748d6c94 --- /dev/null +++ b/sample-data/README-air-routes-latest.txt @@ -0,0 +1,149 @@ + +Information about the most recent Air Routes graph data set. + +Timestamp: Thu, 03 May 2018 07:49:14 -0500 + +This file contains the following sections + 1. Introduction + 2. Some statistics about the graph + + +1. INTRODUCTION + +The graph provides a model of the world commercial airline route network. It +remains a work in progress but I have tried to curate the data carefully. +However,I do not doubt there are errors and omissions that remain. This is +intended as a learning tool and not for actual travel planning purposes! If +you spot any errors such as missing routes or routes that are no longer served, +please do let me know. + +I have tried to verify that a connection between airports is only given if at +least one commercial airline currently operates scheduled service between them. +Given the dynamic nature of the airline industry where routes are added and +removed on a weekly basis any graph such as this one is, invariably, out of +date the day it is published. That said I have tried to keep the graph as up to +date as I can. + +This graph only models airports and routes it does not attempt to model airlines +or route frequency. For example, the graph can tell you that there is a route +between LHR and JFK that at least one airline operates but not which airlines +fly that route nor how many times a day the route is operated. That is an +exercise for another day and for a bigger graph! The graph also does not +currently contain any aircraft information. For the most part I have only +included scheduled flights flown by commercial airlines. I have included a few +unusual routes such as the flights from RAF Brize Norton to RAF Ascension Island +continuing on to Mount Pleasant in the Falkland Islands as I believe this +represents a significant route and is a sort of pseudo-scheduled flight. I do +not include routes flown only by freight carriers like FedEx and UPS. I also +have only mostly included airports with at least one route. There are a few +exceptions such as St. Helena which is a new airport with service pending, but +delayed, due to issues with wind shear. Where an airport has no flights but +remains in the graph it is probably because it was served by commercial airlines +at some point. It is also useful for people learning to search graphs to be able +to query for orphan nodes so for that reason as well I have left them in the +graph. + +All of this said, I believe, as a learning tool there is plenty in the graph to +facilitate writing some interesting queries and if you are so inclined for +producing nice visuals. I hope people have as much fun playing with the graph +as I have had putting it together. + +Please do let me know of any mistakes you find or about airports and/or routes +that are currently missing. I believe I have most of the airports from around +the World that offer scheduled airline service included in the graph at this +point. The one exception is about ten regional airports in China that I +still need to add + +If you require more detailed information about the schema of the graph and +it's overall demographic please see the comments at the top of the +air-routes.graphml file. + + +2. SOME STATISTICS ABOUT THE GRAPH + +Air Routes Graph (v0.78, 2018-May-03) contains: + 3,395 airports + 45,321 routes + 237 countries (and dependent areas) + 7 continents + 3,640 total nodes + 52,111 total edges + +Additional observations: + Longest route is between DOH and AKL (9,025 miles) + Shortest route is between WRY and PPW (2 miles) + Average route distance is 1,182.173 miles. + Longest runway is 18,045ft (BPX) + Shortest runway is 1,300ft (SAB) + Average number of runways is 1.42916 + Furthest North is LYR (latitude: 78.2461013793945) + Furthest South is USH (latitude: -54.8433) + Furthest East is SVU (longitude: 179.341003418) + Furthest West is TVU (longitude: -179.876998901) + Closest to the Equator is MDK (latitude: 0.0226000007242) + Closest to the Greenwich meridian is LDE (longitude: -0.006438999902457) + Highest elevation is DCY (14,472 feet) + Lowest elevation is GUW (-72 feet) + Maximum airport node degree (routes in and out) is 586 (FRA) + Country with the most airports: United States (579) + Continent with the most airports: North America (980) + Average degree (airport nodes) is 26.699 + Average degree (all nodes) is 26.774 + + +Here are the top 50 airports with the most routes + + POS ID CODE TOTAL DETAILS + + 1 52 FRA (586) out:293 in:293 + 2 70 AMS (551) out:274 in:277 + 3 161 IST (546) out:273 in:273 + 4 51 CDG (544) out:272 in:272 + 5 80 MUC (492) out:246 in:246 + 6 64 PEK (485) out:242 in:243 + 7 18 ORD (482) out:241 in:241 + 8 58 DXB (480) out:240 in:240 + 9 1 ATL (472) out:236 in:236 + 10 8 DFW (442) out:221 in:221 + 11 102 DME (436) out:218 in:218 + 12 50 LGW (418) out:209 in:209 + 13 67 PVG (408) out:204 in:204 + 14 74 MAD (400) out:200 in:200 + 15 49 LHR (400) out:200 in:200 + 16 13 LAX (400) out:200 in:200 + 17 73 BCN (388) out:194 in:194 + 18 11 IAH (388) out:194 in:194 + 19 68 FCO (386) out:193 in:193 + 20 31 DEN (382) out:191 in:191 + 21 12 JFK (381) out:191 in:190 + 22 94 STN (378) out:189 in:189 + 23 79 BRU (374) out:187 in:187 + 24 84 MAN (373) out:187 in:186 + 25 35 EWR (372) out:186 in:186 + 26 47 YYZ (366) out:183 in:183 + 27 16 MIA (356) out:178 in:178 + 28 198 DUS (350) out:175 in:175 + 29 106 DOH (344) out:172 in:172 + 30 178 CLT (340) out:170 in:170 + 31 75 VIE (338) out:169 in:169 + 32 60 DUB (334) out:167 in:167 + 33 250 CAN (331) out:166 in:165 + 34 76 ZRH (320) out:160 in:160 + 35 103 SVO (318) out:159 in:159 + 36 177 CPH (314) out:157 in:157 + 37 61 HKG (312) out:156 in:156 + 38 56 SIN (310) out:155 in:155 + 39 93 ARN (308) out:154 in:154 + 40 17 MSP (298) out:149 in:149 + 41 346 LED (296) out:148 in:148 + 42 122 ICN (296) out:148 in:148 + 43 30 LAS (294) out:147 in:147 + 44 9 FLL (292) out:146 in:146 + 45 230 PMI (288) out:144 in:144 + 46 23 SFO (286) out:143 in:143 + 47 101 BKK (284) out:142 in:142 + 48 46 DTW (280) out:140 in:140 + 49 10 IAD (274) out:137 in:137 + 50 15 MCO (266) out:133 in:133 + + diff --git a/sample-data/README-air-routes.txt b/sample-data/README-air-routes.txt index 830dd90d..80b23d21 100644 --- a/sample-data/README-air-routes.txt +++ b/sample-data/README-air-routes.txt @@ -1,4 +1,6 @@ -Information about the Air Routes graph. +Information about the Air Routes graph data set. + +These notes refer to the version of the data set used by the examples in the book. Timestamp: Fri, 06 Oct 2017 11:28:40 -0500 diff --git a/sample-data/README.md b/sample-data/README.md index 0af6643d..c913b7ee 100644 --- a/sample-data/README.md +++ b/sample-data/README.md @@ -1,9 +1,16 @@ -This folder contains the GraphML file for the air-routes graph. This air-routes graph is referenced throughout the book. +This folder contains the GraphML file for the air-routes graph and other sample data. The air-routes graph is referenced throughout the book. -Two files are provided. The file *'air-routes.graphml'* contains the full graph containing over 40,000 routes and over 3,300 airports. The file air-routes-small.graphml just has routes between 46 airports all located in the US. +Two versions of the air-routes data set files are provided. The file *'air-routes.graphml'* contains the full graph containing over 40,000 routes and over 3,300 airports. The file air-routes-small.graphml just has routes between 46 airports all located in the US. +All of the examples in the book are based on *'air-routes.graphml'*. The small script called *'load-air-routes-graph.groovy'* can be used to load the graph into a TinkerGraph from within the Gremlin Console. Edit the script before using it to adjust the location of where you put the GraphML file on your system. -There is also a small Groovy script in the *'sample-code'* folder that can be run from within the Gremlin console to produce some statistics about the graph similr to those found in the boook and in the *'README-air-routes.txt file'*. +There is also a small Groovy script in the *'/sample-code'* folder called *'graph-stats.groovy'* that can be run from within the Gremlin console to produce some statistics about the graph similr to those found in the boook and in the *'README-air-routes.txt file'*. + +The *'aircraft.csv'* file is intended to be used with the *'add-airports.groovy'* sample that is located in the *'/sample-code'* folder. + +The *'edges.csv'* file is intended to be used with the *'GraphFromCSV.java'* sample that is located in the *'/sample-code'* folder. + +The two files *'air-routes-latest.graphml'* and *'air-routes-small-latest.graphml'* contain additional routes and airports added since the original version was uploaded. They are provided in case anyone wants to experiment with some more recent data. The latest data set includes 21 additional airports and 1,921 additional routes. Please check back regularly to see more updates as they are uploaded. diff --git a/sample-data/air-routes-latest.graphml b/sample-data/air-routes-latest.graphml new file mode 100755 index 00000000..8c18e10f --- /dev/null +++ b/sample-data/air-routes-latest.graphml @@ -0,0 +1,254155 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + version + version + 0.78 + Version: 0.78 Generated: 2018-05-03 12:49:29 UTC +Graph created by Kelvin R. Lawrence +Please let me know of any errors you find in the graph. + + + airport + airport + ATL + KATL + Atlanta + Hartsfield - Jackson Atlanta International Airport + US-GA + 5 + 12390 + 1026 + US + 33.6366996765137 + -84.4281005859375 + + + airport + airport + ANC + PANC + Anchorage + Anchorage Ted Stevens + US-AK + 3 + 12400 + 151 + US + 61.1744003295898 + -149.996002197266 + + + airport + airport + AUS + KAUS + Austin + Austin Bergstrom International Airport + US-TX + 2 + 12250 + 542 + US + 30.1944999694824 + -97.6698989868164 + + + airport + airport + BNA + KBNA + Nashville + Nashville International Airport + US-TN + 4 + 11030 + 599 + US + 36.1245002746582 + -86.6781997680664 + + + airport + airport + BOS + KBOS + Boston + Boston Logan + US-MA + 6 + 10083 + 19 + US + 42.36429977 + -71.00520325 + + + airport + airport + BWI + KBWI + Baltimore + Baltimore/Washington International Airport + US-MD + 3 + 10502 + 143 + US + 39.17539978 + -76.66829681 + + + airport + airport + DCA + KDCA + Washington D.C. + Ronald Reagan Washington National Airport + US-DC + 3 + 7169 + 14 + US + 38.8521003723145 + -77.0376968383789 + + + airport + airport + DFW + KDFW + Dallas + Dallas/Fort Worth International Airport + US-TX + 7 + 13401 + 607 + US + 32.896800994873 + -97.0380020141602 + + + airport + airport + FLL + KFLL + Fort Lauderdale + Fort Lauderdale/Hollywood International Airport + US-FL + 2 + 9000 + 64 + US + 26.0725994110107 + -80.152702331543 + + + airport + airport + IAD + KIAD + Washington D.C. + Washington Dulles International Airport + US-VA + 4 + 11500 + 313 + US + 38.94449997 + -77.45580292 + + + airport + airport + IAH + KIAH + Houston + George Bush Intercontinental + US-TX + 5 + 12001 + 96 + US + 29.9843997955322 + -95.3414001464844 + + + airport + airport + JFK + KJFK + New York + New York John F. Kennedy International Airport + US-NY + 4 + 14511 + 12 + US + 40.63980103 + -73.77890015 + + + airport + airport + LAX + KLAX + Los Angeles + Los Angeles International Airport + US-CA + 4 + 12091 + 127 + US + 33.94250107 + -118.4079971 + + + airport + airport + LGA + KLGA + New York + New York La Guardia + US-NY + 2 + 7003 + 20 + US + 40.77719879 + -73.87259674 + + + airport + airport + MCO + KMCO + Orlando + Orlando International Airport + US-FL + 4 + 12005 + 96 + US + 28.4293994903564 + -81.3089981079102 + + + airport + airport + MIA + KMIA + Miami + Miami International Airport + US-FL + 4 + 13016 + 8 + US + 25.7931995391846 + -80.2906036376953 + + + airport + airport + MSP + KMSP + Minneapolis + Minneapolis-St.Paul International Airport + US-MN + 4 + 11006 + 841 + US + 44.8819999695 + -93.2218017578 + + + airport + airport + ORD + KORD + Chicago + Chicago O'Hare International Airport + US-IL + 8 + 13000 + 672 + US + 41.97859955 + -87.90480042 + + + airport + airport + PBI + KPBI + West Palm Beach + Palm Beach International Airport + US-FL + 3 + 10000 + 19 + US + 26.6832008361816 + -80.0955963134766 + + + airport + airport + PHX + KPHX + Phoenix + Phoenix Sky Harbor International Airport + US-AZ + 3 + 11489 + 1135 + US + 33.4342994689941 + -112.012001037598 + + + airport + airport + RDU + KRDU + Raleigh + Raleigh-Durham + US-NC + 3 + 10000 + 435 + US + 35.8776016235352 + -78.7874984741211 + + + airport + airport + SEA + KSEA + Seattle + Seattle-Tacoma + US-WA + 3 + 11901 + 432 + US + 47.4490013122559 + -122.30899810791 + + + airport + airport + SFO + KSFO + San Francisco + San Francisco International Airport + US-CA + 4 + 11870 + 13 + US + 37.6189994812012 + -122.375 + + + airport + airport + SJC + KSJC + San Jose + Norman Y. 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+B789,789,Boeing,787-9 +B78X,78J,Boeing,787-10 +E120,EM2,Embraer,EMB 120 Brasilia +E135,ER3,Embraer,RJ135 +E135,ERD,Embraer,RJ140 +E145,ER4,Embraer,RJ145 +E170,E70,Embraer,170 +E190,E90,Embraer,190 +E195,E95,Embraer,195 +E75L,E75,Embraer,175 (long wing) +E75S,E75,Embraer,175 (short wing) +DC91,D91,McDonnell Douglas,DC-9-10 +DC92,D92,McDonnell Douglas,DC-9-20 +DC93,D93,McDonnell Douglas,DC-9-30 +MD11,M11,McDonnell Douglas,MD-11 +MD81,M81,McDonnell Douglas,MD-81 +MD82,M82,McDonnell Douglas,MD-82 +MD83,M83,McDonnell Douglas,MD-83 +MD87,M87,McDonnell Douglas,MD-87 +MD88,M88,McDonnell Douglas,MD-88 +MD90,M90,McDonnell Douglas,MD-90 +DH8A,DH1,De Havilland Canada,DHC-8-100 Dash 8 / 8Q +DH8B,DH2,De Havilland Canada,DHC-8-200 Dash 8 / 8Q +DH8C,DH3,De Havilland Canada,DHC-8-300 Dash 8 / 8Q +DH8D,DH4,De Havilland Canada,DHC-8-400 Dash 8Q +DHC2,DHP,De Havilland Canada,DHC-2 Beaver +DHC3,DHL,De Havilland Canada,DHC-3 Otter +DHC4,DHC,De Havilland Canada,DHC-4 Caribou +DHC5,DHC,De Havilland Canada,DHC-5 Buffalo +DHC6,DHT,De Havilland Canada,DHC-6 Twin Otter +DHC7,DH7,De Havilland Canada,DHC-7 Dash 7 +BCS1,CS1,Bombardier,CS100 +BCS3,CS3,Bombardier,CS300 +RJ1H,AR1,Avro,RJ100 +RJ70,AR7,Avro,RJ70 +RJ85,AR8,Avro,RJ85 +B461,141,BAe,146-100 +B462,142,BAe,146-200 +B463,143,BAe,146-300 +CRJ1,CR1,Canadair,Regional Jet 100 +CRJX,CRK,Canadair,Regional Jet 1000 +CRJ2,CR2,Canadair,Regional Jet 200 +CRJ7,CR7,Canadair,Regional Jet 700 +CRJ9,CR9,Canadair,Regional Jet 900 +F100,100,Fokker,F100 +F70,F70,Fokker,F70 +IL96,I93,Ilyushin,IL96 diff --git a/sample-code/edges.csv b/sample-data/edges.csv similarity index 100% rename from sample-code/edges.csv rename to sample-data/edges.csv diff --git a/sample-data/load-air-routes-graph.groovy b/sample-data/load-air-routes-graph.groovy index 0eb3da03..09cbcc28 100644 --- a/sample-data/load-air-routes-graph.groovy +++ b/sample-data/load-air-routes-graph.groovy @@ -6,6 +6,7 @@ conf = new BaseConfiguration() conf.setProperty("gremlin.tinkergraph.vertexIdManager","LONG") conf.setProperty("gremlin.tinkergraph.edgeIdManager","LONG") +conf.setProperty("gremlin.tinkergraph.vertexPropertyIdManager","LONG") graph = TinkerGraph.open(conf) // Change the path below to point to wherever you put the graphml file