From 5008db6f3dfb7cc4add0676c255c74c542047cca Mon Sep 17 00:00:00 2001
From: Michele Rastelli <michele@arangodb.com>
Date: Thu, 31 Jul 2025 22:35:06 +0200
Subject: [PATCH 1/3] created demo

---
 demo/pom.xml                                  |    5 +
 demo/src/main/java/org/example/Main.java      |  233 +-
 .../main/resources/air-routes-small.graphml   | 6098 +++++++++++++++++
 demo/src/main/resources/logback-test.xml      |   17 +
 4 files changed, 6341 insertions(+), 12 deletions(-)
 create mode 100644 demo/src/main/resources/air-routes-small.graphml
 create mode 100644 demo/src/main/resources/logback-test.xml

diff --git a/demo/pom.xml b/demo/pom.xml
index f36ae8b..66a99c1 100644
--- a/demo/pom.xml
+++ b/demo/pom.xml
@@ -20,6 +20,11 @@
             <artifactId>arangodb-tinkerpop-provider</artifactId>
             <version>0.0.2-SNAPSHOT</version>
         </dependency>
+        <dependency>
+            <groupId>ch.qos.logback</groupId>
+            <artifactId>logback-classic</artifactId>
+            <version>1.5.18</version>
+        </dependency>
     </dependencies>
 
     <repositories>
diff --git a/demo/src/main/java/org/example/Main.java b/demo/src/main/java/org/example/Main.java
index 8c7948e..00054de 100644
--- a/demo/src/main/java/org/example/Main.java
+++ b/demo/src/main/java/org/example/Main.java
@@ -16,17 +16,50 @@
 
 package org.example;
 
+import com.arangodb.ArangoDB;
+import com.arangodb.ArangoDatabase;
 import com.arangodb.tinkerpop.gremlin.structure.ArangoDBGraph;
 import com.arangodb.tinkerpop.gremlin.utils.ArangoDBConfigurationBuilder;
 import org.apache.commons.configuration2.Configuration;
-import org.apache.tinkerpop.gremlin.structure.Vertex;
+import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.GraphTraversalSource;
+import org.apache.tinkerpop.gremlin.process.traversal.dsl.graph.__;
 
+import java.util.List;
+import java.util.Map;
+
+import static org.apache.tinkerpop.gremlin.process.traversal.Order.desc;
+import static org.apache.tinkerpop.gremlin.process.traversal.Scope.local;
+import static org.apache.tinkerpop.gremlin.structure.Column.values;
+
+/**
+ * Demo program showing how to use Gremlin with ArangoDB TinkerPop provider.
+ * Based on: Practical Gremlin: An Apache TinkerPop Tutorial by Kelvin R. Lawrence
+ * (<a href=https://www.kelvinlawrence.net/book/PracticalGremlin.html>link</a>)
+ */
 public class Main {
     private static final String DB_NAME = "demo";
+    private static final String GRAPHML_FILE = "src/main/resources/air-routes-small.graphml";
 
+    @SuppressWarnings("resource")
     public static void main(String[] args) {
+        System.out.println("Starting ArangoDB TinkerPop Demo with Air Routes Data");
 
-        // create Tinkerpop Graph backed by ArangoDB
+        //region cleanup
+        System.out.println("Cleaning up existing database:");
+        {
+            ArangoDatabase db = new ArangoDB.Builder()
+                    .host("127.0.0.1", 8529)
+                    .password("test")
+                    .build()
+                    .db(DB_NAME);
+            if (db.exists()) {
+                db.drop();
+            }
+            db.arango().shutdown();
+        }
+        //endregion
+
+        // create Tinkerpop graph backed by ArangoDB
         Configuration conf = new ArangoDBConfigurationBuilder()
                 .hosts("127.0.0.1:8529")
                 .user("root")
@@ -34,20 +67,196 @@ public static void main(String[] args) {
                 .database(DB_NAME)
                 .enableDataDefinition(true)
                 .build();
-        ArangoDBGraph g = ArangoDBGraph.open(conf);
+        ArangoDBGraph graph = ArangoDBGraph.open(conf);
+        GraphTraversalSource g = graph.traversal();
 
         // print supported features
-        System.out.println(g.features());
+        System.out.println("Graph Features:");
+        System.out.println(graph.features());
+
+        // Import GraphML data
+        System.out.println("\nImporting Air Routes data from GraphML file...");
+        {
+            g.io(Main.GRAPHML_FILE).read().iterate();
+            System.out.println("Data import completed.");
+        }
+
+        //region Basic Gremlin Queries
+        System.out.println("\n=== Basic Gremlin Queries ===");
+        {
+            System.out.println("Counting vertices and edges:");
+            long vertexCount = g.V().count().next();
+            long edgeCount = g.E().count().next();
+            System.out.println("  Vertices: " + vertexCount);
+            System.out.println("  Edges: " + edgeCount);
+
+            System.out.println("\nVertex labels in the graph:");
+            g.V().label().dedup().forEachRemaining(label -> System.out.println("  " + label));
+
+            System.out.println("\nEdge labels in the graph:");
+            g.E().label().dedup().forEachRemaining(label -> System.out.println("  " + label));
+
+            System.out.println("\nSample of 5 airports:");
+            g.V().hasLabel("airport").limit(5).valueMap().forEachRemaining(
+                    vm -> System.out.println("  " + vm)
+            );
+
+            System.out.println("\nSample of 5 routes:");
+            g.E().hasLabel("route").limit(5).valueMap().forEachRemaining(
+                    vm -> System.out.println("  " + vm)
+            );
+        }
+        //endregion
+
+        //region Explore airports and routes
+        System.out.println("\n=== Exploring Airports and Routes ===");
+        {
+            // Count airports by region
+            System.out.println("\nTop 5 regions by number of airports:");
+            g.V().hasLabel("airport")
+                    .groupCount().by("region")
+                    .order(local).by(values, desc)
+                    .<Map.Entry<String, Long>>unfold().limit(5)
+                    .forEachRemaining(e ->
+                            System.out.println("  " + e.getKey() + ": " + e.getValue() + " airports"));
 
-        // write vertex
-        Vertex v = g.addVertex("person");
-        v.property("name", "Joe");
-        v.property("age", 22);
 
-        // read vertex
-        Vertex joe = g.traversal().V().has("name", "Joe").next();
-        System.out.println("Joe's age: " + joe.property("age").value());
+            // Find airports with most routes
+            System.out.println("\nTop 5 airports with most outgoing routes:");
+            g.V().hasLabel("airport")
+                    .project("airport", "code", "routes")
+                    .by("city")
+                    .by("code")
+                    .by(__.outE("route").count())
+                    .order().by("routes", desc)
+                    .limit(5)
+                    .forEachRemaining(m ->
+                            System.out.println("  " + m.get("airport") + " (" + m.get("code") + "): " + m.get("routes") + " routes"));
+        }
+        //endregion
 
-        g.close();
+        //region Graph Algorithms
+        System.out.println("\n=== Graph Algorithms ===");
+        {
+            // Degree centrality - find most connected airports
+            System.out.println("\nTop 5 airports by degree centrality (most connections):");
+            g.V().hasLabel("airport")
+                    .project("airport", "code", "degree")
+                    .by("city")
+                    .by("code")
+                    .by(__.bothE().count())
+                    .order().by("degree", desc)
+                    .limit(5)
+                    .forEachRemaining(m ->
+                            System.out.println("  " + m.get("airport") + " (" + m.get("code") + "): " + m.get("degree") + " connections"));
+
+            // Find a path between two airports
+            System.out.println("\nFinding shortest path between Boston (BOS) and Atlanta (ATL):");
+
+            g.V().hasLabel("airport")
+                    .has("code", "BOS")
+                    .repeat(__.out().simplePath())
+                    .until(__.has("code", "ATL"))
+                    .limit(5)
+                    .path().by("code")
+                    .forEachRemaining(path ->
+                            System.out.println("  Path (" + (path.size() - 1) + " hops): " +
+                                    String.join(" -> ", path.objects().stream().map(Object::toString).toList())));
+
+            // Find all airports reachable within 2 hops from London
+            System.out.println("\nCount of the airports reachable within 2 hops from Boston (BOS):");
+            Long count = g.V().has("code", "BOS")
+                    .repeat(__.out().simplePath())
+                    .times(2)
+                    .dedup()
+                    .count()
+                    .next();
+            System.out.println("  " + count + " airports");
+        }
+        //endregion
+
+        //region AQL Queries
+        System.out.println("\n=== AQL Queries ===");
+        {
+            // Find weighted k-shortest paths between two airports with an AQL query
+            System.out.println("\nFinding weighted k-shortest paths between Boston (BOS) and San Francisco (SFO) with AQL query:");
+
+            String shortestPathQuery = """
+                    LET start = FIRST(
+                      FOR d IN tinkerpop_vertex
+                      FILTER d.code == @start
+                      RETURN d
+                    )
+                    
+                    LET target = FIRST(
+                      FOR d IN tinkerpop_vertex
+                      FILTER d.code == @target
+                      RETURN d
+                    )
+                    
+                    FOR path IN OUTBOUND K_SHORTEST_PATHS start TO target GRAPH tinkerpop
+                      OPTIONS { weightAttribute: 'dist' }
+                      LIMIT 5
+                      RETURN {
+                        path: path.vertices[*].code,
+                        dist: path.weight
+                      }
+                    """;
+
+            graph.<Map<String, ?>>aql(shortestPathQuery, Map.of(
+                    "start", "BOS",
+                    "target", "SFO"
+            )).forEachRemaining(path ->
+                    System.out.println("  Path (dist: " + path.get("dist") + "): \t" + path.get("path")));
+
+            // AQL traversal to find paths between two airports with constraints on edges
+            System.out.println("\nFinding path between Boston (BOS) and Atlanta (ATL) with max 400 km flights with AQL query:");
+
+            String traversalQuery = """
+                    LET start = FIRST(
+                      FOR d IN tinkerpop_vertex
+                      FILTER d.code == @start
+                      RETURN d
+                    )
+                    
+                    FOR v,e,p IN 1..10 OUTBOUND start GRAPH tinkerpop
+                      PRUNE cond = e.dist > 400
+                      OPTIONS { uniqueVertices: 'path', order: 'bfs' }
+                      FILTER NOT cond
+                      FILTER v.code == @target
+                      LIMIT 5
+                      RETURN p
+                    """;
+
+            graph.<Map<String, ?>>aql(traversalQuery, Map.of(
+                            "start", "BOS",
+                            "target", "ATL"
+                    ))
+                    .project("path", "distances", "tot")
+                    .by(__.select("vertices").unfold().values("code").fold())
+                    .by(__.select("edges").unfold().values("dist").fold())
+                    .by(__.select("edges").unfold().values("dist").sum())
+                    .forEachRemaining(it -> {
+                                @SuppressWarnings("unchecked")
+                                List<String> path = (List<String>) it.get("path");
+                                @SuppressWarnings("unchecked")
+                                List<Number> distances = (List<Number>) it.get("distances");
+                                StringBuilder sb = new StringBuilder();
+                                for (int i = 0; i < distances.size(); i++) {
+                                    sb
+                                            .append(path.get(i))
+                                            .append(" -(")
+                                            .append(distances.get(i))
+                                            .append(")-> ");
+                                }
+                                sb.append(path.getLast());
+                                System.out.println("  Path (dist: " + it.get("tot") + "): \t" + sb);
+                            }
+                    );
+        }
+        //endregion
+
+        graph.close();
     }
+
 }
\ No newline at end of file
diff --git a/demo/src/main/resources/air-routes-small.graphml b/demo/src/main/resources/air-routes-small.graphml
new file mode 100644
index 0000000..50473f2
--- /dev/null
+++ b/demo/src/main/resources/air-routes-small.graphml
@@ -0,0 +1,6098 @@
+<?xml version='1.0' ?>
+<!-- ******************************************************************************* -->
+<!-- Sample file containing selected air routes between selected airports.           -->
+<!-- Intended for learning purposes only and not for travel planning!                -->
+<!--                                                                                 -->
+<!-- Author: Kelvin R. Lawrence.                                                     -->
+<!-- Graph version : 0.77 , 2017-Oct-06                                              -->
+<!--                                                                                 -->
+<!-- Please send details of errors in the graph or suggestions to:                   -->
+<!-- e-mail: gfxman@yahoo.com , twitter: @gfxman                                     -->
+<!--                                                                                 -->
+<!-- PLEASE NOTE:                                                                    -->
+<!-- The data in this graph is all available in the public domain but assembling it  -->
+<!-- all took considerable work! While I have tried to make the data as accurate     -->
+<!-- as possiblle I am sure errors remain. This data is intended as a learning aid   -->
+<!-- only and not for any commercial use.                                            -->
+<!--                                                                                 -->
+<!-- Significant amounts of my own research has gone into into the creation of this  -->
+<!-- graph. Aviation has always been a hobby of mine and this has been an            -->
+<!-- interesting spare time project. I have tried to error check and curate the      -->
+<!-- data which was a really big job but there are almost certainly still errors in  -->
+<!-- here. I have also been working at adding more recently announced routes and     -->
+<!-- this graph remains a work in progress as time permits.                          -->
+<!--                                                                                 -->
+<!-- Given that airlines are adding many routes on a weekly basis across the globe   -->
+<!-- a static graph such as this one will always be out of date the day it is        -->
+<!-- released! Thankfully new airports open less often and I hope I am more or less  -->
+<!-- including the majority of airports that have scheduled airline service.         -->
+<!--                                                                                 -->
+<!-- I do not include many airports without commercial passenger service as it is    -->
+<!-- passenger routes that I am most interested in. I also decided for now not to    -->
+<!-- include seaplane bases or heliports in the graph even if they offer             -->
+<!-- scheduled passenger service. Airports that are classified as only supporting    -->
+<!-- general aviation (private planes) and cargo are not included in the graph.      -->
+<!-- However, You will find some airports with no flights. This is mainly caused by  -->
+<!-- those airports having had commercial service at some prior time and I decided   -->
+<!-- to leave them in the graph in case one day in the future, service resumes.      -->
+<!--                                                                                 -->
+<!-- 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.                                              -->
+<!--                                                                                 -->
+<!-- Route distances were calculated using a standard Haversine Great Circle formula.-->
+<!-- This file is built using Ruby scripts that process all the collected data that  -->
+<!-- I have amassed!                                                                 -->
+<!--                                                                                 -->
+<!-- The graph has a simple schema as you will see from the GraphML markup below.    -->
+<!--                                                                                 -->
+<!-- There are four basic node types:                                                -->
+<!-- 1. Airport   - Contains properties like code (eg DFW), city, lat, lon etc.      -->
+<!-- 2. Country   - A convenient way to find all airports in a specific country.     -->
+<!-- 3. Continent - As above but for continents.                                     -->
+<!-- 4. Version   - A single stand alone node used to version the graph.             -->
+<!--                                                                                 -->
+<!-- There are two edge types:                                                       -->
+<!-- 1. Route    - Represents link between two airports with the distance in Great   -->
+<!--               Circle miles as a property.                                       -->
+<!-- 2. Contains - Connects countries and continents with airports.                  -->
+<!--                                                                                 -->
+<!-- This version of the graph contains 46 airports and 1326 routes.              -->
+<!--                                                                                 -->
+<!-- ******************************************************************************* -->
+<graphml xmlns='http://graphml.graphdrawing.org/xmlns'>
+  <key id='type'    for='node' attr.name='type'    attr.type='string'></key>
+  <key id='code'    for='node' attr.name='code'    attr.type='string'></key>
+  <key id='icao'    for='node' attr.name='icao'    attr.type='string'></key>
+  <key id='desc'    for='node' attr.name='desc'    attr.type='string'></key>
+  <key id='region'  for='node' attr.name='region'  attr.type='string'></key>
+  <key id='runways' for='node' attr.name='runways' attr.type='int'></key>
+  <key id='longest' for='node' attr.name='longest' attr.type='int'></key>
+  <key id='elev'    for='node' attr.name='elev'    attr.type='int'></key>
+  <key id='country' for='node' attr.name='country' attr.type='string'></key>
+  <key id='city'    for='node' attr.name='city'    attr.type='string'></key>
+  <key id='lat'     for='node' attr.name='lat'     attr.type='double'></key>
+  <key id='lon'     for='node' attr.name='lon'     attr.type='double'></key>
+  <key id='dist'    for='edge' attr.name='dist'    attr.type='int'></key>
+  <key id='labelV'  for='node' attr.name='labelV'  attr.type='string'></key>
+  <key id='labelE'  for='edge' attr.name='labelE'  attr.type='string'></key>
+
+  <graph id='routes' edgedefault='directed'>
+  <node id='1'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>ATL</data>
+    <data key='icao'>KATL</data>
+    <data key='city'>Atlanta</data>
+    <data key='desc'>Hartsfield - Jackson Atlanta International Airport</data>
+    <data key='region'>US-GA</data>
+    <data key='runways'>5</data>
+    <data key='longest'>12390</data>
+    <data key='elev'>1026</data>
+    <data key='country'>US</data>
+    <data key='lat'>33.6366996765137</data>
+    <data key='lon'>-84.4281005859375</data>
+  </node>
+  <node id='2'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>ANC</data>
+    <data key='icao'>PANC</data>
+    <data key='city'>Anchorage</data>
+    <data key='desc'>Anchorage Ted Stevens</data>
+    <data key='region'>US-AK</data>
+    <data key='runways'>3</data>
+    <data key='longest'>12400</data>
+    <data key='elev'>151</data>
+    <data key='country'>US</data>
+    <data key='lat'>61.1744003295898</data>
+    <data key='lon'>-149.996002197266</data>
+  </node>
+  <node id='3'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>AUS</data>
+    <data key='icao'>KAUS</data>
+    <data key='city'>Austin</data>
+    <data key='desc'>Austin Bergstrom International Airport</data>
+    <data key='region'>US-TX</data>
+    <data key='runways'>2</data>
+    <data key='longest'>12250</data>
+    <data key='elev'>542</data>
+    <data key='country'>US</data>
+    <data key='lat'>30.1944999694824</data>
+    <data key='lon'>-97.6698989868164</data>
+  </node>
+  <node id='4'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>BNA</data>
+    <data key='icao'>KBNA</data>
+    <data key='city'>Nashville</data>
+    <data key='desc'>Nashville International Airport</data>
+    <data key='region'>US-TN</data>
+    <data key='runways'>4</data>
+    <data key='longest'>11030</data>
+    <data key='elev'>599</data>
+    <data key='country'>US</data>
+    <data key='lat'>36.1245002746582</data>
+    <data key='lon'>-86.6781997680664</data>
+  </node>
+  <node id='5'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>BOS</data>
+    <data key='icao'>KBOS</data>
+    <data key='city'>Boston</data>
+    <data key='desc'>Boston Logan</data>
+    <data key='region'>US-MA</data>
+    <data key='runways'>6</data>
+    <data key='longest'>10083</data>
+    <data key='elev'>19</data>
+    <data key='country'>US</data>
+    <data key='lat'>42.36429977</data>
+    <data key='lon'>-71.00520325</data>
+  </node>
+  <node id='6'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>BWI</data>
+    <data key='icao'>KBWI</data>
+    <data key='city'>Baltimore</data>
+    <data key='desc'>Baltimore/Washington International Airport</data>
+    <data key='region'>US-MD</data>
+    <data key='runways'>3</data>
+    <data key='longest'>10502</data>
+    <data key='elev'>143</data>
+    <data key='country'>US</data>
+    <data key='lat'>39.17539978</data>
+    <data key='lon'>-76.66829681</data>
+  </node>
+  <node id='7'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>DCA</data>
+    <data key='icao'>KDCA</data>
+    <data key='city'>Washington D.C.</data>
+    <data key='desc'>Ronald Reagan Washington National Airport</data>
+    <data key='region'>US-DC</data>
+    <data key='runways'>3</data>
+    <data key='longest'>7169</data>
+    <data key='elev'>14</data>
+    <data key='country'>US</data>
+    <data key='lat'>38.8521003723145</data>
+    <data key='lon'>-77.0376968383789</data>
+  </node>
+  <node id='8'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>DFW</data>
+    <data key='icao'>KDFW</data>
+    <data key='city'>Dallas</data>
+    <data key='desc'>Dallas/Fort Worth International Airport</data>
+    <data key='region'>US-TX</data>
+    <data key='runways'>7</data>
+    <data key='longest'>13401</data>
+    <data key='elev'>607</data>
+    <data key='country'>US</data>
+    <data key='lat'>32.896800994873</data>
+    <data key='lon'>-97.0380020141602</data>
+  </node>
+  <node id='9'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>FLL</data>
+    <data key='icao'>KFLL</data>
+    <data key='city'>Fort Lauderdale</data>
+    <data key='desc'>Fort Lauderdale/Hollywood International Airport</data>
+    <data key='region'>US-FL</data>
+    <data key='runways'>2</data>
+    <data key='longest'>9000</data>
+    <data key='elev'>64</data>
+    <data key='country'>US</data>
+    <data key='lat'>26.0725994110107</data>
+    <data key='lon'>-80.152702331543</data>
+  </node>
+  <node id='10'>
+    <data key='labelV'>airport</data>
+    <data key='type'>airport</data>
+    <data key='code'>IAD</data>
+    <data key='icao'>KIAD</data>
+    <data key='city'>Washington D.C.</data>
+    <data key='desc'>Washington Dulles International Airport</data>
+    <data key='region'>US-VA</data>
+    <data key='runways'>4</data>
+    <data key='longest'>11500</data>
+    <data key='elev'>313</data>
+    <data key='country'>US</data>
+    <data key='lat'>38.94449997</data>
+    <data key='lon'>-77.45580292</data>
+  </node>
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diff --git a/demo/src/main/resources/logback-test.xml b/demo/src/main/resources/logback-test.xml
new file mode 100644
index 0000000..8b98906
--- /dev/null
+++ b/demo/src/main/resources/logback-test.xml
@@ -0,0 +1,17 @@
+<configuration>
+
+    <appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
+        <!-- encoders are assigned the type
+             ch.qos.logback.classic.encoder.PatternLayoutEncoder by default -->
+        <encoder>
+            <pattern>%d{HH:mm:ss.SSS} %-5level %logger{36} - %msg%n</pattern>
+        </encoder>
+    </appender>
+
+    <root level="INFO">
+        <appender-ref ref="STDOUT"/>
+    </root>
+
+<!--      <logger name="com.arangodb.internal.net.Communication" level="debug" />-->
+
+</configuration>

From 150c32366b3b145dc3c43c4c9fc47714212378e3 Mon Sep 17 00:00:00 2001
From: Michele Rastelli <michele@arangodb.com>
Date: Thu, 31 Jul 2025 22:48:35 +0200
Subject: [PATCH 2/3] CI: added demo

---
 .circleci/config.yml | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index a674be4..2294a82 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -117,6 +117,21 @@ jobs:
           command: mvn test -Dtest.graph.type=<<parameters.graphType>> <<parameters.args>>
       - store_cache
 
+  test-demo:
+    executor: 'j21'
+    steps:
+      - timeout
+      - checkout
+      - setup_remote_docker
+      - start-db
+      - load_cache
+      - install
+      - run:
+          name: Run demo
+          command: mvn compile exec:java -Dexec.mainClass="org.example.Main"
+          working_directory: demo
+      - store_cache
+
   test-console:
     executor: 'j21'
     steps:
@@ -252,6 +267,12 @@ workflows:
     jobs:
       - test-plugin
 
+  test-demo:
+    when:
+      not: <<pipeline.parameters.docker-img>>
+    jobs:
+      - test-demo
+
   deploy:
     jobs:
       - deploy:

From 86117240eaecdf140e33ad265055710992d93dc6 Mon Sep 17 00:00:00 2001
From: Michele Rastelli <michele@arangodb.com>
Date: Thu, 31 Jul 2025 22:54:17 +0200
Subject: [PATCH 3/3] CI: fixed db host

---
 README.md                                | 2 +-
 demo/src/main/java/org/example/Main.java | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 1abd481..ea58331 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ gremlin:
           - "e1:[a]->[b]"
           - "e2:[b,c]->[e,f]"
       driver:
-        hosts: [ "127.0.0.1:8529" ]
+        hosts: [ "172.28.0.1:8529" ]
         password: test
 ```
 
diff --git a/demo/src/main/java/org/example/Main.java b/demo/src/main/java/org/example/Main.java
index 00054de..3c22268 100644
--- a/demo/src/main/java/org/example/Main.java
+++ b/demo/src/main/java/org/example/Main.java
@@ -48,7 +48,7 @@ public static void main(String[] args) {
         System.out.println("Cleaning up existing database:");
         {
             ArangoDatabase db = new ArangoDB.Builder()
-                    .host("127.0.0.1", 8529)
+                    .host("172.28.0.1", 8529)
                     .password("test")
                     .build()
                     .db(DB_NAME);
@@ -61,7 +61,7 @@ public static void main(String[] args) {
 
         // create Tinkerpop graph backed by ArangoDB
         Configuration conf = new ArangoDBConfigurationBuilder()
-                .hosts("127.0.0.1:8529")
+                .hosts("172.28.0.1:8529")
                 .user("root")
                 .password("test")
                 .database(DB_NAME)