1. Open a terminal session on the system where property graph support is installed.
2. Either start a local (embedded) in-memory analyst instance or connect to a remote in-memory analyst instance
   • Java example of starting a local (embedded) instance:

     import java.util.Map;
     import java.util.HashMap;
     import oracle.pgx.api.*;
     import oracle.pgx.config.PgxConfig.Field;
      
     String url = Pgx.EMBEDDED_URL; // local JVM
     ServerInstance instance = Pgx.getInstance(url);
     instance.startEngine(); // will use default configuration
     PgxSession session = instance.createSession("test");

   • Java example of connecting to a remote instance:

     import java.util.Map;
     import java.util.HashMap;
     import oracle.pgx.api.*;
     import oracle.pgx.config.PgxConfig.Field;
      
     String url = "http://my-server.com:8080/pgx" // replace with base URL of your setup
     ServerInstance instance = Pgx.getInstance(url);
     PgxSession session = instance.createSession("test");

3. In the shell, enter the following commands, but select only one of the commands to start or connect to the desired type of instance:

   export PGX_HOME=$ORACLE_HOME/md/property_graph/pgx
   cd $PGX_HOME
   ./bin/pgx --help
   ./bin/pgx --version
    
   # start embedded shell
   ./bin/pgx
    
   # start remote shell
   ./bin/pgx --base_url http://my-server.com:8080/pgx

   For the embedded shell, the output should be similar to the following:

   10:43:46,666 [main] INFO Ctrl$2 - >>> PGX engine running.
   pgx>

4. Optionally, show the predefined variables:

   pgx> instance
   ==> PGX Server Instance running on embedded mode
   pgx> session
   ==> PGX session pgxShell registered at PGX Server Instance running on embedded mode
   pgx> analyst
   ==> Analyst for PGX session pgxShell registered at PGX Server Instance running on embedded mode
   pgx>

   Examples in some other topics assume that the instance and session variables have been set as shown here.

The in-memory analyst shell provides a help system, which you access using the :help command.

This topic presents an example of providing graph metadata in a configuration file. Follow these steps oo create a directory and some example files.

1. Create a directory to hold the example files that you will create. For example:

   mkdir -p ${ORACLE_HOME}/md/property_graph/examples/pgx/graphs/

2. In that directory, create a text file named sample.adj.json with the following content for the graph configuration file. This configuration file describes how the in-memory analyst reads the graph.

   {
     "uri": "sample.adj", 
     "format": "adj_list",
     "node_props": [{ 
       "name": "prop", 
       "type": "integer" 
     }],
     "edge_props": [{ 
       "name": "cost", 
       "type": "double" 
     }],
     "separator": " "
   }

3. In the same directory, create a text file named sample.adj with the following content for the graph data:

   128 10 1908 27.03 99 8.51 
   99 2 333 338.0
   1908 889
   333 6 128 51.09

In the configuration file, the uri field provides the location of the graph data. This path resolves relative to the parent directory of the configuration file. When the in-memory analyst loads the graph, it searches for a file named sample.adj containing the graph data.

The other fields in the configuration file indicate that the graph data is provided in adjacency list format, and consists of one node property of type integer and one edge property of type double.

The following figure shows a property graph created from the data:

Figure 3-1 Property Graph Rendered by sample.adj Data

Description of "Figure 3-1 Property Graph Rendered by sample.adj Data"

To read a graph into memory, you must pass the following information:

• The path to the graph configuration file that specifies the graph metadata

• A unique alphanumeric name that you can use to reference the graph

  An error results if you previously loaded a different graph with the same name.

Example: Using the Shell to Read a Graph

pgx> graph = session.readGraphWithProperties("<ORACLE_HOME>/md/property_graph/examples/pgx/graphs/sample.adj.json", "sample");
==> PGX Graph named sample bound to PGX session pgxShell ...
pgx> graph.getNumVertices()
==> 4

Example: Using Java to Read a Graph

import oracle.pgx.api.*;
 
PgxGraph graph = session.readGraphWithProperties("<ORACLE_HOME>/md/property_graph/examples/pgx/graphs/sample.adj.json");

The following topics contain additional examples of reading a property graph into memory:

• Read a Graph Stored in Oracle Database into Memory
  
• Read a Graph Stored in the Local File System into Memory
  

This example creates a small, simple graph in adjacency list format with no vertex or edge properties. Each line contains the vertex (node) ID, followed by the vertex IDs to which its outgoing edges point:

1 2
2 3 4
3 4
4 2

In this list, a single space separates the individual tokens. The in-memory analyst supports other separators, which you can specify in the graph configuration file.

The following figure shows the data rendered as a property graph with 4 vertices and 5 edges. (There are two edges between vertex 2 and vertex 4, each pointing in a direction opposite form the other.)

Figure 3-2 Simple Custom Property Graph

Description of "Figure 3-2 Simple Custom Property Graph"

Reading a graph into the in-memory analyst requires a graph configuration. You can provide the graph configuration using either of these methods:

• Write the configuration settings in JSON format into a file

• Using a Java GraphConfigBuilder object.

The following examples show both methods.

{
    "uri": "graph.adj",
    "format":"adj_list",
    "separator":" "
}

import oracle.pgx.config.FileGraphConfig;
import oracle.pgx.config.Format;
import oracle.pgx.config.GraphConfigBuilder;
FileGraphConfig config = GraphConfigBuilder 
   .forFileFormat(Format.ADJ_LIST) 
   .setUri("graph.adj") 
   .setSeparator(" ") 
   .build();

The graph in Creating a Simple Graph File consists of vertices and edges, without vertex or edge properties. Vertex properties are positioned directly after the source vertex ID in each line. The graph data would look like this if you added a double vertex (node) property with values 0.1, 2.0, 0.3, and 4.56789 to the graph:

1 0.1 2
2 2.0 3 4
3 0.3 4
4 4.56789 2

For the in-memory analyst to read the modified data file, you must add a vertex (node) property in the configuration file or the builder code. The following examples provide a descriptive name for the property and set the type to double.

{
    "uri": "graph.adj",
    "format":"adj_list",
    "separator":" ",
    "node_props":[{
        "name":"double-prop",
        "type":"double"
    }]
}

import oracle.pgx.common.types.PropertyType;
import oracle.pgx.config.FileGraphConfig;
import oracle.pgx.config.Format;
import oracle.pgx.config.GraphConfigBuilder;

FileGraphConfig config = GraphConfigBuilder.forFileFormat(Format.ADJ_LIST) 
    .setUri("graph.adj") 
    .setSeparator(" ") 
    .addNodeProperty("double-prop", PropertyType.DOUBLE) 
    .build();

The previous examples used integer vertex (node) IDs. The default in In-Memory Analytics is integer vertex IDs, but you can define a graph to use string vertex IDs instead.

This data file uses "node 1", "node 2", and so forth instead of just the digit:

"node 1" 0.1 "node 2"
"node 2" 2.0 "node 3" "node 4"
"node 3" 0.3 "node 4"
"node 4" 4.56789 "node 2"

Again, you must modify the graph configuration to match the data file:

{
    "uri": "graph.adj",
    "format":"adj_list",
    "separator":" ",
    "node_props":[{
        "name":"double-prop",
        "type":"double"
    }],
    "node_id_type":"string"
}

import oracle.pgx.common.types.IdType;
import oracle.pgx.common.types.PropertyType;
import oracle.pgx.config.FileGraphConfig;
import oracle.pgx.config.Format;
import oracle.pgx.config.GraphConfigBuilder;

FileGraphConfig config = GraphConfigBuilder.forFileFormat(Format.ADJ_LIST) 
    .setUri("graph.adj") 
    .setSeparator(" ") 
    .addNodeProperty("double-prop", PropertyType.DOUBLE) 
    .setNodeIdType(IdType.STRING) 
    .build();

This example adds an edge property of type string to the graph. The edge properties are positioned after the destination vertex (node) ID.

"node1" 0.1 "node2" "edge_prop_1_2"
"node2" 2.0 "node3" "edge_prop_2_3" "node4" "edge_prop_2_4"
"node3" 0.3 "node4" "edge_prop_3_4"
"node4" 4.56789 "node2" "edge_prop_4_2"

The graph configuration must match the data file:

{
    "uri": "graph.adj",
    "format":"adj_list",
    "separator":" ",
    "node_props":[{
        "name":"double-prop",
        "type":"double"
    }],
    "node_id_type":"string",
     "edge_props":[{
        "name":"edge-prop",
        "type":"string"
    }]
}

import oracle.pgx.common.types.IdType;
import oracle.pgx.common.types.PropertyType;
import oracle.pgx.config.FileGraphConfig;
import oracle.pgx.config.Format;
import oracle.pgx.config.GraphConfigBuilder;

FileGraphConfig config = GraphConfigBuilder.forFileFormat(Format.ADJ_LIST) 
    .setUri("graph.adj") 
    .setSeparator(" ") 
    .addNodeProperty("double-prop", PropertyType.DOUBLE) 
    .setNodeIdType(IdType.STRING) 
    .addEdgeProperty("edge-prop", PropertyType.STRING) 
    .build();

This example reads a graph into memory and analyzes it using the Pagerank algorithm. This analysis creates a new vertex property to store the PageRank values.

pgx> g = session.readGraphWithProperties("<ORACLE_HOME>/md/property_graph/examples/pgx/graphs/sample.adj.json", "my-graph")
==> ...
pgx> rank = analyst.pagerank(g, 0.001, 0.85, 100)

PgxGraph g = session.readGraphWithProperties("<ORACLE_HOME>/md /property_graph/examples/pgx/graphs/sample.adj.json", "my-graph");
VertexProperty<Integer, Double> rank = session.createAnalyst().pagerank(g, 0.001, 0.85, 100);

This example stores the graph, the result of the Pagerank analysis, and all original edge properties as a file in edge-list format on disk.

To store a graph, you must specify:

• The graph format

• A path where the file will be stored

• The properties to be stored. Specify VertexProperty.ALL or EdgeProperty.ALL to store all properties, or VertexProperty.NONE or EdgePropery.NONE to store no properties. To specify individual properties, pass in the VertexProperty or EdgeProperty objects you want to store.

• A flag that indicates whether to overwrite an existing file with the same name

The following examples store the graph data in /tmp/sample_pagerank.elist, with the /tmp/sample_pagerank.elist.json configuration file. The return value is the graph configuration for the stored file. You can use it to read the graph again.

pgx> config = g.store(Format.EDGE_LIST, "/tmp/sample_pagerank.elist", [rank], EdgeProperty.ALL, false)
==> {"uri":"/tmp/sample_pagerank.elist","edge_props":[{"type":"double","name":"cost"}],"vertex_id_type":"integer","loading":{},"format":"edge_list","attributes":{},"vertex_props":[{"type":"double","name":"pagerank"}],"error_handling":{}}

import oracle.pgx.api.*;
import oracle.pgx.config.*;
 
FileGraphConfig config = g.store(Format.EDGE_LIST, "/tmp/sample_pagerank.elist", Collections.singletonList(rank), EdgeProperty.ALL, false);

The in-memory analyst contains a set of built-in algorithms that are available as Java APIs. The details of the APIs are documented in the Javadoc that is included in the product documentation library. Specifically, see the BuiltinAlgorithms interface Method Summary for a list of the supported in-memory analyst methods.

For example, this is the Pagerank procedure signature:

/**
   * Classic pagerank algorithm. Time complexity: O(E * K) with E = number of edges, K is a given constant (max
   * iterations)
   *
   * @param graph
   *          graph
   * @param e
   *          maximum error for terminating the iteration
   * @param d
   *          damping factor
   * @param max
   *          maximum number of iterations
   * @return Vertex Property holding the result as a double
   */
  public <ID extends Comparable<ID>> VertexProperty<ID, Double> pagerank(PgxGraph graph, double e, double d, int max);

For triangle counting, the sortByDegree boolean parameter of countTriangles() allows you to control whether the graph should first be sorted by degree (true) or not (false). If true, more memory will be used, but the algorithm will run faster; however, if your graph is very large, you might want to turn this optimization off to avoid running out of memory.

pgx> analyst.countTriangles(graph, true)
==> 1

import oracle.pgx.api.*;
 
Analyst analyst = session.createAnalyst();
long triangles = analyst.countTriangles(graph, true);

Pagerank computes a rank value between 0 and 1 for each vertex (node) in the graph and stores the values in a double property. The algorithm therefore creates a vertex property of type double for the output.

In the in-memory analyst, there are two types of vertex and edge properties:

• Persistent Properties: Properties that are loaded with the graph from a data source are fixed, in-memory copies of the data on disk, and are therefore persistent. Persistent properties are read-only, immutable and shared between sessions.

• Transient Properties: Values can only be written to transient properties, which are session private. You can create transient properties by calling createVertexProperty and createEdgeProperty on PgxGraph objects.

This example obtains the top three vertices with the highest Pagerank values. It uses a transient vertex property of type double to hold the computed Pagerank values. The Pagerank algorithm uses the following default values for the input parameters: error (tolerance = 0.001, damping factor = 0.85, and maximum number of iterations = 100.

pgx> rank = analyst.pagerank(graph, 0.001, 0.85, 100);
==> ...
pgx> rank.getTopKValues(3)
==> 128=0.1402019732468347
==> 333=0.12002296283541904
==> 99=0.09708583862990475

import java.util.Map.Entry;
import oracle.pgx.api.*;
 
Analyst analyst = session.createAnalyst();
VertexProperty<Integer, Double> rank = analyst.pagerank(graph, 0.001, 0.85, 100);
for (Entry<Integer, Double> entry : rank.getTopKValues(3)) {
 System.out.println(entry.getKey() + "=" + entry.getValue());
}

Filter expressions are expressions that are evaluated for each edge. The expression can define predicates that an edge must fulfil to be contained in the result, in this case a subgraph.

Consider the graph in Providing Graph Metadata in a Configuration File, which consists of four vertices (nodes) and four edges. For an edge to match the filter expression src.prop == 10, the source vertex prop property must equal 10. Two edges match that filter expression, as shown in the following figure.

Figure 3-3 Edges Matching src.prop == 10

Description of "Figure 3-3 Edges Matching src.prop == 10"

The following figure shows the graph that results when the filter is applied. The filter excludes the edges associated with vertex 333, and the vertex itself.

Figure 3-4 Graph Created by the Simple Filter

Description of "Figure 3-4 Graph Created by the Simple Filter"

Using filter expressions to select a single vertex or a set of vertices is difficult. For example, selecting only the vertex with the property value 10 is impossible, because the only way to match the vertex is to match an edge where 10 is either the source or destination property value. However, when you match an edge you automatically include the source vertex, destination vertex, and the edge itself in the result.

The following examples create the subgraph described in About Filter Expressions.

subgraph = graph.filter(new VertexFilter("vertex.prop == 10"))

import oracle.pgx.api.*;
import oracle.pgx.api.filter.*;

PgxGraph graph = session.readGraphWithProperties(...);
PgxGraph subgraph = graph.filter(new VertexFilter("vertex.prop == 10"));

This example uses a slightly more complex filter. It uses the outDegree function, which calculates the number of outgoing edges for an identifier (source src or destination dst). The following filter expression matches all edges with a cost property value greater than 50 and a destination vertex (node) with an outDegree greater than 1.

dst.outDegree() > 1 && edge.cost > 50

One edge in the sample graph matches this filter expression, as shown in the following figure.

Figure 3-5 Edges Matching the outDegree Filter

Description of "Figure 3-5 Edges Matching the outDegree Filter"

The following figure shows the graph that results when the filter is applied. The filter excludes the edges associated with vertixes 99 and 1908, and so excludes those vertices also.

Figure 3-6 Graph Created by the outDegree Filter

Description of "Figure 3-6 Graph Created by the outDegree Filter"

You can create a bipartite subgraph by specifying a set of vertices (nodes), which are used as the left side. A bipartite subgraph has edges only between the left set of vertices and the right set of vertices. There are no edges within those sets, such as between two nodes on the left side. In the in-memory analyst, vertices that are isolated because all incoming and outgoing edges were deleted are not part of the bipartite subgraph.

The following figure shows a bipartite subgraph. No properties are shown.

Description of the illustration pgx_bipartite_graph.png

The following examples create a bipartite subgraph from the simple graph created in Providing Graph Metadata in a Configuration File. They create a vertex collection and fill it with the vertices for the left side.

pgx> s = graph.createVertexSet()
==> ...
pgx> s.addAll([graph.getVertex(333), graph.getVertex(99)])
==> ...
pgx> s.size()
==> 2
pgx> bGraph = graph.bipartiteSubGraphFromLeftSet(s)
==> PGX Bipartite Graph named sample-sub-graph-4

import oracle.pgx.api.*;
 
VertexSet<Integer> s = graph.createVertexSet();
s.addAll(graph.getVertex(333), graph.getVertex(99));
BipartiteGraph bGraph = graph.bipartiteSubGraphFromLeftSet(s);

When you create a subgraph, the in-memory analyst automatically creates a Boolean vertex (node) property that indicates whether the vertex is on the left side. You can specify a unique name for the property.

The resulting bipartite subgraph looks like this:

Description of the illustration pgx_bipartite_subgraph.png

Vertex 1908 is excluded from the bipartite subgraph. The only edge that connected that vertex extended from 128 to 1908. The edge was removed, because it violated the bipartite properties of the subgraph. Vertex 1908 had no other edges, and so was removed also.

Because auto-refresh can create many snapshots and therefore may lead to a high memory usage, by default the option to enable auto-refresh for graphs is available only to administrators.

To allow all users to auto-refresh graphs, you must include the following line into the in-memory analyst configuration file (located in $ORACLE_HOME/md/property_graph/pgx/conf/pgx.conf):

{
  "allow_user_auto_refresh": true
}

Auto-refresh is configured in the loading section of the graph configuration. The example in this topic sets up auto-refresh to check for updates every minute, and to create a new snapshot when the data source has changed.

The following block (JSON format) enables the auto-refresh feature in the configuration file of the sample graph:

{
  "format": "pg",
  "jdbc_url": "jdbc:oracle:thin:@mydatabaseserver:1521/dbName",
  "username": "scott",
  "password": "<password>",
  "name": "my_graph",
  "vertex_props": [{
    "name": "prop",
    "type": "integer"
  }],
  "edge_props": [{
    "name": "cost",
    "type": "double"
  }],
  "separator": " ",
  "loading": {
    "auto_refresh": true,
    "update_interval_sec": 60
  },
}

Notice the additional loading section containing the auto-refresh settings. You can also use the Java APIs to construct the same graph configuration programmatically:

GraphConfig config = GraphConfigBuilder.forPropertyGraphRdbms()
  .setJdbcUrl("jdbc:oracle:thin:@mydatabaseserver:1521/dbName")
  .setUsername("scott")
  .setPassword("<password>")
  .setName("my_graph")
  .addVertexProperty("prop", PropertyType.INTEGER)
  .addEdgeProperty("cost", PropertyType.DOUBLE)
  .setAutoRefresh(true)
  .setUpdateIntervalSec(60)
  .build();

After creating the graph configuration, you can load the graph into the in-memory analyst using the regular APIs.

pgx> G = session.readGraphWithProperties("graphs/my-config.pg.json")

After the graph is loaded, a background task is started automatically, and it periodically checks the data source for updates.

The database is queried every minute for updates. If the graph has changed in the database after the time interval passed, the graph is reloaded and a new snapshot is created in-memory automatically.

You can "check out" (move a pointer to a different version of) the available in-memory snapshots of the graph using the getAvailableSnapshots() method of PgxSession. Example output is as follows:

pgx> session.getAvailableSnapshots(G)
==> GraphMetaData [getNumVertices()=4, getNumEdges()=4, memoryMb=0, dataSourceVersion=1453315103000, creationRequestTimestamp=1453315122669 (2016-01-20 10:38:42.669), creationTimestamp=1453315122685 (2016-01-20 10:38:42.685), vertexIdType=integer, edgeIdType=long]
==> GraphMetaData [getNumVertices()=5, getNumEdges()=5, memoryMb=3, dataSourceVersion=1452083654000, creationRequestTimestamp=1453314938744 (2016-01-20 10:35:38.744), creationTimestamp=1453314938833 (2016-01-20 10:35:38.833), vertexIdType=integer, edgeIdType=long]

The preceding example output contains two entries, one for the originally loaded graph with 4 vertices and 4 edges, and one for the graph created by auto-refresh with 5 vertices and 5 edges.

To check out out a specific snapshot of the graph, use the setSnapshot() methods of PgxSession and give it the creationTimestamp of the snapshot you want to load.

For example, if G is pointing to the newer graph with 5 vertices and 5 edges, but you want to analyze the older version of the graph, you need to set the snapshot to 1453315122685. In the in-memory analyst shell:

pgx> G.getNumVertices()
==> 5
pgx> G.getNumEdges()
==> 5

pgx> session.setSnapshot( G, 1453315122685 )
==> null

pgx> G.getNumVertices()
==> 4
pgx> G.getNumEdges()
==> 4

You can also load a specific snapshot of a graph directly using the readGraphAsOf() method of PgxSession. This is a shortcut for loading a graph with readGraphWithProperty() followed by a setSnapshot(). For example:

pgx> G = session.readGraphAsOf( config, 1453315122685 )

If you do not know or care about what snapshots are currently available in-memory, you can also specify a time span of how “old” a snapshot is acceptable by specifying a maximum allowed age. For example, to specify a maximum snapshot age of 60 minutes, you can use the following:

pgx> G = session.readGraphWithProperties( config, 60l, TimeUnit.MINUTES )

If there are one or more snapshots in memory younger (newer) than the specified maximum age, the youngest (newest) of those snapshots will be returned. If all the available snapshots are older than the specified maximum age, or if there is no snapshot available at all, then a new snapshot will be created automatically.

You can specify advanced options for auto-refresh configuration.

Internally, the in-memory analyst fetches the changes since the last check from the database and creates a new snapshot by applying the delta (changes) to the previous snapshot. There are two timers: one for fetching and caching the deltas from the database, the other for actually applying the deltas and creating a new snapshot.

Additionally, you can specify a threshold for the number of cached deltas. If the number of cached changes grows above this threshold, a new snapshot is created automatically. The number of cached changes is a simple sum of the number of vertex changes plus the number of edge changes.

The deltas are fetched periodically and cached on the in-memory analyst server for two reasons:

• To speed up the actual snapshot creation process

• To account for the case that the database can "forget" changes after a while

You can specify both a threshold and an update timer, which means that both conditions will be checked before new snapshot is created. At least one of these parameters (threshold or update timer) must be specified to prevent the delta cache from becoming too large. The interval at which the source is queried for changes must not be omitted.

The following parameters show a configuration where the data source is queried for new deltas every 5 minutes. New snapshots are created every 20 minutes or if the cached deltas reach a size of 1000 changes.

{
  "format": "pg",
  "jdbc_url": "jdbc:oracle:thin:@mydatabaseserver:1521/dbName",
  "username": "scott",
  "password": "<your_password>",
  "name": "my_graph",

  "loading": {
    "auto_refresh": true,
    "fetch_interval_sec": 300,
    "update_interval_sec": 1200,
    "update_threshold": 1000,
    "create_edge_id_index": true,
    "create_edge_id_mapping": true
  }
}

The in-memory analyst web deployment uses BASIC Auth by default. You should change to a more secure authentication mechanism for a production deployment.

To change the authentication mechanism, modify the security-constraint element of the web.xml deployment descriptor in the web application archive (WAR) file.

To download and install the latest version of Oracle WebLogic Server, see

http://www.oracle.com/technetwork/middleware/weblogic/documentation/index.html

To deploy the in-memory analyst to Oracle WebLogic, use commands like the following. Substitute your administrative credentials and WAR file for the values shown in this example:

. $MW_HOME/user_projects/domains/mydomain/bin/setDomainEnv.sh
. $MW_HOME/wlserver/server/bin/setWLSEnv.sh
java weblogic.Deployer -adminurl http://localhost:7001 -username username -password password -deploy -source $PGX_HOME/server/pgx-webapp-<version>-wls.war

export PGX_HOME=$ORACLE_HOME/md/property_graph/pgx

If the script runs successfully, you will see a message like this one:

Target state: deploy completed on Server myserver

Verify that you can connect to the server by entering a command in the following format:

$PGX_HOME/bin/pgx --base_url http://scott:<password>@localhost:7001/pgx

The in-memory analyst web deployment uses BASIC Auth by default. You should change to a more secure authentication mechanism for a production deployment.

To change the authentication mechanism, modify the security-constraint element of the web.xml deployment descriptor in the web application archive (WAR) file.

The simplest way to connect to an in-memory analyst instance is to specify the base URL of the server. The following base URL can connect the SCOTT user to the local instance listening on port 8080:

http://scott:<password>@localhost:8080/pgx

To start the in-memory analyst shell with this base URL, you use the --base_url command line argument

cd $PGX_HOME
./bin/pgx --base_url http://scott:<password>@localhost:8080/pgx

You can connect to a remote instance the same way. However, the in-memory analyst currently does not provide remote support for the Control API.

• About Logging HTTP Requests
  

You can specify the base URL when you initialize the in-memory analyst using Java. An example is as follows. A URL to an in-memory analyst server is provided to the getInMemAnalyst API call.

import oracle.pg.rdbms.*;
import oracle.pgx.api.*;
 
PgRdbmsGraphConfigcfg = GraphConfigBuilder.forPropertyGraphRdbms().setJdbcUrl("jdbc:oracle:thin:@127.0.0.1:1521:orcl") 
   .setUsername("scott").setPassword("<password>")  .setName("mygraph") 
   .setMaxNumConnections(2) .setLoadEdgeLabel(false) 
   .addVertexProperty("name", PropertyType.STRING, "default_name")  
   .addEdgeProperty("weight", PropertyType.DOUBLE, "1000000")  
   .build();OraclePropertyGraph opg = OraclePropertyGraph.getInstance(cfg);
ServerInstance remoteInstance = Pgx.getInstance("http://scott:<password>@hostname:port/pgx");
PgxSession session = remoteInstance.createSession("my-session");
 
PgxGraph graph = session.readGraphWithProperties(opg.getConfig());

You can connect to an in-memory analyst instance using the REST API PGX endpoints. This enables you to interact with the in-memory analyst in a language other than Java to implement your own client.

The examples in this topic assume that:

• Linux with curl is installed. curl is a simple command-line utility to interact with REST endpoints.)
• The PGX server is up and running on http://localhost:7007.
• The PGX server has authentication/authorization disabled; that is, $ORACLE_HOME/md/property_graph/pgx/conf/server.conf contains "enable_tls": false. (This is a non-default setting and not recommended for production).
• PGX allows reading graphs from the local file system; that is, $ORACLE_HOME/md/property_graph/pgx/conf/pgx.conf contains "allow_local_filesystem": true. (This is a non-default setting and not recommended for production).

For the Swagger specification, you can see a full list of supported endpoints in JSON by opening http://localhost:7007/swagger.json in your browser.

• Step 1: Obtain a CSRF token
• Step 2: Create a session
• Step 3: Read a graph
• Step 4: Create a property
• Step 5: Run the PageRank algorithm on the loaded graph
• Step 6: Execute a PGQL query

curl -v http://localhost:7007/token

*   Trying 127.0.0.1...
* Connected to localhost (127.0.0.1) port 7007 (#0)
> GET /token HTTP/1.1
> Host: localhost:7007
> User-Agent: curl/7.47.0
> Accept: */*
> 
< HTTP/1.1 201
< SET-COOKIE: _csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0;Version=1; HttpOnly
< Content-Length: 0

curl -v --cookie '_csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0' -H 'content-type: application/json' -X POST http://localhost:7007/core/v1/sessions -d '{"source":"my-application", "idleTimeout":0, "taskTimeout":0, "timeUnitName":"MILLISECONDS", "_csrf_token":"9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0"}'

*   Trying 127.0.0.1...
* Connected to localhost (127.0.0.1) port 7007 (#0)
> POST /core/v1/sessions HTTP/1.1
> Host: localhost:7007
> User-Agent: curl/7.47.0
> Accept: */*
> Cookie: _csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0
> content-type: application/json
> Content-Length: 159
> 
* upload completely sent off: 159 out of 159 bytes
< HTTP/1.1 201
< SET-COOKIE: SID=abae2811-6dd2-48b0-93a8-8436e078907d;Version=1; HttpOnly
< Content-Length: 0

curl -v -X POST --cookie '_csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0;SID=abae2811-6dd2-48b0-93a8-8436e078907d'  http://localhost:7007/core/v1/loadGraph -H 'content-type: application/json'  -d  '{"graphConfig":<graph-config>,"graphName":null,"csrf_token":"9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0"}'

{
  "format": "pg",
  "db_engine": "RDBMS",
  "jdbc_url":"jdbc:oracle:thin:@127.0.0.1:1521:orcl122",
  "username":"scott",
  "password":"tiger",
  "max_num_connections": 8,
  "name": "connections",
  "vertex_props": [
    {"name":"name", "type":"string"},
    {"name":"role", "type":"string"},
    {"name":"occupation", "type":"string"},
    {"name":"country", "type":"string"},
    {"name":"political party", "type":"string"},
    {"name":"religion", "type":"string"}
  ],
  "edge_props": [
    {"name":"weight", "type":"double", "default":"1"}
  ],
  "edge_label": true,
  "loading": {
    "load_edge_label": true
  }
}

* upload completely sent off: 315 out of 315 bytes
< HTTP/1.1 202
< Location: http://localhost:7007/core/v1/futures/8a46ef65-01a9-4bd0-87d3-ffe9dfd2ce3c/status
< Content-Type: application/json;charset=utf-8
< Content-Length: 51
< Date: Mon, 05 Nov 2018 17:22:22 GMT
<
* Connection #0 to host localhost left intact
{"futureId":"8a46ef65-01a9-4bd0-87d3-ffe9dfd2ce3c"}

curl -v --cookie 'SID=abae2811-6dd2-48b0-93a8-8436e078907d'  http://localhost:7007/core/v1/futures/8a46ef65-01a9-4bd0-87d3-ffe9dfd2ce3c/status

< HTTP/1.1 200
< Content-Type: application/json;charset=utf-8
< Content-Length: 730
< Date: Mon, 05 Nov 2018 17:35:19 GMT
< 
* Connection #0 to host localhost left intact
{"id":"eb17f75b-e4c1-4a66-81a0-4ff0f8b4cb92","links":[{"href":"http://localhost:7007/core/v1/futures/eb17f75b-e4c1-4a66-81a0-4ff0f8b4cb92/status","rel":"self","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/futures/eb17f75b-e4c1-4a66-81a0-4ff0f8b4cb92","rel":"abort","method":"DELETE","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/futures/eb17f75b-e4c1-4a66-81a0-4ff0f8b4cb92/status","rel":"canonical","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/futures/eb17f75b-e4c1-4a66-81a0-4ff0f8b4cb92/value","rel":"related","method":"GET","interaction":["async-polling"]}],"progress":"succeeded","completed":true,"intervalToPoll":1}

curl -X GET --cookie 'SID=abae2811-6dd2-48b0-93a8-8436e078907d' http://localhost:7007/core/v1/futures/cdc15a38-3422-42a1-baf4-343c140cf95d/value

{"id":"sample","links":[{"href":"http://localhost:7007/core/v1/graphs/sample","rel":"self","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/graphs/sample","rel":"canonical","method":"GET","interaction":["async-polling"]}],"nodeProperties":{"prop1":{"id":"prop1","links":[{"href":"http://localhost:7007/core/v1/graphs/sample/properties/prop1","rel":"self","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/graphs/sample/properties/prop1","rel":"canonical","method":"GET","interaction":["async-polling"]}],"dimension":0,"name":"prop1","entityType":"vertex","type":"integer","transient":false}},"vertexLabels":null,"edgeLabel":null,"metaData":{"id":null,"links":null,"numVertices":4,"numEdges":4,"memoryMb":0,"dataSourceVersion":"1536029578000","config":{"format":"adj_list","separator":" ","edge_props":[{"type":"double","name":"cost"}],"error_handling":{},"vertex_props":[{"type":"integer","name":"prop1"}],"vertex_uris":["PATH_TO_FILE"],"vertex_id_type":"integer","loading":{}},"creationRequestTimestamp":1541242100335,"creationTimestamp":1541242100774,"vertexIdType":"integer","edgeIdType":"long","directed":true},"graphName":"sample","edgeProperties":{"cost":{"id":"cost","links":[{"href":"http://localhost:7007/core/v1/graphs/sample/properties/cost","rel":"self","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/graphs/sample/properties/cost","rel":"canonical","method":"GET","interaction":["async-polling"]}],"dimension":0,"name":"cost","entityType":"edge","type":"double","transient":false}},"ageMs":0,"transient":false}

curl -v -X POST --cookie '_csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0;SID=abae2811-6dd2-48b0-93a8-8436e078907d'  http://localhost:7007/core/v1/graphs/sample/properties -H 'content-type: application/json'  -d '{"entityType":"vertex","type":"double","name":"pagerank", "hardName":false,"dimension":0,"_csrf_token":"9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0"}'

{"id":"pagerank","links":[{"href":"http://localhost:7007/core/v1/graphs/sample/properties/pagerank","rel":"self","method":"GET","interaction":["async-polling"]},{"href":"http://localhost:7007/core/v1/graphs/sample/properties/pagerank","rel":"canonical","method":"GET","interaction":["async-polling"]}],"dimension":0,"name":"pagerank","entityType":"vertex","type":"double","transient":true}

curl -v -X POST --cookie '_csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0;SID=abae2811-6dd2-48b0-93a8-8436e078907d' http://localhost:7007/core/v1/analyses/pgx_builtin_k1a_pagerank/run -H  'content-type: application/json' -d  '{"args":[{"type":"GRAPH","value":"sample"},{"type":"DOUBLE_IN","value":0.001},{"type":"DOUBLE_IN","value":0.85},{"type":"INT_IN","value":100},{"type":"BOOL_IN","value":true},{"type":"NODE_PROPERTY","value":"pagerank"}],"expectedReturnType":"void","workloadCharacteristics":["PARALLELISM.PARALLEL"],"_csrf_token":"9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0"}'

{"success":true,"canceled":false,"exception":null,"returnValue":null,"executionTimeMs":50}

curl -v -X POST --cookie '_csrf_token=9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0;SID=abae2811-6dd2-48b0-93a8-8436e078907d' http://localhost:7007/core/v1/pgql/run -H 'content-type: application/json'  -d '{"pgqlQuery":"SELECT x.pagerank MATCH (x) WHERE x.pagerank > 0","semantic":"HOMOMORPHISM", "schemaStrictnessMode":true, "graphName" : "sample", "_csrf_token":"9bf51c8f-1c75-455e-9b57-ec3ca1c63cc0"}'

{"id":"pgql_1","links":[{"href":"http://localhost:7007/core/v1/pgqlProxies/pgql_1","rel":"self","method":"GET","interaction":["sync"]},{"href":"http://localhost:7007/core/v1/pgqlResultProxies/pgql_1/elements","rel":"related","method":"GET","interaction":["sync"]},{"href":"http://localhost:7007/core/v1/pgqlResultProxies/pgql_1/results","rel":"related","method":"GET","interaction":["sync"]},{"href":"http://localhost:7007/core/v1/pgqlProxies/pgql_1","rel":"canonical","method":"GET","interaction":["async-polling"]}],"exists":true,"graphName":"sample","resultSetId":"pgql_1","numResults":4}

curl -X GET  --cookie 'SID=abae2811-6dd2-48b0-93a8-8436e078907d' http://localhost:7007/core/v1/pgqlProxies/pgql_1/results?size=2048

{"id":"/pgx/core/v1/pgqlProxies/pgql_1/results","links":[{"href":"http://localhost:7007/core/v1/pgqlProxies/pgql_1/results","rel":"self","method":"GET","interaction":["sync"]},{"href":"http://localhost:7007/core/v1/pgqlProxies/pgql_1/results","rel":"canonical","method":"GET","interaction":["async-polling"]}],"count":4,"totalItems":4,"items":[[0.3081206521195582],[0.21367103988538017],[0.21367103988538017],[0.2645372681096815]],"hasMore":false,"offset":0,"limit":4,"showTotalResults":true}