Java UDF Guide

Data Files

There are six files associated with the Java UDF tutorial. All the files can be found in the Java Tutorial Git Repo, which is cloned in the API Download and Installation section.

• A management file (written in Java) that creates the schema, input & output tables, and creates the proc and executes it.
• A UDF (written using the Java UDF API) that contains a table copying example.
• A CSV input file
• Three Project Object Model (POM) files:
  • The main POM file contained at the top-level of the project that is used to compile the two module JARs (the manager and the UDF)
  • The manager POM file contained within the manager/ sub-directory that is used to compile the manager JAR
  • The UDF POM file contained within the udf/ sub-directory that is used to compile the UDF JAR

Software

• Java 1.7 (or greater)

  Note

  The location of java should be placed in the PATH environment variable and the JAVA_HOME should be set. If it is not, you'll need to use the full path to java executables in the relevant instructions below.

• Maven

• Python 2.7 (or greater) or pip

  Note

  The locations of python and pip should be placed in the PATH environment variable. If they are not, you'll need to use the full path to the python and pip executables in the relevant instructions below. Also, administrative access will most likely be required when installing the Python packages.

Compile

The proc files must be compiled into a JAR prior to usage; the files will need to be re-compiled after making any changes to the proc code. Re-compiling this tutorial using the provided main pom.xml file will actually create two JARs: one for the proc itself and one for the manager file.

To compile the Proc & example manager files and create JARs:

mvn clean package

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<artifactId>kinetica-udf-table-copy-example</artifactId>
<groupId>com.kinetica</groupId>
<version>1.0</version>
<packaging>pom</packaging>
<name>Kinetica UDF Table Copy Example</name>

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<modules>
    <module>manager</module>
    <module>udf</module>
</modules>

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<plugin>
    <artifactId>maven-antrun-plugin</artifactId>
    <version>${maven-antrun-plugin.version}</version>
    <executions>
        <execution>
            <id>copy</id>
            <phase>package</phase>
            <configuration>
                <target>
                    <copy todir="${basedir}/output" overwrite="true" flatten="true">
                        <fileset dir="${basedir}">
                            <include name="rank_tom.csv" />
                            <include name="manager/target/*.jar" />
                            <include name="udf/target/*.jar" />
                        </fileset>
                    </copy>
                </target>
            </configuration>
            <goals>
                <goal>run</goal>
            </goals>
        </execution>
    </executions>
</plugin>

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<parent>
    <groupId>com.kinetica</groupId>
    <artifactId>kinetica-udf-table-copy-example</artifactId>
    <version>1.0</version>
</parent>

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<artifactId>kinetica-table-copy-manager</artifactId>
<packaging>jar</packaging>

<properties>
    <gpudb-api.version>[7.1.0.0,7.2.0.0-SNAPSHOT)</gpudb-api.version>
    <kinetica-proc-api.version>[7.1.0.0,7.2.0.0-SNAPSHOT)</kinetica-proc-api.version>
    <java.version>1.7</java.version>
    <maven-compiler-plugin.version>3.1</maven-compiler-plugin.version>
    <maven-assembly-plugin.version>2.6</maven-assembly-plugin.version>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>

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<repositories>
    <repository>
        <id>gpudb-releases</id>
        <url>https://nexus.kinetica.com/repository/releases/</url>
    </repository>

    <repository>
        <id>gpudb-snapshots</id>
        <url>https://nexus.kinetica.com/repository/snapshots/</url>
    </repository>
</repositories>

<dependencies>
    <dependency>
        <groupId>com.gpudb</groupId>
        <artifactId>gpudb-api</artifactId>
        <version>${gpudb-api.version}</version>
        <type>jar</type>
    </dependency>
</dependencies>

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<build>
    <sourceDirectory>../src/main/java</sourceDirectory>

    <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>${maven-compiler-plugin.version}</version>
            <configuration>
                <includes>
                    <include>**/manager/**</include>
                </includes>
                <source>${java.version}</source>
                <target>${java.version}</target>
            </configuration>
        </plugin>

        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>${maven-assembly-plugin.version}</version>
            <executions>
                <execution>
                    <id>manager</id>
                    <configuration>
                        <archive>
                            <manifest>
                                <mainClass>UdfTcManager</mainClass>
                            </manifest>
                        </archive>
                        <descriptorRefs>
                            <descriptorRef>jar-with-dependencies</descriptorRef>
                        </descriptorRefs>
                    <finalName>kinetica-udf-table-copy-manager</finalName>
                    </configuration>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

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<parent>
    <groupId>com.kinetica</groupId>
    <artifactId>kinetica-udf-table-copy-example</artifactId>
    <version>1.0</version>
</parent>

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<artifactId>kinetica-table-copy-udf</artifactId>
<packaging>jar</packaging>

<properties>
    <kinetica-proc-api.version>[7.1.0.0,7.2.0.0-SNAPSHOT)</kinetica-proc-api.version>
    <java.version>1.7</java.version>
    <maven-compiler-plugin.version>3.1</maven-compiler-plugin.version>
    <maven-assembly-plugin.version>2.6</maven-assembly-plugin.version>
    <maven-antrun-plugin.version>1.8</maven-antrun-plugin.version>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
</properties>

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<dependencies>
    <dependency>
        <groupId>com.kinetica</groupId>
        <artifactId>kinetica-proc-api</artifactId>
        <version>${kinetica-proc-api.version}</version>
    </dependency>
</dependencies>

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<build>
    <sourceDirectory>../src/main/java</sourceDirectory>

    <plugins>
        <plugin>
            <artifactId>maven-compiler-plugin</artifactId>
            <version>${maven-compiler-plugin.version}</version>
            <configuration>
                <includes>
                    <include>**/udf/**</include>
                </includes>
                <source>${java.version}</source>
                <target>${java.version}</target>
            </configuration>
        </plugin>

        <plugin>
            <artifactId>maven-assembly-plugin</artifactId>
            <version>${maven-assembly-plugin.version}</version>
            <executions>
                <execution>
                    <id>proc</id>
                    <configuration>
                        <archive>
                            <manifest>
                                <mainClass>com.kinetica.UdfTcJavaProc</mainClass>
                            </manifest>
                        </archive>
                        <descriptorRefs>
                            <descriptorRef>jar-with-dependencies</descriptorRef>
                        </descriptorRefs>
                    <finalName>kinetica-udf-table-copy-proc</finalName>
                    </configuration>
                    <phase>package</phase>
                    <goals>
                        <goal>single</goal>
                    </goals>
                </execution>
            </executions>
        </plugin>
    </plugins>
</build>

Test

A UDF can be tested using the UDF simulator in the native Python API repository without writing anything to the database.

1. Run the UDF manager JAR with the init option, optionally specifying the database host and a username & password:

   java -jar output/kinetica-udf-table-copy-manager-jar-with-dependencies.jar "init" [<hostname> [<username> <password>]]
   

2. In the native Python API directory, run the UDF simulator in execute mode with the following options to simulate running the UDF, where -i is the schema-qualified UDF input table, -o is the schema-qualified UDF output table, and -K is the Kinetica URL (using the appropriate values for your environment). Username (-U) & password (-P) can be specified, if your instance requires authentication:

   python ../../kinetica-api-python/examples/udfsim.py execute -d \
       -i [<schema>.]<input-table> -o [<schema>.]<output-table> \
       -K http://<kinetica-host>:<kinetica-port> \
       [-U <kinetica-user> -P <kinetica-pass>]
   

   For instance:

   python ../../kinetica-api-python/examples/udfsim.py execute -d \
       -i tutorial_udf_java.udf_tc_java_in_table -o tutorial_udf_java.udf_tc_java_out_table \
       -K http://127.0.0.1:9191 \
       -U admin -P admin123
   

3. Copy & execute the export command output by the previous command; this will prepare the execution environment for simulating the UDF:

   export KINETICA_PCF=/tmp/udf-sim-control-files/kinetica-udf-sim-icf-xMGW32
   

   Important

   The export command shown above is an example of what the udfsim.py script will output--it should not be copied to the terminal in which this example is being run. Make sure to copy & execute the actual command output by udfsim.py in the previous step.

4. Run the UDF Proc JAR:

   java -jar output/kinetica-udf-table-copy-proc-jar-with-dependencies.jar
   

5. Run the UDF Simulator in output mode to output the results to Kinetica (use the dry run flag -d to avoid writing to Kinetica), ensuring you replace the Kinetica URL and port with the appropriate values. The results map will be returned (even if there's nothing in it) as well as the amount of records that were (or will be in the case of a dry run) added to the given output table:

   python ../../kinetica-api-python/examples/udfsim.py output \
       -K http://<kinetica-host>:<kinetica-port> \
       [-U <kinetica-user> -P <kinetica-pass>]
   

   For instance:

   python ../../kinetica-api-python/examples/udfsim.py output \
       -K http://127.0.0.1:9191 \
       -U admin -P admin123
   

   This should output the following:

   No results
   Output:
   
   tutorial_udf_java.udf_tc_java_out_table: 10000 records
   

6. Clean the control files output by the UDF simulator:

   python ../../kinetica-api-python/examples/udfsim.py clean
   

   Important

   The clean command is only necessary if data was output to Kinetica; otherwise, the UDF simulator can be re-run as many times as desired without having to clean the output files and enter another export command.

Initialization (UdfTcManager.java init mode)

The init mode calls the init() method of the UdfTcManager.java file. This method will create the schema, an input type and table for the UDF to copy data from, and an output type and table to copy data to. Sample data will also be generated and placed in the input table.

To create tables using the Java API, a Type needs to be defined in the system first. The type is a class, extended from RecordObject, using annotations to describe which class instance variables are fields (i.e. columns), what type they are, and any special handling they should receive. Each field consists of a name and a data type:

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public static class InTable extends RecordObject
{
    @RecordObject.Column(order=0, properties = {"int16", "primary_key"})
    public Integer id;
    @RecordObject.Column(order=1)
    public Float x;
    @RecordObject.Column(order=2)
    public Float y;

    public InTable() {}

    public InTable(Integer id, Float x, Float y)
    {
        this.id = id;
        this.x = x;
        this.y = y;
    }
}

public static class OutTable extends RecordObject
{
    @RecordObject.Column(order=0, properties = {"int16", "primary_key"})
    public Integer id;
    @RecordObject.Column(order=1)
    public Float a;
    @RecordObject.Column(order=2)
    public Float b;

    public OutTable() {}

    public OutTable(Integer id, Float a, Float b)
    {
        this.id = id;
        this.a = a;
        this.b = b;
    }
}

To interact with Kinetica, you must first instantiate an object of the GPUdb class while providing the connection URL and username & password to use for logging in. This database object is later passed to the init() and exec() methods, which take a database object hDB:

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GPUdbBase.Options options = new GPUdbBase.Options();
options.setUsername(username);
options.setPassword(password);
GPUdb kinetica = new GPUdb(url, options);

The InTable type and table are created, but the table is removed first if it already exists. Then the table creation is verified using showTable():

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hDb.clearTable(INPUT_TABLE, null, GPUdb.options("no_error_if_not_exists", "true"));
String inTableId = RecordObject.createType(InTable.class, hDb);
hDb.createTable(INPUT_TABLE, inTableId, null);
System.out.println("Input table successfully created:");
ShowTableResponse showInputTable = hDb.showTable(INPUT_TABLE, null);
System.out.println(showInputTable.getTableNames().get(0) + " with type id " + showInputTable.getTypeIds().get(0));

Next, sample data is generated and inserted into the new input table:

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ArrayList<InTable> allRecords = new ArrayList<>();
for (int i = 0; i < MAX_RECORDS; i++) {
    InTable singleRecord = new InTable();
    singleRecord.id = i;
    singleRecord.x = (float) rand.nextGaussian() * 1 + 1;
    singleRecord.y = (float) rand.nextGaussian() * 1 + 2;
    allRecords.add(singleRecord);
}
hDb.insertRecords(INPUT_TABLE, allRecords, null);
GetRecordsResponse getRecordsResponse = hDb.getRecords(INPUT_TABLE, 0, GPUdbBase.END_OF_SET, null);
System.out.println("Number of records inserted into the input table: " + getRecordsResponse.getTotalNumberOfRecords());
System.out.println();

Lastly, an OutTable type and table are created, but the table is removed first if it already exists. Then the table creation is verified using showTable():

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hDb.clearTable(OUTPUT_TABLE, null, GPUdb.options("no_error_if_not_exists", "true"));
String outTableId = RecordObject.createType(OutTable.class, hDb);
hDb.createTable(OUTPUT_TABLE, outTableId, null);
System.out.println("Output table successfully created:");
ShowTableResponse showOutputTable = hDb.showTable(OUTPUT_TABLE, null);
System.out.println(showOutputTable.get(0) + " with type id " + showOutputTable.get(3));

UDF (UdfTcJavaProc.java)

First, instantiate a handle to the ProcData class:

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ProcData procData = ProcData.get();

Initialize a boolean that will be switched to true if a rank/TOM pair-CSV file value match is found:

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boolean foundMatch = false;

Retrieve each pair of uniquely-identifying rank/TOM pairs from the CSV file containing the list of processing nodes whose data should be copied by the UDF:

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final String procRankNum = procData.getRequestInfo().get("rank_number");
final String procTomNum = procData.getRequestInfo().get("tom_number");

Then, the CSV file mentioned in Data Files is read (skipping the header):

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Scanner scanner = new Scanner(new File("rank_tom.csv"));
scanner.nextLine();
while (scanner.hasNextLine())

Compare the rank and TOM of the current UDF instance's processing node to each rank/TOM pair in the file to determine if the current UDF instance should copy the data on its corresponding processing node:

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String[] row = scanner.nextLine().split(",", -1);
final String fileRankNum = row[0];
final String fileTomNum = row[1];
if (procRankNum.equals(fileRankNum) && procTomNum.equals(fileTomNum))

For each input and output table found in the inputData and outputData objects (respectively), set the output tables' size to the input tables' size. This will allocate enough memory to copy all input records to the output table:

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ProcData.InputTable inputTable = procData.getInputData().getTable(i);
ProcData.OutputTable outputTable = procData.getOutputData().getTable(i);
outputTable.setSize(inputTable.getSize());

For each input column in the input table(s) and for each output column in the output table(s), copy the input columns' values to the output columns:

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for (int j = 0; j < inputTable.getColumnCount(); j++)
{
    ProcData.InputColumn inputColumn = inputTable.getColumn(j);
    ProcData.OutputColumn outputColumn = outputTable.getColumn(j);

    for (long k = 0; k < inputTable.getSize(); k++)
    {
        switch (inputColumn.getType())
        {
            case BYTES: outputColumn.appendVarBytes(inputColumn.getVarBytes(k)); break;
            case CHAR1: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR2: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR4: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR8: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR16: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR32: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR64: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR128: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case CHAR256: outputColumn.appendChar(inputColumn.getChar(k)); break;
            case DATE: outputColumn.appendCalendar(inputColumn.getCalendar(k)); break;
            case DATETIME: outputColumn.appendCalendar(inputColumn.getCalendar(k)); break;
            case DECIMAL: outputColumn.appendBigDecimal(inputColumn.getBigDecimal(k)); break;
            case DOUBLE: outputColumn.appendDouble(inputColumn.getDouble(k)); break;
            case FLOAT: outputColumn.appendFloat(inputColumn.getFloat(k)); break;
            case INT: outputColumn.appendInt(inputColumn.getInt(k)); break;
            case INT8: outputColumn.appendByte(inputColumn.getByte(k)); break;
            case INT16: outputColumn.appendShort(inputColumn.getShort(k)); break;
            case IPV4: outputColumn.appendInet4Address(inputColumn.getInet4Address(k)); break;
            case LONG: outputColumn.appendLong(inputColumn.getLong(k)); break;
            case STRING: outputColumn.appendVarString(inputColumn.getVarString(k)); break;
            case TIME: outputColumn.appendCalendar(inputColumn.getCalendar(k)); break;
            case TIMESTAMP: outputColumn.appendLong(inputColumn.getLong(k)); break;
            default:
                throw new RuntimeException();
        }
    }
}

If no matches were found, finish processing:

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if (!foundMatch)
    System.out.println("No rank or tom matches");

Call complete() to tell Kinetica the proc code is finished:

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procData.complete();

Execution (UdfTcManager.java exec mode)

The exec mode calls the exec() method of the UdfTcManager.java file. This method will read files in as bytes, create a proc, and upload the files to the proc. The method will then execute the proc.

To upload the UdfTcManager.jar and rank_tom.csv files to Kinetica, they will first need to be read in as bytes and added to a file data map:

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Map<String, ByteBuffer> filesMap = new HashMap<>();
for (String fileName : Arrays.asList(CSV_FILE, PROC_JAR_FILE))
{
    byte [] fileAsBytes = Files.readAllBytes(new File(fileName).toPath());
    ByteBuffer fileByteBuffer = ByteBuffer.wrap(fileAsBytes);
    filesMap.put(fileName, fileByteBuffer);
}

After the files are placed in a data map, the distributed UdfTcJavaProc proc can be created in Kinetica and the files can be associated with it:

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CreateProcResponse createProcResponse = hDb.createProc(
        PROC_NAME,
        "distributed",
        filesMap,
        "java",
        Arrays.asList("-jar", PROC_JAR_FILE),
        null
);

java -jar output/kinetica-udf-table-copy-proc-jar-with-dependencies.jar

Finally, after the proc is created, it can be executed. The input table and output table created in the Initialization section are passed in here:

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ExecuteProcResponse executeProcResponse = hDb.executeProc(
        PROC_NAME,
        null,
        null,
        Collections.singletonList(INPUT_TABLE),
        null,
        Collections.singletonList(OUTPUT_TABLE),
        null
);