Write To Aerospike From Spark

     Read from Hdfs and write to Aerospike from Spark via Map Transformation





package com.big.data.spark;

import com.aerospike.client.AerospikeClient;
import com.aerospike.client.Bin;
import com.aerospike.client.Key;
import com.aerospike.client.policy.WritePolicy;
import com.big.data.avro.schema.Employee;
import com.databricks.spark.avro.SchemaConverters;
import org.apache.commons.io.IOUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.types.StructType;

import java.io.Closeable;
import java.io.IOException;

public class ReadFromHdfsWriteToAerospikeSpark extends Configured implements Tool, Closeable {

    public static final String INPUT_PATH = "spark.input.path";
    public static final String OUTPUT_PATH = "spark.output.path";
    public static final String IS_RUN_LOCALLY = "spark.is.run.local";
    public static final String DEFAULT_FS = "spark.default.fs";
    public static final String NUM_PARTITIONS = "spark.num.partitions";

    //Aerospike related properties
    public static final String AEROSPIKE_HOSTNAME = "aerospike.host.name";
    public static final String AEROSPIKE_PORT = "aerospike.port";
    public static final String AEROSPIKE_NAMESPACE = "aerospike.name.space";
    public static final String AEROSPIKE_SETNAME = "aerospike.set.name";

    // For Dem key is emp_id and value is emp_name
    public static final String KEY_NAME = "avro.key.name";
    public static final String VALUE_NAME = "avro.value.name";

    private SQLContext sqlContext;
    private JavaSparkContext javaSparkContext;

    protected <T> JavaSparkContext getJavaSparkContext(final boolean isRunLocal,
                                                       final String defaultFs,
                                                       final Class<T> tClass) {
        final SparkConf sparkConf = new SparkConf()
                //Set spark conf here , 
                //after one gets spark context you can set hadoop configuration for InputFormats
                .set("spark.serializer", "org.apache.spark.serializer.KryoSerializer");

        if (isRunLocal) {

        final JavaSparkContext sparkContext = new JavaSparkContext(sparkConf);

        if (defaultFs != null) {
            sparkContext.hadoopConfiguration().set("fs.defaultFS", defaultFs);

        return sparkContext;

    public int run(String[] args) throws Exception {

        //The arguments passed has been split into Key value by ToolRunner
        Configuration conf = getConf();
        String inputPath = conf.get(INPUT_PATH);
        String outputPath = conf.get(OUTPUT_PATH);
        String aerospikeHostname = conf.get(AEROSPIKE_HOSTNAME);
        int aerospikePort = conf.getInt(AEROSPIKE_PORT, 3000);
        String namespace = conf.get(AEROSPIKE_NAMESPACE);
        String setName = conf.get(AEROSPIKE_SETNAME);

        String keyName = conf.get(KEY_NAME);
        String valueName = conf.get(VALUE_NAME);

        //Get spark context, This is the central context , 
        //which can be wrapped in Any Other context
        javaSparkContext = getJavaSparkContext(conf.getBoolean(IS_RUN_LOCALLY, Boolean.FALSE), 
                                                 conf.get(DEFAULT_FS), this.getClass());
        sqlContext = new SQLContext(javaSparkContext);

        // No input path has been read, no job has not been started yet .
        //To set any configuration use javaSparkContext.hadoopConfiguration().set(Key,value);
        // To set any custom inputformat use javaSparkContext.newAPIHadoopFile() and get a RDD

        // Avro schema to StructType conversion
        final StructType outPutSchemaStructType = 
                                        (StructType) SchemaConverters
        final StructType inputSchema = (StructType) SchemaConverters

        // read data from parquetfile, 
        // the schema of the data is taken from the avro schema
        DataFrame inputDf = sqlContext.read().schema(inputSchema).parquet(inputPath);

        // convert DataFrame into JavaRDD
        // the rows read from the parquetfile is converted into a Row object . 
        // Row has same schema as that of the parquet file roe
        JavaRDD<Row> rowJavaRDD = inputDf.javaRDD();

        // Data read from parquet has same schema as that of avro (Empoyee Avro). 
        // Key is employeeId and value is EmployeeName
        // In the map there is no special function to initialize or shutdown the Aerospike client.
        JavaRDD<Row> returnedRowJavaRDD = rowJavaRDD.map(
              new InsertIntoAerospike(aerospikeHostname, aerospikePort, 
                                            namespace, setName, keyName,valueName));

        //Map is just a transformation in Spark hence a action 
        //like write(), collect() is needed to carry out the action.
        // Remeber spark does Lazy evaluation, without a action transformations will not execute.

        DataFrame outputDf = sqlContext.createDataFrame(returnedRowJavaRDD, outPutSchemaStructType);

        // Convert JavaRDD to dataframe and save into parquet file

        return 0;

    // Do remember all the lambda function are instantiated on driver, serialized and sent to driver.
    // No need to initialize the Service(Aerospike , Hbase on driver ) hence making it transient
    // In the map , for each record insert into Aerospike , this can be converted into batch too
    public static class InsertIntoAerospike implements Function<Row, Row> {

        // not making it static , as it will not be serialized and sent to executors
        private final String aerospikeHostName;
        private final int aerospikePortNo;
        private final String aerospikeNamespace;
        private final String aerospikeSetName;
        private final String keyColumnName;
        private final String valueColumnName;

        // The Aerospike client is not serializable and 
        // neither there is a need to instatiate on driver
        private transient AerospikeClient client;
        private transient WritePolicy policy;

        public InsertIntoAerospike(String hostName, int portNo, String nameSpace,
                            String setName, String keyColumnName, String valueColumnName) {
            this.aerospikeHostName = hostName;
            this.aerospikePortNo = portNo;
            this.aerospikeNamespace = nameSpace;
            this.aerospikeSetName = setName;
            this.keyColumnName = keyColumnName;
            this.valueColumnName = valueColumnName;

            //Add Shutdown hook to close the client gracefully
            //This is the place where u can gracefully clean your Service resources 
            //as there is no cleanup() function in Spark Map
            JVMShutdownHook jvmShutdownHook = new JVMShutdownHook();


        public Row call(Row row) throws Exception {
            // Intitialize on the first call
            if (client == null) {
                policy = new WritePolicy();
                // how to close the client gracefully ?
                client = new AerospikeClient(aerospikeHostName, aerospikePortNo);

            // As rows have schema with fieldName and Values being part of the Row
            Key key = new Key(aerospikeNamespace, aerospikeSetName,
                                              (Integer) row.get(row.fieldIndex(keyColumnName)));
            Bin bin = new Bin(valueColumnName, (String) row.get(row.fieldIndex(valueColumnName)));
            client.put(policy, key, bin);

            return row;

        //When JVM is going down close the client
        private class JVMShutdownHook extends Thread {
            public void run() {
                System.out.println("JVM Shutdown Hook: Thread initiated ,
                                                  shutting down service gracefully");

    public void close() throws IOException {

    public static void main(String[] args) throws Exception {
        ToolRunner.run(new ReadFromHdfsWriteToAerospikeSpark(), args);


Key Take Aways:

  • The Map Transformation doesn’t has a start or stop function .
  • Map is a transformation and not a action, hence a action is needed for map to execute.
  • write() , collect() can be used to make sure the transformation executes.
  • For service cleanup a ShutdownHook is needed
  • A number of Task can run in the same JVM on a given executor
  • A static variable for service means , multiple task can access the service , the service API needs to be thread safe in that case.


Aerospike Unit Test framework:


Integration Test:

package com.big.data.spark;

import com.aerospike.client.AerospikeClient;
import com.aerospike.client.Key;
import com.aerospike.client.Record;
import com.aerospike.client.policy.WritePolicy;
import com.aerospike.unit.impls.AerospikeRunTimeConfig;
import com.aerospike.unit.impls.AerospikeSingleNodeCluster;
import com.aerospike.unit.utils.AerospikeUtils;
import com.big.data.avro.AvroUtils;
import com.big.data.avro.schema.Employee;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.commons.io.FileUtils;
import org.apache.hadoop.fs.Path;
import org.junit.After;
import org.junit.Assert;
import org.junit.Before;
import org.junit.Test;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import parquet.avro.AvroParquetReader;
import parquet.avro.AvroParquetWriter;
import parquet.hadoop.ParquetReader;
import parquet.hadoop.ParquetWriter;
import parquet.hadoop.metadata.CompressionCodecName;

import java.io.File;

public class ReadFromHdfsWriteToAerospikeSparkTest {

    private static final Logger LOG = LoggerFactory.getLogger(ReadWriteAvroParquetFilesTest.class);
    private static final String BASEDIR =
    "/tmp/ReadFromHdfsWriteToASparkTest/avroparquetInputFile/" + System.currentTimeMillis() + "/";
    private String input;
    private String output;

    private AerospikeSingleNodeCluster cluster;
    private AerospikeRunTimeConfig runtimConfig;
    private AerospikeClient client;

    //Aerospike unit related Params
    private String memorySize = "64M";
    private String setName = "BIGTABLE";
    private String binName = "BIGBIN";
    private String nameSpace = "RandomNameSpace";

    public void setUp() throws Exception {

        input = BASEDIR + "input/";
        output = BASEDIR + "output/";

        Employee employee = new Employee();

        Employee employee1 = new Employee();

        //Write parquet file2 with GZIP compression
        ParquetWriter<Object> writer = AvroParquetWriter
                                            .builder(new Path(input + "1.gz.parquet"))

        // Start Aerospike MiniCluster
        // Instatiate the cluster with NameSpaceName , memory Size.
        // One can use the default constructor and retieve nameSpace,
        // Memory info from cluster.getRunTimeConfiguration();
        cluster = new AerospikeSingleNodeCluster(nameSpace, memorySize);
        // Get the runTime configuration of the cluster
        runtimConfig = cluster.getRunTimeConfiguration();
        client = new AerospikeClient("", runtimConfig.getServicePort());


    public void testSuccess() throws Exception {

        String[] args = new String[]{"-D" + ReadWriteAvroParquetFiles.INPUT_PATH + "=" + input,
                "-D" + ReadFromHdfsWriteToAerospikeSpark.OUTPUT_PATH + "=" + output,
                "-D" + ReadFromHdfsWriteToAerospikeSpark.IS_RUN_LOCALLY + "=true",
                "-D" + ReadFromHdfsWriteToAerospikeSpark.DEFAULT_FS + "=file:///",
                "-D" + ReadFromHdfsWriteToAerospikeSpark.NUM_PARTITIONS + "=1",
                "-D" + ReadFromHdfsWriteToAerospikeSpark.AEROSPIKE_NAMESPACE + "="
                                                            + runtimConfig.getNameSpaceName(),
                "-D" + ReadFromHdfsWriteToAerospikeSpark.AEROSPIKE_HOSTNAME + "=",
                "-D" + ReadFromHdfsWriteToAerospikeSpark.AEROSPIKE_PORT + "=" 
                                                            + runtimConfig.getServicePort(),
                "-D" + ReadFromHdfsWriteToAerospikeSpark.AEROSPIKE_SETNAME + "=" + setName,
                "-D" + ReadFromHdfsWriteToAerospikeSpark.KEY_NAME + "=emp_id",
                "-D" + ReadFromHdfsWriteToAerospikeSpark.VALUE_NAME + "=emp_name"};


        ParquetReader<GenericRecord> reader = AvroParquetReader.builder(new Path(output))
        //Use .withConf(FS.getConf()) 
        //for reading from a diferent HDFS and not local , by default the fs is local
        GenericData.Record event = null;
        while ((event = (GenericData.Record) reader.read()) != null) {
            Employee outputEvent = AvroUtils.convertByteArraytoAvroPojo(
                                        Employee.getClassSchema()), Employee.getClassSchema());
            LOG.info("Data read from Sparkoutput is {}", outputEvent.toString());
            Assert.assertTrue(outputEvent.getEmpId().equals(1) || outputEvent.getEmpId().equals(2));

        //Fetch both the keys from Aerospike
        WritePolicy policy = new WritePolicy();
        Key key1 = new Key(runtimConfig.getNameSpaceName(), setName, 1);
        Record result1 = client.get(policy, key1);
        Assert.assertEquals(result1.getValue("emp_name").toString(), "Maverick1");

        Key key2 = new Key(runtimConfig.getNameSpaceName(), setName, 2);
        Record result2 = client.get(policy, key2);
        Assert.assertEquals(result2.getValue("emp_name").toString(), "Maverick2");


    public void cleanup() throws Exception {
        FileUtils.deleteDirectory(new File(BASEDIR));

        if (cluster != null) {
            // Stop the cluster
        if (client != null) {