run_spark_hdfs.py

#!/usr/bin/env python

#The MIT License (MIT)
#Copyright (c) 2018 University of California, Los Angeles and Intel Corporation
#Copyright (c) 2019 Omics Data Automation, Inc.

#Permission is hereby granted, free of charge, to any person obtaining a copy of 
#this software and associated documentation files (the "Software"), to deal in 
#the Software without restriction, including without limitation the rights to 
#use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 
#the Software, and to permit persons to whom the Software is furnished to do so, 
#subject to the following conditions:

#The above copyright notice and this permission notice shall be included in all 
#copies or substantial portions of the Software.

#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 
#FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 
#COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 
#IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 
#CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

import json
import jsondiff
import tempfile
import subprocess
import hashlib
import os
import sys
import shutil
import difflib
import errno
from collections import OrderedDict

import common

query_json_template_string="""
{   
        "workspace" : "",
        "array" : "",
        "vcf_header_filename" : ["inputs/template_vcf_header.vcf"],
        "query_column_ranges" : [ [ [0, 10000000000 ] ] ],
        "query_row_ranges" : [ [ [0, 3 ] ] ],
        "query_block_size" : 10000,
        "query_block_size_margin" : 500,
        "reference_genome" : "inputs/chr1_10MB.fasta.gz",
        "query_attributes" : [ "REF", "ALT", "BaseQRankSum", "MQ", "RAW_MQ", "MQ0", "ClippingRankSum", "MQRankSum", "ReadPosRankSum", "DP", "GT", "GQ", "SB", "AD", "PL", "DP_FORMAT", "MIN_DP", "PID", "PGT" ]
}"""

vcf_query_attributes_order = [ "END", "REF", "ALT", "BaseQRankSum", "ClippingRankSum", "MQRankSum", "ReadPosRankSum", "MQ", "RAW_MQ", "MQ0", "DP", "GT", "GQ", "SB", "AD", "PL", "PGT", "PID", "MIN_DP", "DP_FORMAT", "FILTER" ];
query_attributes_with_DS_ID = [ "REF", "ALT", "BaseQRankSum", "MQ", "RAW_MQ", "MQ0", "ClippingRankSum", "MQRankSum", "ReadPosRankSum", "DP", "GT", "GQ", "SB", "AD", "PL", "DP_FORMAT", "MIN_DP", "PID", "PGT", "DS", "ID" ];
query_attributes_with_PL_only = [ "PL" ]
query_attributes_with_MLEAC_only = [ "MLEAC" ]
default_segment_size = 40

def query_column_ranges_for_PB(qcr):
    return [{ 'column_or_interval_list': [{'column_interval': {'tiledb_column_interval':{'begin':qcr[0],'end':qcr[1]}}}]}]

def create_query_json(ws_dir, test_name, query_param_dict, test_dir):
    test_dict=json.loads(query_json_template_string);
    test_dict["workspace"] = ws_dir
    test_dict["array"] = test_name
    if (test_name == "t6_7_8"):
        test_dict["query_column_ranges"] = query_column_ranges_for_PB(query_param_dict["query_column_ranges"])
        if ('query_row_ranges' in test_dict):
            del test_dict['query_row_ranges']
    else:
        test_dict["query_column_ranges"] = [ [query_param_dict["query_column_ranges"] ] ]
    if("vid_mapping_file" in query_param_dict):
        test_dict["vid_mapping_file"] = query_param_dict["vid_mapping_file"];
    if("callset_mapping_file" in query_param_dict):
        test_dict["callset_mapping_file"] = query_param_dict["callset_mapping_file"];
    if("query_attributes" in query_param_dict):
        test_dict["query_attributes"] = query_param_dict["query_attributes"];
    if('segment_size' in query_param_dict):
        test_dict['segment_size'] = query_param_dict['segment_size'];
    else:
        test_dict['segment_size'] = default_segment_size;
    if('produce_GT_field' in query_param_dict):
        test_dict['produce_GT_field'] = query_param_dict['produce_GT_field'];
    if('produce_FILTER_field' in query_param_dict):
        test_dict['produce_FILTER_field'] = query_param_dict['produce_FILTER_field'];
    if('query_block_size' in query_param_dict):
        test_dict['query_block_size'] = query_param_dict['query_block_size'];
    if('query_block_size_margin' in query_param_dict):
        test_dict['query_block_size_margin'] = query_param_dict['query_block_size_margin'];
    if('vid_mapping_file' in test_dict):
        test_dict['vid_mapping_file'] = test_dir+os.path.sep+test_dict['vid_mapping_file'];
    if('callset_mapping_file' in test_dict):
        test_dict['callset_mapping_file'] = test_dir+os.path.sep+test_dict['callset_mapping_file'];
    if('vcf_header_filename' in test_dict):
        for i,val in enumerate(test_dict['vcf_header_filename']):
            test_dict['vcf_header_filename'][i] = test_dir+os.path.sep+val;
    if('reference_genome' in test_dict):
        test_dict['reference_genome'] = test_dir+os.path.sep+test_dict['reference_genome'];
    return test_dict;


loader_json_template_string="""
{
    "row_based_partitioning" : false,
    "column_partitions" : [
        {"begin": 0, "workspace":"", "array": "" }
    ],
    "callset_mapping_file" : "",
    "vid_mapping_file" : "inputs/vid.json",
    "size_per_column_partition": 700 ,
    "treat_deletions_as_intervals" : true,
    "vcf_header_filename": "inputs/template_vcf_header.vcf",
    "reference_genome" : "inputs/chr1_10MB.fasta.gz",
    "num_parallel_vcf_files" : 1,
    "do_ping_pong_buffering" : false,
    "offload_vcf_output_processing" : false,
    "discard_vcf_index": true,
    "produce_combined_vcf": true,
    "produce_tiledb_array" : true,
    "delete_and_create_tiledb_array" : true,
    "compress_tiledb_array" : true,
    "segment_size" : 1048576,
    "num_cells_per_tile" : 3
}""";

def create_loader_json(ws_dir, test_name, test_params_dict, col_part, test_dir):
    test_dict=json.loads(loader_json_template_string);
    test_dict['column_partitions'] = col_part;
    for col_part in test_dict['column_partitions']:
        col_part["workspace"] = ws_dir;
        col_part["array"] = test_name+col_part["array"];
    test_dict["callset_mapping_file"] = test_params_dict['callset_mapping_file'];
    if('vid_mapping_file' in test_params_dict):
        test_dict['vid_mapping_file'] = test_params_dict['vid_mapping_file'];
    if('size_per_column_partition' in test_params_dict):
        test_dict['size_per_column_partition'] = test_params_dict['size_per_column_partition'];
    if('segment_size' in test_params_dict):
        test_dict['segment_size'] = test_params_dict['segment_size'];
    else:
        test_dict['segment_size'] = default_segment_size;
    test_dict['vid_mapping_file'] = test_dir+os.path.sep+test_dict['vid_mapping_file'];
    test_dict['callset_mapping_file'] = test_dir+os.path.sep+test_dict['callset_mapping_file'];
    test_dict['vcf_header_filename'] = test_dir+os.path.sep+test_dict['vcf_header_filename'];
    test_dict['reference_genome'] = test_dir+os.path.sep+test_dict['reference_genome'];
    return test_dict;

def add_hdfs_to_loader_json(test_dict, namenode):
    for col_part in test_dict['column_partitions']:
        col_part['workspace'] = namenode+col_part['workspace'];
    return test_dict;

def move_arrays_to_hdfs(ws_dir, namenode):
#    pid = subprocess.Popen('hadoop fs -rm -r '+namenode+ws_dir+'/*', shell=True, stdout=subprocess.PIPE);
#    stdout_string = pid.communicate()[0]
#    if(pid.returncode != 0):
#        sys.stderr.write('Error deleting arrays from workspace in HDFS:'namenode+ws_dir+'\n');
#        sys.exit(-1);
    pid = subprocess.Popen('hadoop fs -put '+ws_dir+'/* '+namenode+ws_dir, shell=True, stdout=subprocess.PIPE);
    stdout_string = pid.communicate()[0]
    if(pid.returncode != 0):
        sys.stderr.write('Error copying array to HDFS workspace:'+namenode+ws_dir+'\n');
        sys.exit(-1);

def get_file_content_and_md5sum(filename):
    with open(filename, 'rb') as fptr:
        data = fptr.read();
        data_list = data.splitlines(True);
        data_list_filter = [k for k in data_list if not k.startswith('##')];
        data_filter = "".join(data_list_filter);
        md5sum_hash_str = str(hashlib.md5(data_filter).hexdigest());

        fptr.close();
        return (data_filter, md5sum_hash_str);

def get_json_from_file(filename):
    with open(filename, 'rb') as fptr:
        data = fptr.read();
        json_out = json.loads(data)
        fptr.close();

        return json_out;

def print_diff(golden_output, test_output):
    print("=======Golden output:=======");
    print(golden_output);
    print("=======Test output:=======");
    print(test_output);
    print("=======END=======");

def cleanup_and_exit(namenode, tmpdir, exit_code):
    if(exit_code == 0):
        shutil.rmtree(tmpdir, ignore_errors=True)
        if("://" in namenode):
            pid = subprocess.Popen('hadoop fs -rm -r '+namenode+tmpdir, shell=True, stdout=subprocess.PIPE);
            pid = subprocess.Popen('hadoop fs -rm -r '+namenode+'/home/hadoop/.tiledb/', shell=True, stdout=subprocess.PIPE);
    sys.exit(exit_code);

def main():
    if(len(sys.argv) < 8):
        sys.stderr.write('Usage: ./run_spark_hdfs.py <build_dir> <install_dir> <spark_master> <hdfs_namenode> <spark_deploy> <genomicsdb_version> <test_dir> [<build_type>]\n');
        sys.stderr.write('   Optional Argument 8 - build_type=Release|Coverage|...\n')
        sys.exit(-1);
    exe_path = sys.argv[2]+os.path.sep+'bin';
    spark_master = sys.argv[3];
    namenode = sys.argv[4];
    jar_dir = sys.argv[1]+os.path.sep+'target';
    spark_deploy = sys.argv[5];
    genomicsdb_version = sys.argv[6];
    test_dir = sys.argv[7];
    if (len(sys.argv) == 9):
        build_type = sys.argv[8]
    else:
        build_type = "default"
    #Switch to tests directory
    parent_dir=os.path.dirname(os.path.realpath(__file__))
    os.chdir(parent_dir)
    hostfile_path=parent_dir+os.path.sep+'hostfile';
    vid_path=parent_dir+os.path.sep;
    template_vcf_header_path=parent_dir+os.path.sep+'inputs'+os.path.sep+'template_vcf_header.vcf';
    tmpdir = tempfile.mkdtemp()
    ws_dir=tmpdir+os.path.sep+'ws';
    jacoco, jacoco_report_cmd = common.setup_jacoco(os.path.abspath(sys.argv[1]), build_type)
    loader_tests = [
            { "name" : "t0_1_2", 'golden_output' : 'golden_outputs/t0_1_2_loading',
                'callset_mapping_file': 'inputs/callsets/t0_1_2.json',
                "column_partitions": [
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test0"} ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test1"},
                      {"begin": 10000, "workspace":"/tmp/ws", "array": "test2"}
                       ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test3"},
                      {"begin": 3000, "workspace":"/tmp/ws", "array": "test4"},
                      {"begin": 6000, "workspace":"/tmp/ws", "array": "test5"},
                      {"begin": 9000, "workspace":"/tmp/ws", "array": "test6"},
                      {"begin": 12000, "workspace":"/tmp/ws", "array": "test7"}
                       ]
                    ],
                "query_params": [
                    { "query_column_ranges" : [12100, 12200], "golden_output": {
                        "spark"   : "golden_outputs/spark_t0_1_2_vcf_at_12100",
                        } },
                    { "query_column_ranges" : [0, 100000], "golden_output": {
                        "spark"   : "golden_outputs/spark_t0_1_2_vcf_at_0",
                        } },
                    { "query_column_ranges" : [12150, 100000], "golden_output": {
                        "spark"   : "golden_outputs/spark_t0_1_2_vcf_at_12150",
                        } },
                    ]
            },
            { "name" : "t0_overlapping", 'golden_output': 'golden_outputs/t0_overlapping',
                'callset_mapping_file': 'inputs/callsets/t0_overlapping.json',
                "column_partitions": [
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test0"} ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test1"},
                      {"begin": 10000, "workspace":"/tmp/ws", "array": "test2"}
                       ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test3"},
                      {"begin": 3000, "workspace":"/tmp/ws", "array": "test4"},
                      {"begin": 6000, "workspace":"/tmp/ws", "array": "test5"},
                      {"begin": 9000, "workspace":"/tmp/ws", "array": "test6"},
                      {"begin": 12000, "workspace":"/tmp/ws", "array": "test7"}
                       ]
                    ],
                "query_params": [
                    { "query_column_ranges" : [12202, 100000], "golden_output": {
                        "spark"        : "golden_outputs/spark_t0_overlapping_at_12202",
                        }
                    }
                ]
            },
            { "name" : "t6_7_8", 'golden_output' : 'golden_outputs/t6_7_8_loading',
                'callset_mapping_file': 'inputs/callsets/t6_7_8.json',
                "column_partitions": [
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test0"} ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test1"},
                      {"begin": 500000, "workspace":"/tmp/ws", "array": "test2"},
                      {"begin": 1000000, "workspace":"/tmp/ws", "array": "test3"}
                       ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test4"},
                      {"begin": 250000, "workspace":"/tmp/ws", "array": "test5"},
                      {"begin": 500000, "workspace":"/tmp/ws", "array": "test6"},
                      {"begin": 750000, "workspace":"/tmp/ws", "array": "test7"},
                      {"begin": 1000000, "workspace":"/tmp/ws", "array": "test8"}
                       ]
                    ],
                "query_params": [
                    { "query_column_ranges" : [0, 10000000], "golden_output": {
                        "spark": "golden_outputs/spark_t6_7_8_vcf_at_0",
                        },
                      "query_block_size" : 1000000, "query_block_size_margin": 50000 },
                    { "query_column_ranges" : [8029500, 10000000], "golden_output": {
                        "spark": "golden_outputs/spark_t6_7_8_vcf_at_8029500",
                        },
                      "query_block_size" : 100000, "query_block_size_margin": 5000 },
                    { "query_column_ranges" : [8029500, 8029500], "golden_output": {
                        "spark"        : "golden_outputs/spark_t6_7_8_vcf_at_8029500-8029500",
                        } }
                    ]
            },
            { "name" : "t0_1_2_combined", 'golden_output' : 'golden_outputs/t0_1_2_combined',
                'callset_mapping_file': 'inputs/callsets/t0_1_2_combined.json',
                "column_partitions": [
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test0"} ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test1"},
                      {"begin": 10000, "workspace":"/tmp/ws", "array": "test2"}
                       ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test3"},
                      {"begin": 3000, "workspace":"/tmp/ws", "array": "test4"},
                      {"begin": 6000, "workspace":"/tmp/ws", "array": "test5"},
                      {"begin": 9000, "workspace":"/tmp/ws", "array": "test6"},
                      {"begin": 12000, "workspace":"/tmp/ws", "array": "test7"}
                       ]
                    ],
                "query_params": [
                    { "query_column_ranges" : [0, 1000000], "golden_output": {
                        "spark": "golden_outputs/spark_t0_1_2_combined",
                        },
                      "query_block_size" : 100000, "query_block_size_margin": 5000 },
                    ]
            }, 
            { "name" : "t0_haploid_triploid_1_2_3_triploid_deletion",
                'golden_output' : 'golden_outputs/t0_haploid_triploid_1_2_3_triploid_deletion_loading',
                'callset_mapping_file': 'inputs/callsets/t0_haploid_triploid_1_2_3_triploid_deletion.json',
                "vid_mapping_file": "inputs/vid_DS_ID_phased_GT.json",
                'size_per_column_partition': 1200,
                'segment_size': 100,
                "column_partitions": [
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test0"} ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test1"},
                      {"begin": 10000, "workspace":"/tmp/ws", "array": "test2"}
                       ],
                    [ {"begin": 0, "workspace":"/tmp/ws", "array": "test3"},
                      {"begin": 3000, "workspace":"/tmp/ws", "array": "test4"},
                      {"begin": 6000, "workspace":"/tmp/ws", "array": "test5"},
                      {"begin": 9000, "workspace":"/tmp/ws", "array": "test6"},
                      {"begin": 12000, "workspace":"/tmp/ws", "array": "test7"}
                       ]
                    ],
                "query_params": [
                    { "query_column_ranges" : [0, 1000000],
                      'callset_mapping_file': 'inputs/callsets/t0_haploid_triploid_1_2_3_triploid_deletion.json',
                      "vid_mapping_file": "inputs/vid_DS_ID_phased_GT.json",
                      'segment_size': 100,
                        "golden_output": {
                        "spark"   : "golden_outputs/spark_t0_haploid_triploid_1_2_3_triploid_deletion_java_vcf",
                        },
                      "query_block_size" : 100000, "query_block_size_margin": 5000 },
                    { "query_column_ranges" : [0, 1000000],
                      'callset_mapping_file': 'inputs/callsets/t0_haploid_triploid_1_2_3_triploid_deletion.json',
                      "vid_mapping_file": "inputs/vid_DS_ID_phased_GT.json",
                      'produce_GT_field': True,
                      'segment_size': 100,
                        "golden_output": {
                        "spark"   : "golden_outputs/spark_t0_haploid_triploid_1_2_3_triploid_deletion_java_vcf_produce_GT",
                        },
                      "query_block_size" : 100000, "query_block_size_margin": 5000 }
                ]
            },
    ];
    if("://" in namenode):
        pid = subprocess.Popen('hadoop fs -mkdir -p '+namenode+'/home/hadoop/.tiledb/', shell=True, stdout=subprocess.PIPE);
        stdout_string = pid.communicate()[0]
        if(pid.returncode != 0):
            sys.stderr.write('Error creating hdfs:///home/hadoop/.tiledb/');
            sys.exit(-1);
    for test_params_dict in loader_tests:
        test_name = test_params_dict['name']
        
        for col_part in test_params_dict['column_partitions']:
            test_loader_dict = create_loader_json(ws_dir, test_name, test_params_dict, col_part, test_dir);
            if(test_name == "t0_1_2"):
                test_loader_dict["compress_tiledb_array"] = True;
            if("://" in namenode):
                test_loader_dict = add_hdfs_to_loader_json(test_loader_dict, namenode);
            loader_json_filename = tmpdir+os.path.sep+test_name+'-loader.json'
            with open(loader_json_filename, 'wb') as fptr:
                json.dump(test_loader_dict, fptr, indent=4, separators=(',', ': '));
                fptr.close();
            # invoke vcf2tiledb -r <rank> where <rank> goes from 0 to num partitions
            # otherwise this only loads the first partition
            for i in range(0, len(col_part)):
                etl_cmd=exe_path+os.path.sep+'vcf2tiledb -r '+str(i)+' '+loader_json_filename
                pid = subprocess.Popen(etl_cmd, shell=True,
                        stdout=subprocess.PIPE, stderr=subprocess.PIPE);
                stdout_string, stderr_string = pid.communicate()
                if(pid.returncode != 0):
                    sys.stderr.write('Loading failed for test: '+test_name+' rank '+str(i)+'\n');
                    sys.stderr.write('Loading command: '+etl_cmd+'\n');
                    sys.stderr.write('Loader file :'+str(test_loader_dict)+'\n');
                    sys.stderr.write('Loading stdout: '+stdout_string+'\n');
                    sys.stderr.write('Loading stderr: '+stderr_string+'\n');
                    cleanup_and_exit(namenode, tmpdir, -1);
                else:
                    sys.stdout.write('Loading passed for test: '+test_name+' rank '+str(i)+'\n');
            with open(loader_json_filename, 'wb') as fptr:
                json.dump(test_loader_dict, fptr, indent=4, separators=(',', ': '));
                fptr.close();
            for query_param_dict in test_params_dict['query_params']:
                if("://" in namenode):
                    test_query_dict = create_query_json(namenode+ws_dir, test_name, query_param_dict, test_dir)
                else:
                    test_query_dict = create_query_json(ws_dir, test_name, query_param_dict, test_dir)
                test_query_dict['query_attributes'] = vcf_query_attributes_order;
                query_json_filename = tmpdir+os.path.sep+test_name+'-query.json'
                with open(query_json_filename, 'wb') as fptr:
                    json.dump(test_query_dict, fptr, indent=4, separators=(',', ': '));
                    fptr.close();
                spark_cmd = 'spark-submit --class TestGenomicsDBSparkHDFS --master '+spark_master+' --deploy-mode '+spark_deploy+' --total-executor-cores 1 --executor-memory 512M --conf "spark.yarn.executor.memoryOverhead=3700" --conf "spark.executor.extraJavaOptions='+jacoco+'" --conf "spark.driver.extraJavaOptions='+jacoco+'" --jars '+jar_dir+'/genomicsdb-'+genomicsdb_version+'-allinone.jar '+jar_dir+'/genomicsdb-'+genomicsdb_version+'-examples.jar --loader '+loader_json_filename+' --query '+query_json_filename+' --template_vcf_header '+template_vcf_header_path+' --spark_master '+spark_master+' --jar_dir '+jar_dir;
                if (test_name == "t6_7_8"):
                    spark_cmd = spark_cmd + ' --use-query-protobuf';
                if (test_name == "t0_overlapping"):
                    spark_cmd = spark_cmd + ' --hostfile ' + hostfile_path
                pid = subprocess.Popen(spark_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE);
                stdout_string, stderr_string = pid.communicate()
                if(pid.returncode != 0):
                    sys.stderr.write('Query test: '+test_name+' with query file '+query_json_filename+' failed\n');
                    sys.stderr.write('Spark command was: '+spark_cmd+'\n');
                    sys.stderr.write('Spark stdout was: '+stdout_string+'\n');
                    sys.stderr.write('Spark stderr was: '+stderr_string+'\n');
                    sys.stderr.write('Query file was: '+json.dumps(test_query_dict)+'\n');
                    cleanup_and_exit(namenode, tmpdir, -1);
                stdout_list = stdout_string.splitlines(True);
                stdout_list_filter = [k for k in stdout_list if not k.startswith('##')];
                stdout_filter = "".join(stdout_list_filter);
                md5sum_hash_str = str(hashlib.md5(stdout_filter).hexdigest())
                if('golden_output' in query_param_dict and 'spark' in query_param_dict['golden_output']):
                    golden_stdout, golden_md5sum = get_file_content_and_md5sum(query_param_dict['golden_output']['spark']);
                    if(golden_md5sum != md5sum_hash_str):
                        sys.stdout.write('Mismatch in query test: '+test_name+' with column ranges: '+str(query_param_dict['query_column_ranges'])+' and loaded with '+str(len(col_part))+' partitions\n');
                        print_diff(golden_stdout, stdout_filter);
                        sys.stderr.write('Spark command was: '+spark_cmd+'\n');
                        sys.stderr.write('Spark stdout was: '+stdout_string+'\n');
                        sys.stderr.write('Spark stderr was: '+stderr_string+'\n');
                        sys.stderr.write('Query file was: '+json.dumps(test_query_dict)+'\n');
                        cleanup_and_exit(namenode, tmpdir, -1);
                    else:
                        sys.stdout.write('Query test: '+test_name+' with column ranges: '+str(query_param_dict['query_column_ranges'])+' and loaded with '+str(len(col_part))+' partitions passed\n');
                # add another spark run command to test datasourcev2 stuff
                if('vid_mapping_file' in query_param_dict):
                    vid_path_final=vid_path+query_param_dict['vid_mapping_file'];
                else:
                    vid_path_final=vid_path+"inputs"+os.path.sep+"vid.json";
                spark_cmd_v2 = 'spark-submit --class TestGenomicsDBDataSourceV2 --master '+spark_master+' --deploy-mode '+spark_deploy+' --total-executor-cores 1 --executor-memory 512M --conf "spark.yarn.executor.memoryOverhead=3700" --conf "spark.executor.extraJavaOptions='+jacoco+'" --conf "spark.driver.extraJavaOptions='+jacoco+'" --jars '+jar_dir+'/genomicsdb-'+genomicsdb_version+'-allinone.jar '+jar_dir+'/genomicsdb-'+genomicsdb_version+'-examples.jar --loader '+loader_json_filename+' --query '+query_json_filename+' --vid '+vid_path_final+' --spark_master '+spark_master;
                if (test_name == "t6_7_8"):
                  spark_cmd_v2 = spark_cmd_v2 + ' --use-query-protobuf';
                if (test_name == "t0_overlapping"):
                    spark_cmd = spark_cmd_v2 + ' --hostfile ' + hostfile_path
                pid = subprocess.Popen(spark_cmd_v2, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE);
                stdout_string, stderr_string = pid.communicate()
                if(pid.returncode != 0):
                    sys.stderr.write('Query test V2: '+test_name+' with query file '+query_json_filename+' failed\n');
                    sys.stderr.write('Spark command was: '+spark_cmd_v2+'\n');
                    sys.stderr.write('Spark stdout was: '+stdout_string+'\n');
                    sys.stderr.write('Spark stderr was: '+stderr_string+'\n');
                    sys.stderr.write('Query file was: '+json.dumps(test_query_dict)+'\n');
                    cleanup_and_exit(namenode, tmpdir, -1);
                stdout_list = stdout_string.splitlines(True);
                stdout_filter = "".join(stdout_list);
                stdout_json = json.loads(stdout_filter);
                if('golden_output' in query_param_dict and 'spark' in query_param_dict['golden_output']):
                    json_golden = get_json_from_file(query_param_dict['golden_output']['spark']+'_v2');
                    checkdiff = jsondiff.diff(stdout_json, json_golden);
                    if (not checkdiff):
                        sys.stdout.write('Query test V2: '+test_name+' with column ranges: '+str(query_param_dict['query_column_ranges'])+' and loaded with '+str(len(col_part))+' partitions passed\n');
                    else:
                        sys.stdout.write('Mismatch in query test V2: '+test_name+' with column ranges: '+str(query_param_dict['query_column_ranges'])+' and loaded with '+str(len(col_part))+' partitions\n');
                        print(checkdiff);
                        sys.stderr.write('Spark stdout was: '+stdout_string+'\n');
                        sys.stderr.write('Spark stderr was: '+stderr_string+'\n');
                        cleanup_and_exit(namenode, tmpdir, -1);
        rc = common.report_jacoco_coverage(jacoco_report_cmd)
        if (rc != 0):
            cleanup_and_exit(namenode, tmpdir, -1)
    cleanup_and_exit(namenode, tmpdir, 0)

if __name__ == '__main__':
    main()