/**
* @param rdd
* @param fname
* @param inSingleFile
* @throws DMLRuntimeException
*/
private void customSaveTextFile(JavaRDD<String> rdd, String fname, boolean inSingleFile)
throws DMLRuntimeException {
if (inSingleFile) {
Random rand = new Random();
String randFName = fname + "_" + rand.nextLong() + "_" + rand.nextLong();
try {
while (MapReduceTool.existsFileOnHDFS(randFName)) {
randFName = fname + "_" + rand.nextLong() + "_" + rand.nextLong();
}
rdd.saveAsTextFile(randFName);
MapReduceTool.mergeIntoSingleFile(randFName, fname); // Faster version :)
// rdd.coalesce(1, true).saveAsTextFile(randFName);
// MapReduceTool.copyFileOnHDFS(randFName + "/part-00000", fname);
} catch (IOException e) {
throw new DMLRuntimeException(
"Cannot merge the output into single file: " + e.getMessage());
} finally {
try {
// This is to make sure that we donot create random files on HDFS
MapReduceTool.deleteFileIfExistOnHDFS(randFName);
} catch (IOException e) {
throw new DMLRuntimeException(
"Cannot merge the output into single file: " + e.getMessage());
}
}
} else {
rdd.saveAsTextFile(fname);
}
}
@Override
public void writeMatrixToHDFS(
MatrixBlock src, String fname, long rlen, long clen, int brlen, int bclen, long nnz)
throws IOException, DMLRuntimeException, DMLUnsupportedOperationException {
// prepare file access
JobConf job = new JobConf(ConfigurationManager.getCachedJobConf());
Path path = new Path(fname);
// if the file already exists on HDFS, remove it.
MapReduceTool.deleteFileIfExistOnHDFS(fname);
// core write
writeTextCellMatrixToHDFS(path, job, src, rlen, clen);
}
public void configure(JobConf job) {
super.configure(job);
if (resultIndexes.length > 1) throw new RuntimeException("MMCJMR only outputs one result");
outputDummyRecords = MapReduceTool.getUniqueKeyPerTask(job, false).equals("0");
try {
// valueBuffer=valueClass.newInstance();
valueBuffer = buffer;
remainingbuffer = new RemainIndexValue(valueClass);
} catch (Exception e) {
throw new RuntimeException(e);
}
int blockRlen = dim1.getRowsPerBlock();
int blockClen = dim2.getColsPerBlock();
int elementSize = (int) Math.ceil((double) (77 + 8 * blockRlen * blockClen + 20 + 12) / 0.75);
OUT_CACHE_SIZE =
((long) OptimizerUtils.getLocalMemBudget() // current jvm max mem
- MRJobConfiguration.getMMCJCacheSize(job))
/ elementSize;
outCache = new HashMap<MatrixIndexes, MatrixValue>(1024);
}
@Override
public void processInstruction(ExecutionContext ec)
throws DMLRuntimeException, DMLUnsupportedOperationException {
SparkExecutionContext sec = (SparkExecutionContext) ec;
// get filename (literal or variable expression)
String fname =
ec.getScalarInput(input2.getName(), ValueType.STRING, input2.isLiteral()).getStringValue();
try {
// if the file already exists on HDFS, remove it.
MapReduceTool.deleteFileIfExistOnHDFS(fname);
// prepare output info according to meta data
String outFmt = input3.getName();
OutputInfo oi = OutputInfo.stringToOutputInfo(outFmt);
// get input rdd
JavaPairRDD<MatrixIndexes, MatrixBlock> in1 =
sec.getBinaryBlockRDDHandleForVariable(input1.getName());
MatrixCharacteristics mc = sec.getMatrixCharacteristics(input1.getName());
if (oi == OutputInfo.MatrixMarketOutputInfo || oi == OutputInfo.TextCellOutputInfo) {
// recompute nnz if necessary (required for header if matrix market)
if (isInputMatrixBlock && !mc.nnzKnown())
mc.setNonZeros(SparkUtils.computeNNZFromBlocks(in1));
JavaRDD<String> header = null;
if (outFmt.equalsIgnoreCase("matrixmarket")) {
ArrayList<String> headerContainer = new ArrayList<String>(1);
// First output MM header
String headerStr =
"%%MatrixMarket matrix coordinate real general\n"
+
// output number of rows, number of columns and number of nnz
mc.getRows()
+ " "
+ mc.getCols()
+ " "
+ mc.getNonZeros();
headerContainer.add(headerStr);
header = sec.getSparkContext().parallelize(headerContainer);
}
JavaRDD<String> ijv =
in1.flatMap(
new ConvertMatrixBlockToIJVLines(mc.getRowsPerBlock(), mc.getColsPerBlock()));
if (header != null) customSaveTextFile(header.union(ijv), fname, true);
else customSaveTextFile(ijv, fname, false);
} else if (oi == OutputInfo.CSVOutputInfo) {
JavaRDD<String> out = null;
Accumulator<Double> aNnz = null;
if (isInputMatrixBlock) {
// piggyback nnz computation on actual write
if (!mc.nnzKnown()) {
aNnz = sec.getSparkContext().accumulator(0L);
in1 = in1.mapValues(new ComputeBinaryBlockNnzFunction(aNnz));
}
out =
RDDConverterUtils.binaryBlockToCsv(
in1, mc, (CSVFileFormatProperties) formatProperties, true);
} else {
// This case is applicable when the CSV output from transform() is written out
@SuppressWarnings("unchecked")
JavaPairRDD<Long, String> rdd =
(JavaPairRDD<Long, String>)
((MatrixObject) sec.getVariable(input1.getName())).getRDDHandle().getRDD();
out = rdd.values();
String sep = ",";
boolean hasHeader = false;
if (formatProperties != null) {
sep = ((CSVFileFormatProperties) formatProperties).getDelim();
hasHeader = ((CSVFileFormatProperties) formatProperties).hasHeader();
}
if (hasHeader) {
StringBuffer buf = new StringBuffer();
for (int j = 1; j < mc.getCols(); j++) {
if (j != 1) {
buf.append(sep);
}
buf.append("C" + j);
}
ArrayList<String> headerContainer = new ArrayList<String>(1);
headerContainer.add(0, buf.toString());
JavaRDD<String> header = sec.getSparkContext().parallelize(headerContainer);
out = header.union(out);
}
}
customSaveTextFile(out, fname, false);
if (isInputMatrixBlock && !mc.nnzKnown()) mc.setNonZeros((long) aNnz.value().longValue());
} else if (oi == OutputInfo.BinaryBlockOutputInfo) {
// piggyback nnz computation on actual write
Accumulator<Double> aNnz = null;
if (!mc.nnzKnown()) {
aNnz = sec.getSparkContext().accumulator(0L);
in1 = in1.mapValues(new ComputeBinaryBlockNnzFunction(aNnz));
}
// save binary block rdd on hdfs
in1.saveAsHadoopFile(
fname, MatrixIndexes.class, MatrixBlock.class, SequenceFileOutputFormat.class);
if (!mc.nnzKnown()) mc.setNonZeros((long) aNnz.value().longValue());
} else {
// unsupported formats: binarycell (not externalized)
throw new DMLRuntimeException("Unexpected data format: " + outFmt);
}
// write meta data file
MapReduceTool.writeMetaDataFile(fname + ".mtd", ValueType.DOUBLE, mc, oi, formatProperties);
} catch (IOException ex) {
throw new DMLRuntimeException("Failed to process write instruction", ex);
}
}
/**
* @param sparseM1
* @param sparseM2
* @param instType
*/
private void runMinMaxComparisonTest(
OpType type,
DataType dtM1,
DataType dtM2,
boolean sparseM1,
boolean sparseM2,
ExecType instType) {
// rtplatform for MR
RUNTIME_PLATFORM platformOld = rtplatform;
rtplatform = (instType == ExecType.MR) ? RUNTIME_PLATFORM.HADOOP : RUNTIME_PLATFORM.HYBRID;
// get the testname
String TEST_NAME = null;
int minFlag = (type == OpType.MIN) ? 1 : 0;
boolean s1Flag = (dtM1 == DataType.SCALAR);
boolean s2Flag = (dtM2 == DataType.SCALAR);
if (s1Flag && s2Flag) TEST_NAME = TEST_NAME4;
else if (s1Flag) TEST_NAME = TEST_NAME2;
else if (s2Flag) TEST_NAME = TEST_NAME3;
else TEST_NAME = TEST_NAME1;
String TEST_CACHE_DIR = "";
if (TEST_CACHE_ENABLED) {
int mrows1 = (dtM1 == DataType.MATRIX) ? rows : 1;
int mrows2 = (dtM2 == DataType.MATRIX) ? rows : 1;
double sparsityLeft = sparseM1 ? sparsity2 : sparsity1;
double sparsityRight = sparseM2 ? sparsity2 : sparsity1;
TEST_CACHE_DIR =
minFlag + "_" + mrows1 + "_" + mrows2 + "_" + sparsityLeft + "_" + sparsityRight + "/";
}
try {
TestConfiguration config = getTestConfiguration(TEST_NAME);
loadTestConfiguration(config, TEST_CACHE_DIR);
// This is for running the junit test the new way, i.e., construct the arguments directly
String HOME = SCRIPT_DIR + TEST_DIR;
fullDMLScriptName = HOME + TEST_NAME + ".dml";
programArgs =
new String[] {
"-explain", "-args", input("A"), input("B"), Integer.toString(minFlag), output("C")
};
fullRScriptName = HOME + TEST_NAME_R + ".R";
rCmd =
"Rscript"
+ " "
+ fullRScriptName
+ " "
+ inputDir()
+ " "
+ minFlag
+ " "
+ expectedDir();
// generate actual dataset
int mrows1 = (dtM1 == DataType.MATRIX) ? rows : 1;
int mcols1 = (dtM1 == DataType.MATRIX) ? cols : 1;
int mrows2 = (dtM2 == DataType.MATRIX) ? rows : 1;
int mcols2 = (dtM2 == DataType.MATRIX) ? cols : 1;
double[][] A = getRandomMatrix(mrows1, mcols1, -1, 1, sparseM1 ? sparsity2 : sparsity1, 7);
writeInputMatrix("A", A, true);
MatrixCharacteristics mc1 = new MatrixCharacteristics(mrows1, mcols1, 1000, 1000);
MapReduceTool.writeMetaDataFile(
input("A.mtd"), ValueType.DOUBLE, mc1, OutputInfo.TextCellOutputInfo);
double[][] B = getRandomMatrix(mrows2, mcols2, -1, 1, sparseM2 ? sparsity2 : sparsity1, 3);
writeInputMatrix("B", B, true);
MatrixCharacteristics mc2 = new MatrixCharacteristics(mrows2, mcols2, 1000, 1000);
MapReduceTool.writeMetaDataFile(
input("B.mtd"), ValueType.DOUBLE, mc2, OutputInfo.TextCellOutputInfo);
// run test
runTest(true, false, null, -1);
runRScript(true);
// compare matrices
HashMap<CellIndex, Double> dmlfile = readDMLMatrixFromHDFS("C");
HashMap<CellIndex, Double> rfile = readRMatrixFromFS("C");
TestUtils.compareMatrices(dmlfile, rfile, eps, "Stat-DML", "Stat-R");
} catch (IOException e) {
e.printStackTrace();
throw new RuntimeException(e);
} finally {
rtplatform = platformOld;
}
}
