/**
* @param path
* @param job
* @param fs
* @param dest
* @param rlen
* @param clen
* @param brlen
* @param bclen
* @throws IOException
* @throws IllegalAccessException
* @throws InstantiationException
*/
@SuppressWarnings("deprecation")
private void readBinaryBlockMatrixBlocksFromHDFS(
Path path,
JobConf job,
FileSystem fs,
Collection<IndexedMatrixValue> dest,
long rlen,
long clen,
int brlen,
int bclen)
throws IOException {
MatrixIndexes key = new MatrixIndexes();
MatrixBlock value = new MatrixBlock();
// set up preferred custom serialization framework for binary block format
if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION)
MRJobConfiguration.addBinaryBlockSerializationFramework(job);
for (Path lpath : getSequenceFilePaths(fs, path)) // 1..N files
{
// directly read from sequence files (individual partfiles)
SequenceFile.Reader reader = new SequenceFile.Reader(fs, lpath, job);
try {
while (reader.next(key, value)) {
int row_offset = (int) (key.getRowIndex() - 1) * brlen;
int col_offset = (int) (key.getColumnIndex() - 1) * bclen;
int rows = value.getNumRows();
int cols = value.getNumColumns();
// bound check per block
if (row_offset + rows < 0
|| row_offset + rows > rlen
|| col_offset + cols < 0
|| col_offset + cols > clen) {
throw new IOException(
"Matrix block ["
+ (row_offset + 1)
+ ":"
+ (row_offset + rows)
+ ","
+ (col_offset + 1)
+ ":"
+ (col_offset + cols)
+ "] "
+ "out of overall matrix range [1:"
+ rlen
+ ",1:"
+ clen
+ "].");
}
// copy block to result
dest.add(new IndexedMatrixValue(new MatrixIndexes(key), new MatrixBlock(value)));
}
} finally {
IOUtilFunctions.closeSilently(reader);
}
}
}
public void configure(JobConf job) {
super.configure(job);
try {
comb_instructions = MRJobConfiguration.getCombineInstruction(job);
} catch (Exception e) {
throw new RuntimeException(e);
}
for (int i = 0; i < resultIndexes.length; i++) {
MatrixCharacteristics stat =
MRJobConfiguration.getMatrixCharacteristicsForOutput(job, resultIndexes[i]);
outputBlockSizes.put(
resultIndexes[i],
new Pair<Integer, Integer>(stat.getRowsPerBlock(), stat.getColsPerBlock()));
}
for (MRInstruction ins : comb_instructions) {
outputIndexesMapping.put(ins.output, getOutputIndexes(ins.output));
}
}
public static JobReturn runJob(
MRJobInstruction inst,
String[] inputs,
InputInfo[] inputInfos,
long[] rlens,
long[] clens,
int[] brlens,
int[] bclens,
String combineInstructions,
int numReducers,
int replication,
byte[] resultIndexes,
String[] outputs,
OutputInfo[] outputInfos)
throws Exception {
JobConf job;
job = new JobConf(CombineMR.class);
job.setJobName("Standalone-MR");
boolean inBlockRepresentation = MRJobConfiguration.deriveRepresentation(inputInfos);
// whether use block representation or cell representation
MRJobConfiguration.setMatrixValueClass(job, inBlockRepresentation);
byte[] inputIndexes = new byte[inputs.length];
for (byte b = 0; b < inputs.length; b++) inputIndexes[b] = b;
// set up the input files and their format information
MRJobConfiguration.setUpMultipleInputs(
job,
inputIndexes,
inputs,
inputInfos,
brlens,
bclens,
true,
inBlockRepresentation ? ConvertTarget.BLOCK : ConvertTarget.CELL);
// set up the dimensions of input matrices
MRJobConfiguration.setMatricesDimensions(job, inputIndexes, rlens, clens);
// set up the block size
MRJobConfiguration.setBlocksSizes(job, inputIndexes, brlens, bclens);
// set up unary instructions that will perform in the mapper
MRJobConfiguration.setInstructionsInMapper(job, "");
// set up the aggregate instructions that will happen in the combiner and reducer
MRJobConfiguration.setAggregateInstructions(job, "");
// set up the instructions that will happen in the reducer, after the aggregation instrucions
MRJobConfiguration.setInstructionsInReducer(job, "");
MRJobConfiguration.setCombineInstructions(job, combineInstructions);
// set up the replication factor for the results
job.setInt("dfs.replication", replication);
// set up what matrices are needed to pass from the mapper to reducer
HashSet<Byte> mapoutputIndexes =
MRJobConfiguration.setUpOutputIndexesForMapper(
job, inputIndexes, null, null, combineInstructions, resultIndexes);
// set up the multiple output files, and their format information
MRJobConfiguration.setUpMultipleOutputs(
job, resultIndexes, null, outputs, outputInfos, inBlockRepresentation);
// configure mapper and the mapper output key value pairs
job.setMapperClass(GMRMapper.class);
job.setMapOutputKeyClass(MatrixIndexes.class);
if (inBlockRepresentation) job.setMapOutputValueClass(TaggedMatrixBlock.class);
else job.setMapOutputValueClass(TaggedMatrixCell.class);
// configure reducer
job.setReducerClass(InnerReducer.class);
// job.setReducerClass(PassThroughReducer.class);
MatrixChar_N_ReducerGroups ret =
MRJobConfiguration.computeMatrixCharacteristics(
job,
inputIndexes,
null,
null,
null,
combineInstructions,
resultIndexes,
mapoutputIndexes,
false);
MatrixCharacteristics[] stats = ret.stats;
// set up the number of reducers
MRJobConfiguration.setNumReducers(job, ret.numReducerGroups, numReducers);
// Print the complete instruction
if (LOG.isTraceEnabled()) inst.printCompleteMRJobInstruction(stats);
// By default, the job executes in "cluster" mode.
// Determine if we can optimize and run it in "local" mode.
MatrixCharacteristics[] inputStats = new MatrixCharacteristics[inputs.length];
for (int i = 0; i < inputs.length; i++) {
inputStats[i] = new MatrixCharacteristics(rlens[i], clens[i], brlens[i], bclens[i]);
}
// set unique working dir
MRJobConfiguration.setUniqueWorkingDir(job);
RunningJob runjob = JobClient.runJob(job);
return new JobReturn(stats, runjob.isSuccessful());
}
/**
* Note: For efficiency, we directly use SequenceFile.Reader instead of SequenceFileInputFormat-
* InputSplits-RecordReader (SequenceFileRecordReader). First, this has no drawbacks since the
* SequenceFileRecordReader internally uses SequenceFile.Reader as well. Second, it is
* advantageous if the actual sequence files are larger than the file splits created by
* informat.getSplits (which is usually aligned to the HDFS block size) because then there is
* overhead for finding the actual split between our 1k-1k blocks. This case happens if the read
* matrix was create by CP or when jobs directly write to large output files (e.g., parfor matrix
* partitioning).
*
* @param path
* @param job
* @param fs
* @param dest
* @param rlen
* @param clen
* @param brlen
* @param bclen
* @throws IOException
* @throws IllegalAccessException
* @throws InstantiationException
* @throws DMLRuntimeException
*/
@SuppressWarnings("deprecation")
private static void readBinaryBlockMatrixFromHDFS(
Path path,
JobConf job,
FileSystem fs,
MatrixBlock dest,
long rlen,
long clen,
int brlen,
int bclen)
throws IOException, DMLRuntimeException {
boolean sparse = dest.isInSparseFormat();
MatrixIndexes key = new MatrixIndexes();
MatrixBlock value = new MatrixBlock();
// set up preferred custom serialization framework for binary block format
if (MRJobConfiguration.USE_BINARYBLOCK_SERIALIZATION)
MRJobConfiguration.addBinaryBlockSerializationFramework(job);
for (Path lpath : getSequenceFilePaths(fs, path)) // 1..N files
{
// directly read from sequence files (individual partfiles)
SequenceFile.Reader reader = new SequenceFile.Reader(fs, lpath, job);
try {
// note: next(key, value) does not yet exploit the given serialization classes, record
// reader does but is generally slower.
while (reader.next(key, value)) {
// empty block filter (skip entire block)
if (value.isEmptyBlock(false)) continue;
int row_offset = (int) (key.getRowIndex() - 1) * brlen;
int col_offset = (int) (key.getColumnIndex() - 1) * bclen;
int rows = value.getNumRows();
int cols = value.getNumColumns();
// bound check per block
if (row_offset + rows < 0
|| row_offset + rows > rlen
|| col_offset + cols < 0
|| col_offset + cols > clen) {
throw new IOException(
"Matrix block ["
+ (row_offset + 1)
+ ":"
+ (row_offset + rows)
+ ","
+ (col_offset + 1)
+ ":"
+ (col_offset + cols)
+ "] "
+ "out of overall matrix range [1:"
+ rlen
+ ",1:"
+ clen
+ "].");
}
// copy block to result
if (sparse) {
dest.appendToSparse(value, row_offset, col_offset);
// note: append requires final sort
} else {
dest.copy(
row_offset, row_offset + rows - 1, col_offset, col_offset + cols - 1, value, false);
}
}
} finally {
IOUtilFunctions.closeSilently(reader);
}
}
if (sparse && clen > bclen) {
// no need to sort if 1 column block since always sorted
dest.sortSparseRows();
}
}
