kbendick commented on a change in pull request #2803:
URL: https://github.com/apache/iceberg/pull/2803#discussion_r667569657
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File path: spark2/src/main/java/org/apache/iceberg/spark/source/Reader.java
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@@ -205,35 +215,62 @@ public StructType readSchema() {
// broadcast the table metadata as input partitions will be sent to
executors
Broadcast<Table> tableBroadcast =
sparkContext.broadcast(SerializableTable.copyOf(table));
- List<InputPartition<ColumnarBatch>> readTasks = Lists.newArrayList();
- for (CombinedScanTask task : tasks()) {
- readTasks.add(new ReadTask<>(
- task, tableBroadcast, expectedSchemaString, caseSensitive,
- localityPreferred, new BatchReaderFactory(batchSize)));
+ int taskSize = tasks().size();
+ InputPartition<ColumnarBatch>[] readTasks = new InputPartition[taskSize];
+ Long startTime = System.currentTimeMillis();
+ try {
+ pool.submit(() -> IntStream.range(0, taskSize).parallel()
+ .mapToObj(taskId -> {
+ LOG.trace("The size of scanTasks is {}, current taskId is {},
current thread id is {}",
+ taskSize, taskId, Thread.currentThread().getName());
+ readTasks[taskId] = new ReadTask<>(
+ tasks().get(taskId), tableBroadcast,
expectedSchemaString, caseSensitive,
+ localityPreferred, new BatchReaderFactory(batchSize));
+ return true;
+ }).collect(Collectors.toList())).get();
+ } catch (Exception e) {
+ e.printStackTrace();
+ System.exit(-1);
}
- LOG.info("Batching input partitions with {} tasks.", readTasks.size());
-
- return readTasks;
+ Long endTime = System.currentTimeMillis();
+ LOG.info("Batching input partitions with {} tasks.", readTasks.length);
+ LOG.info("It took {} s to construct {} readTasks with localityPreferred =
{}.", (endTime - startTime) / 1000,
+ taskSize, localityPreferred);
+ return Arrays.asList(readTasks.clone());
}
/**
* This is called in the Spark Driver when data is to be materialized into
{@link InternalRow}
*/
+ @SuppressWarnings({"checkstyle:LocalVariableName",
"checkstyle:RegexpSinglelineJava"})
@Override
public List<InputPartition<InternalRow>> planInputPartitions() {
String expectedSchemaString = SchemaParser.toJson(lazySchema());
// broadcast the table metadata as input partitions will be sent to
executors
Broadcast<Table> tableBroadcast =
sparkContext.broadcast(SerializableTable.copyOf(table));
-
- List<InputPartition<InternalRow>> readTasks = Lists.newArrayList();
- for (CombinedScanTask task : tasks()) {
- readTasks.add(new ReadTask<>(
- task, tableBroadcast, expectedSchemaString, caseSensitive,
- localityPreferred, InternalRowReaderFactory.INSTANCE));
+ int taskSize = tasks().size();
+ InputPartition<InternalRow>[] readTasks = new InputPartition[taskSize];
+ Long start_time = System.currentTimeMillis();
+ try {
+ pool.submit(() -> IntStream.range(0, taskSize).parallel()
+ .mapToObj(taskId -> {
+ LOG.trace("The size of scanTasks is {}, current taskId is {},
current thread id is {}",
+ taskSize, taskId, Thread.currentThread().getName());
+ readTasks[taskId] = new ReadTask<>(
+ tasks().get(taskId), tableBroadcast,
expectedSchemaString, caseSensitive,
+ localityPreferred, InternalRowReaderFactory.INSTANCE);
+ return true;
+ }).collect(Collectors.toList())).get();
+ } catch (Exception e) {
+ e.printStackTrace();
+ System.exit(-1);
Review comment:
I would avoid explicitly calling System.exit and instead let the
exception bubble up (possibly catching it and then rethrowing it with
additional information added or as another exception type). This would make it
easier for end users to track down their exceptions, particularly when working
in a notebook where there's limited space to display stack traces already.
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