kfaraz commented on code in PR #13303:
URL: https://github.com/apache/druid/pull/13303#discussion_r1092765726
##########
indexing-hadoop/src/main/java/org/apache/druid/indexer/DeterminePartitionsJob.java:
##########
@@ -346,10 +361,14 @@ protected void innerMap(
rollupGranularity.bucketStart(inputRow.getTimestamp()).getMillis(),
inputRow
);
- context.write(
- new
BytesWritable(HadoopDruidIndexerConfig.JSON_MAPPER.writeValueAsBytes(groupKey)),
- NullWritable.get()
- );
+
+ final byte[] bytes =
HadoopDruidIndexerConfig.JSON_MAPPER.writeValueAsBytes(groupKey);
+ if (sample <= 1 || Math.abs(HASH_FUNCTION.hashBytes(bytes).asInt()) %
sample == 0) {
Review Comment:
Why are we using a hash of the bytes here? Doesn't this mean that we will
never emit certain group keys?
##########
core/src/main/java/org/apache/druid/indexer/partitions/DimensionRangePartitionsSpec.java:
##########
@@ -144,6 +146,17 @@ public List<String> getPartitionDimensions()
return partitionDimensions;
}
+ public DimensionRangeShardSpec createShardSpec(
Review Comment:
It is better to have this logic in `DeterminePartitionsJob` since it is only
used there. You can check the number of dimensions and create either a
`DimensionRangeShardSpec` or a `SingleDimensionShardSpec`.
##########
indexing-hadoop/src/main/java/org/apache/druid/indexer/DeterminePartitionsJob.java:
##########
@@ -346,10 +361,14 @@ protected void innerMap(
rollupGranularity.bucketStart(inputRow.getTimestamp()).getMillis(),
inputRow
);
- context.write(
- new
BytesWritable(HadoopDruidIndexerConfig.JSON_MAPPER.writeValueAsBytes(groupKey)),
- NullWritable.get()
- );
+
+ final byte[] bytes =
HadoopDruidIndexerConfig.JSON_MAPPER.writeValueAsBytes(groupKey);
+ if (sample <= 1 || Math.abs(HASH_FUNCTION.hashBytes(bytes).asInt()) %
sample == 0) {
+ context.write(
Review Comment:
Nit: This can be put on a single-line now.
##########
indexing-hadoop/src/main/java/org/apache/druid/indexer/DeterminePartitionsJob.java:
##########
@@ -476,22 +502,29 @@ void emitDimValueCounts(
final byte[] groupKey = buf.array();
// Emit row-counter value.
- write(context, groupKey, new DimValueCount("", "", 1));
-
- for (final Map.Entry<String, Iterable<String>> dimAndValues :
dims.entrySet()) {
- final String dim = dimAndValues.getKey();
-
- if (partitionDimension == null || partitionDimension.equals(dim)) {
- final Iterable<String> dimValues = dimAndValues.getValue();
-
- if (Iterables.size(dimValues) == 1) {
- // Emit this value.
- write(context, groupKey, new DimValueCount(dim,
Iterables.getOnlyElement(dimValues), 1));
+ write(context, groupKey, new DimValueCount(Collections.emptyList(),
StringTuple.create(), 1));
+
+ Iterator<List<String>> dimensionGroupIterator =
dimensionGroupingSet.iterator();
Review Comment:
Ah, I see it now. Thanks for the clarification.
In the case of single_dim too, I was wondering if we even need this check
anymore. I don't think we can submit a spec with null partition dimension even
for single_dim, atleast for index_parallel. There is a check in
`ParallelIndexTuningConfig` which validates that partition dimensions are
always specified.
We should have a similar check in `HadoopTuningConfig` too. Secretly
choosing a partition dimension behind the scenes is probably not so great. It's
better to fail if no partition dimension has been specified. What do you think?
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