huyuanfeng2018 commented on code in PR #879:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/879#discussion_r1756214335
##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/JobVertexScaler.java:
##########
@@ -378,28 +405,70 @@ protected static int scale(
// Cap parallelism at either maxParallelism(number of key groups or
source partitions) or
// parallelism upper limit
- final int upperBound = Math.min(maxParallelism, parallelismUpperLimit);
+ int upperBound = Math.min(maxParallelism, parallelismUpperLimit);
// Apply min/max parallelism
newParallelism = Math.min(Math.max(parallelismLowerLimit,
newParallelism), upperBound);
var adjustByMaxParallelism =
inputShipStrategies.isEmpty() ||
inputShipStrategies.contains(HASH);
if (!adjustByMaxParallelism) {
- return newParallelism;
+ return Tuple2.of(newParallelism, Optional.empty());
}
- // When the shuffle type of vertex inputs contains keyBy or vertex is
a source, we try to
- // adjust the parallelism such that it divides the maxParallelism
without a remainder
- // => data is evenly spread across subtasks
- for (int p = newParallelism; p <= maxParallelism / 2 && p <=
upperBound; p++) {
- if (maxParallelism % p == 0) {
- return p;
+ if (numPartitions <= 0) {
+ // When the shuffle type of vertex inputs contains keyBy or vertex
is a source,
+ // we try to adjust the parallelism such that it divides the
maxParallelism without a
+ // remainder => data is evenly spread across subtasks
+ for (int p = newParallelism; p <= maxParallelism / 2 && p <=
upperBound; p++) {
+ if (maxParallelism % p == 0) {
+ return Tuple2.of(p, Optional.empty());
+ }
+ }
+ // If parallelism adjustment fails, use originally computed
parallelism
+ return Tuple2.of(newParallelism, Optional.empty());
+ } else {
+
+ // When we know the numPartitions at a vertex,
+ // adjust the parallelism such that it divides the numPartitions
without a remainder
+ // => Data is evenly distributed among subtasks
+ for (int p = newParallelism; p <= upperBound && p <=
numPartitions; p++) {
+ if (numPartitions % p == 0) {
+ return Tuple2.of(p, Optional.empty());
+ }
}
Review Comment:
> Right, I missed that. I was trying to generalize the two code blocks. How
about the following?
Thansk, Fine with me , However, I do not recommend overriding the values
of MaxParallelism and UpperBound, so I added two new variables instead:
1. adjustableMaxParallelism( Indicates the MaxParallelism in the adjustment
process )
2. adjustableUpperBound (Indicates the UpperBound in the adjustment process)
##########
flink-autoscaler/src/main/java/org/apache/flink/autoscaler/JobVertexScaler.java:
##########
@@ -378,28 +405,70 @@ protected static int scale(
// Cap parallelism at either maxParallelism(number of key groups or
source partitions) or
// parallelism upper limit
- final int upperBound = Math.min(maxParallelism, parallelismUpperLimit);
+ int upperBound = Math.min(maxParallelism, parallelismUpperLimit);
// Apply min/max parallelism
newParallelism = Math.min(Math.max(parallelismLowerLimit,
newParallelism), upperBound);
var adjustByMaxParallelism =
inputShipStrategies.isEmpty() ||
inputShipStrategies.contains(HASH);
if (!adjustByMaxParallelism) {
- return newParallelism;
+ return Tuple2.of(newParallelism, Optional.empty());
}
- // When the shuffle type of vertex inputs contains keyBy or vertex is
a source, we try to
- // adjust the parallelism such that it divides the maxParallelism
without a remainder
- // => data is evenly spread across subtasks
- for (int p = newParallelism; p <= maxParallelism / 2 && p <=
upperBound; p++) {
- if (maxParallelism % p == 0) {
- return p;
+ if (numPartitions <= 0) {
+ // When the shuffle type of vertex inputs contains keyBy or vertex
is a source,
+ // we try to adjust the parallelism such that it divides the
maxParallelism without a
+ // remainder => data is evenly spread across subtasks
+ for (int p = newParallelism; p <= maxParallelism / 2 && p <=
upperBound; p++) {
+ if (maxParallelism % p == 0) {
+ return Tuple2.of(p, Optional.empty());
+ }
+ }
+ // If parallelism adjustment fails, use originally computed
parallelism
+ return Tuple2.of(newParallelism, Optional.empty());
+ } else {
+
+ // When we know the numPartitions at a vertex,
+ // adjust the parallelism such that it divides the numPartitions
without a remainder
+ // => Data is evenly distributed among subtasks
+ for (int p = newParallelism; p <= upperBound && p <=
numPartitions; p++) {
+ if (numPartitions % p == 0) {
+ return Tuple2.of(p, Optional.empty());
+ }
}
Review Comment:
> Right, I missed that. I was trying to generalize the two code blocks. How
about the following?
Thansk, fine with me , However, I do not recommend overriding the values
of MaxParallelism and UpperBound, so I added two new variables instead:
1. adjustableMaxParallelism( Indicates the MaxParallelism in the adjustment
process )
2. adjustableUpperBound (Indicates the UpperBound in the adjustment process)
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