mxm commented on code in PR #571:
URL:
https://github.com/apache/flink-kubernetes-operator/pull/571#discussion_r1172429181
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/metrics/ScalingMetrics.java:
##########
@@ -153,37 +169,64 @@ private static double getNumRecordsInPerSecond(
}
private static double getNumRecordsOutPerSecond(
- Map<FlinkMetric, AggregatedMetric> flinkMetrics,
- JobVertexID jobVertexID,
- boolean isSource,
- double numRecordsInPerSecond) {
- if (numRecordsInPerSecond <= 0) {
- // If the input rate is zero, we also need to flatten the output
rate.
- // Otherwise, the OUTPUT_RATIO would be outrageously large,
leading to
- // a rapid scale up.
- return 0;
- }
+ Map<FlinkMetric, AggregatedMetric> flinkMetrics, JobVertexID
jobVertexID) {
+
AggregatedMetric numRecordsOutPerSecond =
flinkMetrics.get(FlinkMetric.NUM_RECORDS_OUT_PER_SEC);
+
if (numRecordsOutPerSecond == null) {
- if (isSource) {
- numRecordsOutPerSecond =
-
flinkMetrics.get(FlinkMetric.SOURCE_TASK_NUM_RECORDS_OUT_PER_SEC);
- }
- if (numRecordsOutPerSecond == null) {
- LOG.warn("Received null output rate for {}. Returning NaN.",
jobVertexID);
- return Double.NaN;
- }
+ LOG.warn("Received null output rate for {}. Returning NaN.",
jobVertexID);
+ return Double.NaN;
}
return numRecordsOutPerSecond.getSum();
}
- private static double computeOutputRatio(
- double numRecordsInPerSecond, double numRecordsOutPerSecond) {
- if (numRecordsInPerSecond <= 0) {
- return 0;
+ private static double computeEdgeOutPerSecond(
+ JobTopology topology,
+ Map<JobVertexID, Map<FlinkMetric, AggregatedMetric>> flinkMetrics,
+ JobVertexID from,
+ JobVertexID to) {
+ var toMetrics = flinkMetrics.get(to);
+
+ var toVertexInputs = topology.getInputs().get(to);
+ // Case 1: Downstream vertex has a single input (from) so we can use
the most reliable num
+ // records in
+ if (toVertexInputs.size() == 1) {
+ LOG.debug(
+ "Computing edge ({}, {}) data rate for single input
downstream task", from, to);
+ return getNumRecordsInPerSecond(toMetrics, to, false);
+ }
+
+ // Case 2: Downstream vertex has only inputs from upstream vertices
which don't have other
+ // outputs
+ double sumOtherUpstreamInputs = 0;
+ for (JobVertexID input : toVertexInputs) {
+ if (input.equals(from)) {
+ continue;
Review Comment:
```suggestion
// Exclude source edge because we only want to consider
other input edges
continue;
```
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/metrics/ScalingMetrics.java:
##########
@@ -153,37 +169,64 @@ private static double getNumRecordsInPerSecond(
}
private static double getNumRecordsOutPerSecond(
- Map<FlinkMetric, AggregatedMetric> flinkMetrics,
- JobVertexID jobVertexID,
- boolean isSource,
- double numRecordsInPerSecond) {
- if (numRecordsInPerSecond <= 0) {
- // If the input rate is zero, we also need to flatten the output
rate.
- // Otherwise, the OUTPUT_RATIO would be outrageously large,
leading to
- // a rapid scale up.
- return 0;
- }
+ Map<FlinkMetric, AggregatedMetric> flinkMetrics, JobVertexID
jobVertexID) {
+
AggregatedMetric numRecordsOutPerSecond =
flinkMetrics.get(FlinkMetric.NUM_RECORDS_OUT_PER_SEC);
+
if (numRecordsOutPerSecond == null) {
- if (isSource) {
- numRecordsOutPerSecond =
-
flinkMetrics.get(FlinkMetric.SOURCE_TASK_NUM_RECORDS_OUT_PER_SEC);
- }
- if (numRecordsOutPerSecond == null) {
- LOG.warn("Received null output rate for {}. Returning NaN.",
jobVertexID);
- return Double.NaN;
- }
+ LOG.warn("Received null output rate for {}. Returning NaN.",
jobVertexID);
+ return Double.NaN;
}
return numRecordsOutPerSecond.getSum();
}
- private static double computeOutputRatio(
- double numRecordsInPerSecond, double numRecordsOutPerSecond) {
- if (numRecordsInPerSecond <= 0) {
- return 0;
+ private static double computeEdgeOutPerSecond(
+ JobTopology topology,
+ Map<JobVertexID, Map<FlinkMetric, AggregatedMetric>> flinkMetrics,
+ JobVertexID from,
+ JobVertexID to) {
+ var toMetrics = flinkMetrics.get(to);
+
+ var toVertexInputs = topology.getInputs().get(to);
+ // Case 1: Downstream vertex has a single input (from) so we can use
the most reliable num
+ // records in
+ if (toVertexInputs.size() == 1) {
+ LOG.debug(
+ "Computing edge ({}, {}) data rate for single input
downstream task", from, to);
+ return getNumRecordsInPerSecond(toMetrics, to, false);
+ }
+
+ // Case 2: Downstream vertex has only inputs from upstream vertices
which don't have other
+ // outputs
+ double sumOtherUpstreamInputs = 0;
Review Comment:
```suggestion
double numRecordsOutFromUpstreamInputs = 0;
```
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/AutoScalerInfo.java:
##########
@@ -86,20 +87,24 @@ public AutoScalerInfo(Map<String, String> data) {
configMap.setData(Preconditions.checkNotNull(data));
}
- @SneakyThrows
- public SortedMap<Instant, Map<JobVertexID, Map<ScalingMetric, Double>>>
getMetricHistory() {
+ public SortedMap<Instant, CollectedMetrics> getMetricHistory() {
var historyYaml = configMap.getData().get(COLLECTED_METRICS_KEY);
if (historyYaml == null) {
return new TreeMap<>();
}
- return YAML_MAPPER.readValue(decompress(historyYaml), new
TypeReference<>() {});
+ try {
+ return YAML_MAPPER.readValue(decompress(historyYaml), new
TypeReference<>() {});
+ } catch (JsonProcessingException e) {
+ LOG.error(
+ "Could not deserialize metric history, possibly the format
changed. Discarding...");
+ return new TreeMap<>();
Review Comment:
This will clear all existing metrics when we deploy this new version.
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/metrics/CollectedMetrics.java:
##########
@@ -17,18 +17,19 @@
package org.apache.flink.kubernetes.operator.autoscaler.metrics;
-import org.apache.flink.kubernetes.operator.autoscaler.topology.JobTopology;
import org.apache.flink.runtime.jobgraph.JobVertexID;
-import lombok.Value;
+import lombok.AllArgsConstructor;
+import lombok.Data;
+import lombok.NoArgsConstructor;
-import java.time.Instant;
import java.util.Map;
-import java.util.SortedMap;
-/** Topology and collected metric history. */
-@Value
+/** Collected scaling metrics. */
+@Data
+@NoArgsConstructor
+@AllArgsConstructor
public class CollectedMetrics {
- JobTopology jobTopology;
- SortedMap<Instant, Map<JobVertexID, Map<ScalingMetric, Double>>>
metricHistory;
+ private Map<JobVertexID, Map<ScalingMetric, Double>> vertexMetrics;
+ private Map<Edge, Double> outputRatios;
Review Comment:
This will increase the size of the metric history. We may have to pursue
https://issues.apache.org/jira/browse/FLINK-31866 for future upgrades.
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/AutoScalerInfo.java:
##########
@@ -86,20 +87,24 @@ public AutoScalerInfo(Map<String, String> data) {
configMap.setData(Preconditions.checkNotNull(data));
}
- @SneakyThrows
- public SortedMap<Instant, Map<JobVertexID, Map<ScalingMetric, Double>>>
getMetricHistory() {
+ public SortedMap<Instant, CollectedMetrics> getMetricHistory() {
var historyYaml = configMap.getData().get(COLLECTED_METRICS_KEY);
if (historyYaml == null) {
return new TreeMap<>();
}
- return YAML_MAPPER.readValue(decompress(historyYaml), new
TypeReference<>() {});
+ try {
+ return YAML_MAPPER.readValue(decompress(historyYaml), new
TypeReference<>() {});
+ } catch (JsonProcessingException e) {
+ LOG.error(
+ "Could not deserialize metric history, possibly the format
changed. Discarding...");
+ return new TreeMap<>();
Review Comment:
I think this will introduce a bug where only a single metric observation in
a window will be considered: Fixed via
https://issues.apache.org/jira/browse/FLINK-31866 or
https://issues.apache.org/jira/browse/FLINK-31867.
##########
flink-kubernetes-operator-autoscaler/src/main/java/org/apache/flink/kubernetes/operator/autoscaler/metrics/CollectedMetricHistory.java:
##########
@@ -0,0 +1,32 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.kubernetes.operator.autoscaler.metrics;
+
+import org.apache.flink.kubernetes.operator.autoscaler.topology.JobTopology;
+
+import lombok.Value;
+
+import java.time.Instant;
+import java.util.SortedMap;
+
+/** Topology and collected metric history. */
+@Value
+public class CollectedMetricHistory {
+ JobTopology jobTopology;
Review Comment:
Why do we need to persist the JobTopology? Could we save some bytes here?
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