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feat: add scaler for temporal #4863

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2 changes: 1 addition & 1 deletion CHANGELOG.md
Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@ To learn more about active deprecations, we recommend checking [GitHub Discussio

### New

- TODO ([#XXX](https://github.com/kedacore/keda/issue/XXX))
- **General**: Add Temporal Scaler `temporal` ([#4724](https://github.com/kedacore/keda/issues/4724))

### Improvements

Expand Down
269 changes: 269 additions & 0 deletions pkg/scalers/temporal_scaler.go
Original file line number Diff line number Diff line change
@@ -0,0 +1,269 @@
package scalers

import (
"context"
"errors"
"fmt"
"strconv"
"strings"
"sync"
"time"

"github.com/go-logr/logr"
kedautil "github.com/kedacore/keda/v2/pkg/util"
tclfilter "go.temporal.io/api/filter/v1"
workflowservice "go.temporal.io/api/workflowservice/v1"
sdk "go.temporal.io/sdk/client"
"google.golang.org/grpc"
v2 "k8s.io/api/autoscaling/v2"
"k8s.io/metrics/pkg/apis/external_metrics"
)

const (
defaultTargetWorkflowLength = 5
defaultActivationTargetWorkflowLength = 0
temporalClientTimeOut = 30
)

type executionInfo struct {
workflowId string
runId string
}

type temporalWorkflowScaler struct {
metricType v2.MetricTargetType
metadata *temporalWorkflowMetadata
tcl sdk.Client
logger logr.Logger
}

type temporalWorkflowMetadata struct {
activationTargetWorkflowLength int64
endpoint string
namespace string
workflowName string
activities []string
scalerIndex int
targetQueueSize int64
metricName string
}

// NewTemporalWorkflowScaler creates a new instance of temporalWorkflowScaler.
func NewTemporalWorkflowScaler(config *ScalerConfig) (Scaler, error) {
metricType, err := GetMetricTargetType(config)
if err != nil {
return nil, fmt.Errorf("failed to get scaler metric type: %w", err)
}

meta, err := parseTemporalMetadata(config)
if err != nil {
return nil, fmt.Errorf("failed to parse Temporal metadata: %w", err)
}

logger := InitializeLogger(config, "temporal_workflow_scaler")

c, err := sdk.Dial(sdk.Options{
HostPort: meta.endpoint,
ConnectionOptions: sdk.ConnectionOptions{
DialOptions: []grpc.DialOption{
grpc.WithTimeout(time.Duration(temporalClientTimeOut) * time.Second),
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As gRPC is HTTP at the of the day, I think that we should use the environment variable that it's in config.GlobalHTTPTimeout

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Please, add the TLS options too. There is a helper you should use that unifies all TLS configs like minVersion or custom CAs.

},
},
})

if err != nil {
return nil, fmt.Errorf("failed to create Temporal client: %w", err)
}

return &temporalWorkflowScaler{
metricType: metricType,
metadata: meta,
tcl: c,
logger: logger,
}, nil
}

// Close closes the Temporal client connection.
func (s *temporalWorkflowScaler) Close(context.Context) error {
if s.tcl != nil {
s.tcl.Close()
}
return nil
}

// GetMetricSpecForScaling returns the metric specification for scaling.
func (s *temporalWorkflowScaler) GetMetricSpecForScaling(context.Context) []v2.MetricSpec {
externalMetric := &v2.ExternalMetricSource{
Metric: v2.MetricIdentifier{
Name: s.metadata.metricName,
},
Target: GetMetricTarget(s.metricType, s.metadata.targetQueueSize),
}
metricSpec := v2.MetricSpec{
External: externalMetric,
Type: externalMetricType,
}
return []v2.MetricSpec{metricSpec}
}

// GetMetricsAndActivity returns metrics and activity for the scaler.
func (s *temporalWorkflowScaler) GetMetricsAndActivity(ctx context.Context, metricName string) ([]external_metrics.ExternalMetricValue, bool, error) {
queueSize, err := s.getQueueSize(ctx)
if err != nil {
return nil, false, fmt.Errorf("failed to get Temporal queue size: %w", err)
}

metric := GenerateMetricInMili(metricName, float64(queueSize))

return []external_metrics.ExternalMetricValue{metric}, queueSize > s.metadata.activationTargetWorkflowLength, nil
}

// getQueueSize returns the queue size of open workflows.
func (s *temporalWorkflowScaler) getQueueSize(ctx context.Context) (int64, error) {
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This does not get an accurate queue size (this paginates). You can use .CountWorkflow but that's only for workflows, it doesn't help with activities (and often it's activities that are the reason for needing to scale).

The proper way to scale Temporal workers is to use the temporal_worker_task_slots_available metric on the workers. See https://docs.temporal.io/dev-guide/worker-performance.

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It would make more sense if we consider multiple activities within a single workflow and deploy workers for each activity. However, the current scaling mechanism relies on pending workflows rather than individual activities. I plan to review the SDK documentation to explore the possibility of integrating activities into the scaling process.

Notably, "temporal_worker_task_slots_available" serves as a Prometheus metric, which could potentially be employed alongside the Prometheus scaler for those interested in scaling based on this particular metric.

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It would make more sense if we consider multiple activities within a single workflow and deploy workers for each activity.

I don't think it's a reasonable scaler if you don't consider activities. And I don't think the scaler is working that well if it's only for a single workflow type.

However, the current scaling mechanism relies on pending workflows rather than individual activities.

Pending activities matter too (maybe more). Even if you were only doing pending workflows, list open workflows is paginated, you are not getting full counts. Regardless, scaling a worker based on a single workflow is not the best way to write a scaler.

Notably, "temporal_worker_task_slots_available" serves as a Prometheus metric, which could potentially be employed alongside the Prometheus scaler for those interested in scaling based on this particular metric.

This is the metric that should be scaled on and is the one Temporal recommends scaling on (assuming you've configured individual worker resources properly based on your workflows/activites), see https://docs.temporal.io/dev-guide/worker-performance. The current scaler which doesn't include activities, only works for a single workflow type, etc is not sufficient IMO.

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I have transitioned this process to a paginated approach. Unfortunately, I haven't discovered a method to integrate activity counts into the current setup. It seems that further research is necessary to explore potential solutions in this regard.

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I have transitioned this process to a paginated approach.

You should just use CountWorkflow, not list every workflow. But regardless, we do not have a way for you to easily get all pending activities from the server for a task queue. The scaler needs to use the slots metric per the worker performance doc. Using list/count is not the best way to write the scaler.

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@cretz Can you please review the recent changes?

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@cretz cretz Aug 21, 2023

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The idea of a target queue size and listing workflows is not the recommended approach to determining whether to scale up or down (ntm it'd be better to use count with a query checking whether running). We recommend using the temporal_worker_task_slots_available metric (with check whether worker type is activity or workflow).

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@cretz is one of the main contributors of temporal.io SDK repo.
I think that we should follow his recommendations at this point @Prajithp . Could you implement it? 🙏

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@JorTurFer I am unsure about the feasibility of achieving this through the CountWorkflow method, as it might not provide visibility into whether the activity is presently running or not. As recommended by him, individuals aiming to scale based on Prometheus data can make use of the query specified above.

Can we please leave this pull request open for a while? This would allow for the possibility of additional suggestions from others. In the meantime, we will maintain our own fork and deploy it in our production environment.

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Can we please leave this pull request open for a while?

Yeas sure, no problem at all

we will maintain our own fork and deploy it in our production environment

I'd suggest using external scaler/metrics api scaler instead of maintaining your own fork. I mean, KEDA can be extended using those scalers from a 3rd party service that you can develop with the code that you prefer. Using this approach instead of maintaining your own fork can brings you the option to upgrade KEDA without the hard effort of rebase it and adapt the code (as you have develop just a scaler this shouldn't be a drama, but extending is always better than modifying)


var executions []executionInfo
var nextPageToken []byte
for {
listOpenWorkflowExecutionsRequest := &workflowservice.ListOpenWorkflowExecutionsRequest{
Namespace: s.metadata.namespace,
MaximumPageSize: 1000,
NextPageToken: nextPageToken,
Filters: &workflowservice.ListOpenWorkflowExecutionsRequest_TypeFilter{
TypeFilter: &tclfilter.WorkflowTypeFilter{
Name: s.metadata.workflowName,
},
},
}
ws, err := s.tcl.ListOpenWorkflow(ctx, listOpenWorkflowExecutionsRequest)
if err != nil {
return 0, fmt.Errorf("failed to get workflows: %w", err)
}

for _, exec := range ws.GetExecutions() {
execution := executionInfo{
workflowId: exec.Execution.GetWorkflowId(),
runId: exec.Execution.RunId,
}
executions = append(executions, execution)
}

if nextPageToken = ws.NextPageToken; len(nextPageToken) == 0 {
break
}
}
Comment on lines +126 to +153
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I'm afraid about the performance impact of this. Could we face with any infinite (or almost infinite loop)? If the backend responds slowly and we have to browse idk, 50 pages, what will happen?
Is adding a limit for the pages doable? Maybe just with a parameter that users can modify under their own risk?
WDYT?


pendingCh := make(chan string, len(executions))
var wg sync.WaitGroup

for _, execInfo := range executions {
wg.Add(1)
go func(e executionInfo) {
defer wg.Done()

workflowId := e.workflowId
runId := e.runId

if !s.isActivityRunning(ctx, workflowId, runId) {
executionId := workflowId + "__" + runId
pendingCh <- executionId
}

}(execInfo)
}
wg.Wait()
close(pendingCh)
Comment on lines +158 to +174
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Same as above, if there are thousand of pending executions, What will happen?
Probably I'm wrong, but I understand that we are listing all the workflows and inside the workflows, we are checking all the executions to decide if it's running or not. This suggests me some questions:

  • Are executed activities removed at any moment or will we have this queue growing and growing?
  • Doesn't the workflow have any option to give that information during the first requests instead of having to navigate over all the activities?


var queueLength int64
for range pendingCh {
queueLength++
}
return queueLength, nil
}

// isActivityRunning checks if there are running activities associated with a specific workflow execution.
func (s *temporalWorkflowScaler) isActivityRunning(ctx context.Context, workflowId, runId string) bool {
resp, err := s.tcl.DescribeWorkflowExecution(ctx, workflowId, runId)
if err != nil {
s.logger.Error(err, "error describing workflow execution", "workflowId", workflowId, "runId", runId)
return false
}

// If there is no activityName and there are running activities, return true.
if len(s.metadata.activities) == 0 && len(resp.GetPendingActivities()) > 0 {
return true
}

// Store the IDs of running activities. Make sure no duplicates incase of anything.
runningActivities := make(map[string]struct{})
for _, pendingActivity := range resp.GetPendingActivities() {
activityName := pendingActivity.ActivityType.GetName()
if s.hasMatchingActivityName(activityName) {
runningActivities[pendingActivity.ActivityId] = struct{}{}
}
}

// Return true if there are any running activities, otherwise false.
return len(runningActivities) > 0
}

// hasMatchingActivityName checks if the provided activity name matches any of the defined activity names in the metadata.
func (s *temporalWorkflowScaler) hasMatchingActivityName(activityName string) bool {
for _, activity := range s.metadata.activities {
if activityName == activity {
return true
}
}
return false
}

// parseTemporalMetadata parses the Temporal metadata from the ScalerConfig.
func parseTemporalMetadata(config *ScalerConfig) (*temporalWorkflowMetadata, error) {
meta := &temporalWorkflowMetadata{}
meta.activationTargetWorkflowLength = defaultActivationTargetWorkflowLength
meta.targetQueueSize = defaultTargetWorkflowLength

if config.TriggerMetadata["endpoint"] == "" {
return nil, errors.New("no Temporal gRPC endpoint provided")
}
meta.endpoint = config.TriggerMetadata["endpoint"]

if config.TriggerMetadata["namespace"] == "" {
meta.namespace = "default"
} else {
meta.namespace = config.TriggerMetadata["namespace"]
}

if config.TriggerMetadata["workflowName"] == "" {
return nil, errors.New("no workflow name provided")
}
meta.workflowName = config.TriggerMetadata["workflowName"]

if activities := config.TriggerMetadata["activityName"]; activities != "" {
meta.activities = strings.Split(activities, ",")
}

if size, ok := config.TriggerMetadata["targetQueueSize"]; ok {
queueSize, err := strconv.ParseInt(size, 10, 64)
if err != nil {
return nil, fmt.Errorf("invalid targetQueueSize - must be an integer")
}
meta.targetQueueSize = queueSize
}

if size, ok := config.TriggerMetadata["activationTargetQueueSize"]; ok {
activationTargetQueueSize, err := strconv.ParseInt(size, 10, 64)
if err != nil {
return nil, fmt.Errorf("invalid activationTargetQueueSize - must be an integer")
}
meta.activationTargetWorkflowLength = activationTargetQueueSize
}

meta.metricName = GenerateMetricNameWithIndex(
config.ScalerIndex, kedautil.NormalizeString(
fmt.Sprintf("temporal-%s-%s", meta.namespace, meta.workflowName),
),
)
meta.scalerIndex = config.ScalerIndex
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I think that this line it's not necessary because you are already generating the metrics name here (and it's the only reason to use the scalerIndex IIRC)


return meta, nil
}
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