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Revert "Merge remote-tracking branch 'yaron/perf-1' into perf-benchmarks-v1.16"
This reverts commit 91c4eab, reversing changes made to 8de08db.
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‎.github/workflows/website-root.yml‎

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name: Azure Static Web App Root
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on:
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workflow_dispatch:
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push:
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branches:
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- v1.15
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pull_request:
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types: [opened, synchronize, reopened, closed]
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branches:
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- v1.15
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concurrency:
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# Cancel the previously triggered build for only PR build.
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group: website-${{ github.event.pull_request.number || github.sha }}
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cancel-in-progress: true
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jobs:
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build_and_deploy_job:
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name: Build Hugo Website
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if: github.event.action != 'closed'
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runs-on: ubuntu-latest
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env:
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SWA_BASE: 'proud-bay-0e9e0e81e'
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HUGO_ENV: production
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steps:
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- name: Checkout docs repo
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uses: actions/checkout@v4
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with:
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submodules: true
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- name: Setup Node
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uses: actions/setup-node@v4
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with:
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node-version: '18'
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- name: Setup Hugo
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uses: peaceiris/[email protected]
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with:
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hugo-version: 0.147.9
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extended: true
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- name: Setup Submodules
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run: |
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cd daprdocs
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git submodule update --init --recursive
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cd ..
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sudo npm install -D --save autoprefixer
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sudo npm install -D --save postcss-cli
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- name: Build Hugo Website
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run: |
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git config --global --add safe.directory /github/workspace
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if [ $GITHUB_EVENT_NAME == 'pull_request' ]; then
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STAGING_URL="https://${SWA_BASE}-${{github.event.number}}.westus2.azurestaticapps.net/"
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fi
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hugo ${STAGING_URL+-b "$STAGING_URL"}
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- name: Deploy docs site
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uses: Azure/static-web-apps-deploy@v1
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with:
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azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_PROUD_BAY_0E9E0E81E }}
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repo_token: ${{ secrets.GITHUB_TOKEN }}
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action: "upload"
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app_location: "/daprdocs/public/"
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output_location: "/"
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skip_app_build: true
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skip_deploy_on_missing_secrets: true
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- name: Upload Hugo artifacts
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uses: actions/upload-artifact@v4
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with:
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name: hugo_build
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path: ./daprdocs/public/
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if-no-files-found: error
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close_staging_site:
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if: github.event_name == 'pull_request' && github.event.action == 'closed'
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runs-on: ubuntu-latest
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name: Close Pull Request Job
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steps:
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- name: Close Pull Request
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id: closepullrequest
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uses: Azure/static-web-apps-deploy@v1
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with:
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azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_PROUD_BAY_0E9E0E81E }}
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action: "close"
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skip_deploy_on_missing_secrets: true
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algolia_index:
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name: Index site for Algolia
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if: github.event_name == 'push'
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needs: ['build_and_deploy_job']
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runs-on: ubuntu-latest
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env:
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ALGOLIA_APP_ID: ${{ secrets.ALGOLIA_APP_ID }}
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ALGOLIA_API_WRITE_KEY: ${{ secrets.ALGOLIA_API_WRITE_KEY }}
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ALGOLIA_INDEX_NAME: daprdocs
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steps:
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- name: Checkout docs repo
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uses: actions/checkout@v4
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with:
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submodules: false
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- name: Download Hugo artifacts
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uses: actions/download-artifact@v3
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with:
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name: hugo_build
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path: site/
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- name: Install Python packages
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run: |
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pip install --upgrade bs4
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pip install --upgrade 'algoliasearch>=2.0,<3.0'
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- name: Index site
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run: python ./.github/scripts/algolia.py ./site

‎daprdocs/content/en/operations/configuration/configuration-overview.md‎

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The OpenTelemetry (`otel`) endpoint can also be configured via an environment variable. The presence of the `OTEL_EXPORTER_OTLP_ENDPOINT` environment variable
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turns on tracing for the sidecar.
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| Environment Variable | Description |
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|----------------------|-----------------------------------------------------------------|
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| Environment Variable | Description |
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|----------------------|-------------|
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| `OTEL_EXPORTER_OTLP_ENDPOINT` | Sets the Open Telemetry (OTEL) server address, turns on tracing |
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| `OTEL_EXPORTER_OTLP_INSECURE` | Sets the connection to the endpoint as unencrypted (true/false) |
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| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) |
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| `OTEL_SERVICE_NAME` | Optional override to specify the service name used in traces |
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| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) |
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See [Observability distributed tracing]({{% ref "tracing-overview.md" %}}) for more information.
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‎daprdocs/content/en/operations/performance-and-scalability/perf-actors-activation.md‎

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* Sidecar Injector (control plane)
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* Sentry (optional, control plane)
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## Performance summary for Dapr v1.12
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## Performance summary for Dapr v1.0
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The actors API in Dapr sidecar identifies which hosts are registered for a given actor type and routes the request to the appropriate host for a given actor ID. The host runs an instance of the application and uses the Dapr SDK (.Net, Java, Python, Go) to handle actors requests via HTTP.
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The actors API in Dapr sidecar will identify which hosts are registered for a given actor type and route the request to the appropriate host for a given actor ID. The host runs an instance of the application and uses the Dapr SDK (.Net, Java, Python or PHP) to handle actors requests via HTTP.
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This test uses invokes actors via Dapr's HTTP API directly.
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* Sidecar limited to 0.5 vCPU
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* mTLS enabled
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* Sidecar telemetry enabled (tracing with a sampling rate of 0.1)
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* Payload of an empty JSON object: `{}`
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### Results
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* The requested throughput was 500 qps.
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* The actual throughput was 500 qps.
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* The tp90 latency was ~3.2ms.
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* The tp99 latency was ~7ms.
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* Dapr app consumed ~339m CPU and ~336Mb of Memory
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* Dapr sidecar consumed 93m CPU and ~60Mb of Memory
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* The actual throughput was ~500 qps.
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* The tp90 latency was ~3ms.
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* The tp99 latency was ~6.2ms.
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* Dapr app consumed ~523m CPU and ~304.7Mb of Memory
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* Dapr sidecar consumed 2m CPU and ~18.2Mb of Memory
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* No app restarts
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* No sidecar restarts
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## Related links
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* For more information see [overview of Dapr on Kubernetes]({{% ref kubernetes-overview.md %}})

‎daprdocs/content/en/operations/performance-and-scalability/perf-pubsub.md‎

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‎daprdocs/content/en/operations/performance-and-scalability/perf-service-invocation.md‎

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For more information see [overview of Dapr on Kubernetes]({{% ref kubernetes-overview.md %}}).
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## Performance summary for Dapr v1.12
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## Performance summary for Dapr v1.0
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The service invocation API is a reverse proxy with built-in service discovery to connect to other services. This includes tracing, metrics, mTLS for in-transit encryption of traffic, together with resiliency in the form of retries for network partitions and connection errors.
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| Component | vCPU | Memory
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| ------------- | ------------- | -------------
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| Operator | 0.003 | 18 Mb
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| Sentry | 0.01 | 33 Mb
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| Sidecar Injector | 0.008 | 17 Mb
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| Placement | 0.005 | 25 Mb
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| Operator | 0.001 | 12.5 Mb
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| Sentry | 0.005 | 13.6 Mb
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| Sidecar Injector | 0.002 | 14.6 Mb
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| Placement | 0.001 | 20.9 Mb
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There are a number of variants that affect the CPU and memory consumption for each of the system components. These variants are shown in the table below.
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### Data plane performance
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The Dapr sidecar uses 0.48 vCPU and 23Mb per 1000 requests per second.
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End-to-end, the Dapr sidecars (client and server) add ~1.40 ms to the 90th percentile latency, and ~2.10 ms to the 99th percentile latency. End-to-end here is a call from one app to another app receiving a response. This is shown by steps 1-7 in [this diagram]({{% ref service-invocation-overview.md %}}).
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This performance is on par or better than commonly used service meshes.
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### Latency
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In the test setup, requests went through the Dapr sidecar both on the client side (serving requests from the load tester tool) and the server side (the target app).
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mTLS and telemetry (tracing with a sampling rate of 0.1) and metrics were enabled on the Dapr test, and disabled for the baseline test.
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The Dapr sidecar uses 0.45 vCPU and 38Mb per 1000 requests per second.
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<img src="/images/perf_invocation_p90.png" alt="Latency for 90th percentile">
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End-to-end, the Dapr sidecars (client and server) add ~1.20 ms to the 90th percentile latency, and ~2.50 ms to the 99th percentile latency. End-to-end here is a call from one app to another app receiving a response. This is shown by steps 1-7 in [this diagram]({{< ref service-invocation-overview.md >}}).
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<br>
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This performance is on par or better than commonly used service meshes.
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<img src="/images/perf_invocation_p99.png" alt="Latency for 99th percentile">

‎daprdocs/content/en/operations/performance-and-scalability/perf-state.md‎

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