diff --git a/.github/workflows/link_validation.yaml b/.github/workflows/link_validation.yaml index 4b7840e3cb1..350f8407a33 100644 --- a/.github/workflows/link_validation.yaml +++ b/.github/workflows/link_validation.yaml @@ -13,7 +13,7 @@ jobs: validate: runs-on: ubuntu-latest env: - PYTHON_VER: 3.7 + PYTHON_VER: 3.12 steps: - uses: actions/checkout@v2 - name: Check Microsoft URLs do not pin localized versions @@ -27,7 +27,7 @@ jobs: exit 1 fi - name: Set up Python ${{ env.PYTHON_VER }} - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ env.PYTHON_VER }} - name: Install dependencies diff --git a/.github/workflows/website-root.yml b/.github/workflows/website-root.yml deleted file mode 100644 index 17989accb7d..00000000000 --- a/.github/workflows/website-root.yml +++ /dev/null @@ -1,109 +0,0 @@ -name: Azure Static Web App Root - -on: - workflow_dispatch: - push: - branches: - - v1.12 - pull_request: - types: [opened, synchronize, reopened, closed] - branches: - - v1.12 - -concurrency: - # Cancel the previously triggered build for only PR build. - group: website-${{ github.event.pull_request.number || github.sha }} - cancel-in-progress: true - -jobs: - build_and_deploy_job: - name: Build Hugo Website - if: github.event.action != 'closed' - runs-on: ubuntu-latest - env: - SWA_BASE: 'proud-bay-0e9e0e81e' - HUGO_ENV: production - steps: - - name: Checkout docs repo - uses: actions/checkout@v3 - with: - submodules: true - - name: Setup Node - uses: actions/setup-node@v2 - with: - node-version: '14' - - name: Setup Hugo - uses: peaceiris/actions-hugo@v2.5.0 - with: - hugo-version: 0.102.3 - extended: true - - name: Setup Docsy - run: | - cd daprdocs - git submodule update --init --recursive - sudo npm install -D --save autoprefixer - sudo npm install -D --save postcss-cli - - name: Build Hugo Website - run: | - cd daprdocs - git config --global --add safe.directory /github/workspace - if [ $GITHUB_EVENT_NAME == 'pull_request' ]; then - STAGING_URL="https://${SWA_BASE}-${{github.event.number}}.westus2.azurestaticapps.net/" - fi - hugo ${STAGING_URL+-b "$STAGING_URL"} - - name: Deploy docs site - uses: Azure/static-web-apps-deploy@v1 - with: - azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_PROUD_BAY_0E9E0E81E }} - repo_token: ${{ secrets.GITHUB_TOKEN }} - action: "upload" - app_location: "daprdocs/public/" - api_location: "daprdocs/public/" - output_location: "" - skip_app_build: true - skip_deploy_on_missing_secrets: true - - name: Upload Hugo artifacts - uses: actions/upload-artifact@v3 - with: - name: hugo_build - path: ./daprdocs/public/ - if-no-files-found: error - - close_staging_site: - if: github.event_name == 'pull_request' && github.event.action == 'closed' - runs-on: ubuntu-latest - name: Close Pull Request Job - steps: - - name: Close Pull Request - id: closepullrequest - uses: Azure/static-web-apps-deploy@v1 - with: - azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_PROUD_BAY_0E9E0E81E }} - action: "close" - skip_deploy_on_missing_secrets: true - - algolia_index: - name: Index site for Algolia - if: github.event_name == 'push' - needs: ['build_and_deploy_job'] - runs-on: ubuntu-latest - env: - ALGOLIA_APP_ID: ${{ secrets.ALGOLIA_APP_ID }} - ALGOLIA_API_WRITE_KEY: ${{ secrets.ALGOLIA_API_WRITE_KEY }} - ALGOLIA_INDEX_NAME: daprdocs - steps: - - name: Checkout docs repo - uses: actions/checkout@v2 - with: - submodules: false - - name: Download Hugo artifacts - uses: actions/download-artifact@v3 - with: - name: hugo_build - path: site/ - - name: Install Python packages - run: | - pip install --upgrade bs4 - pip install --upgrade 'algoliasearch>=2.0,<3.0' - - name: Index site - run: python ./.github/scripts/algolia.py ./site diff --git a/.github/workflows/website-v1-12.yml b/.github/workflows/website-v1-12.yml index a16d54978ef..d16d3796cf9 100644 --- a/.github/workflows/website-v1-12.yml +++ b/.github/workflows/website-v1-12.yml @@ -1,6 +1,7 @@ name: Azure Static Web App v1.12 on: + workflow_dispatch: push: branches: - v1.12 diff --git a/README.md b/README.md index 98bc0a4c4ff..e99d95a2590 100644 --- a/README.md +++ b/README.md @@ -14,8 +14,8 @@ The following branches are currently maintained: | Branch | Website | Description | | ------------------------------------------------------------ | -------------------------- | ------------------------------------------------------------------------------------------------ | -| [v1.12](https://github.com/dapr/docs) (primary) | https://docs.dapr.io | Latest Dapr release documentation. Typo fixes, clarifications, and most documentation goes here. | -| [v1.13](https://github.com/dapr/docs/tree/v1.13) (pre-release) | https://v1-13.docs.dapr.io/ | Pre-release documentation. Doc updates that are only applicable to v1.13+ go here. | +| [v1.13](https://github.com/dapr/docs) (primary) | https://docs.dapr.io | Latest Dapr release documentation. Typo fixes, clarifications, and most documentation goes here. | +| [v1.14](https://github.com/dapr/docs/tree/v1.14) (pre-release) | https://v1-14.docs.dapr.io/ | Pre-release documentation. Doc updates that are only applicable to v1.14+ go here. | For more information visit the [Dapr branch structure](https://docs.dapr.io/contributing/docs-contrib/contributing-docs/#branch-guidance) document. diff --git a/daprdocs/config.toml b/daprdocs/config.toml index bcc1eb945eb..fd8e19ae615 100644 --- a/daprdocs/config.toml +++ b/daprdocs/config.toml @@ -1,5 +1,5 @@ # Site Configuration -baseURL = "https://docs.dapr.io" +baseURL = "https://v1-12.docs.dapr.io" title = "Dapr Docs" theme = "docsy" disableFastRender = true @@ -183,17 +183,20 @@ github_subdir = "daprdocs" github_branch = "v1.12" # Versioning -version_menu = "v1.12 (latest)" +version_menu = "v1.12" version = "v1.12" -archived_version = false +archived_version = true url_latest_version = "https://docs.dapr.io" [[params.versions]] - version = "v1.13 (preview)" - url = "https://v1-13.docs.dapr.io" + version = "v1.14 (preview)" + url = "https://v1-14.docs.dapr.io" [[params.versions]] - version = "v1.12 (latest)" + version = "v1.13 (latest)" url = "#" +[[params.versions]] + version = "v1.12" + url = "https://v1-12.docs.dapr.io" [[params.versions]] version = "v1.11" url = "https://v1-11.docs.dapr.io" diff --git a/daprdocs/content/en/operations/configuration/configuration-overview.md b/daprdocs/content/en/operations/configuration/configuration-overview.md index f900beed05c..8732e3f998b 100644 --- a/daprdocs/content/en/operations/configuration/configuration-overview.md +++ b/daprdocs/content/en/operations/configuration/configuration-overview.md @@ -89,11 +89,12 @@ set `samplingRate : "0"` in the configuration. The valid range of samplingRate i The OpenTelemetry (otel) endpoint can also be configured via an environment variables. The presence of the OTEL_EXPORTER_OTLP_ENDPOINT environment variable turns on tracing for the sidecar. -| Environment Variable | Description | -|----------------------|-------------| +| Environment Variable | Description | +|----------------------|-----------------------------------------------------------------| | `OTEL_EXPORTER_OTLP_ENDPOINT` | Sets the Open Telemetry (OTEL) server address, turns on tracing | | `OTEL_EXPORTER_OTLP_INSECURE` | Sets the connection to the endpoint as unencrypted (true/false) | -| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) | +| `OTEL_EXPORTER_OTLP_PROTOCOL` | Transport protocol (`grpc`, `http/protobuf`, `http/json`) | +| `OTEL_SERVICE_NAME` | Optional override to specify the service name used in traces | See [Observability distributed tracing]({{< ref "tracing-overview.md" >}}) for more information. diff --git a/daprdocs/content/en/operations/performance-and-scalability/perf-actors-activation.md b/daprdocs/content/en/operations/performance-and-scalability/perf-actors-activation.md index ddc604142fa..9382177dd2e 100644 --- a/daprdocs/content/en/operations/performance-and-scalability/perf-actors-activation.md +++ b/daprdocs/content/en/operations/performance-and-scalability/perf-actors-activation.md @@ -19,9 +19,9 @@ For applications using actors in Dapr there are two aspects to be considered. Fi * Sidecar Injector (control plane) * Sentry (optional, control plane) -## Performance summary for Dapr v1.0 +## Performance summary for Dapr v1.12 -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. +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. This test uses invokes actors via Dapr's HTTP API directly. @@ -40,17 +40,14 @@ Test parameters: * Sidecar limited to 0.5 vCPU * mTLS enabled * Sidecar telemetry enabled (tracing with a sampling rate of 0.1) -* Payload of an empty JSON object: `{}` ### Results -* The actual throughput was ~500 qps. -* The tp90 latency was ~3ms. -* The tp99 latency was ~6.2ms. -* Dapr app consumed ~523m CPU and ~304.7Mb of Memory -* Dapr sidecar consumed 2m CPU and ~18.2Mb of Memory +* The requested throughput was 500 qps. +* The actual throughput was 500 qps. +* The tp90 latency was ~3.2ms. +* The tp99 latency was ~7ms. +* Dapr app consumed ~339m CPU and ~336Mb of Memory +* Dapr sidecar consumed 93m CPU and ~60Mb of Memory * No app restarts * No sidecar restarts - -## Related links -* For more information see [overview of Dapr on Kubernetes]({{< ref kubernetes-overview.md >}}) \ No newline at end of file diff --git a/daprdocs/content/en/operations/performance-and-scalability/perf-pubsub.md b/daprdocs/content/en/operations/performance-and-scalability/perf-pubsub.md new file mode 100644 index 00000000000..2717bd87db2 --- /dev/null +++ b/daprdocs/content/en/operations/performance-and-scalability/perf-pubsub.md @@ -0,0 +1,50 @@ +--- +type: docs +title: "Pub/sub performance" +linkTitle: "Pub/sub performance" +weight: 30000 +description: "" +--- +This article provides pub/sub API performance benchmarks and resource utilization in Dapr on Kubernetes. + +## System overview + +Dapr consists of a data plane, the sidecar that runs next to your app, and a control plane that configures the sidecars and provides capabilities such as cert and identity management. + +### Kubernetes components + +* Sidecar (data plane) +* Placement (required for actors, control plane mapping actor types to hosts) +* Operator (control plane) +* Sidecar Injector (control plane) +* Sentry (optional, control plane) +* Kafka cluster with 3 replicas + +## Performance summary for Dapr v1.12 + +The Pub/Sub API is used to publish messages to a message broker. Dapr accepts requests from the app via HTTP or gRPC, wraps them in a cloud event if needed, and sends the request to the message broker. + +Performance varies based on the underlying message broker. The Pub/Sub performance test measures the added latency when publishing a message with Dapr compared with the baseline latency when publishing directly to the message broker. + +### Kubernetes performance test setup + +The test was conducted on a 3 node Kubernetes cluster, using commodity hardware running 4 cores and 8GB of RAM, without any network acceleration. + +Test parameters: + +* 1000 requests per second +* 1 replica +* 1 minute duration +* Sidecar limited to 0.5 vCPU +* Sidecar telemetry enabled (tracing with a sampling rate of 0.1) +* Payload of a 1kb size + +### Results + +* The requested throughput was 1000 qps +* The actual throughput was 1000 qps +* Added latency for 90th percentile was 0.64ms for gRPC and 0.49ms for HTTP +* Added latency for 99th percentile was 1.91ms for gRPC and 1.21ms for HTTP +* Dapr app consumed ~0.2 vCPU and ~30Mb of Memory for both gRPC and HTTP +* No app restarts +* No sidecar restarts diff --git a/daprdocs/content/en/operations/performance-and-scalability/perf-service-invocation.md b/daprdocs/content/en/operations/performance-and-scalability/perf-service-invocation.md index 3b201e56ecd..48032a143a9 100644 --- a/daprdocs/content/en/operations/performance-and-scalability/perf-service-invocation.md +++ b/daprdocs/content/en/operations/performance-and-scalability/perf-service-invocation.md @@ -29,7 +29,7 @@ For more information see [overview of Dapr in self-hosted mode]({{< ref self-hos For more information see [overview of Dapr on Kubernetes]({{< ref kubernetes-overview.md >}}). -## Performance summary for Dapr v1.0 +## Performance summary for Dapr v1.12 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. @@ -59,10 +59,10 @@ When running in a highly available production setup, the Dapr control plane cons | Component | vCPU | Memory | ------------- | ------------- | ------------- -| Operator | 0.001 | 12.5 Mb -| Sentry | 0.005 | 13.6 Mb -| Sidecar Injector | 0.002 | 14.6 Mb -| Placement | 0.001 | 20.9 Mb +| Operator | 0.003 | 18 Mb +| Sentry | 0.01 | 33 Mb +| Sidecar Injector | 0.008 | 17 Mb +| Placement | 0.005 | 25 Mb 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. @@ -75,18 +75,11 @@ There are a number of variants that affect the CPU and memory consumption for ea ### Data plane performance -The Dapr sidecar uses 0.48 vCPU and 23Mb per 1000 requests per second. -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 >}}). - -This performance is on par or better than commonly used service meshes. - -### Latency - 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). 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. -Latency for 90th percentile +The Dapr sidecar uses 0.45 vCPU and 38Mb per 1000 requests per second. -
+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 >}}). -Latency for 99th percentile +This performance is on par or better than commonly used service meshes. diff --git a/daprdocs/content/en/operations/performance-and-scalability/perf-state.md b/daprdocs/content/en/operations/performance-and-scalability/perf-state.md new file mode 100644 index 00000000000..8db9d1a4284 --- /dev/null +++ b/daprdocs/content/en/operations/performance-and-scalability/perf-state.md @@ -0,0 +1,50 @@ +--- +type: docs +title: "State performance" +linkTitle: "State performance" +weight: 40000 +description: "" +--- +This article provides state API performance benchmarks and resource utilization in Dapr on Kubernetes. + +## System overview + +Dapr consists of a data plane, the sidecar that runs next to your app, and a control plane that configures the sidecars and provides capabilities such as cert and identity management. + +### Kubernetes components + +* Sidecar (data plane) +* Placement (required for actors, control plane mapping actor types to hosts) +* Operator (control plane) +* Sidecar Injector (control plane) +* Sentry (optional, control plane) +* PosgreSQL database (single node) + +## Performance summary for Dapr v1.12 + +The state API is used to persist state to a database, commonly called state store in Dapr. + +Performance varies based on the underlying state store. The state API performance test measures the added latency when using Dapr to get state compared with the baseline latency when getting state directly from the state store. + +### Kubernetes performance test setup + +The test was conducted on a 3 node Kubernetes cluster, using commodity hardware running 4 cores and 8GB of RAM, without any network acceleration. + +Test parameters: + +* 1000 requests per second +* 1 replica +* 1 minute duration +* Sidecar limited to 0.5 vCPU +* Sidecar telemetry enabled (tracing with a sampling rate of 0.1) +* Payload of a 1kb size + +### Results + +* The requested throughput was 1000 qps +* The actual throughput was 1000 qps +* Added latency for 90th percentile was 0.75ms for gRPC +* Added latency for 99th percentile was 1.52ms for gRPC +* Dapr app consumed ~0.3 vCPU and ~48 of Memory for gRPC +* No app restarts +* No sidecar restarts