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move some things around for launch (#1436)
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frontend/docs/pages/home/_meta.json

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"your-first-task": "Tasks",
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"workers": "Workers",
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"running-your-task": "Running Tasks",
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"orchestration": "Workflow Orchestration",
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"environments": "Environments",
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"--running-workflows": {
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"title": "Ways of Running Tasks",
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"title": "Workflows",
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"type": "separator"
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},
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"orchestration": "Task Orchestration",
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"dags": {
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"title": "Directed Acyclic Graphs (DAGs)"
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},
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"conditional-workflows": "Conditional Workflows",
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"child-spawning": {
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"title": "Child Spawning"
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},
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"additional-metadata": {
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"title": "Additional Metadata"
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},
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"--error-handling": {
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"title": "Error Handling",
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"type": "separator"
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},
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"timeouts": "Timeouts",
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"on-failure-tasks": "On Failure Tasks",
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"bulk-retries-and-cancellations": "Bulk Retries and Cancellations",
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"retries": {
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"title": "Retries"
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},
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},
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"docker": "Running with Docker",
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"worker-healthchecks": "Worker Health Checks",
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"--flow-control": {
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"title": "Flow Control",
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"type": "separator"
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"title": "Bulk Cancellation",
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"display": "hidden"
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},
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"--v1-new-features": {
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"title": "V1 New Features",
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"type": "separator"
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},
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"v1-new-features-overview": "Overview",
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"v1-complex-workflows": "Complex Workflows",
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"v1-durable-execution": {
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"title": "Durable Execution",
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"display": "hidden"
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},
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"v1-bulk-operations": "Bulk Cancel and Replay",
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"v1-sdk-improvements": {
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"title": "SDK Improvements"
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},
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"--v1-migration-guides": {
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"title": "V1 Migration Guides",
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"type": "separator"
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},
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"v1-sdk-improvements": {
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"title": "SDK Improvements"
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},
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"migration-guide-engine": "Engine Migration Guide",
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"migration-guide-python": "Python Migration Guide",
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"migration-guide-typescript": "Typescript Migration Guide",

frontend/docs/pages/home/v1-bulk-operations.mdx renamed to frontend/docs/pages/home/bulk-retries-and-cancellations.mdx

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### Bulk Operations by Filters
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### Bulk Operations by Run Ids
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The second way to bulk cancel or replay runs is by providing a list of filters. This is the most powerful way to cancel or replay runs in bulk, as it allows you to cancel or replay all runs matching a set of arbitrary filters without needing to provide IDs for the runs in advance.
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<UniversalTabs items={["Python"]}>
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frontend/docs/pages/home/orchestration.mdx

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export const getStaticProps = ({}) => getSnippets([SimpleTs, SimplePy]);
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# What is Orchestration?
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# Task Orchestration
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Orchestration can be very overloaded in the software development world. In Hatchet, we define orchestration as the both process of coordinating the execution of tasks on "compute" and the coordination of the compute resources themselves.
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## Orchestrating Tasks
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In Hatchet there are two primary ways to orchestrate tasks:
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Not only can you run a single task in Hatchet, but you can also orchestrate multiple tasks together based on a shape that you define. For example, you can run a task that depends on the output of another task, or you can run a task that waits for a certain condition to be met before running.
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1. [Declarative Workflow Design (DAGs)](./dags.mdx) -- which is a way to declaratively define the sequence and dependencies of tasks in a workflow when you know the dependencies ahead of time.
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2. [Procedural Child Spawning](./child-spawning.mdx) -- which is a way to orchestrate tasks in a workflow when you don't know the dependencies ahead of time or when the dependencies are dynamic.
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## Orchestrating Tasks With Flow Controls
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In addition to coordinating the execution of tasks, Hatchet also provides a set of flow control primitives that allow you to orchestrate tasks in a workflow.
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1. [Worker Slots](./workers.mdx#understanding-slots) -- which is a way to control the number of tasks that can be executed concurrently on a given compute process.
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2. [Concurrency Control](./concurrency-keys.mdx) -- which is a global way to control the concurrent execution of tasks based on a specific key.
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3. [Rate Limiting](./rate-limits.mdx) -- which is a global way to control the rate of task execution based on time period.
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## Orchestrating Compute
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In addition to coordinating the execution of tasks, Hatchet also coordinates the compute resources themselves. Autoscaling compute resources to match the workload is a core feature of Hatchet managed compute and we're working on plugins for other compute orchestrators.
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To read about waiting on additional conditions in a workflow, see the [Conditional Workflows](./conditional-workflows.mdx) guide.

frontend/docs/pages/home/v1-new-features-overview.mdx

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