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Pipeline retrieved successfully Schema — OPTIONAL |
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created_at date-timeTimestamp when the pipeline was originally created. | deployment_config object — OPTIONALPipeline deployment configuration.
It represents configuration entries directly provided by the user
(e.g., runtime configuration) and entries derived from the schema
of the compiled program (e.g., connectors). Storage configuration,
if applicable, is set by the runner. clock_resolution_usecs int64 — OPTIONALReal-time clock resolution in microseconds. This parameter controls the execution of queries that use the NOW() function. The output of such
queries depends on the real-time clock and can change over time without any external
inputs. The pipeline will update the clock value and trigger incremental recomputation
at most each clock_resolution_usecs microseconds. It is set to 100 milliseconds (100,000 microseconds) by default. Set to null to disable periodic clock updates. | cpu_profiler boolean — OPTIONALEnable CPU profiler. The default value is true . | max_buffering_delay_usecs int64 — OPTIONALMaximal delay in microseconds to wait for min_batch_size_records to
get buffered by the controller, defaults to 0. | min_batch_size_records int64 — OPTIONALMinimal input batch size. The controller delays pushing input records to the circuit until at
least min_batch_size_records records have been received (total
across all endpoints) or max_buffering_delay_usecs microseconds
have passed since at least one input records has been buffered.
Defaults to 0. | min_storage_bytes integer — OPTIONALThe minimum estimated number of bytes in a batch of data to write it to
storage. This is provided for debugging and fine-tuning and should
ordinarily be left unset. It only has an effect when storage is set to
true. A value of 0 will write even empty batches to storage, and nonzero
values provide a threshold. usize::MAX would effectively disable
storage. | resources object — OPTIONALcpu_cores_max int64 — OPTIONALThe maximum number of CPU cores to reserve
for an instance of this pipeline | cpu_cores_min int64 — OPTIONALThe minimum number of CPU cores to reserve
for an instance of this pipeline | memory_mb_max int64 — OPTIONALThe maximum memory in Megabytes to reserve
for an instance of this pipeline | memory_mb_min int64 — OPTIONALThe minimum memory in Megabytes to reserve
for an instance of this pipeline | storage_class string — OPTIONALStorage class to use for an instance of this pipeline.
The class determines storage performance such as IOPS and throughput. | storage_mb_max int64 — OPTIONALThe total storage in Megabytes to reserve
for an instance of this pipeline |
| storage boolean — OPTIONALShould persistent storage be enabled for this pipeline?
-
If false (default), the pipeline's state is kept in in-memory data-structures.
This is useful if the pipeline is ephemeral and does not need to be recovered
after a restart. The pipeline will most likely run faster since it does not
need to read from, or write to disk
-
If true , the pipeline state is stored in the specified location,
is persisted across restarts, and can be checkpointed and recovered.
This feature is currently experimental.
| tracing boolean — OPTIONAL | tracing_endpoint_jaeger string — OPTIONALJaeger tracing endpoint to send tracing information to. | workers int32 — OPTIONALNumber of DBSP worker threads. | inputs objectInput endpoint configuration. | name string — OPTIONAL | outputs object — OPTIONALOutput endpoint configuration. | storage_config object — OPTIONALConfiguration for persistent storage in a [PipelineConfig ]. cache stringPossible values: [page_cache , feldera_cache ] How to cache access to storage within a Feldera pipeline. | path stringThe location where the pipeline state is stored or will be stored. It should point to a path on the file-system of the machine/container
where the pipeline will run. If that path doesn't exist yet, or if it
does not contain any checkpoints, then the pipeline creates it and
starts from an initial state in which no data has yet been received. If
it does exist, then the pipeline starts from the most recent checkpoint
that already exists there. In either case, (further) checkpoints will be
written there. |
|
| deployment_desired_status stringPossible values: [Shutdown , Paused , Running ] | deployment_error object — OPTIONALInformation returned by REST API endpoints on error. details objectDetailed error metadata.
The contents of this field is determined by error_code . | error_code stringError code is a string that specifies this error type. | message stringHuman-readable error message. |
| deployment_location string — OPTIONALLocation where the pipeline can be reached at runtime
(e.g., a TCP port number or a URI). | deployment_status stringPossible values: [Shutdown , Provisioning , Initializing , Paused , Running , ShuttingDown , Failed ] Pipeline status. This type represents the state of the pipeline tracked by the pipeline
runner and observed by the API client via the GET /v0/pipelines/{name} endpoint. The lifecycle of a pipelineThe following automaton captures the lifecycle of the pipeline. Individual
states and transitions of the automaton are described below.
-
In addition to the transitions shown in the diagram, all states have an
implicit "forced shutdown" transition to the Shutdown state. This
transition is triggered when the pipeline runner is unable to communicate
with the pipeline and thereby forces a shutdown.
-
States labeled with the hourglass symbol (⌛) are timed states. The
automaton stays in timed state until the corresponding operation completes
or until the runner performs a forced shutdown of the pipeline after a
pre-defined timeout period.
-
State transitions labeled with API endpoint names (/start , /pause ,
/shutdown ) are triggered by invoking corresponding endpoint,
e.g., POST /v0/pipelines/{name}/start .
Shutdown◄────┐ │ │ /deploy│ │ │ ⌛ShuttingDown ▼ ▲ ⌛Provisioning │ │ │ │ │ ▼ │/shutdown ⌛Initializing │ │ │ ┌────────┴─────────┴─┐ │ ▼ │ │ Paused │ │ │ ▲ │ │/start│ │/pause │ │ ▼ │ │ │ Running │ └──────────┬─────────┘ │ ▼ Failed
Desired and actual statusWe use the desired state model to manage the lifecycle of a pipeline.
In this model, the pipeline has two status attributes associated with
it at runtime: the desired status, which represents what the user
would like the pipeline to do, and the current status, which
represents the actual state of the pipeline. The pipeline runner
service continuously monitors both fields and steers the pipeline
towards the desired state specified by the user.
Only three of the states in the pipeline automaton above can be
used as desired statuses: Paused , Running , and Shutdown .
These statuses are selected by invoking REST endpoints shown
in the diagram. The user can monitor the current state of the pipeline via the
GET /v0/pipelines/{name} endpoint, which returns an object of type ExtendedPipelineDescr .
In a typical scenario, the user first sets
the desired state, e.g., by invoking the /deploy endpoint, and
then polls the GET /v0/pipelines/{name} endpoint to monitor the actual status
of the pipeline until its deployment_status attribute changes
to "paused" indicating that the pipeline has been successfully
initialized, or "failed", indicating an error. | deployment_status_since date-timeTime when the pipeline was assigned its current status
of the pipeline. | description string | id uuid | name string | program_binary_url string — OPTIONALURL where to download the program binary from. | program_code string | program_config objectprofile string — OPTIONALPossible values: [dev , unoptimized , optimized ] Enumeration of possible compilation profiles that can be passed to the Rust compiler
as an argument via cargo build --profile <> . A compilation profile affects among
other things the compilation speed (how long till the program is ready to be run)
and runtime speed (the performance while running). |
| program_info object — OPTIONALProgram information which includes schema, input connectors and output connectors. input_connectors objectInput connectors derived from the schema. | output_connectors objectOutput connectors derived from the schema. | schema objectA struct containing the tables (inputs) and views for a program. Parse from the JSON data-type of the DDL generated by the SQL compiler. inputs object[]case_sensitive boolean | name string | fields object[]case_sensitive boolean | name string | columntype (circular) |
| materialized boolean — OPTIONAL | properties object — OPTIONAL |
| outputs object[]case_sensitive boolean | name string | fields object[]case_sensitive boolean | name string | columntype (circular) |
| materialized boolean — OPTIONAL | properties object — OPTIONAL |
|
|
| program_status Program compilation status. | program_status_since date-timeTimestamp when the current program status was set. | program_version int64 | runtime_config objectGlobal pipeline configuration settings. This is the publicly
exposed type for users to configure pipelines. clock_resolution_usecs int64 — OPTIONALReal-time clock resolution in microseconds. This parameter controls the execution of queries that use the NOW() function. The output of such
queries depends on the real-time clock and can change over time without any external
inputs. The pipeline will update the clock value and trigger incremental recomputation
at most each clock_resolution_usecs microseconds. It is set to 100 milliseconds (100,000 microseconds) by default. Set to null to disable periodic clock updates. | cpu_profiler boolean — OPTIONALEnable CPU profiler. The default value is true . | max_buffering_delay_usecs int64 — OPTIONALMaximal delay in microseconds to wait for min_batch_size_records to
get buffered by the controller, defaults to 0. | min_batch_size_records int64 — OPTIONALMinimal input batch size. The controller delays pushing input records to the circuit until at
least min_batch_size_records records have been received (total
across all endpoints) or max_buffering_delay_usecs microseconds
have passed since at least one input records has been buffered.
Defaults to 0. | min_storage_bytes integer — OPTIONALThe minimum estimated number of bytes in a batch of data to write it to
storage. This is provided for debugging and fine-tuning and should
ordinarily be left unset. It only has an effect when storage is set to
true. A value of 0 will write even empty batches to storage, and nonzero
values provide a threshold. usize::MAX would effectively disable
storage. | resources object — OPTIONALcpu_cores_max int64 — OPTIONALThe maximum number of CPU cores to reserve
for an instance of this pipeline | cpu_cores_min int64 — OPTIONALThe minimum number of CPU cores to reserve
for an instance of this pipeline | memory_mb_max int64 — OPTIONALThe maximum memory in Megabytes to reserve
for an instance of this pipeline | memory_mb_min int64 — OPTIONALThe minimum memory in Megabytes to reserve
for an instance of this pipeline | storage_class string — OPTIONALStorage class to use for an instance of this pipeline.
The class determines storage performance such as IOPS and throughput. | storage_mb_max int64 — OPTIONALThe total storage in Megabytes to reserve
for an instance of this pipeline |
| storage boolean — OPTIONALShould persistent storage be enabled for this pipeline?
-
If false (default), the pipeline's state is kept in in-memory data-structures.
This is useful if the pipeline is ephemeral and does not need to be recovered
after a restart. The pipeline will most likely run faster since it does not
need to read from, or write to disk
-
If true , the pipeline state is stored in the specified location,
is persisted across restarts, and can be checkpointed and recovered.
This feature is currently experimental.
| tracing boolean — OPTIONAL | tracing_endpoint_jaeger string — OPTIONALJaeger tracing endpoint to send tracing information to. | workers int32 — OPTIONALNumber of DBSP worker threads. |
| version int64 |
|