feldera package

feldera.pipeline_builder module

class feldera.pipeline_builder.PipelineBuilder(client: ~feldera.rest.feldera_client.FelderaClient, name: str, sql: str, udf_rust: str = '', udf_toml: str = '', description: str = '', compilation_profile: ~feldera.enums.CompilationProfile = CompilationProfile.OPTIMIZED, runtime_config: ~feldera.runtime_config.RuntimeConfig = <feldera.runtime_config.RuntimeConfig object>)

Bases: object

A builder for creating a Feldera Pipeline.

Parameters:

• client – The .FelderaClient instance

• name – The name of the pipeline

• description – The description of the pipeline

• sql – The SQL code of the pipeline

• udf_rust – Rust code for UDFs

• udf_toml – Rust dependencies required by UDFs (in the TOML format)

• compilation_profile – The compilation profile to use

• runtime_config – The runtime config to use

create() → Pipeline

Create the pipeline if it does not exist.

Returns:

The created pipeline

create_or_replace() → Pipeline

Creates a pipeline if it does not exist and replaces it if it exists.

If the pipeline exists and is running, it will be stopped and replaced.

feldera.pipeline module

class feldera.pipeline.Pipeline(name: str, client: FelderaClient)

Bases: object

delete()

Deletes the pipeline.

execute(query: str)

Executes an ad-hoc SQL query on the current pipeline, discarding its result. Unlike the query() method which returns a generator for retrieving query results lazily, this method processes the query eagerly and fully before returning.

This method is suitable for SQL operations like INSERT and DELETE, where the user needs confirmation of successful query execution, but does not require the query result. If the query fails, an exception will be raised.

Parameters:

query – The SQL query to be executed.

foreach_chunk(view_name: str, callback: Callable[[DataFrame, int], None])

Run the given callback on each chunk of the output of the specified view.

Parameters:

• view_name – The name of the view.

• callback –

  The callback to run on each chunk. The callback should take two arguments:

  • chunk -> The chunk as a pandas DataFrame

  • seq_no -> The sequence number. The sequence number is a monotonically increasing integer that starts from 0. Note that the sequence number is unique for each chunk, but not necessarily contiguous.

Please note that the callback is run in a separate thread, so it should be thread-safe. Please note that the callback should not block for a long time, as by default, backpressure is enabled and will block the pipeline.

Note

• The callback must be thread-safe as it will be run in a separate thread.

static get(name: str, client: FelderaClient) → Pipeline

Get the pipeline if it exists.

Parameters:

• name – The name of the pipeline.

• client – The FelderaClient instance.

input_json(table_name: str, data: Dict | list, update_format: str = 'raw', force: bool = False)

Push this JSON data to the specified table of the pipeline.

Parameters:

• table_name – The name of the table to push data into.

• data – The JSON encoded data to be pushed to the pipeline. The data should be in the form: {‘col1’: ‘val1’, ‘col2’: ‘val2’} or [{‘col1’: ‘val1’, ‘col2’: ‘val2’}, {‘col1’: ‘val1’, ‘col2’: ‘val2’}]

• update_format – The update format of the JSON data to be pushed to the pipeline. Must be one of: “raw”, “insert_delete”. <https://docs.feldera.com/formats/json#the-insertdelete-format>

• force – True to push data even if the pipeline is paused. False by default.

input_pandas(table_name: str, df: DataFrame, force: bool = False)

Push all rows in a pandas DataFrame to the pipeline.

Parameters:

• table_name – The name of the table to insert data into.

• df – The pandas DataFrame to be pushed to the pipeline.

• force – True to push data even if the pipeline is paused. False by default.

listen(view_name: str) → OutputHandler

Listen to the output of the provided view so that it is available in the notebook / python code. When the pipeline is shutdown, these listeners are dropped.

Parameters:

view_name – The name of the view to listen to.

pause()

Pause the pipeline.

query(query: str) → Generator[Mapping[str, Any], None, None]

Executes an ad-hoc SQL query on this pipeline and returns the result in the specified format. For INSERT and DELETE queries, consider using execute() instead.

Important:

This method is lazy. It returns a generator and is not evaluated until you consume the result.

Parameters:

query – The SQL query to be executed.

Returns:

A generator that yields the rows of the result as Python dictionaries.

query_parquet(query: str, path: str)

Executes an ad-hoc SQL query on this pipeline and saves the result to the specified path as a parquet file. If the extension isn’t parquet, it will be automatically appended to path.

Parameters:

• query – The SQL query to be executed.

• path – The path of the parquet file.

query_tabular(query: str) → Generator[str, None, None]

Executes a SQL query on this pipeline and returns the result as a formatted string.

Important:

This method is lazy. It returns a generator and is not evaluated until you consume the result.

Parameters:

query – The SQL query to be executed.

Returns:

A generator that yields a string representing the query result in a human-readable, tabular format.

restart()

Restarts the pipeline.

resume()

Resumes the pipeline.

shutdown()

Shut down the pipeline.

start()

Starts this pipeline.

Raises:

RuntimeError – If the pipeline returns unknown metrics.

status() → PipelineStatus

Return the current status of the pipeline.

wait_for_completion(shutdown: bool = False)

Block until the pipeline has completed processing all input records.

This method blocks until (1) all input connectors attached to the pipeline have finished reading their input data sources and issued end-of-input notifications to the pipeline, and (2) all inputs received from these connectors have been fully processed and corresponding outputs have been sent out through the output connectors.

This method will block indefinitely if at least one of the input connectors attached to the pipeline is a streaming connector, such as Kafka, that does not issue the end-of-input notification.

Parameters:

shutdown – If True, the pipeline will be shutdown after completion. False by default.

Raises:

RuntimeError – If the pipeline returns unknown metrics.

wait_for_idle(idle_interval_s: float = 5.0, timeout_s: float = 600.0, poll_interval_s: float = 0.2)

Wait for the pipeline to become idle and then returns.

Idle is defined as a sufficiently long interval in which the number of input and processed records reported by the pipeline do not change, and they equal each other (thus, all input records present at the pipeline have been processed).

Parameters:

• idle_interval_s – Idle interval duration (default is 5.0 seconds).

• timeout_s – Timeout waiting for idle (default is 600.0 seconds).

• poll_interval_s – Polling interval, should be set substantially smaller than the idle interval (default is 0.2 seconds).

Raises:

• ValueError – If idle interval is larger than timeout, poll interval is larger than timeout, or poll interval is larger than idle interval.

• RuntimeError – If the metrics are missing or the timeout was reached.

feldera.enums module

class feldera.enums.BuildMode(value)

Bases: Enum

An enumeration.

CREATE = 1

GET = 2

GET_OR_CREATE = 3

class feldera.enums.CompilationProfile(value)

Bases: Enum

The compilation profile to use when compiling the program.

DEV = 'dev'

The development compilation profile.

OPTIMIZED = 'optimized'

The optimized compilation profile, the default for this API.

SERVER_DEFAULT = None

The compiler server default compilation profile.

UNOPTIMIZED = 'unoptimized'

The unoptimized compilation profile.

class feldera.enums.PipelineStatus(value)

Bases: Enum

Represents the state that this pipeline is currently in.

Shutdown     ◄────┐
│         │
/deploy   │       │
│   ⌛ShuttingDown
▼         ▲
⌛Provisioning    │
│         │
Provisioned        │
▼         │/shutdown
⌛Initializing     │
│        │
┌────────┴─────────┴─┐
│        ▼           │
│      Paused        │
│      │    ▲        │
│/start│    │/pause  │
│      ▼    │        │
│     Running        │
└──────────┬─────────┘
           │
           ▼
        Failed

FAILED = 8

The pipeline remains in this state until the users acknowledge the failure by issuing a call to shutdown the pipeline; transitions to the PipelineStatus.SHUTDOWN state.

INITIALIZING = 4

The pipeline is initializing its internal state and connectors.

This state is part of the pipeline’s deployment process. In this state, the pipeline’s HTTP server is up and running, but its query engine and input and output connectors are still initializing.

The pipeline remains in this state until:

1. Initialization completes successfully; the pipeline transitions to the PipelineStatus.PAUSED state.

2. Initialization fails; transitions to the PipelineStatus.FAILED state.

3. A pre-defined timeout has passed. The runner performs forced shutdown of the pipeline; returns to the PipelineStatus.SHUTDOWN state.

4. The user cancels the pipeline by invoking the /shutdown endpoint. The manager performs forced shutdown of the pipeline; returns to the PipelineStatus.SHUTDOWN state.

NOT_FOUND = 1

The pipeline has not been created yet.

PAUSED = 5

The pipeline is fully initialized, but data processing has been paused.

The pipeline remains in this state until:

1. The user starts the pipeline by invoking the /start endpoint. The manager passes the request to the pipeline; transitions to the PipelineStatus.RUNNING state.

2. The user cancels the pipeline by invoking the /shutdown endpoint. The manager passes the shutdown request to the pipeline to perform a graceful shutdown; transitions to the PipelineStatus.SHUTTING_DOWN state.

3. An unexpected runtime error renders the pipeline PipelineStatus.FAILED.

PROVISIONING = 3

The runner triggered a deployment of the pipeline and is waiting for the pipeline HTTP server to come up.

In this state, the runner provisions a runtime for the pipeline, starts the pipeline within this runtime and waits for it to start accepting HTTP requests.

The user is unable to communicate with the pipeline during this time. The pipeline remains in this state until:

1. Its HTTP server is up and running; the pipeline transitions to the PipelineStatus.INITIALIZING state.

2. A pre-defined timeout has passed. The runner performs forced shutdown of the pipeline; returns to the PipelineStatus.SHUTDOWN state.

3. The user cancels the pipeline by invoking the /shutdown endpoint. The manager performs forced shutdown of the pipeline, returns to the PipelineStatus.SHUTDOWN state.

RUNNING = 6

The pipeline is processing data.

The pipeline remains in this state until:

1. The user pauses the pipeline by invoking the /pause endpoint. The manager passes the request to the pipeline; transitions to the PipelineStatus.PAUSED state.

2. The user cancels the pipeline by invoking the /shutdown endpoint. The runner passes the shutdown request to the pipeline to perform a graceful shutdown; transitions to the PipelineStatus.SHUTTING_DOWN state.

3. An unexpected runtime error renders the pipeline PipelineStatus.FAILED.

SHUTDOWN = 2

Pipeline has not been started or has been shut down.

The pipeline remains in this state until the user triggers a deployment by invoking the /deploy endpoint.

SHUTTING_DOWN = 7

Graceful shutdown in progress.

In this state, the pipeline finishes any ongoing data processing, produces final outputs, shuts down input/output connectors and terminates.

The pipeline remains in this state until:

1. Shutdown completes successfully; transitions to the PipelineStatus.SHUTDOWN state.

2. A pre-defined timeout has passed. The manager performs forced shutdown of the pipeline; returns to the PipelineStatus.SHUTDOWN state.

UNAVAILABLE = 9

The pipeline was at least once initialized, but in the most recent status check either could not be reached or returned it is not yet ready.

static from_str(value)

feldera.output_handler module

class feldera.output_handler.OutputHandler(client: FelderaClient, pipeline_name: str, view_name: str, queue: Queue | None)

Bases: object

start()

Starts the output handler in a separate thread

to_dict(clear_buffer: bool = True)

Returns the output of the pipeline as a list of python dictionaries

Parameters:

clear_buffer – Whether to clear the buffer after getting the output.

to_pandas(clear_buffer: bool = True)

Returns the output of the pipeline as a pandas DataFrame

Parameters:

clear_buffer – Whether to clear the buffer after getting the output.

feldera.runtime_config module

class feldera.runtime_config.Resources(config: Mapping[str, Any] | None = None, cpu_cores_max: int | None = None, cpu_cores_min: int | None = None, memory_mb_max: int | None = None, memory_mb_min: int | None = None, storage_class: str | None = None, storage_mb_max: int | None = None)

Bases: object

Class used to specify the resource configuration for a pipeline.

Parameters:

• config – A dictionary containing all the configuration values.

• cpu_cores_max – The maximum number of CPU cores to reserve for an instance of the pipeline.

• cpu_cores_min – The minimum number of CPU cores to reserve for an instance of the pipeline.

• memory_mb_max – The maximum memory in Megabytes to reserve for an instance of the pipeline.

• memory_mb_min – The minimum memory in Megabytes to reserve for an instance of the pipeline.

• storage_class – The storage class to use for the pipeline. The class determines storage performance such as IOPS and throughput.

• storage_mb_max – The storage in Megabytes to reserve for an instance of the pipeline.

class feldera.runtime_config.RuntimeConfig(workers: int | None = None, storage: bool | None = False, tracing: bool | None = False, tracing_endpoint_jaeger: str | None = '', cpu_profiler: bool = True, max_buffering_delay_usecs: int = 0, min_batch_size_records: int = 0, min_storage_bytes: int | None = None, clock_resolution_usecs: int | None = None, resources: Resources | None = None)

Bases: object

Runtime configuration class to define the configuration for a pipeline.

classmethod from_dict(d: Mapping[str, Any])

Create a .RuntimeConfig object from a dictionary.

Subpackages

• feldera.rest package
  • Submodules
  • feldera.rest.config module
    • Config
  • feldera.rest.errors module
    • FelderaAPIError
    • FelderaCommunicationError
    • FelderaError
    • FelderaTimeoutError
  • feldera.rest.feldera_client module
    • FelderaClient
      • FelderaClient.checkpoint_pipeline()
      • FelderaClient.create_or_update_pipeline()
      • FelderaClient.create_pipeline()
      • FelderaClient.delete_pipeline()
      • FelderaClient.get_pipeline()
      • FelderaClient.get_pipeline_stats()
      • FelderaClient.get_runtime_config()
      • FelderaClient.listen_to_pipeline()
      • FelderaClient.patch_pipeline()
      • FelderaClient.pause_pipeline()
      • FelderaClient.pipelines()
      • FelderaClient.push_to_pipeline()
      • FelderaClient.query_as_json()
      • FelderaClient.query_as_parquet()
      • FelderaClient.query_as_text()
      • FelderaClient.shutdown_pipeline()
      • FelderaClient.start_pipeline()
  • feldera.rest.pipeline module
    • Pipeline
      • Pipeline.from_dict()
  • feldera.rest.sql_table module
    • SQLTable
      • SQLTable.from_dict()
  • feldera.rest.sql_view module
    • SQLView
      • SQLView.from_dict()
  • Module contents