import logging
import time
from datetime import datetime
import pathlib
import pandas
from uuid import UUID
from typing import List, Dict, Callable, Optional, Generator, Mapping, Any
from collections import deque
from feldera.rest.errors import FelderaAPIError
from feldera.enums import (
BootstrapPolicy,
PipelineFieldSelector,
PipelineStatus,
ProgramStatus,
CheckpointStatus,
TransactionStatus,
StorageStatus,
DeploymentDesiredStatus,
DeploymentResourcesDesiredStatus,
DeploymentResourcesStatus,
DeploymentRuntimeDesiredStatus,
DeploymentRuntimeStatus,
)
from feldera.rest.pipeline import Pipeline as InnerPipeline
from feldera.rest.feldera_client import FelderaClient
from feldera._callback_runner import CallbackRunner
from feldera.output_handler import OutputHandler
from feldera._helpers import ensure_dataframe_has_columns, chunk_dataframe
from feldera.rest.sql_table import SQLTable
from feldera.rest.sql_view import SQLView
from feldera.runtime_config import RuntimeConfig
from feldera.stats import PipelineStatistics
[docs]
class Pipeline:
def __init__(self, client: FelderaClient):
self.client: FelderaClient = client
self._inner: InnerPipeline | None = None
@staticmethod
def _from_inner(inner: InnerPipeline, client: FelderaClient) -> "Pipeline":
pipeline = Pipeline(client)
pipeline._inner = inner
return pipeline
[docs]
def refresh(self, field_selector: PipelineFieldSelector):
"""
Calls the backend to get the updated, latest version of the pipeline.
:param field_selector: Choose what pipeline information to refresh; see PipelineFieldSelector enum definition.
:raises FelderaConnectionError: If there is an issue connecting to the backend.
"""
self._inner = self.client.get_pipeline(self.name, field_selector)
[docs]
def status(self) -> PipelineStatus:
"""
Return the current status of the pipeline.
"""
try:
self.refresh(PipelineFieldSelector.STATUS)
return PipelineStatus.from_str(self._inner.deployment_status)
except FelderaAPIError as err:
if err.status_code == 404:
return PipelineStatus.NOT_FOUND
else:
raise err
[docs]
def wait_for_status(
self, expected_status: PipelineStatus, timeout: Optional[int] = None
) -> None:
"""
Wait for the pipeline to reach the specified status.
:param expected_status: The status to wait for
:param timeout: Maximum time to wait in seconds. If None, waits forever (default: None)
:raises TimeoutError: If the expected status is not reached within the timeout
"""
start_time = time.time()
while True:
current_status = self.status()
if current_status == expected_status:
return
if timeout is not None and time.time() - start_time >= timeout:
raise TimeoutError(
f"Pipeline did not reach {expected_status.name} status within {timeout} seconds"
)
time.sleep(1)
[docs]
def stats(self) -> PipelineStatistics:
"""Gets the pipeline metrics and performance counters."""
return PipelineStatistics.from_dict(self.client.get_pipeline_stats(self.name))
[docs]
def logs(self) -> Generator[str, None, None]:
"""Gets the pipeline logs."""
return self.client.get_pipeline_logs(self.name)
[docs]
def pause_connector(self, table_name: str, connector_name: str):
"""
Pause the specified input connector.
Connectors allow feldera to fetch data from a source or write data to a sink.
This method allows users to **PAUSE** a specific **INPUT** connector.
All connectors are RUNNING by default.
Refer to the connector documentation for more information:
https://docs.feldera.com/connectors/#input-connector-orchestration
:param table_name: The name of the table that the connector is attached to.
:param connector_name: The name of the connector to pause.
:raises FelderaAPIError: If the connector is not found, or if the pipeline is not running.
"""
self.client.pause_connector(self.name, table_name, connector_name)
[docs]
def resume_connector(self, table_name: str, connector_name: str):
"""
Resume the specified connector.
Connectors allow feldera to fetch data from a source or write data to a sink.
This method allows users to **RESUME / START** a specific **INPUT** connector.
All connectors are RUNNING by default.
Refer to the connector documentation for more information:
https://docs.feldera.com/connectors/#input-connector-orchestration
:param table_name: The name of the table that the connector is attached to.
:param connector_name: The name of the connector to resume.
:raises FelderaAPIError: If the connector is not found, or if the pipeline is not running.
"""
self.client.resume_connector(self.name, table_name, connector_name)
[docs]
def listen(self, view_name: str) -> OutputHandler:
"""
Follow the change stream (i.e., the output) of the provided view.
Returns an output handle to read the changes.
When the pipeline is stopped, the handle is dropped.
The handle will only receive changes from the point in time when the listener is created.
In order to receive all changes since the pipeline started, you can create the pipeline in the `PAUSED` state
using :meth:`start_paused`, attach listeners and unpause the pipeline using :meth:`resume`.
:param view_name: The name of the view to listen to.
"""
if self.status() not in [PipelineStatus.PAUSED, PipelineStatus.RUNNING]:
raise RuntimeError("Pipeline must be running or paused to listen to output")
handler = OutputHandler(self.client, self.name, view_name)
handler.start()
return handler
[docs]
def foreach_chunk(
self, view_name: str, callback: Callable[[pandas.DataFrame, int], None]
):
"""
Run the given callback on each chunk of the output of the specified view.
The callback will only receive changes from the point in time when the listener is created.
In order to receive all changes since the pipeline started, you can create the pipeline in the `PAUSED` state
using :meth:`start_paused`, attach listeners and unpause the pipeline using :meth:`resume`.
:param view_name: The name of the view.
:param 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.
"""
if self.status() not in [PipelineStatus.RUNNING, PipelineStatus.PAUSED]:
raise RuntimeError("Pipeline must be running or paused to listen to output")
handler = CallbackRunner(
self.client, self.name, view_name, callback, lambda exception: None
)
handler.start()
[docs]
def wait_for_completion(
self, force_stop: bool = False, timeout_s: float | None = None
):
"""
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.
:param force_stop: If True, the pipeline will be forcibly stopped after
completion. False by default. No checkpoints will be made.
:param timeout_s: Optional. The maximum time (in seconds) to wait for
the pipeline to complete. The default is None, which means wait
indefinitely.
:raises RuntimeError: If the pipeline returns unknown metrics.
"""
if self.status() not in [
PipelineStatus.RUNNING,
PipelineStatus.INITIALIZING,
PipelineStatus.PROVISIONING,
PipelineStatus.BOOTSTRAPPING,
]:
raise RuntimeError("Pipeline must be running to wait for completion")
start_time = time.monotonic()
while True:
if timeout_s is not None:
elapsed = time.monotonic() - start_time
if elapsed > timeout_s:
raise TimeoutError(
f"timeout ({timeout_s}s) reached while waiting for"
f" pipeline '{self.name}' to complete"
)
logging.debug(
f"waiting for pipeline {self.name} to complete: elapsed"
f" time {elapsed}s, timeout: {timeout_s}s"
)
pipeline_complete: bool = self.is_complete()
if pipeline_complete is None:
raise RuntimeError(
"received unknown metrics from the pipeline, pipeline_complete is None"
)
elif pipeline_complete:
break
time.sleep(1)
if force_stop:
self.stop(force=True)
[docs]
def is_complete(self) -> bool:
"""
Check if the pipeline has completed processing all input records.
Returns True if (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.
"""
return self.stats().global_metrics.pipeline_complete
[docs]
def wait_for_idle(
self,
idle_interval_s: float = 5.0,
timeout_s: float | None = None,
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).
:param idle_interval_s: Idle interval duration (default is 5.0 seconds).
:param timeout_s: Timeout waiting for idle (default is 600.0 seconds).
:param 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.
:raises RuntimeError: If the metrics are missing or the timeout was
reached.
"""
if timeout_s is not None and idle_interval_s > timeout_s:
raise ValueError(
f"idle interval ({idle_interval_s}s) cannot be larger than"
f" timeout ({timeout_s}s)"
)
if timeout_s is not None and poll_interval_s > timeout_s:
raise ValueError(
f"poll interval ({poll_interval_s}s) cannot be larger than"
f" timeout ({timeout_s}s)"
)
if poll_interval_s > idle_interval_s:
raise ValueError(
f"poll interval ({poll_interval_s}s) cannot be larger "
f"than idle interval ({idle_interval_s}s)"
)
start_time_s = time.monotonic()
idle_started_s = None
prev = (0, 0)
while True:
now_s = time.monotonic()
# Metrics retrieval
metrics = self.stats().global_metrics
total_input_records = metrics.total_input_records
total_processed_records = metrics.total_processed_records
if metrics.total_input_records is None:
raise RuntimeError(
"total_input_records is missing from the pipeline metrics"
)
if metrics.total_processed_records is None:
raise RuntimeError(
"""total_processed_records is missing from the pipeline \
metrics"""
)
# Idle check
unchanged = (
prev[0] == total_input_records and prev[1] == total_processed_records
)
equal = total_input_records == total_processed_records
prev = (total_input_records, total_processed_records)
if unchanged and equal:
if idle_started_s is None:
idle_started_s = now_s
else:
idle_started_s = None
if idle_started_s is not None and now_s - idle_started_s >= idle_interval_s:
return
# Timeout
if timeout_s is not None and now_s - start_time_s >= timeout_s:
raise RuntimeError(f"waiting for idle reached timeout ({timeout_s}s)")
time.sleep(poll_interval_s)
[docs]
def activate(
self, wait: bool = True, timeout_s: Optional[float] = None
) -> Optional[PipelineStatus]:
"""
Activates the pipeline when starting from STANDBY mode. Only applicable
when the pipeline is starting from a checkpoint in object store.
:param wait: Set True to wait for the pipeline to activate. True by
default
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to pause.
"""
return self.client.activate_pipeline(self.name, wait=wait, timeout_s=timeout_s)
[docs]
def start(
self,
bootstrap_policy: Optional[BootstrapPolicy] = None,
wait: bool = True,
timeout_s: Optional[float] = None,
):
"""
.. _start:
Starts this pipeline.
- The pipeline must be in STOPPED state to start.
- If the pipeline is in any other state, an error will be raised.
- If the pipeline is in PAUSED state, use `.meth:resume` instead.
:param bootstrap_policy: The bootstrap policy to use.
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to start.
:param wait: Set True to wait for the pipeline to start. True by default
:raises RuntimeError: If the pipeline is not in STOPPED state.
"""
self.client.start_pipeline(
self.name, bootstrap_policy=bootstrap_policy, wait=wait, timeout_s=timeout_s
)
[docs]
def start_paused(
self,
bootstrap_policy: Optional[BootstrapPolicy] = None,
wait: bool = True,
timeout_s: Optional[float] = None,
):
"""
Starts the pipeline in the paused state.
"""
return self.client.start_pipeline_as_paused(
self.name, bootstrap_policy=bootstrap_policy, wait=wait, timeout_s=timeout_s
)
[docs]
def start_standby(
self,
bootstrap_policy: Optional[BootstrapPolicy] = None,
wait: bool = True,
timeout_s: Optional[float] = None,
):
"""
Starts the pipeline in the standby state.
"""
self.client.start_pipeline_as_standby(
self.name, bootstrap_policy=bootstrap_policy, wait=wait, timeout_s=timeout_s
)
[docs]
def restart(
self,
bootstrap_policy: Optional[BootstrapPolicy] = None,
timeout_s: Optional[float] = None,
):
"""
Restarts the pipeline.
This method forcibly **STOPS** the pipeline regardless of its current
state and then starts it again. No checkpoints are made when stopping
the pipeline.
:param bootstrap_policy: The bootstrap policy to use.
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to restart.
"""
self.stop(force=True, timeout_s=timeout_s)
self.start(bootstrap_policy=bootstrap_policy, timeout_s=timeout_s)
[docs]
def pause(self, wait: bool = True, timeout_s: Optional[float] = None):
"""
Pause the pipeline.
The pipeline can only transition to the PAUSED state from the RUNNING
state. If the pipeline is already paused, it will remain in the PAUSED
state.
:param wait: Set True to wait for the pipeline to pause. True by default
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to pause.
"""
self.client.pause_pipeline(self.name, wait=wait, timeout_s=timeout_s)
[docs]
def stop(self, force: bool, wait: bool = True, timeout_s: Optional[float] = None):
"""
Stops the pipeline.
Stops the pipeline regardless of its current state.
:param force: Set True to immediately scale compute resources to zero.
Set False to automatically checkpoint before stopping.
:param wait: Set True to gracefully shutdown listeners and wait for the
pipeline to stop. True by default.
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to stop.
"""
self.client.stop_pipeline(
self.name, force=force, wait=wait, timeout_s=timeout_s
)
[docs]
def approve(self):
"""
Approves the pipeline to proceed with bootstrapping.
This method is used when a pipeline has been started with
`bootstrap_policy=BootstrapPolicy.AWAIT_APPROVAL` and is currently in the
AWAITINGAPPROVAL state. The pipeline will wait for explicit user approval
before proceeding with the bootstrapping process.
"""
self.client.approve_pipeline(self.name)
[docs]
def resume(self, wait: bool = True, timeout_s: Optional[float] = None):
"""
Resumes the pipeline from the PAUSED state. If the pipeline is already
running, it will remain in the RUNNING state.
:param wait: Set True to wait for the pipeline to resume. True by default
:param timeout_s: The maximum time (in seconds) to wait for the
pipeline to resume.
"""
self.client.resume_pipeline(self.name, wait=wait, timeout_s=timeout_s)
[docs]
def start_transaction(self) -> int:
"""
Start a new transaction.
:return: Transaction ID.
:raises FelderaAPIError: If the pipeline fails to start a transaction, e.g., if the pipeline is not running or
there is already an active transaction.
"""
return self.client.start_transaction(self.name)
[docs]
def commit_transaction(
self,
transaction_id: Optional[int] = None,
wait: bool = True,
timeout_s: Optional[float] = None,
):
"""
Commit the currently active transaction.
:param transaction_id: If provided, the function verifies that the currently active transaction matches this ID.
If the active transaction ID does not match, the function raises an error.
:param wait: If True, the function blocks until the transaction either commits successfully or the timeout is reached.
If False, the function initiates the commit and returns immediately without waiting for completion. The default value is True.
:param timeout_s: Maximum time (in seconds) to wait for the transaction to commit when `wait` is True.
If None, the function will wait indefinitely.
:raises RuntimeError: If there is currently no transaction in progress.
:raises ValueError: If the provided `transaction_id` does not match the current transaction.
:raises TimeoutError: If the transaction does not commit within the specified timeout (when `wait` is True).
:raises FelderaAPIError: If the pipeline fails to commit a transaction.
"""
self.client.commit_transaction(self.name, transaction_id, wait, timeout_s)
[docs]
def transaction_status(self) -> TransactionStatus:
"""
Get pipeline's transaction handling status.
:return: Current transaction handling status of the pipeline.
:raises FelderaAPIError: If pipeline's status couldn't be read, e.g., because the pipeline is not currently running.
"""
return self.stats().global_metrics.transaction_status
[docs]
def transaction_id(self) -> Optional[int]:
"""
Gets the ID of the currently active transaction or None if there is no active transaction.
:return: The ID of the transaction.
"""
transaction_id = self.stats().global_metrics.transaction_id
if transaction_id == 0:
return None
else:
return transaction_id
[docs]
def delete(self, clear_storage: bool = False):
"""
Deletes the pipeline.
The pipeline must be stopped, and the storage cleared before it can be
deleted.
:param clear_storage: True if the storage should be cleared before
deletion. False by default
:raises FelderaAPIError: If the pipeline is not in STOPPED state or the
storage is still bound.
"""
if clear_storage:
self.clear_storage()
self.client.delete_pipeline(self.name)
[docs]
@staticmethod
def get(name: str, client: FelderaClient) -> "Pipeline":
"""
Get the pipeline if it exists.
:param name: The name of the pipeline.
:param client: The FelderaClient instance.
"""
try:
inner = client.get_pipeline(name, PipelineFieldSelector.ALL)
return Pipeline._from_inner(inner, client)
except FelderaAPIError as err:
if err.status_code == 404:
err.message = f"Pipeline with name {name} not found"
raise err
[docs]
def checkpoint(self, wait: bool = False, timeout_s: Optional[float] = None) -> int:
"""
Checkpoints this pipeline.
:param wait: If true, will block until the checkpoint completes.
:param timeout_s: The maximum time (in seconds) to wait for the
checkpoint to complete.
:return: The checkpoint sequence number.
:raises FelderaAPIError: If enterprise features are not enabled.
"""
seq = self.client.checkpoint_pipeline(self.name)
if not wait:
return seq
start = time.time()
while True:
elapsed = time.monotonic() - start
if timeout_s is not None and elapsed > timeout_s:
raise TimeoutError(
f"""timeout ({timeout_s}s) reached while waiting for \
pipeline '{self.name}' to make checkpoint '{seq}'"""
)
status = self.checkpoint_status(seq)
if status == CheckpointStatus.InProgress:
time.sleep(0.1)
continue
return seq
[docs]
def checkpoint_status(self, seq: int) -> CheckpointStatus:
"""
Checks the status of the given checkpoint.
:param seq: The checkpoint sequence number.
"""
resp = self.client.checkpoint_pipeline_status(self.name)
success = resp.get("success")
if seq == success:
return CheckpointStatus.Success
fail = resp.get("failure") or {}
if seq == fail.get("sequence_number"):
failure = CheckpointStatus.Failure
failure.error = fail.get("error", "")
return failure
if (success is None) or seq > success:
return CheckpointStatus.InProgress
if seq < success:
return CheckpointStatus.Unknown
[docs]
def sync_checkpoint(
self, wait: bool = False, timeout_s: Optional[float] = None
) -> str:
"""
Syncs this checkpoint to object store.
:param wait: If true, will block until the checkpoint sync operation
completes.
:param timeout_s: The maximum time (in seconds) to wait for the
checkpoint to complete syncing.
:raises FelderaAPIError: If no checkpoints have been made.
:raises RuntimeError: If syncing the checkpoint fails.
"""
uuid = self.client.sync_checkpoint(self.name)
if not wait:
return uuid
start = time.time()
while True:
elapsed = time.monotonic() - start
if timeout_s is not None and elapsed > timeout_s:
raise TimeoutError(
f"""timeout ({timeout_s}s) reached while waiting for \
pipeline '{self.name}' to sync checkpoint '{uuid}'"""
)
status = self.sync_checkpoint_status(uuid)
if status == CheckpointStatus.Failure:
raise RuntimeError(
f"failed to sync checkpoint '{uuid}': ", status.get_error()
)
if status in [CheckpointStatus.InProgress, CheckpointStatus.Unknown]:
time.sleep(0.1)
continue
break
return uuid
[docs]
def sync_checkpoint_status(self, uuid: str) -> CheckpointStatus:
"""
Checks the status of the given checkpoint sync operation.
If the checkpoint is currently being synchronized, returns
`CheckpointStatus.Unknown`.
Failures are not raised as runtime errors and must be explicitly
checked.
:param uuid: The checkpoint uuid.
"""
resp = self.client.sync_checkpoint_status(self.name)
success = resp.get("success")
fail = resp.get("failure") or {}
if uuid == success:
return CheckpointStatus.Success
fail = resp.get("failure") or {}
if uuid == fail.get("uuid"):
failure = CheckpointStatus.Failure
failure.error = fail.get("error", "")
logging.error(f"failed to sync checkpoint '{uuid}': {failure.error}")
return failure
if (success is None) or UUID(uuid) > UUID(success):
return CheckpointStatus.InProgress
return CheckpointStatus.Unknown
[docs]
def query(self, query: str) -> Generator[Mapping[str, Any], None, None]:
"""
Executes an ad-hoc SQL query on this pipeline and returns a generator
that yields the rows of the result as Python dictionaries. For
``INSERT`` and ``DELETE`` queries, consider using :meth:`.execute`
instead. All floating-point numbers are deserialized as Decimal objects
to avoid precision loss.
Note:
You can only ``SELECT`` from materialized tables and views.
Important:
This method is lazy. It returns a generator and is not evaluated
until you consume the result.
:param query: The SQL query to be executed.
:return: A generator that yields the rows of the result as Python
dictionaries.
:raises FelderaAPIError: If the pipeline is not in a RUNNING or PAUSED
state.
:raises FelderaAPIError: If querying a non materialized table or view.
:raises FelderaAPIError: If the query is invalid.
"""
return self.client.query_as_json(self.name, query)
[docs]
def query_parquet(self, 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`.
Note:
You can only ``SELECT`` from materialized tables and views.
:param query: The SQL query to be executed.
:param path: The path of the parquet file.
:raises FelderaAPIError: If the pipeline is not in a RUNNING or PAUSED
state.
:raises FelderaAPIError: If querying a non materialized table or view.
:raises FelderaAPIError: If the query is invalid.
"""
self.client.query_as_parquet(self.name, query, path)
[docs]
def query_tabular(self, query: str) -> Generator[str, None, None]:
"""
Executes a SQL query on this pipeline and returns the result as a
formatted string.
Note:
You can only ``SELECT`` from materialized tables and views.
Important:
This method is lazy. It returns a generator and is not evaluated
until you consume the result.
:param query: The SQL query to be executed.
:return: A generator that yields a string representing the query result
in a human-readable, tabular format.
:raises FelderaAPIError: If the pipeline is not in a RUNNING or PAUSED
state.
:raises FelderaAPIError: If querying a non materialized table or view.
:raises FelderaAPIError: If the query is invalid.
"""
return self.client.query_as_text(self.name, query)
[docs]
def query_hash(self, query: str):
"""
Executes an ad-hoc SQL query on this pipeline and returns the result
as a hash of the result set. This is useful for quickly checking
if the result set has changed without retrieving the entire result.
Note:
For a stable hash, the query must be deterministic which means
it should be sorted.
:param query: The SQL query to be executed.
:raises FelderaAPIError: If the pipeline is not in a RUNNING or PAUSED
state.
:raises FelderaAPIError: If querying a non materialized table or view.
:raises FelderaAPIError: If the query is invalid.
"""
return self.client.query_as_hash(self.name, query)
[docs]
def execute(self, query: str):
"""
Executes an ad-hoc SQL query on the current pipeline, discarding its
result. Unlike the :meth:`.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.
Important:
If you try to ``INSERT`` or ``DELETE`` data from a table while the
pipeline is paused, it will block until the pipeline is resumed.
:param query: The SQL query to be executed.
:raises FelderaAPIError: If the pipeline is not in a RUNNING state.
:raises FelderaAPIError: If the query is invalid.
"""
gen = self.query_tabular(query)
deque(gen, maxlen=0)
[docs]
def clear_storage(self):
"""
Clears the storage of the pipeline if it is currently in use.
This action cannot be canceled, and will delete all the pipeline
storage.
"""
if self.storage_status() == StorageStatus.INUSE:
self.client.clear_storage(self.name)
@property
def name(self) -> str:
"""
Return the name of the pipeline.
"""
return self._inner.name
[docs]
def program_code(self) -> str:
"""
Return the program SQL code of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.program_code
[docs]
def modify(
self,
sql: Optional[str] = None,
udf_rust: Optional[str] = None,
udf_toml: Optional[str] = None,
program_config: Optional[Mapping[str, Any]] = None,
runtime_config: Optional[Mapping[str, Any]] = None,
description: Optional[str] = None,
):
"""
Modify the pipeline.
Modify the values of pipeline attributes: SQL code, UDF Rust code,
UDF Rust dependencies (TOML), program config, runtime config, and
description. Only the provided attributes will be modified. Other
attributes will remain unchanged.
The pipeline must be in the STOPPED state to be modified.
:raises FelderaAPIError: If the pipeline is not in a STOPPED state.
"""
self.client.patch_pipeline(
name=self._inner.name,
sql=sql,
udf_rust=udf_rust,
udf_toml=udf_toml,
program_config=program_config,
runtime_config=runtime_config,
description=description,
)
[docs]
def update_runtime(self):
"""
Recompile a pipeline with the Feldera runtime version included in the
currently installed Feldera platform.
Use this endpoint after upgrading Feldera to rebuild pipelines that were
compiled with older platform versions. In most cases, recompilation is not
required—pipelines compiled with older versions will continue to run on the
upgraded platform.
Situations where recompilation may be necessary:
- To benefit from the latest bug fixes and performance optimizations.
- When backward-incompatible changes are introduced in Feldera. In this case,
attempting to start a pipeline compiled with an unsupported version will
result in an error.
If the pipeline is already compiled with the current platform version,
this operation is a no-op.
Note that recompiling the pipeline with a new platform version may change its
query plan. If the modified pipeline is started from an existing checkpoint,
it may require bootstrapping parts of its state from scratch. See Feldera
documentation for details on the bootstrapping process.
:raises FelderaAPIError: If the pipeline is not in a STOPPED state.
"""
self.client.update_pipeline_runtime(self._inner.name)
[docs]
def storage_status(self) -> StorageStatus:
"""
Return the storage status of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return StorageStatus.from_str(self._inner.storage_status)
[docs]
def program_status(self) -> ProgramStatus:
"""
Return the program status of the pipeline.
Program status is the status of compilation of this SQL program.
We first compile the SQL program to Rust code, and then compile the
Rust code to a binary.
"""
self.refresh(PipelineFieldSelector.STATUS)
return ProgramStatus.from_value(self._inner.program_status)
[docs]
def program_status_since(self) -> datetime:
"""
Return the timestamp when the current program status was set.
"""
self.refresh(PipelineFieldSelector.STATUS)
return datetime.fromisoformat(self._inner.program_status_since)
[docs]
def udf_rust(self) -> str:
"""
Return the Rust code for UDFs.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.udf_rust
[docs]
def udf_toml(self) -> str:
"""
Return the Rust dependencies required by UDFs (in the TOML format).
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.udf_toml
[docs]
def program_config(self) -> Mapping[str, Any]:
"""
Return the program config of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.program_config
[docs]
def runtime_config(self) -> RuntimeConfig:
"""
Return the runtime config of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return RuntimeConfig.from_dict(self._inner.runtime_config)
[docs]
def set_runtime_config(self, runtime_config: RuntimeConfig):
"""Updates the runtime config of the pipeline. The pipeline
must be stopped. Changing some pipeline configuration, such
as the number of workers, requires storage to be cleared.
For example, to set 'min_batch_size_records' on a pipeline::
runtime_config = pipeline.runtime_config()
runtime_config.min_batch_size_records = 500
pipeline.set_runtime_config(runtime_config)
"""
self.client.patch_pipeline(
name=self._inner.name, runtime_config=runtime_config.to_dict()
)
[docs]
def id(self) -> str:
"""
Return the ID of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.id
[docs]
def description(self) -> str:
"""
Return the description of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.description
[docs]
def tables(self) -> List[SQLTable]:
"""
Return the tables of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.tables
[docs]
def views(self) -> List[SQLView]:
"""
Return the views of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.views
[docs]
def created_at(self) -> datetime:
"""
Return the creation time of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return datetime.fromisoformat(self._inner.created_at)
[docs]
def version(self) -> int:
"""
Return the version of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.version
[docs]
def program_version(self) -> int:
"""
Return the program version of the pipeline.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.program_version
[docs]
def deployment_status_since(self) -> datetime:
"""
Return the timestamp when the current deployment status of the pipeline
was set.
"""
self.refresh(PipelineFieldSelector.STATUS)
return datetime.fromisoformat(self._inner.deployment_status_since)
[docs]
def deployment_config(self) -> Mapping[str, Any]:
"""
Return the deployment config of the pipeline.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.deployment_config
[docs]
def deployment_desired_status(self) -> DeploymentDesiredStatus:
"""
Return the desired deployment status of the pipeline.
This is the next state that the pipeline should transition to.
"""
self.refresh(PipelineFieldSelector.STATUS)
return DeploymentDesiredStatus.from_str(self._inner.deployment_desired_status)
[docs]
def deployment_resources_desired_status(self) -> DeploymentResourcesDesiredStatus:
"""
Return the desired status of the the deployment resources.
"""
self.refresh(PipelineFieldSelector.STATUS)
return DeploymentResourcesDesiredStatus.from_str(
self._inner.deployment_resources_desired_status
)
[docs]
def deployment_resources_status(self) -> DeploymentResourcesStatus:
"""
Return the status of the deployment resources.
"""
self.refresh(PipelineFieldSelector.STATUS)
return DeploymentResourcesStatus.from_str(
self._inner.deployment_resources_status
)
[docs]
def deployment_runtime_desired_status(self) -> DeploymentRuntimeDesiredStatus:
"""
Return the deployment runtime desired status.
"""
self.refresh(PipelineFieldSelector.STATUS)
return DeploymentRuntimeDesiredStatus.from_str(
self._inner.deployment_runtime_desired_status
)
[docs]
def deployment_runtime_status(self) -> DeploymentRuntimeStatus:
"""
Return the deployment runtime status.
"""
self.refresh(PipelineFieldSelector.STATUS)
return DeploymentRuntimeStatus.from_str(self._inner.deployment_runtime_status)
[docs]
def deployment_runtime_status_details(self) -> Optional[dict]:
"""
Return the deployment runtime status details.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.deployment_runtime_status_details
[docs]
def deployment_error(self) -> Mapping[str, Any]:
"""
Return the deployment error of the pipeline.
Returns an empty string if there is no error.
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.deployment_error
[docs]
def deployment_location(self) -> str:
"""
Return the deployment location of the pipeline.
Deployment location is the location where the pipeline can be reached
at runtime (a TCP port number or a URI).
"""
self.refresh(PipelineFieldSelector.STATUS)
return self._inner.deployment_location
[docs]
def program_info(self) -> Mapping[str, Any]:
"""
Return the program info of the pipeline.
This is the output returned by the SQL compiler, including: the list of
input and output connectors, the generated Rust code for the pipeline,
and the SQL program schema.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.program_info
[docs]
def program_error(self) -> Mapping[str, Any]:
"""
Return the program error of the pipeline.
If there are no errors, the `exit_code` field inside both
`sql_compilation` and `rust_compilation` will be 0.
"""
self.refresh(PipelineFieldSelector.ALL)
return self._inner.program_error
[docs]
def errors(self) -> List[Mapping[str, Any]]:
"""
Returns a list of all errors in this pipeline.
"""
errors = []
perr = self.program_error()
for e in perr.keys():
err = perr.get(e)
if err and err.get("exit_code", 0) != 0:
errors.append({e: err})
derr = self.deployment_error()
if derr:
errors.append(derr)
return errors
[docs]
def support_bundle(
self,
output_path: Optional[str] = None,
*,
circuit_profile: bool = True,
heap_profile: bool = True,
metrics: bool = True,
logs: bool = True,
stats: bool = True,
pipeline_config: bool = True,
system_config: bool = True,
) -> bytes:
"""
Generate a support bundle containing diagnostic information from this pipeline.
This method collects various diagnostic data from the pipeline including
circuit profile, heap profile, metrics, logs, stats, and connector statistics,
and packages them into a single ZIP file for support purposes.
:param output_path: Optional path to save the support bundle file. If None,
the support bundle is only returned as bytes.
:param circuit_profile: Whether to collect circuit profile data (default: True)
:param heap_profile: Whether to collect heap profile data (default: True)
:param metrics: Whether to collect metrics data (default: True)
:param logs: Whether to collect logs data (default: True)
:param stats: Whether to collect stats data (default: True)
:param pipeline_config: Whether to collect pipeline configuration data (default: True)
:param system_config: Whether to collect system configuration data (default: True)
:return: The support bundle as bytes (ZIP archive)
:raises FelderaAPIError: If the pipeline does not exist or if there's an error
"""
# Build query parameters
params = {}
if not circuit_profile:
params["circuit_profile"] = "false"
if not heap_profile:
params["heap_profile"] = "false"
if not metrics:
params["metrics"] = "false"
if not logs:
params["logs"] = "false"
if not stats:
params["stats"] = "false"
if not pipeline_config:
params["pipeline_config"] = "false"
if not system_config:
params["system_config"] = "false"
support_bundle_bytes = self.client.get_pipeline_support_bundle(
self.name, params=params
)
if output_path is not None:
path = pathlib.Path(output_path)
# Ensure the file has .zip extension
if path.suffix != ".zip":
path = path.with_suffix(".zip")
with open(path, "wb") as f:
f.write(support_bundle_bytes)
print(f"Support bundle written to {path}")
return support_bundle_bytes