def test_get_batch_log(requests_mock, mocker):
requests_mock.get(
f"http://example.com/batches/{MOCK_BATCH_ID}/log",
json=MOCK_BATCH_LOG_JSON,
)
mocker.patch.object(BatchLog, "from_json")
client = LivyClient("http://example.com")
batch = client.get_batch_log(MOCK_BATCH_ID)
assert batch == BatchLog.from_json.return_value
BatchLog.from_json.assert_called_once_with(MOCK_BATCH_LOG_JSON)
class LivyBatch:
"""Manages a remote Livy batch and high-level interactions with it.
The py_files, files, jars and archives arguments are lists of URLs, e.g.
["s3://bucket/object", "hdfs://path/to/file", ...] and must be reachable by
the Spark driver process. If the provided URL has no scheme, it's
considered to be relative to the default file system configured in the Livy
server.
URLs in the py_files argument are copied to a temporary staging area and
inserted into Python's sys.path ahead of the standard library paths. This
allows you to import .py, .zip and .egg files in Python.
URLs for jars, py_files, files and archives arguments are all copied to the
same working directory on the Spark cluster.
The driver_memory and executor_memory arguments have the same format as JVM
memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g. 512m,
2g).
See https://spark.apache.org/docs/latest/configuration.html for more
information on Spark configuration properties.
:param url: The URL of the Livy server.
:param file: File containing the application to execute.
:param auth: A requests-compatible auth object to use when making requests.
:param class_name: Application Java/Spark main class.
:param proxy_user: User to impersonate when starting the session.
:param jars: URLs of jars to be used in this session.
:param py_files: URLs of Python files to be used in this session.
:param files: URLs of files to be used in this session.
:param driver_memory: Amount of memory to use for the driver process (e.g.
'512m').
:param driver_cores: Number of cores to use for the driver process.
:param executor_memory: Amount of memory to use per executor process (e.g.
'512m').
:param executor_cores: Number of cores to use for each executor.
:param num_executors: Number of executors to launch for this session.
:param archives: URLs of archives to be used in this session.
:param queue: The name of the YARN queue to which submitted.
:param name: The name of this session.
:param spark_conf: Spark configuration properties.
"""
def __init__(
self,
url: str,
file: str,
auth: Auth = None,
class_name: str = None,
args: List[str] = None,
proxy_user: str = None,
jars: List[str] = None,
py_files: List[str] = None,
files: List[str] = None,
driver_memory: str = None,
driver_cores: int = None,
executor_memory: str = None,
executor_cores: int = None,
num_executors: int = None,
archives: List[str] = None,
queue: str = None,
name: str = None,
spark_conf: Dict[str, Any] = None,
) -> None:
self.client = LivyClient(url, auth)
self.file = file
self.class_name = class_name
self.args = args
self.proxy_user = proxy_user
self.jars = jars
self.py_files = py_files
self.files = files
self.driver_memory = driver_memory
self.driver_cores = driver_cores
self.executor_memory = executor_memory
self.executor_cores = executor_cores
self.num_executors = num_executors
self.archives = archives
self.queue = queue
self.name = name
self.spark_conf = spark_conf
self.batch_id: Optional[int] = None
def start(self) -> None:
"""Create the batch session.
Unlike LivySession, this does not wait for the session to be ready.
"""
batch = self.client.create_batch(
self.file,
self.class_name,
self.args,
self.proxy_user,
self.jars,
self.py_files,
self.files,
self.driver_memory,
self.driver_cores,
self.executor_memory,
self.executor_cores,
self.num_executors,
self.archives,
self.queue,
self.name,
self.spark_conf,
)
self.batch_id = batch.batch_id
def wait(self) -> SessionState:
"""Wait for the batch session to finish."""
intervals = polling_intervals([0.1, 0.5, 1.0, 3.0], 5.0)
while True:
state = self.state
if state in SESSION_STATE_FINISHED:
break
time.sleep(next(intervals))
return state
@property
def state(self) -> SessionState:
"""The state of the managed Spark batch."""
if self.batch_id is None:
raise ValueError("batch session not yet started")
batch = self.client.get_batch(self.batch_id)
if batch is None:
raise ValueError(
"batch session not found - it may have been shut down"
)
return batch.state
def log(self, from_: int = None, size: int = None) -> List[str]:
"""Get logs for this Spark batch.
:param from_: The line number to start getting logs from.
:param size: The number of lines of logs to get.
"""
if self.batch_id is None:
raise ValueError("batch session not yet started")
log = self.client.get_batch_log(self.batch_id, from_, size)
if log is None:
raise ValueError(
"batch session not found - it may have been shut down"
)
return log.lines
def kill(self) -> None:
"""Kill the managed Spark batch session."""
if self.batch_id is not None:
self.client.delete_batch(self.batch_id)
self.client.close()
class LivyBatch:
"""Manages a remote Livy batch and high-level interactions with it.
:param url: The URL of the Livy server.
:param batch_id: The ID of the Livy batch.
:param auth: A requests-compatible auth object to use when making requests.
:param verify: Either a boolean, in which case it controls whether we
verify the server’s TLS certificate, or a string, in which case it must
be a path to a CA bundle to use. Defaults to ``True``.
:param requests_session: A specific ``requests.Session`` to use, allowing
advanced customisation. The caller is responsible for closing the
session.
"""
def __init__(
self,
url: str,
batch_id: int,
auth: Auth = None,
verify: Verify = True,
requests_session: requests.Session = None,
) -> None:
self.client = LivyClient(url, auth, verify, requests_session)
self.batch_id = batch_id
@classmethod
def create(
cls,
url: str,
file: str,
auth: Auth = None,
verify: Verify = True,
requests_session: requests.Session = None,
class_name: str = None,
args: List[str] = None,
proxy_user: str = None,
jars: List[str] = None,
py_files: List[str] = None,
files: List[str] = None,
driver_memory: str = None,
driver_cores: int = None,
executor_memory: str = None,
executor_cores: int = None,
num_executors: int = None,
archives: List[str] = None,
queue: str = None,
name: str = None,
spark_conf: Dict[str, Any] = None,
) -> "LivyBatch":
"""Create a new Livy batch session.
The py_files, files, jars and archives arguments are lists of URLs,
e.g. ["s3://bucket/object", "hdfs://path/to/file", ...] and must be
reachable by the Spark driver process. If the provided URL has no
scheme, it's considered to be relative to the default file system
configured in the Livy server.
URLs in the py_files argument are copied to a temporary staging area
and inserted into Python's sys.path ahead of the standard library
paths. This allows you to import .py, .zip and .egg files in Python.
URLs for jars, py_files, files and archives arguments are all copied to
the same working directory on the Spark cluster.
The driver_memory and executor_memory arguments have the same format as
JVM memory strings with a size unit suffix ("k", "m", "g" or "t") (e.g.
512m, 2g).
See https://spark.apache.org/docs/latest/configuration.html for more
information on Spark configuration properties.
:param url: The URL of the Livy server.
:param file: File containing the application to execute.
:param auth: A requests-compatible auth object to use when making
requests.
:param verify: Either a boolean, in which case it controls whether we
verify the server’s TLS certificate, or a string, in which case it
must be a path to a CA bundle to use. Defaults to ``True``.
:param requests_session: A specific ``requests.Session`` to use,
allowing advanced customisation. The caller is responsible for
closing the session.
:param class_name: Application Java/Spark main class.
:param proxy_user: User to impersonate when starting the session.
:param jars: URLs of jars to be used in this session.
:param py_files: URLs of Python files to be used in this session.
:param files: URLs of files to be used in this session.
:param driver_memory: Amount of memory to use for the driver process
(e.g. '512m').
:param driver_cores: Number of cores to use for the driver process.
:param executor_memory: Amount of memory to use per executor process
(e.g. '512m').
:param executor_cores: Number of cores to use for each executor.
:param num_executors: Number of executors to launch for this session.
:param archives: URLs of archives to be used in this session.
:param queue: The name of the YARN queue to which submitted.
:param name: The name of this session.
:param spark_conf: Spark configuration properties.
"""
client = LivyClient(url, auth, verify, requests_session)
batch = client.create_batch(
file,
class_name,
args,
proxy_user,
jars,
py_files,
files,
driver_memory,
driver_cores,
executor_memory,
executor_cores,
num_executors,
archives,
queue,
name,
spark_conf,
)
client.close()
return cls(url, batch.batch_id, auth, verify, requests_session)
def wait(self) -> SessionState:
"""Wait for the batch session to finish."""
intervals = polling_intervals([0.1, 0.5, 1.0, 3.0], 5.0)
while True:
state = self.state
if state in SESSION_STATE_FINISHED:
break
time.sleep(next(intervals))
return state
@property
def state(self) -> SessionState:
"""The state of the managed Spark batch."""
batch = self.client.get_batch(self.batch_id)
if batch is None:
raise ValueError(
"batch session not found - it may have been shut down"
)
return batch.state
def log(self, from_: int = None, size: int = None) -> List[str]:
"""Get logs for this Spark batch.
:param from_: The line number to start getting logs from.
:param size: The number of lines of logs to get.
"""
log = self.client.get_batch_log(self.batch_id, from_, size)
if log is None:
raise ValueError(
"batch session not found - it may have been shut down"
)
return log.lines
def kill(self) -> None:
"""Kill the managed Spark batch session."""
self.client.delete_batch(self.batch_id)
self.client.close()
