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client.py
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executable file
·1557 lines (1317 loc) · 59.7 KB
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import time
from typing import Dict, Tuple, List, Optional, Any, Union, Sequence
import pandas
try:
import pyarrow
except ImportError:
pyarrow = None
import requests
import json
import os
import decimal
from uuid import UUID
from databricks.sql import __version__
from databricks.sql import *
from databricks.sql.exc import (
OperationalError,
SessionAlreadyClosedError,
CursorAlreadyClosedError,
)
from databricks.sql.thrift_api.TCLIService import ttypes
from databricks.sql.thrift_backend import ThriftBackend
from databricks.sql.utils import (
ExecuteResponse,
ParamEscaper,
inject_parameters,
transform_paramstyle,
ColumnTable,
ColumnQueue,
)
from databricks.sql.parameters.native import (
DbsqlParameterBase,
TDbsqlParameter,
TParameterDict,
TParameterSequence,
TParameterCollection,
ParameterStructure,
dbsql_parameter_from_primitive,
ParameterApproach,
)
from databricks.sql.types import Row, SSLOptions
from databricks.sql.auth.auth import get_python_sql_connector_auth_provider
from databricks.sql.experimental.oauth_persistence import OAuthPersistence
from databricks.sql.thrift_api.TCLIService.ttypes import (
TSparkParameter,
TOperationState,
)
logger = logging.getLogger(__name__)
if pyarrow is None:
logger.warning(
"[WARN] pyarrow is not installed by default since databricks-sql-connector 4.0.0,"
"any arrow specific api (e.g. fetchmany_arrow) and cloud fetch will be disabled."
"If you need these features, please run pip install pyarrow or pip install databricks-sql-connector[pyarrow] to install"
)
DEFAULT_RESULT_BUFFER_SIZE_BYTES = 104857600
DEFAULT_ARRAY_SIZE = 100000
NO_NATIVE_PARAMS: List = []
class Connection:
def __init__(
self,
server_hostname: str,
http_path: str,
access_token: Optional[str] = None,
http_headers: Optional[List[Tuple[str, str]]] = None,
session_configuration: Optional[Dict[str, Any]] = None,
catalog: Optional[str] = None,
schema: Optional[str] = None,
_use_arrow_native_complex_types: Optional[bool] = True,
**kwargs,
) -> None:
"""
Connect to a Databricks SQL endpoint or a Databricks cluster.
Parameters:
:param server_hostname: Databricks instance host name.
:param http_path: Http path either to a DBSQL endpoint (e.g. /sql/1.0/endpoints/1234567890abcdef)
or to a DBR interactive cluster (e.g. /sql/protocolv1/o/1234567890123456/1234-123456-slid123)
:param access_token: `str`, optional
Http Bearer access token, e.g. Databricks Personal Access Token.
Unless if you use auth_type=`databricks-oauth` you need to pass `access_token.
Examples:
```
connection = sql.connect(
server_hostname='dbc-12345.staging.cloud.databricks.com',
http_path='sql/protocolv1/o/6789/12abc567',
access_token='dabpi12345678'
)
```
:param http_headers: An optional list of (k, v) pairs that will be set as Http headers on every request
:param session_configuration: An optional dictionary of Spark session parameters. Defaults to None.
Execute the SQL command `SET -v` to get a full list of available commands.
:param catalog: An optional initial catalog to use. Requires DBR version 9.0+
:param schema: An optional initial schema to use. Requires DBR version 9.0+
Other Parameters:
use_inline_params: `boolean` | str, optional (default is False)
When True, parameterized calls to cursor.execute() will try to render parameter values inline with the
query text instead of using native bound parameters supported in DBR 14.1 and above. This connector will attempt to
sanitise parameterized inputs to prevent SQL injection. The inline parameter approach is maintained for
legacy purposes and will be deprecated in a future release. When this parameter is `True` you will see
a warning log message. To suppress this log message, set `use_inline_params="silent"`.
auth_type: `str`, optional (default is databricks-oauth if neither `access_token` nor `tls_client_cert_file` is set)
`databricks-oauth` : to use Databricks OAuth with fine-grained permission scopes, set to `databricks-oauth`.
`azure-oauth` : to use Microsoft Entra ID OAuth flow, set to `azure-oauth`.
oauth_client_id: `str`, optional
custom oauth client_id. If not specified, it will use the built-in client_id of databricks-sql-python.
oauth_redirect_port: `int`, optional
port of the oauth redirect uri (localhost). This is required when custom oauth client_id
`oauth_client_id` is set
user_agent_entry: `str`, optional
A custom tag to append to the User-Agent header. This is typically used by partners to identify their applications.. If not specified, it will use the default user agent PyDatabricksSqlConnector
experimental_oauth_persistence: configures preferred storage for persisting oauth tokens.
This has to be a class implementing `OAuthPersistence`.
When `auth_type` is set to `databricks-oauth` or `azure-oauth` without persisting the oauth token in a
persistence storage the oauth tokens will only be maintained in memory and if the python process
restarts the end user will have to login again.
Note this is beta (private preview)
For persisting the oauth token in a prod environment you should subclass and implement OAuthPersistence
from databricks.sql.experimental.oauth_persistence import OAuthPersistence, OAuthToken
class MyCustomImplementation(OAuthPersistence):
def __init__(self, file_path):
self._file_path = file_path
def persist(self, token: OAuthToken):
# implement this method to persist token.refresh_token and token.access_token
def read(self) -> Optional[OAuthToken]:
# implement this method to return an instance of the persisted token
connection = sql.connect(
server_hostname='dbc-12345.staging.cloud.databricks.com',
http_path='sql/protocolv1/o/6789/12abc567',
auth_type="databricks-oauth",
experimental_oauth_persistence=MyCustomImplementation()
)
For development purpose you can use the existing `DevOnlyFilePersistence` which stores the
raw oauth token in the provided file path. Please note this is only for development and for prod you should provide your
own implementation of OAuthPersistence.
Examples:
```
# for development only
from databricks.sql.experimental.oauth_persistence import DevOnlyFilePersistence
connection = sql.connect(
server_hostname='dbc-12345.staging.cloud.databricks.com',
http_path='sql/protocolv1/o/6789/12abc567',
auth_type="databricks-oauth",
experimental_oauth_persistence=DevOnlyFilePersistence("~/dev-oauth.json")
)
```
:param _use_arrow_native_complex_types: `bool`, optional
Controls whether a complex type field value is returned as a string or as a native Arrow type. Defaults to True.
When True:
MAP is returned as List[Tuple[str, Any]]
STRUCT is returned as Dict[str, Any]
ARRAY is returned as numpy.ndarray
When False, complex types are returned as a strings. These are generally deserializable as JSON.
"""
# Internal arguments in **kwargs:
# _use_cert_as_auth
# Use a TLS cert instead of a token
# _enable_ssl
# Connect over HTTP instead of HTTPS
# _port
# Which port to connect to
# _skip_routing_headers:
# Don't set routing headers if set to True (for use when connecting directly to server)
# _tls_no_verify
# Set to True (Boolean) to completely disable SSL verification.
# _tls_verify_hostname
# Set to False (Boolean) to disable SSL hostname verification, but check certificate.
# _tls_trusted_ca_file
# Set to the path of the file containing trusted CA certificates for server certificate
# verification. If not provide, uses system truststore.
# _tls_client_cert_file, _tls_client_cert_key_file, _tls_client_cert_key_password
# Set client SSL certificate.
# See https://docs.python.org/3/library/ssl.html#ssl.SSLContext.load_cert_chain
# _retry_stop_after_attempts_count
# The maximum number of attempts during a request retry sequence (defaults to 24)
# _socket_timeout
# The timeout in seconds for socket send, recv and connect operations. Defaults to None for
# no timeout. Should be a positive float or integer.
# _disable_pandas
# In case the deserialisation through pandas causes any issues, it can be disabled with
# this flag.
# _use_arrow_native_decimals
# Databricks runtime will return native Arrow types for decimals instead of Arrow strings
# (True by default)
# _use_arrow_native_timestamps
# Databricks runtime will return native Arrow types for timestamps instead of Arrow strings
# (True by default)
# use_cloud_fetch
# Enable use of cloud fetch to extract large query results in parallel via cloud storage
logger.debug(
"Connection.__init__(server_hostname=%s, http_path=%s)",
server_hostname,
http_path,
)
if access_token:
access_token_kv = {"access_token": access_token}
kwargs = {**kwargs, **access_token_kv}
self.open = False
self.host = server_hostname
self.port = kwargs.get("_port", 443)
self.disable_pandas = kwargs.get("_disable_pandas", False)
self.lz4_compression = kwargs.get("enable_query_result_lz4_compression", True)
auth_provider = get_python_sql_connector_auth_provider(
server_hostname, **kwargs
)
user_agent_entry = kwargs.get("user_agent_entry")
if user_agent_entry is None:
user_agent_entry = kwargs.get("_user_agent_entry")
if user_agent_entry is not None:
logger.warning(
"[WARN] Parameter '_user_agent_entry' is deprecated; use 'user_agent_entry' instead. "
"This parameter will be removed in the upcoming releases."
)
if user_agent_entry:
useragent_header = "{}/{} ({})".format(
USER_AGENT_NAME, __version__, user_agent_entry
)
else:
useragent_header = "{}/{}".format(USER_AGENT_NAME, __version__)
base_headers = [("User-Agent", useragent_header)]
self._ssl_options = SSLOptions(
# Double negation is generally a bad thing, but we have to keep backward compatibility
tls_verify=not kwargs.get(
"_tls_no_verify", False
), # by default - verify cert and host
tls_verify_hostname=kwargs.get("_tls_verify_hostname", True),
tls_trusted_ca_file=kwargs.get("_tls_trusted_ca_file"),
tls_client_cert_file=kwargs.get("_tls_client_cert_file"),
tls_client_cert_key_file=kwargs.get("_tls_client_cert_key_file"),
tls_client_cert_key_password=kwargs.get("_tls_client_cert_key_password"),
)
self.thrift_backend = ThriftBackend(
self.host,
self.port,
http_path,
(http_headers or []) + base_headers,
auth_provider,
ssl_options=self._ssl_options,
_use_arrow_native_complex_types=_use_arrow_native_complex_types,
**kwargs,
)
self._open_session_resp = self.thrift_backend.open_session(
session_configuration, catalog, schema
)
self._session_handle = self._open_session_resp.sessionHandle
self.protocol_version = self.get_protocol_version(self._open_session_resp)
self.use_cloud_fetch = kwargs.get("use_cloud_fetch", True)
self.open = True
logger.info("Successfully opened session " + str(self.get_session_id_hex()))
self._cursors = [] # type: List[Cursor]
self.use_inline_params = self._set_use_inline_params_with_warning(
kwargs.get("use_inline_params", False)
)
def _set_use_inline_params_with_warning(self, value: Union[bool, str]):
"""Valid values are True, False, and "silent"
False: Use native parameters
True: Use inline parameters and log a warning
"silent": Use inline parameters and don't log a warning
"""
if value is False:
return False
if value not in [True, "silent"]:
raise ValueError(
f"Invalid value for use_inline_params: {value}. "
+ 'Valid values are True, False, and "silent"'
)
if value is True:
logger.warning(
"Parameterised queries executed with this client will use the inline parameter approach."
"This approach will be deprecated in a future release. Consider using native parameters."
"Learn more: https://github.com/databricks/databricks-sql-python/tree/main/docs/parameters.md"
'To suppress this warning, set use_inline_params="silent"'
)
return value
# The ideal return type for this method is perhaps Self, but that was not added until 3.11, and we support pre-3.11 pythons, currently.
def __enter__(self) -> "Connection":
return self
def __exit__(self, exc_type, exc_value, traceback):
try:
self.close()
except BaseException as e:
logger.warning(f"Exception during connection close in __exit__: {e}")
if exc_type is None:
raise
return False
def __del__(self):
if self.open:
logger.debug(
"Closing unclosed connection for session "
"{}".format(self.get_session_id_hex())
)
try:
self._close(close_cursors=False)
except OperationalError as e:
# Close on best-effort basis.
logger.debug("Couldn't close unclosed connection: {}".format(e.message))
def get_session_id(self):
return self.thrift_backend.handle_to_id(self._session_handle)
@staticmethod
def get_protocol_version(openSessionResp):
"""
Since the sessionHandle will sometimes have a serverProtocolVersion, it takes
precedence over the serverProtocolVersion defined in the OpenSessionResponse.
"""
if (
openSessionResp.sessionHandle
and hasattr(openSessionResp.sessionHandle, "serverProtocolVersion")
and openSessionResp.sessionHandle.serverProtocolVersion
):
return openSessionResp.sessionHandle.serverProtocolVersion
return openSessionResp.serverProtocolVersion
@staticmethod
def server_parameterized_queries_enabled(protocolVersion):
if (
protocolVersion
and protocolVersion >= ttypes.TProtocolVersion.SPARK_CLI_SERVICE_PROTOCOL_V8
):
return True
else:
return False
def get_session_id_hex(self):
return self.thrift_backend.handle_to_hex_id(self._session_handle)
def cursor(
self,
arraysize: int = DEFAULT_ARRAY_SIZE,
buffer_size_bytes: int = DEFAULT_RESULT_BUFFER_SIZE_BYTES,
) -> "Cursor":
"""
Return a new Cursor object using the connection.
Will throw an Error if the connection has been closed.
"""
if not self.open:
raise Error("Cannot create cursor from closed connection")
cursor = Cursor(
self,
self.thrift_backend,
arraysize=arraysize,
result_buffer_size_bytes=buffer_size_bytes,
)
self._cursors.append(cursor)
return cursor
def close(self) -> None:
"""Close the underlying session and mark all associated cursors as closed."""
self._close()
def _close(self, close_cursors=True) -> None:
if close_cursors:
for cursor in self._cursors:
cursor.close()
logger.info(f"Closing session {self.get_session_id_hex()}")
if not self.open:
logger.debug("Session appears to have been closed already")
try:
self.thrift_backend.close_session(self._session_handle)
except RequestError as e:
if isinstance(e.args[1], SessionAlreadyClosedError):
logger.info("Session was closed by a prior request")
except DatabaseError as e:
if "Invalid SessionHandle" in str(e):
logger.warning(
f"Attempted to close session that was already closed: {e}"
)
else:
logger.warning(
f"Attempt to close session raised an exception at the server: {e}"
)
except Exception as e:
logger.error(f"Attempt to close session raised a local exception: {e}")
self.open = False
def commit(self):
"""No-op because Databricks does not support transactions"""
pass
def rollback(self):
raise NotSupportedError("Transactions are not supported on Databricks")
class Cursor:
def __init__(
self,
connection: Connection,
thrift_backend: ThriftBackend,
result_buffer_size_bytes: int = DEFAULT_RESULT_BUFFER_SIZE_BYTES,
arraysize: int = DEFAULT_ARRAY_SIZE,
) -> None:
"""
These objects represent a database cursor, which is used to manage the context of a fetch
operation.
Cursors are not isolated, i.e., any changes done to the database by a cursor are immediately
visible by other cursors or connections.
"""
self.connection = connection
self.rowcount = -1 # Return -1 as this is not supported
self.buffer_size_bytes = result_buffer_size_bytes
self.active_result_set: Union[ResultSet, None] = None
self.arraysize = arraysize
# Note that Cursor closed => active result set closed, but not vice versa
self.open = True
self.executing_command_id = None
self.thrift_backend = thrift_backend
self.active_op_handle = None
self.escaper = ParamEscaper()
self.lastrowid = None
self.ASYNC_DEFAULT_POLLING_INTERVAL = 2
# The ideal return type for this method is perhaps Self, but that was not added until 3.11, and we support pre-3.11 pythons, currently.
def __enter__(self) -> "Cursor":
return self
def __exit__(self, exc_type, exc_value, traceback):
try:
logger.debug("Cursor context manager exiting, calling close()")
self.close()
except BaseException as e:
logger.warning(f"Exception during cursor close in __exit__: {e}")
if exc_type is None:
raise
return False
def __iter__(self):
if self.active_result_set:
for row in self.active_result_set:
yield row
else:
raise Error("There is no active result set")
def _determine_parameter_approach(
self, params: Optional[TParameterCollection]
) -> ParameterApproach:
"""Encapsulates the logic for choosing whether to send parameters in native vs inline mode
If params is None then ParameterApproach.NONE is returned.
If self.use_inline_params is True then inline mode is used.
If self.use_inline_params is False, then check if the server supports them and proceed.
Else raise an exception.
Returns a ParameterApproach enumeration or raises an exception
If inline approach is used when the server supports native approach, a warning is logged
"""
if params is None:
return ParameterApproach.NONE
if self.connection.use_inline_params:
return ParameterApproach.INLINE
else:
return ParameterApproach.NATIVE
def _all_dbsql_parameters_are_named(self, params: List[TDbsqlParameter]) -> bool:
"""Return True if all members of the list have a non-null .name attribute"""
return all([i.name is not None for i in params])
def _normalize_tparametersequence(
self, params: TParameterSequence
) -> List[TDbsqlParameter]:
"""Retains the same order as the input list."""
output: List[TDbsqlParameter] = []
for p in params:
if isinstance(p, DbsqlParameterBase):
output.append(p)
else:
output.append(dbsql_parameter_from_primitive(value=p))
return output
def _normalize_tparameterdict(
self, params: TParameterDict
) -> List[TDbsqlParameter]:
return [
dbsql_parameter_from_primitive(value=value, name=name)
for name, value in params.items()
]
def _normalize_tparametercollection(
self, params: Optional[TParameterCollection]
) -> List[TDbsqlParameter]:
if params is None:
return []
if isinstance(params, dict):
return self._normalize_tparameterdict(params)
if isinstance(params, Sequence):
return self._normalize_tparametersequence(list(params))
def _determine_parameter_structure(
self,
parameters: List[TDbsqlParameter],
) -> ParameterStructure:
all_named = self._all_dbsql_parameters_are_named(parameters)
if all_named:
return ParameterStructure.NAMED
else:
return ParameterStructure.POSITIONAL
def _prepare_inline_parameters(
self, stmt: str, params: Optional[Union[Sequence, Dict[str, Any]]]
) -> Tuple[str, List]:
"""Return a statement and list of native parameters to be passed to thrift_backend for execution
:stmt:
A string SQL query containing parameter markers of PEP-249 paramstyle `pyformat`.
For example `%(param)s`.
:params:
An iterable of parameter values to be rendered inline. If passed as a Dict, the keys
must match the names of the markers included in :stmt:. If passed as a List, its length
must equal the count of parameter markers in :stmt:.
Returns a tuple of:
stmt: the passed statement with the param markers replaced by literal rendered values
params: an empty list representing the native parameters to be passed with this query.
The list is always empty because native parameters are never used under the inline approach
"""
escaped_values = self.escaper.escape_args(params)
rendered_statement = inject_parameters(stmt, escaped_values)
return rendered_statement, NO_NATIVE_PARAMS
def _prepare_native_parameters(
self,
stmt: str,
params: List[TDbsqlParameter],
param_structure: ParameterStructure,
) -> Tuple[str, List[TSparkParameter]]:
"""Return a statement and a list of native parameters to be passed to thrift_backend for execution
:stmt:
A string SQL query containing parameter markers of PEP-249 paramstyle `named`.
For example `:param`.
:params:
An iterable of parameter values to be sent natively. If passed as a Dict, the keys
must match the names of the markers included in :stmt:. If passed as a List, its length
must equal the count of parameter markers in :stmt:. In list form, any member of the list
can be wrapped in a DbsqlParameter class.
Returns a tuple of:
stmt: the passed statement` with the param markers replaced by literal rendered values
params: a list of TSparkParameters that will be passed in native mode
"""
stmt = stmt
output = [
p.as_tspark_param(named=param_structure == ParameterStructure.NAMED)
for p in params
]
return stmt, output
def _close_and_clear_active_result_set(self):
try:
if self.active_result_set:
self.active_result_set.close()
finally:
self.active_result_set = None
def _check_not_closed(self):
if not self.open:
raise Error("Attempting operation on closed cursor")
def _handle_staging_operation(
self, staging_allowed_local_path: Union[None, str, List[str]]
):
"""Fetch the HTTP request instruction from a staging ingestion command
and call the designated handler.
Raise an exception if localFile is specified by the server but the localFile
is not descended from staging_allowed_local_path.
"""
if isinstance(staging_allowed_local_path, type(str())):
_staging_allowed_local_paths = [staging_allowed_local_path]
elif isinstance(staging_allowed_local_path, type(list())):
_staging_allowed_local_paths = staging_allowed_local_path
else:
raise Error(
"You must provide at least one staging_allowed_local_path when initialising a connection to perform ingestion commands"
)
abs_staging_allowed_local_paths = [
os.path.abspath(i) for i in _staging_allowed_local_paths
]
assert self.active_result_set is not None
row = self.active_result_set.fetchone()
assert row is not None
# Must set to None in cases where server response does not include localFile
abs_localFile = None
# Default to not allow staging operations
allow_operation = False
if getattr(row, "localFile", None):
abs_localFile = os.path.abspath(row.localFile)
for abs_staging_allowed_local_path in abs_staging_allowed_local_paths:
# If the indicated local file matches at least one allowed base path, allow the operation
if (
os.path.commonpath([abs_localFile, abs_staging_allowed_local_path])
== abs_staging_allowed_local_path
):
allow_operation = True
else:
continue
if not allow_operation:
raise Error(
"Local file operations are restricted to paths within the configured staging_allowed_local_path"
)
# May be real headers, or could be json string
headers = (
json.loads(row.headers) if isinstance(row.headers, str) else row.headers
)
handler_args = {
"presigned_url": row.presignedUrl,
"local_file": abs_localFile,
"headers": dict(headers) or {},
}
logger.debug(
f"Attempting staging operation indicated by server: {row.operation} - {getattr(row, 'localFile', '')}"
)
# TODO: Create a retry loop here to re-attempt if the request times out or fails
if row.operation == "GET":
return self._handle_staging_get(**handler_args)
elif row.operation == "PUT":
return self._handle_staging_put(**handler_args)
elif row.operation == "REMOVE":
# Local file isn't needed to remove a remote resource
handler_args.pop("local_file")
return self._handle_staging_remove(**handler_args)
else:
raise Error(
f"Operation {row.operation} is not supported. "
+ "Supported operations are GET, PUT, and REMOVE"
)
def _handle_staging_put(
self, presigned_url: str, local_file: str, headers: Optional[dict] = None
):
"""Make an HTTP PUT request
Raise an exception if request fails. Returns no data.
"""
if local_file is None:
raise Error("Cannot perform PUT without specifying a local_file")
with open(local_file, "rb") as fh:
r = requests.put(url=presigned_url, data=fh, headers=headers)
# fmt: off
# Design borrowed from: https://stackoverflow.com/a/2342589/5093960
OK = requests.codes.ok # 200
CREATED = requests.codes.created # 201
ACCEPTED = requests.codes.accepted # 202
NO_CONTENT = requests.codes.no_content # 204
# fmt: on
if r.status_code not in [OK, CREATED, NO_CONTENT, ACCEPTED]:
raise Error(
f"Staging operation over HTTP was unsuccessful: {r.status_code}-{r.text}"
)
if r.status_code == ACCEPTED:
logger.debug(
f"Response code {ACCEPTED} from server indicates ingestion command was accepted "
+ "but not yet applied on the server. It's possible this command may fail later."
)
def _handle_staging_get(
self, local_file: str, presigned_url: str, headers: Optional[dict] = None
):
"""Make an HTTP GET request, create a local file with the received data
Raise an exception if request fails. Returns no data.
"""
if local_file is None:
raise Error("Cannot perform GET without specifying a local_file")
r = requests.get(url=presigned_url, headers=headers)
# response.ok verifies the status code is not between 400-600.
# Any 2xx or 3xx will evaluate r.ok == True
if not r.ok:
raise Error(
f"Staging operation over HTTP was unsuccessful: {r.status_code}-{r.text}"
)
with open(local_file, "wb") as fp:
fp.write(r.content)
def _handle_staging_remove(
self, presigned_url: str, headers: Optional[dict] = None
):
"""Make an HTTP DELETE request to the presigned_url"""
r = requests.delete(url=presigned_url, headers=headers)
if not r.ok:
raise Error(
f"Staging operation over HTTP was unsuccessful: {r.status_code}-{r.text}"
)
def execute(
self,
operation: str,
parameters: Optional[TParameterCollection] = None,
enforce_embedded_schema_correctness=False,
) -> "Cursor":
"""
Execute a query and wait for execution to complete.
The parameterisation behaviour of this method depends on which parameter approach is used:
- With INLINE mode, parameters are rendered inline with the query text
- With NATIVE mode (default), parameters are sent to the server separately for binding
This behaviour is controlled by the `use_inline_params` argument passed when building a connection.
The paramstyle for these approaches is different:
If the connection was instantiated with use_inline_params=False (default), then parameters
should be given in PEP-249 `named` paramstyle like :param_name. Parameters passed by positionally
are indicated using a `?` in the query text.
If the connection was instantiated with use_inline_params=True, then parameters
should be given in PEP-249 `pyformat` paramstyle like %(param_name)s. Parameters passed by positionally
are indicated using a `%s` marker in the query. Note: this approach is not recommended as it can break
your SQL query syntax and will be removed in a future release.
```python
inline_operation = "SELECT * FROM table WHERE field = %(some_value)s"
native_operation = "SELECT * FROM table WHERE field = :some_value"
parameters = {"some_value": "foo"}
```
Both will result in the query equivalent to "SELECT * FROM table WHERE field = 'foo'
being sent to the server
:returns self
"""
logger.debug(
"Cursor.execute(operation=%s, parameters=%s)", operation, parameters
)
param_approach = self._determine_parameter_approach(parameters)
if param_approach == ParameterApproach.NONE:
prepared_params = NO_NATIVE_PARAMS
prepared_operation = operation
elif param_approach == ParameterApproach.INLINE:
prepared_operation, prepared_params = self._prepare_inline_parameters(
operation, parameters
)
elif param_approach == ParameterApproach.NATIVE:
normalized_parameters = self._normalize_tparametercollection(parameters)
param_structure = self._determine_parameter_structure(normalized_parameters)
transformed_operation = transform_paramstyle(
operation, normalized_parameters, param_structure
)
prepared_operation, prepared_params = self._prepare_native_parameters(
transformed_operation, normalized_parameters, param_structure
)
self._check_not_closed()
self._close_and_clear_active_result_set()
execute_response = self.thrift_backend.execute_command(
operation=prepared_operation,
session_handle=self.connection._session_handle,
max_rows=self.arraysize,
max_bytes=self.buffer_size_bytes,
lz4_compression=self.connection.lz4_compression,
cursor=self,
use_cloud_fetch=self.connection.use_cloud_fetch,
parameters=prepared_params,
async_op=False,
enforce_embedded_schema_correctness=enforce_embedded_schema_correctness,
)
self.active_result_set = ResultSet(
self.connection,
execute_response,
self.thrift_backend,
self.buffer_size_bytes,
self.arraysize,
self.connection.use_cloud_fetch,
)
if execute_response.is_staging_operation:
self._handle_staging_operation(
staging_allowed_local_path=self.thrift_backend.staging_allowed_local_path
)
return self
def execute_async(
self,
operation: str,
parameters: Optional[TParameterCollection] = None,
enforce_embedded_schema_correctness=False,
) -> "Cursor":
"""
Execute a query and do not wait for it to complete and just move ahead
:param operation:
:param parameters:
:return:
"""
param_approach = self._determine_parameter_approach(parameters)
if param_approach == ParameterApproach.NONE:
prepared_params = NO_NATIVE_PARAMS
prepared_operation = operation
elif param_approach == ParameterApproach.INLINE:
prepared_operation, prepared_params = self._prepare_inline_parameters(
operation, parameters
)
elif param_approach == ParameterApproach.NATIVE:
normalized_parameters = self._normalize_tparametercollection(parameters)
param_structure = self._determine_parameter_structure(normalized_parameters)
transformed_operation = transform_paramstyle(
operation, normalized_parameters, param_structure
)
prepared_operation, prepared_params = self._prepare_native_parameters(
transformed_operation, normalized_parameters, param_structure
)
self._check_not_closed()
self._close_and_clear_active_result_set()
self.thrift_backend.execute_command(
operation=prepared_operation,
session_handle=self.connection._session_handle,
max_rows=self.arraysize,
max_bytes=self.buffer_size_bytes,
lz4_compression=self.connection.lz4_compression,
cursor=self,
use_cloud_fetch=self.connection.use_cloud_fetch,
parameters=prepared_params,
async_op=True,
enforce_embedded_schema_correctness=enforce_embedded_schema_correctness,
)
return self
def get_query_state(self) -> "TOperationState":
"""
Get the state of the async executing query or basically poll the status of the query
:return:
"""
self._check_not_closed()
return self.thrift_backend.get_query_state(self.active_op_handle)
def is_query_pending(self):
"""
Checks whether the async executing query is in pending state or not
:return:
"""
operation_state = self.get_query_state()
return not operation_state or operation_state in [
ttypes.TOperationState.RUNNING_STATE,
ttypes.TOperationState.PENDING_STATE,
]
def get_async_execution_result(self):
"""
Checks for the status of the async executing query and fetches the result if the query is finished
Otherwise it will keep polling the status of the query till there is a Not pending state
:return:
"""
self._check_not_closed()
while self.is_query_pending():
# Poll after some default time
time.sleep(self.ASYNC_DEFAULT_POLLING_INTERVAL)
operation_state = self.get_query_state()
if operation_state == ttypes.TOperationState.FINISHED_STATE:
execute_response = self.thrift_backend.get_execution_result(
self.active_op_handle, self
)
self.active_result_set = ResultSet(
self.connection,
execute_response,
self.thrift_backend,
self.buffer_size_bytes,
self.arraysize,
)
if execute_response.is_staging_operation:
self._handle_staging_operation(
staging_allowed_local_path=self.thrift_backend.staging_allowed_local_path
)
return self
else:
raise Error(
f"get_execution_result failed with Operation status {operation_state}"
)
def executemany(self, operation, seq_of_parameters):
"""
Execute the operation once for every set of passed in parameters.
This will issue N sequential request to the database where N is the length of the provided sequence.
No optimizations of the query (like batching) will be performed.
Only the final result set is retained.
:returns self
"""
for parameters in seq_of_parameters:
self.execute(operation, parameters)
return self
def catalogs(self) -> "Cursor":
"""
Get all available catalogs.
:returns self
"""
self._check_not_closed()
self._close_and_clear_active_result_set()
execute_response = self.thrift_backend.get_catalogs(
session_handle=self.connection._session_handle,
max_rows=self.arraysize,
max_bytes=self.buffer_size_bytes,
cursor=self,
)
self.active_result_set = ResultSet(