def __init__(self, driver_name, data_source_name, query, output_types):
"""Creates a `SqlDataset`.
`SqlDataset` allows a user to read data from the result set of a SQL query.
For example:
```python
tf.compat.v1.enable_eager_execution()
dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3",
"SELECT name, age FROM people",
(tf.string, tf.int32))
# Prints the rows of the result set of the above query.
for element in dataset:
print(element)
```
Args:
driver_name: A 0-D `tf.string` tensor containing the database type.
Currently, the only supported value is 'sqlite'.
data_source_name: A 0-D `tf.string` tensor containing a connection string
to connect to the database.
query: A 0-D `tf.string` tensor containing the SQL query to execute.
output_types: A tuple of `tf.DType` objects representing the types of the
columns returned by `query`.
"""
self._driver_name = ops.convert_to_tensor(driver_name,
dtype=dtypes.string,
name="driver_name")
self._data_source_name = ops.convert_to_tensor(data_source_name,
dtype=dtypes.string,
name="data_source_name")
self._query = ops.convert_to_tensor(query,
dtype=dtypes.string,
name="query")
self._structure = structure.NestedStructure(
nest.map_structure(
lambda dtype: structure.TensorStructure(dtype, []),
output_types))
if compat.forward_compatible(2019, 8, 3):
variant_tensor = gen_experimental_dataset_ops.sql_dataset(
self._driver_name, self._data_source_name, self._query,
**self._flat_structure)
else:
variant_tensor = gen_experimental_dataset_ops.experimental_sql_dataset(
self._driver_name, self._data_source_name, self._query,
**self._flat_structure)
super(SqlDatasetV2, self).__init__(variant_tensor)
def __init__(self, driver_name, data_source_name, query, output_types):
"""Creates a `SqlDataset`.
`SqlDataset` allows a user to read data from the result set of a SQL query.
For example:
```python
dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3",
"SELECT name, age FROM people",
(tf.string, tf.int32))
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
# Prints the rows of the result set of the above query.
while True:
try:
print(sess.run(next_element))
except tf.errors.OutOfRangeError:
break
```
Args:
driver_name: A 0-D `tf.string` tensor containing the database type.
Currently, the only supported value is 'sqlite'.
data_source_name: A 0-D `tf.string` tensor containing a connection string
to connect to the database.
query: A 0-D `tf.string` tensor containing the SQL query to execute.
output_types: A tuple of `tf.DType` objects representing the types of the
columns returned by `query`.
"""
self._driver_name = ops.convert_to_tensor(driver_name,
dtype=dtypes.string,
name="driver_name")
self._data_source_name = ops.convert_to_tensor(data_source_name,
dtype=dtypes.string,
name="data_source_name")
self._query = ops.convert_to_tensor(query,
dtype=dtypes.string,
name="query")
self._structure = structure.NestedStructure(
nest.map_structure(
lambda dtype: structure.TensorStructure(dtype, []),
output_types))
variant_tensor = gen_experimental_dataset_ops.experimental_sql_dataset(
self._driver_name, self._data_source_name, self._query,
**dataset_ops.flat_structure(self))
super(SqlDatasetV2, self).__init__(variant_tensor)
def __init__(self, driver_name, data_source_name, query, output_types):
"""Creates a `SqlDataset`.
`SqlDataset` allows a user to read data from the result set of a SQL query.
For example:
```python
tf.enable_eager_execution()
dataset = tf.data.experimental.SqlDataset("sqlite", "/foo/bar.sqlite3",
"SELECT name, age FROM people",
(tf.string, tf.int32))
# Prints the rows of the result set of the above query.
for element in dataset:
print(element)
```
Args:
driver_name: A 0-D `tf.string` tensor containing the database type.
Currently, the only supported value is 'sqlite'.
data_source_name: A 0-D `tf.string` tensor containing a connection string
to connect to the database.
query: A 0-D `tf.string` tensor containing the SQL query to execute.
output_types: A tuple of `tf.DType` objects representing the types of the
columns returned by `query`.
"""
self._driver_name = ops.convert_to_tensor(
driver_name, dtype=dtypes.string, name="driver_name")
self._data_source_name = ops.convert_to_tensor(
data_source_name, dtype=dtypes.string, name="data_source_name")
self._query = ops.convert_to_tensor(
query, dtype=dtypes.string, name="query")
self._structure = structure.NestedStructure(
nest.map_structure(
lambda dtype: structure.TensorStructure(dtype, []), output_types))
variant_tensor = gen_experimental_dataset_ops.experimental_sql_dataset(
self._driver_name, self._data_source_name, self._query,
**dataset_ops.flat_structure(self))
super(SqlDatasetV2, self).__init__(variant_tensor)
def _as_variant_tensor(self):
return gen_experimental_dataset_ops.experimental_sql_dataset(
self._driver_name, self._data_source_name, self._query,
nest.flatten(self.output_types), nest.flatten(self.output_shapes))
def _as_variant_tensor(self):
return gen_experimental_dataset_ops.experimental_sql_dataset(
self._driver_name, self._data_source_name, self._query,
nest.flatten(self.output_types), nest.flatten(self.output_shapes))