prefect_gcp.bigquery
¶
Tasks for interacting with GCP BigQuery
BigQueryWarehouse
¶
Bases: DatabaseBlock
A block for querying a database with BigQuery.
Upon instantiating, a connection to BigQuery is established and maintained for the life of the object until the close method is called.
It is recommended to use this block as a context manager, which will automatically close the connection and its cursors when the context is exited.
It is also recommended that this block is loaded and consumed within a single task or flow because if the block is passed across separate tasks and flows, the state of the block's connection and cursor could be lost.
Attributes:
Name | Type | Description |
---|---|---|
gcp_credentials |
GcpCredentials
|
The credentials to use to authenticate. |
fetch_size |
int
|
The number of rows to fetch at a time when calling fetch_many.
Note, this parameter is executed on the client side and is not
passed to the database. To limit on the server side, add the |
Source code in prefect_gcp/bigquery.py
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|
close
¶
Closes connection and its cursors.
Source code in prefect_gcp/bigquery.py
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|
execute
async
¶
Executes an operation on the database. This method is intended to be used for operations that do not return data, such as INSERT, UPDATE, or DELETE.
Unlike the fetch methods, this method will always execute the operation upon calling.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
operation |
str
|
The SQL query or other operation to be executed. |
required |
parameters |
Optional[Dict[str, Any]]
|
The parameters for the operation. |
None
|
**execution_options |
Dict[str, Any]
|
Additional options to pass to |
{}
|
Examples:
Execute operation with parameters:
from prefect_gcp.bigquery import BigQueryWarehouse
with BigQueryWarehouse.load("BLOCK_NAME") as warehouse:
operation = '''
CREATE TABLE mydataset.trips AS (
SELECT
bikeid,
start_time,
duration_minutes
FROM
bigquery-public-data.austin_bikeshare.bikeshare_trips
LIMIT %(limit)s
);
'''
warehouse.execute(operation, parameters={"limit": 5})
Source code in prefect_gcp/bigquery.py
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|
execute_many
async
¶
Executes many operations on the database. This method is intended to be used for operations that do not return data, such as INSERT, UPDATE, or DELETE.
Unlike the fetch methods, this method will always execute the operations upon calling.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
operation |
str
|
The SQL query or other operation to be executed. |
required |
seq_of_parameters |
List[Dict[str, Any]]
|
The sequence of parameters for the operation. |
required |
Examples:
Create mytable in mydataset and insert two rows into it:
from prefect_gcp.bigquery import BigQueryWarehouse
with BigQueryWarehouse.load("bigquery") as warehouse:
create_operation = '''
CREATE TABLE IF NOT EXISTS mydataset.mytable (
col1 STRING,
col2 INTEGER,
col3 BOOLEAN
)
'''
warehouse.execute(create_operation)
insert_operation = '''
INSERT INTO mydataset.mytable (col1, col2, col3) VALUES (%s, %s, %s)
'''
seq_of_parameters = [
("a", 1, True),
("b", 2, False),
]
warehouse.execute_many(
insert_operation,
seq_of_parameters=seq_of_parameters
)
Source code in prefect_gcp/bigquery.py
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|
fetch_all
async
¶
Fetch all results from the database.
Repeated calls using the same inputs to any of the fetch methods of this block will skip executing the operation again, and instead, return the next set of results from the previous execution, until the reset_cursors method is called.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
operation |
str
|
The SQL query or other operation to be executed. |
required |
parameters |
Optional[Dict[str, Any]]
|
The parameters for the operation. |
None
|
**execution_options |
Dict[str, Any]
|
Additional options to pass to |
{}
|
Returns:
Type | Description |
---|---|
List[Row]
|
A list of tuples containing the data returned by the database, where each row is a tuple and each column is a value in the tuple. |
Examples:
Execute operation with parameters, fetching all rows:
from prefect_gcp.bigquery import BigQueryWarehouse
with BigQueryWarehouse.load("BLOCK_NAME") as warehouse:
operation = '''
SELECT word, word_count
FROM `bigquery-public-data.samples.shakespeare`
WHERE corpus = %(corpus)s
AND word_count >= %(min_word_count)s
ORDER BY word_count DESC
LIMIT 3;
'''
parameters = {
"corpus": "romeoandjuliet",
"min_word_count": 250,
}
result = warehouse.fetch_all(operation, parameters=parameters)
Source code in prefect_gcp/bigquery.py
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|
fetch_many
async
¶
Fetch a limited number of results from the database.
Repeated calls using the same inputs to any of the fetch methods of this block will skip executing the operation again, and instead, return the next set of results from the previous execution, until the reset_cursors method is called.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
operation |
str
|
The SQL query or other operation to be executed. |
required |
parameters |
Optional[Dict[str, Any]]
|
The parameters for the operation. |
None
|
size |
Optional[int]
|
The number of results to return; if None or 0, uses the value of
|
None
|
**execution_options |
Dict[str, Any]
|
Additional options to pass to |
{}
|
Returns:
Type | Description |
---|---|
List[Row]
|
A list of tuples containing the data returned by the database, where each row is a tuple and each column is a value in the tuple. |
Examples:
Execute operation with parameters, fetching two new rows at a time:
from prefect_gcp.bigquery import BigQueryWarehouse
with BigQueryWarehouse.load("BLOCK_NAME") as warehouse:
operation = '''
SELECT word, word_count
FROM `bigquery-public-data.samples.shakespeare`
WHERE corpus = %(corpus)s
AND word_count >= %(min_word_count)s
ORDER BY word_count DESC
LIMIT 6;
'''
parameters = {
"corpus": "romeoandjuliet",
"min_word_count": 250,
}
for _ in range(0, 3):
result = warehouse.fetch_many(
operation,
parameters=parameters,
size=2
)
print(result)
Source code in prefect_gcp/bigquery.py
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|
fetch_one
async
¶
Fetch a single result from the database.
Repeated calls using the same inputs to any of the fetch methods of this block will skip executing the operation again, and instead, return the next set of results from the previous execution, until the reset_cursors method is called.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
operation |
str
|
The SQL query or other operation to be executed. |
required |
parameters |
Optional[Dict[str, Any]]
|
The parameters for the operation. |
None
|
**execution_options |
Dict[str, Any]
|
Additional options to pass to |
{}
|
Returns:
Type | Description |
---|---|
Row
|
A tuple containing the data returned by the database, where each row is a tuple and each column is a value in the tuple. |
Examples:
Execute operation with parameters, fetching one new row at a time:
from prefect_gcp.bigquery import BigQueryWarehouse
with BigQueryWarehouse.load("BLOCK_NAME") as warehouse:
operation = '''
SELECT word, word_count
FROM `bigquery-public-data.samples.shakespeare`
WHERE corpus = %(corpus)s
AND word_count >= %(min_word_count)s
ORDER BY word_count DESC
LIMIT 3;
'''
parameters = {
"corpus": "romeoandjuliet",
"min_word_count": 250,
}
for _ in range(0, 3):
result = warehouse.fetch_one(operation, parameters=parameters)
print(result)
Source code in prefect_gcp/bigquery.py
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|
get_connection
¶
Get the opened connection to BigQuery.
Source code in prefect_gcp/bigquery.py
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|
reset_cursors
¶
Tries to close all opened cursors.
Source code in prefect_gcp/bigquery.py
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|
bigquery_create_table
async
¶
Creates table in BigQuery.
Args:
dataset: Name of a dataset in that the table will be created.
table: Name of a table to create.
schema: Schema to use when creating the table.
gcp_credentials: Credentials to use for authentication with GCP.
clustering_fields: List of fields to cluster the table by.
time_partitioning: bigquery.TimePartitioning
object specifying a partitioning
of the newly created table
project: Project to initialize the BigQuery Client with; if
not provided, will default to the one inferred from your credentials.
location: The location of the dataset that will be written to.
external_config: The external data source. # noqa
Returns:
Table name.
Example:
from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.bigquery import bigquery_create_table
from google.cloud.bigquery import SchemaField
@flow
def example_bigquery_create_table_flow():
gcp_credentials = GcpCredentials(project="project")
schema = [
SchemaField("number", field_type="INTEGER", mode="REQUIRED"),
SchemaField("text", field_type="STRING", mode="REQUIRED"),
SchemaField("bool", field_type="BOOLEAN")
]
result = bigquery_create_table(
dataset="dataset",
table="test_table",
schema=schema,
gcp_credentials=gcp_credentials
)
return result
example_bigquery_create_table_flow()
Source code in prefect_gcp/bigquery.py
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|
bigquery_insert_stream
async
¶
Insert records in a Google BigQuery table via the streaming API.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset |
str
|
Name of a dataset where the records will be written to. |
required |
table |
str
|
Name of a table to write to. |
required |
records |
List[dict]
|
The list of records to insert as rows into the BigQuery table; each item in the list should be a dictionary whose keys correspond to columns in the table. |
required |
gcp_credentials |
GcpCredentials
|
Credentials to use for authentication with GCP. |
required |
project |
Optional[str]
|
The project to initialize the BigQuery Client with; if not provided, will default to the one inferred from your credentials. |
None
|
location |
str
|
Location of the dataset that will be written to. |
'US'
|
Returns:
Type | Description |
---|---|
List
|
List of inserted rows. |
Example
from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.bigquery import bigquery_insert_stream
from google.cloud.bigquery import SchemaField
@flow
def example_bigquery_insert_stream_flow():
gcp_credentials = GcpCredentials(project="project")
records = [
{"number": 1, "text": "abc", "bool": True},
{"number": 2, "text": "def", "bool": False},
]
result = bigquery_insert_stream(
dataset="integrations",
table="test_table",
records=records,
gcp_credentials=gcp_credentials
)
return result
example_bigquery_insert_stream_flow()
Source code in prefect_gcp/bigquery.py
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|
bigquery_load_cloud_storage
async
¶
Run method for this Task. Invoked by calling this Task within a Flow context, after initialization. Args: uri: GCS path to load data from. dataset: The id of a destination dataset to write the records to. table: The name of a destination table to write the records to. gcp_credentials: Credentials to use for authentication with GCP. schema: The schema to use when creating the table. job_config: Dictionary of job configuration parameters; note that the parameters provided here must be pickleable (e.g., dataset references will be rejected). project: The project to initialize the BigQuery Client with; if not provided, will default to the one inferred from your credentials. location: Location of the dataset that will be written to.
Returns:
Type | Description |
---|---|
LoadJob
|
The response from |
Example
from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.bigquery import bigquery_load_cloud_storage
@flow
def example_bigquery_load_cloud_storage_flow():
gcp_credentials = GcpCredentials(project="project")
result = bigquery_load_cloud_storage(
dataset="dataset",
table="test_table",
uri="uri",
gcp_credentials=gcp_credentials
)
return result
example_bigquery_load_cloud_storage_flow()
Source code in prefect_gcp/bigquery.py
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|
bigquery_load_file
async
¶
Loads file into BigQuery.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
dataset |
str
|
ID of a destination dataset to write the records to; if not provided here, will default to the one provided at initialization. |
required |
table |
str
|
Name of a destination table to write the records to; if not provided here, will default to the one provided at initialization. |
required |
path |
Union[str, Path]
|
A string or path-like object of the file to be loaded. |
required |
gcp_credentials |
GcpCredentials
|
Credentials to use for authentication with GCP. |
required |
schema |
Optional[List[SchemaField]]
|
Schema to use when creating the table. |
None
|
job_config |
Optional[dict]
|
An optional dictionary of job configuration parameters; note that the parameters provided here must be pickleable (e.g., dataset references will be rejected). |
None
|
rewind |
bool
|
if True, seek to the beginning of the file handle before reading the file. |
False
|
size |
Optional[int]
|
Number of bytes to read from the file handle. If size is None or large, resumable upload will be used. Otherwise, multipart upload will be used. |
None
|
project |
Optional[str]
|
Project to initialize the BigQuery Client with; if not provided, will default to the one inferred from your credentials. |
None
|
location |
str
|
location of the dataset that will be written to. |
'US'
|
Returns:
Type | Description |
---|---|
LoadJob
|
The response from |
Example
from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.bigquery import bigquery_load_file
from google.cloud.bigquery import SchemaField
@flow
def example_bigquery_load_file_flow():
gcp_credentials = GcpCredentials(project="project")
result = bigquery_load_file(
dataset="dataset",
table="test_table",
path="path",
gcp_credentials=gcp_credentials
)
return result
example_bigquery_load_file_flow()
Source code in prefect_gcp/bigquery.py
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|
bigquery_query
async
¶
Runs a BigQuery query.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
query |
str
|
String of the query to execute. |
required |
gcp_credentials |
GcpCredentials
|
Credentials to use for authentication with GCP. |
required |
query_params |
Optional[List[tuple]]
|
List of 3-tuples specifying BigQuery query parameters; currently only scalar query parameters are supported. See the Google documentation for more details on how both the query and the query parameters should be formatted. |
None
|
dry_run_max_bytes |
Optional[int]
|
If provided, the maximum number of bytes the query
is allowed to process; this will be determined by executing a dry run
and raising a |
None
|
dataset |
Optional[str]
|
Name of a destination dataset to write the query results to,
if you don't want them returned; if provided, |
None
|
table |
Optional[str]
|
Name of a destination table to write the query results to,
if you don't want them returned; if provided, |
None
|
to_dataframe |
bool
|
If provided, returns the results of the query as a pandas
dataframe instead of a list of |
False
|
job_config |
Optional[dict]
|
Dictionary of job configuration parameters; note that the parameters provided here must be pickleable (e.g., dataset references will be rejected). |
None
|
project |
Optional[str]
|
The project to initialize the BigQuery Client with; if not provided, will default to the one inferred from your credentials. |
None
|
result_transformer |
Optional[Callable[[List[Row]], Any]]
|
Function that can be passed to transform the result of a query before returning. The function will be passed the list of rows returned by BigQuery for the given query. |
None
|
location |
str
|
Location of the dataset that will be queried. |
'US'
|
Returns:
Type | Description |
---|---|
Any
|
A list of rows, or pandas DataFrame if to_dataframe, |
Any
|
matching the query criteria. |
Example
Queries the public names database, returning 10 results.
from prefect import flow
from prefect_gcp import GcpCredentials
from prefect_gcp.bigquery import bigquery_query
@flow
def example_bigquery_query_flow():
gcp_credentials = GcpCredentials(
service_account_file="/path/to/service/account/keyfile.json",
project="project"
)
query = '''
SELECT word, word_count
FROM `bigquery-public-data.samples.shakespeare`
WHERE corpus = @corpus
AND word_count >= @min_word_count
ORDER BY word_count DESC;
'''
query_params = [
("corpus", "STRING", "romeoandjuliet"),
("min_word_count", "INT64", 250)
]
result = bigquery_query(
query, gcp_credentials, query_params=query_params
)
return result
example_bigquery_query_flow()
Source code in prefect_gcp/bigquery.py
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