Query

exception analyze.query.QueryDecodeError
analyze.query.allocate(project_id, source_query, driver_query, source_target_columns, driver_target_columns, target_frame_id)

Performs an allocation based on the provided sqlalchemy source and driver data queries

Parameters

  • project_id (str) – Unique project identifier

  • source_query (str) – Base64 encoded pickled sqlalchemy query for source data

  • driver_query (str) – Base64 encoded pickled sqlalchemy query for driver data

  • source_target_columns (dict) – Target columns for the allocation source data

  • driver_target_columns (dict) – Target columns for the driver data

  • target_frame_id (str) – Unique target table identifier

  • append (bool) – Append results to target table

Returns

None

analyze.query.delete(project_id, delete_query, recreate=False)

Performs a table delete

Parameters

  • project_id (str) – Unique project identifier

  • delete_query (str) – Base64 encoded pickled sqlalchemy query for delete statement

  • recreate (bool) – Whether to recreate the table (prevent increase in size)

Returns

None

analyze.query.dialect()

Returns the current datastore dialect, for use with sqlalchemy

Args:

Returns

The dialect of the database backend (‘postgresql’, ‘greenplum’ or ‘hana’)

Return type

str

analyze.query.download_archive(project_id, model_id, table_name)

Generates a table archive download from the specified table_name

Parameters

  • project_id (str) – Unique project identifier

  • model_id (str) –

  • table_name (str) –

Returns

None

download_csv(project_id, project_id, table_name=None, query=None, params=None, encoding='UTF8')

Generates a CSV download stream from the specified query.

Parameters

  • project_id (str) – Unique project identifier

  • table_name (str) – The fully-qualified name (i.e. “schema”.”table”) of the table to download.

  • query (str) – The query to execute. If params is set, this value is assumed to be a query template.

  • params (str) – The parameter collection (if any) to combine with the query template.

  • encoding (str) – The character encoding for the database to use when exporting data as CSV.

Returns

Entire result set as CSV, streamed using chunked transfer encoding.

Notes

query takes precedence over table_name. In other words, if both are specified, query will be used.

analyze.query.download_typed_csv(project_id, query, delimiter, quote_char, escape_char)

Generates a data typed CSV download from the specified query

Parameters

  • project_id (str) – Unique project identifier

  • query (str) – The query to execute. The result will be what is output to CSV

  • delimiter (str) – What to use as a delimiter

  • quote_char (str) – What to use as a quote character

  • escape_char (str) – What to use as an escape character

Returns

The download URI, in the uri keword of a dict

Return type

dict

analyze.query.download_xls(project_id, query, params=None, sheetname='sheet1', headers=True, types=None)

Generates a .xls file download from the specified query

Parameters

  • project_id (str) – Unique project identifier

  • query (str) – The query to execute. The results will be wrtten to the xls file

  • params (dict) – Excel publishing parameters

  • sheetname (str, optional) – What to name the worksheet

  • headers (bool, optional) – Should headers be included in the xls file?

  • types (str) – Types

Returns

The download URI, in the uri keword of a dict

Return type

dict

analyze.query.download_xlsx(project_id, query, params=None, sheetname='sheet1', headers=True, types=None)

Generates a .xlsx file download from the specified query

Parameters

  • project_id (str) – Unique project identifier

  • query (str) – The query to execute. The results will be wrtten to the xls file

  • params (dict) – Excel publishing parameters

  • sheetname (str, optional) – What to name the worksheet

  • headers (bool, optional) – Should headers be included in the xls file?

  • types (str) – Types

Returns

The download URI, in the uri keword of a dict

Return type

dict

analyze.query.facets(project_id, query, params=None)

Generates table facet information by column

Parameters

  • project_id (str) – Unique project identifier

  • query (str) – The query to execute. Facets will be determined based on the results of this query.

  • params (dict) – Publishing parameters

Returns

A dict containing facets for each column

Return type

dict

analyze.query.melt(project_id, source_query, target_frame_id, id_vars, value_vars=None, value_name='value', var_name='variable')

Performs an table unpivot/melt based on the provided sqlalchemy source query

Parameters

  • project_id (str) – Unique project identifier

  • source_query (str) – Base64 encoded pickled sqlalchemy query for source data

  • target_frame_id (str) –

  • id_vars (list) – Base64 encoded pickled sqlalchemy query for driver data

  • value_vars (list, optional) – Target columns for the allocation source data

  • value_name (str) – Target columns for the driver data

  • var_name (str) – Target columns for the driver data

Returns

None

analyze.query.pivot(project_id, source_query, category_column, value_column, target_frame_id)

Performs an table pivot based on the provided sqlalchemy source, given the columns for headers and values

Parameters

  • project_id (str) – Unique project identifier

  • source_query (str) – Base64 encoded pickled sqlalchemy query for source data

  • category_column (str) – Column to transform into column headers

  • value_column (str) – Column to use for values

  • agg_function (str) – Aggregation method

  • target_frame_id (str) – Unique target table identifier

Returns

None

analyze.query.query(project_id, query, params=None, max_rows=None)

Executes the specified query and returns the results

Parameters

  • project_id (str) – Unique project identifier

  • query (str) – The data-returning query to execute

  • params (dict, optional) – The query parameters

  • max_rows (int, optional) – The maximum number of rows to return

  • is_encoded (bool, optional) – If the query is and encoded, pickled SQLAlchemy object

Returns

The results of the query, represented as a list of dicts

Return type

list

analyze.query.query_config(project_id, config)

Builds a query based on the supplied config, executes the query and returns the results

Parameters

  • project_id (str) – Unique project identifier

  • config (dict) – The data-returning query to execute

Returns

The results of the query, represented as a list of dicts

Return type

list

analyze.query.table(project_id, table_id, version_id=None)

Provides detailed table information

Parameters

  • project_id (str) – Unique project identifier

  • table_id (str) – Unique table identifier

  • version_id – version??

Returns

dict with the following properties

  • meta (dict): Table and query meta data

  • rows (list): Data rows

Return type

dict

analyze.query.update(project_id, update_query)

Performs a table update

Parameters

  • project_id (str) – Unique project identifier

  • update_query (str) – Base64 encoded pickled sqlalchemy query for update statement

Returns

None

analyze.query.upsert(project_id, update_query, insert_query, recreate=False)

Performs a table upsert

Parameters

  • project_id (str) – Unique project identifier

  • update_query (str) – Base64 encoded pickled sqlalchemy query for update statement

  • insert_query (str) – Base64 encoded pickled sqlalchemy query for insert statement

  • recreate (bool) – Whether to recreate the table (prevent increase in size)

Returns

None