bi_etl.components.data_analyzer module

Created on Oct 9, 2015

@author: Derek Wood

class bi_etl.components.data_analyzer.DataAnalyzer(task: ETLTask | None = None, logical_name: str = 'DataAnalyzer', **kwargs)[source]

Bases: ETLComponent

Class that analyzes the data rows passed to it. * Tracks distinct columns passed in * Tracks datatype of each column * Tracks valid values of each column

Parameters:

  • task (ETLTask) – The instance to register in (if not None)

  • logical_name (str) – The logical name of this source. Used for log messages.

COLUMN_HEADERS = 'Column Name                                    Data Type              Non-Null Rows     Cardinality   Message'
COLUMN_HEADERS_DICT = {'cardinality': 'Cardinality', 'col': 'Column Name', 'most_common_value': 'Most Common Value', 'msg': 'Message', 'non_null_rows': 'Non-Null Rows', 'not_present_on_rows': 'Rows without this column', 'present': 'Rows with this column', 'type': 'Data Type'}
COLUMN_HEADERS_FORMAT = '{col:45}  {type:20} {non_null_rows:>15} {cardinality:>15}   {msg}'
DEFAULT_FORMAT = '{col:45}  {type:20} {non_null_rows:15,} {cardinality:15,}   {msg}'
DEFAULT_PROGRESS_FREQUENCY = 10

Default for number of seconds between progress messages when reading from this component. See ETLComponent.progress_frequency` to override.

DEFAULT_PROGRESS_MESSAGE = '{logical_name} current row # {row_number:,}'

Default progress message when reading from this component. See ETLComponent.progress_message` to override.

class DataType(name, length=None, precision=None, fmt=None)[source]

Bases: object

__init__(name, length=None, precision=None, fmt=None)[source]
EQUALS_FORMAT = '{col:45} type = {type:20} non_null_rows={non_null_rows:15,} cardinality={cardinality:15,} {msg}'
FULL_ITERATION_HEADER = 'full'

Constant value passed into ETLComponent.Row() to request all columns in the row. Deprecated: Please use ETLComponent.full_row_instance() to get a row with all columns.

PIPE_FORMAT = '{col}|{type}|{present}|{not_present_on_rows}|{non_null_rows}|{cardinality}|{most_common_value}|{msg}'
PIPE_HEADERS = 'Column Name|Data Type|Rows with this column|Rows without this column|Non-Null Rows|Cardinality|Most Common Value|Message'
Row(data: MutableMapping | Iterator | None = None, iteration_header: RowIterationHeader | str | None = None) → Row

Make a new empty row with this components structure.

__init__(task: ETLTask | None = None, logical_name: str = 'DataAnalyzer', **kwargs)[source]
analyze_column(column_name, column_value, column_number=None)[source]
analyze_row(row)[source]

Analyze the data row passed in. Call this for all the rows that should be analyzed.

build_row(source_row: Row, source_excludes: frozenset | None = None, target_excludes: frozenset | None = None, stat_name: str = 'build_row_safe', parent_stats: Statistics | None = None) → Row

Use a source row to build a row with correct data types for this table.

Parameters:

  • source_row –

  • source_excludes –

  • target_excludes –

  • stat_name – Name of this step for the ETLTask statistics. Default = ‘build rows’

  • parent_stats –

Return type:

Row

build_row_dynamic_source(source_row: Row, source_excludes: frozenset | None = None, target_excludes: frozenset | None = None, stat_name: str = 'build_row_dynamic_source', parent_stats: Statistics | None = None) → Row

Use a source row to build a row with correct data types for this table. This version expects dynamically changing source rows, so it sanity checks all rows.

Parameters:

  • source_row –

  • source_excludes –

  • target_excludes –

  • stat_name – Name of this step for the ETLTask statistics. Default = ‘build rows’

  • parent_stats –

Return type:

Row

cache_commit()
cache_row(row: Row, allow_update: bool = False, allow_insert: bool = True)
property check_row_limit
clear_cache()

Clear all lookup caches. Sets to un-cached state (unknown state v.s. empty state which is what init_cache gives)

clear_statistics()
close(error: bool = False)[source]
property column_names: List[str]

The list of column names for this component.

property column_names_set: set

A set containing the column names for this component. Usable to quickly check if the component contains a certain column.

debug_log(state: bool = True)
define_lookup(lookup_name: str, lookup_keys: list, lookup_class: Type[Lookup] = None, lookup_class_kwargs: dict | None = None)

Define a new lookup.

Parameters:

  • lookup_name – Name for the lookup. Used to refer to it later.

  • lookup_keys – list of lookup key columns

  • lookup_class – Optional python class to use for the lookup. Defaults to value of default_lookup_class attribute.

  • lookup_class_kwargs – Optional dict of additional parameters to pass to lookup constructor. Defaults to empty dict.

property empty_iteration_header: RowIterationHeader
fill_cache(progress_frequency: float = 10, progress_message='{component} fill_cache current row # {row_number:,}', criteria_list: list = None, criteria_dict: dict = None, column_list: list = None, exclude_cols: frozenset = None, order_by: list = None, assume_lookup_complete: bool = None, allow_duplicates_in_src: bool = False, row_limit: int = None, parent_stats: Statistics = None)

Fill all lookup caches from the table.

Parameters:

  • progress_frequency – How often (in seconds) to output progress messages. Default 10. None for no progress messages.

  • progress_message – The progress message to print. Default is "{component} fill_cache current row # {row_number:,}". Note logical_name and row_number substitutions applied via format().

  • criteria_list – Each string value will be passed to sqlalchemy.sql.expression.Select.where(). https://goo.gl/JlY9us

  • criteria_dict – Dict keys should be columns, values are set using = or in

  • column_list – List of columns to include

  • exclude_cols (frozenset) – Optional. Columns to exclude when filling the cache

  • order_by – list of columns to sort by when filling the cache (helps range caches)

  • assume_lookup_complete – Should later lookup calls assume the cache is complete? If so, lookups will raise an Exception if a key combination is not found. Default to False if filtering criteria was used, otherwise defaults to True.

  • allow_duplicates_in_src – Should we quietly let the source provide multiple rows with the same key values? Default = False

  • row_limit – limit on number of rows to cache.

  • row_limit – limit on number of rows to cache.

  • parent_stats – Optional Statistics object to nest this steps statistics in. Default is to place statistics in the ETLTask level statistics.

fill_cache_from_source(source: ETLComponent, progress_frequency: float = 10, progress_message='{component} fill_cache current row # {row_number:,}', criteria_list: list = None, criteria_dict: dict = None, column_list: list = None, exclude_cols: frozenset = None, order_by: list = None, assume_lookup_complete: bool = None, allow_duplicates_in_src: bool = False, row_limit: int = None, parent_stats: Statistics = None)

Fill all lookup caches from the database table. Note that filtering criteria can be specified so that the resulting cache is not the entire current contents. See assume_lookup_complete for how the lookup will handle cache misses – note only database table backed components have the ability to fall back to querying the existing data on cache misses.

Parameters:

  • source – Source component to get rows from.

  • progress_frequency – How often (in seconds) to output progress messages. Default 10. None for no progress messages.

  • progress_message – The progress message to print. Default is "{component} fill_cache current row # {row_number:,}". Note logical_name and row_number substitutions applied via format().

  • criteria_list – Each string value will be passed to sqlalchemy.sql.expression.Select.where(). https://goo.gl/JlY9us

  • criteria_dict – Dict keys should be columns, values are set using = or in

  • column_list – List of columns to include

  • exclude_cols – Optional. Columns to exclude when filling the cache

  • order_by – list of columns to sort by when filling the cache (helps range caches)

  • assume_lookup_complete – Should later lookup calls assume the cache is complete? If so, lookups will raise an Exception if a key combination is not found. Default to False if filtering criteria was used, otherwise defaults to True.

  • allow_duplicates_in_src – Should we quietly let the source provide multiple rows with the same key values? Default = False

  • row_limit – limit on number of rows to cache.

  • parent_stats – Optional Statistics object to nest this steps statistics in. Default is to place statistics in the ETLTask level statistics.

property full_iteration_header: RowIterationHeader
full_row_instance(data: MutableMapping | Iterator | None = None) → Row

Build a full row (all columns) using the source data.

Note: If data is passed here, it uses bi_etl.components.row.row.Row.update() to map the data into the columns. That is nicely automatic, but slower since it has to try various ways to read the data container object.

Consider using the appropriate one of the more specific update methods based on the source data container.

generate_iteration_header(logical_name: str | None = None, columns_in_order: list | None = None, result_primary_key: list | None = None) → RowIterationHeader
get_analysis_str() → str[source]
get_by_lookup(lookup_name: str, source_row: Row, stats_id: str = 'get_by_lookup', parent_stats: Statistics | None = None, fallback_to_db: bool = False) → Row

Get by an alternate key. Returns a Row

Throws:

NoResultFound

get_column_name(column: str)
get_lookup(lookup_name: str) → Lookup
get_lookup_keys(lookup_name: str) → list
get_lookup_tuple(lookup_name: str, row: Row) → tuple
get_qualified_lookup_name(base_lookup_name: str) → str
get_stats_entry(stats_id: str, parent_stats: Statistics | None = None, print_start_stop_times: bool | None = None)
get_unique_stats_entry(stats_id: str, parent_stats: Statistics | None = None, print_start_stop_times: bool | None = None)
init_cache()

Initialize all lookup caches as empty.

property is_closed
iter_result(result_list: object, columns_in_order: list | None = None, criteria_dict: dict | None = None, logical_name: str | None = None, progress_frequency: int | None = None, stats_id: str | None = None, parent_stats: Statistics | None = None) → Iterable[Row]
Yields:

row (Row) – next row

static kwattrs_order() → Dict[str, int]

Certain values need to be set before others in order to work correctly. This method should return a dict mapping those key values = arg name to a value less than the default of 9999, which will be used for any arg not explicitly listed here.

log_analysis()[source]
log_progress(row: Row, stats: Statistics)
logging_level_reported = False

Has the logging level of this component been reported (logged) yet? Stored at class level so that it can be logged only once.

property lookups
next_row()[source]
static null_safe_max(a: int | None, b: int | None) → int | None[source]
property primary_key: list

The name of the primary key column(s). Only impacts trace messages. Default=Empty list.

property primary_key_tuple: tuple

The name of the primary key column(s) in a tuple. Used when a hashable PK definition is needed.

print_analysis(out: TextIOBase = None, valid_value_limit: int = 10, columns_header: str = None, columns_out_fmt: str = None)[source]

Print the data analysis results.

Parameters:

  • out – The File to write the results to. Default=``stdout`` valid_value_limit (int): How many valid values should be printed.

  • valid_value_limit – The number of valid values to output

  • columns_header – The table header for the columns list

  • columns_out_fmt – The format to use for lines

property progress_frequency: int

How often (in seconds) to output progress messages. None for no progress messages.

property row_name: str
property rows_read: int

int The number of rows read and returned.

sanity_check_example_row(example_source_row, source_excludes=None, target_excludes=None, ignore_source_not_in_target=None, ignore_target_not_in_source=None)
sanity_check_source_mapping(source_definition: ETLComponent, source_name: str = None, source_excludes: frozenset = None, target_excludes: frozenset = None, ignore_source_not_in_target: bool = None, ignore_target_not_in_source: bool = None, raise_on_source_not_in_target: bool = None, raise_on_target_not_in_source: bool = None)
set_kwattrs(**kwargs)
property statistics
property trace_data: bool

boolean Should a debug message be printed with the parsed contents (as columns) of each row.

uncache_row(row)
uncache_where(key_names, key_values_dict)
where(criteria_list: list | None = None, criteria_dict: dict | None = None, order_by: list | None = None, column_list: List[Column | str] = None, exclude_cols: FrozenSet[Column | str] = None, use_cache_as_source: bool | None = None, progress_frequency: int | None = None, stats_id: str | None = None, parent_stats: Statistics | None = None) → Iterable[Row]
Parameters:
Return type:

rows