utilities for reconciling data
Project description
Data Reconciler
A set of utilities which can used to validate data (tables only at the moment) using polars LazyFrames.
Comparing Tables
The following examples use dbqq to manage database login details and grab table data
The queries
Let's work with the two following queries, in our example we have a module tests
with the following queries query1 and query2
select
nvl (to_char (settlementdate, 'YYYY-MM-DD'), -1) as settlementdate,
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid,
nvl (
to_char (max(effective_from_timestamp), 'YYYY-MM-DD'),
-1
) as max_effective_from_timestamp,
nvl (sum(energy), -1) as sum_energy,
nvl (sum(marketfeevalue), -1) as sum_marketfeevalue,
nvl (sum(feerate), -1) as sum_feerate,
nvl (sum(feeunits), -1) as sum_feeunits
from
sds.info_g_setmarketfees
where
1 = 1
and effective_from_timestamp < sysdate - 2
and effective_to_timestamp > sysdate
group by
nvl (to_char (settlementdate, 'YYYY-MM-DD'), -1),
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid
order by
settlementdate,
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid
fetch first 10000 rows only
select
nvl (cast(date (settlementdate) as string), -1) as settlementdate,
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid,
nvl (
cast(date (max(effective_from_timestamp)) as string),
-1
) as max_effective_from_timestamp,
nvl (sum(energy), -1) as sum_energy,
nvl (sum(marketfeevalue), -1) as sum_marketfeevalue,
nvl (sum(feerate), -1) as sum_feerate,
nvl (sum(feeunits), -1) as sum_feeunits
from
ea_edp_prod_infoserver_generation_raw.setmarketfees
where
1 = 1
and active_ind = 'Y'
group by
nvl (cast(date (settlementdate) as string), -1),
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid
order by
nvl (cast(date (settlementdate) as string), -1),
runno,
participantid,
periodid,
marketfeeid,
participantcategoryid
limit 10500
The Imports
from dbqq import connectors
from datarec import tables
from tests.queries import query1, query2
Table Comparison
The datarec package provides two methods for comparing tables:
datarec.tables.is_equal: Compares data from two tables, ensuring that the values are equal. datarec.tables.is_close_numeric: Compares numeric values from two tables, ensuring that the values are close based on absolute tolerance a_tol and relative tolerance r_tol. Both comparison methods share similar keyword arguments, except for the a_tol and r_tol arguments exclusive to is_close_numeric.
Index Arguments During testing, each table is indexed, and each testable entry in the row is validated. Each column has a suffix:
...: Specifies the table location....: Specifies the test results.- ...: Specifies if the column was found in either the left, right, or both tables.
- No surrounding text for the suffix: Specifies columns used as indexes.
The most useful arguments for the comparison methods are columns_to_ignore and column_indexes. When no column_indexes are provided, a row number is used as the index.
Indexes can be defined using an iterable object. In the indexed example, we set the range to [0,1,2,3,4,5]. These indexes are not validated and do not have a suffix. The group of indexes must be distinct and match rows between tables.
Like indexes, ignored columns can be set. Ignored columns are not indexed or validated, but their values can be compared visually.
Lastly, at the end of the table are the left, right, and both identifier columns. These columns indicate if the matched columns via index are in the left table, right table, or both tables.
Without Index
connector1 = connectors.oracle.improd()
connector2 = connectors.databricks.prod()
p1 = connector1.cache()(query1)
p2 = connector2.cache()(query2)
validation = tables.is_equal(
p1,
p2,
pl1_name="left",
pl2_name="right",
show_both_first=True,
show_failed_first=True,
interlaced=True,
column_case="upper",
columns_to_ignore=None,
column_indexes=None,
)
validation.results.collect()
shape: (10_500, 37)
┌────────────┬───────────────────────┬────────────────────────┬────────────────┬──────────────┬───────────────┬──────────────────────┬──────────────────────┬───────────────────────┬──────────────────────────────┬───┬────────────────────┬────────────────────┬─────────────────────┬────────────────────────────┬─────────────────────┬──────────────────────┬─────────────────────────────┬──────────┬───────────┬──────────┐
│ row_number ┆ SETTLEMENTDATE ~LEFT~ ┆ SETTLEMENTDATE ~RIGHT~ ┆ SETTLEMENTDATE ┆ RUNNO ~LEFT~ ┆ RUNNO ~RIGHT~ ┆ RUNNO ~*VALIDATION*~ ┆ PARTICIPANTID ~LEFT~ ┆ PARTICIPANTID ~RIGHT~ ┆ PARTICIPANTID ~*VALIDATION*~ ┆ … ┆ SUM_MARKETFEEVALUE ┆ SUM_FEERATE ~LEFT~ ┆ SUM_FEERATE ~RIGHT~ ┆ SUM_FEERATE ~*VALIDATION*~ ┆ SUM_FEEUNITS ~LEFT~ ┆ SUM_FEEUNITS ~RIGHT~ ┆ SUM_FEEUNITS ~*VALIDATION*~ ┆ **LEFT** ┆ **RIGHT** ┆ **BOTH** │
│ --- ┆ --- ┆ --- ┆ ~*VALIDATION*~ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ ┆ ~*VALIDATION*~ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ u32 ┆ str ┆ str ┆ --- ┆ i64 ┆ i64 ┆ bool ┆ str ┆ str ┆ bool ┆ ┆ --- ┆ f64 ┆ f64 ┆ bool ┆ f64 ┆ f64 ┆ bool ┆ bool ┆ bool ┆ bool │
│ ┆ ┆ ┆ bool ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ bool ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │
╞════════════╪═══════════════════════╪════════════════════════╪════════════════╪══════════════╪═══════════════╪══════════════════════╪══════════════════════╪═══════════════════════╪══════════════════════════════╪═══╪════════════════════╪════════════════════╪═════════════════════╪════════════════════════════╪═════════════════════╪══════════════════════╪═════════════════════════════╪══════════╪═══════════╪══════════╡
│ 0 ┆ 1998-07-12 ┆ 1998-07-12 ┆ true ┆ 5 ┆ 5 ┆ true ┆ YALLOURN ┆ YALLOURN ┆ true ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 1 ┆ 1998-07-12 ┆ 1998-07-12 ┆ true ┆ 5 ┆ 5 ┆ true ┆ YALLOURN ┆ YALLOURN ┆ true ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 2 ┆ 1998-07-12 ┆ 1998-07-12 ┆ true ┆ 5 ┆ 5 ┆ true ┆ YALLOURN ┆ YALLOURN ┆ true ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 3 ┆ 1998-07-12 ┆ 1998-07-12 ┆ true ┆ 5 ┆ 5 ┆ true ┆ YALLOURN ┆ YALLOURN ┆ true ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 10496 ┆ null ┆ 1998-08-10 ┆ false ┆ null ┆ 3 ┆ false ┆ null ┆ YALLOURN ┆ false ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 10497 ┆ null ┆ 1998-08-10 ┆ false ┆ null ┆ 3 ┆ false ┆ null ┆ YALLOURN ┆ false ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 10498 ┆ null ┆ 1998-08-10 ┆ false ┆ null ┆ 3 ┆ false ┆ null ┆ YALLOURN ┆ false ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 10499 ┆ null ┆ 1998-08-10 ┆ false ┆ null ┆ 3 ┆ false ┆ null ┆ YALLOURN ┆ false ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
└────────────┴───────────────────────┴────────────────────────┴────────────────┴──────────────┴───────────────┴──────────────────────┴──────────────────────┴───────────────────────┴──────────────────────────────┴───┴────────────────────┴────────────────────┴─────────────────────┴────────────────────────────┴─────────────────────┴──────────────────────┴─────────────────────────────┴──────────┴───────────┴──────────┘
With Index
connector1 = connectors.oracle.improd()
connector2 = connectors.databricks.prod()
p1 = connector1.cache()(query1)
p2 = connector2.cache()(query2)
validation = tables.is_close_numeric(
p1,
p2,
pl1_name="left",
pl2_name="right",
show_both_first=True,
show_failed_first=True,
interlaced=True,
column_case="upper",
column_indexes=range(0, 6),
columns_to_ignore=[0],
a_tol=10e-3,
r_tol=10e-4,
)
validation.results.collect()
shape: (10_500, 23)
┌────────────────┬───────┬───────────────┬──────────┬─────────────┬───────────────────────┬─────────────────────────────────────┬──────────────────────────────────────┬───────────────────┬────────────────────┬───┬───────────────────────────────────┬────────────────────┬─────────────────────┬────────────────────────────┬─────────────────────┬──────────────────────┬─────────────────────────────┬──────────┬───────────┬──────────┐
│ SETTLEMENTDATE ┆ RUNNO ┆ PARTICIPANTID ┆ PERIODID ┆ MARKETFEEID ┆ PARTICIPANTCATEGORYID ┆ MAX_EFFECTIVE_FROM_TIMESTAMP ~LEFT~ ┆ MAX_EFFECTIVE_FROM_TIMESTAMP ~RIGHT~ ┆ SUM_ENERGY ~LEFT~ ┆ SUM_ENERGY ~RIGHT~ ┆ … ┆ SUM_MARKETFEEVALUE ~*VALIDATION*~ ┆ SUM_FEERATE ~LEFT~ ┆ SUM_FEERATE ~RIGHT~ ┆ SUM_FEERATE ~*VALIDATION*~ ┆ SUM_FEEUNITS ~LEFT~ ┆ SUM_FEEUNITS ~RIGHT~ ┆ SUM_FEEUNITS ~*VALIDATION*~ ┆ **LEFT** ┆ **RIGHT** ┆ **BOTH** │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str ┆ i64 ┆ str ┆ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ ┆ bool ┆ f64 ┆ f64 ┆ bool ┆ f64 ┆ f64 ┆ bool ┆ bool ┆ bool ┆ bool │
╞════════════════╪═══════╪═══════════════╪══════════╪═════════════╪═══════════════════════╪═════════════════════════════════════╪══════════════════════════════════════╪═══════════════════╪════════════════════╪═══╪═══════════════════════════════════╪════════════════════╪═════════════════════╪════════════════════════════╪═════════════════════╪══════════════════════╪═════════════════════════════╪══════════╪═══════════╪══════════╡
│ 1998-07-12 ┆ 5 ┆ YALLOURN ┆ 0 ┆ F_ADMIN ┆ ALL ┆ 2019-08-01 ┆ 2022-07-06 ┆ 0.0 ┆ 0.0 ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 1998-07-12 ┆ 5 ┆ YALLOURN ┆ 1 ┆ V_ADMIN ┆ ALL ┆ 2019-08-01 ┆ 2022-07-06 ┆ -4.66778 ┆ -4.66778 ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 1998-07-12 ┆ 5 ┆ YALLOURN ┆ 1 ┆ V_EST ┆ ALL ┆ 2019-08-01 ┆ 2022-07-06 ┆ -4.66778 ┆ -4.66778 ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ 1998-07-12 ┆ 5 ┆ YALLOURN ┆ 1 ┆ V_METERING ┆ ALL ┆ 2019-08-01 ┆ 2022-07-06 ┆ -4.66778 ┆ -4.66778 ┆ … ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ -1.0 ┆ -1.0 ┆ true ┆ true ┆ true ┆ true │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 17 ┆ V_SETT ┆ ALL ┆ null ┆ 2022-07-06 ┆ null ┆ 0.0 ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_ADMIN ┆ ALL ┆ null ┆ 2022-07-06 ┆ null ┆ 0.0 ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_EST ┆ ALL ┆ null ┆ 2022-07-06 ┆ null ┆ 0.0 ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_METERING ┆ ALL ┆ null ┆ 2022-07-06 ┆ null ┆ 0.0 ┆ … ┆ false ┆ null ┆ -1.0 ┆ false ┆ null ┆ -1.0 ┆ false ┆ false ┆ true ┆ false │
└────────────────┴───────┴───────────────┴──────────┴─────────────┴───────────────────────┴─────────────────────────────────────┴──────────────────────────────────────┴───────────────────┴────────────────────┴───┴───────────────────────────────────┴────────────────────┴─────────────────────┴────────────────────────────┴─────────────────────┴──────────────────────┴─────────────────────────────┴──────────┴───────────┴──────────┘
TableReconciliationData Objects
After a table reconciliation a TableReconciliationData object is returned, we
can view the content of the object as dictionary using the dataclasses.asdict
method. Using the solution from the With Index section
Content
import dataclasses
from pprint import pprint
pprint(dataclasses.asdict(validation), width=20, sort_dicts=False)
{'results': <polars.LazyFrame object at 0x25973615A30>,
'left': 'LEFT',
'right': 'RIGHT',
'columns_all': ['SETTLEMENTDATE',
'RUNNO',
'PARTICIPANTID',
'PERIODID',
'MARKETFEEID',
'PARTICIPANTCATEGORYID',
'MAX_EFFECTIVE_FROM_TIMESTAMP',
'SUM_ENERGY',
'SUM_MARKETFEEVALUE',
'SUM_FEERATE',
'SUM_FEEUNITS'],
'columns_indexes': ['SETTLEMENTDATE',
'RUNNO',
'PARTICIPANTID',
'PERIODID',
'MARKETFEEID',
'PARTICIPANTCATEGORYID'],
'columns_ignored': ['MAX_EFFECTIVE_FROM_TIMESTAMP'],
'columns_tested': ['SUM_ENERGY',
'SUM_MARKETFEEVALUE',
'SUM_FEERATE',
'SUM_FEEUNITS']}
Left and Right Rows
We can also isolate the rows which are contained only in the left and right tables.
[!Note]
When extracting rows only in the left and right tables, suffixed and location columns are not included
validation.get_rows_left_only().collect()
shape: (0, 11)
┌────────────────┬───────┬───────────────┬──────────┬─────────────┬───────────────────────┬──────────────────────────────┬────────────┬────────────────────┬─────────────┬──────────────┐
│ SETTLEMENTDATE ┆ RUNNO ┆ PARTICIPANTID ┆ PERIODID ┆ MARKETFEEID ┆ PARTICIPANTCATEGORYID ┆ MAX_EFFECTIVE_FROM_TIMESTAMP ┆ SUM_ENERGY ┆ SUM_MARKETFEEVALUE ┆ SUM_FEERATE ┆ SUM_FEEUNITS │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str ┆ i64 ┆ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════════╪═══════╪═══════════════╪══════════╪═════════════╪═══════════════════════╪══════════════════════════════╪════════════╪════════════════════╪═════════════╪══════════════╡
└────────────────┴───────┴───────────────┴──────────┴─────────────┴───────────────────────┴──────────────────────────────┴────────────┴────────────────────┴─────────────┴──────────────┘
validation.get_rows_right_only().collect()
shape: (500, 11)
┌────────────────┬───────┬───────────────┬──────────┬─────────────┬───────────────────────┬──────────────────────────────┬────────────┬────────────────────┬─────────────┬──────────────┐
│ SETTLEMENTDATE ┆ RUNNO ┆ PARTICIPANTID ┆ PERIODID ┆ MARKETFEEID ┆ PARTICIPANTCATEGORYID ┆ MAX_EFFECTIVE_FROM_TIMESTAMP ┆ SUM_ENERGY ┆ SUM_MARKETFEEVALUE ┆ SUM_FEERATE ┆ SUM_FEEUNITS │
│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ str ┆ i64 ┆ str ┆ str ┆ str ┆ f64 ┆ f64 ┆ f64 ┆ f64 │
╞════════════════╪═══════╪═══════════════╪══════════╪═════════════╪═══════════════════════╪══════════════════════════════╪════════════╪════════════════════╪═════════════╪══════════════╡
│ 1998-08-08 ┆ 3 ┆ YALLOURN ┆ 4 ┆ V_ADMIN ┆ ALL ┆ 2022-07-06 ┆ -5.08651 ┆ -0.55494 ┆ -1.0 ┆ -1.0 │
│ 1998-08-08 ┆ 3 ┆ YALLOURN ┆ 4 ┆ V_EST ┆ ALL ┆ 2022-07-06 ┆ -5.08651 ┆ -0.35758 ┆ -1.0 ┆ -1.0 │
│ 1998-08-08 ┆ 3 ┆ YALLOURN ┆ 4 ┆ V_METERING ┆ ALL ┆ 2022-07-06 ┆ -5.08651 ┆ -0.04018 ┆ -1.0 ┆ -1.0 │
│ 1998-08-08 ┆ 3 ┆ YALLOURN ┆ 4 ┆ V_NECA ┆ ALL ┆ 2022-07-06 ┆ -5.08651 ┆ -0.20143 ┆ -1.0 ┆ -1.0 │
│ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … ┆ … │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 17 ┆ V_SETT ┆ ALL ┆ 2022-07-06 ┆ 0.0 ┆ 0.0 ┆ -1.0 ┆ -1.0 │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_ADMIN ┆ ALL ┆ 2022-07-06 ┆ 0.0 ┆ 0.0 ┆ -1.0 ┆ -1.0 │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_EST ┆ ALL ┆ 2022-07-06 ┆ 0.0 ┆ 0.0 ┆ -1.0 ┆ -1.0 │
│ 1998-08-10 ┆ 3 ┆ YALLOURN ┆ 18 ┆ V_METERING ┆ ALL ┆ 2022-07-06 ┆ 0.0 ┆ 0.0 ┆ -1.0 ┆ -1.0 │
└────────────────┴───────┴───────────────┴──────────┴─────────────┴───────────────────────┴──────────────────────────────┴────────────┴────────────────────┴─────────────┴──────────────┘
Summarizing Results
To summarize the results of a table validation we can call a
summarize_reconciliation object with a summarization join method. The join
method can be either left, right,inner and outer. The join method will
specify how the summarization of the results is performed
import dataclasses
from pprint import pprint
pprint(
dataclasses.asdict(tables.summarize_reconciliation(validation_no_index, "left")),
width=20,
sort_dicts=False
)
outputs
{'n_tested_rows': 10000,
'n_tested_cols': 4,
'n_tested_entries': 40000,
'n_tested_entries_passed': 40000,
'n_tested_entries_failed': 0,
'n_tested_rows_passed': 10000,
'n_tested_rows_passed_partially': 0,
'n_tested_rows_failed': 0,
'validation_ratio_entries': 1.0,
'validation_ratio_rows': 1.0,
'stats_invalidations_per_row_avg': 0.0,
'stats_invalidations_per_row_std': 0.0,
'n_total_rows_left': 10000,
'n_total_rows_right': 10500,
'n_total_rows_intersecting': 10000,
'n_total_rows_union': 10500,
'pass_ratio': 1}
To check whether or not the test has passed
tables.summarize_reconciliation(validation_no_index, "left").PASS
returns a boolean
True
we can call flag a string describing the outcome of the test
tables.summarize_reconciliation(validation_no_index, "left").flag
returns
'PASSED'
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