From b20b6e2a0e7bbb4833786b3f5f408093f0c940f1 Mon Sep 17 00:00:00 2001
From: lchen-2101 <73617864+lchen-2101@users.noreply.github.com>
Date: Thu, 6 Feb 2025 10:18:05 -0800
Subject: [PATCH] Feature/335 parquet lf processing (#338)

closes #335

---------

Co-authored-by: Adam <41971533+jcadam14@users.noreply.github.com>
---
 poetry.lock                                   | 1469 ++++-------------
 pyproject.toml                                |    6 +-
 src/regtech_data_validator/cli.py             |   31 +-
 src/regtech_data_validator/data_formatters.py |    5 +-
 .../validation_results.py                     |    1 +
 src/regtech_data_validator/validator.py       |  253 +--
 tests/test_schema_functions.py                |    5 +-
 7 files changed, 522 insertions(+), 1248 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 700a9ea..8e744db 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,166 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
-
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-
-[package.extras]
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-
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-    {file = "yarl-1.18.3.tar.gz", hash = "sha256:ac1801c45cbf77b6c99242eeff4fffb5e4e73a800b5c4ad4fc0be5def634d2e1"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-propcache = ">=0.2.0"
-
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.12,<4"
-content-hash = "0b78d214e46cf07373b811fe18ef3785e10a09a788624a21834e28d087151f3d"
+content-hash = "11a3c9f0a1af81642e4f9224005f3640c077b337d210ddf005d47505890e7936"
diff --git a/pyproject.toml b/pyproject.toml
index e98bad0..08fd727 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,21 +14,17 @@ packages = [{ include = "regtech_data_validator", from = "src" }]
 python = ">=3.12,<4"
 pandas = "^2.2.2"
 pandera = "^0.22.1"
-requests = "^2.32.3"
 tabulate = "^0.9.0"
 ujson = "^5.9.0"
-fsspec = "^2024.6.1"
 polars-lts-cpu = "^1.21.0"
 pyarrow = "^19.0.0"
-boto3 = "~1.36.3"
-#pinning due to snyk high vulnerability find
-s3fs = { version = "^2024.9.0", extras = ["aiohttp=^3.11.10"] }
 
 [tool.poetry.group.dev.dependencies]
 pytest = "8.3.4"
 pytest-cov = "6.0.0"
 beautifulsoup4 = "^4.12.3"
 lxml = "^5.3.0"
+requests = "^2.32.3"
 
 [tool.poetry.group.data.dependencies]
 openpyxl = "^3.1.5"
diff --git a/src/regtech_data_validator/cli.py b/src/regtech_data_validator/cli.py
index 1ab0397..cdf5211 100644
--- a/src/regtech_data_validator/cli.py
+++ b/src/regtech_data_validator/cli.py
@@ -1,6 +1,5 @@
 from dataclasses import dataclass
 from enum import StrEnum
-from pathlib import Path
 from regtech_data_validator.data_formatters import df_to_csv, df_to_str, df_to_json, df_to_table, df_to_download
 from typing import Annotated, Optional
 
@@ -8,7 +7,7 @@
 import typer
 import typer.core
 
-from regtech_data_validator.validator import validate_batch_csv
+from regtech_data_validator.validator import validate_batch_csv, validate_lazy_frame
 from regtech_data_validator.validation_results import ValidationPhase
 
 # Need to do this because the latest version of typer, if the rich package exists
@@ -22,6 +21,11 @@
 app = typer.Typer(no_args_is_help=True, pretty_exceptions_enable=False)
 
 
+class FileType(StrEnum):
+    CSV = 'csv'
+    PARQUET = 'parquet'
+
+
 @dataclass
 class KeyValueOpt:
     key: str
@@ -57,7 +61,7 @@ def describe() -> None:
 @app.command(no_args_is_help=True)
 def validate(
     path: Annotated[
-        Path,
+        str,
         typer.Argument(
             exists=True,
             dir_okay=False,
@@ -77,6 +81,7 @@ def validate(
         ),
     ] = None,
     output: Annotated[Optional[OutputFormat], typer.Option()] = OutputFormat.TABLE,
+    filetype: Annotated[Optional[FileType], typer.Option()] = FileType.CSV,
 ) -> tuple[bool, pl.DataFrame]:
     """
     Validate CFPB data submission
@@ -87,11 +92,21 @@ def validate(
     final_phase = ValidationPhase.LOGICAL
     all_findings = []
     final_df = pl.DataFrame()
-    # path = "s3://cfpb-devpub-regtech-sbl-filing-main/upload/2024/1234364890REGTECH006/156.csv"
-    for validation_results in validate_batch_csv(path, context_dict, batch_size=50000, batch_count=1):
-        total_findings += validation_results.error_counts.total_count + validation_results.warning_counts.total_count
-        final_phase = validation_results.phase
-        all_findings.append(validation_results)
+    if filetype == FileType.CSV:
+        for validation_results in validate_batch_csv(path, context_dict, batch_size=50000, batch_count=1):
+            total_findings += (
+                validation_results.error_counts.total_count + validation_results.warning_counts.total_count
+            )
+            final_phase = validation_results.phase
+            all_findings.append(validation_results)
+    elif filetype == FileType.PARQUET:
+        lf = pl.scan_parquet(path, allow_missing_columns=True)
+        for validation_results in validate_lazy_frame(lf, context_dict, batch_size=50000, batch_count=1):
+            total_findings += (
+                validation_results.error_counts.total_count + validation_results.warning_counts.total_count
+            )
+            final_phase = validation_results.phase
+            all_findings.append(validation_results)
 
     if all_findings:
         final_df = pl.concat([v.findings for v in all_findings], how="diagonal")
diff --git a/src/regtech_data_validator/data_formatters.py b/src/regtech_data_validator/data_formatters.py
index faf7864..ab34a29 100644
--- a/src/regtech_data_validator/data_formatters.py
+++ b/src/regtech_data_validator/data_formatters.py
@@ -1,6 +1,7 @@
 import ujson
 import polars as pl
 
+from typing import Dict, Any
 from tabulate import tabulate
 
 from functools import partial
@@ -54,7 +55,7 @@ def format_findings(df: pl.DataFrame, phase, checks):
                 "record_no",
                 "uid",
             ],
-            columns="field_number",
+            on="field_number",
             values=["field_name", "field_value"],
             aggregate_function="first",
         )
@@ -261,7 +262,7 @@ def process_group_data(group_df, json_results, group_size, checks):
     return group_df
 
 
-def process_chunk(df: pl.DataFrame, validation_id: str, check: SBLCheck) -> [dict]:
+def process_chunk(df: pl.DataFrame, validation_id: str, check: SBLCheck) -> Dict[str, Any]:
     # once we have a grouped dataframe, working with the data as a
     # python dict is much faster
     findings_json = ujson.loads(df.write_json())
diff --git a/src/regtech_data_validator/validation_results.py b/src/regtech_data_validator/validation_results.py
index 40a6352..f9262f2 100644
--- a/src/regtech_data_validator/validation_results.py
+++ b/src/regtech_data_validator/validation_results.py
@@ -26,3 +26,4 @@ class ValidationResults(object):
     is_valid: bool
     findings: pl.DataFrame
     phase: ValidationPhase
+    record_count: int = 0
diff --git a/src/regtech_data_validator/validator.py b/src/regtech_data_validator/validator.py
index a32c309..ff76173 100644
--- a/src/regtech_data_validator/validator.py
+++ b/src/regtech_data_validator/validator.py
@@ -1,7 +1,9 @@
 """Creates two DataFrameSchema objects by rendering the schema template
 with validations listed in phase 1 and phase 2."""
 
+import logging
 from pathlib import Path
+from typing import Dict, List
 import polars as pl
 import pandera.polars as pa
 from pandera import Check
@@ -12,17 +14,14 @@
 from regtech_data_validator.validation_results import ValidationPhase, Counts, ValidationResults
 from regtech_data_validator.data_formatters import format_findings
 
-from fsspec import AbstractFileSystem, filesystem
-
-import shutil
-import os
-
 from regtech_data_validator.phase_validations import (
     get_phase_1_schema_for_lei,
     get_phase_2_schema_for_lei,
     get_register_schema,
 )
 
+log = logging.getLogger(__name__)
+
 
 # Gets all associated field names from the check
 def _get_check_fields(check: Check, primary_column: str) -> list[str]:
@@ -55,9 +54,11 @@ def _filter_valid_records(df: pl.DataFrame, check_output: pl.Series, fields: lis
 
 
 def _records_to_fields(failed_records_df: pl.DataFrame) -> pl.DataFrame:
-    # Melts the DataFrame with columns per Check field to DataFrame with a row per field
-    failed_record_fields_df = failed_records_df.melt(
-        variable_name='field_name', value_name='field_value', id_vars=['record_no']
+    # Unpivot the DataFrame with columns per Check field to DataFrame with a row per field
+    failed_record_fields_df = failed_records_df.unpivot(
+        variable_name='field_name',
+        value_name='field_value',
+        index=['record_no'],
     )
     return failed_record_fields_df
 
@@ -93,49 +94,50 @@ def validate(
         #       `list[dict[str,Any]]`, but it's actually of type `SchemaError`
         schema_error: SchemaError
 
-        if process_errors:
-            for schema_error in err.schema_errors:
-                check = schema_error.check
-                column_name = schema_error.schema.name
-
-                # CHECK_ERROR is thrown by pandera polars if the check itself has a coding error, NOT if the check data results in an error
-                if (
-                    schema_error.reason_code is SchemaErrorReason.CHECK_ERROR
-                    or schema_error.reason_code is SchemaErrorReason.COLUMN_NOT_IN_DATAFRAME
-                ):
-                    raise RuntimeError(schema_error) from schema_error
-                if not check:
-                    raise RuntimeError(
-                        f'SchemaError occurred with no associated Check for {column_name} column'
-                    ) from schema_error
-
-                if not isinstance(check, SBLCheck):
-                    raise RuntimeError(
-                        f'Check {check} type on {column_name} column not supported. Must be of type {SBLCheck}'
-                    ) from schema_error
-
-                schema_error = gather_errors(schema_error)
-
-                fields = _get_check_fields(check, column_name)
-                check_output: pl.Series | None = schema_error.check_output
-
-                if check_output is not None:
-                    # Filter data not associated with failed Check, and update index for merging with findings_df
-                    check_output = check_output.with_columns(pl.col('index').add(row_start))
+        # if process_errors:
+        for schema_error in err.schema_errors:
+            check = schema_error.check
+            column_name = schema_error.schema.name
+
+            # CHECK_ERROR is thrown by pandera polars if the check itself has a coding error, NOT if the check data results in an error
+            if (
+                schema_error.reason_code is SchemaErrorReason.CHECK_ERROR
+                or schema_error.reason_code is SchemaErrorReason.COLUMN_NOT_IN_DATAFRAME
+            ):
+                raise RuntimeError(schema_error) from schema_error
+            if not check:
+                raise RuntimeError(
+                    f'SchemaError occurred with no associated Check for {column_name} column'
+                ) from schema_error
+
+            if not isinstance(check, SBLCheck):
+                raise RuntimeError(
+                    f'Check {check} type on {column_name} column not supported. Must be of type {SBLCheck}'
+                ) from schema_error
+
+            fields = _get_check_fields(check, column_name)
+            check_output = gather_errors(schema_error.check_output)
+
+            if check_output is not None:
+                # Filter data not associated with failed Check, and update index for merging with findings_df
+                check_output = check_output.with_columns(pl.col('index').add(row_start))
+                if process_errors:
                     failed_records_df = _filter_valid_records(submission_df, check_output, fields)
                     failed_record_fields_df = _records_to_fields(failed_records_df)
                     findings = _add_validation_metadata(failed_record_fields_df, check)
-                    check_findings.append(findings)
                 else:
-                    # The above exception handling _should_ prevent this from ever happenin, but...just in case.
-                    raise RuntimeError(
-                        f'No check output for "{check.name}" check.  Pandera SchemaError: {schema_error}'
+                    findings = _add_validation_metadata(check_output, check)
+                    findings = findings.with_columns(
+                        pl.lit(check.scope).alias("scope"), pl.lit(check.severity.value).alias("validation_type")
                     )
-            if check_findings:
-                findings_df = pl.concat(check_findings)
+                check_findings.append(findings)
+            else:
+                # The above exception handling _should_ prevent this from ever happenin, but...just in case.
+                raise RuntimeError(f'No check output for "{check.name}" check.  Pandera SchemaError: {schema_error}')
+        if check_findings:
+            findings_df = pl.concat(check_findings)
 
-    updated_df = add_uid(findings_df, submission_df, row_start)
-    return updated_df
+    return add_uid(findings_df, submission_df, row_start) if process_errors else findings_df
 
 
 # Add the uid for the record throwing the error/warning to the error dataframe
@@ -151,16 +153,14 @@ def add_uid(results_df: pl.DataFrame, submission_df: pl.DataFrame, offset: int)
 # This function is a Generator, and will yield the results of each batch of processing, along with the
 # phase (SYNTACTICAL/LOGICAL) that the findings were found.  Callers of this function will want to
 # store or concat each iteration of findings
-def validate_batch_csv(
-    path: Path | str,
+def validate_data(
+    source: Path | str | pl.LazyFrame,
     context: dict[str, str] | None = None,
     batch_size: int = 50000,
     batch_count: int = 1,
     max_errors=1000000,
 ):
     has_syntax_errors = False
-    real_path = get_real_file_path(path)
-    # process the data first looking for syntax (phase 1) errors, then looking for logical (phase 2) errors/warnings
     syntax_schema = get_phase_1_schema_for_lei(context)
     syntax_checks = [check for col_schema in syntax_schema.columns.values() for check in col_schema.checks]
 
@@ -169,8 +169,10 @@ def validate_batch_csv(
 
     all_uids = []
 
-    for validation_results, uids in validate_chunks(
-        syntax_schema, real_path, batch_size, batch_count, max_errors, syntax_checks
+    batch_validation = validate_lazy_chunks if isinstance(source, pl.LazyFrame) else validate_chunks
+
+    for validation_results, uids in batch_validation(
+        syntax_schema, source, batch_size, batch_count, max_errors, syntax_checks
     ):
         all_uids.extend(uids)
         # validate, and therefore validate_chunks, can return an empty dataframe for findings
@@ -179,31 +181,34 @@ def validate_batch_csv(
         yield validation_results
 
     if not has_syntax_errors:
-        register_schema = get_register_schema(context)
-        validation_results = validate(register_schema, pl.DataFrame({"uid": all_uids}), 0, True)
-        if not validation_results.is_empty():
-            validation_results = format_findings(
-                validation_results,
-                ValidationPhase.LOGICAL.value,
-                [check for col_schema in register_schema.columns.values() for check in col_schema.checks],
-            )
-        error_counts, warning_counts = get_scope_counts(validation_results)
-        results = ValidationResults(
-            error_counts=error_counts,
-            warning_counts=warning_counts,
-            is_valid=((error_counts.total_count + warning_counts.total_count) == 0),
-            findings=validation_results,
-            phase=register_schema.name,
-        )
-        yield results
+        yield validate_register_level(context, all_uids)
 
-        for validation_results, _ in validate_chunks(
-            logic_schema, real_path, batch_size, batch_count, max_errors, logic_checks
+        for validation_results, _ in batch_validation(
+            logic_schema, source, batch_size, batch_count, max_errors, logic_checks
         ):
             yield validation_results
 
-    if os.path.isdir("/tmp/s3"):
-        shutil.rmtree("/tmp/s3")
+
+def validate_batch_csv(
+    path: Path | str,
+    context: dict[str, str] | None = None,
+    batch_size: int = 50000,
+    batch_count: int = 1,
+    max_errors=1000000,
+):
+    for results in validate_data(path, context, batch_size, batch_count, max_errors):
+        yield results
+
+
+def validate_lazy_frame(
+    lf: pl.LazyFrame,
+    context: dict[str, str] | None = None,
+    batch_size: int = 50000,
+    batch_count: int = 1,
+    max_errors=1000000,
+):
+    for results in validate_data(lf, context, batch_size, batch_count, max_errors):
+        yield results
 
 
 # Reads in a path to a csv in batches, using batch_size to determine number of rows to read into the buffer,
@@ -213,55 +218,91 @@ def validate_batch_csv(
 # batch_count adds processing cylces (time) but can significantly reduce resources.
 def validate_chunks(schema, path, batch_size, batch_count, max_errors, checks):
     reader = pl.read_csv_batched(path, infer_schema_length=0, missing_utf8_is_empty_string=True, batch_size=batch_size)
-    batches = reader.next_batches(batch_count)
     process_errors = True
     total_count = 0
     row_start = 0
+    batches = reader.next_batches(batch_count)
     while batches:
         df = pl.concat(batches)
-        validation_results = validate(schema, df, row_start, process_errors)
-        if not validation_results.is_empty():
-
-            validation_results = format_findings(validation_results, schema.name.value, checks)
-
-        error_counts, warning_counts = get_scope_counts(validation_results)
-        results = ValidationResults(
-            error_counts=error_counts,
-            warning_counts=warning_counts,
-            is_valid=((error_counts.total_count + warning_counts.total_count) == 0),
-            findings=validation_results,
-            phase=schema.name,
+        validation_results, total_count, process_errors = validate_chunk(
+            schema, df, total_count, row_start, max_errors, process_errors, checks
         )
+        row_start += df.height
+        yield validation_results, df["uid"].to_list()
+        batches = reader.next_batches(batch_count)
 
-        total_count += results.findings.height
-
-        if total_count > max_errors and process_errors:
-            process_errors = False
-            head_count = results.findings.height - (total_count - max_errors)
-            results.findings = results.findings.head(head_count)
 
+def validate_lazy_chunks(schema, lf: pl.LazyFrame, batch_size: int, batch_count, max_errors, checks):
+    process_errors = True
+    total_count = 0
+    row_start = 0
+    df = lf.slice(row_start, batch_size).collect()
+    while df.height:
+        validation_results, total_count, process_errors = validate_chunk(
+            schema, df, total_count, row_start, max_errors, process_errors, checks
+        )
         row_start += df.height
-        batches = reader.next_batches(batch_count)
-        yield results, df["uid"].to_list()
+        yield validation_results, df["uid"].to_list()
+        df = lf.slice(row_start, batch_size).collect()
+
+
+def validate_chunk(schema, df: pl.DataFrame, total_count, row_start, max_errors, process_errors, checks):
+    log.debug("Start UID: %s, Last UID: %s", df['uid'][0], df['uid'][-1])
+    validation_results = validate(schema, df, row_start, process_errors)
+    if process_errors and not validation_results.is_empty():
+        validation_results = format_findings(validation_results, schema.name.value, checks)
+
+    error_counts, warning_counts = get_scope_counts(validation_results)
+    results = ValidationResults(
+        record_count=df.height,
+        error_counts=error_counts,
+        warning_counts=warning_counts,
+        is_valid=((error_counts.total_count + warning_counts.total_count) == 0),
+        findings=validation_results if process_errors else pl.DataFrame(),
+        phase=schema.name,
+    )
+
+    total_count += error_counts.total_count + warning_counts.total_count
+    log.debug("Counts: %d %d", error_counts, warning_counts)
+    if total_count > max_errors and process_errors:
+        log.debug("Reached max errors, adjusting results")
+        process_errors = False
+        head_count = results.findings.height - (total_count - max_errors)
+        log.debug(
+            "Results height: %d, total count: %d, head count: %d", results.findings.height, total_count, head_count
+        )
+        results.findings = results.findings.head(head_count)
+        log.debug("Results height after heading %d", results.findings.height)
+
+    return results, total_count, process_errors
 
 
-def get_real_file_path(path):
-    path = str(path)
-    if path.startswith("s3://"):
-        fs: AbstractFileSystem = filesystem(protocol="filecache", target_protocol="s3", cache_storage="/tmp/s3")
-        path = fs.unstrip_protocol(path)
-        with fs.open(path, "r") as f:
-            return f.name
-    return path
+def validate_register_level(context: Dict[str, str] | None, all_uids: List[str]):
+    log.debug("Processing register logic errors")
+    register_schema = get_register_schema(context)
+    validation_results = validate(register_schema, pl.DataFrame({"uid": all_uids}), 0, True)
+    if not validation_results.is_empty():
+        validation_results = format_findings(
+            validation_results,
+            ValidationPhase.LOGICAL.value,
+            [check for col_schema in register_schema.columns.values() for check in col_schema.checks],
+        )
+    error_counts, warning_counts = get_scope_counts(validation_results)
+    results = ValidationResults(
+        error_counts=error_counts,
+        warning_counts=warning_counts,
+        is_valid=((error_counts.total_count + warning_counts.total_count) == 0),
+        findings=validation_results,
+        phase=register_schema.name,
+    )
+    log.debug("Register counts: %d %d", error_counts, warning_counts)
+    return results
 
 
 # This function adds an index column (polars dataframes do not normally have one), and filters out
 # any row that did not fail a check.
-def gather_errors(schema_error: SchemaError):
-    schema_error.check_output = schema_error.check_output.with_row_index()
-    error_indices = schema_error.check_output.filter(~pl.col("check_output"))["index"].to_list()
-    schema_error.check_output = schema_error.check_output.filter(pl.col("index").is_in(error_indices))
-    return schema_error
+def gather_errors(check_output: pl.DataFrame):
+    return check_output.with_row_index().filter(~pl.col("check_output"))
 
 
 def get_scope_counts(error_frame: pl.DataFrame):
diff --git a/tests/test_schema_functions.py b/tests/test_schema_functions.py
index d0b10b0..f824da4 100644
--- a/tests/test_schema_functions.py
+++ b/tests/test_schema_functions.py
@@ -2,6 +2,7 @@
 import pytest
 import tempfile
 from pathlib import Path
+from polars.exceptions import ColumnNotFoundError
 
 from regtech_data_validator.validator import validate_batch_csv
 from regtech_data_validator.validation_results import ValidationPhase
@@ -271,9 +272,9 @@ def test_with_non_matching_lei(self, csv_df_file):
         assert results[2].phase == ValidationPhase.LOGICAL
 
     def test_column_not_found_in_df(self, csv_df_mission_column_file):
-        with pytest.raises(RuntimeError) as re:
+        with pytest.raises(ColumnNotFoundError) as re:
             list(validate_batch_csv(csv_df_mission_column_file))
-        assert "column 'uid' not in dataframe" in str(re.value)
+        assert '"uid" not found' in str(re.value)
 
     def test_same_uids(self, csv_df_same_uids_file):
         results = list(validate_batch_csv(csv_df_same_uids_file))