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Export column types for scan-related pandas tables

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This commit is contained in:
Michael Hohn
2022-08-08 16:48:17 -07:00
committed by =Michael Hohn
parent 7e996e746c
commit 505ee8ea66
1 changed files with 58 additions and 53 deletions
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111
sarif_cli/scan_tables.py
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@@ -9,6 +9,60 @@ from . import snowflake_id
class ZeroResults(Exception): class ZeroResults(Exception):
pass pass
#
# Column types for scan-related pandas tables
#
class ScanTablesTypes:
scans = {
"id" : pd.UInt64Dtype(),
"commit_id" : pd.StringDtype(),
"project_id" : pd.UInt64Dtype(),
"db_create_start" : numpy.datetime64(),
"db_create_stop" : numpy.datetime64(),
"scan_start_date" : numpy.datetime64(),
"scan_stop_date" : numpy.datetime64(),
"tool_name" : pd.StringDtype(),
"tool_version" : pd.StringDtype(),
"tool_query_commit_id" : pd.StringDtype(),
"sarif_file_name" : pd.StringDtype(),
"results_count" : pd.Int64Dtype(),
"rules_count" : pd.Int64Dtype(),
}
results = {
'id' : pd.UInt64Dtype(),
'scan_id' : pd.UInt64Dtype(),
'query_id' : pd.StringDtype(),
'result_type' : pd.StringDtype(),
'codeFlow_id' : pd.UInt64Dtype(),
'message' : pd.StringDtype(),
'message_object' : numpy.dtype('O'),
'location' : pd.StringDtype(),
'source_startLine' : pd.Int64Dtype(),
'source_startCol' : pd.Int64Dtype(),
'source_endLine' : pd.Int64Dtype(),
'source_endCol' : pd.Int64Dtype(),
'sink_startLine' : pd.Int64Dtype(),
'sink_startCol' : pd.Int64Dtype(),
'sink_endLine' : pd.Int64Dtype(),
'sink_endCol' : pd.Int64Dtype(),
# TODO Find high-level info from query name or tags?
'source_object' : numpy.dtype('O'),
'sink_object' : numpy.dtype('O'),
}
projects = {
"id" : pd.UInt64Dtype(),
"project_name" : pd.StringDtype(),
"creation_date" : numpy.datetime64(),
"repo_url" : pd.StringDtype(),
"primary_language" : pd.StringDtype(),
"languages_analyzed" : pd.StringDtype(),
}
# #
# Projects table # Projects table
# #
@@ -36,18 +90,10 @@ def joins_for_projects(basetables, external_info, scantables):
"repo_url" : repo_url, "repo_url" : repo_url,
"primary_language" : b.project['semmle.sourceLanguage'][0], # TODO: external info "primary_language" : b.project['semmle.sourceLanguage'][0], # TODO: external info
"languages_analyzed" : ",".join(list(b.project['semmle.sourceLanguage'])) "languages_analyzed" : ",".join(list(b.project['semmle.sourceLanguage']))
},index=[0]) }, index=[0])
# Force all column types to ensure appropriate formatting # Force all column types to ensure appropriate formatting
res1 = res.astype({ res1 = res.astype(ScanTablesTypes.projects).reset_index(drop=True)
"id" : pd.UInt64Dtype(),
"project_name" : pd.StringDtype(),
"creation_date" : numpy.datetime64(),
"repo_url" : pd.StringDtype(),
"primary_language" : pd.StringDtype(),
"languages_analyzed" : pd.StringDtype(),
}).reset_index(drop=True)
return res1 return res1
# #
@@ -82,22 +128,7 @@ def joins_for_scans(basetables, external_info, scantables):
},index=[0]) },index=[0])
# Force all column types to ensure correct writing and type checks on reading. # Force all column types to ensure correct writing and type checks on reading.
res1 = res.astype({ res1 = res.astype(ScanTablesTypes.scans).reset_index(drop=True)
"id" : pd.UInt64Dtype(),
"commit_id" : pd.StringDtype(),
"project_id" : pd.UInt64Dtype(),
"db_create_start" : numpy.datetime64(),
"db_create_stop" : numpy.datetime64(),
"scan_start_date" : numpy.datetime64(),
"scan_stop_date" : numpy.datetime64(),
"tool_name" : pd.StringDtype(),
"tool_version" : pd.StringDtype(),
"tool_query_commit_id" : pd.StringDtype(),
"sarif_file_name" : pd.StringDtype(),
"results_count" : pd.Int64Dtype(),
"rules_count" : pd.Int64Dtype(),
}).reset_index(drop=True)
return res1 return res1
# #
@@ -129,33 +160,7 @@ def joins_for_results(basetables, external_info):
res = tables[0] res = tables[0]
# Force all column types to ensure appropriate formatting # Force all column types to ensure appropriate formatting
res1 = res.astype({ res1 = res.astype(ScanTablesTypes.results).reset_index(drop=True)
'id' : pd.UInt64Dtype(),
'scan_id' : pd.UInt64Dtype(),
'query_id' : pd.StringDtype(),
'result_type' : pd.StringDtype(),
'codeFlow_id' : pd.UInt64Dtype(),
'message' : pd.StringDtype(),
'message_object' : numpy.dtype('O'),
'location' : pd.StringDtype(),
'source_startLine' : pd.Int64Dtype(),
'source_startCol' : pd.Int64Dtype(),
'source_endLine' : pd.Int64Dtype(),
'source_endCol' : pd.Int64Dtype(),
'sink_startLine' : pd.Int64Dtype(),
'sink_startCol' : pd.Int64Dtype(),
'sink_endLine' : pd.Int64Dtype(),
'sink_endCol' : pd.Int64Dtype(),
# TODO Find high-level info from query name or tags?
'source_object' : numpy.dtype('O'),
'sink_object' : numpy.dtype('O'),
}).reset_index(drop=True)
return res1 return res1
def _results_from_kind_problem(basetables, external_info): def _results_from_kind_problem(basetables, external_info):