feat(ingest/sql): auto extract and use mode query user metadata #11307
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| Original file line number | Diff line number | Diff line change |
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| import json | ||
| import logging | ||
| from dataclasses import dataclass, field | ||
| from typing import Callable, Dict, List, Optional, Tuple, Union | ||
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| from typing_extensions import Protocol | ||
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| from datahub.ingestion.api.report import Report | ||
| from datahub.metadata.urns import CorpGroupUrn, CorpUserUrn | ||
| from datahub.utilities.stats_collections import int_top_k_dict | ||
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| UrnStr = str | ||
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| logger = logging.getLogger(__name__) | ||
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| class QueryLog(Protocol): | ||
| """Represents Query Log Entry | ||
| expected by QueryMetaExractor interface | ||
| """ | ||
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| query_text: str | ||
| user: Optional[Union[CorpUserUrn, CorpGroupUrn]] | ||
|
There was a problem hiding this comment. is the idea that both preparsed queries and observed queries will match this interface? There was a problem hiding this comment. Yes, ultimately, that's the goal . Anything that has "query", "user" and "extra_info" can use this. |
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| extra_info: Optional[dict] | ||
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| def _get_last_line(query: str) -> str: | ||
| return query.rstrip().rsplit("\n", maxsplit=1)[-1] | ||
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| @dataclass | ||
| class ToolMetaExtractorReport(Report): | ||
| num_queries_meta_extracted: Dict[str, int] = field(default_factory=int_top_k_dict) | ||
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| class ToolMetaExtractor: | ||
| """Enriches input query log entry with tool-specific details captured as part of query log | ||
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| Such as | ||
| - Queries executed on warehouse by Mode BI tool contain information of actual user interacting | ||
| with BI tool which is more useful as compared to service account used to execute query as reported | ||
| by warehouse query logs. | ||
| """ | ||
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| def __init__(self) -> None: | ||
| self.report = ToolMetaExtractorReport() | ||
| self.known_tool_extractors: List[Tuple[str, Callable[[QueryLog], bool]]] = [ | ||
| ( | ||
| "mode", | ||
| self._extract_mode_query, | ||
| ) | ||
| ] | ||
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| def _extract_mode_query(self, entry: QueryLog) -> bool: | ||
| """ | ||
| Returns: | ||
| bool: whether QueryLog entry is that of mode and mode user info | ||
| is extracted into entry. | ||
| """ | ||
| last_line = _get_last_line(entry.query_text) | ||
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| if not ( | ||
| last_line.startswith("--") | ||
| and '"url":"https://modeanalytics.com' in last_line | ||
| ): | ||
| return False | ||
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| mode_json_raw = last_line[2:] | ||
| mode_json = json.loads(mode_json_raw) | ||
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| original_user = entry.user | ||
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| entry.user = email_to_user_urn(mode_json["email"]) | ||
| entry.extra_info = entry.extra_info or {} | ||
| entry.extra_info["user_via"] = original_user | ||
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| # TODO: Generate an "origin" urn. | ||
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| return True | ||
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| def extract_bi_metadata(self, entry: QueryLog) -> bool: | ||
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| for tool, meta_extractor in self.known_tool_extractors: | ||
| try: | ||
| if meta_extractor(entry): | ||
| self.report.num_queries_meta_extracted[tool] += 1 | ||
| return True | ||
| except Exception: | ||
| logger.debug("Tool metadata extraction failed with error : {e}") | ||
| return False | ||
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| # NOTE: This is implementing the most common user urn generation scenario | ||
| # however may need to be revisited at later point | ||
| def email_to_user_urn(email: str) -> CorpUserUrn: | ||
| return CorpUserUrn(email.split("@", 1)[0]) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,246 @@ | ||
| [ | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.view-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "upstreamLineage", | ||
| "aspect": { | ||
| "json": { | ||
| "upstreams": [ | ||
| { | ||
| "auditStamp": { | ||
| "time": 1643871600000, | ||
| "actor": "urn:li:corpuser:_ingestion" | ||
| }, | ||
| "created": { | ||
| "time": 1643871600000, | ||
| "actor": "urn:li:corpuser:foo" | ||
| }, | ||
| "dataset": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.table-1,PROD)", | ||
| "type": "TRANSFORMED", | ||
| "query": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12" | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.table-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "datasetUsageStatistics", | ||
| "aspect": { | ||
| "json": { | ||
| "timestampMillis": 1643846400000, | ||
| "eventGranularity": { | ||
| "unit": "DAY", | ||
| "multiple": 1 | ||
| }, | ||
| "partitionSpec": { | ||
| "partition": "FULL_TABLE_SNAPSHOT", | ||
| "type": "FULL_TABLE" | ||
| }, | ||
| "uniqueUserCount": 1, | ||
| "totalSqlQueries": 4, | ||
| "topSqlQueries": [ | ||
| "create view `bigquery-dataset-1`.`view-1` as select * from `bigquery-dataset-1`.`table-1`", | ||
| "select * from `bigquery-dataset-1`.`table-1`" | ||
| ], | ||
| "userCounts": [ | ||
| { | ||
| "user": "urn:li:corpuser:foo", | ||
| "count": 4 | ||
| } | ||
| ], | ||
| "fieldCounts": [] | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "query", | ||
| "entityUrn": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "queryProperties", | ||
| "aspect": { | ||
| "json": { | ||
| "statement": { | ||
| "value": "create view `bigquery-dataset-1`.`view-1` as select * from `bigquery-dataset-1`.`table-1`", | ||
| "language": "SQL" | ||
| }, | ||
| "source": "SYSTEM", | ||
| "created": { | ||
| "time": 1643871600000, | ||
| "actor": "urn:li:corpuser:foo" | ||
| }, | ||
| "lastModified": { | ||
| "time": 1643871600000, | ||
| "actor": "urn:li:corpuser:foo" | ||
| } | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.table-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "status", | ||
| "aspect": { | ||
| "json": { | ||
| "removed": false | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "query", | ||
| "entityUrn": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "dataPlatformInstance", | ||
| "aspect": { | ||
| "json": { | ||
| "platform": "urn:li:dataPlatform:bigquery" | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.view-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "datasetUsageStatistics", | ||
| "aspect": { | ||
| "json": { | ||
| "timestampMillis": 1643846400000, | ||
| "eventGranularity": { | ||
| "unit": "DAY", | ||
| "multiple": 1 | ||
| }, | ||
| "partitionSpec": { | ||
| "partition": "FULL_TABLE_SNAPSHOT", | ||
| "type": "FULL_TABLE" | ||
| }, | ||
| "uniqueUserCount": 1, | ||
| "totalSqlQueries": 4, | ||
| "topSqlQueries": [ | ||
| "select * from `bigquery-dataset-1`.`view-1`\nLIMIT 100\n-- {\"user\":\"@bar\",\"email\":\"[email protected]\",\"url\":\"https://modeanalytics.com/acryltest/reports/6234ff78bc7d/runs/662b21949629/queries/f0aad24d5b37\",\"scheduled\":false}\n" | ||
| ], | ||
| "userCounts": [ | ||
| { | ||
| "user": "urn:li:corpuser:bar", | ||
| "count": 4 | ||
| } | ||
| ], | ||
| "fieldCounts": [] | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "query", | ||
| "entityUrn": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "querySubjects", | ||
| "aspect": { | ||
| "json": { | ||
| "subjects": [ | ||
| { | ||
| "entity": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.table-1,PROD)" | ||
| }, | ||
| { | ||
| "entity": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.view-1,PROD)" | ||
| } | ||
| ] | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "query", | ||
| "entityUrn": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "status", | ||
| "aspect": { | ||
| "json": { | ||
| "removed": false | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.view-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "operation", | ||
| "aspect": { | ||
| "json": { | ||
| "timestampMillis": 1643871600000, | ||
| "partitionSpec": { | ||
| "partition": "FULL_TABLE_SNAPSHOT", | ||
| "type": "FULL_TABLE" | ||
| }, | ||
| "actor": "urn:li:corpuser:foo", | ||
| "operationType": "CREATE", | ||
| "customProperties": { | ||
| "query_urn": "urn:li:query:176428c30cc3197730c20f6d0161efe869a0a041876d23c044aa2cd60d4c7a12" | ||
| }, | ||
| "lastUpdatedTimestamp": 1643871600000 | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| }, | ||
| { | ||
| "entityType": "dataset", | ||
| "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:bigquery,project-id-1.bigquery-dataset-1.view-1,PROD)", | ||
| "changeType": "UPSERT", | ||
| "aspectName": "status", | ||
| "aspect": { | ||
| "json": { | ||
| "removed": false | ||
| } | ||
| }, | ||
| "systemMetadata": { | ||
| "lastObserved": 1643871600000, | ||
| "runId": "bigquery-2022_02_03-07_00_00", | ||
| "lastRunId": "no-run-id-provided" | ||
| } | ||
| } | ||
| ] |
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this is a bit tricky - we only need it here, since observed query calls this?
imo it's a bit confusing because ObservedQuery also has extra_info, but that isn't copied over by
add()oradd_observed_queryThere was a problem hiding this comment.
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Right.
How about removing
extra_infoentirely. I do not believe we are doing anything withuser_viajust yet.Also I believe we could place the line
self._tool_meta_extractor.extract_bi_metadata(parsed)inadd()right before invokingadd_preparsed_queryoradd_observed_queryinstead of here and also refractorPreparsedQuery/ObservedQueryto support base protocol - renaming ofquery_texttoquery, etc. What do you think? Would it be less confusing then ?There was a problem hiding this comment.
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Added some refactoring + comment + passing extra_info to PreParsedQuery.