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How to use checksum_crc32c method in localstack

Best Python code snippet using localstack_python

verify_release_manifest.py

Source:verify_release_manifest.py

1"""2Given a file containing a list of constituent staging dirs for a DCP release (aka a manifest),3verify that data has been loaded from each of them to the target DCP dataset and the count of loaded files4matches the # in the staging area.5Files are determined to be loaded if they exist at the desired target path and crc as defined in the staging6areas descriptors. It's possible that an expected file was loaded by another staging dir (i.e,. they both7contain the same file). While this is discouraged, it's technically possible and we need to accommodate that.8So, we check if the target path was loaded, disregarding the source staging dir.9Additionally, this will check that metadata was loaded properly (including links) by pull the entity_id, version and10content from the files in GS and checking that the expected row is present in the given dataset. If a newer version11is present in the repo than is staged, we consider that valid.12Example invocation:13python verify_release_manifest.py -f testing.csv -g fake-gs-project -b fake-bq-project -d fake-dataset14"""15import argparse16import json17import logging18import sys19from dataclasses import dataclass20from datetime import datetime21from collections import defaultdict22from functools import partial23from multiprocessing import Pool24from typing import Tuple25from urllib.parse import urlparse26from dateutil import parser27from google.cloud import bigquery, storage28from google.cloud.storage.client import Client29from dagster_utils.contrib.google import get_credentials30from hca_orchestration.solids.load_hca.data_files.load_data_metadata_files import FileMetadataTypes31from hca_orchestration.solids.load_hca.non_file_metadata.load_non_file_metadata import NonFileMetadataTypes32from hca_orchestration.support.dates import parse_version_to_datetime33logging.basicConfig(level=logging.INFO, format='%(message)s')34@dataclass(frozen=True)35class PathWithCrc:36    path: str37    crc32c: str38@dataclass(frozen=True)39class StagingAreaVerificationResult:40    has_metadata_errors: bool41    has_file_errors: bool42    def has_errors(self) -> bool:43        return self.has_metadata_errors or self.has_file_errors44def get_staging_area_file_descriptors(storage_client: Client, staging_areas: set[str]) -> dict[str, set[PathWithCrc]]:45    """46    Given a set of GS staging areas, return the downloaded descriptors present in each area47    """48    expected: dict[str, set[PathWithCrc]] = defaultdict(set[PathWithCrc])49    for staging_area in staging_areas:50        url = urlparse(staging_area)51        for file_type in FileMetadataTypes:52            prefix = f"{url.path.lstrip('/')}/descriptors/{file_type.value}"53            blobs = list(storage_client.list_blobs(url.netloc, prefix=prefix))54            for blob in blobs:55                parsed = json.loads(blob.download_as_text())56                path_with_crc = PathWithCrc(target_path_from_descriptor(parsed), parsed["crc32c"])57                expected[staging_area].add(path_with_crc)58    return expected59def target_path_from_descriptor(descriptor: dict[str, str]) -> str:60    return f"/v1/{descriptor['file_id']}/{descriptor['crc32c']}/{descriptor['file_name']}"61def find_files_in_load_history(bq_project: str, dataset: str,62                               areas: dict[str, set[PathWithCrc]]) -> dict[str, set[PathWithCrc]]:63    client = bigquery.Client(project=bq_project)64    loaded_paths = {}65    for area, paths_with_crc in areas.items():66        logging.debug(f"\tPulling loaded files for area {area}...")67        target_paths = [path_with_crc.path for path_with_crc in paths_with_crc]68        query = f"""69            SELECT target_path, checksum_crc32c70            FROM `datarepo_{dataset}.datarepo_load_history` dlh71            WHERE  state = 'succeeded'72            AND target_path IN UNNEST(@paths)73        """74        job_config = bigquery.QueryJobConfig(75            query_parameters=[76                bigquery.ArrayQueryParameter("paths", "STRING", target_paths),77            ]78        )79        query_job = client.query(query, job_config=job_config)80        loaded_paths[area] = {PathWithCrc(row["target_path"], row["checksum_crc32c"]) for row in81                              query_job}82    return loaded_paths83def parse_manifest_file(manifest_file: str) -> list[str]:84    with open(manifest_file) as manifest:85        # some of the staging areas submitted via the form need slight cleanup86        return [area.rstrip('\n/').strip() for area in manifest]87def process_staging_area(area: str, gs_project: str, bq_project: str, dataset: str,88                         release_cutoff: datetime) -> StagingAreaVerificationResult:89    logging.info(f"Processing staging area = {area}")90    creds = get_credentials()91    storage_client = storage.Client(project=gs_project, credentials=creds)92    expected_loaded_paths = get_staging_area_file_descriptors(storage_client, {area})93    loaded_paths_by_staging_area = find_files_in_load_history(bq_project, dataset, expected_loaded_paths)94    has_file_error = False95    for area, paths_with_crc in expected_loaded_paths.items():96        load_paths_for_staging_area = loaded_paths_by_staging_area[area]97        diff = paths_with_crc - load_paths_for_staging_area98        loaded = len(load_paths_for_staging_area)99        staged = len(paths_with_crc)100        if diff:101            logging.warning(102                f"â area = {area} - (data files) Mismatched loaded paths; expected files loaded = {staged}, actual loaded = {loaded}"103            )104            logging.debug(diff)105            has_file_error = True106        else:107            logging.info(108                f"â area = {area} - (data files) expected files loaded = {staged}, actual loaded = {loaded}"109            )110    has_metadata_error = verify_metadata(area, bq_project, dataset, release_cutoff)111    return StagingAreaVerificationResult(has_metadata_error, has_file_error)112def inspect_entities_at_path(storage_client: Client, bq_client: bigquery.Client, bq_project: str,113                             bq_dataset: str, staging_area: str, prefix: str, entity_type: str,114                             release_cutoff: datetime) -> bool:115    metadata_entities: dict[str, Tuple[str, str]] = {}116    url = urlparse(staging_area)117    if prefix:118        prefix = f"{url.path.lstrip('/')}/{prefix}/{entity_type}"119    else:120        prefix = f"{url.path.lstrip('/')}/{entity_type}"121    blobs = list(storage_client.list_blobs(url.netloc, prefix=prefix))122    for blob in blobs:123        content = blob.download_as_text()124        file_name = blob.name.split('/')[-1]125        entity_id = file_name.split('_')[0]126        version = file_name.split('_')[1].replace('.json', '')127        # files may be staged after we import, guard against those versions being present128        version_timestamp = parse_version_to_datetime(version)129        if version_timestamp > release_cutoff:130            logging.info(f"Ignoring file {file_name} staged after cutoff")131            continue132        # multiple versions may be staged, the latest one should win133        if entity_id in metadata_entities:134            existing_version, _ = metadata_entities[entity_id]135            if existing_version >= version:136                continue137        metadata_entities[entity_id] = (version, content)138    if len(metadata_entities) == 0:139        if entity_type == 'links':140            logging.debug(f"area = {staging_area} no links data found")141            return False142        logging.debug(f"ï¸area = {staging_area} No metadata for {entity_type} expected, skipping")143        return False144    logging.debug(f"Querying for metadata entities of type {entity_type} [area={staging_area}]")145    entity_ids = metadata_entities.keys()146    query = f"""147    SELECT {entity_type}_id, content, version FROM `{bq_project}.datarepo_{bq_dataset}.{entity_type}`148    WHERE {entity_type}_id IN UNNEST(@entity_ids)149    """150    job_config = bigquery.QueryJobConfig(151        query_parameters=[152            bigquery.ArrayQueryParameter("entity_ids", "STRING", entity_ids),153        ]154    )155    query_job = bq_client.query(query, job_config=job_config)156    rows = {row[f'{entity_type}_id']: (row['version'], row['content']) for row in query_job.result()}157    has_error = False158    for key, (version, content) in metadata_entities.items():159        if key not in rows.keys():160            logging.info(f"â area = {staging_area} {entity_type} ID {key} not in table")161            return True162        row = rows[key]163        parsed_version = parser.parse(version)164        if parsed_version < row[0]:165            # old version staged but a newer version was present, ignore166            logging.debug(167                f"Newer version of entity present in repo, ignoring. [area={staging_area}, entity_type={entity_type}, id={key}]"168            )169            continue170        if not parser.parse(version) == row[0]:171            has_error = True172            logging.info(f"â area = {staging_area} {entity_type} ID {key} version is incorrect")173        if not json.loads(content) == json.loads(row[1]):174            has_error = True175            logging.info(f"â area = {staging_area} {entity_type} ID {key} content is incorrect")176    logging.debug(177        f"â area = {staging_area} - (metadata) all {entity_type} entities found ({len(metadata_entities.keys())} entities)")178    return has_error179def verify_metadata(staging_area: str, bq_project: str, bq_dataset: str, release_cutoff: datetime) -> bool:180    creds = get_credentials()181    storage_client = storage.Client(project="broad-dsp-monster-hca-prod", credentials=creds)182    client = bigquery.Client(project=bq_project)183    logging.debug(f"Verifying metadata for {staging_area}")184    links_errors = inspect_entities_at_path(185        storage_client,186        client,187        bq_project,188        bq_dataset,189        staging_area,190        "",191        "links",192        release_cutoff193    )194    non_file_metadata_errors = [195        inspect_entities_at_path(196            storage_client,197            client,198            bq_project,199            bq_dataset,200            staging_area,201            "metadata",202            non_file_metadata_type.value,203            release_cutoff204        ) for non_file_metadata_type in205        NonFileMetadataTypes]206    file_metadata_errors = [207        inspect_entities_at_path(208            storage_client,209            client, bq_project,210            bq_dataset,211            staging_area,212            "metadata",213            file_metadata_type.value,214            release_cutoff215        ) for file_metadata_type in FileMetadataTypes]216    return any(file_metadata_errors) or any(non_file_metadata_errors) or links_errors217def verify(manifest_file: str, gs_project: str, bq_project: str,218           dataset: str, pool_size: int, release_cutoff: str) -> int:219    staging_areas = parse_manifest_file(manifest_file)220    parsed_cutoff = datetime.fromisoformat(release_cutoff)221    logging.info("Parsing manifest...")222    logging.info(f"Release cutoff = {release_cutoff}")223    logging.info(f"{len(staging_areas)} staging areas in manifest.")224    logging.info(f"Inspecting staging areas (pool_size = {pool_size})...")225    # we multiprocess because this takes quite awhile for > 10 projects, which is common for our releases226    frozen = partial(227        process_staging_area,228        gs_project=gs_project,229        bq_project=bq_project,230        dataset=dataset,231        release_cutoff=parsed_cutoff)232    if pool_size > 0:233        with Pool(pool_size) as p:234            results = p.map(frozen, staging_areas)235    else:236        results = [frozen(area) for area in staging_areas]237    logging.info('-' * 80)238    if any(map(lambda x: x.has_errors(), results)):239        logging.error(f"â Manifest {manifest_file} had errors")240        return 1241    else:242        logging.info(f"â Manifest {manifest_file} had no errors")243    return 0244if __name__ == '__main__':245    argparser = argparse.ArgumentParser()246    argparser.add_argument("-f", "--manifest-file", required=True)247    argparser.add_argument("-g", "--gs-project", required=True)248    argparser.add_argument("-b", "--bq-project", required=True)249    argparser.add_argument("-d", "--dataset", required=True)250    argparser.add_argument("-p", "--pool-size", type=int, default=4)251    argparser.add_argument("-r", "--release-cutoff", required=True)252    args = argparser.parse_args()253    exit_code = verify(254        args.manifest_file,255        args.gs_project,256        args.bq_project,257        args.dataset,258        args.pool_size,259        args.release_cutoff)...

strings.py

Source:strings.py

...99def checksum_crc32(string: Union[str, bytes]) -> str:100    bytes = to_bytes(string)101    checksum = zlib.crc32(bytes)102    return base64.b64encode(checksum.to_bytes(4, "big")).decode()103def checksum_crc32c(string: Union[str, bytes]):104    checksum = CrtCrc32cChecksum()105    checksum.update(to_bytes(string))106    return base64.b64encode(checksum.digest()).decode()107def hash_sha1(string: Union[str, bytes]) -> str:108    digest = hashlib.sha1(to_bytes(string)).digest()109    return base64.b64encode(digest).decode()110def hash_sha256(string: Union[str, bytes]) -> str:111    digest = hashlib.sha256(to_bytes(string)).digest()112    return base64.b64encode(digest).decode()113def base64_to_hex(b64_string: str) -> bytes:114    return binascii.hexlify(base64.b64decode(b64_string))115def base64_decode(data: Union[str, bytes]) -> bytes:116    """Decode base64 data - with optional padding, and able to handle urlsafe encoding (containing -/_)."""117    data = to_str(data)...

load_data_metadata_files.py

Source:load_data_metadata_files.py

1from enum import Enum2from dagster import (3    DynamicOutputDefinition,4    Failure,5    Optional,6    composite_solid,7    configured,8    solid,9)10from dagster.core.execution.context.compute import (11    AbstractComputeExecutionContext,12)13from google.cloud.bigquery.client import RowIterator14from hca_manage.common import JobId15from hca_orchestration.contrib.bigquery import BigQueryService16from hca_orchestration.models.hca_dataset import TdrDataset17from hca_orchestration.resources.config.scratch import ScratchConfig18from hca_orchestration.solids.load_hca.ingest_metadata_type import (19    ingest_metadata_type,20)21from hca_orchestration.solids.load_hca.load_table import (22    export_data,23    load_table_solid,24)25from hca_orchestration.support.typing import (26    HcaScratchDatasetName,27    MetadataType,28    MetadataTypeFanoutResult,29)30class FileMetadataTypes(Enum):31    """32    This Enum captures MetadataTypes that are directly describing a file type in the HCA33    """34    ANALYSIS_FILE = MetadataType('analysis_file')35    IMAGE_FILE = MetadataType('image_file')36    REFERENCE_FILE = MetadataType('reference_file')37    SEQUENCE_FILE = MetadataType('sequence_file')38    SUPPLEMENTARY_FILE = MetadataType('supplementary_file')39ingest_file_metadata_type = configured(ingest_metadata_type, name="ingest_file_metadata_type")(40    {"metadata_types": FileMetadataTypes, "prefix": "file-metadata-with-ids"})41class NullFileIdException(Failure):42    pass43def _inject_file_ids(44        target_hca_dataset: TdrDataset,45        scratch_config: ScratchConfig,46        file_metadata_type: str,47        scratch_dataset_name: HcaScratchDatasetName,48        bigquery_service: BigQueryService49) -> RowIterator:50    fq_dataset_id = target_hca_dataset.fully_qualified_jade_dataset_name()51    query = f"""52    SELECT S.{file_metadata_type}_id, S.version, J.file_id, S.content, S.descriptor53    FROM {file_metadata_type} S LEFT JOIN `{target_hca_dataset.project_id}.{fq_dataset_id}.datarepo_load_history` J54    ON J.state = 'succeeded'55    AND JSON_EXTRACT_SCALAR(S.descriptor, '$.crc32c') = J.checksum_crc32c56    AND '/v1/' || JSON_EXTRACT_SCALAR(S.descriptor, '$.file_id') || '/' || JSON_EXTRACT_SCALAR(S.descriptor, '$.crc32c') || '/' || JSON_EXTRACT_SCALAR(S.descriptor, '$.file_name') = J.target_path57    """58    destination_table_name = f"{file_metadata_type}_with_ids"59    source_path = f"{scratch_config.scratch_area()}/metadata/{file_metadata_type}/*"60    rows = bigquery_service.run_query_using_external_schema(61        query,62        source_paths=[source_path],63        schema=[64            {65                "mode": "REQUIRED",66                "name": f"{file_metadata_type}_id",67                "type": "STRING"68            },69            {70                "mode": "REQUIRED",71                "name": "version",72                "type": "TIMESTAMP"73            },74            {75                "mode": "REQUIRED",76                "name": "content",77                "type": "STRING"78            },79            {80                "mode": "REQUIRED",81                "name": "crc32c",82                "type": "STRING"83            },84            {85                "mode": "REQUIRED",86                "name": "descriptor",87                "type": "STRING"88            }89        ],90        table_name=file_metadata_type,91        destination=f"{scratch_dataset_name}.{destination_table_name}",92        bigquery_project=scratch_config.scratch_bq_project,93        location=target_hca_dataset.bq_location94    )95    for row in rows:96        # check for rows with no file_id97        # we allow null file_ids in the case where there is a `drs_uri` in the file descriptor (i.e., the file is98        # hosted in a repo external to TDR but we still want to expose the file metadata)99        if not row["file_id"] and "drs_uri" not in row["descriptor"]:100            raise NullFileIdException(101                f"File metadata with null file ID detected, will not ingest. Check crc32c and target_path [table={file_metadata_type}]")102    return rows103@solid(104    required_resource_keys={"bigquery_service", "target_hca_dataset", "scratch_config", "data_repo_client"}105)106def inject_file_ids_solid(107        context: AbstractComputeExecutionContext,108        file_metadata_fanout_result: MetadataTypeFanoutResult109) -> MetadataTypeFanoutResult:110    bigquery_service = context.resources.bigquery_service111    target_hca_dataset = context.resources.target_hca_dataset112    scratch_config = context.resources.scratch_config113    file_metadata_type = file_metadata_fanout_result.metadata_type114    scratch_dataset_name = file_metadata_fanout_result.scratch_dataset_name115    _inject_file_ids(116        target_hca_dataset=target_hca_dataset,117        scratch_config=scratch_config,118        file_metadata_type=file_metadata_fanout_result.metadata_type,119        scratch_dataset_name=file_metadata_fanout_result.scratch_dataset_name,120        bigquery_service=bigquery_service121    )122    export_data(123        "file-metadata-with-ids",124        table_name_extension="_with_ids",125        metadata_type=file_metadata_type,126        scratch_config=scratch_config,127        scratch_dataset_name=scratch_dataset_name,128        bigquery_service=bigquery_service129    )130    return file_metadata_fanout_result131@composite_solid(132    output_defs=[DynamicOutputDefinition(dagster_type=Optional[JobId])]133)134def file_metadata_fanout(result: list[JobId], scratch_dataset_name: HcaScratchDatasetName) -> Optional[JobId]:135    results = ingest_file_metadata_type(result, scratch_dataset_name).map(inject_file_ids_solid)...

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