import sys import json import requests import elasticsearch from confluent_kafka import Consumer, KafkaException from fatcat_openapi_client import ReleaseEntity, ContainerEntity, ApiClient, ChangelogEntry from fatcat_tools import (public_api, entity_from_json, release_to_elasticsearch, container_to_elasticsearch, changelog_to_elasticsearch, ) from fatcat_web.search import get_elastic_container_stats from .worker_common import FatcatWorker class ElasticsearchReleaseWorker(FatcatWorker): """ Consumes from release-updates topic and pushes into (presumably local) elasticsearch. Uses a consumer group to manage offset. """ def __init__(self, kafka_hosts, consume_topic, poll_interval=10.0, offset=None, elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat", elasticsearch_release_index="fatcat_releases", batch_size=200, api_host="https://api.fatcat.wiki/v0", query_stats=False): super().__init__(kafka_hosts=kafka_hosts, consume_topic=consume_topic) self.consumer_group = "elasticsearch-updates3" self.batch_size = batch_size self.poll_interval = poll_interval self.elasticsearch_backend = elasticsearch_backend self.elasticsearch_index = elasticsearch_index self.elasticsearch_release_index = elasticsearch_release_index self.entity_type = ReleaseEntity self.transform_func = release_to_elasticsearch self.api_host = api_host self.query_stats = query_stats def run(self): ac = ApiClient() api = public_api(self.api_host) # only used by container indexing query_stats code path es_client = elasticsearch.Elasticsearch(self.elasticsearch_backend) def fail_fast(err, partitions): if err is not None: print("Kafka consumer commit error: {}".format(err), file=sys.stderr) print("Bailing out...", file=sys.stderr) # TODO: should it be sys.exit(-1)? raise KafkaException(err) for p in partitions: # check for partition-specific commit errors if p.error: print("Kafka consumer commit error: {}".format(p.error), file=sys.stderr) print("Bailing out...", file=sys.stderr) # TODO: should it be sys.exit(-1)? raise KafkaException(p.error) #print("Kafka consumer commit successful") pass def on_rebalance(consumer, partitions): for p in partitions: if p.error: raise KafkaException(p.error) print("Kafka partitions rebalanced: {} / {}".format( consumer, partitions), file=sys.stderr) consumer_conf = self.kafka_config.copy() consumer_conf.update({ 'group.id': self.consumer_group, 'on_commit': fail_fast, # messages don't have offset marked as stored until pushed to # elastic, but we do auto-commit stored offsets to broker 'enable.auto.commit': True, 'enable.auto.offset.store': False, # user code timeout; if no poll after this long, assume user code # hung and rebalance (default: 5min) 'max.poll.interval.ms': 60000, 'default.topic.config': { 'auto.offset.reset': 'latest', }, }) consumer = Consumer(consumer_conf) consumer.subscribe([self.consume_topic], on_assign=on_rebalance, on_revoke=on_rebalance, ) while True: batch = consumer.consume( num_messages=self.batch_size, timeout=self.poll_interval) if not batch: if not consumer.assignment(): print("... no Kafka consumer partitions assigned yet", file=sys.stderr) print("... nothing new from kafka, try again (interval: {}".format(self.poll_interval), file=sys.stderr) continue print("... got {} kafka messages".format(len(batch)), file=sys.stderr) # first check errors on entire batch... for msg in batch: if msg.error(): raise KafkaException(msg.error()) # ... then process bulk_actions = [] for msg in batch: json_str = msg.value().decode('utf-8') entity = entity_from_json(json_str, self.entity_type, api_client=ac) if self.entity_type == ChangelogEntry: key = entity.index # might need to fetch from API if not (entity.editgroup and entity.editgroup.editor): entity = api.get_changelog_entry(entity.index) else: key = entity.ident if self.entity_type != ChangelogEntry and entity.state == 'wip': print(f"WARNING: skipping state=wip entity: {self.entity_type.__name__} {entity.ident}", file=sys.stderr) continue if self.entity_type == ContainerEntity and self.query_stats: stats = get_elastic_container_stats( entity.ident, es_client=es_client, es_index=self.elasticsearch_release_index, merge_shadows=True, ) doc_dict = container_to_elasticsearch(entity, stats=stats) else: doc_dict = self.transform_func(entity) # TODO: handle deletions from index bulk_actions.append(json.dumps({ "index": { "_id": key, }, })) bulk_actions.append(json.dumps(doc_dict)) # if only WIP entities, then skip if not bulk_actions: for msg in batch: consumer.store_offsets(message=msg) continue print("Upserting, eg, {} (of {} {} in elasticsearch)".format(key, len(batch), self.entity_type.__name__), file=sys.stderr) elasticsearch_endpoint = "{}/{}/_bulk".format( self.elasticsearch_backend, self.elasticsearch_index) resp = requests.post(elasticsearch_endpoint, headers={"Content-Type": "application/x-ndjson"}, data="\n".join(bulk_actions) + "\n") resp.raise_for_status() if resp.json()['errors']: desc = "Elasticsearch errors from post to {}:".format(elasticsearch_endpoint) print(desc, file=sys.stderr) print(resp.content, file=sys.stderr) raise Exception(desc) for msg in batch: # offsets are *committed* (to brokers) automatically, but need # to be marked as processed here consumer.store_offsets(message=msg) class ElasticsearchContainerWorker(ElasticsearchReleaseWorker): def __init__(self, kafka_hosts, consume_topic, poll_interval=10.0, offset=None, query_stats=False, elasticsearch_release_index="fatcat_release", elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat", batch_size=200): super().__init__(kafka_hosts=kafka_hosts, consume_topic=consume_topic, poll_interval=poll_interval, offset=offset, elasticsearch_backend=elasticsearch_backend, elasticsearch_index=elasticsearch_index, elasticsearch_release_index=elasticsearch_release_index, query_stats=query_stats, batch_size=batch_size) # previous group got corrupted (by pykafka library?) self.consumer_group = "elasticsearch-updates3" self.entity_type = ContainerEntity self.transform_func = container_to_elasticsearch class ElasticsearchChangelogWorker(ElasticsearchReleaseWorker): """ Pulls changelog messages from Kafka, runs transformations and indexes them. Note: Very early versions of changelog entries did not contain details about the editor or extra fields. """ def __init__(self, kafka_hosts, consume_topic, poll_interval=10.0, offset=None, elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat_changelog", batch_size=200): super().__init__(kafka_hosts=kafka_hosts, consume_topic=consume_topic) self.consumer_group = "elasticsearch-updates3" self.batch_size = batch_size self.poll_interval = poll_interval self.elasticsearch_backend = elasticsearch_backend self.elasticsearch_index = elasticsearch_index self.entity_type = ChangelogEntry self.transform_func = changelog_to_elasticsearch