import json
import time
import requests
from confluent_kafka import Consumer, Producer, KafkaException

from fatcat_client import ReleaseEntity, ApiClient
from fatcat_tools import *
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=5.0, offset=None,
            elasticsearch_backend="http://localhost:9200", elasticsearch_index="fatcat",
            batch_size=200):
        super().__init__(kafka_hosts=kafka_hosts,
                         consume_topic=consume_topic)
        self.consumer_group = "elasticsearch-updates"
        self.batch_size = batch_size
        self.poll_interval = poll_interval
        self.elasticsearch_backend = elasticsearch_backend
        self.elasticsearch_index = elasticsearch_index

    def run(self):
        ac = ApiClient()

        def on_rebalance(consumer, partitions):
            for p in partitions:
                if p.error:
                    raise KafkaException(p.error)
            print("Kafka partitions rebalanced: {} / {}".format(
                consumer, partitions))

        consumer_conf = self.kafka_config.copy()
        consumer_conf.update({
            'group.id': self.consumer_group,
            'enable.auto.offset.store': False,
            '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")
                print("... nothing new from kafka, try again (interval: {}".format(self.poll_interval))
                continue
            print("... got {} kafka messages".format(len(batch)))
            # 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, ReleaseEntity, api_client=ac)
                print("Upserting: release/{}".format(entity.ident))
                bulk_actions.append(json.dumps({
                    "index": { "_id": entity.ident, },
                }))
                bulk_actions.append(json.dumps(
                    release_to_elasticsearch(entity)))
            elasticsearch_endpoint = "{}/{}/release/_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)
                print(resp.content)
                raise Exception(desc)
            consumer.store_offsets(batch[-1])