366 lines
14 KiB
Python
366 lines
14 KiB
Python
from __future__ import unicode_literals
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import logging
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from operator import methodcaller
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from ..exceptions import ElasticsearchException, TransportError
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from ..compat import map, string_types
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logger = logging.getLogger('elasticsearch.helpers')
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class BulkIndexError(ElasticsearchException):
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@property
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def errors(self):
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""" List of errors from execution of the last chunk. """
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return self.args[1]
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class ScanError(ElasticsearchException):
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def __init__(self, scroll_id, *args, **kwargs):
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super(ScanError, self).__init__(*args, **kwargs)
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self.scroll_id = scroll_id
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def expand_action(data):
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"""
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From one document or action definition passed in by the user extract the
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action/data lines needed for elasticsearch's
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:meth:`~elasticsearch.Elasticsearch.bulk` api.
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"""
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# when given a string, assume user wants to index raw json
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if isinstance(data, string_types):
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return '{"index":{}}', data
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# make sure we don't alter the action
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data = data.copy()
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op_type = data.pop('_op_type', 'index')
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action = {op_type: {}}
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for key in ('_index', '_parent', '_percolate', '_routing', '_timestamp',
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'_ttl', '_type', '_version', '_version_type', '_id', '_retry_on_conflict'):
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if key in data:
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action[op_type][key] = data.pop(key)
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# no data payload for delete
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if op_type == 'delete':
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return action, None
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return action, data.get('_source', data)
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def _chunk_actions(actions, chunk_size, max_chunk_bytes, serializer):
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"""
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Split actions into chunks by number or size, serialize them into strings in
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the process.
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"""
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bulk_actions = []
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size, action_count = 0, 0
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for action, data in actions:
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action = serializer.dumps(action)
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cur_size = len(action) + 1
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if data is not None:
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data = serializer.dumps(data)
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cur_size += len(data) + 1
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# full chunk, send it and start a new one
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if bulk_actions and (size + cur_size > max_chunk_bytes or action_count == chunk_size):
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yield bulk_actions
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bulk_actions = []
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size, action_count = 0, 0
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bulk_actions.append(action)
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if data is not None:
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bulk_actions.append(data)
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size += cur_size
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action_count += 1
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if bulk_actions:
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yield bulk_actions
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def _process_bulk_chunk(client, bulk_actions, raise_on_exception=True, raise_on_error=True, **kwargs):
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"""
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Send a bulk request to elasticsearch and process the output.
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"""
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# if raise on error is set, we need to collect errors per chunk before raising them
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errors = []
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try:
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# send the actual request
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resp = client.bulk('\n'.join(bulk_actions) + '\n', **kwargs)
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except TransportError as e:
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# default behavior - just propagate exception
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if raise_on_exception:
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raise e
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# if we are not propagating, mark all actions in current chunk as failed
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err_message = str(e)
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exc_errors = []
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# deserialize the data back, thisis expensive but only run on
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# errors if raise_on_exception is false, so shouldn't be a real
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# issue
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bulk_data = map(client.transport.serializer.loads, bulk_actions)
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while True:
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try:
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# collect all the information about failed actions
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action = next(bulk_data)
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op_type, action = action.popitem()
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info = {"error": err_message, "status": e.status_code, "exception": e}
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if op_type != 'delete':
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info['data'] = next(bulk_data)
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info.update(action)
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exc_errors.append({op_type: info})
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except StopIteration:
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break
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# emulate standard behavior for failed actions
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if raise_on_error:
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raise BulkIndexError('%i document(s) failed to index.' % len(exc_errors), exc_errors)
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else:
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for err in exc_errors:
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yield False, err
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return
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# go through request-reponse pairs and detect failures
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for op_type, item in map(methodcaller('popitem'), resp['items']):
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ok = 200 <= item.get('status', 500) < 300
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if not ok and raise_on_error:
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errors.append({op_type: item})
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if ok or not errors:
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# if we are not just recording all errors to be able to raise
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# them all at once, yield items individually
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yield ok, {op_type: item}
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if errors:
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raise BulkIndexError('%i document(s) failed to index.' % len(errors), errors)
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def streaming_bulk(client, actions, chunk_size=500, max_chunk_bytes=100 * 1024 * 1024,
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raise_on_error=True, expand_action_callback=expand_action,
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raise_on_exception=True, **kwargs):
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"""
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Streaming bulk consumes actions from the iterable passed in and yields
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results per action. For non-streaming usecases use
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:func:`~elasticsearch.helpers.bulk` which is a wrapper around streaming
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bulk that returns summary information about the bulk operation once the
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entire input is consumed and sent.
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:arg client: instance of :class:`~elasticsearch.Elasticsearch` to use
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:arg actions: iterable containing the actions to be executed
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:arg chunk_size: number of docs in one chunk sent to es (default: 500)
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:arg max_chunk_bytes: the maximum size of the request in bytes (default: 100MB)
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:arg raise_on_error: raise ``BulkIndexError`` containing errors (as `.errors`)
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from the execution of the last chunk when some occur. By default we raise.
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:arg raise_on_exception: if ``False`` then don't propagate exceptions from
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call to ``bulk`` and just report the items that failed as failed.
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:arg expand_action_callback: callback executed on each action passed in,
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should return a tuple containing the action line and the data line
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(`None` if data line should be omitted).
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"""
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actions = map(expand_action_callback, actions)
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for bulk_actions in _chunk_actions(actions, chunk_size, max_chunk_bytes, client.transport.serializer):
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for result in _process_bulk_chunk(client, bulk_actions, raise_on_exception, raise_on_error, **kwargs):
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yield result
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def bulk(client, actions, stats_only=False, **kwargs):
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"""
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Helper for the :meth:`~elasticsearch.Elasticsearch.bulk` api that provides
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a more human friendly interface - it consumes an iterator of actions and
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sends them to elasticsearch in chunks. It returns a tuple with summary
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information - number of successfully executed actions and either list of
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errors or number of errors if `stats_only` is set to `True`.
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See :func:`~elasticsearch.helpers.streaming_bulk` for more accepted
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parameters
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:arg client: instance of :class:`~elasticsearch.Elasticsearch` to use
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:arg actions: iterator containing the actions
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:arg stats_only: if `True` only report number of successful/failed
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operations instead of just number of successful and a list of error responses
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Any additional keyword arguments will be passed to
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:func:`~elasticsearch.helpers.streaming_bulk` which is used to execute
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the operation.
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"""
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success, failed = 0, 0
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# list of errors to be collected is not stats_only
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errors = []
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for ok, item in streaming_bulk(client, actions, **kwargs):
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# go through request-reponse pairs and detect failures
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if not ok:
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if not stats_only:
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errors.append(item)
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failed += 1
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else:
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success += 1
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return success, failed if stats_only else errors
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def parallel_bulk(client, actions, thread_count=4, chunk_size=500,
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max_chunk_bytes=100 * 1024 * 1024,
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expand_action_callback=expand_action, **kwargs):
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"""
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Parallel version of the bulk helper run in multiple threads at once.
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:arg client: instance of :class:`~elasticsearch.Elasticsearch` to use
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:arg actions: iterator containing the actions
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:arg thread_count: size of the threadpool to use for the bulk requests
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:arg chunk_size: number of docs in one chunk sent to es (default: 500)
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:arg max_chunk_bytes: the maximum size of the request in bytes (default: 100MB)
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:arg raise_on_error: raise ``BulkIndexError`` containing errors (as `.errors`)
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from the execution of the last chunk when some occur. By default we raise.
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:arg raise_on_exception: if ``False`` then don't propagate exceptions from
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call to ``bulk`` and just report the items that failed as failed.
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:arg expand_action_callback: callback executed on each action passed in,
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should return a tuple containing the action line and the data line
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(`None` if data line should be omitted).
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"""
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# Avoid importing multiprocessing unless parallel_bulk is used
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# to avoid exceptions on restricted environments like App Engine
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from multiprocessing.dummy import Pool
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actions = map(expand_action_callback, actions)
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pool = Pool(thread_count)
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try:
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for result in pool.imap(
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lambda chunk: list(_process_bulk_chunk(client, chunk, **kwargs)),
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_chunk_actions(actions, chunk_size, max_chunk_bytes, client.transport.serializer)
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):
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for item in result:
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yield item
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finally:
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pool.close()
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pool.join()
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def scan(client, query=None, scroll='5m', raise_on_error=True,
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preserve_order=False, size=1000, **kwargs):
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"""
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Simple abstraction on top of the
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:meth:`~elasticsearch.Elasticsearch.scroll` api - a simple iterator that
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yields all hits as returned by underlining scroll requests.
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By default scan does not return results in any pre-determined order. To
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have a standard order in the returned documents (either by score or
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explicit sort definition) when scrolling, use ``preserve_order=True``. This
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may be an expensive operation and will negate the performance benefits of
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using ``scan``.
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:arg client: instance of :class:`~elasticsearch.Elasticsearch` to use
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:arg query: body for the :meth:`~elasticsearch.Elasticsearch.search` api
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:arg scroll: Specify how long a consistent view of the index should be
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maintained for scrolled search
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:arg raise_on_error: raises an exception (``ScanError``) if an error is
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encountered (some shards fail to execute). By default we raise.
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:arg preserve_order: don't set the ``search_type`` to ``scan`` - this will
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cause the scroll to paginate with preserving the order. Note that this
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can be an extremely expensive operation and can easily lead to
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unpredictable results, use with caution.
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:arg size: size (per shard) of the batch send at each iteration.
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Any additional keyword arguments will be passed to the initial
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:meth:`~elasticsearch.Elasticsearch.search` call::
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scan(es,
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query={"query": {"match": {"title": "python"}}},
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index="orders-*",
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doc_type="books"
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)
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"""
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if not preserve_order:
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body = query.copy() if query else {}
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body["sort"] = "_doc"
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# initial search
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resp = client.search(body=query, scroll=scroll, size=size, **kwargs)
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scroll_id = resp.get('_scroll_id')
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if scroll_id is None:
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return
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try:
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first_run = True
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while True:
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# if we didn't set search_type to scan initial search contains data
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if first_run:
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first_run = False
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else:
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resp = client.scroll(scroll_id, scroll=scroll)
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for hit in resp['hits']['hits']:
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yield hit
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# check if we have any errrors
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if resp["_shards"]["failed"]:
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logger.warning(
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'Scroll request has failed on %d shards out of %d.',
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resp['_shards']['failed'], resp['_shards']['total']
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)
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if raise_on_error:
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raise ScanError(
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scroll_id,
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'Scroll request has failed on %d shards out of %d.' %
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(resp['_shards']['failed'], resp['_shards']['total'])
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)
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scroll_id = resp.get('_scroll_id')
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# end of scroll
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if scroll_id is None or not resp['hits']['hits']:
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break
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finally:
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if scroll_id:
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client.clear_scroll(body={'scroll_id': [scroll_id]}, ignore=(404, ))
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def reindex(client, source_index, target_index, query=None, target_client=None,
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chunk_size=500, scroll='5m', scan_kwargs={}, bulk_kwargs={}):
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"""
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Reindex all documents from one index that satisfy a given query
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to another, potentially (if `target_client` is specified) on a different cluster.
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If you don't specify the query you will reindex all the documents.
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.. note::
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This helper doesn't transfer mappings, just the data.
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:arg client: instance of :class:`~elasticsearch.Elasticsearch` to use (for
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read if `target_client` is specified as well)
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:arg source_index: index (or list of indices) to read documents from
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:arg target_index: name of the index in the target cluster to populate
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:arg query: body for the :meth:`~elasticsearch.Elasticsearch.search` api
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:arg target_client: optional, is specified will be used for writing (thus
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enabling reindex between clusters)
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:arg chunk_size: number of docs in one chunk sent to es (default: 500)
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:arg scroll: Specify how long a consistent view of the index should be
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maintained for scrolled search
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:arg scan_kwargs: additional kwargs to be passed to
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:func:`~elasticsearch.helpers.scan`
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:arg bulk_kwargs: additional kwargs to be passed to
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:func:`~elasticsearch.helpers.bulk`
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"""
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target_client = client if target_client is None else target_client
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docs = scan(client,
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query=query,
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index=source_index,
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scroll=scroll,
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stored_fields=('_source', '_parent', '_routing', '_timestamp'),
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**scan_kwargs
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)
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def _change_doc_index(hits, index):
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for h in hits:
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h['_index'] = index
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if 'fields' in h:
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h.update(h.pop('fields'))
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yield h
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kwargs = {
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'stats_only': True,
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}
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kwargs.update(bulk_kwargs)
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return bulk(target_client, _change_doc_index(docs, target_index),
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chunk_size=chunk_size, **kwargs)
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