class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(self.indices, doc_type, body, doc_id,
**kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(self.indices, doc_type, body, doc_id,
**kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(self.indices, doc_type, doc_id, body,
**kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(self.indices, doc_type, body,
**kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(self.indices, doc_type, doc_id, body,
**kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(self.indices, doc_type, body,
**kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(self.indices, doc_type, doc_id, body,
**kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(self.indices, doc_type, doc_id,
body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(self.indices, doc_type, doc_id, body,
**kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
class BaseElasticsearchBackend(Base):
"""Base connection wrapper based on the ElasticSearch official library.
It uses two entry points to configure the underlying connection:
* ``transport_class``: the transport class from ``elasticsearch``. By
default ``elasticsearch.transport.Transport``.
* ``connection_class``: the connection class used by the transport class.
It's undefined by default, as it is on the subclasses to provide one.
If any of these elements is not defined, an ``ImproperlyConfigured`` error
will be raised when the backend will try to configure the client.
"""
#: ElasticSearch transport class used by the client class to perform
#: requests.
transport_class = Transport
#: ElasticSearch connection class used by the transport class to perform
#: requests.
connection_class = None
def configure_client(self):
"""Instantiate and configure the ElasticSearch client.
It simply takes the given HOSTS list and uses PARAMS as the keyword
arguments of the ElasticSearch class.
The client's transport_class is given by the class attribute
``transport_class``, and the connection class used by the transport
class is given by the class attribute ``connection_class``.
An ``ImproperlyConfigured`` exception is raised if any of these
elements is undefined.
"""
hosts = self.server['HOSTS']
params = self.server['PARAMS']
if not self.transport_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no transport class provided' % self.__class__)
if not self.connection_class:
raise ImproperlyConfigured(
'Djangoes backend %r is not properly configured: '
'no connection class provided' % self.__class__)
#pylint: disable=star-args
self.client = Elasticsearch(hosts,
transport_class=self.transport_class,
connection_class=self.connection_class,
**params)
# Server methods
# ==============
# The underlying client does not require index names to perform server
# related queries, such as "ping" or "info". The connection wrapper act
# for them as a proxy.
def ping(self, **kwargs):
return self.client.ping(**kwargs)
def info(self, **kwargs):
return self.client.info(**kwargs)
def put_script(self, lang, script_id, body, **kwargs):
return self.client.put_script(lang, script_id, body, **kwargs)
def get_script(self, lang, script_id, **kwargs):
return self.client.get_script(lang, script_id, **kwargs)
def delete_script(self, lang, script_id, **kwargs):
return self.client.delete_script(lang, script_id, **kwargs)
def put_template(self, template_id, body, **kwargs):
return self.client.put_template(template_id, body, **kwargs)
def get_template(self, template_id, body=None, **kwargs):
return self.client.get_template(template_id, body, **kwargs)
def delete_template(self, template_id=None, **kwargs):
return self.client.delete_template(template_id, **kwargs)
# Bulk methods
# ============
# The underlying client does not require index names, but it can be used.
# As it makes sense to not give an index, developers are free to use these
# as they want, as long as they are careful.
def mget(self, body, index=None, doc_type=None, **kwargs):
return self.client.mget(body, index, doc_type, **kwargs)
def bulk(self, body, index=None, doc_type=None, **kwargs):
return self.client.bulk(body, index, doc_type, **kwargs)
def msearch(self, body, index=None, doc_type=None, **kwargs):
return self.client.msearch(body, index, doc_type, **kwargs)
def mpercolate(self, body, index=None, doc_type=None, **kwargs):
return self.client.mpercolate(body, index, doc_type, **kwargs)
# Scroll methods
# ==============
# The underlying client does not require an index to perform scroll.
def scroll(self, scroll_id, **kwargs):
return self.client.scroll(scroll_id, **kwargs)
def clear_scroll(self, scroll_id, body=None, **kwargs):
return self.client.clear_scroll(scroll_id, body, **kwargs)
# Query methods
# =============
# The underlying client requires index names (or alias names) to perform
# queries. The connection wrapper overrides these client methods to
# automatically uses the configured names (indices and/or aliases).
def create(self, doc_type, body, doc_id=None, **kwargs):
return self.client.create(
self.indices, doc_type, body, doc_id, **kwargs)
def index(self, doc_type, body, doc_id=None, **kwargs):
return self.client.index(
self.indices, doc_type, body, doc_id, **kwargs)
def exists(self, doc_id, doc_type='_all', **kwargs):
return self.client.exists(self.indices, doc_id, doc_type, **kwargs)
def get(self, doc_id, doc_type='_all', **kwargs):
return self.client.get(self.indices, doc_id, doc_type, **kwargs)
def get_source(self, doc_id, doc_type='_all', **kwargs):
return self.client.get_source(self.indices, doc_id, doc_type, **kwargs)
def update(self, doc_type, doc_id, body=None, **kwargs):
return self.client.update(
self.indices, doc_type, doc_id, body, **kwargs)
def search(self, doc_type=None, body=None, **kwargs):
return self.client.search(self.indices, doc_type, body, **kwargs)
def search_shards(self, doc_type=None, **kwargs):
return self.client.search_shards(self.indices, doc_type, **kwargs)
def search_template(self, doc_type=None, body=None, **kwargs):
return self.client.search_template(
self.indices, doc_type, body, **kwargs)
def explain(self, doc_type, doc_id, body=None, **kwargs):
return self.client.explain(
self.indices, doc_type, doc_id, body, **kwargs)
def delete(self, doc_type, doc_id, **kwargs):
return self.client.delete(self.indices, doc_type, doc_id, **kwargs)
def count(self, doc_type=None, body=None, **kwargs):
return self.client.count(self.indices, doc_type, body, **kwargs)
def delete_by_query(self, doc_type=None, body=None, **kwargs):
return self.client.delete_by_query(
self.indices, doc_type, body, **kwargs)
def suggest(self, body, **kwargs):
return self.client.suggest(body, self.indices, **kwargs)
def percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def count_percolate(self, doc_type, doc_id=None, body=None, **kwargs):
return self.client.count_percolate(
self.indices, doc_type, doc_id, body, **kwargs)
def mlt(self, doc_type, doc_id, body=None, **kwargs):
return self.client.mlt(self.indices, doc_type, doc_id, body, **kwargs)
def termvector(self, doc_type, doc_id, body=None, **kwargs):
return self.client.termvector(
self.indices, doc_type, doc_id, body, **kwargs)
def mtermvectors(self, doc_type=None, body=None, **kwargs):
return self.client.mtermvectors(self.indices, doc_type, body, **kwargs)
def benchmark(self, doc_type=None, body=None, **kwargs):
return self.client.benchmark(self.indices, doc_type, body, **kwargs)
def abort_benchmark(self, name=None, **kwargs):
return self.client.abort_benchmark(name, **kwargs)
def list_benchmarks(self, doc_type=None, **kwargs):
return self.client.list_benchmarks(self.indices, doc_type, **kwargs)
class ElastAlerter():
""" The main Elastalert runner. This class holds all state about active rules,
controls when queries are run, and passes information between rules and alerts.
:param args: An argparse arguments instance. Should contain debug and start
:param conf: The configuration dictionary. At the top level, this
contains global options, and under 'rules', contains all state relating
to rules and alerts. In each rule in conf['rules'], the RuleType and Alerter
instances live under 'type' and 'alerts', respectively. The conf dictionary
should not be passed directly from a configuration file, but must be populated
by config.py:load_rules instead. """
def parse_args(self, args):
parser = argparse.ArgumentParser()
parser.add_argument('--config',
action='store',
dest='config',
default="config.yaml",
help='Global config file (default: config.yaml)')
parser.add_argument(
'--debug',
action='store_true',
dest='debug',
help='Suppresses alerts and prints information instead')
parser.add_argument(
'--rule',
dest='rule',
help=
'Run only a specific rule (by filename, must still be in rules folder)'
)
parser.add_argument(
'--silence',
dest='silence',
help=
'Silence rule for a time period. Must be used with --rule. Usage: '
'--silence <units>=<number>, eg. --silence hours=2')
parser.add_argument(
'--start',
dest='start',
help=
'YYYY-MM-DDTHH:MM:SS Start querying from this timestamp. (Default: present)'
)
parser.add_argument(
'--end',
dest='end',
help=
'YYYY-MM-DDTHH:MM:SS Query to this timestamp. (Default: present)')
parser.add_argument(
'--verbose',
action='store_true',
dest='verbose',
help='Increase verbosity without suppressing alerts')
parser.add_argument(
'--pin_rules',
action='store_true',
dest='pin_rules',
help='Stop ElastAlert from monitoring config file changes')
self.args = parser.parse_args(args)
def __init__(self, args):
self.parse_args(args)
self.conf = load_rules(self.args.config, use_rule=self.args.rule)
self.max_query_size = self.conf['max_query_size']
self.rules = self.conf['rules']
self.debug = self.args.debug
self.verbose = self.args.verbose
self.writeback_index = self.conf['writeback_index']
self.es_host = self.conf['es_host']
self.es_port = self.conf['es_port']
self.run_every = self.conf['run_every']
self.alert_time_limit = self.conf['alert_time_limit']
self.old_query_limit = self.conf['old_query_limit']
self.alerts_sent = 0
self.num_hits = 0
self.current_es = None
self.current_es_addr = None
self.buffer_time = self.conf['buffer_time']
self.silence_cache = {}
self.rule_hashes = get_rule_hashes(self.conf)
self.writeback_es = Elasticsearch(host=self.es_host, port=self.es_port)
if self.debug:
self.verbose = True
if self.verbose:
logging.getLogger().setLevel(logging.INFO)
for rule in self.rules:
rule = self.init_rule(rule)
if self.args.silence:
self.silence()
@staticmethod
def get_index(rule, starttime=None, endtime=None):
""" Gets the index for a rule. If strftime is set and starttime and endtime
are provided, it will return a comma seperated list of indices. If strftime
is set but starttime and endtime are not provided, it will replace all format
tokens with a wildcard. """
index = rule['index']
if rule.get('use_strftime_index'):
if starttime and endtime:
return format_index(index, starttime, endtime)
else:
# Replace the substring containing format characters with a *
format_start = index.find('%')
format_end = index.rfind('%') + 2
return index[:format_start] + '*' + index[format_end:]
else:
return index
@staticmethod
def get_query(filters,
starttime=None,
endtime=None,
sort=True,
timestamp_field='@timestamp'):
""" Returns a query dict that will apply a list of filters, filter by
start and end time, and sort results by timestamp.
:param filters: A list of elasticsearch filters to use.
:param starttime: A timestamp to use as the start time of the query.
:param endtime: A timestamp to use as the end time of the query.
:param sort: If true, sort results by timestamp. (Default True)
:return: A query dictionary to pass to elasticsearch.
"""
starttime = dt_to_ts(starttime)
endtime = dt_to_ts(endtime)
filters = copy.copy(filters)
query = {'filter': {'bool': {'must': filters}}}
if starttime and endtime:
query['filter']['bool']['must'].append({
'range': {
timestamp_field: {
'from': starttime,
'to': endtime
}
}
})
if sort:
query['sort'] = [{timestamp_field: {'order': 'asc'}}]
return query
def get_terms_query(self, query, size, field):
""" Takes a query generated by get_query and outputs a aggregation query """
if 'sort' in query:
query.pop('sort')
query.update(
{'aggs': {
'counts': {
'terms': {
'field': field,
'size': size
}
}
}})
aggs_query = {'aggs': {'filtered': query}}
return aggs_query
def get_index_start(self, index, timestamp_field='@timestamp'):
""" Query for one result sorted by timestamp to find the beginning of the index.
:param index: The index of which to find the earliest event.
:return: Timestamp of the earliest event.
"""
query = {'sort': {timestamp_field: {'order': 'asc'}}}
try:
res = self.current_es.search(index=index,
size=1,
body=query,
_source_include=[timestamp_field],
ignore_unavailable=True)
except ElasticsearchException as e:
self.handle_error("Elasticsearch query error: %s" % (e),
{'index': index})
return '1969-12-30T00:00:00Z'
if len(res['hits']['hits']) == 0:
# Index is completely empty, return a date before the epoch
return '1969-12-30T00:00:00Z'
timestamp = res['hits']['hits'][0]['_source'][timestamp_field]
return timestamp
def get_hits(self, rule, starttime, endtime, index):
""" Query elasticsearch for the given rule and return the results.
:param rule: The rule configuration.
:param starttime: The earliest time to query.
:param endtime: The latest time to query.
:return: A list of hits, bounded by self.max_query_size.
"""
query = self.get_query(rule['filter'],
starttime,
endtime,
timestamp_field=rule['timestamp_field'])
try:
res = self.current_es.search(index=index,
size=self.max_query_size,
body=query,
_source_include=rule['include'],
ignore_unavailable=True)
except ElasticsearchException as e:
# Elasticsearch sometimes gives us GIGANTIC error messages
# (so big that they will fill the entire terminal buffer)
if len(str(e)) > 1024:
e = str(e)[:1024] + '... (%d characters removed)' % (
len(str(e)) - 1024)
self.handle_error('Error running query: %s' % (e),
{'rule': rule['name']})
return None
hits = res['hits']['hits']
self.num_hits += len(hits)
lt = rule.get('use_local_time')
logging.info("Queried rule %s from %s to %s: %s hits" %
(rule['name'], pretty_ts(
starttime, lt), pretty_ts(endtime, lt), len(hits)))
self.replace_ts(hits, rule)
# Record doc_type for use in get_top_counts
if 'doc_type' not in rule and len(hits):
rule['doc_type'] = hits[0]['_type']
return hits
def replace_ts(self, hits, rule):
for hit in hits:
hit['_source'][rule['timestamp_field']] = ts_to_dt(
hit['_source'][rule['timestamp_field']])
def get_hits_count(self, rule, starttime, endtime, index):
""" Query elasticsearch for the count of results and returns a list of timestamps
equal to the endtime. This allows the results to be passed to rules which expect
an object for each hit.
:param rule: The rule configuration dictionary.
:param starttime: The earliest time to query.
:param endtime: The latest time to query.
:return: A dictionary mapping timestamps to number of hits for that time period.
"""
query = self.get_query(rule['filter'],
starttime,
endtime,
timestamp_field=rule['timestamp_field'],
sort=False)
query = {'query': {'filtered': query}}
try:
res = self.current_es.count(index=index,
doc_type=rule['doc_type'],
body=query,
ignore_unavailable=True)
except ElasticsearchException as e:
# Elasticsearch sometimes gives us GIGANTIC error messages
# (so big that they will fill the entire terminal buffer)
if len(str(e)) > 1024:
e = str(e)[:1024] + '... (%d characters removed)' % (
len(str(e)) - 1024)
self.handle_error('Error running count query: %s' % (e),
{'rule': rule['name']})
return None
self.num_hits += res['count']
lt = rule.get('use_local_time')
logging.info("Queried rule %s from %s to %s: %s hits" %
(rule['name'], pretty_ts(
starttime, lt), pretty_ts(endtime, lt), res['count']))
return {endtime: res['count']}
def get_hits_terms(self, rule, starttime, endtime, index, key, qk=None):
rule_filter = copy.copy(rule['filter'])
if qk:
filter_key = rule['query_key']
if rule.get('raw_count_keys',
True) and not rule['query_key'].endswith('.raw'):
filter_key += '.raw'
rule_filter.extend([{'term': {filter_key: qk}}])
base_query = self.get_query(rule_filter,
starttime,
endtime,
timestamp_field=rule['timestamp_field'],
sort=False)
query = self.get_terms_query(base_query, rule.get('terms_size', 5),
key)
try:
res = self.current_es.search(index=index,
doc_type=rule['doc_type'],
body=query,
search_type='count',
ignore_unavailable=True)
except ElasticsearchException as e:
# Elasticsearch sometimes gives us GIGANTIC error messages
# (so big that they will fill the entire terminal buffer)
if len(str(e)) > 1024:
e = str(e)[:1024] + '... (%d characters removed)' % (
len(str(e)) - 1024)
self.handle_error('Error running query: %s' % (e),
{'rule': rule['name']})
return None
buckets = res['aggregations']['filtered']['counts']['buckets']
self.num_hits += len(buckets)
lt = rule.get('use_local_time')
logging.info('Queried rule %s from %s to %s: %s buckets' %
(rule['name'], pretty_ts(
starttime, lt), pretty_ts(endtime, lt), len(buckets)))
return {endtime: buckets}
def remove_duplicate_events(self, data, rule):
# Remove data we've processed already
data = [
event for event in data
if event['_id'] not in rule['processed_hits']
]
# Remember the new data's IDs
for event in data:
rule['processed_hits'][event['_id']] = event['_source'][
rule['timestamp_field']]
return [event['_source'] for event in data]
def remove_old_events(self, rule):
# Anything older than the buffer time we can forget
now = ts_now()
remove = []
buffer_time = rule.get('buffer_time', self.buffer_time)
for _id, timestamp in rule['processed_hits'].iteritems():
if now - timestamp > buffer_time:
remove.append(_id)
map(rule['processed_hits'].pop, remove)
def run_query(self, rule, start=None, end=None):
""" Query for the rule and pass all of the results to the RuleType instance.
:param rule: The rule configuration.
:param start: The earliest time to query.
:param end: The latest time to query.
Returns True on success and False on failure.
"""
if start is None:
start = self.get_index_start(rule['index'])
if end is None:
end = ts_now()
# Reset hit counter and query
rule_inst = rule['type']
prev_num_hits = self.num_hits
max_size = rule.get('max_query_size', self.max_query_size)
index = self.get_index(rule, start, end)
if rule.get('use_count_query'):
data = self.get_hits_count(rule, start, end, index)
elif rule.get('use_terms_query'):
data = self.get_hits_terms(rule, start, end, index,
rule['query_key'])
else:
data = self.get_hits(rule, start, end, index)
if data:
data = self.remove_duplicate_events(data, rule)
# There was an exception while querying
if data is None:
return False
elif data:
if rule.get('use_count_query'):
rule_inst.add_count_data(data)
elif rule.get('use_terms_query'):
rule_inst.add_terms_data(data)
else:
rule_inst.add_data(data)
# Warn if we hit max_query_size
if self.num_hits - prev_num_hits == max_size and not rule.get(
'use_count_query'):
logging.warning("Hit max_query_size (%s) while querying for %s" %
(max_size, rule['name']))
return True
def get_starttime(self, rule):
""" Query ES for the last time we ran this rule.
:param rule: The rule configuration.
:return: A timestamp or None.
"""
query = {
'filter': {
'term': {
'rule_name': '%s' % (rule['name'])
}
},
'sort': {
'@timestamp': {
'order': 'desc'
}
}
}
try:
if self.writeback_es:
res = self.writeback_es.search(
index=self.writeback_index,
doc_type='elastalert_status',
size=1,
body=query,
_source_include=['endtime', 'rule_name'])
if res['hits']['hits']:
endtime = ts_to_dt(
res['hits']['hits'][0]['_source']['endtime'])
if ts_now() - endtime < self.old_query_limit:
return endtime
else:
logging.info(
"Found expired previous run for %s at %s" %
(rule['name'], endtime))
return None
except (ElasticsearchException, KeyError) as e:
self.handle_error('Error querying for last run: %s' % (e),
{'rule': rule['name']})
self.writeback_es = None
return None
def set_starttime(self, rule, endtime):
""" Given a rule and an endtime, sets the appropriate starttime for it. """
# This means we are starting fresh
if 'starttime' not in rule:
# Try to get the last run from elasticsearch
last_run_end = self.get_starttime(rule)
if last_run_end:
rule['starttime'] = last_run_end
return
# Use buffer for normal queries, or run_every increments otherwise
buffer_time = rule.get('buffer_time', self.buffer_time)
if not rule.get('use_count_query') and not rule.get('use_terms_query'):
rule['starttime'] = endtime - buffer_time
else:
rule['starttime'] = endtime - self.run_every
def run_rule(self, rule, endtime, starttime=None):
""" Run a rule for a given time period, including querying and alerting on results.
:param rule: The rule configuration.
:param starttime: The earliest timestamp to query.
:param endtime: The latest timestamp to query.
:return: The number of matches that the rule produced.
"""
run_start = time.time()
self.current_es = Elasticsearch(host=rule['es_host'],
port=rule['es_port'])
self.current_es_addr = (rule['es_host'], rule['es_port'])
# If there are pending aggregate matches, try processing them
for x in range(len(rule['agg_matches'])):
match = rule['agg_matches'].pop()
self.add_aggregated_alert(match, rule)
# Start from provided time if it's given
if starttime:
rule['starttime'] = starttime
else:
self.set_starttime(rule, endtime)
rule['original_starttime'] = rule['starttime']
# Don't run if starttime was set to the future
if ts_now() <= rule['starttime']:
logging.warning(
"Attempted to use query start time in the future (%s), sleeping instead"
% (starttime))
return 0
# Run the rule
# If querying over a large time period, split it up into chunks
self.num_hits = 0
tmp_endtime = endtime
buffer_time = rule.get('buffer_time', self.buffer_time)
while endtime - rule['starttime'] > buffer_time:
tmp_endtime = rule['starttime'] + self.run_every
if not self.run_query(rule, rule['starttime'], tmp_endtime):
return 0
rule['starttime'] = tmp_endtime
if not self.run_query(rule, rule['starttime'], endtime):
return 0
rule['type'].garbage_collect(endtime)
# Process any new matches
num_matches = len(rule['type'].matches)
while rule['type'].matches:
match = rule['type'].matches.pop(0)
# If realert is set, silence the rule for that duration
# Silence is cached by query_key, if it exists
# Default realert time is 0 seconds
# concatenate query_key (or none) with rule_name to form silence_cache key
if 'query_key' in rule:
try:
key = '.' + match[rule['query_key']]
except KeyError:
# Some matches may not have a query key
key = ''
else:
key = ''
if self.is_silenced(rule['name'] + key) or self.is_silenced(
rule['name']):
logging.info('Ignoring match for silenced rule %s%s' %
(rule['name'], key))
continue
if rule['realert']:
self.set_realert(
rule['name'] + key,
dt_to_ts(datetime.datetime.utcnow() + rule['realert']))
# If no aggregation, alert immediately
if not rule['aggregation']:
self.alert([match], rule)
continue
# Add it as an aggregated match
self.add_aggregated_alert(match, rule)
time_taken = time.time() - run_start
# Write to ES that we've run this rule against this time period
body = {
'rule_name': rule['name'],
'endtime': endtime,
'starttime': rule['starttime'],
'matches': num_matches,
'hits': self.num_hits,
'@timestamp': ts_now(),
'time_taken': time_taken
}
self.writeback('elastalert_status', body)
return num_matches
def init_rule(self, new_rule, new=True):
''' Copies some necessary non-config state from an exiting rule to a new rule. '''
if 'download_dashboard' in new_rule['filter']:
# Download filters from kibana and set the rules filters to them
db_filters = self.filters_from_kibana(
new_rule, new_rule['filter']['download_dashboard'])
if db_filters is not None:
new_rule['filter'] = db_filters
else:
raise EAException("Could not download filters from %s" %
(new_rule['filter']['download_dashboard']))
blank_rule = {
'agg_matches': [],
'current_aggregate_id': None,
'processed_hits': {}
}
rule = blank_rule
# Set rule to either a blank template or existing rule with same name
if not new:
for rule in self.rules:
if rule['name'] == new_rule['name']:
break
else:
logging.warning(
"Couldn't find existing rule %s, starting from scratch" %
(new_rule['name']))
rule = blank_rule
copy_properties = [
'agg_matches', 'current_aggregate_id', 'processed_hits',
'starttime'
]
for prop in copy_properties:
if prop == 'starttime' and 'starttime' not in rule:
continue
new_rule[prop] = rule[prop]
return new_rule
def load_rule_changes(self):
''' Using the modification times of rule config files, syncs the running rules
to match the files in rules_folder by removing, adding or reloading rules. '''
rule_hashes = get_rule_hashes(self.conf)
# Check each current rule for changes
for rule_file, hash_value in self.rule_hashes.iteritems():
if rule_file not in rule_hashes:
# Rule file was deleted
logging.info(
'Rule file %s not found, stopping rule execution' %
(rule_file))
self.rules = [
rule for rule in self.rules
if rule['rule_file'] != rule_file
]
continue
if hash_value != rule_hashes[rule_file]:
# Rule file was changed, reload rule
try:
new_rule = load_configuration(
os.path.join(self.conf['rules_folder'], rule_file))
except EAException as e:
self.handle_error('Could not load rule %s: %s' %
(rule_file, e))
continue
logging.info("Reloading configuration for rule %s" %
(rule_file))
# Initialize the rule that matches rule_file
self.rules = [
rule if rule['rule_file'] != rule_file else self.init_rule(
new_rule, False) for rule in self.rules
]
# Load new rules
if not self.args.rule:
for rule_file in set(rule_hashes.keys()) - set(
self.rule_hashes.keys()):
try:
new_rule = load_configuration(
os.path.join(self.conf['rules_folder'], rule_file))
except EAException as e:
self.handle_error('Could not load rule %s: %s' %
(rule_file, e))
continue
logging.info('Loaded new rule %s' % (rule_file))
self.rules.append(self.init_rule(new_rule))
self.rule_hashes = rule_hashes
def start(self):
""" Periodically go through each rule and run it """
starttime = self.args.start
if starttime:
try:
starttime = ts_to_dt(starttime)
except (TypeError, ValueError):
self.handle_error(
"%s is not a valid ISO 8601 timestamp (YYYY-MM-DDTHH:MM:SS+XX:00)"
% (starttime))
exit(1)
while True:
# If writeback_es errored, it's disabled until the next query cycle
if not self.writeback_es:
self.writeback_es = Elasticsearch(host=self.es_host,
port=self.es_port)
self.send_pending_alerts()
next_run = datetime.datetime.utcnow() + self.run_every
for rule in self.rules:
# Set endtime based on the rule's delay
delay = rule.get('query_delay')
if hasattr(self.args, 'end') and self.args.end:
endtime = ts_to_dt(self.args.end)
elif delay:
endtime = ts_now() - delay
else:
endtime = ts_now()
try:
num_matches = self.run_rule(rule, endtime, starttime)
except EAException as e:
self.handle_error(
"Error running rule %s: %s" % (rule['name'], e),
{'rule': rule['name']})
else:
old_starttime = pretty_ts(rule.get('original_starttime'),
rule.get('use_local_time'))
logging.info(
"Ran %s from %s to %s: %s query hits, %s matches,"
" %s alerts sent" %
(rule['name'], old_starttime,
pretty_ts(endtime, rule.get('use_local_time')),
self.num_hits, num_matches, self.alerts_sent))
self.alerts_sent = 0
self.remove_old_events(rule)
if next_run < datetime.datetime.utcnow():
# We were processing for longer than our refresh interval
# This can happen if --start was specified with a large time period
# or if we are running too slow to process events in real time.
logging.warning("Querying from %s to %s took longer than %s!" %
(old_starttime, endtime, self.run_every))
continue
# Only force starttime once
starttime = None
if not self.args.pin_rules:
self.load_rule_changes()
# Wait before querying again
sleep_for = (next_run - datetime.datetime.utcnow()).seconds
logging.info("Sleeping for %s seconds" % (sleep_for))
time.sleep(sleep_for)
def generate_kibana_db(self, rule, match):
''' Uses a template dashboard to upload a temp dashboard showing the match.
Returns the url to the dashboard. '''
db = copy.deepcopy(kibana.dashboard_temp)
# Set filters
for filter in rule['filter']:
if filter:
kibana.add_filter(db, filter)
kibana.set_included_fields(db, rule['include'])
# Set index
index = self.get_index(rule)
kibana.set_index_name(db, index)
return self.upload_dashboard(db, rule, match)
def upload_dashboard(self, db, rule, match):
''' Uploads a dashboard schema to the kibana-int elasticsearch index associated with rule.
Returns the url to the dashboard. '''
# Set time range
start = ts_add(match[rule['timestamp_field']],
-rule.get('timeframe', datetime.timedelta(minutes=10)))
end = ts_add(match[rule['timestamp_field']],
datetime.timedelta(minutes=10))
kibana.set_time(db, start, end)
# Set dashboard name
db_name = 'ElastAlert - %s - %s' % (rule['name'], end)
kibana.set_name(db, db_name)
# Add filter for query_key value
if 'query_key' in rule:
if rule['query_key'] in match:
term = {'term': {rule['query_key']: match[rule['query_key']]}}
kibana.add_filter(db, term)
# Convert to json
db_js = json.dumps(db)
db_body = {
'user': '******',
'group': 'guest',
'title': db_name,
'dashboard': db_js
}
# Upload
es = Elasticsearch(host=rule['es_host'], port=rule['es_port'])
res = es.create(index='kibana-int', doc_type='temp', body=db_body)
# Return dashboard URL
kibana_url = rule.get('kibana_dashboard')
if not kibana_url:
kibana_url = 'http://%s:%s/_plugin/kibana/' % (rule['es_host'],
rule['es_port'])
return kibana_url + '#/dashboard/temp/%s' % (res['_id'])
def get_dashboard(self, rule, db_name):
""" Download dashboard which matches use_kibana_dashboard from elasticsearch. """
es = Elasticsearch(host=rule['es_host'], port=rule['es_port'])
if not db_name:
raise EAException("use_kibana_dashboard undefined")
query = {'query': {'term': {'_id': db_name}}}
try:
res = es.search(index='kibana-int',
doc_type='dashboard',
body=query,
_source_include=['dashboard'])
except ElasticsearchException as e:
raise EAException("Error querying for dashboard: %s" % (e))
if res['hits']['hits']:
return json.loads(res['hits']['hits'][0]['_source']['dashboard'])
else:
raise EAException("Could not find dashboard named %s" % (db_name))
def use_kibana_link(self, rule, match):
""" Uploads an existing dashboard as a temp dashboard modified for match time.
Returns the url to the dashboard. """
# Download or get cached dashboard
dashboard = rule.get('dashboard_schema')
if not dashboard:
db_name = rule.get('use_kibana_dashboard')
dashboard = self.get_dashboard(rule, db_name)
if dashboard:
rule['dashboard_schema'] = dashboard
else:
return None
dashboard = copy.deepcopy(dashboard)
return self.upload_dashboard(dashboard, rule, match)
def filters_from_kibana(self, rule, db_name):
""" Downloads a dashboard from kibana and returns corresponding filters, None on error. """
try:
db = rule.get('dashboard_schema')
if not db:
db = self.get_dashboard(rule, db_name)
filters = kibana.filters_from_dashboard(db)
except EAException:
return None
return filters
def alert(self, matches, rule, alert_time=None):
""" Send out an alert.
:param matches: A list of matches.
:param rule: A rule configuration.
"""
if alert_time is None:
alert_time = ts_now()
# Compute top count keys
if rule.get('top_count_keys'):
for match in matches:
if 'query_key' in rule and rule['query_key'] in match:
qk = match[rule['query_key']]
else:
qk = None
start = ts_to_dt(match[rule['timestamp_field']]) - rule.get(
'timeframe', datetime.timedelta(minutes=10))
end = ts_to_dt(
match[rule['timestamp_field']]) + datetime.timedelta(
minutes=10)
keys = rule.get('top_count_keys')
counts = self.get_top_counts(rule, start, end, keys,
rule.get('top_count_number'), qk)
match.update(counts)
# Generate a kibana dashboard for the first match
if rule.get('generate_kibana_link') or rule.get(
'use_kibana_dashboard'):
try:
if rule.get('generate_kibana_link'):
kb_link = self.generate_kibana_db(rule, matches[0])
else:
kb_link = self.use_kibana_link(rule, matches[0])
except EAException as e:
self.handle_error(
"Could not generate kibana dash for %s match: %s" %
(rule['name'], e))
else:
if kb_link:
matches[0]['kibana_link'] = kb_link
for enhancement in rule['match_enhancements']:
for match in matches:
try:
enhancement.process(match)
except EAException as e:
self.handle_error(
"Error running match enhancement: %s" % (e),
{'rule': rule['name']})
# Don't send real alerts in debug mode
if self.debug:
alerter = DebugAlerter(rule)
alerter.alert(matches)
return
# Run the alerts
alert_sent = False
alert_exception = None
for alert in rule['alert']:
try:
alert.alert(matches)
except EAException as e:
self.handle_error(
'Error while running alert %s: %s' %
(alert.get_info()['type'], e), {'rule': rule['name']})
alert_exception = str(e)
else:
self.alerts_sent += 1
alert_sent = True
# Write the alert(s) to ES
agg_id = None
for match in matches:
alert_body = self.get_alert_body(match, rule, alert_sent,
alert_time, alert_exception)
# Set all matches to aggregate together
if agg_id:
alert_body['aggregate_id'] = agg_id
res = self.writeback('elastalert', alert_body)
if res and not agg_id:
agg_id = res['_id']
def get_alert_body(self,
match,
rule,
alert_sent,
alert_time,
alert_exception=None):
body = {'match_body': match}
body['rule_name'] = rule['name']
# TODO record info about multiple alerts
body['alert_info'] = rule['alert'][0].get_info()
body['alert_sent'] = alert_sent
body['alert_time'] = alert_time
# If the alert failed to send, record the exception
if not alert_sent:
body['alert_exception'] = alert_exception
return body
def writeback(self, doc_type, body):
# Convert any datetime objects to timestamps
for key in body.keys():
if isinstance(body[key], datetime.datetime):
body[key] = dt_to_ts(body[key])
if self.debug:
logging.info("Skipping writing to ES: %s" % (body))
return None
if '@timestamp' not in body:
body['@timestamp'] = dt_to_ts(ts_now())
if self.writeback_es:
try:
res = self.writeback_es.create(index=self.writeback_index,
doc_type=doc_type,
body=body)
return res
except ElasticsearchException as e:
logging.exception(
"Error writing alert info to elasticsearch: %s" % (e))
self.writeback_es = None
return None
def find_recent_pending_alerts(self, time_limit):
""" Queries writeback_es to find alerts that did not send
and are newer than time_limit """
query = {
'query': {
'query_string': {
'query': 'alert_sent:false'
}
},
'filter': {
'range': {
'alert_time': {
'from': dt_to_ts(ts_now() - time_limit),
'to': dt_to_ts(ts_now())
}
}
}
}
if self.writeback_es:
try:
res = self.writeback_es.search(index=self.writeback_index,
doc_type='elastalert',
body=query,
size=1000)
if res['hits']['hits']:
return res['hits']['hits']
except:
pass
return []
def send_pending_alerts(self):
pending_alerts = self.find_recent_pending_alerts(self.alert_time_limit)
for alert in pending_alerts:
_id = alert['_id']
alert = alert['_source']
try:
rule_name = alert.pop('rule_name')
alert_time = alert.pop('alert_time')
match_body = alert.pop('match_body')
except KeyError:
# Malformed alert, drop it
continue
agg_id = alert.get('aggregate_id', None)
if agg_id:
# Aggregated alerts will be taken care of by get_aggregated_matches
continue
# Find original rule
for rule in self.rules:
if rule['name'] == rule_name:
break
else:
# Original rule is missing, drop alert
continue
# Retry the alert unless it's a future alert
if ts_now() > ts_to_dt(alert_time):
aggregated_matches = self.get_aggregated_matches(_id)
if aggregated_matches:
matches = [match_body] + [
agg_match['match_body']
for agg_match in aggregated_matches
]
self.alert(matches, rule, alert_time=alert_time)
rule['current_aggregate_id'] = None
else:
self.alert([match_body], rule, alert_time=alert_time)
# Delete it from the index
try:
self.writeback_es.delete(index=self.writeback_index,
doc_type='elastalert',
id=_id)
except:
self.handle_error("Failed to delete alert %s at %s" %
(_id, alert_time))
# Send in memory aggregated alerts
for rule in self.rules:
if rule['agg_matches']:
if ts_now() > rule['aggregate_alert_time']:
self.alert(rule['agg_matches'], rule)
rule['agg_matches'] = []
def get_aggregated_matches(self, _id):
""" Removes and returns all matches from writeback_es that have aggregate_id == _id """
query = {
'query': {
'query_string': {
'query': 'aggregate_id:%s' % (_id)
}
}
}
matches = []
if self.writeback_es:
try:
res = self.writeback_es.search(index=self.writeback_index,
doc_type='elastalert',
body=query)
for match in res['hits']['hits']:
matches.append(match['_source'])
self.writeback_es.delete(index=self.writeback_index,
doc_type='elastalert',
id=match['_id'])
except (KeyError, ElasticsearchException) as e:
self.handle_error(
"Error fetching aggregated matches: %s" % (e), {'id': _id})
return matches
def add_aggregated_alert(self, match, rule):
""" Save a match as a pending aggregate alert to elasticsearch. """
if not rule['current_aggregate_id'] or rule[
'aggregate_alert_time'] < ts_to_dt(
match[rule['timestamp_field']]):
# First match, set alert_time
match_time = ts_to_dt(match[rule['timestamp_field']])
alert_time = match_time + rule['aggregation']
rule['aggregate_alert_time'] = alert_time
agg_id = None
else:
# Already pending aggregation, use existing alert_time
alert_time = rule['aggregate_alert_time']
agg_id = rule['current_aggregate_id']
logging.info(
'Adding alert for %s to aggregation, next alert at %s' %
(rule['name'], alert_time))
alert_body = self.get_alert_body(match, rule, False, alert_time)
if agg_id:
alert_body['aggregate_id'] = agg_id
res = self.writeback('elastalert', alert_body)
# If new aggregation, save _id
if res and not agg_id:
rule['current_aggregate_id'] = res['_id']
# Couldn't write the match to ES, save it in memory for now
if not res:
rule['agg_matches'].append(match)
return res
def silence(self):
""" Silence an alert for a period of time. --silence and --rule must be passed as args. """
if not self.args.rule:
logging.error('--silence must be used with --rule')
exit(1)
# With --rule, self.rules will only contain that specific rule
rule_name = self.rules[0]['name']
try:
unit, num = self.args.silence.split('=')
silence_time = datetime.timedelta(**{unit: int(num)})
silence_ts = dt_to_ts(silence_time + datetime.datetime.utcnow())
except (ValueError, TypeError):
logging.error('%s is not a valid time period' %
(self.args.silence))
exit(1)
if not self.set_realert(rule_name, silence_ts):
logging.error('Failed to save silence command to elasticsearch')
exit(1)
logging.info('Success. %s will be silenced until %s' %
(rule_name, silence_ts))
def set_realert(self, rule_name, timestamp):
""" Write a silence to elasticsearch for rule_name until timestamp. """
body = {
'rule_name': rule_name,
'@timestamp': ts_now(),
'until': timestamp
}
self.silence_cache[rule_name] = timestamp
return self.writeback('silence', body)
def is_silenced(self, rule_name):
""" Checks if rule_name is currently silenced. Returns false on exception. """
if rule_name in self.silence_cache:
if ts_now() < ts_to_dt(self.silence_cache[rule_name]):
return True
else:
self.silence_cache.pop(rule_name)
return False
query = {
'filter': {
'term': {
'rule_name': rule_name
}
},
'sort': {
'until': {
'order': 'desc'
}
}
}
if self.writeback_es:
try:
res = self.writeback_es.search(index=self.writeback_index,
doc_type='silence',
size=1,
body=query,
_source_include=['until'])
except ElasticsearchException as e:
self.handle_error(
"Error while querying for alert silence status: %s" % (e),
{'rule': rule_name})
return False
if res['hits']['hits']:
until_ts = res['hits']['hits'][0]['_source']['until']
if ts_now() < ts_to_dt(until_ts):
self.silence_cache[rule_name] = until_ts
return True
return False
def handle_error(self, message, data=None):
''' Logs message at error level and writes message, data and traceback to Elasticsearch. '''
if not self.writeback_es:
self.writeback_es = Elasticsearch(host=self.es_host,
port=self.es_port)
logging.error(message)
body = {'message': message}
tb = traceback.format_exc()
body['traceback'] = tb.strip().split('\n')
if data:
body['data'] = data
self.writeback('elastalert_error', body)
def get_top_counts(self,
rule,
starttime,
endtime,
keys,
number=5,
qk=None):
""" Counts the number of events for each unique value for each key field.
Returns a dictionary with top_events_<key> mapped to the top 5 counts for each key. """
all_counts = {}
for key in keys:
index = self.get_index(rule, starttime, endtime)
buckets = self.get_hits_terms(rule, starttime, endtime, index, key,
qk).values()[0]
# get_hits_terms adds to num_hits, but we don't want to count these
self.num_hits -= len(buckets)
terms = {}
for bucket in buckets:
terms[bucket['key']] = bucket['doc_count']
counts = terms.items()
counts.sort(key=lambda x: x[1], reverse=True)
# Save a dict with the top 5 events by key
all_counts['top_events_%s' % (key)] = dict(counts[:number])
return all_counts
