# -*- coding: utf-8 -*- # Copyright 2010 - 2017 RhodeCode GmbH and the AppEnlight project authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Utility functions. """ import logging import requests import hashlib import json import copy import uuid import appenlight.lib.helpers as h from collections import namedtuple from datetime import timedelta, datetime, date from dogpile.cache.api import NO_VALUE from appenlight.models import Datastores from appenlight.validators import (LogSearchSchema, TagListSchema, accepted_search_params) from itsdangerous import TimestampSigner from ziggurat_foundations.permissions import ALL_PERMISSIONS from ziggurat_foundations.models.services.user import UserService from dateutil.relativedelta import relativedelta from dateutil.rrule import rrule, MONTHLY, DAILY log = logging.getLogger(__name__) Stat = namedtuple('Stat', 'start_interval value') def default_extractor(item): """ :param item - item to extract date from """ if hasattr(item, 'start_interval'): return item.start_interval return item['start_interval'] # fast gap generator def gap_gen_default(start, step, itemiterator, end_time=None, iv_extractor=None): """ generates a list of time/value items based on step and itemiterator if there are entries missing from iterator time/None will be returned instead :param start - datetime - what time should we start generating our values :param step - timedelta - stepsize :param itemiterator - iterable - we will check this iterable for values corresponding to generated steps :param end_time - datetime - when last step is >= end_time stop iterating :param iv_extractor - extracts current step from iterable items """ if not iv_extractor: iv_extractor = default_extractor next_step = start minutes = step.total_seconds() / 60.0 while next_step.minute % minutes != 0: next_step = next_step.replace(minute=next_step.minute - 1) for item in itemiterator: item_start_interval = iv_extractor(item) # do we have a match for current time step in our data? # no gen a new tuple with 0 values while next_step < item_start_interval: yield Stat(next_step, None) next_step = next_step + step if next_step == item_start_interval: yield Stat(item_start_interval, item) next_step = next_step + step if end_time: while next_step < end_time: yield Stat(next_step, None) next_step = next_step + step class DateTimeEncoder(json.JSONEncoder): """ Simple datetime to ISO encoder for json serialization""" def default(self, obj): if isinstance(obj, date): return obj.isoformat() if isinstance(obj, datetime): return obj.isoformat() return json.JSONEncoder.default(self, obj) def channelstream_request(secret, endpoint, payload, throw_exceptions=False, servers=None): responses = [] if not servers: servers = [] signer = TimestampSigner(secret) sig_for_server = signer.sign(endpoint) for secret, server in [(s['secret'], s['server']) for s in servers]: response = {} secret_headers = {'x-channelstream-secret': sig_for_server, 'x-channelstream-endpoint': endpoint, 'Content-Type': 'application/json'} url = '%s%s' % (server, endpoint) try: response = requests.post(url, data=json.dumps(payload, cls=DateTimeEncoder), headers=secret_headers, verify=False, timeout=2).json() except requests.exceptions.RequestException as e: if throw_exceptions: raise responses.append(response) return responses def add_cors_headers(response): # allow CORS response.headers.add('Access-Control-Allow-Origin', '*') response.headers.add('XDomainRequestAllowed', '1') response.headers.add('Access-Control-Allow-Methods', 'GET, POST, OPTIONS') # response.headers.add('Access-Control-Allow-Credentials', 'true') response.headers.add('Access-Control-Allow-Headers', 'Content-Type, Depth, User-Agent, X-File-Size, X-Requested-With, If-Modified-Since, X-File-Name, Cache-Control, Pragma, Origin, Connection, Referer, Cookie') response.headers.add('Access-Control-Max-Age', '86400') from sqlalchemy.sql import compiler from psycopg2.extensions import adapt as sqlescape # or use the appropiate escape function from your db driver def compile_query(query): dialect = query.session.bind.dialect statement = query.statement comp = compiler.SQLCompiler(dialect, statement) comp.compile() enc = dialect.encoding params = {} for k, v in comp.params.items(): if isinstance(v, str): v = v.encode(enc) params[k] = sqlescape(v) return (comp.string.encode(enc) % params).decode(enc) def convert_es_type(input_data): """ This might need to convert some text or other types to corresponding ES types """ return str(input_data) ProtoVersion = namedtuple('ProtoVersion', ['major', 'minor', 'patch']) def parse_proto(input_data): try: parts = [int(x) for x in input_data.split('.')] while len(parts) < 3: parts.append(0) return ProtoVersion(*parts) except Exception as e: log.info('Unknown protocol version: %s' % e) return ProtoVersion(99, 99, 99) def es_index_name_limiter(start_date=None, end_date=None, months_in_past=6, ixtypes=None): """ This function limits the search to 6 months by default so we don't have to query 300 elasticsearch indices for 20 years of historical data for example """ # should be cached later def get_possible_names(): return list(Datastores.es.aliases().keys()) possible_names = get_possible_names() es_index_types = [] if not ixtypes: ixtypes = ['reports', 'metrics', 'logs'] for t in ixtypes: if t == 'reports': es_index_types.append('rcae_r_%s') elif t == 'logs': es_index_types.append('rcae_l_%s') elif t == 'metrics': es_index_types.append('rcae_m_%s') elif t == 'uptime': es_index_types.append('rcae_u_%s') elif t == 'slow_calls': es_index_types.append('rcae_sc_%s') if start_date: start_date = copy.copy(start_date) else: if not end_date: end_date = datetime.utcnow() start_date = end_date + relativedelta(months=months_in_past * -1) if not end_date: end_date = start_date + relativedelta(months=months_in_past) index_dates = list(rrule(MONTHLY, dtstart=start_date.date().replace(day=1), until=end_date.date(), count=36)) index_names = [] for ix_type in es_index_types: to_extend = [ix_type % d.strftime('%Y_%m') for d in index_dates if ix_type % d.strftime('%Y_%m') in possible_names] index_names.extend(to_extend) for day in list(rrule(DAILY, dtstart=start_date.date(), until=end_date.date(), count=366)): ix_name = ix_type % day.strftime('%Y_%m_%d') if ix_name in possible_names: index_names.append(ix_name) return index_names def build_filter_settings_from_query_dict( request, params=None, override_app_ids=None, resource_permissions=None): """ Builds list of normalized search terms for ES from query params ensuring application list is restricted to only applications user has access to :param params (dictionary) :param override_app_ids - list of application id's to use instead of applications user normally has access to """ params = copy.deepcopy(params) applications = [] if not resource_permissions: resource_permissions = ['view'] if request.user: applications = UserService.resources_with_perms( request.user, resource_permissions, resource_types=['application']) # CRITICAL - this ensures our resultset is limited to only the ones # user has view permissions all_possible_app_ids = set([app.resource_id for app in applications]) # if override is preset we force permission for app to be present # this allows users to see dashboards and applications they would # normally not be able to if override_app_ids: all_possible_app_ids = set(override_app_ids) schema = LogSearchSchema().bind(resources=all_possible_app_ids) tag_schema = TagListSchema() filter_settings = schema.deserialize(params) tag_list = [] for k, v in list(filter_settings.items()): if k in accepted_search_params: continue tag_list.append({"name": k, "value": v, "op": 'eq'}) # remove the key from filter_settings filter_settings.pop(k, None) tags = tag_schema.deserialize(tag_list) filter_settings['tags'] = tags return filter_settings def gen_uuid(): return str(uuid.uuid4()) def gen_uuid4_sha_hex(): return hashlib.sha1(uuid.uuid4().bytes).hexdigest() def permission_tuple_to_dict(data): out = { "user_name": None, "perm_name": data.perm_name, "owner": data.owner, "type": data.type, "resource_name": None, "resource_type": None, "resource_id": None, "group_name": None, "group_id": None } if data.user: out["user_name"] = data.user.user_name if data.perm_name == ALL_PERMISSIONS: out['perm_name'] = '__all_permissions__' if data.resource: out['resource_name'] = data.resource.resource_name out['resource_type'] = data.resource.resource_type out['resource_id'] = data.resource.resource_id if data.group: out['group_name'] = data.group.group_name out['group_id'] = data.group.id return out def get_cached_buckets(request, stats_since, end_time, fn, cache_key, gap_gen=None, db_session=None, step_interval=None, iv_extractor=None, rerange=False, *args, **kwargs): """ Takes "fn" that should return some data and tries to load the data dividing it into daily buckets - if the stats_since and end time give a delta bigger than 24hours, then only "todays" data is computed on the fly :param request: (request) request object :param stats_since: (datetime) start date of buckets range :param end_time: (datetime) end date of buckets range - utcnow() if None :param fn: (callable) callable to use to populate buckets should have following signature: def get_data(request, since_when, until, *args, **kwargs): :param cache_key: (string) cache key that will be used to build bucket caches :param gap_gen: (callable) gap generator - should return step intervals to use with out `fn` callable :param db_session: (Session) sqlalchemy session :param step_interval: (timedelta) optional step interval if we want to override the default determined from total start/end time delta :param iv_extractor: (callable) used to get step intervals from data returned by `fn` callable :param rerange: (bool) handy if we want to change ranges from hours to days when cached data is missing - will shorten execution time if `fn` callable supports that and we are working with multiple rows - like metrics :param args: :param kwargs: :return: iterable """ if not end_time: end_time = datetime.utcnow().replace(second=0, microsecond=0) delta = end_time - stats_since # if smaller than 3 days we want to group by 5min else by 1h, # for 60 min group by min if not gap_gen: gap_gen = gap_gen_default if not iv_extractor: iv_extractor = default_extractor # do not use custom interval if total time range with new iv would exceed # end time if not step_interval or stats_since + step_interval >= end_time: if delta < h.time_deltas.get('12h')['delta']: step_interval = timedelta(seconds=60) elif delta < h.time_deltas.get('3d')['delta']: step_interval = timedelta(seconds=60 * 5) elif delta > h.time_deltas.get('2w')['delta']: step_interval = timedelta(days=1) else: step_interval = timedelta(minutes=60) if step_interval >= timedelta(minutes=60): log.info('cached_buckets:{}: adjusting start time ' 'for hourly or daily intervals'.format(cache_key)) stats_since = stats_since.replace(hour=0, minute=0) ranges = [i.start_interval for i in list(gap_gen(stats_since, step_interval, [], end_time=end_time))] buckets = {} storage_key = 'buckets:' + cache_key + '{}|{}' # this means we basicly cache per hour in 3-14 day intervals but i think # its fine at this point - will be faster than db access anyways if len(ranges) >= 1: last_ranges = [ranges[-1]] else: last_ranges = [] if step_interval >= timedelta(minutes=60): for r in ranges: k = storage_key.format(step_interval.total_seconds(), r) value = request.registry.cache_regions.redis_day_30.get(k) # last buckets are never loaded from cache is_last_result = ( r >= end_time - timedelta(hours=6) or r in last_ranges) if value is not NO_VALUE and not is_last_result: log.info("cached_buckets:{}: " "loading range {} from cache".format(cache_key, r)) buckets[r] = value else: log.info("cached_buckets:{}: " "loading range {} from storage".format(cache_key, r)) range_size = step_interval if (step_interval == timedelta(minutes=60) and not is_last_result and rerange): range_size = timedelta(days=1) r = r.replace(hour=0, minute=0) log.info("cached_buckets:{}: " "loading collapsed " "range {} {}".format(cache_key, r, r + range_size)) bucket_data = fn( request, r, r + range_size, step_interval, gap_gen, bucket_count=len(ranges), *args, **kwargs) for b in bucket_data: b_iv = iv_extractor(b) buckets[b_iv] = b k2 = storage_key.format( step_interval.total_seconds(), b_iv) request.registry.cache_regions.redis_day_30.set(k2, b) log.info("cached_buckets:{}: saving cache".format(cache_key)) else: # bucket count is 1 for short time ranges <= 24h from now bucket_data = fn(request, stats_since, end_time, step_interval, gap_gen, bucket_count=1, *args, **kwargs) for b in bucket_data: buckets[iv_extractor(b)] = b return buckets def get_cached_split_data(request, stats_since, end_time, fn, cache_key, db_session=None, *args, **kwargs): """ Takes "fn" that should return some data and tries to load the data dividing it into 2 buckets - cached "since_from" bucket and "today" bucket - then the data can be reduced into single value Data is cached if the stats_since and end time give a delta bigger than 24hours - then only 24h is computed on the fly """ if not end_time: end_time = datetime.utcnow().replace(second=0, microsecond=0) delta = end_time - stats_since if delta >= timedelta(minutes=60): log.info('cached_split_data:{}: adjusting start time ' 'for hourly or daily intervals'.format(cache_key)) stats_since = stats_since.replace(hour=0, minute=0) storage_key = 'buckets_split_data:' + cache_key + ':{}|{}' old_end_time = end_time.replace(hour=0, minute=0) final_storage_key = storage_key.format(delta.total_seconds(), old_end_time) older_data = None cdata = request.registry.cache_regions.redis_day_7.get( final_storage_key) if cdata: log.info("cached_split_data:{}: found old " "bucket data".format(cache_key)) older_data = cdata if (stats_since < end_time - h.time_deltas.get('24h')['delta'] and not cdata): log.info("cached_split_data:{}: didn't find the " "start bucket in cache so load older data".format(cache_key)) recent_stats_since = old_end_time older_data = fn(request, stats_since, recent_stats_since, db_session=db_session, *args, **kwargs) request.registry.cache_regions.redis_day_7.set(final_storage_key, older_data) elif stats_since < end_time - h.time_deltas.get('24h')['delta']: recent_stats_since = old_end_time else: recent_stats_since = stats_since log.info("cached_split_data:{}: loading fresh " "data bucksts from last 24h ".format(cache_key)) todays_data = fn(request, recent_stats_since, end_time, db_session=db_session, *args, **kwargs) return older_data, todays_data def in_batches(seq, size): """ Splits am iterable into batches of specified size :param seq (iterable) :param size integer """ return (seq[pos:pos + size] for pos in range(0, len(seq), size))