Implement Druid DB fetching
This commit is contained in:
180
core/db/druid.py
180
core/db/druid.py
@@ -1,17 +1,9 @@
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import logging
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import random
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import string
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import time
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from datetime import datetime
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from math import floor, log10
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from pprint import pprint
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import orjson
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import requests
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from django.conf import settings
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from siphashc import siphash
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from core import r
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from core.db import StorageBackend
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from core.db.processing import parse_druid
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from core.views import helpers
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@@ -32,30 +24,26 @@ class DruidBackend(StorageBackend):
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"limit": size,
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"queryType": "scan",
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"dataSource": index,
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"filter": {
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"type": "and",
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"fields": [
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],
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},
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# "resultFormat": "list",
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# "columns":[],
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"intervals": ["1000-01-01/3000-01-01"],
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# "batchSize": 20480,
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"intervals": ["1999-01-01/2999-01-01"],
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}
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to_add = {
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"type": "search",
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"dimension": "msg",
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"query": {
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"type": "insensitive_contains",
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"value": query,
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},
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},
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base_filter = {
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"type": "and",
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"fields": [],
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}
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to_add = {
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"type": "search",
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"dimension": "msg",
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"query": {
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"type": "insensitive_contains",
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"value": query,
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},
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}
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if blank:
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return search_query
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else:
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search_query["filter"] = base_filter
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search_query["filter"]["fields"].append(to_add)
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return search_query
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@@ -65,12 +53,15 @@ class DruidBackend(StorageBackend):
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return parsed
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def run_query(self, user, search_query):
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ss = orjson.dumps(search_query, option=orjson.OPT_INDENT_2)
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ss = ss.decode()
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print(ss)
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response = requests.post("http://broker:8082/druid/v2", json=search_query)
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response = orjson.loads(response.text)
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print("RESPONSE LEN", len(response))
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ss = orjson.dumps(list(response), option=orjson.OPT_INDENT_2)
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ss = ss.decode()
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print(ss)
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# ss = orjson.dumps(response, option=orjson.OPT_INDENT_2)
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# ss = ss.decode()
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# print(ss)
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return response
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def filter_blacklisted(self, user, response):
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@@ -89,12 +80,24 @@ class DruidBackend(StorageBackend):
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tags=None,
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):
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add_bool = []
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add_top = []
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add_in = {}
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helpers.add_defaults(query_params)
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# Check size
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# Now, run the helpers for SIQTSRSS/ADR
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# S - Size
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# I - Index
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# Q - Query
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# T - Tags
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# S - Source
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# R - Ranges
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# S - Sort
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# S - Sentiment
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# A - Annotate
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# D - Dedup
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# R - Reverse
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# S - Size
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if request.user.is_anonymous:
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sizes = settings.MAIN_SIZES_ANON
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else:
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@@ -104,37 +107,80 @@ class DruidBackend(StorageBackend):
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if isinstance(size, dict):
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return size
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# Check index
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# I - Index
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index = self.parse_index(request.user, query_params)
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if isinstance(index, dict):
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return index
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# Create the search query
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search_query = self.parse_query(query_params, tags, size, index, custom_query, add_bool)
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if isinstance(search_query, dict):
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# Q/T - Query/Tags
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search_query = self.parse_query(
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query_params, tags, size, index, custom_query, add_bool
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)
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# Query should be a dict, so check if it contains message here
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if "message" in search_query:
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return search_query
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# S - Sources
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sources = self.parse_source(request.user, query_params)
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# TODO
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add_top_tmp = {"bool": {"should": []}}
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total_count = 0
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for source_iter in sources:
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add_top_tmp["bool"]["should"].append({"equals": {"src": source_iter}})
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total_count += 1
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total_sources = len(settings.MAIN_SOURCES) + len(
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settings.SOURCES_RESTRICTED
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)
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if not total_count == total_sources:
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add_top.append(add_top_tmp)
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if isinstance(sources, dict):
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return sources
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total_count = len(sources)
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total_sources = len(settings.MAIN_SOURCES) + len(settings.SOURCES_RESTRICTED)
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if total_count != total_sources:
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add_in["src"] = sources
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print("SIZE IS", size)
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# R - Ranges
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from_ts, to_ts = self.parse_date_time(query_params)
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if from_ts:
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addendum = f"{from_ts}/{to_ts}"
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search_query["intervals"] = [addendum]
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# S - Sort
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sort = self.parse_sort(query_params)
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if isinstance(sort, dict):
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return sort
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if sort:
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search_query["order"] = sort
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# S - Sentiment
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sentiment_r = self.parse_sentiment(query_params)
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if isinstance(sentiment_r, dict):
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return sentiment_r
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if sentiment_r:
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sentiment_method, sentiment = sentiment_r
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sentiment_query = {"type": "bound", "dimension": "sentiment"}
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if sentiment_method == "below":
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sentiment_query["upper"] = sentiment
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elif sentiment_method == "above":
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sentiment_query["lower"] = sentiment
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elif sentiment_method == "exact":
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sentiment_query["lower"] = sentiment
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sentiment_query["upper"] = sentiment
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elif sentiment_method == "nonzero":
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sentiment_query["lower"] = -0.0001
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sentiment_query["upper"] = 0.0001
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sentiment_query["lowerStrict"] = True
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sentiment_query["upperStrict"] = True
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# add_bool.append(sentiment_query)
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self.add_filter(search_query)
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search_query["filter"]["fields"].append(sentiment_query)
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# Add in the additional information we already populated
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if add_bool:
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self.add_bool(search_query, add_bool)
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self.add_type("and", search_query, add_bool)
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if add_in:
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self.add_in(search_query, add_in)
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response = self.query(request.user, search_query)
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# print("RESP", response)
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# A/D/R - Annotate/Dedup/Reverse
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self.process_results(
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response,
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annotate=annotate,
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dedup=dedup,
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dedup_fields=dedup_fields,
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reverse=reverse,
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)
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# ss = orjson.dumps(list(response), option=orjson.OPT_INDENT_2)
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# ss = ss.decode()
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# print(ss)
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@@ -143,11 +189,29 @@ class DruidBackend(StorageBackend):
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context = response
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return context
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def add_bool(self, search_query, add_bool):
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if "filter" in search_query:
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if "fields" in search_query["filter"]:
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search_query["filter"]["fields"].append({"bool": {"should": add_bool}})
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else:
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search_query["filter"]["fields"] = [{"bool": {"should": add_bool}}]
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else:
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search_query["filter"] = {"bool": {"should": add_bool}}
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def add_filter(self, search_query):
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if "filter" not in search_query:
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search_query["filter"] = {
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"type": "and",
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"fields": [],
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}
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def add_in(self, search_query, add_in):
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self.add_filter(search_query)
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for key, value in add_in.items():
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to_add = {"type": "in", "dimension": key, "values": value}
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search_query["filter"]["fields"].append(to_add)
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def add_type(self, gate, search_query, add_bool):
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top_level_bool = {"type": gate, "fields": []}
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self.add_filter(search_query)
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for item in add_bool:
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for key, value in item.items():
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to_add = {"type": "selector", "dimension": key, "value": value}
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top_level_bool["fields"].append(to_add)
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search_query["filter"]["fields"].append(top_level_bool)
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def check_valid_query(self, query_params, custom_query):
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# We can do blank queries with this data source
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pass
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