| #!/usr/bin/env python3 |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| """ASF Infrastructure Reporting Dashboard - Download Statistics Tasks""" |
| import asyncio |
| from ..lib import middleware, config, asfuid |
| import elasticsearch |
| import elasticsearch_dsl |
| from .. import plugins |
| import re |
| import time |
| import os |
| import ua_parser.user_agent_parser |
| import aiohttp |
| import json |
| import datetime |
| |
| DEFAULT_PROJECTS_LIST = "https://whimsy.apache.org/public/public_ldap_projects.json" |
| MAX_HITS = 60 # Max number of artifacts to track in a single search |
| MAX_HITS_UA = 60 # Max number of user agents to collate |
| DOWNLOADS_CACHE_ITEMS = 200 # Keep the 200 latest search results in cache. 200 results is ~50MB |
| DOWNLOADS_CACHE_TTL = 7200 # Only cache items for 2 hours |
| PERSISTENT_REPORTS_BACKFILL_MONTHS = 6 # Try to backfill download reports six months back if possible |
| |
| INTERNAL_AGENTS = { |
| "Windows Package Manager": ("winget-cli", "Microsoft-Delivery-Optimization", "WindowsPackageManager", "Microsoft BITS",), |
| "NSIS (plugin)": ("NSIS_Inetc", ), |
| "Transmission": ("Transmission/", ), |
| "Free Download Manager": ("FDM", ), |
| "Patch My PC Client": ("Patch My PC Publishing Service", ), |
| "Artifactory": ("Artifactory", ), |
| "Scoop/Shovel": ("Scoop/", "Shovel/", ), |
| "BigFix": ("BigFix", ), |
| } |
| |
| # Common crawlers to ignore in stats. |
| IGNORED_BOTS = ("bingbot", "amazonbot", "diffbot", "googlebot", "slurp", "yandex", "baidu", ) |
| |
| # Ignore certain IPs that are known scanners |
| IGNORED_IPS = ( |
| '18.233.217.21', # Unknown AWS machine, does millions of downloads |
| '93.159.231.13', # Kaspersky Labs, testing binaries |
| ) |
| |
| # Different indices have different field names, account for it here: |
| FIELD_NAMES = { |
| "fastly": { # the index prefix |
| "geo_country": "geo_country_code", |
| "bytes": "bytes", |
| "vhost": "vhost", |
| "uri": "url", |
| "timestamp": "timestamp", |
| "_vhost_": "dlcdn.apache.org", # This is a variable field value, not a name |
| "request_method": "request", |
| "useragent": "request_user_agent", |
| }, |
| "loggy": { # the index prefix |
| "geo_country": "geo_country", |
| "bytes": "bytes", |
| "vhost": "vhost", |
| "uri": "uri", |
| "timestamp": "@timestamp", |
| "_vhost_": "downloads.apache.org", # This is a variable field value, not a name |
| "request_method": "request_method", |
| "useragent": "useragent", |
| }, |
| } |
| |
| dataurl = "http://localhost:9200" |
| datadir = None # Where to store persistent data |
| if hasattr(config.reporting, "downloads"): # If prod... |
| dataurl = config.reporting.downloads["dataurl"] |
| datadir = config.reporting.downloads.get("datadir") |
| |
| es_client = elasticsearch.AsyncElasticsearch(hosts=[dataurl], timeout=45) |
| |
| if datadir: |
| if not os.path.exists(datadir): |
| print(f"Setting up persistent data dir for download stats: {datadir}") |
| try: |
| os.mkdir(datadir) |
| except OSError as e: |
| print(f"Could not set up data directory {datadir}, will not store persistent download stats!") |
| |
| # WARNING: whilst operations on lists are generally thread-safe, this cache is not, |
| # because updating the cache requires several operations which are not currently protected by a lock. |
| # However, it appears that access to instances of this code are single-threaded by hypercorn, |
| # so the lack of thread safety should not be a problem. |
| downloads_data_cache = [] |
| |
| async def make_query(provider, field_names, project, duration, filters, max_hits=MAX_HITS, max_ua=MAX_HITS_UA, downscaled=False): |
| q = elasticsearch_dsl.Search(using=es_client) |
| if isinstance(duration, str) and "-" in duration: # Whole month math, e.g. now-1M/M for this month only |
| q = q.filter("range", **{field_names["timestamp"]: {"gte": duration, "lte": duration}}) |
| else: |
| q = q.filter("range", **{field_names["timestamp"]: {"gte": f"now-{duration}d"}}) |
| q = q.filter("match", **{field_names["request_method"]: "GET"}) |
| q = q.filter("range", bytes={"gt": 5000}) # this filters out hashes and (most?) sigs |
| # Query project for both TLP and podling download locations. It may be TLP now, it may be Podling, |
| # it may have graduated somewhere in between. |
| query_tlp = elasticsearch_dsl.Q("prefix", **{field_names["uri"] + ".keyword": f"/{project}/"}) |
| query_podling = elasticsearch_dsl.Q("prefix", **{field_names["uri"] + ".keyword": f"/incubator/{project}/"}) |
| q = q.query(elasticsearch_dsl.query.Bool(should=[query_tlp, query_podling], minimum_should_match=1)) |
| |
| q = q.filter("match", **{field_names["vhost"]: field_names["_vhost_"]}) |
| |
| # Various standard filters for weeding out bogus requests |
| uas_to_ignore = list(IGNORED_BOTS) |
| if "empty_ua" in filters: # Empty User-Agent header, usually automation gone wrong |
| q = q.exclude("terms", **{field_names["useragent"]+".keyword": ["", "-"]}) |
| if uas_to_ignore: |
| q = q.exclude("terms", **{field_names["useragent"]: uas_to_ignore}) |
| # Exclude binary scanner machines |
| q = q.exclude("terms", **{"client_ip.keyword": list(IGNORED_IPS)}) |
| |
| # TODO: Make this not extremely slow. For now, we'll filter in post. |
| #if "no_query" in filters: # Don't show results with query strings in them |
| # q = q.exclude("wildcard", **{field_names["uri"]+".keyword": "*="}) |
| |
| # Bucket sorting by most downloaded items |
| main_bucket = q.aggs.bucket( |
| "most_downloads", elasticsearch_dsl.A("terms", field=f"{field_names['uri']}.keyword", size=max_hits) |
| ) |
| main_bucket.metric("useragents", "terms", field=field_names["useragent"]+".keyword", size=max_ua) |
| main_bucket.bucket("per_day", "date_histogram", interval="day", field=field_names["timestamp"] |
| ).metric( |
| "bytes_sum", "sum", field=field_names["bytes"] |
| ).metric( |
| "unique_ips", "cardinality", field="client_ip.keyword" |
| ).metric( |
| "cca2", "terms", field=field_names["geo_country"] + ".keyword" |
| ) |
| |
| # Bucket sorting by most bytes downloaded (may differ from most downloads top 60!) |
| main_bucket = q.aggs.bucket( |
| "most_traffic", elasticsearch_dsl.A("terms", field=f"{field_names['uri']}.keyword", size=max_hits, order={"bytes_sum": "desc"}) |
| ) |
| main_bucket.metric("useragents", "terms", field=field_names["useragent"]+".keyword", size=max_ua) |
| main_bucket.metric( |
| "bytes_sum", "sum", field=field_names["bytes"] |
| ).bucket("per_day", "date_histogram", interval="day", field=field_names["timestamp"] |
| ).metric( |
| "bytes_sum", "sum", field=field_names["bytes"] |
| ).metric( |
| "unique_ips", "cardinality", field="client_ip.keyword" |
| ).metric( |
| "cca2", "terms", field=field_names["geo_country"] + ".keyword" |
| ) |
| try: |
| resp = await es_client.search(index=f"{provider}-*", body=q.to_dict(), size=0, timeout="60s") |
| if downscaled and resp: |
| resp["downscaled"] = True |
| return resp |
| except elasticsearch.TransportError as e: |
| # If too many buckets for us to handle, downscale the UA search |
| if isinstance(e.info, dict) and 'too_many_buckets_exception' in e.info["error"].get("caused_by", {}).get("type", ""): |
| max_ua = int(max_ua*0.67) |
| max_hits = int(max_hits*0.67) |
| print(f"Too many buckets for {project}, downscaling query by 33%") |
| if max_ua > 2: |
| return await make_query(provider, field_names, project, duration, filters, max_hits, max_ua, True) |
| return {"downscaled": downscaled} |
| |
| |
| async def generate_stats(project: str, duration: str, filters: str="empty_ua,no_query"): |
| original_duration = duration |
| if isinstance(duration, str) and "M/M" not in duration: |
| if duration.endswith("d"): |
| duration = duration[:-1] |
| try: |
| duration = int(duration) |
| except ValueError: |
| return {"success": False, "message": "Invalid duration window! Please specify a whole number of days"} |
| |
| downloaded_artifacts = {} |
| query_parameters = { |
| "filters": filters, |
| "timespan": original_duration, |
| "project": project, |
| "daily_stats_4_tuple": ["utc_epoch", "downloads", "unique_clients", "bytes_transferred",], |
| "hosts_tracked": [x["_vhost_"] for x in FIELD_NAMES.values()], |
| "max_hits": MAX_HITS, |
| "max_hits_useragent": MAX_HITS_UA, |
| } |
| |
| # Check if we have a cached result |
| cache_found = False |
| # TODO: the cache key needs to take account of form_data filters as they affect the content |
| cache_key = f"{project}-{duration}" |
| cache_timeout_ts = time.time() - DOWNLOADS_CACHE_TTL |
| epochs = [] |
| for item in downloads_data_cache: # (cache_key, cache_ts, cache_data) |
| if item[0] == cache_key and item[1] >= cache_timeout_ts: |
| cache_found = True |
| downloaded_artifacts = item[2] |
| query_parameters = item[3] |
| break |
| |
| if not cache_found: |
| downscaled = False |
| for provider, field_names in FIELD_NAMES.items(): |
| resp = await make_query(provider, field_names, project, duration, filters) |
| if "aggregations" not in resp: # Skip this provider if no data is available |
| continue |
| if resp.get("downscaled"): # Too many damn buckets |
| downscaled = True |
| for methodology in ( |
| "most_downloads", |
| "most_traffic", |
| ): |
| for entry in resp["aggregations"][methodology]["buckets"]: |
| # url, shortened = /incubator/ponymail/foo.tar.gz -> foo.tar.gz |
| url = re.sub(r"/+", "/", entry["key"]).replace(f"/{project}/", "", 1) |
| # TODO: Address in OpenSearch later on... |
| if "no_query" in filters and "?" in url: |
| continue |
| if "." not in url or url.endswith("/") or url.endswith("KEYS"): # Never count KEYS or non-files |
| continue |
| if url not in downloaded_artifacts: |
| downloaded_artifacts[url] = { |
| "bytes": 0, |
| "hits": 0, |
| "hits_unique": 0, |
| "cca2": {}, |
| "daily_stats": {}, |
| "useragents": {}, |
| } |
| no_bytes = 0 |
| no_hits = 0 |
| no_hits_unique = 0 |
| cca2_hits = {} |
| daily_data = [] |
| |
| # User Agent (Browser + OS) summation |
| uas = {} |
| for uaentry in entry["useragents"]["buckets"]: |
| ua_agent = uaentry["key"] # the full agent string |
| # NOTE: ua_parser will set OS and UA Family to "Other" when it doesn't recognize the UA string. |
| ua = ua_parser.user_agent_parser.Parse(ua_agent) |
| ua_os_family = ua.get("os", {}).get("family", "Unknown") |
| # If OS is "Other", we'll adjust it to "Unknown" ourselves. |
| if ua_os_family == "Other": |
| ua_os_family = "Unknown" |
| # UA family will typically be "Other" when unknown to the parser, we'll address this below. |
| # If the family is empty, we'll also set to Other and adjust later on. |
| ua_agent_family = ua.get("user_agent", {}).get("family", "Other") |
| # Adjust for various package managers we know of |
| if ua_agent_family == "Other": |
| for ia_key, ia_names in INTERNAL_AGENTS.items(): |
| if any(x in ua_agent for x in ia_names): |
| ua_agent_family = ia_key |
| break |
| # If we still don't know what this is, mark as "Unknown", to distinguish from the combined "Other" chart group. |
| if ua_agent_family == "Other": |
| ua_agent_family = "Unknown" |
| ua_key = ua_os_family + " / " + ua_agent_family |
| uas[ua_key] = uas.get(ua_key, 0) + uaentry["doc_count"] |
| for key, val in uas.items(): |
| # There will be duplicate entries here, so we are going to go for the highest count found for each URL |
| downloaded_artifacts[url]["useragents"][key] = max(downloaded_artifacts[url]["useragents"].get(key, 0), val) |
| |
| for daily_entry in entry["per_day"]["buckets"]: |
| day_ts = int(daily_entry["key"] / 1000) |
| epochs.append(day_ts) |
| nb_daily = int(daily_entry["bytes_sum"]["value"]) |
| nh_daily = int(daily_entry["doc_count"]) |
| no_bytes += nb_daily |
| |
| visits_unique = int(daily_entry["unique_ips"]["value"]) |
| no_hits += nh_daily |
| no_hits_unique += visits_unique |
| |
| for ccaentry in daily_entry["cca2"]["buckets"]: |
| cca2 = ccaentry["key"] |
| cca2_count = ccaentry["doc_count"] |
| if cca2 and cca2 != "-": |
| cca2_hits[cca2] = cca2_hits.get(cca2, 0) + cca2_count |
| daily_data.append([day_ts, nh_daily, visits_unique, nb_daily]) |
| |
| # The prevailing agg (most hits or most traffic) wins |
| if no_bytes > downloaded_artifacts[url]["bytes"]: |
| downloaded_artifacts[url]["bytes"] += no_bytes |
| downloaded_artifacts[url]["daily_stats"] = daily_data |
| if no_hits > downloaded_artifacts[url]["hits"]: |
| downloaded_artifacts[url]["hits"] += no_hits |
| downloaded_artifacts[url]["daily_stats"] = daily_data |
| if no_hits_unique > downloaded_artifacts[url]["hits_unique"]: |
| downloaded_artifacts[url]["hits_unique"] += no_hits_unique |
| if sum([x for x in cca2_hits.values()]) > sum([x for x in downloaded_artifacts[url]["cca2"].values()]): |
| downloaded_artifacts[url]["cca2"] = cca2_hits |
| # Ensure all entries are properly marked if query was downscaled |
| if downscaled: |
| for key, val in downloaded_artifacts.items(): |
| val["downscaled"] = True |
| |
| if epochs: |
| min_epoch = time.strftime("%Y-%m-%d 00:00:00", time.gmtime(min(epochs))) |
| max_epoch = time.strftime("%Y-%m-%d 23:59:59", time.gmtime(max(epochs))) |
| query_parameters["timespan"] = f"{min_epoch} (UTC) -> {max_epoch} (UTC)" |
| # Set cache data and cull old cache list if needed |
| new_cache_list = [item for item in downloads_data_cache if item[1] >= cache_timeout_ts] |
| downloads_data_cache.clear() |
| downloads_data_cache.extend(new_cache_list) |
| # Make sure there is room to add another entry |
| # entries are added in date order, so [0] is the oldest |
| while len(downloads_data_cache) >= DOWNLOADS_CACHE_ITEMS: |
| del downloads_data_cache[0] |
| downloads_data_cache.append((cache_key, time.time(), downloaded_artifacts, query_parameters)) |
| |
| return downloaded_artifacts or {}, query_parameters |
| |
| |
| async def downloads_scan_loop(): |
| projects = [] |
| while True: |
| # Update list of projects, if possible - otherwise, fall back to cache |
| projects_list = DEFAULT_PROJECTS_LIST |
| if hasattr(config.reporting, "downloads"): # If prod... |
| projects_list = config.reporting.downloads.get("projects_list", DEFAULT_PROJECTS_LIST) |
| |
| async with aiohttp.ClientSession(timeout=aiohttp.ClientTimeout(total=30)) as hc: |
| try: |
| async with hc.get(projects_list) as req: |
| if req.status == 200: |
| projects = (await req.json())["projects"].keys() |
| except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as e: |
| print(f"Download stats: Could not fetch list of projects from {projects_list}: {e}") |
| print("Download stats: Using cached entry instead") |
| |
| # For each project, run scans if needed |
| for project in projects: |
| if datadir: |
| # Ensure the project data dir exists, otherwise make it |
| project_datadir = os.path.join(datadir, project) |
| if not os.path.isdir(project_datadir): |
| print(f"Download stats: Setting up downloads data dir for {project}: {project_datadir}") |
| try: |
| os.mkdir(project_datadir) |
| except OSError as e: |
| print(f"Download stats: Could not set up {project_datadir}: {e}") |
| print(f"Download stats: No persistent download data will be saved for this project") |
| continue |
| |
| # Make a list of the reports to gather. We want this month, and perhaps last N months, if the |
| # reports are outdated or missing. |
| months_to_process = [] |
| today = datetime.datetime.utcnow() |
| for m in range(0, PERSISTENT_REPORTS_BACKFILL_MONTHS): |
| today = today.replace(day=1) # Round down to first day of the month |
| monthly_filename = os.path.join(project_datadir, f"{today.year}-{today.month:02}.json") |
| monthly_query = f"now-{m}M/M" # OpenSearch whole-month query |
| monthly_deadline = (today.replace(year=today.year if today.month != 12 else today.year+1, month=today.month % 12+1)).timestamp() |
| report_stat = os.stat(monthly_filename) if os.path.exists(monthly_filename) else None |
| # If no report file, empty file, or it was not updated after the month was done, schedule it |
| if not report_stat or report_stat.st_size == 0 or report_stat.st_mtime < monthly_deadline: |
| months_to_process.append((monthly_filename, monthly_query)) |
| today = today - datetime.timedelta(days=1) # Wind clock back one month |
| |
| for entry in months_to_process: |
| monthly_filename, monthly_query = entry |
| if not os.path.exists(monthly_filename): |
| try: |
| open(monthly_filename, "w+").write("") |
| except OSError as e: |
| print(f"Download stats: Cannot write to data file {monthly_filename}, skipping: {e}") |
| continue |
| |
| # only scan if we don't have a recent stored result |
| json_stat = os.stat(monthly_filename) |
| #Skip if file is present, >0 bytes, and was written to recently |
| if json_stat and json_stat.st_mtime > (time.time() - 86400) and json_stat.st_size > 0: |
| #print(f"Skipping {monthly_filename}") |
| continue |
| # Grab scan results, write to disk |
| stats, query_params = await generate_stats(project, monthly_query) |
| json_result = { |
| "query": query_params, |
| "files": stats, |
| } |
| json.dump(json_result, open(monthly_filename, "w"), indent=2) |
| #print(f"Wrote {monthly_filename}") |
| |
| # Sleep for a couple of hours (4), then check if we need to scan again |
| await asyncio.sleep(4*3600) |
| |
| |
| plugins.root.register(downloads_scan_loop, slug="downloads", title="Real-time Download Stats", icon="bi-cloud-download", private=True) |
| plugins.root.register(slug="downloads_static", title="Public Download Statistics", icon="bi-cloud-download", private=False) |
