commit 8936cccd29becbbcc7dbd99f4d38693537feb3ec
Author: Emily Doherty <git@emily.sh>
Date: Fri, 8 Sep 2023 23:20:13 -0700
Initial implementations
Okay not really, but I have lost the original git repo so this is where we're at qq
Diffstat:
2 files changed, 311 insertions(+), 0 deletions(-)
diff --git a/wow-token-current.py b/wow-token-current.py
@@ -0,0 +1,62 @@
+import boto3
+import datetime
+import json
+import os
+
+dynamo_region_map = {
+ 'us-west-1': 'us-west-1',
+ 'us-west-2': 'us-west-2',
+ 'us-east-1': 'us-east-1',
+ 'us-east-2': 'us-east-2',
+ 'ap-south-1': 'eu-north-1',
+ 'ap-northeast-3': 'ap-northeast-1',
+ 'ap-northeast-2': 'ap-northeast-1',
+ 'ap-southeast-1': 'ap-southeast-2',
+ 'ap-southeast-2': 'ap-southeast-2',
+ 'ap-northeast-1': 'ap-northeast-1',
+ 'ca-central-1': 'us-east-1',
+ 'eu-central-1': 'eu-north-1',
+ 'eu-west-1': 'eu-north-1',
+ 'eu-west-2': 'eu-north-1',
+ 'eu-west-3': 'eu-north-1',
+ 'eu-north-1': 'eu-north-1',
+ 'sa-east-1': 'sa-east-1',
+ 'eu-south-1': 'eu-north-1'
+} # This is a rough first pass at an intelligent region selector based on what is replicated
+local_region = ''
+if os.environ['AWS_REGION'] in dynamo_region_map:
+ local_region = dynamo_region_map[os.environ['AWS_REGION']]
+else:
+ local_region = 'eu-north-1'
+
+dynamodb_client = boto3.resource('dynamodb', region_name=local_region)
+table = dynamodb_client.Table('wow-token-price')
+
+regions = ['us', 'eu', 'tw', 'kr']
+regional_data = {
+ 'us': {'current_time': 0, 'price': 0},
+ 'eu': {'current_time': 0, 'price': 0},
+ 'tw': {'current_time': 0, 'price': 0},
+ 'kr': {'current_time': 0, 'price': 0}
+}
+
+
+def token_data():
+ items = table.scan()['Items']
+ data = {
+ 'current_time': datetime.datetime.utcfromtimestamp(int(items[0]['current_time'])).replace(
+ tzinfo=datetime.timezone.utc).isoformat(),
+ 'price_data': {}
+ }
+ for item in items:
+ data['price_data'][item['region']] = int(int(item['price']) / 10000)
+ return data
+
+
+def lambda_handler(event, context):
+ data = token_data()
+ response = {'status': '200', 'statusDescription': 'OK', 'headers': {}}
+ response['headers']['content-type'] = [{'key': 'Content-Type', 'value': 'application/json'}]
+ response['body'] = json.dumps(data)
+ print('AWS Region:' + os.environ['AWS_REGION'] + '\tdynamodb_connect_region: ' + local_region)
+ return response
diff --git a/wow-token-historical.py b/wow-token-historical.py
@@ -0,0 +1,249 @@
+import sys
+
+import boto3
+from boto3.dynamodb.conditions import Key
+import datetime
+import calendar
+import json
+import os
+import statistics
+
+dynamo_region_map = {
+ 'us-west-1': 'us-west-1',
+ 'us-west-2': 'us-west-2',
+ 'us-east-1': 'us-east-1',
+ 'us-east-2': 'us-east-2',
+ 'ap-south-1': 'eu-north-1',
+ 'ap-northeast-3': 'ap-northeast-1',
+ 'ap-northeast-2': 'ap-northeast-1',
+ 'ap-southeast-1': 'ap-southeast-2',
+ 'ap-southeast-2': 'ap-southeast-2',
+ 'ap-northeast-1': 'ap-northeast-1',
+ 'ca-central-1': 'us-east-1',
+ 'eu-central-1': 'eu-north-1',
+ 'eu-west-1': 'eu-west-3',
+ 'eu-west-2': 'eu-west-3',
+ 'eu-west-3': 'eu-west-3',
+ 'eu-north-1': 'eu-north-1',
+ 'sa-east-1': 'sa-east-1',
+ 'eu-south-1': 'eu-north-1',
+
+} # This is a rough first pass at an intelligent region selector based on what is replicated
+local_region = ''
+if os.environ['AWS_REGION'] in dynamo_region_map:
+ local_region = dynamo_region_map[os.environ['AWS_REGION']]
+else:
+ local_region = 'eu-west-3'
+
+
+timestream_client = boto3.client('timestream-query', region_name='us-east-1')
+dynamodb_client = boto3.resource('dynamodb', region_name=local_region)
+table = dynamodb_client.Table('wow-token-price-recent')
+
+
+def historical_data(time, region):
+ # This shim is to permanently change the URL of 30d to 720h for local caching,
+ # There seems to be at least 1 person using 30d (strangely with no .json) which was deprecated
+ # as the data source for 1 month of data years ago
+ if time == '30d':
+ time = '720h'
+
+ if time[-1] == 'h':
+ return dynamo_data(time, region)
+ else:
+ return timestream_data(time, region)
+
+
+def timestream_data(time, region):
+ query_table = {
+ '30d': {'interval': "INTERVAL '1' MONTH", 'bin': '15m'},
+ '90d': {'interval': "INTERVAL '3' MONTH", 'bin': '30m'},
+ '6m': {'interval': "INTERVAL '6' MONTH", 'bin': '1h'},
+ '1y': {'interval': "INTERVAL '1' YEAR", 'bin': '2h'},
+ '2y': {'interval': "INTERVAL '2' YEAR", 'bin': '2h'},
+ 'all': {'interval': "INTERVAL '10' YEAR", 'bin': '3h'},
+ }
+ timestream_query = 'WITH binned_query AS (' + \
+ 'SELECT BIN(time, ' + query_table[time]['bin'] + ') AS binned_time,' + \
+ 'CAST(AVG(measure_value::bigint) AS bigint) as average ' + \
+ 'FROM "wow-token-price-history"."' + region + '-price-history" ' + \
+ "WHERE measure_name = 'price' " + \
+ 'AND time > now() - (' + query_table[time]['interval'] + ') ' + \
+ 'GROUP BY BIN(time, ' + query_table[time]['bin'] + "), 'price') " + \
+ 'SELECT CREATE_TIME_SERIES(binned_time, average) AS timeseries_data ' + \
+ 'FROM binned_query ' + \
+ 'ORDER BY timeseries_data ASC' # This is so ugly
+ response = timestream_client.query(QueryString=timestream_query)
+ rows = response['Rows'][0]['Data'][0]['TimeSeriesValue']
+ data = []
+ # TODO: Should we inject the aggregation functions into this loop to reduce the cost of looping?
+ for row in rows:
+ row_isotime = row['Time'].split('.')[0]
+ row_datetime = datetime.datetime.fromisoformat(row_isotime).replace(
+ tzinfo=datetime.timezone.utc)
+ data.append({
+ 'time': row_datetime.isoformat(),
+ 'value': int(int(row['Value']['ScalarValue']) / 10000)
+ })
+ return data
+
+
+def dynamo_data(time, region):
+ time_stripped = int(time[:-1])
+ start_time = datetime.datetime.utcnow() - datetime.timedelta(hours=time_stripped)
+ start_time_utc = start_time.replace(tzinfo=datetime.timezone.utc)
+ response = table.query(
+ KeyConditionExpression=(
+ Key('region').eq(region) &
+ Key('timestamp').gte(int(start_time_utc.timestamp()))))
+ data = []
+ for item in response['Items']:
+ item_time = datetime.datetime.utcfromtimestamp(int(item['timestamp'])).replace(
+ tzinfo=datetime.timezone.utc).isoformat()
+ data.append({
+ 'time': item_time,
+ 'value': int(int(item['price']) / 10000)
+ })
+ return data
+
+
+def aggregate_data(aggregate_function: str, data: list):
+ if aggregate_function == 'daily_max':
+ return max_min(1, 1, data)
+ elif aggregate_function == 'daily_min':
+ return max_min(-1, 1, data)
+ elif aggregate_function == 'daily_mean':
+ return mean(1, data)
+ elif aggregate_function == 'weekly_max':
+ return max_min(1, 7, data)
+ elif aggregate_function == 'weekly_min':
+ return max_min(-1, 7, data)
+ elif aggregate_function == 'weekly_mean':
+ return mean(7, data)
+
+
+def date_in_range(day_range: tuple, date: datetime.datetime):
+ month_range = calendar.monthrange(date.year, date.month)
+ if day_range[0] <= date.day < day_range[1]:
+ return True
+ elif date.day < day_range[1] and date.day < day_range[0]:
+ # TODO: I am probably missing a sanity check here, come back to it
+ return True
+ else:
+ return False
+
+
+def day_bucket(bucket_size: int, date: datetime.datetime) -> tuple[datetime.datetime, datetime.datetime]:
+ month_range = calendar.monthrange(date.year, date.month)
+ days_to_reset = {0: 1, 1: 0, 2: 6, 3: 5, 4: 4, 5: 3, 6: 2}
+ # We want the bucket boundaries for a bucket size of 7 to fall on
+ # reset day (index 1), and for a month (31) to fall on the actual boundaries of that month
+ # this means month-to-month, there are dynamic sizing of buckets
+ # TODO: Monthly boundaries
+ if bucket_size == 7 and date.weekday() != 1:
+ # This is WoW, the week starts on Tuesday (datetime index 1)
+ bucket_size = days_to_reset[date.weekday()]
+
+ return tuple((date, date + datetime.timedelta(days=bucket_size)))
+
+
+def is_new_bucket(d_datetime: datetime.datetime, current_bucket_day: datetime.datetime.day, bucket: tuple) -> bool:
+ if d_datetime.day != current_bucket_day and (d_datetime >= bucket[1] or d_datetime.weekday() == 1):
+ return True
+ return False
+
+
+def max_min(fn: int, bucket_size: int, data: list) -> list:
+ new_data = []
+ first_date = datetime.datetime.fromisoformat(data[0]['time'])
+ current_bucket_day = first_date.day
+ # I hate working with dates
+ bucket = day_bucket(bucket_size, first_date)
+ min_max = {'minimum': 999_999_999, 'maximum': 0}
+ min_max_date = {'minimum_date': datetime.datetime.min, 'maximum_date': datetime.datetime.max}
+
+ for d in data:
+ d_datetime = datetime.datetime.fromisoformat(d['time'])
+ # current_day is used to check if this 'if' has triggered for a new bucket and bypass if it has
+ if is_new_bucket(d_datetime, current_bucket_day, bucket):
+ current_bucket_day = d_datetime.day
+ bucket = day_bucket(bucket_size, d_datetime)
+ if fn == -1: # Minimum function
+ new_data.append({'time': min_max_date['minimum_date'], 'value': min_max['minimum']})
+ elif fn == 1: # Maximum function
+ new_data.append({'time': min_max_date['maximum_date'], 'value': min_max['maximum']})
+ min_max = {'minimum': 999_999_999, 'maximum': 0}
+ min_max_date = {
+ 'minimum_date': datetime.datetime.min.isoformat(),
+ 'maximum_date': datetime.datetime.max.isoformat()
+ }
+
+ if d['value'] < min_max['minimum']:
+ min_max['minimum'] = d['value']
+ min_max_date['minimum_date'] = d_datetime.isoformat()
+
+ if d['value'] > min_max['maximum']:
+ min_max['maximum'] = d['value']
+ min_max_date['maximum_date'] = d_datetime.isoformat()
+
+ return new_data
+
+
+def mean(bucket_size: int, data: list) -> list:
+ new_data = []
+ first_date = datetime.datetime.fromisoformat(data[0]['time'])
+ current_bucket_day = first_date.day
+ bucket = day_bucket(bucket_size, first_date)
+ mean_bucket = []
+ bucket_date = first_date
+
+ for d in data:
+ d_datetime = datetime.datetime.fromisoformat(d['time'])
+ if is_new_bucket(d_datetime, current_bucket_day, bucket):
+ current_bucket_day = d_datetime.day
+ bucket = day_bucket(bucket_size, d_datetime)
+ new_data.append({'time': bucket[0].isoformat(), 'value': int(statistics.mean(mean_bucket))})
+ mean_bucket = []
+
+ mean_bucket.append(d['value'])
+
+ return new_data
+
+
+def validate_path(split_uri: str) -> bool:
+ return validate_region(split_uri[-2]) and validate_time(split_uri[-1].split('.')[0])
+
+
+def validate_time(time: str) -> bool:
+ valid_times = ['24h', '48h', '72h', '120h', '168h', '336h', '720h', '30d', '2190h', '90d', '6m', '1y', '2y', 'all']
+ return time in valid_times
+
+
+def validate_region(region: str) -> bool:
+ valid_regions = ['us', 'eu', 'tw', 'kr']
+ return region in valid_regions
+
+
+def validate_aggregate(aggregate_function: str) -> bool:
+ valid_aggregates = ['daily_max', 'daily_min', 'daily_mean', 'weekly_max', 'weekly_min', 'weekly_mean']
+ return aggregate_function in valid_aggregates
+
+
+def lambda_handler(event, context):
+ uri = event['Records'][0]['cf']['request']['uri']
+ split_uri = uri.split('/')
+ if validate_path(split_uri):
+ time = split_uri[-1].split('.')[0]
+ region = split_uri[-2]
+ aggregate_function = split_uri[-3]
+ data = historical_data(time, region)
+
+ if validate_aggregate(aggregate_function):
+ data = aggregate_data(aggregate_function, data)
+
+ response = {'status': '200', 'statusDescription': 'OK', 'headers': {}}
+ response['headers']['content-type'] = [{'key': 'Content-Type', 'value': 'application/json'}]
+ response['body'] = json.dumps(data)
+ return response
+ else:
+ return {'status': '404', 'statusDescription': 'NotFound', 'headers': {}}
