[英]Developing REST APIs using Python (Sanic)
from sanic import Sanic
from sanic import response
app = Sanic(__name__)
@app.route('/v1/ok', methods=['GET'])
async def post_handler(request):
return response.text("hey all good")
if __name__ == '__main__':
app.run(host="0.0.0.0", port=8001, debug=True)
我正在嘗試使用sanic在python中編寫REST API
這是我的結論:
我嘗試使用wrk和運行30s測試的50個線程對這個GET API進行基准測試。 使用機器的AWS EC2 t2.medium具有4GB RAM和2個CPU命令
wrk -t50 -c4000 -d30s http://XXX.XX.XXX.XXX:8001/v1/ok
標桿結果
Running 30s test @ http://XXX.XX.XXX.XXX:8001/v1/ok
50 threads and 4000 connections
Thread Stats Avg Stdev Max +/- Stdev
Latency 559.30ms 117.86ms 1.99s 94.47%
Req/Sec 41.92 44.33 361.00 86.14%
53260 requests in 30.10s, 6.70MB read
Socket errors: connect 1493, read 15631, write 0, timeout 4
Requests/sec: 1769.21
Transfer/sec: 228.06KB
我的疑問是,我該如何改善
在POST請求的情況下,這非常糟糕,其中我試圖加載keras模型並進行預測。
代碼編寫方式有問題嗎?
要么
這是Sanic的局限性嗎?
我應該嘗試其他REST框架嗎?
PS:就延遲和超時請求而言,我對flask的體驗甚至更糟。
import sys
import os
import json
import pandas
import numpy
import optparse
from keras.models import Sequential, load_model
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
from collections import OrderedDict
from sanic import Sanic
from sanic import response
import time
app = Sanic(__name__)
@app.route('/v1/mal/prediction', methods=['POST'])
async def post_handler(request):
csv_file = 'alerts.csv'
log_entry = request.json
dataframe = pandas.read_csv(csv_file, engine='python', quotechar='|', header=None)
dataset = dataframe.values
X = dataset[:,0]
for index, item in enumerate(X):
reqJson = json.loads(item, object_pairs_hook=OrderedDict)
del reqJson['timestamp']
del reqJson['headers']
del reqJson['source']
del reqJson['route']
del reqJson['responsePayload']
X[index] = json.dumps(reqJson, separators=(',', ':'))
tokenizer = Tokenizer(filters='\t\n', char_level=True)
tokenizer.fit_on_texts(X)
seq = tokenizer.texts_to_sequences([log_entry])
max_log_length = 1024
log_entry_processed = sequence.pad_sequences(seq, maxlen=max_log_length)
model = load_model('model.h5')
model.load_weights('weights.h5')
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
prediction = model.predict(log_entry_processed)
return response.text(prediction[0])
if __name__ == '__main__':
app.run(host="0.0.0.0", port=8000, debug=True)
請提出更好的方法來改善API響應時間並減少超時請求?
禁用debug
並將workers
設置為您實例中的CPU數量(t2.med為2):
app.run(host="0.0.0.0", port=8001, workers=2)
這里的游戲有點晚了,但是我相信為了使其真正異步,您需要添加await
調用。 否則,您只是在調用阻塞函數。
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