Google的App Engine是否有过多的停机时间，特别是在数据存储区写入方面？
此外，停机时间似乎是在高流量时间安排的，例如在下午中午和凌晨3:00。这是正常的吗？随着技术的成熟，它会改善吗？
答案 0 :(得分：10)
下午与清晨停工时间。数据存储在下午的写入次数比上午的凌晨时间多20-30％（太平洋时间;包括看跌期权） ，更新和删除可用性）。
注意：我确信Google会在非高峰时段喜欢停机时间。因此，我希望他们会继续尽量减少停机时间，或尽可能安排在非高峰时段。
停机时间趋势。数据存储区不可用的15分钟时段数量一直在减少。在过去的366天中，平均每天有3.8个15分钟的数据存储空间不可用。在过去的200天里，这已经 60％降低到每天2.3。过去几个月的停机时间确实非常好 - 从3月1日起，每天写入停机时间不到0.25分钟。这是数据存储写停机时间的图表： Downtime trending http://imagebin.ca/img/4wkHVQPc.png
为了回答您的问题，我写了this script，从GAE's Datastore Status page中提取停机时间数据。
alt text http://imagebin.ca/img/p9ScWTm.png
alt text http://imagebin.ca/img/9FbLut2G.png
alt text http://imagebin.ca/img/t3XKLk.png
alt text http://imagebin.ca/img/k36T9h.png
（如果你想收集自己的参数略有不同的数据，你可以调整脚本顶部的变量）：
# RAW Data: Each element counts the number of days in which the datastore
# was unavailable for at least some portion of a given 15-minute window. The
# first element corresponds to the time chunk from 00:00 to 00:15, and so on.
RESULTS_SINCE_2010JAN01_BIN15 = [0, 0, 0, 0, 3, 11, 3, 3, 3, 3, 12, 3, 3, 3, 4, 14, 4, 4, 4, 4, 12, 2, 2, 2, 2, 14, 4, 4, 4, 4, 11, 2, 2, 2, 2, 11, 5, 5, 5, 5, 13, 4, 4, 4, 4, 14, 7, 5, 5, 5, 14, 4, 3, 3, 3, 13, 2, 2, 2, 2, 12, 5, 4, 4, 4, 14, 5, 3, 3, 3, 12, 7, 2, 2, 2, 5, 5, 0, 0, 0, 2, 9, 3, 2, 2, 2, 10, 1, 1, 1, 2, 9, 3, 3, 3, 15]
RESULTS_SINCE_2009JUL20_BIN15 = [0, 0, 0, 0, 11, 21, 5, 5, 5, 5, 29, 6, 6, 6, 7, 38, 11, 11, 11, 11, 37, 7, 7, 7, 7, 44, 12, 12, 12, 12, 37, 10, 10, 10, 10, 34, 7, 7, 7, 7, 46, 11, 11, 11, 11, 39, 15, 13, 13, 13, 44, 13, 12, 12, 12, 44, 5, 5, 5, 5, 34, 11, 10, 10, 10, 40, 13, 11, 11, 11, 31, 21, 12, 12, 11, 19, 21, 4, 4, 4, 13, 28, 10, 9, 9, 16, 36, 10, 10, 10, 12, 32, 7, 7, 6, 35]
RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN15 = [0, 0, 0, 0, 4, 12, 4, 4, 4, 4, 22, 6, 6, 6, 7, 27, 7, 7, 7, 7, 21, 6, 6, 6, 6, 32, 9, 9, 9, 9, 26, 8, 8, 8, 8, 27, 7, 7, 7, 7, 30, 7, 7, 7, 7, 27, 10, 8, 8, 8, 28, 10, 9, 9, 9, 28, 4, 4, 4, 4, 21, 4, 4, 4, 4, 25, 6, 4, 4, 4, 18, 14, 9, 10, 9, 16, 17, 2, 2, 2, 8, 18, 7, 6, 6, 9, 19, 5, 5, 5, 6, 18, 5, 5, 4, 21]
# RESULTS DISTILLED FROM COLLECTED_RESULTS
RESULTS_SINCE_2010JAN01_BIN60 = [RESULTS_SINCE_2010JAN01_BIN15[i*4]+RESULTS_SINCE_2010JAN01_BIN15[i*4+1]+RESULTS_SINCE_2010JAN01_BIN15[i*4+2]+RESULTS_SINCE_2010JAN01_BIN15[i*4+3] for i in xrange(24)]
RESULTS_SINCE_2010JAN01_BIN240 = [RESULTS_SINCE_2010JAN01_BIN60[i*4]+RESULTS_SINCE_2010JAN01_BIN60[i*4+1]+RESULTS_SINCE_2010JAN01_BIN60[i*4+2]+RESULTS_SINCE_2010JAN01_BIN60[i*4+3] for i in xrange(6)]
RESULTS_SINCE_2010JAN01_BIN480 = [RESULTS_SINCE_2010JAN01_BIN60[i*2]+RESULTS_SINCE_2010JAN01_BIN60[i*2+1] for i in xrange(3)]
RESULTS_SINCE_2009JUL20_BIN60 = [RESULTS_SINCE_2009JUL20_BIN15[i*4]+RESULTS_SINCE_2009JUL20_BIN15[i*4+1]+RESULTS_SINCE_2009JUL20_BIN15[i*4+2]+RESULTS_SINCE_2009JUL20_BIN15[i*4+3] for i in xrange(24)]
RESULTS_SINCE_2009JUL20_BIN240 = [RESULTS_SINCE_2009JUL20_BIN60[i*4]+RESULTS_SINCE_2009JUL20_BIN60[i*4+1]+RESULTS_SINCE_2009JUL20_BIN60[i*4+2]+RESULTS_SINCE_2009JUL20_BIN60[i*4+3] for i in xrange(6)]
RESULTS_SINCE_2009JUL20_BIN480 = [RESULTS_SINCE_2009JUL20_BIN240[i*2]+RESULTS_SINCE_2009JUL20_BIN240[i*2+1] for i in xrange(3)]
RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN60 = [RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN15[i*4]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN15[i*4+1]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN15[i*4+2]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN15[i*4+3] for i in xrange(24)]
RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN240 = [RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN60[i*4]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN60[i*4+1]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN60[i*4+2]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN60[i*4+3] for i in xrange(6)]
RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN480 = [RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN240[i*2]+RESULTS_WRITE_DOWNTIME_SINCE_2009JUL20_BIN240[i*2+1] for i in xrange(3)]