App Engine 停机时间

发布于 2024-09-11 00:29:44 字数 117 浏览 10 评论 0原文

Google 的 App Engine 是否有过多的停机时间，特别是在数据存储写入方面？

此外，停机时间似乎安排在高流量时段，例如，下午中间与凌晨 3:00。这是正常的吗？随着技术的成熟，它会有所改善吗？

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只是偏爱你 2024-09-18 00:29:44

简短回答

下午与凌晨停机时间。下午数据存储不可用的写入频率比凌晨高出约 20-30%（太平洋时间；包括 put、更新和删除可用性）。
注意：我确信 Google希望在非高峰时段发生停机。因此，我预计他们会继续尝试最大程度地减少停机时间，或尽可能将停机时间安排在非高峰时间。
停机趋势。数据存储不可用的 15 分钟时间段的数量一直在减少。在过去 366 天中，平均每天有 3.8 个 15 分钟的时间段数据存储不可用。在过去 200 天里，这一数字减少了 60% 至每天 2.3 次。过去几个月的写入停机时间实际上相当不错——自 3 月 1 日以来，每天的 15 分钟写入停机时间块少于 0.25 个。下面是数据存储写入停机时间的图表：
停机趋势 http://imagebin.ca/img/4wkHVQPc.png

答案来源

为了回答您的问题，我写了< a href="http://gist.github.com/483827">此脚本，用于从 GAE 的数据存储状态页面。

图表

原始数据

（如果您想使用稍微不同的参数收集自己的数据，您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)]

Short Answers

Afternoon vs early morning downtime. The datastore has been unavailable for writes about 20-30% more often in the afternoon than in the wee hours of the morning (Pacific time; includes put, update, and delete availability).
Note: I'm sure Google would like downtime to occur during off-peak hours. Thus I expect they'll continue to try to minimize downtime, or schedule it for off-peak hours whenever possible.
Downtime trending. The number of 15-minute periods during which the datastore has been unavailable has been decreasing. In the past 366 days, there were an average of 3.8 15-minute periods in which the datastore was unavailable per day. In the past 200 days, this has decreased by 60% to 2.3 per day. Write downtime over the past few months has actually been quite good - since March 1st, there have been less than 0.25 15-minute chunks of write downtime per day. Here's a graph of datastore write downtime:
Downtime trending http://imagebin.ca/img/4wkHVQPc.png

Source of Answers

To answer your question, I wrote this script which extracts downtime data from GAE's Datastore Status page.

Graphs

Datastore write downtime from 2009-Jul-20 to 2010-Jul-20 (4 hour bins):

alt text http://imagebin.ca/img/p9ScWTm.png

Datastore write downtime from 2009-Jul-20 to 2010-Jul-20 (1 hour bins):

alt text http://imagebin.ca/img/9FbLut2G.png

Datastore downtime from 2009-Jul-20 to 2010-Jul-20 (4 hour bins):

alt text http://imagebin.ca/img/t3XKLk.png

Datastore downtime from 2010-Jan-01 to 2010-Jul-20 (4 hour bins):

alt text http://imagebin.ca/img/k36T9h.png

Raw data

(you can tweak the variables at the top of the script if you'd like to collect your own data with slightly different parameters):

# 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)]

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