Azure表存储事务限制

我正在针对ATS运行性能测试，当针对同一表/存储帐户使用多个虚拟机时，它表现得有些奇怪.

I'm running performance tests against ATS and its behaving a bit weird when using multiple virtual machines against the same table / storage account.

整个管道是非阻塞的(等待/异步)，并使用TPL进行并发和并行执行.

The entire pipeline is non blocking (await/async) and using TPL for concurrent and parallel execution.

首先非常奇怪的是，使用这种设置，我只能插入约1200次.它运行在L VM盒上，即4核+ 800mbps.

First of all its very strange that with this setup i'm only getting about 1200 insertions. This is running on a L VM box, that is 4 cores + 800mbps.

我要插入100.000行，这些行具有唯一的PK和唯一的RK，应该利用最终的分布.

I'm inserting 100.000 rows with unique PK and unique RK, that should leverage the ultimate distribution.

以下是更具确定性的行为.

Even more deterministic behavior is the following.

当我运行1个VM时，我每秒获得约1200次插入. 当我运行3个VM时，每秒每次插入可获得大约730.

When I run 1 VM i get about 1200 insertions per second. When I run 3 VM i get about 730 on each insertions per second.

阅读博客文章时很幽默，他们在其中指定了目标. https://azure.microsoft.com/zh-CN/blog/windows-azures-flat-network-storage-and-2012-scalability-targets/

Its quite humors to read the blog post where they are specifying their targets. https://azure.microsoft.com/en-gb/blog/windows-azures-flat-network-storage-and-2012-scalability-targets/

单个表分区–表分区是表中具有相同分区键值的所有实体，通常表具有多个分区.单个表分区的吞吐量目标为:

Single Table Partition– a table partition are all of the entities in a table with the same partition key value, and usually tables have many partitions. The throughput target for a single table partition is:

每秒最多2,000个实体

Up to 2,000 entities per second

注意，这是针对单个分区而不是单个表的.因此，具有良好分区的表最多可以处理20,000个实体/秒，这是上述的总体帐户目标.

Note, this is for a single partition, and not a single table. Therefore, a table with good partitioning, can process up to the 20,000 entities/second, which is the overall account target described above.

我应该怎么做才能每秒使用20k，如何才能在每个VM上执行超过1,2k?

What shall I do to be able to utilize the 20k per second, and how would it be possible to execute more than 1,2k per VM?

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更新:

我现在还尝试为每个单独的节点使用3个存储帐户，并且仍在获得性能/节流行为.我找不到逻辑上的原因.

I've now also tried using 3 storage accounts for each individual node and is still getting the performance / throttling behavior. Which i can't find a logical reason for.

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更新2:

我进一步优化了代码，现在可以执行大约1550.

I've optimized the code further and now i'm possible to execute about 1550.

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更新3:

我现在也在美国西部尝试过.那里的表现较差.降低约33％.

I've now also tried in US West. The performance is worse there. About 33% lower.

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更新4:

我尝试从XL机器执行代码.这是8核而不是4核，并且内存和带宽增加了一倍，并且性能提高了2％，因此显然这个问题不在我一边.

I tried executing the code from a XL machine. Which is 8 cores instead of 4 and the double amount of memory and bandwidth and got a 2% increase in performance so clearly this problem is not on my side..