Amazon EC2 推出 I3 系列機器

Amazon EC2 推出使用 NVMe SSD 的機器,I3 系列:「Now Available – I3 Instances for Demanding, I/O Intensive Applications」。

以東京區的價錢來看,r4.16xlarge 與 i3.16xlarge 都是 64 vCPU 與 488GB RAM。不一樣的地方只有兩個:

  • 第一個是 r4 只有 195 vCPU,而 i3 有 200 vCPU,快了一些。
  • 第二個是 i3 多了 8 個 1900 NVMe SSD。

但價錢卻只差一些 ($5.12/hr 與 $5.856/hr),如果速度可以善用 SSD 的話,跟 r4.* 比起來其實頗超值的...

Linode 推出 $5/month 方案,DigitalOcean 推出 load balancer

LinodeDigitalOcean 這兩家有名的 VPS 都推出新的功能:「High-Memory Instances and $5 Linodes」、「Load Balancers: Simplifying High Availability」。

Linode 另外將 $10/month 方案的硬碟空間加大:

And finally, the existing Linode 2GB ($10/mo) plan is receiving a free storage upgrade from 24GiB to 30GiB.

本來對外的速度限制在 125Mbps max 拉到 1000Mbps:(本來的資料可以參考之前的頁面)

And finally finally, we’ve also increased the outbound network speed limit on all plans to be at minimum 1000 Mbits. Existing Linodes will need to reboot to pick up the new value, that’s it!

EC2 的 r4 系列機器開出來了...

Amazon EC2 的 r4.* 總算是開出來了:「Amazon EC2 R4 instances are now available in new regions」。

Amazon EC2 R4 instances are now available in the following regions: Asia Pacific (Tokyo), Asia Pacific (Singapore), South America (São Paulo), Asia Pacific (Seoul), Asia Pacific (Mumbai), Canada (Central), and EU (London).

r4.16xlarge (488GB) 算是補上中間的 r3.8xlarge (244GB) 與 x1.16xlarge (976GB) 中間的一塊洞了,不然之前得開 p2.8xlarge (488GB),但也不是每一區都有,而且用不到 GPU 就浪費了...

Google Chrome 55 的記憶體改善

前陣子 Google Chrome 55 出了,其中最讓人期待的是對記憶體的改善,有人整理了數據:「Chrome 55 uses ~30% less memory than Chrome 54」。

依照作者拿 測試,前者的記憶體省了 26%,後者省了 30%,都相當明顯。我自己在升到 55 後有明顯感覺到改善,尤其是重開 Chrome 時重新讀取頁面的速度也快了不少...

這些改善主要是出自於「Fall cleaning: Optimizing V8 memory consumption」這邊提到對 V8 engine 的改寫,我感覺到速度變快應該是記憶體用量降低使得 CPU cache rate 提高的關係吧...

Mark Callaghan 講最近的 MySQL 的行銷活動...

Mark Callaghan 這篇倒是沒提到什麼技術的東西,主要是講最近 MySQL 的兩大 conference,一個是 OracleOracle Open World,另外一個是 PerconaPercona Live Amsterdam 2016,然後用了 benchmarketing 這個酸酸的詞 XDDD:「Peak benchmarketing season for MySQL」。


My joke is that each of these makes a different group happy: performance -> marketing, usability -> developers, manageability -> operations, availability -> end users, efficiency -> management.

另外提到了 RocksDB 建出來的 MyRocks 在 memory fit 時可能會比 InnoDB 還要好:

One last disclaimer. If you care about read-mostly/in-memory workloads then InnoDB is probably an excellent choice. MyRocks can still be faster than InnoDB for in-memory workloads. That is more likely when the bottleneck for InnoDB is page write-back performance. So write-heavy/in-memory can still be a winner for MyRocks.


Python 3.6 對 Dict 的改善

Python 3 的 Dict 將會有重大的改變:「[Python-Dev] Python 3.6 dict becomes compact and gets a private version; and keywords become ordered」。

在 3.5 時:

Python 3.5.1 (default, Jun 20 2016, 14:48:22)
>>> def func(**kw): print(kw.keys())
>>> func(a=1, b=2, c=3, d=4, e=5)
dict_keys(['c', 'd', 'e', 'b', 'a'])   # random order

對上目前還在開發的 3.6:

Python 3.6.0a4+ (default:d43f819caea7, Sep  8 2016, 13:05:34)
>>> def func(**kw): print(kw.keys())
>>> func(a=1, b=2, c=3, d=4, e=5)
dict_keys(['a', 'b', 'c', 'd', 'e'])   # expected order

在「Compact and ordered dict」這邊可以看到記憶體的使用量降低:

It seems like the memory usage is between 20% and 25% smaller. Great job!

Memory usage, Python 3.5 => Python 3.6 on Linux x86_64:

./python -c 'import sys; print(sys.getsizeof({str(i):i for i in range(10)}))'

* 10 items: 480 B => 384 B (-20%)
* 100 items: 6240 B => 4720 B (-24%)
* 1000 items: 49248 B => 36984 B (-25%)

Note: the size is the the size of the container itself, not of keys nor values.


3% slowdown in microbench is not surprising.
Compact dict introduces one additional indirection.

Instead, I've added freelist for most compact PyDictKeys.
So I think overall performance is almost same to before compact dict.

不過也有人提到應該拿 3.5 + patch 測,而不是直接拿 3.6 測:

There are a lot of other changes in interpreter core between 3.5 and 3.5 (such as new bytecode and optimized function calls). Could you compare the performance between the version just before adding new dict implementation and the version just after this?


Linode 記憶體升級,以及新的日本機房計畫

Linode 的 13 歲禮物:「Linode’s 13th Birthday – Gifts for All!」。包括了記憶體的升級計畫:

Old PlanNew PlanPrice
Linode 1 GBLinode 2 GB$10/mo ($0.015/hr)
Linode 2 GBLinode 4 GB$20/mo ($0.03/hr)
Linode 4 GBLinode 8 GB$40/mo ($0.06/hr)
Linode 8 GBLinode 12 GB$80/mo ($0.12/hr)
Linode 16 GBLinode 24 GB$160/mo ($0.24/hr)
Linode 32 GBLinode 48 GB$320/mo ($0.48/hr)
Linode 48 GBLinode 64 GB$480/mo ($0.72/hr)
Linode 64 GBLinode 80 GB$640/mo ($0.96/hr)
Linode 96 GBLinode 120 GB$960/mo ($1.44/hr)

比較小的機器都是 double RAM,比較大的機器就沒那麼明顯了... 但這樣就超越 DigitalOcean 的規格,而且還領先其他 VPS 不少。


Unfortunately, since Tokyo is sold out, the upgrade is not available there. We hope to have our second Tokyo facility online before the end of the year.

是個好消息 XD

AWS 最新的 x1.32xlarge...

Amazon EC2 推出了 x1.32xlarge:「X1 Instances for EC2 – Ready for Your Memory-Intensive Workloads」。


Processor: 4 x Intel™ Xeon E7 8880 v3 (Haswell) running at 2.3 GHz – 64 cores / 128 vCPUs.
Memory: 1,952 GiB with Single Device Data Correction (SDDC+1).
Instance Storage: 2 x 1,920 GB SSD.


If you are ready to start using the X1 instances in the US East (Northern Virginia), US West (Oregon), Europe (Ireland), Europe (Frankfurt), Asia Pacific (Tokyo), Asia Pacific (Singapore), or Asia Pacific (Sydney) Regions, please request access and we’ll get you going as soon as possible.

這樣以後就不能說「用 C4 就對了」...

調整 MySQL 的記憶體用量

Percona 的「Best Practices for Configuring Optimal MySQL Memory Usage」這篇給了個蠻不錯的建議:

Don’t allow the mysqld process VSZ exceed 90% of the system memory

ps ax -O vsz | grep mysqld 可以看到 mysqld 吃了多少 VSZ,然後自己除整台機器的記憶體大小,就可以算出來目前吃了多少,然後調整 innodb_buffer_pool_size 的數字。

另外 performance schema 也會有不少影響:

MySQL is workload related – if you have many connections active at the same time that run heavy selects using a lot of memory for sorting or temporary tables, you might need a lot of memory (especially if Performance Schema is enabled). In other cases this amount of memory is minimal. You’ll generally need somewhere between 1 and 10GB for this purpose.


Another thing you need to account for is memory fragmentation. Depending on the memory allocation library you’re using (glibc, TCMalloc, jemalloc, etc.), the operating system settings such as Transparent Huge Pages (THP) and workload may show memory usage to grow over time (until it reaches some steady state). Memory fragmentation can also account for 10% or more of additional memory usage.


Google Compute Engine 推出 Custom Machine Type

Google Compute Engine 推出了可以自己設定 CPU 與 RAM 的機器種類:「Custom Machine Types - Compute Engine — Google Cloud Platform」。

可以從 1 個 vCPU 到 32 個 vCPU,而記憶體最多是 6.5GB * vCPU 數,所以理論上最高是 208GB?

Create a machine type with as little as 1 vCPU and up to 32 vCPUs, or any even number of vCPUs in between. Memory can be configured up to 6.5 GB of RAM per vCPU.

計價方式就是 vCPU 算一份,記憶體算一份。記得以前有比較小的 Cloud Service 有提供過類似的計價方式,後來都收掉了...