FYI, for more comprehensive, multi-cloud data, please check out https://sparecores.com/!
Cloudperf is a python helper for running benchmarks on cloud providers' infrastructure (currently Amazon AWS/EC2 is supported) and getting/comparing the results along with their price.
Features include:
- fetch and upload EC2 instance prices (both on-demand and spot) from/to S3
- run defined benchmarks on all EC2 node types in docker images ** ability to run more sophisticated benchmark setups (on one node), like launching a multi-instance distributed DB server and a benchmark client ** any docker image can be used for the benchmarks, the only requirement is to be able to produce a benchmark score
- CLI for updating/getting the gathered data
You can install the application with a simple pip install cloudperf.
Then you'll have the cloudperf executable, which is the main entry point to the
application.
Cloudperf works with tabular data, stored in S3 (world readable published) in compressed JSON files. The three files are:
- Prices: https://cloudperf-data.s3-us-west-2.amazonaws.com/prices.json.gz
- Performance: https://cloudperf-data.s3-us-west-2.amazonaws.com/performance.json.gz
- Combined: https://cloudperf-data.s3-us-west-2.amazonaws.com/combined.json.gz
Prices are constantly updated for many reasons:
- new instance types come and old ones go
- the prices can change
- the spot prices change dynamically, very similarly to a stock exchange
The Performance data is updated in a much less frequent manner. The Combined file is updated approximately daily and it's mainly for embedded web, not for general use.
The CLI has two modes of operation:
- get/show mode
- update mode
The first is when you use the already compiled data, which contains the current prices and benchmark results. The second can be used to update either the price list or (re)run the benchmarks and store the data. You can use this internally as well to run your own benchmarks.
The get mode has some common arguments:
--colsfor the columns you want to see. There are many columns in the data tables, many of them are hidden by default, to make the output easier to read.--sortthe column(s) to sort on--filteryou can filter on the given column's data. You can use basic operators, like>,<,>=,<=and=
If you execute cloudperf prices, it will fetch actual pricing data from S3 and
show it to you, like:
$ cloudperf prices
instanceType region spot-az vcpu memory price
t3.nano eu-north-1 eu-north-1c 2 0.500 0.00160
t3.nano us-east-2 us-east-2c 2 0.500 0.00160
t3.nano us-east-1 us-east-1a 2 0.500 0.00160
t3.nano us-east-1 us-east-1f 2 0.500 0.00160
t3.nano us-east-1 us-east-1d 2 0.500 0.00160
t3.nano us-east-1 us-east-1c 2 0.500 0.00160
t3.nano us-east-1 us-east-1b 2 0.500 0.00160
t3.nano eu-north-1 eu-north-1a 2 0.500 0.00160
t3.nano us-west-2 us-west-2a 2 0.500 0.00160
t3.nano us-east-2 us-east-2b 2 0.500 0.00160
t3.nano eu-north-1 eu-north-1b 2 0.500 0.00160
t3.nano us-east-2 us-east-2a 2 0.500 0.00160
t3.nano eu-west-1 eu-west-1a 2 0.500 0.00170
t3.nano eu-west-1 eu-west-1c 2 0.500 0.00170
t3.nano ca-central-1 ca-central-1b 2 0.500 0.00170
t3.nano ap-south-1 ap-south-1b 2 0.500 0.00170
t3.nano ap-south-1 ap-south-1a 2 0.500 0.00170
t3.nano us-west-2 us-west-2c 2 0.500 0.00170
t3.nano ca-central-1 ca-central-1a 2 0.500 0.00170
t3.nano eu-west-1 eu-west-1b 2 0.500 0.00170
t3.nano eu-west-2 eu-west-2b 2 0.500 0.00180
t3.nano eu-central-1 eu-central-1c 2 0.500 0.00180
t3.nano us-west-2 us-west-2b 2 0.500 0.00180
t3.nano eu-west-2 eu-west-2a 2 0.500 0.00180
t3.nano eu-west-2 eu-west-2c 2 0.500 0.00180
t3.nano eu-central-1 eu-central-1a 2 0.500 0.00180
t3.nano eu-central-1 eu-central-1b 2 0.500 0.00180
t3.nano us-west-1 us-west-1b 2 0.500 0.00190
t3.nano us-west-1 us-west-1a 2 0.500 0.00190
t3.nano ap-northeast-1 ap-northeast-1a 2 0.500 0.00200
t1.micro eu-west-1 eu-west-1b 1 0.613 0.00200
t3.nano ap-southeast-2 ap-southeast-2b 2 0.500 0.00200
t3.nano ap-southeast-2 ap-southeast-2c 2 0.500 0.00200
t3.nano ap-southeast-2 ap-southeast-2a 2 0.500 0.00200
t3.nano ap-southeast-1 ap-southeast-1c 2 0.500 0.00200
t1.micro us-east-1 us-east-1b 1 0.613 0.00200
t1.micro us-east-1 us-east-1c 1 0.613 0.00200
You can filter for any columns, like for a given region:
$ cloudperf prices --filter region=us-west-2 | head -10
instanceType region spot-az vcpu memory price
t3.nano us-west-2 us-west-2a 2 0.500 0.0016
t3.nano us-west-2 us-west-2c 2 0.500 0.0017
t3.nano us-west-2 us-west-2b 2 0.500 0.0018
t1.micro us-west-2 us-west-2a 1 0.613 0.0020
t1.micro us-west-2 us-west-2b 1 0.613 0.0020
t1.micro us-west-2 us-west-2c 1 0.613 0.0020
t3.micro us-west-2 us-west-2c 2 1.000 0.0031
t3.micro us-west-2 us-west-2a 2 1.000 0.0031
t3.micro us-west-2 us-west-2b 2 1.000 0.0031
You can have multiple filters:
$ cloudperf prices --filter 'vcpu>=128' --filter region=us-west-2
instanceType region spot-az vcpu memory price
x1.32xlarge us-west-2 us-west-2c 128 1952.0 4.0014
x1.32xlarge us-west-2 us-west-2b 128 1952.0 4.0014
x1.32xlarge us-west-2 None 128 1952.0 13.3380
x1.32xlarge us-west-2 us-west-2a 128 1952.0 13.3380
x1e.32xlarge us-west-2 None 128 3904.0 26.6880
x1e.32xlarge us-west-2 us-west-2b 128 3904.0 26.6880
x1e.32xlarge us-west-2 us-west-2a 128 3904.0 26.6880
The spot-az column contains the Availability Zone (AZ) and the last known spot price if available. spot-az None marks the on-demand price for the instance in that region.
You can issue cloudperf performance --no-combined to get the latest benchmark
results. This will include all the benchmarks for the benchmarked instances and
their latest score.
By default, this will only show the benchmark results with the vCPU-numbered
concurrency (meaning the benchmark program ran with vCPU parallelism).
If you want to see benchmark results for each number of vCPUs, you can use for
example:
$ cloudperf performance --no-combined --no-maxcpu --filter instanceType=m5.24xlarge --filter benchmark_id=stress-ng:crc16
instanceType benchmark_id benchmark_cpus benchmark_score
m5.24xlarge stress-ng:crc16 1 153.4700
m5.24xlarge stress-ng:crc16 2 305.4200
m5.24xlarge stress-ng:crc16 3 457.2700
m5.24xlarge stress-ng:crc16 4 607.5400
m5.24xlarge stress-ng:crc16 5 750.4900
m5.24xlarge stress-ng:crc16 6 897.9900
m5.24xlarge stress-ng:crc16 7 1042.1200
m5.24xlarge stress-ng:crc16 8 1186.4000
m5.24xlarge stress-ng:crc16 9 1333.7000
m5.24xlarge stress-ng:crc16 10 1480.6400
m5.24xlarge stress-ng:crc16 11 1624.5700
m5.24xlarge stress-ng:crc16 12 1773.9300
m5.24xlarge stress-ng:crc16 13 1921.5400
m5.24xlarge stress-ng:crc16 14 2066.2900
m5.24xlarge stress-ng:crc16 15 2213.0600
m5.24xlarge stress-ng:crc16 16 2359.9000
m5.24xlarge stress-ng:crc16 17 2507.6800
m5.24xlarge stress-ng:crc16 18 2653.8500
m5.24xlarge stress-ng:crc16 19 2800.1600
m5.24xlarge stress-ng:crc16 20 2945.6200
m5.24xlarge stress-ng:crc16 21 3092.3500
m5.24xlarge stress-ng:crc16 22 3238.4400
m5.24xlarge stress-ng:crc16 23 3383.5900
m5.24xlarge stress-ng:crc16 24 3528.6500
m5.24xlarge stress-ng:crc16 25 3673.7700
m5.24xlarge stress-ng:crc16 26 3821.0200
m5.24xlarge stress-ng:crc16 27 3963.2900
m5.24xlarge stress-ng:crc16 28 4109.5900
m5.24xlarge stress-ng:crc16 29 4253.9200
m5.24xlarge stress-ng:crc16 30 4400.6100
m5.24xlarge stress-ng:crc16 31 4543.7900
m5.24xlarge stress-ng:crc16 32 4690.0000
m5.24xlarge stress-ng:crc16 33 4826.8500
m5.24xlarge stress-ng:crc16 34 4938.4700
m5.24xlarge stress-ng:crc16 35 5114.5600
m5.24xlarge stress-ng:crc16 36 5256.2100
m5.24xlarge stress-ng:crc16 37 5391.5700
m5.24xlarge stress-ng:crc16 38 5532.8000
m5.24xlarge stress-ng:crc16 39 5641.4900
m5.24xlarge stress-ng:crc16 40 5797.2500
m5.24xlarge stress-ng:crc16 41 5941.1000
m5.24xlarge stress-ng:crc16 42 6072.0700
m5.24xlarge stress-ng:crc16 45 6207.2000
m5.24xlarge stress-ng:crc16 43 6208.1700
m5.24xlarge stress-ng:crc16 44 6333.4600
m5.24xlarge stress-ng:crc16 46 6508.2700
m5.24xlarge stress-ng:crc16 47 6709.0500
m5.24xlarge stress-ng:crc16 48 6869.4300
m5.24xlarge stress-ng:crc16 50 6973.1700
m5.24xlarge stress-ng:crc16 49 6973.2600
m5.24xlarge stress-ng:crc16 51 7127.6400
m5.24xlarge stress-ng:crc16 52 7327.4600
m5.24xlarge stress-ng:crc16 53 7444.9100
m5.24xlarge stress-ng:crc16 54 7546.5700
m5.24xlarge stress-ng:crc16 55 7658.1300
m5.24xlarge stress-ng:crc16 56 7708.0800
m5.24xlarge stress-ng:crc16 57 7885.0400
m5.24xlarge stress-ng:crc16 58 7994.7500
m5.24xlarge stress-ng:crc16 59 8099.9600
m5.24xlarge stress-ng:crc16 60 8211.5600
m5.24xlarge stress-ng:crc16 61 8314.0200
m5.24xlarge stress-ng:crc16 62 8434.1600
m5.24xlarge stress-ng:crc16 63 8543.7900
m5.24xlarge stress-ng:crc16 64 8650.0700
m5.24xlarge stress-ng:crc16 65 8751.9500
m5.24xlarge stress-ng:crc16 66 8861.1900
m5.24xlarge stress-ng:crc16 67 8974.5600
m5.24xlarge stress-ng:crc16 68 9083.7900
m5.24xlarge stress-ng:crc16 69 9194.9700
m5.24xlarge stress-ng:crc16 70 9305.0300
m5.24xlarge stress-ng:crc16 71 9408.9300
m5.24xlarge stress-ng:crc16 72 9504.9400
m5.24xlarge stress-ng:crc16 73 9630.7000
m5.24xlarge stress-ng:crc16 74 9739.4700
m5.24xlarge stress-ng:crc16 75 9849.7800
m5.24xlarge stress-ng:crc16 76 9955.6900
m5.24xlarge stress-ng:crc16 77 10067.6800
m5.24xlarge stress-ng:crc16 78 10163.1200
m5.24xlarge stress-ng:crc16 79 10284.8700
m5.24xlarge stress-ng:crc16 80 10392.3300
m5.24xlarge stress-ng:crc16 81 10489.5500
m5.24xlarge stress-ng:crc16 82 10609.9500
m5.24xlarge stress-ng:crc16 83 10660.9400
m5.24xlarge stress-ng:crc16 84 10819.9800
m5.24xlarge stress-ng:crc16 85 10931.2500
m5.24xlarge stress-ng:crc16 86 11042.3300
m5.24xlarge stress-ng:crc16 87 11142.7500
m5.24xlarge stress-ng:crc16 88 11259.1200
m5.24xlarge stress-ng:crc16 89 11366.6500
m5.24xlarge stress-ng:crc16 90 11432.5700
m5.24xlarge stress-ng:crc16 91 11569.4600
m5.24xlarge stress-ng:crc16 92 11680.7600
m5.24xlarge stress-ng:crc16 93 11787.9300
m5.24xlarge stress-ng:crc16 94 11902.5500
m5.24xlarge stress-ng:crc16 95 11995.5200
m5.24xlarge stress-ng:crc16 96 12102.4500
Here you can see how scalable is that instance (mainly the hypervisor/CPU and of course the OS).
The main reason for this program to exist is to conduct a performance/price ratio for the benchmarked instances in order to know which instances should we use for executing large batch jobs.
You can list the instances' relative performance/price results with a command similar to this:
$ cloudperf performance --filter benchmark_id=stress-ng:crc16 --filter region=us-west-2
instanceType benchmark_id benchmark_cpus perf/price price benchmark_score region spot-az
[...]
t2.2xlarge stress-ng:crc16 8 8765.0808 0.1114 976.4300 us-west-2 us-west-2b
t2.2xlarge stress-ng:crc16 8 8765.0808 0.1114 976.4300 us-west-2 us-west-2a
t2.xlarge stress-ng:crc16 4 8862.2980 0.0557 493.6300 us-west-2 us-west-2a
t2.xlarge stress-ng:crc16 4 8862.2980 0.0557 493.6300 us-west-2 us-west-2b
t2.xlarge stress-ng:crc16 4 8862.2980 0.0557 493.6300 us-west-2 us-west-2c
t3.xlarge stress-ng:crc16 4 9067.4651 0.0501 454.2800 us-west-2 us-west-2b
c4.8xlarge stress-ng:crc16 36 9370.8534 0.4992 4677.9300 us-west-2 us-west-2c
c4.8xlarge stress-ng:crc16 36 9529.2931 0.4909 4677.9300 us-west-2 us-west-2b
c4.8xlarge stress-ng:crc16 36 9535.1203 0.4906 4677.9300 us-west-2 us-west-2a
t3.2xlarge stress-ng:crc16 8 9742.1769 0.1029 1002.4700 us-west-2 us-west-2a
t3.2xlarge stress-ng:crc16 8 10004.6906 0.1002 1002.4700 us-west-2 us-west-2b
t3.small stress-ng:crc16 2 12400.0000 0.0209 259.1600 us-west-2 us-west-2d
t3.small stress-ng:crc16 2 12459.6154 0.0208 259.1600 us-west-2 None
t2.medium stress-ng:crc16 2 17940.2878 0.0139 249.3700 us-west-2 us-west-2c
t2.medium stress-ng:crc16 2 17940.2878 0.0139 249.3700 us-west-2 us-west-2b
t2.medium stress-ng:crc16 2 17940.2878 0.0139 249.3700 us-west-2 us-west-2a
t3.small stress-ng:crc16 2 41136.5079 0.0063 259.1600 us-west-2 us-west-2a
t3.small stress-ng:crc16 2 41136.5079 0.0063 259.1600 us-west-2 us-west-2b
t3.small stress-ng:crc16 2 41136.5079 0.0063 259.1600 us-west-2 us-west-2c
Here you can see if nothing more is important to you than to get decent performance for your money, t3.small would be the best. WARNING! The benchmarks are executed with unlimited CPU credits (where applicable), but this is not reflected in the prices!
Excluding burstable instances from the list gives:
c5d.18xlarge stress-ng:crc16 72 14177.0380 0.7029 9965.0400 us-east-2 us-east-2b
c5.18xlarge stress-ng:crc16 72 14339.3896 0.6946 9960.1400 us-east-2 us-east-2c
c5.18xlarge stress-ng:crc16 72 14368.3497 0.6932 9960.1400 us-east-2 us-east-2b
c5d.2xlarge stress-ng:crc16 8 14375.2618 0.0764 1098.2700 us-east-2 us-east-2b
c4.4xlarge stress-ng:crc16 16 14386.1878 0.1448 2083.1200 us-east-2 us-east-2c
c4.4xlarge stress-ng:crc16 16 14386.1878 0.1448 2083.1200 us-east-2 us-east-2b
c4.4xlarge stress-ng:crc16 16 14386.1878 0.1448 2083.1200 us-east-2 us-east-2a
c5d.2xlarge stress-ng:crc16 8 14450.9211 0.0760 1098.2700 us-east-2 us-east-2c
c5.4xlarge stress-ng:crc16 16 14595.0690 0.1521 2219.9100 us-east-2 us-east-2a
c5.4xlarge stress-ng:crc16 16 14595.0690 0.1521 2219.9100 us-east-2 us-east-2c
c5.4xlarge stress-ng:crc16 16 14604.6711 0.1520 2219.9100 us-east-2 us-east-2b
c5d.9xlarge stress-ng:crc16 36 14605.6416 0.3421 4996.5900 us-east-2 us-east-2a
c5d.9xlarge stress-ng:crc16 36 14605.6416 0.3421 4996.5900 us-east-2 us-east-2b
c5d.9xlarge stress-ng:crc16 36 14605.6416 0.3421 4996.5900 us-east-2 us-east-2c
a1.xlarge stress-ng:crc16 4 15625.8883 0.0197 307.8300 us-east-2 us-east-2b
a1.xlarge stress-ng:crc16 4 15625.8883 0.0197 307.8300 us-east-2 us-east-2a
c4.8xlarge stress-ng:crc16 36 16153.0732 0.2896 4677.9300 us-east-2 us-east-2b
c4.8xlarge stress-ng:crc16 36 16153.0732 0.2896 4677.9300 us-east-2 us-east-2a
c4.8xlarge stress-ng:crc16 36 16153.0732 0.2896 4677.9300 us-east-2 us-east-2c
Which means at the time of writing, a spot c4.8xlarge instance is the winner of the price/performance contest in the us-east-2 region.
Because running benchmarks cost money, the range of them is quite limited ATM. We use stress-ng to do multiprocess benchmarking. We don't really care about the absolute numbers just the relation between them.
stress-ng:hdd_rndwr_512: this writes 512 byte blocks with O_DIRECT, O_DSYNC flagsstress-ng:hdd_rndwr_4k: this writes 4k byte blocks with O_DIRECT, O_DSYNC flagsstress-ng:crc16: this computes 1024 rounds of CCITT CRC16 on random datastress-ng:matrixprod: matrix product of two 128 x 128 matrices of double floats
This script and running the benchmarks is sponsored by System1. System1 uses Amazon's infrastructure to run its business and has a lot of batch jobs, which should be executed in the most cost effective manner. Cloudperf helps to achieve that: it shows which is the most performing instance for a given price at the time of the execution.
The actual prices can be browsed here: https://bra-fsn.github.io/cloudperf/prices.html
To compare the performance of instances see: https://bra-fsn.github.io/cloudperf/benchmarks/index.html