Perfsight OpenAPI

1 signature

1.1 signature parameters

name	type	requeired	description
access_token	string	yes	token generated by PerfSight, click to get
env	string	yes	China enviroment: v2; Singapore environment: sgp;
app_id	string	yes	Project ID
username	string	yes	Username to log in to the PerfSight platform

1.2 signature method

Taking python as an example, assume that the actual parameters of api are

payload = {
    "platform": 0,
}

1. Set the token expiration time (take 120 seconds as an example)

exp = int(time.time() + 120)

2. Add exp to parameters

payload = {
    "platform": 0,
    "exp": exp
}

3. Use jwt to generate token

headers = {
  "alg": "HS256",
  "typ": "JWT"
}
jwt_token = jwt.encode(payload=payload, key=access_token, algorithm='HS256', headers=headers)

4. Construct request parameters

params = {
    'app_id': "123",
    'env': 'sgp',
    'username': 'abc',
    'token': jwt_token,
}

5. Send request

headers = {'Content-Type': 'application/json'}
url = 'http://9.134.150.248:8086/openapi/scene/dayAggregateData'
response = requests.post(url, json=params, headers=headers)
print(response.json())

6. Format of response

{
  {
  "ret": 200, // return code
  "msg": "", // error message
  "data": [ // data
    {
      "merge_count": 581179108,
      "fps_mean": 52.56,
      "fps_swing_hour": 11.03,
      "jank_frame_hour": 5.99,
      "pss": 1425.3,
      "vss": 12507.61,
      "psmall_jank": 3.51,
      "frame100": 0.02,
      "mono": 183.34
    }
  ],
  "requestid": "94e2ac8b38b08ec238d492a708e3d023", // Server request ID
  "flush_time": "2023-02-24 17:21:54" // response time
}
}

1.3 request url

China: https://api.perfsight.qq.com

IEGG: http://101.32.118.205:80

2 api description and instructions

2.1 get scene analysis summary/time range data

path：/openapi/scene/dayAggregateData

method：POST

request body：

name	type	requeired	description
platform	int	yes	platform ID, Android:0, iOS:1, PC:5
project_version	string	yes	version
scene	string	yes	scene name
is_customize	bool	yes	whether to use custom image quality
customize_arr	array	yes	custom image quality array
region	string	yes	country/region
start	string	yes	from time format:"2023-02-20 00:00:00"
end	string	yes	end time format:"2023-02-20 00:00:00"
time_level	int	yes	1:summary, 4:time range

"is_customize": true
"customize_arr": [
            {
                "category": "device classification",
                "category_items": ["1st Gear"]
            },
            {
                "category": "target fps",
                "category_items": ["high"]
            }
        ],

response:

name	type	description
merge_count	int	Game Number
fps_mean	float	Avg(FPS)
fps_jetter_hour	float	Avg(FPS Jitter)
fps_jank_hour	float	Avg(FPS Jank)
pss_average_peak	float	Avg(Max(PSS Memory))
vss_average_peak	float	Avg(Max(VSS Memory))
perfdog_small_jank	float	Avg(Perfdog Jank)
frametime_gt_100ms_percent	float	Percent(FrameTime > 100ms / Frames)
mono_reserved_peak	float	Max(MONO memory)

2.2 get model analysis data

path：/openapi/device/deviceAnalysis

method：POST

request body：

name	type	requeired	description
platform	int	yes	platform ID, Android:0, iOS:1, PC:5
project_version	string	yes	version
scene	string	yes	scene name
is_customize	bool	yes	whether to use custom image quality
customize_arr	array	yes	custom image quality array
device_class	array	yes	device classification,array of int; 0:comprehensive, 1:high, 2:medium, 3:low
device_search	array	yes	model, array of string
region	string	yes	country/region
start	string	yes	from time format:"2023-02-20 00:00:00"
end	string	yes	end time format:"2023-02-20 00:00:00"
time_level	int	yes	1:summary, 4:time range

"customize_arr": [
            {
                "category": "device classification",
                "category_items": ["1st Gear"]
            },
            {
                "category": "target fps",
                "category_items": ["high"]
            }
        ],

response:

name	type	description
calendar_time	string	time
device_model	string	model
ram	int	Ram
device_cpu_core	int	cpu cores
os	string
device_cpu_freq	int	cpu frequency
gpu_renderer	string	gpu
gpu_version	string	gpu version
device_manu	string	manu
scene_label	string	scene name
def_class	int	customized device classification
merge_count	int	game number
quality	string	quality
benchmark_main_top	string	benchmark device classification
region_id	int	country/region ID
region_name	string	country/region name
cpu_avg	float	Avg(CPU Utilization)
avg_online_time	float	Avg(online time)
count_rate	float	Percent(game number of sub scene)
x64_support	string	whether to support x64
fps_mean	float	Avg(FPS)
fps_jetter_hour	float	Avg(FPS Jitter)
fps_low_hour	float	Times of Low FPS
fps_jank_hour	float	Avg(FPS Jank)
perfdog_small_jank	float	Avg(Perfdog Jank)
perfdog_big_jank	float	Avg(Perfdog BigJank)
frametime_gt_100ms_percent	float	Percent(FrameTime > 100ms / Frames)
cpu_decline_percent	float	Percentage of CPU Throttling Game Number
stutter	float	Stutter
fps_attainment	float	Frame Rate Compliance Rate
fps_section1	float	FPS Segment 1
fps_section2	float	FPS Segment 2
fps_section3	float	FPS Segment 3
fps_section4	float	FPS Segment 4
fps_section5	float	FPS Segment 5
pss_average_peak	float	Avg(Max(PSS Memory))
pss_average_peak_top1	float	Avg(Max(Top 1% PSS Memory))
pss_peak_peak	float	Max(Max(PSS Memory))
vss_average_peak	float	Avg(Max(VSS Memory))
vss_average_peak_top1	float	Avg(Max(Top 1% VSS Memory))
vss_peak_peak	float	Max(Max(VSS Memory))
dist_data	map	Segment data
cpu_mean	float64	CPU Average
current_mean	float64	Average Current
power_mean	float64	Average Power Consumption
power_consumption	float64	Electricity Consumption
cpu_temperateure_mean	float64	Average cpu temperature
battery_temperature_mean	float64	average battery temperature
loading_time	float	scene Loading Time
loading_tiem_mean_r1000	float	Scene Loading Time
mean_scene_time	int	Scene Duration