极速阅览

Update Site:

Sort: A-Z Sort: Most Used Sort: Newest

Compare To:

+ / %

Time Window:

Daily Daily (7-day avg) Monthly Yearly Ever/Cumulative

Count Type:

Unique IPs Total Checks

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Analyzing the data yourself

You can download the raw data directly from individual update sites. Each site has 8 different statistics files available, plus a metadata index:

Data Types:

stats-unique-daily.txt.gz - Unique IP addresses per day
stats-total-daily.txt.gz - Total update checks per day
stats-unique-monthly.txt.gz - Unique IP addresses per month
stats-total-monthly.txt.gz - Total update checks per month
stats-unique-yearly.txt.gz - Unique IP addresses per year
stats-total-yearly.txt.gz - Total update checks per year
stats-unique-ever.txt.gz - Cumulative unique IP addresses by day
stats-total-ever.txt.gz - Cumulative total checks by day
sites.json - Metadata index with site list and summary statistics

URL Format: https://sites.imagej.net/{SITE_NAME}/{STATS_FILE}

Sites Index: https://sites.imagej.net/sites.json

Example URLs:

Data Format: Each line contains a datestamp and count value separated by a space:

20250723 458
20250724 672
20250725 543

Date formats:

Daily/Ever: YYYYMMDD (e.g., 20250723)
Monthly: YYYYMM (e.g., 202507)
Yearly: YYYY (e.g., 2025)

Sites Metadata Format: The sites.json file contains metadata for each update site:

{
  "Java-8": {
    "date_range": {
      "start": "20151220",
      "end": "20250904"
    },
    "total_unique_ips": 12534,
    "total_requests": 89472,
    "days_with_data": 3546,
    "last_generated": "2025-09-05"
  }
}

Here is an example Python script to analyze total yearly downloads for a specific site:

import gzip
import urllib.request

def fetch_yearly_stats(site_name, stat_type='total'):
    """Fetch and parse yearly statistics for a site."""
    url = f'https://sites.imagej.net/{site_name}/stats-{stat_type}-yearly.txt.gz'

    with urllib.request.urlopen(url) as response:
        with gzip.open(response, 'rt') as f:
            data = {}
            for line in f:
                if line.strip():
                    year, count = line.strip().split()
                    data[int(year)] = int(count)
    return data

# Example: Get Java-8 yearly download statistics
site = 'Java-8'
yearly_stats = fetch_yearly_stats(site, 'unique')

print(f"Yearly unique IP statistics for {site}:")
for year in sorted(yearly_stats.keys()):
    print(f"  {year}: {yearly_stats[year]:,} unique IPs")

# Find the most popular year
best_year = max(yearly_stats.keys(), key=lambda y: yearly_stats[y])
print(f"\nBest year: {best_year} with {yearly_stats[best_year]:,} unique IPs")

And another example to list all sites sorted by total unique IPs:

import json
import urllib.request

with urllib.request.urlopen('https://sites.imagej.net/sites.json') as response:
    sites_data = json.load(response)

# Sort sites by total unique IPs
ranked_sites = sorted(
    sites_data.items(),
    key=lambda x: x[1]['total_unique_ips'],
    reverse=True
)

print("Sites ranked by total unique IPs:")
for site_name, metadata in ranked_sites:
    print(f"  {site_name}: {metadata['total_unique_ips']:,} unique IPs "
          f"({metadata['days_with_data']} days of data)")

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Update Site Statistics

Analyzing the data yourself

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