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https://github.com/tahoe-lafs/tahoe-lafs.git
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51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
#!/usr/bin/env python
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import sys, os.path
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if len(sys.argv) > 1 and sys.argv[1] == "config":
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print("""\
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graph_title Tahoe File Estimate
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graph_vlabel files
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graph_category tahoe
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graph_info This graph shows the estimated number of files and directories present in the grid
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files.label files
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files.draw LINE2""")
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sys.exit(0)
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# Edit this to point at some subset of storage directories.
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node_dirs = [os.path.expanduser("~amduser/prodnet/storage1"),
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os.path.expanduser("~amduser/prodnet/storage2"),
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os.path.expanduser("~amduser/prodnet/storage3"),
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os.path.expanduser("~amduser/prodnet/storage4"),
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]
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sections = ["aa", "ab", "ac", "ad", "ae", "af", "ag", "ah", "ai", "aj"]
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# and edit this to reflect your default encoding's "total_shares" value, and
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# the total number of servers.
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N = 10
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num_servers = 20
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index_strings = set()
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for base in node_dirs:
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for section in sections:
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sampledir = os.path.join(base, "storage", "shares", section)
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indices = os.listdir(sampledir)
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index_strings.update(indices)
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unique_strings = len(index_strings)
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# the chance that any given file appears on any given server
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chance = 1.0 * N / num_servers
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# the chance that the file does *not* appear on the servers that we're
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# examining
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no_chance = (1-chance) ** len(node_dirs)
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# if a file has a 25% chance of not appearing in our sample, then we need to
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# raise our estimate by (1.25/1)
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correction = 1+no_chance
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#print "correction", correction
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files = unique_strings * (32*32/len(sections)) * correction
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print("files.value %d" % int(files))
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