1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
|
// skate-map runs a given map function over input data. We mostly want to
// extract a key from a json document. For simple cases, you can use `jq` and
// other tools. Some key derivations require a bit more.
//
// An example with mostly unix tools. We want to extract the DOI and sort by
// it; we also want to do this fast, hence parallel, LC_ALL, etc.
//
// $ zstdcat -T0 file.zst | (1)
// LC_ALL=C tr -d '\t' | (2) *
// parallel -j 16 --block 10M --pipe (3) *
// "jq -rc 'select(.biblio.doi != null) | (4) *
// [.biblio.doi, (.|tostring)] | @tsv'" | (5) *
// LC_ALL=C sed 's/\\\\/\\/g' | (6) *
// LC_ALL=C awk -F $'\t' -v OFS='\t' '$1=tolower($1)' | (7) *
// skate-to-doi -B -S -f 1 | (8) *
// LC_ALL=C sort -S 30% --parallel 6 -k1,1 | (9)
// zstd -c -T0 > skate.out
//
// (1) zstd is fast! "~4x faster than zlib" (https://is.gd/HT1DUs)
// (2) we use tab as column separator and we want clean this up before (could
// be skipped, if we limit number of splits)
// (3) we pass the data to jq, with a bit larger buffer (default is 1MB)
// (4) we want no "null" output
// (5) tostring prints input as string, because we need to carry the document forward
// (6) but we need some cleanup, too
// (7) we normalize the DOI to lowercase
// (8) a custom filter to normalize a DOI in a specific column
// (9) sorting by DOI
//
// This is reasonably fast, but some cleanup is ugly. We also want more complex
// keys, e.g. more normalizations, etc. We'd like to encapsulate (2) to (8).
package main
import (
"flag"
"fmt"
"log"
"os"
"runtime"
"text/tabwriter"
"git.archive.org/martin/cgraph/skate"
"git.archive.org/martin/cgraph/skate/parallel"
)
var (
mapperName = flag.String("m", "", "mapper to run")
numWorkers = flag.Int("w", runtime.NumCPU(), "number of workers")
batchSize = flag.Int("b", 50000, "batch size")
verbose = flag.Bool("verbose", false, "show progress")
keyPrefix = flag.String("p", "", "a key prefix to use")
extraValue = flag.String("x", "", "extra value to pass to configurable mappers")
)
func main() {
flag.Parse()
// TODO
// [ ] add prefixes and a way to derive multiple keys in one go
// [ ] how to store multiple keys, sorted?
// [ ] maybe wrap jq and parallel for arbitrary nested keys
availableMappers := map[string]skate.Mapper{
"id": skate.Identity,
"ff": skate.CreateFixedMapper(*extraValue),
"ti": skate.MapperTitle,
"tn": skate.MapperTitleNormalized,
"ty": skate.MapperTitleNysiis,
"ts": skate.MapperTitleSandcrawler,
}
switch {
case *mapperName != "":
if f, ok := availableMappers[*mapperName]; !ok {
log.Fatalf("unknown mapper name: %v", *mapperName)
} else {
if *keyPrefix != "" {
f = skate.WithPrefix(f, *keyPrefix)
}
pp := parallel.NewProcessor(os.Stdin, os.Stdout, f.AsTSV)
pp.NumWorkers = *numWorkers
pp.BatchSize = *batchSize
pp.Verbose = *verbose
if err := pp.Run(); err != nil {
log.Fatal(err)
}
}
default:
fmt.Println("skate-map available mappers")
fmt.Println()
w := tabwriter.NewWriter(os.Stdout, 0, 0, 4, ' ', 0)
for k, v := range availableMappers {
fmt.Fprintf(w, "%s\t%s\n", k, skate.NameOf(v))
}
w.Flush()
}
}
|
