// skate-map runs a given "map" function over input data. Here, 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, hence a dedicated program.
//
// An example with mostly unix tools. We want to extract the DOI from newline
// delimited JSON 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 the input as string, because we need to carry the document forward ...
// (6) ... but we'll 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; in short: we'd like to encapsulate (2)
// to (8) with `skate-map`.
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")
	bestEffort  = flag.Bool("B", false, "best effort")
	logFile     = flag.String("log", "", "log filename")
	skipOnEmpty = flag.Int("skip-on-empty", -1, "omit docs with empty value in given column (zero indexed)")

	help = `skate-map available mappers

    $ skate-map -m ts < file.ndj > file.tsv
	`
)

func main() {
	flag.Parse()
	availableMappers := map[string]skate.Mapper{
		// Add new mapper functions here.
		"id": skate.Identity,
		"ff": skate.CreateFixedMapper(*extraValue),
		"ti": skate.MapperTitle,
		"tn": skate.MapperTitleNormalized,
		"ty": skate.MapperTitleNysiis,
		"ts": skate.MapperTitleSandcrawler,
		"ru": skate.MapperURLFromRef,
	}
	if *logFile != "" {
		f, err := os.OpenFile(*logFile, os.O_CREATE|os.O_APPEND, 0644)
		if err != nil {
			log.Fatal(err)
		}
		defer f.Close()
		log.SetOutput(f)
	}
	switch {
	case *mapperName != "":
		if mapf, ok := availableMappers[*mapperName]; !ok {
			log.Fatalf("unknown mapper name: %v", *mapperName)
		} else {
			if *skipOnEmpty >= 0 {
				mapf = skate.WithSkipOnEmpty(mapf, *skipOnEmpty)
			}
			if *keyPrefix != "" {
				mapf = skate.WithPrefix(mapf, *keyPrefix)
			}
			if *bestEffort {
				mapf = skate.WithBestEffort(mapf)
			}
			pp := parallel.NewProcessor(os.Stdin, os.Stdout, mapf.AsTSV)
			pp.NumWorkers = *numWorkers
			pp.BatchSize = *batchSize
			pp.Verbose = *verbose
			if err := pp.Run(); err != nil {
				log.Fatal(err)
			}
		}
	default:
		fmt.Println(help)
		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()
	}
}