Fast parallel bulk loading utility for elasticsearch.
$ go get github.com/miku/esbulk/cmd/esbulk
For deb or rpm packages, see: https://github.com/miku/esbulk/releases
$ esbulk -h
Usage: ./esbulk [OPTIONS] JSON
-cpuprofile string
write cpu profile to file
-host string
elasticsearch host (default "localhost")
-id string
name of field to use as id field, by default ids are autogenerated
-index string
index name
-mapping string
mapping string or filename to apply before indexing
-memprofile string
write heap profile to file
-port int
elasticsearch port (default 9200)
-purge
purge any existing index before indexing
-server string
elasticsearch server, this works with https as well (default "http://localhost:9200")
-size int
bulk batch size (default 1000)
-type string
elasticsearch doc type (default "default")
-v prints current program version
-verbose
output basic progress
-w int
number of workers to use (default 4)
-z unzip gz'd file on the fly
To index a JSON file, that contains one document per line, just run:
$ esbulk -index example file.ldj
Where file.ldj is line delimited JSON, like:
{"name": "esbulk", "version": "0.2.4"}
{"name": "estab", "version": "0.1.3"}
...
By default esbulk will use as many parallel
workers, as there are cores. To tweak the indexing
process, adjust the -size and -w parameters.
You can index from gzipped files as well, using
the -z flag:
$ esbulk -z -index example file.ldj.gz
Starting with 0.3.7 the preferred method to set a
non-default server hostport is via -server, e.g.
$ esbulk -server https://0.0.0.0:9201
This way, you can use https as well, which was not
possible before. Options -host and -port are
kept for backwards compatibility.
Since version 0.3.8: If you want to reuse IDs from your documents in elasticsearch, you
can specify the ID field via -id flag:
$ cat file.json
{"x": "doc-1", "db": "mysql"}
{"x": "doc-2", "db": "mongo"}
Here, we would like to reuse the ID from field x.
$ esbulk -id x -index throwaway -verbose file.json
...
$ curl -s http://localhost:9200/throwaway/_search | jq
{
"took": 2,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1,
"hits": [
{
"_index": "throwaway",
"_type": "default",
"_id": "doc-2",
"_score": 1,
"_source": {
"x": "doc-2",
"db": "mongo"
}
},
{
"_index": "throwaway",
"_type": "default",
"_id": "doc-1",
"_score": 1,
"_source": {
"x": "doc-1",
"db": "mysql"
}
}
]
}
}
A similar project has been started for solr, called solrbulk.
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