From the fsriver doc:
curl -XPUT 'localhost:9200/_river/mydocs/_meta' -d '{
"type": "fs",
"fs": {
"url": "/tmp",
"update_rate": 900000,
"includes": ".doc,.pdf",
"excludes": "resume"
}
}'
How does this translate to the Python API?
Thanks,
Kent
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Hello Kent,
you can always access the raw transport and send any request you wish
for the unsupported APIs:
from elasticsearch import Elasticsearch
es = Elasticsearch()
data, status = es.transport.perform_request('PUT', '/_river/....',
body={'type': 'fs',})
Hope this helps,
Honza Kral
On Thu, Mar 13, 2014 at 9:57 PM, Kent Tenney ktenney@gmail.com wrote:
From the fsriver doc:
curl -XPUT 'localhost:9200/_river/mydocs/_meta' -d '{
"type": "fs",
"fs": {
"url": "/tmp",
"update_rate": 900000,
"includes": ".doc,.pdf",
"excludes": "resume"
}
}'
How does this translate to the Python API?
Thanks,
Kent
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It doesn't look like the elasticsearch-py API covers the river use case.
When I've run into things like this I've always just run a manual CURL
request, or if I need to do it from within a script I just do a basic
command with requests, ala
requests.put("http://localhost:9200/_river/mydocs/_meta" data='{"type":
"fs", "fs": { "url": "/tmp", "update_rate": 900000, "includes":
".doc,.pdf", "excludes": "resume" }}')
Not the most elegant approach, but it works!
On Thursday, March 13, 2014 1:57:55 PM UTC-7, Kent Tenney wrote:
From the fsriver doc:
curl -XPUT 'localhost:9200/_river/mydocs/_meta' -d '{
"type": "fs",
"fs": {
"url": "/tmp",
"update_rate": 900000,
"includes": ".doc,.pdf",
"excludes": "resume"
}
}'
How does this translate to the Python API?
Thanks,
Kent
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