Best way to index and search 1 GB+ existing data in couchDB?


(shadow) #1

Hello all

I'm a beginner with couchDB and Elastic Search. After having read a bunch
of articles and blogs, I decided to send you a message and see if you have
a solution/a better way of doing what I'm trying to do.

So I have this 1 GB of text data in a couchDB (this data may bet changed
(edited/appended/deleted) on a daily basis. I want to provide a good search
functionality (text based). For that I decided to use river of elastic
search for indexing. Now I know how to set up a river and keep trace of
changes

(basically just seeing up a curl -XPOST as shown in multiple examples on
net), but what I also want to do is to index the already present data...

Is there a way in elastic search to do that. If yes, can you point me to
the right documentation/ sample code?

Thanks


(David Pilato) #2

Hey

Did you look at : https://github.com/elasticsearch/elasticsearch-river-couchdb ?

Just two steps :

· Install the plugin and start your ES node

· Create the river

All your existing docs will be sent to ES. New docs, updates, deletes will also be sent to ES.

Is it what you are looking for ?

David.

De : elasticsearch@googlegroups.com [mailto:elasticsearch@googlegroups.com] De la part de Shadow
Envoyé : mercredi 27 juin 2012 18:17
À : elasticsearch@googlegroups.com
Objet : Best way to index and search 1 GB+ existing data in couchDB?

Hello all

I'm a beginner with couchDB and Elastic Search. After having read a bunch of articles and blogs, I decided to send you a message and see if you have a solution/a better way of doing what I'm trying to do.

So I have this 1 GB of text data in a couchDB (this data may bet changed (edited/appended/deleted) on a daily basis. I want to provide a good search functionality (text based). For that I decided to use river of elastic search for indexing. Now I know how to set up a river and keep trace of changes

(basically just seeing up a curl -XPOST as shown in multiple examples on net), but what I also want to do is to index the already present data...

Is there a way in elastic search to do that. If yes, can you point me to the right documentation/ sample code?

Thanks


(shadow) #3

Yes I did. I actually have setup elastic search indexing of my couched on
my localhost right now...
Initially I wasn't sure if it would index from start or not, but after
giving it some time, it did. So that worked.

But what I'm trying to do however is do searches on that indexing and
looking for examples of how people write search programs on top of these
indexes... do you/ anyone else have any examples which could guide me in
that direction?

Thanks

On Wednesday, June 27, 2012 12:23:02 PM UTC-7, David Pilato wrote:

Hey

Did you look at :
https://github.com/elasticsearch/elasticsearch-river-couchdb ?

Just two steps :

· Install the plugin and start your ES node

· Create the river

All your existing docs will be sent to ES. New docs, updates, deletes will
also be sent to ES.

Is it what you are looking for ?

David.

De : elasticsearch@googlegroups.com [mailto:
elasticsearch@googlegroups.com] De la part de Shadow
Envoyé : mercredi 27 juin 2012 18:17
À : elasticsearch@googlegroups.com
Objet : Best way to index and search 1 GB+ existing data in couchDB?

Hello all

I'm a beginner with couchDB and Elastic Search. After having read a bunch
of articles and blogs, I decided to send you a message and see if you have
a solution/a better way of doing what I'm trying to do.

So I have this 1 GB of text data in a couchDB (this data may bet changed
(edited/appended/deleted) on a daily basis. I want to provide a good search
functionality (text based). For that I decided to use river of elastic
search for indexing. Now I know how to set up a river and keep trace of
changes

(basically just seeing up a curl -XPOST as shown in multiple examples on
net), but what I also want to do is to index the already present data...

Is there a way in elastic search to do that. If yes, can you point me to
the right documentation/ sample code?

Thanks


(system) #4