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It's these little details I love, when a project actually cares about operations and not just "well here's the API"
I've been using ElasticSearch only for Logstash, but i've been blown away so far as how easy it is to deal with.
The github lays it out well.
Lucene is one of those projects which hardly has any real competition. That's surprising given how many real world software projects have a search requirement. While Lucene is excellent, it's not without flaws and competition is always great.
Elasticsearch is really awesome for searching, but what most people don't realize is that it makes a better MongoDB than MongoDB while giving you that searching too.
The one drawback ES had in the bad old days was that backup and restore was a nightmare... ESPECIALLY on AWS. The new system they introduced was so simple I was concerned about updating to it because I was SURE something would go south.
But it all just worked.
I still have the Couch to ES replication running because I'm anal like that... but really... yeah... you can do without Couchbase, Mongo et al... ES will probably do everything you need PLUS everything you can't do in the others.
Elasticsearch is really awesome for searching, but what most people don't realize is that it makes a better MongoDB than MongoDB while giving you that searching too.
I'm surprised so many people miss this. Out of the box, Elasticsearch is a distributed NoSQL store with better write consistency (and arguably performance) than MongoDB offers in its default configuration. The major missing feature was backup snapshots and restores, which 1.0 delivers—along with aggregations that more than rival MongoDBs. The team has intentionally avoided marketing themselves as a NoSQL store (was t…
It was two weeks ago, and our startup was on the precipice of a major launch. We had completely rewritten our online publication site, which drives the bulk of our traffic. The product had to be shipped on-time - we had press releases, eager investors and a launch party dependent on it. A few days before launch, things were not looking good. As admins manipulated articles in preparation for the launch, the servers ke…
Elasticsearch mostly "just works". The latest version of Solr has made clustering easier (requires managing Zookeeper), but before that, it was either ES or nightmare. Lucene is one of those projects which hardly has any real competition. That's surprising given how many real world software projects have a search requirement. While Lucene is excellent, it's not without flaws and competition is always great.
Elasticsearch is really awesome for searching, but what most people don't realize is that it makes a better MongoDB than MongoDB while giving you that searching too.
What I'm doing is slamming the full text output of OCRed PDFs into a MyISAM table, the entire document in a text field.
What I'm afraid I'm not doing right is creating the web interface to search elasticsearch. What I'm using filters with the query string syntax[1] in the search box, pointing directly at that fulltext column. I'm also using the highlight functionality so that I can specify how many highlight blurbs to return with the result. The query string syntax works great with the OCR'd text, because most of it is near-garbage (as most ocr is) so you can search for something like "net sales"~50 to find those two terms within 50 words of each other. I think the results were something like: net sales 15,000 results "net sales" 120 results "net sales"~50 550 results
Can anyone point me at a good web based search implementation using elasticsearch that explains how they're doing it?
What I have works pretty good, I just want to... check my work, I guess.
[1]: http://www.elasticsearch.org/guide/en/elasticsearch/referenc...
Elasticsearch is really awesome for searching, but what most people don't realize is that it makes a better MongoDB than MongoDB while giving you that searching too.
What limitations should one be aware of that would make ElasticSearch not a viable candidate where something like MongoDB would be a better fit?
You create a number of shards for each index(database) that you can't later expand.