I just discovered a nice video which explains the Zipf’s law.
I’m wondering if I can index the french lexique from Université de Savoie and find some funny things based on that…
I gave a BBL talk recently and while chatting with attendees, one of them told me a simple use case he covered with elasticsearch: indexing metadata files on a NAS with a simple
ls -lR like command.
His need is to be able to search on a NAS for files when a user wants to restore a deleted file.
As you can imagine a search engine is super helpful when you have hundreds of millions files!
I found this idea great and this is by the way why I love speaking at conferences or in companies: you always get great ideas when you listen to others!
I decided then to adapt this idea using the ELK stack.
Some months ago, I published a recipe on how to index Twitter with Logstash and Elasticsearch.
I have the same need today as I want to monitor Twitter when we run the elastic FR meetup (join us by the way if you are in France!).
Well, this recipe can be really simplified and actually I don’t want to waste my time anymore on building and managing elasticsearch and Kibana clusters anymore.
Let’s use a Found by elastic cluster instead.
This article is based on Recommender System with Mahout and Elasticsearch tutorial created by MapR.
It now uses the 20M MovieLens dataset which contains: 20 million ratings and 465 000 tag applications applied to 27 000 movies by 138 000 users and was released in 4/2015. The format with this recent version has changed a bit so I needed to adapt the existing scripts to the new format.
Recently, I got a database MySQL dump and I was thinking of importing it into elasticsearch.
The first idea which pops up was:
Well. I found that some of the steps are really not needed.
I can actually use ELK stack and create a simple recipe which can be used to import SQL dump scripts without needing to actually load the data to a database and then read it again from the database.
Let’s do it!
Interview I gave for Voxxed Istanbul.
I’m often running some demos during conferences where we have a booth. As many others, I’m using Twitter feed as my datasource.
Let’s do it!
Sometimes, you would like to reindex your data to change your mapping or to change your index settings or to move from one server to another or to one cluster to another (think about multiple data centers for example).
For the later you can use Snapshot and Restore feature but if you need to change any index settings, you need something else.
Let’s do it!
Using Found by elastic cluster helps a lot to have a ready to use and managed elasticsearch cluster.
I started my own cluster yesterday to power brownbaglunch.fr website (work in progress) and it was ready to use after some clicks!
It’s a kind of magic!
I gave recently a talk at Voxxed Istanbul 2015 and I’d like to share here the story of this talk.
The talk was about adding a real search engine for your legacy application. Here “legacy” means an application which is still using SQL statements to execute search requests.