Sounds like a cool music, right? At least this is one of my favorite tracks. May be some of you already know that, I enjoy doing some DeeJaying for my friends. But today, I want to speak about another kind of beats. Elastic beats! Elastic Beats Actually my favorite funky music track is a one from Georges Duke: Reach out! But this is another story… Beats So what are beats?
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… Download french words wget http://www.lexique.org/listes/liste_mots.txt head -20 liste_mots.txt What do we have? It’s a CSV file (tabulation as separator): 1_graph 8_frantfreqparm 0 279.84 1 612.10 2 1043.90 3 839.32 4 832.23 5 913.87 6 603.42 7 600.61 8 908.03 9 1427.
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!
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.
I’m often running some demos during conferences where we have a booth. As many others, I’m using Twitter feed as my datasource. I have been using Twitter river plugin for many years but, you know, rivers have been deprecated. Logstash 1.5.0 provides a safer and more flexible way to deal with tweets with its twitter input. Let’s do it! Let’s assume that you have already elasticsearch 1.5.2, Logstash 1.5.0 and Kibana 4.0.2 running on your laptop or on a cloud instance.
I gave recently a talk at Devoxx France 2015 with Colin Surprenant and I’d like to share here some of the examples we used for the talk. The talk was about “what my data look like?”. We said that our manager was asking us to answer some questions: who are our customers? how do they use our services? what do they think about us on Twitter? Our CRM database So we have a PostgreSQL database containing our data.
Recently I saw a tweet where Capitaine Train team started to open data they have collected and enriched or corrected. Ouvrez, ouvrez, les données structurées. Capitaine Train libère les gares : https://t.co/y6DjWsbALF #opendata — Trainline France (@trainline_fr) April 23, 2015 I decided to play a bit with ELK stack and create a simple recipe which can be used with any other CSV like data. Prerequisites You will need: Logstash: I’m using 1.5.0-rc3. Elasticsearch: I’m using 1.