We recently worked on moving Quepid from a Python backend to a Ruby backend, something Joel Spolsky would frown upon. Here's why.
We will be exhibiting Quepid and talking about the importance of relevant search
AWS's biggest event of the year is always good for refined ideas. Let's review a few of Joe's favorite practices, sessions and lessons learned from this year's conference.
Graphs. In Solr!
We will be speaking and sharing war stories about building always on always available discovery systems using Cassandra
We will be workshopping *Enabling a Sophisticated Search User Experience* at KMWorld
When you have a lot of fields, sometimes you just want to exclude a few.
Quepid & OSC can help you transition from legacy search to a Solr or Elasticsearch solution while helping you avoid the negative impacts and even improve your search in the process.
beCamp, that most Charlottesville of conferences brings together tech-curious folks from all walks of life.
A quick trip review of Cassandra Summit 2015.
There's something new cooking in how Lucene scores documents. Instead of the traditional "TF*IDF," Lucene just switched to something called BM25 in trunk. That means a new scoring formula for Solr (likely Solr 6) and Elasticsearch down the line.
*The* conference focused on Solr, we've made the pilgrimage since 2010
Courtesy of Grant Ingersoll's keynote speech, some bad behaviors we all, including me, engage in when tuning search results!
When the Charlottesville Women in Technology (CWiT) team first contacted Opensource Connections (OSC) about whether or not we wanted to sponsor CWiT for the year, to be honest I knew very little about the details of gender inequality in Science and Technology and even less about ways to help address that inequality.
We're going to be at Elastic ON sharing how to tune ES for better search results!
Amazon Web Services' premiere conference.
Doug, author of Relevant Search, will be sharing some advanced techniques
Good news! We're proud to announce our test-driven search toolbench Quepid now supports Elasticsearch. Quepid helps by bringing test-driven principles to tuning search results -- what we call Test-Driven Relevancy. It helps define what good search results by incorporating your own business expertise from colleagues that know users the best
Notes from pulling and pushing data in Solr using Spark and DataStax Enterprise
Returning for another year, John, Matt, Eric, and Chris will all be at Cassandra Summit, sharing war stories from our C* projects for the Federal Government and Commercial clients
I love stringing together custom analyzers to solve my search problems. Analyzers control how search and document text are transformed, step-by-step into individual terms for matching. This in turn gives you tremendous low-level control of your relevance. Yet one thing has always bugged me with Elasticsearch. You can't inspect the step-by-step behavior of an analyzer very easily. You have the _analyze API, which helps a great deal see the final output of the lengthy analysis process. But you can't pry into each step to see what's happening.
In Chapter 4 of Relevant Search, we talk a LOT about Elasticsearch analyzers. Without analyzers, your search engine would be a rather unintelligent string comparison system instead of a smart, powerful search engine. Analyzers are the text-processing pipeline that feed the search engine's core data structures, controlling whether two tokens (basically words) match during a search.
When I first got involved in search work, I noticed a fairly shocking shortcoming: improving the quality of search results is an abysmal experience. Despite the fact that search drives the user experience of many apps, it feels miserable to work with.
Joe is presenting a talk about logging with Logstash, Kafka, and Elasticsearch.
Starting in the summer of 2015, users can create their own scorer to accommodate any scale of ranking results and make their own custom scoring algorithm that that work best for their situation.
Quepid has added Organizations to make it easier to collaboratively solve search relevancy problems with your team!
We will be sharing war stories about building always on always available discovery systems using Cassandra
AWS CLI documentation only covers using JMESPath result queries briefly. Let's explore how much more you can do.
Do you know about Splainer? It's our handy-dandy, free and open source tool for working with Solr search results. It's become my favorite go to tool for tweaking a specific Solr query. Let's face it: nobody likes working with Solr in their browser's URL bar. It's a royal pain.
The release of Quepid v0.2.0 (July 3, 2015) added several new features as well as enhanced some existing features. The Release Notes below provide a quick look to whet your appetite. Individual posts detailing the how Organizations and Custom Scorers work are coming soon!
It’s time to fill out your timesheet, again. You’ve put in a full week of work but remembering everything you’ve accomplished can be difficult when you’re jumping between projects. What if you could just quickly copy your git commits for the week and be done?
Make the most out of your location data by using OpenLayers to provide a visualization.
Trying to answer hard policy questions like the impact of Pre-K attendance on 8th grade graduation rates? VLDS is your friend.
We're pleased to announce that Chapters 4 and 5 are available for early access for Relevant Search! Please read and give us feedback. This is early access for a reason: we want to hear what you think!
I often want to intercept the Solr docs in a format I can use offline. Clients have complex ingestion systems. I shouldn't need to have the full ingestion apparatus to do some Solr work. With documents offline, I can script something simple and stupid that throws documents at Solr to test my search relevancy work without having the full system at hand to populate Solr.
VLDS is the Virginia Longitudinal Data System, providing educational and workforce training data to improve public education. Eric will be looking at how we can help improve educational outcomes.
Something amazing happened today on our Quepid project. We did a code review. Instead of trying to extract value from reviewing pull requests in isolation, we realized actually talking to each other was the only way to move the dial on understanding each other's work.
Takeaways from Cassandra Day DC