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Open Source Search Server

Open Source Search Server
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Getting Started - Google Maps JavaScript API v3 Audience This documentation is designed for people familiar with JavaScript programming and object-oriented programming concepts. You should also be familiar with Google Maps from a user's point of view. There are many JavaScript tutorials available on the Web. This conceptual documentation is designed to let you quickly start exploring and developing applications with the Google Maps API. Obtaining an API Key All Maps API applications* should load the Maps API using an API key. * Google Maps API for Business developers must not include a key in their requests. To create your API key: Visit the APIs Console at and log in with your Google Account. By default, a key can be used on any site. Hello, World The easiest way to start learning about the Google Maps API is to see a simple example. View example (map-simple.html) Even in this simple example, there are a few things to note: We declare the application as HTML5 using the <! Loading the Google Maps API

Whoosh 2.4.0 Fast, pure-Python full text indexing, search, and spell checking library. Package Documentation Whoosh is a fast, featureful full-text indexing and searching library implemented in pure Python. Programmers can use it to easily add search functionality to their applications and websites. Some of Whoosh's features include: Pythonic API.Pure-Python. Whoosh might be useful in the following circumstances: Anywhere a pure-Python solution is desirable to avoid having to build/compile native libraries (or force users to build/compile them).As a research platform (at least for programmers that find Python easier to read and work with than Java ;)When an easy-to-use Pythonic interface is more important to you than raw speed. Whoosh was created and is maintained by Matt Chaput. This software is licensed under the terms of the simplified BSD (A.K.A. If you have setuptools or pip installed, you can use easy_install or pip to download and install Whoosh automatically:

Getting Started with elasticsearch and AngularJS: Part1 - Searching The ability to deliver sophisticated client-side JavaScript applications is an important aspect of data discovery and visualization. It’s no secret that elasticsearch is phenomenal at extracting meaning from enormous data sets in near real-time. Exposing that power to end users requires equally impressive applications. Elasticsearch has made search more approachable by exposing Web friendly APIs (REST + JSON) that reduce the impedance mismatch associated with relational models, at no sacrifice to query capability. The goal here is to write a series of articles that help folks gain some insight into how these technologies fit together. Getting Started Loading Data Throughout the series, I’ll be using the StackOverflow data that Matt used in this post, which also describes how to aquire and load the data. Application Module Angular provides its own module system for loading and bootstrapping applications. // app.jsangular.module('demo', []); Creating a Search Controller Executing Searches <! <!

Search engine indexing Popular engines focus on the full-text indexing of online, natural language documents.[1] Media types such as video and audio[2] and graphics[3] are also searchable. Meta search engines reuse the indices of other services and do not store a local index, whereas cache-based search engines permanently store the index along with the corpus. Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size. Indexing[edit] The purpose of storing an index is to optimize speed and performance in finding relevant documents for a search query. Index design factors[edit] Major factors in designing a search engine's architecture include: Merge factors Storage techniques How to store the index data, that is, whether information should be data compressed or filtered. Index size How much computer storage is required to support the index. Lookup speed How quickly a word can be found in the inverted index. Maintenance How the index is maintained over time.[5] Fault tolerance

The Xapian Project Elasticsearch: Five Things I was Doing Wrong | gibrown Update: Also check out my series on scaling Elasticsearch. I’ve been working with Elasticsearch off and on for over a year, but recently I attended’s training class (well worth the time and money) and discovered a few significant things that I was doing just plain wrong. Before using Elasticsearch I used Lucene directly, and so a few of the errors I made were due to not understanding some of the things ES does for you behind the scenes. As background, most of the data I’m indexing conforms to the WordPress database schema. 1. For some reason I had neglected to use arrays when creating fileds such as a list of tags attached to a document. Or, for fields that were lists of URLs I just separated them by spaces and used the whitespace analyzer. Using an array of items is a much easier way, but somehow, after initially reading about the array mapping, I completely forgot that it existed. 2. In the end this was a case of premature optimization. 3. 4. 5. Like this: Related

TinyMCE, jQuery and Ajax forms Even thought this really seems easy, I believe there's stuff missing. I'm trying to make jquery and TinyMCE works together in ajax mode (silent submit and reload), but I really can't get anything working... some help would be appreciated. romain May 16, 2008 #1 "tinyMCE.triggerSave();" was all it took. I just wish I read your entry 4 hours earlier :) would have saved the headache. Mark L. ghjghjghj guga Aug 26, 2008 #3 Thanks bro! Paul S Sep 2, 2008 #4 Thanks, saved me after a few hours of head scratching! Karl Sep 26, 2008 #5 Scott Oct 22, 2008 #6 Thank you very much. Jason Smith Dec 14, 2008 #7 thanks! cwd May 2, 2009 #8 Where does this go ? Jun 4, 2009 #9 Thanks a lot! Jon Jun 5, 2009 #10 Thanks, you saved my day. Andrea Jun 10, 2009 #11 Doh! TomM Jun 18, 2009 #12 thanks so much - you saved me a ton of time trying to figure that one out! Dan Pickett Jun 22, 2009 #13 Thanks! marss Jul 28, 2009 #14 Thank you very much... darcon3371 Aug 24, 2009 #15 Gingah Aug 25, 2009 #16 You safe my life! Brilliant. hi

Realtime Search: Solr vs Elasticsearch | Socialcast Engineering What is Elasticsearch? Elasticsearch is REST based, distributed search engine powered by the excellent Lucene library. The built in JSON + HTTP API provides an elegant platform perfect for integrating with (ex: the elastic_searchable ruby gem). Why is it better than Solr? First of all, let’s set the record straight: Solr is fast. Unfortunately, it is really easy to break Solr as well. Now throw a few million documents into the index and Solr will be buckling at the knees while Elasticsearch doesn’t break a sweat! It is painfully apparent that Solr’s architecture was not built for realtime search applications. Realworld Results… After transitioning our search infrastructure from Solr to Elasticsearch, we saw an instant ~50x improvement in search performance! And now for something a bit more interesting… The typical realtime search architecture goes something like this: Elasticsearch can support this model quite well, but it also offers a feature that turns this entire workflow on it’s head.

Quick 'n' Comfortable Web Development in PHP | Nette Framework MySQL: Błąd „#1005 – Can’t create table (errno: 121)” podczas tworzenia kluczy obcych. Powoli wyjaśnia się moja sytuacja na uczelni, także mam czas na opisywanie bardziej ambitnych problemów. Dzisiejszy wpis sponsoruje przedmiot Hurtownie i Eksploracja Danych, w ramach którego w tym semestrze naszym zadaniem było m. in. zaprojektowanie rzeczonej hurtowni. Ze względu na to, że uwielbiam przedmioty pozwalające wykorzystać posiadaną wiedzę w praktyce, z przyjemnością zająłem się wykonaniem projektu. ;] Strukturę bazy danych obsługującej hurtownię projektowałem w narzędziu MySQL WorkBench, które z tego miejsca szczerze polecam – jak na darmowe oprogramowanie jest naprawdę bardzo solidnym produktem. Po wykonaniu części opisowej przypomniałem sobie, że trzeba jeszcze wstawić kod tworzący całą strukturę [zestaw zapytań CREATE TABLE wraz z odpowiednimi ALTER TABLE dla relacji]. Przywołałem okno Firefoksa, spojrzałem na strukturę bazy i nieco zdziwiony zobaczyłem tylko kilka tabel „liści”, nie posiadających żadnych kluczy obcych. #1005 – Can’t create table ‚. Gdzie jest błąd?