视频1 视频21 视频41 视频61 视频文章1 视频文章21 视频文章41 视频文章61 推荐1 推荐3 推荐5 推荐7 推荐9 推荐11 推荐13 推荐15 推荐17 推荐19 推荐21 推荐23 推荐25 推荐27 推荐29 推荐31 推荐33 推荐35 推荐37 推荐39 推荐41 推荐43 推荐45 推荐47 推荐49 关键词1 关键词101 关键词201 关键词301 关键词401 关键词501 关键词601 关键词701 关键词801 关键词901 关键词1001 关键词1101 关键词1201 关键词1301 关键词1401 关键词1501 关键词1601 关键词1701 关键词1801 关键词1901 视频扩展1 视频扩展6 视频扩展11 视频扩展16 文章1 文章201 文章401 文章601 文章801 文章1001 资讯1 资讯501 资讯1001 资讯1501 标签1 标签501 标签1001 关键词1 关键词501 关键词1001 关键词1501 专题2001
AnalyzingYourMongoDBDatawithAnalytica
2020-11-09 13:25:04 责编:小采
文档


SET .byHashtag = group(tweets.by(entities.hashtags.text)) //group our tweets by hashtag and store them in a calculated (virtual) collection called 'byHashtag'
SET .byHashtag.count = count(tweets) // counts up the number of tweets for each hashtags in our virtual collection
SET .tophashtags = orderdesc(byHashtag.by(count)) //sort the results in descending order

Analytica uses dot notion to specify what collections, documents, or properties to operate on. Each SET command in Analytica results in a computation or the transformation of a set of documents, the results of which are stored in what we call calculated properties or calculated collections. These are intermediate results, stored in Analytica (at the database, collection, or document level - depending on how you specify them), which can be used in subsequent computations. Finally the command ‘.tophashtags.(text, count)’ retrieves the text of the hashtags along with the count of how many tweets use that hashtag.

Since we wanted to graph out our results, we used Analytica’s plug in for Excel to enter a series of Analytica script expressions. In addition to calculating the most tweeted hashtags, we also looked at the frequency of tweets per month from the @mongodb account, analyzed the content of @mongodb’s tweets to see how hashtags and URLs were being used, and computed a few other metrics. With this quick analysis, we saw that @mongodb’s tweeting patterns have changed over time (a lot more tweets recently!), figured out that over 80% of @mongodb’s tweets are retweeted at least once, and learnt (perhaps not surprisingly!) that the most popular tweets are about new releases. We graphed out the results and generated the HTML page to share with the MongoDB community.

We’re holding a webinar with 10gen?on February 12 so that you can learn more about Analytica and ask questions. In the webinar, we’ll go through how you can use Analytica on your own data to produce in-depth analyses, dashboards and reports and become a data whiz! In the meantime you can?learn more and download the beta version of Analytica. You’ll be able to run Analytica against your own datasets or in an example we’ve put together on data from StackOverflow.

If you are looking for other datasets to try, I’d recommend checking out Twitter’s API, Foursquare’s API, the NYTimes API, or Sunlight Labs API. Each of these has JSON, CSV or XML data that you can easily import into MongoDB to start analyzing with Analytica or MongoDB’s query language and aggregation framework. We’ll also post a step-by-step guide soon, which will describe how you can run an analysis on your own history. We’d love to hear from you - you can email?with questions or feedback.

  • Analytica Documentation
  • Learn more about MongoDB and Analytica in the Webinar on Data Analytics and Business Intelligence with MongoDB and Analytica February 12 ?
  • Follow Analytica on Twitter
  • 下载本文
    显示全文
    专题