{"id":1357,"date":"2013-08-20T07:48:53","date_gmt":"2013-08-20T05:48:53","guid":{"rendered":"http:\/\/blog.gocept.com\/?p=1357"},"modified":"2013-11-06T18:15:01","modified_gmt":"2013-11-06T17:15:01","slug":"viewing-scales-metrics-from-pyramid","status":"publish","type":"post","link":"https:\/\/blog.gocept.com\/2013\/08\/20\/viewing-scales-metrics-from-pyramid\/","title":{"rendered":"Viewing scales metrics from Pyramid"},"content":{"rendered":"

We’ve recently started experimenting with the excellent scales<\/a> library to collect in-process metrics (see Coda Hale’s CodeConf talk “Metrics everywhere<\/a>” among many others for reasons why one definitely wants to do that).<\/p>\n

Scales comes with a flask-based HTTP server that allows viewing the collected measurements and dumping them as JSON. But if you already are in a web application, there’s no real need to spin up yet another thread, open another port etc. to do this. In our case, we’re using Pyramid, so here’s a quick recipe to get the same view that greplin.scales.flaskhandler provides:<\/p>\n

Update 2013-11-06:<\/strong> This code is now released as pyramid_scales<\/a>.<\/p>\n

# in your Pyramid setup\r\nconfig.add_route('scales', '\/scales\/*prefix')\r\n\r\nfrom StringIO import StringIO\r\nfrom pyramid.view import view_config\r\nimport greplin.scales\r\nimport greplin.scales.formats\r\n\r\n@view_config(route_name='scales', renderer='string')\r\ndef scales_stats(request):\r\n\u00a0\u00a0\u00a0 parts = request.matchdict.get('prefix')\r\n\u00a0\u00a0\u00a0 path = '\/'.join(parts)\r\n\u00a0\u00a0\u00a0 stats = greplin.scales.util.lookup(greplin.scales.getStats(), parts)\r\n\r\n\u00a0\u00a0\u00a0 output = StringIO()\r\n\u00a0\u00a0\u00a0 outputFormat = request.params.get('format', 'html')\r\n\u00a0\u00a0\u00a0 query = request.params.get('query', None)\r\n\u00a0\u00a0\u00a0 if outputFormat == 'json':\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 request.response.content_type = 'application\/json'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 greplin.scales.formats.jsonFormat(output, stats, query)\r\n\u00a0\u00a0\u00a0 elif outputFormat == 'prettyjson':\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 request.response.content_type = 'application\/json'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 greplin.scales.formats.jsonFormat(output, stats, query, pretty=True)\r\n\u00a0\u00a0\u00a0 else:\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 request.response.content_type = 'text\/html'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 # XXX Dear pyramid.renderers.string_renderer_factory,\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 # you can't be serious\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 request.response.default_content_type = 'not-text\/html'\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 output.write('<html>')\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 greplin.scales.formats.htmlHeader(output, '\/' + path, __name__, query)\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 greplin.scales.formats.htmlFormat(output, tuple(parts), stats, query)\r\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 output.write('<\/html>')\r\n\r\n\u00a0\u00a0\u00a0 return output.getvalue()<\/pre>\n","protected":false},"excerpt":{"rendered":"
We’ve recently started experimenting with the excellent scales library to collect in-process metrics (see Coda Hale’s CodeConf talk “Metrics everywhere” among many others for reasons why one definitely wants to do that). Scales comes with a flask-based HTTP server that allows viewing the collected measurements and dumping them as JSON. But if you already are … Continue reading “Viewing scales metrics from Pyramid”<\/span><\/a><\/p>\n","protected":false},"author":10315341,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[10221],"tags":[7092,144718,832,63064],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/pFP3y-lT","jetpack-related-posts":[{"id":1327,"url":"https:\/\/blog.gocept.com\/2013\/07\/09\/monitoringlove-sprint-takeaway\/","url_meta":{"origin":1357,"position":0},"title":"#monitoringlove sprint takeaway","author":"Daniel Havlik","date":"July 9, 2013","format":false,"excerpt":"A few weeks ago I co-organised and participated in a #monitoringlove sprint in Berlin. 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