With a RethinkDB query, you can’t do “count” and “sum” quite the same as you’d do in SQL.<\/p>\n
Since it uses a map-reduce style engine underneath, you set count to “1” on each of your rows and then add these up later:<\/p>\n
\nr.db('performance')\n .table('query_timings')\n .pluck('DbId')\n .group('DbId')\n .map(function(row) {\n return {\n DbId: row('DbId'),\n Duration: row('Duration'),\n count: 1\n }\n })\n .reduce(function(left, right) {\n var dur = left('Duration').add(right('Duration'));\n var cnt = left('count').add(right('count')); \n \n var avg = cnt > 0 ? dur \/ avg : 0;\n \n return {\n Duration: dur,\n count: cnt,\n avg: avg\n }\n })\n<\/pre>\nNote however, that with this implementation you’re computing the average over and over, which is not ideal. <\/p>\n
If you want to do “count distinct” you’d want to create a hashmap to track the distinct values:<\/p>\n
\nr.db('performance')\n .table('query_timings')\n .group('DbId')\n .map(function(row) {\n var IP = row('IP');\n \n var o = r.object(IP, 1);\n \n return {\n DbId: row('DbId'),\n IP: IP,\n o\n }\n })\n .reduce(function(left, right) {\n var qL = left('o');\n var qR = right('o');\n \n var m = qL.merge(qR);\n \n return {\n DbId: left('DbId'),\n IPs: m.keys(),\n found: m.keys().count(),\n o: m\n }\n })\n .ungroup()\n .map(function(group) {\n return {\n 'DbId': group('group'),\n 'found': group('reduction')('found')\n }\n })\n<\/pre>\n","protected":false},"excerpt":{"rendered":"With a RethinkDB query, you can’t do “count” and “sum” quite the same as you’d do in SQL. Since it uses a map-reduce style engine underneath, you set count to “1” on each of your rows and then add these up later: r.db(‘performance’) .table(‘query_timings’) .pluck(‘DbId’) .group(‘DbId’) .map(function(row) { return { DbId: row(‘DbId’), Duration: row(‘Duration’), count: … <\/p>\n