{"action":"created","comment":{"url":"https://api.github.com/repos/elastic/stack-docs/pulls/comments/299146573","pull_request_review_id":256471045,"id":299146573,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDI5OTE0NjU3Mw==","diff_hunk":"@@ -0,0 +1,333 @@\n+[role=\"xpack\"]\n+[testenv=\"basic\"]\n+[[dataframe-examples]]\n+== {dataframe-cap} examples\n+++++\n+<titleabbrev>Examples</titleabbrev>\n+++++\n+\n+beta[]\n+\n+These examples demonstrate how to use {dataframe-transforms} to derive useful \n+insights from your data. All the examples use one of the \n+{kibana-ref}/add-sample-data.html[{kib} sample datasets]. For a more detailed, \n+step-by-step example, see \n+<<ecommerce-dataframes,Transforming your data with {dataframes}>>.\n+\n+* <<example-best-customers>>\n+* <<example-airline>>\n+* <<example-clientips>>\n+\n+[float]\n+[[example-best-customers]]\n+=== Finding your best customers\n+\n+In this example, we use the eCommerce orders sample dataset to find the customers \n+who spent the most in our hypothetical webshop. Let's transform the data such \n+that the destination index contains the number of orders, the total price of \n+the orders, the amount of unique products and the average price per order, \n+and the total amount of ordered products for each customer.\n+\n+[source,js]\n+----------------------------------\n+POST _data_frame/transforms/_preview\n+{\n+  \"source\": {\n+    \"index\": \"kibana_sample_data_ecommerce\"\n+  },\n+  \"dest\" : {\n+    \"index\" : \"sample_ecommerce_orders_by_customer\"\n+  },\n+  \"pivot\": {\n+    \"group_by\": { <1>\n+      \"user\": { \"terms\": { \"field\": \"user\" }}, \n+      \"customer_id\": { \"terms\": { \"field\": \"customer_id\" }}\n+    },\n+    \"aggregations\": {\n+      \"order_count\": { \"value_count\": { \"field\": \"order_id\" }},\n+      \"total_order_amt\": { \"sum\": { \"field\": \"taxful_total_price\" }},\n+      \"avg_amt_per_order\": { \"avg\": { \"field\": \"taxful_total_price\" }},\n+      \"avg_unique_products_per_order\": { \"avg\": { \"field\": \"total_unique_products\" }},\n+      \"total_unique_products\": { \"cardinality\": { \"field\": \"products.product_id\" }}\n+    }\n+  }\n+}\n+----------------------------------\n+// CONSOLE\n+// TEST[skip:SETUP]\n+\n+<1> Two `group_by` fields have been selected. This means the {dataframe} will \n+contain a unique row per `user` and `customer_id` combination. Within this \n+dataset both these fields are unique. By including both in the {dataframe} it \n+gives more context to the final results.\n+\n+NOTE: In the example above, condensed JSON formatting has been used for easier \n+readability of the pivot object.\n+\n+The preview {dataframe-transforms} API enables you to see the layout of the \n+{dataframe} in advance, populated with some sample values. For example:\n+\n+[source,js]\n+----------------------------------\n+{\n+  \"preview\" : [\n+    {\n+      \"total_order_amt\" : 3946.9765625,\n+      \"order_count\" : 59.0,\n+      \"total_unique_products\" : 116.0,\n+      \"avg_unique_products_per_order\" : 2.0,\n+      \"customer_id\" : \"10\",\n+      \"user\" : \"recip\",\n+      \"avg_amt_per_order\" : 66.89790783898304\n+    },\n+    ...\n+    ]\n+  }\n+----------------------------------\n+// CONSOLE\n+// TEST[skip:SETUP]\n+\n+This {dataframe} makes it easier to answer questions such as:\n+\n+* Which customers spend the most?\n+\n+* Which customers spend the most per order?\n+\n+* Which customers order most often?\n+\n+* Which customers ordered the least number of different products?\n+\n+It's possible to answer these questions using aggregations alone, however \n+{dataframes} allow us to persist this data as a customer centric index. This \n+enables us to analyze data at scale and gives more flexibility to explore and \n+navigate data from a customer centric perspective. In some cases, it can even \n+make creating visualizations much simpler.\n+\n+[float]\n+[[example-airline]]\n+=== Finding air carriers with the most delays\n+\n+In this example, we use the Flights sample dataset to find out which air carrier \n+had the most delays. First, we filter the source data such that it excludes all \n+the cancelled flights by using a query filter. Then we transform the data to \n+contain the distinct number of flights, the sum of delayed minutes, and the sum \n+of the flight minutes by air carrier. Finally, we use a \n+{ref}/search-aggregations-pipeline-bucket-script-aggregation.html[`bucket_script`] \n+to determine what percentage of the flight time was actually delay.\n+\n+[source,js]\n+----------------------------------\n+POST _data_frame/transforms/_preview\n+{\n+  \"source\": {\n+    \"index\": \"kibana_sample_data_flights\",\n+    \"query\": { <1>\n+      \"bool\": {\n+        \"filter\": [\n+          { \"term\":  { \"Cancelled\": false } }\n+        ]\n+      }\n+    }\n+  },\n+  \"dest\" : {\n+    \"index\" : \"sample_flight_delays_by_carrier\"\n+  },\n+  \"pivot\": {\n+    \"group_by\": { <2>\n+      \"carrier\": { \"terms\": { \"field\": \"Carrier\" }}\n+    },\n+    \"aggregations\": {\n+      \"flights_count\": { \"value_count\": { \"field\": \"FlightNum\" }},\n+      \"delay_mins_total\": { \"sum\": { \"field\": \"FlightDelayMin\" }},\n+      \"flight_mins_total\": { \"sum\": { \"field\": \"FlightTimeMin\" }},\n+      \"delay_time_percentage\": { <3>\n+        \"bucket_script\": {\n+          \"buckets_path\": {\n+            \"delay_time\": \"delay_mins_total.value\",\n+            \"flight_time\": \"flight_mins_total.value\"\n+          },\n+          \"script\": \"(params.delay_time / params.flight_time) * 100\"\n+        }\n+      }\n+    }\n+  }\n+}\n+----------------------------------\n+// CONSOLE\n+// TEST[skip:SETUP]\n+\n+<1> Filter the source data to select only flights that were not cancelled.\n+<2> The data is grouped by the `Carrier` field which contains the airline name.\n+<3> This `bucket_script` performs calculations on the results that are returned \n+by the aggregation. In this particular example, it calculates what percentage of \n+travel time was taken up by delays.\n+\n+The preview shows you that the new index would contain data like this for each \n+carrier:\n+\n+[source,js]\n+----------------------------------\n+{\n+  \"preview\" : [\n+    {\n+      \"carrier\" : \"ES-Air\",\n+      \"flights_count\" : 2802.0,\n+      \"flight_mins_total\" : 1436927.5130677223,\n+      \"delay_time_percentage\" : 9.335543983955839,\n+      \"delay_mins_total\" : 134145.0\n+    },\n+    ...\n+  ]\n+}\n+----------------------------------\n+// CONSOLE\n+// TEST[skip:SETUP]\n+\n+This {dataframe} makes it easier to answer questions such as:\n+\n+* Which air carrier has the most delays as a percentage of flight time?\n+\n+NOTE: This data is fictional and does not reflect actual delays \n+or flight stats for any of the featured destination or origin airports.\n+\n+[float]\n+[[example-clientips]]\n+=== Finding suspicious client IPs by using scripted metrics\n+\n+With {dataframe-transforms}, you can use \n+{ref}/search-aggregations-metrics-scripted-metric-aggregation.html[scripted \n+metric aggregations] on your data. These aggregations are flexible and make \n+it possible to perform very complex processing. Let's use scripted metrics to \n+identify suspicious client IPs in the web log sample dataset.\n+\n+We transform the data such that the new index contains the sum of bytes and the \n+number of distinct URLs, agents, incoming requests by location, and geographic \n+destinations for each client IP. We also use a scripted field to count the \n+specific types of HTTP responses that each client IP receives. Ultimately, the \n+example below transforms web log data into an entity centric index where the \n+entity is `clientip`.\n+\n+[source,js]\n+----------------------------------\n+POST _data_frame/transforms/_preview\n+{\n+  \"source\": {\n+    \"index\": \"kibana_sample_data_logs\",\n+    \"query\": { <1>\n+      \"range\" : {\n+        \"timestamp\" : {\n+          \"gte\" : \"now-30d/d\"\n+        }\n+      }\n+    }\n+  },\n+  \"dest\" : {\n+    \"index\" : \"sample_weblogs_by_clientip\"\n+  },  \n+  \"pivot\": {\n+    \"group_by\": {  <2>\n+      \"clientip\": { \"terms\": { \"field\": \"clientip\" } }\n+      },\n+    \"aggregations\": {\n+      \"url_dc\": { \"cardinality\": { \"field\": \"url.keyword\" }},\n+      \"bytes_sum\": { \"sum\": { \"field\": \"bytes\" }},\n+      \"geo.src_dc\": { \"cardinality\": { \"field\": \"geo.src\" }},\n+      \"agent_dc\": { \"cardinality\": { \"field\": \"agent.keyword\" }},\n+      \"geo.dest_dc\": { \"cardinality\": { \"field\": \"geo.dest\" }},\n+      \"responses.total\": { \"value_count\": { \"field\": \"timestamp\" }},\n+      \"responses.counts\": { <3>\n+        \"scripted_metric\": { \n+          \"init_script\": \"state.responses = ['error':0L,'success':0L,'other':0L]\",\n+          \"map_script\": \"\"\"\n+            def code = doc['response.keyword'].value;\n+            if (code.startsWith('5') || code.startsWith('4')) {\n+              state.responses.error += 1 ;\n+            } else if(code.startsWith('2')) {\n+              state.responses.success += 1;\n+            } else {\n+              state.responses.other += 1;\n+            }\n+            \"\"\",\n+          \"combine_script\": \"state.responses\",\n+          \"reduce_script\": \"\"\"\n+            def counts = ['error': 0L, 'success': 0L, 'other': 0L];\n+            for (responses in states) {\n+              counts.error += responses['error'];\n+              counts.success += responses['success'];\n+              counts.other += responses['other'];\n+            }\n+            return counts;\n+            \"\"\"\n+          }\n+        },\n+      \"timestamp.min\": { \"min\": { \"field\": \"timestamp\" }},\n+      \"timestamp.max\": { \"max\": { \"field\": \"timestamp\" }},\n+      \"timestamp.duration_ms\": { <4>\n+        \"bucket_script\": {\n+          \"buckets_path\": {\n+            \"min_time\": \"timestamp.min.value\",\n+            \"max_time\": \"timestamp.max.value\"\n+          },\n+          \"script\": \"(params.max_time - params.min_time)\"\n+        }\n+      }\n+    }\n+  }\n+}\n+----------------------------------\n+// CONSOLE\n+// TEST[skip:SETUP]\n+\n+<1> This range query limits the transform to documents that are within the \n+last 30 days at the point in time the {dataframe-transform} is started.","path":"docs/en/stack/data-frames/dataframe-examples.asciidoc","position":282,"original_position":282,"commit_id":"d2e55c9e94becf16f85417bcaa59e09d5a14e71e","original_commit_id":"d2e55c9e94becf16f85417bcaa59e09d5a14e71e","user":{"login":"sophiec20","id":4185750,"node_id":"MDQ6VXNlcjQxODU3NTA=","avatar_url":"https://avatars2.githubusercontent.com/u/4185750?v=4","gravatar_id":"","url":"https://api.github.com/users/sophiec20","html_url":"https://github.com/sophiec20","followers_url":"https://api.github.com/users/sophiec20/followers","following_url":"https://api.github.com/users/sophiec20/following{/other_user}","gists_url":"https://api.github.com/users/sophiec20/gists{/gist_id}","starred_url":"https://api.github.com/users/sophiec20/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/sophiec20/subscriptions","organizations_url":"https://api.github.com/users/sophiec20/orgs","repos_url":"https://api.github.com/users/sophiec20/repos","events_url":"https://api.github.com/users/sophiec20/events{/privacy}","received_events_url":"https://api.github.com/users/sophiec20/received_events","type":"User","site_admin":false},"body":"The range query is evaluated as each checkpoint is processed. For batch data frames, there is only ever 1 checkpoint so this statement is true. However I think we should make this statement also cover continuous data frames which are coming soon. \r\n\r\nPerhaps something along the lines of...\r\n`This range query limits the transform to documents that are within the \r\nlast 30 days at the point in time the {dataframe-transform} checkpoint is processed. For batch data frames this occurs once.` ","created_at":"2019-07-01T17:32:58Z","updated_at":"2019-07-01T17:37:18Z","html_url":"https://github.com/elastic/stack-docs/pull/389#discussion_r299146573","pull_request_url":"https://api.github.com/repos/elastic/stack-docs/pulls/389","author_association":"NONE","_links":{"self":{"href":"https://api.github.com/repos/elastic/stack-docs/pulls/comments/299146573"},"html":{"href":"https://github.com/elastic/stack-docs/pull/389#discussion_r299146573"},"pull_request":{"href":"https://api.github.com/repos/elastic/stack-docs/pulls/389"}}},"pull_request":{"url":"https://api.github.com/repos/elastic/stack-docs/pulls/389","id":291989009,"node_id":"MDExOlB1bGxSZXF1ZXN0MjkxOTg5MDA5","html_url":"https://github.com/elastic/stack-docs/pull/389","diff_url":"https://github.com/elastic/stack-docs/pull/389.diff","patch_url":"https://github.com/elastic/stack-docs/pull/389.patch","issue_url":"https://api.github.com/repos/elastic/stack-docs/issues/389","number":389,"state":"open","locked":false,"title":"[DOCS] Creates a data frame examples page","user":{"login":"szabosteve","id":22324794,"node_id":"MDQ6VXNlcjIyMzI0Nzk0","avatar_url":"https://avatars3.githubusercontent.com/u/22324794?v=4","gravatar_id":"","url":"https://api.github.com/users/szabosteve","html_url":"https://github.com/szabosteve","followers_url":"https://api.github.com/users/szabosteve/followers","following_url":"https://api.github.com/users/szabosteve/following{/other_user}","gists_url":"https://api.github.com/users/szabosteve/gists{/gist_id}","starred_url":"https://api.github.com/users/szabosteve/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/szabosteve/subscriptions","organizations_url":"https://api.github.com/users/szabosteve/orgs","repos_url":"https://api.github.com/users/szabosteve/repos","events_url":"https://api.github.com/users/szabosteve/events{/privacy}","received_events_url":"https://api.github.com/users/szabosteve/received_events","type":"User","site_admin":false},"body":"This PR creates a data frame examples page under data-frames and indicates it in the index file. 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