DOC-4568 added map transform example page

content/integrate/redis-data-integration/data-pipelines/transform-examples/map-example.md

@@ -0,0 +1,170 @@
+---
+Title: Restructure JSON or hash objects
+alwaysopen: false
+categories:
+- docs
+- integrate
+- rs
+- rdi
+description: null
+group: di
+linkTitle: Restructure objects
+summary: Redis Data Integration keeps Redis in sync with a primary database in near
+  real time.
+type: integration
+weight: 40
+---
+
+By default, RDI adds fields to
+[hash]({{< relref "/develop/data-types/hashes" >}}) or
+[JSON]({{< relref "/develop/data-types/json" >}}) objects in the target
+database that closely match the columns of the source table.
+The examples below show how you can create a completely new object structure
+from existing fields using the
+[`map`]({{< relref "/integrate/redis-data-integration/reference/data-transformation/map" >}})
+transformation.
+
+## Map to a new JSON structure
+
+The first
+[job file]({{< relref "/integrate/redis-data-integration/data-pipelines/data-pipelines#job-files" >}})
+example creates a new [JSON]({{< relref "/develop/data-types/json" >}})
+object structure to write to the target.
+The `source` section selects the `employee` table of the
+[`chinook`](https://github.com/Redislabs-Solution-Architects/rdi-quickstart-postgres)
+database (the optional `db` value here corresponds to the
+`sources.<source-name>.connection.database` value defined in
+[`config.yaml`]({{< relref "/integrate/redis-data-integration/data-pipelines/data-pipelines#the-configyaml-file" >}})).
+
+In the `transform` section, the `map` transformation uses a [JMESPath](https://jmespath.org/)
+expression to specify the new JSON format. (Note that the vertical bar "|" in the `expression`
+line indicates that the following indented lines should be interpreted as a single string.)
+The expression resembles JSON notation but with data values supplied from
+table fields and
+[JMESPath functions]({{< relref "/integrate/redis-data-integration/reference/jmespath-custom-functions" >}}).
+
+Here, we rename the
+`employeeid` field to `id` and create two nested objects for the `address`
+and `contact` information. The `name` field is the concatenation of the existing
+`firstname` and `lastname` fields, with `lastname` converted to uppercase.
+In the `contact` subobject, the `email` address is obfuscated slightly, using the
+`replace()` function to hide the '@' sign and dots.
+
+In the `output` section of the job file, we specify that we want to write
+to a JSON object with a custom key. Note that in the `output` section, you must refer to
+fields defined in the `map` transformation, so we use the new name `id`
+for the key instead of `employeeid`.
+
+The full example is shown below:
+
+```yaml
+source:
+  db: chinook
+  table: employee
+transform:
+  - uses: map
+    with:
+      expression: |
+        {
+          "id": employeeid,
+          "name": concat([firstname, ' ', upper(lastname)]),
+          "address": {
+            "street": address,
+            "city": city,
+            "state": state,
+            "postalCode": postalcode,
+            "country": country
+          },
+          "contact": {
+            "phone": phone,
+            "safeEmail": replace(replace(email, '@', '_at_'), '.', '_dot_')
+          }
+        }
+      language: jmespath
+output:
+  - uses: redis.write
+    with:
+      connection: target
+      data_type: json
+      key:
+        expression: concat(['emp:', id])
+        language: jmespath
+```
+
+If you query one of the new JSON objects, you see output like the following:
+
+```bash
+> JSON.GET emp:1 $
+"[{\"id\":1,\"name\":\"Andrew ADAMS\",\"address\":{\"street\":\"11120 Jasper Ave NW\",\"city\":\"Edmonton\",\"state\":\"AB\",\"postalCode\":\"T5K 2N1\",\"country\":\"Canada\"},\"contact\":{\"phone\":\"+1 (780) 428-9482\",\"safeEmail\":\"andrew_at_chinookcorp_dot_com\"}}]"
+```
+
+Formatted in the usual JSON style, the output looks like the sample below:
+
+```json
+{
+  "id": 1,
+  "name": "Andrew ADAMS",
+  "address": {
+    "street": "11120 Jasper Ave NW",
+    "city": "Edmonton",
+    "state": "AB",
+    "postalCode": "T5K 2N1",
+    "country": "Canada"
+  },
+  "contact": {
+    "phone": "+1 (780) 428-9482",
+    "safeEmail": "andrew_at_chinookcorp_dot_com"
+  }
+}
+```
+
+## Map to a hash structure
+
+This example creates a new [hash]({{< relref "/develop/data-types/hashes" >}})
+object structure for items from the `track` table. Here, the `map` transformation uses
+[SQL](https://en.wikipedia.org/wiki/SQL) for the expression because this is often
+more suitable for hashes or "flat"
+JSON objects without subobjects or arrays. The expression renames some of the fields.
+It also calculates more human-friendly representations for the track duration (originally
+stored in the `milliseconds` field) and the storage size (originally stored in the
+`bytes` field).
+
+The full example is shown below:
+
+```yaml
+source:
+  db: chinook
+  table: track
+transform:
+  - uses: map
+    with:
+      expression:
+          id: trackid
+          name: name
+          duration: concat(floor(milliseconds / 60000), ':', floor(mod(milliseconds / 1000, 60)))
+          storagesize: concat(round(bytes / 1048576.0, 2), 'MB')
+      language: sql
+output:
+  - uses: redis.write
+    with:
+      connection: target
+      data_type: hash
+      key:
+        expression: concat('track:', id)
+        language: sql
+```
+
+If you query the data for one of the `track` hash objects, you see output
+like the following:
+
+```bash
+> hgetall track:16
+1) "id"
+2) "16"
+3) "name"
+4) "Dog Eat Dog"
+5) "duration"
+6) "3:35.0"
+7) "storagesize"
+8) "6.71MB"
+```