# Navigating the Mapper Screen

Abstract

SummaryDetailed explanation of the Mapper UI.

##### 1 Event type list

Lists all the event types received from the inputs. These event types may be from your inputs or they may have been added/modified by the Code Engine. Each event type is color-coded to indicate its status.

• Unmapped: None of the fields of this event type are mapped.

• Mapped: All the fields of this event type are mapped, discarded, or are metadata.

Note: Metadata fields do not have to be mapped or discarded for an event type to be in the Mapped state.

• Partially Mapped: Some of the fields of this event type are still red, meaning unmapped.

• Discarded: This event type will be discarded by Alooma (not replicated into your target database).

Clicking on an event type in the list displays the number of events of this type that have been received by Alooma.

##### 2 Event fields

Lists the event fields in the event type’s JSON. These names may be from the input or they may have been added/modified by the Code Engine. The color of the dot next to each event field indicates its status.

• Unmapped: This event field has not been handled. You can map it or discard it.

# Note

Alooma lets you apply a mapping (go live) even if it still contains unmapped fields. Alooma then handles events that arrive with these unmapped fields according to the mapping configuration you define.

• Mapped: This event field is mapped to a column in the target table.

• Discarded: This event field will be discarded by Alooma (not replicated into the target).

• Invalid: This event field mapping is invalid. Hover over this event field to display the reason. You cannot apply the mapping until the invalid event fields have been resolved.

You can expand a dictionary field to see its sub-fields by clicking the caret next to the field name.

A variety of metadata fields are automatically added by Alooma to each event type according to the input, such as timestamps, source IPs and so on. Metadata fields do not have to be mapped or discarded - you can map them to your data destination or not as you see fit. Metadata also includes a field named event_type, which defines the event's event type, which you can modify in the Code Engine.

##### 3 Target column name

Lists the name of the column in the selected data destination’s table into which to replicate the event field. You can change this name by simply typing it, or if a table has already been selected, you can select a column name by typing any part of its name.

##### 4 Target data type

Lists the data types of each column in the target table into which to replicate each event field. When you use Alooma’s auto-mapping feature, the data type of structured data (such as MySQL, PostgreSQL or Salesforce) is automatically assigned according to the data source’s schema, and for unstructured data is set by Alooma’s data detection heuristics. You can modify the data type, if desired.

To modify the data type, simply select one of the options offered by Alooma in the drop-down menu.

# Note

You can only change the data type when defining a new table using Alooma, not when mapping to an existing table's column. If you need to alter a table, see Altering a Column via the Mapper.

##### 5 Event type field statistics

Provides insights describing the occurrence of the selected event field and its values – helping you make more informed mapping decisions.

• Occurrence: Some events only contain a subset of all event type fields. These statistics show the percentage of events that contain this field. Alooma also shows the breakdown by data type of the field’s values. For unstructured inputs, Alooma infers each field’s data type by the values that it contains. For example, in the image above 89.3% of the values have the data type number and 10.7% are null.

• Most Frequent Values: Lists the most frequently occurring data values in this field.

##### 6 Select table

Select an existing table or create a new one in the data destination. In the case where your data destination supports schemas (such as Redshift or Snowflake) or datasets (such as Google BigQuery), you'll see a schema or dataset drop-down before the table one, which defaults to the default schema or dataset you defined in your data destination connection.

Now that you know your way around the mapper screen, time to get mapping!