Description:

Reads XML content and creates Record objects. Records are expected in the second level of XML data, embedded in an enclosing root tag.

Tags:

xml, record, reader, parser

Properties:

In the list below, the names of required properties appear in bold. Any other properties (not in bold) are considered optional. The table also indicates any default values, and whether a property supports the Expression Language Guide.

Name	Default Value	Allowable Values	Description
Schema Access Strategy	infer-schema	* Use 'Schema Name' Property  * Use 'Schema Text' Property  * HWX Schema Reference Attributes  * HWX Content-Encoded Schema Reference  * Confluent Content-Encoded Schema Reference  * Infer Schema	Specifies how to obtain the schema that is to be used for interpreting the data.
Schema Registry		Controller Service API:  SchemaRegistry Implementations:  ConfluentSchemaRegistry HortonworksSchemaRegistry	Specifies the Controller Service to use for the Schema Registry
Schema Name	${schema.name}		Specifies the name of the schema to lookup in the Schema Registry property Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Schema Version			Specifies the version of the schema to lookup in the Schema Registry. If not specified then the latest version of the schema will be retrieved. Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Schema Branch			Specifies the name of the branch to use when looking up the schema in the Schema Registry property. If the chosen Schema Registry does not support branching, this value will be ignored. Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Schema Text	${avro.schema}		The text of an Avro-formatted Schema Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Schema Inference Cache		Controller Service API:  RecordSchemaCacheService Implementation:  VolatileSchemaCache	Specifies a Schema Cache to use when inferring the schema. If not populated, the schema will be inferred each time. However, if a cache is specified, the cache will first be consulted and if the applicable schema can be found, it will be used instead of inferring the schema.
Expect Records as Array	false	* false  * true  * Use attribute 'xml.stream.is.array'	This property defines whether the reader expects a FlowFile to consist of a single Record or a series of Records with a "wrapper element". Because XML does not provide for a way to read a series of XML documents from a stream directly, it is common to combine many XML documents by concatenating them and then wrapping the entire XML blob with a "wrapper element". This property dictates whether the reader expects a FlowFile to consist of a single Record or a series of Records with a "wrapper element" that will be ignored.
Attribute Prefix			If this property is set, the name of attributes will be prepended with a prefix when they are added to a record. Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Field Name for Content			If tags with content (e. g. <field>content</field>) are defined as nested records in the schema, the name of the tag will be used as name for the record and the value of this property will be used as name for the field. If tags with content shall be parsed together with attributes (e. g. <field attribute="123">content</field>), they have to be defined as records. For additional information, see the section of processor usage. Supports Expression Language: true (will be evaluated using flow file attributes and variable registry)
Date Format			Specifies the format to use when reading/writing Date fields. If not specified, Date fields will be assumed to be number of milliseconds since epoch (Midnight, Jan 1, 1970 GMT). If specified, the value must match the Java Simple Date Format (for example, MM/dd/yyyy for a two-digit month, followed by a two-digit day, followed by a four-digit year, all separated by '/' characters, as in 01/01/2017).
Time Format			Specifies the format to use when reading/writing Time fields. If not specified, Time fields will be assumed to be number of milliseconds since epoch (Midnight, Jan 1, 1970 GMT). If specified, the value must match the Java Simple Date Format (for example, HH:mm:ss for a two-digit hour in 24-hour format, followed by a two-digit minute, followed by a two-digit second, all separated by ':' characters, as in 18:04:15).
Timestamp Format			Specifies the format to use when reading/writing Timestamp fields. If not specified, Timestamp fields will be assumed to be number of milliseconds since epoch (Midnight, Jan 1, 1970 GMT). If specified, the value must match the Java Simple Date Format (for example, MM/dd/yyyy HH:mm:ss for a two-digit month, followed by a two-digit day, followed by a four-digit year, all separated by '/' characters; and then followed by a two-digit hour in 24-hour format, followed by a two-digit minute, followed by a two-digit second, all separated by ':' characters, as in 01/01/2017 18:04:15).

State management:

This component does not store state.

Restricted:

This component is not restricted.

Summary:

The XMLReader Controller Service reads XML content and creates Record objects. The Controller Service must be configured with a schema that describes the structure of the XML data. Fields in the XML data that are not defined in the schema will be skipped. Depending on whether the property “Expect Records as Array” is set to “false” or “true”, the reader either expects a single record or an array of records for each FlowFile.

Example: Single record

            <record>
              <field1>content</field1>
              <field2>content</field2>
            </record>

An array of records has to be enclosed by a root tag. Example: Array of records
<root>
<record>
<field1>content</field1>
<field2>content</field2>
</record>
<record>
<field1>content</field1>
<field2>content</field2>
</record>
</root>

Example: Simple Fields

The simplest kind of data within XML data are tags / fields only containing content (no attributes, no embedded tags). They can be described in the schema by simple types (e. g. INT, STRING, …).
<root>
<record>
<simple_field>content</simple_field>
</record>
</root>

This record can be described by a schema containing one field (e. g. of type string). By providing this schema, the reader expects zero or one occurrences of “simple_field” in the record.
{
“namespace”: “nifi”,
“name”: “test”,
“type”: “record”,
“fields”: [
{ “name”: “simple_field”, “type”: “string” }
]
}

Example: Arrays with Simple Fields

Arrays are considered as repetitive tags / fields in XML data. For the following XML data, “array_field” is considered to be an array enclosing simple fields, whereas “simple_field” is considered to be a simple field not enclosed in an array.
<record>
<array_field>content</array_field>
<array_field>content</array_field>
<simple_field>content</simple_field>
</record>

This record can be described by the following schema:
{
“namespace”: “nifi”,
“name”: “test”,
“type”: “record”,
“fields”: [
{ “name”: “array_field”, “type”:
{ “type”: “array”, “items”: “string” }
},
{ “name”: “simple_field”, “type”: “string” }
]
}

If a field in a schema is embedded in an array, the reader expects zero, one or more occurrences of the field in a record. The field “array_field” principally also could be defined as a simple field, but then the second occurrence of this field would replace the first in the record object. Moreover, the field “simple_field” could also be defined as an array. In this case, the reader would put it into the record object as an array with one element.

Example: Tags with Attributes

XML fields frequently not only contain content, but also attributes. The following record contains a field with an attribute “attr” and content:
<record>
<field_with_attribute attr=”attr_content”>content of field</field_with_attribute>
</record>

To parse the content of the field “field_with_attribute” together with the attribute “attr”, two requirements have to be fulfilled:

• In the schema, the field has to be defined as record.
• The property “Field Name for Content” has to be set.
• As an option, the property “Attribute Prefix” also can be set.
  For the example above, the following property settings are assumed:

Property Name	Property Value
Field Name for Content	field_name_for_content
Attribute Prefix	prefix_

The schema can be defined as follows:
{
“name”: “test”,
“namespace”: “nifi”,
“type”: “record”,
“fields”: [
{
“name”: “field_with_attribute”,
“type”: {
“name”: “RecordForTag”,
“type”: “record”,
“fields” : [
{“name”: “attr”, “type”: “string”},
{“name”: “field_name_for_content”, “type”: “string”}
]
}
]
}

Note that the field “field_name_for_content” not only has to be defined in the property section, but also in the schema, whereas the prefix for attributes is not part of the schema. It will be appended when an attribute named “attr” is found at the respective position in the XML data and added to the record. The record object of the above example will be structured as follows:
Record (
Record “field_with_attribute” (
RecordField “prefix_attr” = “attr_content”,
RecordField “field_name_for_content” = “content of field”
)
)

Principally, the field “field_with_attribute” could also be defined as a simple field. In this case, the attributes simply would be ignored. Vice versa, the simple field in example 1 above could also be defined as a record (assuming that the property “Field Name for Content” is set.

Example: Tags within tags

XML data is frequently nested. In this case, tags enclose other tags:
<record>
<field_with_embedded_fields attr=”attr_content”>
<embedded_field>embedded content</embedded_field>
<another_embedded_field>another embedded content</another_embedded_field>
</field_with_embedded_fields>
</record>

The enclosing fields always have to be defined as records, irrespective whether they include attributes to be parsed or not. In this example, the tag “field_with_embedded_fields” encloses the fields “embedded_field” and “another_embedded_field”, which are both simple fields. The schema can be defined as follows:
{
“name”: “test”,
“namespace”: “nifi”,
“type”: “record”,
“fields”: [
{
“name”: “field_with_embedded_fields”,
“type”: {
“name”: “RecordForEmbedded”,
“type”: “record”,
“fields” : [
{“name”: “attr”, “type”: “string”},
{“name”: “embedded_field”, “type”: “string”},
{“name”: “another_embedded_field”, “type”: “string”}
]
}
]
}

Notice that this case does not require the property “Field Name for Content” to be set as this is only required for tags containing attributes and content.

Example: Array of records

For further explanation of the logic of this reader, an example of an array of records shall be demonstrated. The following record contains the field “array_field”, which repeatedly occurs. The field contains two embedded fields.
<record>
<array_field>
<embedded_field>embedded content 1</embedded_field>
<another_embedded_field>another embedded content 1</another_embedded_field>
</array_field>
<array_field>
<embedded_field>embedded content 2</embedded_field>
<another_embedded_field>another embedded content 2</another_embedded_field>
</array_field>
</record>

This XML data can be parsed similarly to the data in example 4. However, the record defined in the schema of example 4 has to be embedded in an array.
{
“namespace”: “nifi”,
“name”: “test”,
“type”: “record”,
“fields”: [
{ “name”: “array_field”,
“type”: {
“type”: “array”,
“items”: {
“name”: “RecordInArray”,
“type”: “record”,
“fields” : [
{“name”: “embedded_field”, “type”: “string”},
{“name”: “another_embedded_field”, “type”: “string”}
]
}
}
}
]
}

Example: Array in record

In XML data, arrays are frequently enclosed by tags:
<record>
<field_enclosing_array>
<element>content 1</element>
<element>content 2</element>
</field_enclosing_array>
<field_without_array> content 3</field_without_array>
</record>

For the schema, embedded tags have to be described by records. Therefore, the field “field_enclosing_array” is a record that embeds an array with elements of type string:
{
“namespace”: “nifi”,
“name”: “test”,
“type”: “record”,
“fields”: [
{ “name”: “field_enclosing_array”,
“type”: {
“name”: “EmbeddedRecord”,
“type”: “record”,
“fields” : [
{
“name”: “element”,
“type”: {
“type”: “array”,
“items”: “string”
}
}
]
}
},
{ “name”: “field_without_array”, “type”: “string” }
]
}

Example: Maps

A map is a field embedding fields with different names:
<record>
<map_field>
<field1>content</field1>
<field2>content</field2>
…
</map_field>
<simple_field>content</simple_field>
</record>

This data can be processed using the following schema:
{
“namespace”: “nifi”,
“name”: “test”,
“type”: “record”,
“fields”: [
{ “name”: “map_field”, “type”:
{ “type”: “map”, “items”: string }
},
{ “name”: “simple_field”, “type”: “string” }
]
}

Schema Inference

While NiFi’s Record API does require that each Record have a schema, it is often convenient to infer the schema based on the values in the data, rather than having to manually create a schema. This is accomplished by selecting a value of “Infer Schema” for the “Schema Access Strategy” property. When using this strategy, the Reader will determine the schema by first parsing all data in the FlowFile, keeping track of all fields that it has encountered and the type of each field. Once all data has been parsed, a schema is formed that encompasses all fields that have been encountered.
A common concern when inferring schemas is how to handle the condition of two values that have different types. For example, consider a FlowFile with the following two records:

    John
    8
    N/A


    Jane
    Ten
    8
    Ten

It is clear that the “name” field will be inferred as a STRING type. However, how should we handle the “age” field? Should the field be an CHOICE between INT and STRING? Should we prefer LONG over INT? Should we just use a STRING? Should the field be considered nullable?

To help understand how this Record Reader infers schemas, we have the following list of rules that are followed in the inference logic:

• All fields are inferred to be nullable.
• When two values are encountered for the same field in two different records (or two values are encountered for an ARRAY type), the inference engine prefers to use a “wider” data type over using a CHOICE data type. A data type “A” is said to be wider than data type “B” if and only if data type “A” encompasses all values of “B” in addition to other values. For example, the LONG type is wider than the INT type but not wider than the BOOLEAN type (and BOOLEAN is also not wider than LONG). INT is wider than SHORT. The STRING type is considered wider than all other types with the Exception of MAP, RECORD, ARRAY, and CHOICE.
• If two values are encountered for the same field in two different records (or two values are encountered for an ARRAY type), but neither value is of a type that is wider than the other, then a CHOICE type is used. In the example above, the “values” field will be inferred as a CHOICE between a STRING or an ARRRAY<STRING>.
• If the “Time Format,” “Timestamp Format,” or “Date Format” properties are configured, any value that would otherwise be considered a STRING type is first checked against the configured formats to see if it matches any of them. If the value matches the Timestamp Format, the value is considered a Timestamp field. If it matches the Date Format, it is considered a Date field. If it matches the Time Format, it is considered a Time field. In the unlikely event that the value matches more than one of the configured formats, they will be matched in the order: Timestamp, Date, Time. I.e., if a value matched both the Timestamp Format and the Date Format, the type that is inferred will be Timestamp. Because parsing dates and times can be expensive, it is advisable not to configure these formats if dates, times, and timestamps are not expected, or if processing the data as a STRING is acceptable. For use cases when this is important, though, the inference engine is intelligent enough to optimize the parsing by first checking several very cheap conditions. For example, the string’s length is examined to see if it is too long or too short to match the pattern. This results in far more efficient processing than would result if attempting to parse each string value as a timestamp.
• The MAP type is never inferred. Instead, the RECORD type is used.
• If two elements exist with the same name and the same parent (i.e., two sibling elements have the same name), the field will be inferred to be of type ARRAY.
• If a field exists but all values are null, then the field is inferred to be of type STRING.

Caching of Inferred Schemas

This Record Reader requires that if a schema is to be inferred, that all records be read in order to ensure that the schema that gets inferred is applicable for all records in the FlowFile. However, this can become expensive, especially if the data undergoes many different transformations. To alleviate the cost of inferring schemas, the Record Reader can be configured with a “Schema Inference Cache” by populating the property with that name. This is a Controller Service that can be shared by Record Readers and Record Writers.
Whenever a Record Writer is used to write data, if it is configured with a “Schema Cache,” it will also add the schema to the Schema Cache. This will result in an identifier for that schema being added as an attribute to the FlowFile.
Whenever a Record Reader is used to read data, if it is configured with a “Schema Inference Cache”, it will first look for a “schema.cache.identifier” attribute on the FlowFile. If the attribute exists, it will use the value of that attribute to lookup the schema in the schema cache. If it is able to find a schema in the cache with that identifier, then it will use that schema instead of reading, parsing, and analyzing the data to infer the schema. If the attribute is not available on the FlowFile, or if the attribute is available but the cache does not have a schema with that identifier, then the Record Reader will proceed to infer the schema as described above.
The end result is that users are able to chain together many different Processors to operate on Record-oriented data. Typically, only the first such Processor in the chain will incur the “penalty” of inferring the schema. For all other Processors in the chain, the Record Reader is able to simply lookup the schema in the Schema Cache by identifier. This allows the Record Reader to infer a schema accurately, since it is inferred based on all data in the FlowFile, and still allows this to happen efficiently since the schema will typically only be inferred once, regardless of how many Processors handle the data.