Overview
Apache NiFi offers a very robust set of Processors that are capable of ingesting, processing, routing, transforming, and delivering data of any format. This is possible because the NiFi framework itself is data-agnostic. It doesn’t care whether your data is a 100-byte JSON message or a 100-gigabyte video. This is an incredibly powerful feature. However, there are many patterns that have quickly developed to handle data of differing types.
One class of data that is often processed by NiFi is record-oriented data. When we say record-oriented data, we are often (but not always) talking about structured data such as JSON, CSV, and Avro. There are many other types of data that can also be represented as "records" or "messages," though. As a result, a set of Controller Services have been developed for parsing these different data formats and representing the data in a consistent way by using the RecordReader API. This allows data that has been written in any data format to be treated the same, so long as there is a RecordReader that is capable of producing a Record object that represents the data.
When we talk about a Record, this is an abstraction that allows us to treat data in the same way, regardless of the format that it is in. A Record is made up of one or more Fields. Each Field has a name and a Type associated with it. The Fields of a Record are described using a Record Schema. The Schema indicates which fields make up a specific type of Record. The Type of a Field will be one of the following:
-
String
-
Boolean
-
Byte
-
Character
-
Short
-
Integer
-
Long
-
BigInt
-
Float
-
Double
-
Date - Represents a Date without a Time component
-
Time - Represents a Time of Day without a Date component
-
Timestamp - Represents a Date and Time
-
Embedded Record - Hierarchical data, such as JSON, can be represented by allowing a field to be of Type Record itself.
-
Choice - A field may be any one of several types.
-
Array - All elements of an array have the same type.
-
Map - All Map Keys are of type String. The Values are of the same type.
Once a stream of data has been converted into Records, the RecordWriter API allows us to then serialize those Records back into streams of bytes so that they can be passed onto other systems.
Of course, there’s not much point in reading and writing this data if we aren’t going to do something with the data in between. There are several processors that have already been developed for NiFi that provide some very powerful capabilities for routing, querying, and transforming Record-oriented data. Often times, in order to perform the desired function, a processor will need input from the user in order to determine which fields in a Record or which values in a Record should be operated on.
Enter the NiFi RecordPath language. RecordPath is intended to be a simple, easy-to-use Domain-Specific Language (DSL) to specify which fields in a Record we care about or want to access when configuring a processor.
Structure of a RecordPath
A Record in NiFi is made up of (potentially) many fields, and each of these fields could actually be itself a Record. This means that a Record can be thought of as having a hierarchical, or nested, structure. We talk about an "inner Record" as being the child of the "outer Record." The child of an inner Record, then, is a descendant of the outer-most Record. Similarly, we can refer to an outer Record as being an ancestor of an inner Record.
Child Operator
The RecordPath language is structured in such a way that we are able to easily reference fields of the outer-most Record, or fields of a
child Record, or descendant Record. To accomplish this, we separate the names of the children with a slash character (/
), which we
refer to as the child
operator. For example,
let’s assume that we have a Record that is made up of two fields: name
and details
. Also, assume that details
is a field that is
itself a Record and has two Fields: identifier
and address
. Further, let’s consider that address
is itself a Record that contains
5 fields: number
, street
, city
, state
, and zip
. An example, written here in JSON for illustrative purposes may look like this:
{ "name": "John Doe", "details": { "identifier": 100, "address": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" } } }
We can reference the zip
field by using the RecordPath: /details/address/zip
. This tells us that we want to use the details
field of
the "root" Record. We then want to reference the address
field of the child Record and the zip
field of that Record.
Descendant Operator
In addition to providing an explicit path to reach the zip
field, it may sometimes be useful to reference the zip
field without knowing
the full path. In such a case, we can use the descendant
operator (//
) instead of the child
operator (/
). To reach the same zip
field as above, we can accomplish this by simply using the path //zip
.
There is a very important distinction, though, between the child
operator and the descendant
operator: the descendant
operator may match
many fields, whereas the child
operator will match at most one field. To help understand this, consider the following Record:
{ "name": "John Doe", "workAddress": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" }, "homeAddress": { "number": "456", "street": "116th Avenue", "city": "New York", "state": "NY", "zip": "11697" } }
Now, if we use the RecordPath /workAddress/zip
, we will be referencing the zip
field that has a value of "10020." The RecordPath /homeAddress/zip
will
reference the zip
field that has a value of "11697." However, the RecordPath //zip
will reference both of these fields.
Filters
With the above examples and explanation, we are able to easily reference a specific field within a Record. However, in real scenarios, the data is rarely as
simple as in the examples above. Often times, we need to filter out or refine which fields we are referencing. Examples of when we might want to do this are
when we reference an Array field and want to only reference some of the elements in the array; when we reference a Map field and want to reference one or a few
specific entries in the Map; or when we want to reference a Record only if it adheres to some criteria. We can accomplish this by providing our criteria to the
RecordPath within square brackets (using the [
and ]
characters). We will go over each of these cases below.
Function Usage
In addition to retrieving a field from a Record, as outlined above in the Filters section, we sometimes need to refine which fields we want to select. Or we
may want to return a modified version of a field. To do this, we rely on functions. The syntax for a function is <function name> <open parenthesis> <args> <close parenthesis>,
where <args> represents one or more arguments separated by commas. An argument may be a string literal (such as 'hello'
) or a number literal (such as 48
), or could be
a relative or absolute RecordPath (such as ./name
or /id
). Additionally, we can use functions within a filter. For example, we could use a RecordPath such as
/person[ isEmpty('name') ]/id
to retrieve the id
field of any person whose name is empty. A listing of functions that are available and their corresponding documentation
can be found below in the Functions section.
Arrays
When we reference an Array field, the value of the field may be an array that contains several elements, but we may want only a few of those elements. For example, we may want to reference only the first element; only the last element; or perhaps the first, second, third, and last elements. We can reference a specific element simply by using the index of the element within square brackets (the index is 0-based). So let us consider a modified version of the Record above:
{ "name": "John Doe", "addresses": [ "work": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" }, "home": { "number": "456", "street": "116th Avenue", "city": "New York", "state": "NY", "zip": "11697" } ] }
We can now reference the first element in the addresses
array by using the RecordPath /addresses[0]
. We can access the second element using the RecordPath /addresses[1]
.
There may be times, though, that we don’t know how many elements will exist in the array. So we can use negative indices to count backward from the end of the array. For example,
we can access the last element as /addresses[-1]
or the next-to-last element as /addresses[-2]
. If we want to reference several elements, we can use a comma-separated list of
elements, such as /addresses[0, 1, 2, 3]
. Or, to access elements 0 through 8, we can use the range
operator (..
), as in /addresses[0..8]
. We can also mix these, and reference
all elements by using the syntax /addresses[0..-1]
or even /addresses[0, 1, 4, 6..-1]
. Of course, not all of the indices referenced here will match on the Record above, because
the addresses
array has only 2 elements. The indices that do not match will simply be skipped.
Maps
Similar to an Array field, a Map field may actually consist of several different values. RecordPath gives us the ability to select a set of values based on their keys. We do this by using a quoted String within square brackets. As an example, let’s re-visit our original Record from above:
{ "name": "John Doe", "details": { "identifier": 100, "address": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" } } }
Now, though, let’s consider that the Schema that is associated with the Record indicates that the address
field is not a Record but rather a Map
field.
In this case, if we attempt to reference the zip
using the RecordPath /details/address/zip
the RecordPath will not match because the address
field is not a Record
and therefore does not have any Child Record named zip
. Instead, it is a Map field with keys and values of type String.
Unfortunately, when looking at JSON this may seem a bit confusing because JSON does not truly have a Type system. When we convert the JSON into a Record object in order
to operate on the data, though, this distinction can be important.
In the case laid out above, we can still access the zip
field using RecordPath. We must now use the a slightly different syntax, though: /details/address['zip']
. This
is telling the RecordPath that we want to access the details
field at the highest level. We then want to access its address
field. Since the address
field is a Map
field we can use square brackets to indicate that we want to specify a Map Key, and we can then specify the key in quotes.
Further, we can select more than one Map Key, using a comma-separated list: /details/address['city', 'state', 'zip']
. We can also select all of the fields, if we want,
using the Wildcard operator (*
): /details/address[*]
. Map fields do not contain any sort of ordering, so it is not possible to reference the keys by numeric indices.
Predicates
Thus far, we have discussed two different types of filters. Each of them allows us to select one or more elements out from a field that allows for many values.
Often times, though, we need to apply a filter that allows us to restrict which Record fields are selected. For example, what if we want to select the zip
field but
only for an address
field where the state is not New York? The above examples do not give us any way to do this.
RecordPath provides the user the ability to specify a Predicate. A Predicate is simply a filter that can be applied to a field in order to determine whether or not the
field should be included in the results. Like other filters, a Predicate is specified within square brackets. The syntax of the Predicate is
<Relative RecordPath> <Operator> <Expression>
. The Relative RecordPath
works just like any other RecordPath but must start with a .
(to reference the current field)
or a ..
(to reference the current field’s parent) instead of a slash and references
fields relative to the field that the Predicate applies to. The Operator
must be one of:
-
Equals (
=
) -
Not Equal (
!=
) -
Greater Than (
>
) -
Greater Than or Equal To (
>=
) -
Less Than (
<
) -
Less Than or Equal To (
<=
)
The Expression
can be a literal value such as 50
or Hello
or can be another RecordPath.
To illustrate this, let’s take the following Record as an example:
{ "name": "John Doe", "workAddress": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" }, "homeAddress": { "number": "456", "street": "Grand St", "city": "Jersey City", "state": "NJ", "zip": "07304" }, "details": { "position": "Dataflow Engineer", "preferredState": "NY" } }
Now we can use a Predicate to choose only the fields where the state is not New York. For example, we can use /*[./state != 'NY']
. This will select any Record field
that has a state
field if the state does not have a value of "NY". Note that the details
Record will not be returned because it does not have a field named state
.
So in this example, the RecordPath will select only the homeAddress
field. Once we have selected that field, we can continue on with our RecordPath. As we stated
above, we can select the zip
field: /*[./state != 'NY']/zip
. This RecordPath will result in selecting the zip
field only from the homeAddress
field.
We can also compare the value in one field with the value in another field. For example, we can select the address that is in the person’s preferred state by using
the RecordPath /*[./state = /details/preferredState]
. In this example, this RecordPath will retrieve the workAddress
field because its state
field matches the
value of the preferredState
field.
Additionally, we can write a RecordPath that references the "city" field of any record whose state is "NJ" by using the parent operator (..
): /*/city[../state = 'NJ']
.
Functions
In the Function Usage section above, we describe how and why to use a function in RecordPath. Here, we will describe the different functions that are available,
what they do, and how they work. Functions can be divided into two groups: Standalone Functions, which can be the 'root' of a RecordPath, such as substringAfter( /name, ' ' )
and Filter Functions, which are to be used as a filter, such as /name[ contains('John') ]
. A Standalone Function can also be used within a filter but does not return a boolean
(true
or false
value) and therefore cannot itself be an entire filter. For example, we can use a path such as /name[ substringAfter(., ' ') = 'Doe']
but we cannot simply use
/name[ substringAfter(., ' ') ]
because doing so doesn’t really make sense, as filters must be boolean values.
Unless otherwise noted, all of the examples below are written to operate on the following Record:
{ "name": "John Doe", "workAddress": { "number": "123", "street": "5th Avenue", "city": "New York", "state": "NY", "zip": "10020" }, "homeAddress": { "number": "456", "street": "Grand St", "city": "Jersey City", "state": "NJ", "zip": "07304" }, "details": { "position": "Dataflow Engineer", "preferredState": "NY", "employer": "", "vehicle": null, "phrase": " " } }
Standalone Functions
substring
The substring function returns a portion of a String value. The function requires 3 arguments: The value to take a portion of, the 0-based start index (inclusive),
and the 0-based end index (exclusive). The start index and end index can be 0
to indicate the first character of a String, a positive integer to indicate the nth index
into the string, or a negative integer. If the value is a negative integer, say -n
, then this represents the n`th character for the end. A value of `-1
indicates the last
character in the String. So, for example, substring( 'hello world', 0, -1 )
means to take the string hello
, and return characters 0 through the last character, so the return
value will be hello world
.
RecordPath |
Return value |
|
John Doe |
|
John |
|
<empty string> |
|
John Doe |
|
<empty string> |
substringAfter
Returns the portion of a String value that occurs after the first occurrence of some other value.
RecordPath |
Return value |
|
Doe |
|
hn Doe |
|
John Doe |
|
John Doe |
substringAfterLast
Returns the portion of a String value that occurs after the last occurrence of some other value.
RecordPath |
Return value |
|
Doe |
|
e |
|
John Doe |
|
John Doe |
substringBefore
Returns the portion of a String value that occurs before the first occurrence of some other value.
RecordPath |
Return value |
|
John |
|
J |
|
John Doe |
|
John Doe |
substringBeforeLast
Returns the portion of a String value that occurs before the last occurrence of some other value.
RecordPath |
Return value |
|
John |
|
John D |
|
John Doe |
|
John Doe |
replace
Replaces all occurrences of a String with another String.
RecordPath |
Return value |
|
Jxhn Dxe |
|
Jxyzhn Dxyze |
|
John Doe |
|
John New York |
replaceRegex
Evaluates a Regular Expression against the contents of a String value and replaces any match with another value.
This function requires 3 arguments: the String to run the regular expression against, the regular expression to run,
and the replacement value. The replacement value may optionally use back-references, such as $1
and ${named_group}
RecordPath |
Return value |
|
Jxhn Dxe |
|
Jxyzhn Dxyze |
|
John Doe |
|
John New York |
|
Jxohn Dxoe |
|
Jxohn Dxoe |
concat
Concatenates all the arguments together.
RecordPath |
Return value |
|
John Doe lives in Jersey City |
join
Joins together multiple values with a separator.
RecordPath |
Return value |
|
123, 5th Avenue, New York, NY, 10020 |
anchored
Allows evaluating a RecordPath while anchoring the root context to a child record.
RecordPath |
Return value |
|
Jersey City |
Additionally, this can be used in conjunction with arrays. For example, if we have the following record:
{ "id": "1234", "elements": [{ "name": "book", "color": "red" }, { "name": "computer", "color": "black" }] }
We can evaluate hte following Record paths:
RecordPath |
Return value |
|
The array containing |
|
The array containing |
fieldName
Normally, when a path is given to a particular field in a Record, what is returned is the value of that field. It
can sometimes be useful, however, to obtain the name of the field instead of the value. To do this, we can use the
fieldName
function.
RecordPath |
Return value |
|
|
|
Jersey City |
In the above example, the first RecordPath returns two separate field names: "workAddress" and "homeAddress". The second
RecordPath, in contrast, returns the value of a "city" field and uses the fieldName
function as a predicate. The second
RecordPath finds a "city" field whose parent does not have a name that begins with "work". This means that it will return
the value of the "city" field whose parent is "homeAddress" but not the value of the "city" field whose parent is "workAddress".
toDate
Converts a String to a date. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" }, { "name": "eventDate", "type" : "string"} ] }
and a record such as:
{ "name" : "My Event", "eventDate" : "2017-10-20T00:00:00Z" }
The following record path would parse the eventDate field into a Date:
toDate( /eventDate, "yyyy-MM-dd’T’HH:mm:ss’Z'")
toDate( /eventDate, "yyyy-MM-dd’T’HH:mm:ss’Z'", "GMT+8:00")
toString
Converts a value to a String, using the given character set if the input type is "bytes". For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" }, { "name": "bytes", "type" : "bytes"} ] }
and a record such as:
{ "name" : "My Event", "bytes" : "Hello World!" }
The following record path would parse the bytes field into a String:
toString( /bytes, "UTF-8")
toBytes
Converts a String to byte[], using the given character set. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" }, { "name": "s", "type" : "string"} ] }
and a record such as:
{ "name" : "My Event", "s" : "Hello World!" }
The following record path would convert the String field into a byte array using UTF-16 encoding:
toBytes( /s, "UTF-16")
coalesce
Returns the first value from the given arguments that is non-null. For example, given a record such as:
{ "id": null, "name": "John Doe" }
The following record path would return "John Doe":
coalesce(/id, /name)
Given the record:
{ "id": "1234", "name": null }
The same record path would return "1234".
Given the record:
{ "id": null, "name": null }
The record path would return null
.
Given the record:
{ "id": "null", "name": "John Doe" }
The record path would return the String "null". Note here the very important difference in that the id
field does not have a null value but rather the value of the field is the literal string "null".
Given the record:
{ "name": null }
The record path would return null
. Given that the id
field is not present, it is treated as a null
value.
Given the record:
{ "id": "1234", "name": "John Doe" }
The record path would return "1234". However, the record path coalesce(/name, /id)
would return "John Doe" because
both fields given are non-null, so the coalesce
function returns the first value that is referenced in its arguments,
not the first value that is encountered in the Record itself.
format
Converts a Date to a String in the given format with an optional time zone. The function defaults to the system local time zone when the second argument is not provided.
The first argument to this function must be a Date or a Number, and the second argument must be a format String that follows the Java DateTimeFormatter, and the third argument, optional, must be a format String that either an abbreviation such as "PST", a full name such as "America/Los_Angeles", or a custom ID such as "GMT-8:00"
For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" }, { "name": "eventDate", "type" : { "type" : "long", "logicalType" : "timestamp-millis" } } ] }
and a record such as:
{ "name" : "My Event", "eventDate" : 1508457600000 }
The following record path expressions would format the date as a String:
RecordPath |
Return value |
|
2017-10-20T00:00:00Z |
|
2017-10-20 |
|
2017-10-20 08:00:00 +0800 |
|
2017-10-20 |
In the case where the field is declared as a String, the toDate function must be called before formatting.
For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" }, { "name": "eventDate", "type" : "string"} ] }
and a record such as:
{ "name" : "My Event", "eventDate" : "2017-10-20T00:00:00Z" }
The following record path expression would re-format the date String:
RecordPath |
Return value |
|
2017-10-20 |
trim
Removes whitespace from the start and end of a string.
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : " John Smith " }
The following record path expression would remove extraneous whitespace:
RecordPath |
Return value |
|
John Smith |
toUpperCase
Change the entire String to upper case
{ "type": "record", "name": "events", "fields": [ { "name": "fullName", "type": "string" } ] }
and a record such as:
{ "fullName" : "john smith" }
The following record path expression change lower case letters to upper case:
RecordPath |
Return value |
|
JOHN SMITH |
toLowerCase
Changes the entire string to lower case.
{ "type": "record", "name": "events", "fields": [ { "name": "message", "type": "string" } ] }
and a record such as:
{ "name" : "hEllO wORLd" }
The following record path expression change upper case letters to lower case:
RecordPath |
Return value |
|
hello world |
base64Encode
Converts a String or byte[] using Base64 encoding, using the UTF-8 character set. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : "John" }
The following record path expression would encode the String using Base64:
RecordPath |
Return value |
|
Sm9obg== |
base64Decode
Decodes a Base64-encoded String or byte[]. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : "Sm9obg==" }
The following record path expression would decode the String using Base64:
RecordPath |
Return value |
|
John |
escapeJson
JSON Stringifies a Record, Array or simple field (e.g. String), using the UTF-8 character set. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [{ "name": "person", "type": "record", "fields": [ { "name": "name", "type": "string" }, { "name": "age", "type": "int" } ] }] }
and a record such as:
{ "person": { "name" : "John", "age" : 30 } }
The following record path expression would convert the record into an escaped JSON String:
RecordPath |
Return value |
|
"{\"person\":{\"name\":\"John\",\"age\":30}}" |
|
"\"John\"" |
|
"30" |
unescapeJson
Converts a stringified JSON element to a Record, Array or simple field (e.g. String), using the UTF-8 character set.
This function takes up to three arguments:
-
The Record Path of the stringified JSON element to be parsed
-
(optional) whether to convert parsed JSON Objects from Maps to Records, default =
false
-
(optional) whether to recursively convert nested JSON Objects from Maps to Records, default =
false
Note: RecordSetWriters will not be able to serialise Maps, so fields may be omitted if not converted to Records.
For example, using unescapeJson
to replace the root ("/") of a Record in UpdateRecord
without using the Record conversion,
would result in an empty output because the RecordSetWriter is unable to match the resultant Map content to a RecordSchema.
For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [{ "name": "person", "type": "record", "fields": [ { "name": "name", "type": "string" }, { "name": "age", "type": "int" } ] }] }
and a record such as:
{ "json_str": "{\"person\":{\"name\":\"John\",\"age\":30}}" }
The following record path expression would populate the record with unescaped JSON fields:
RecordPath |
Return value |
|
{"person": {"name": "John", "age": 30}}" |
Given a record such as:
{ "json_str": "{\"name\":\"John\",\"age\":30}" }
The following record path expression would return:
RecordPath |
Return value |
|
{"name": "John", "age": 30} (as a Record) |
|
{"name"="John", "age"=30} (as a Map) |
|
{"name"="John", "age"=30} (as a Map) |
Given a record such as:
{ "json_str": "{\"name\":\"John\",\"age\":30,\"addresses\"[{\"address_1\": \"123 Fake Street\"}]}" }
The following record path expression would return:
RecordPath |
Return value |
|
{"name"="John", "age"=30, "addresses"=[{"address_1"="123 Fake Street"}]} (as a Map, with each entry in "addresses" as a Map) |
|
{"name": "John", "age": 30, "addresses"=[{"address_1"="123 Fake Street"}]} (as a Record, with each entry in "addresses" as a Map) |
|
{"name": "John", "age": 30, "addresses": [{"address_1": "123 Fake Street"}]} (as a Record, with each entry in "addresses" as a Record) |
Given a record such as:
{ "json_str": "\"John\"" }
The following record path expression would return:
RecordPath |
Return value |
|
"John" |
Note that the target schema must be pre-defined if the unescaped
JSON is to be set in a Record’s fields - Infer Schema will not currently do this automatically.
hash
Converts a String using a hash algorithm. For example, given a schema such as:
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : "John" }
The following record path expression would hash the String using one of these, [SHA-384, SHA-224, SHA-256, MD2, SHA, SHA-512, MD5] algorithms.
RecordPath |
Return value |
|
527bd5b5d689e2c32ae974c6229ff785 |
padLeft
Prepends characters to the input String until it reaches the desired length.
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : "john smith" }
The following record path expression would prepend '@' characters to the input String:
RecordPath |
Return value |
|
@@@@@john smith |
padRight
Appends characters to the input String until it reaches the desired length.
{ "type": "record", "name": "events", "fields": [ { "name": "name", "type": "string" } ] }
and a record such as:
{ "name" : "john smith" }
The following record path expression would append '@' characters to the input String:
RecordPath |
Return value |
|
john smith@@@@@ |
uuid5
Inserts a UUID v5 into the target field.
There are two ways to use this function: with or without a namespace. Given this schema:
{ "type": "record", "name": "events", "fields": [ { "name": "input", "type": "string" }, { "name": "id_ns", "type": "string" } ] }
and a record such as:
{ "input" : "john smith", "id_ns": "02b317d3-7fec-421a-89c5-3ad0eb83c79e" }
There are two options for using this function:
uuid5(/input)
uuid5(/input, /id_ns)
The first option will generate a simple UUID v5 that does not use a namespace in the generation process. The second will take the value of the supplied record path and use it as the namespace.
Please note that the namespace must always be a valid UUID string. An empty string, another data type, etc. will result in an error. This is by design because the most common use case for UUID v5 is to uniquely identify records across data sets.
count
Returns the count of the number of elements. This is commonly used in conjunction with arrays. For example, if we have the following record:
{ "id": "1234", "elements": [{ "name": "book", "color": "red" }, { "name": "computer", "color": "black" }] }
We could determine the number of elements
by using the count
function. Using:
count(/elements[*])
Would yield a value of 2
. We could also use this as a filter, such as:
/id[ count(/elements[*]) = 2 ]
Which will return the id
element with a value of 1234
.
mapOf
Creates a map of Strings with the given parameters. For example, if we have the following record:
{ "firstName": "John", "lastName": "Snow" }
We could use the UpdateRecord
processor with
/fullName => mapOf("firstName", /firstName, "lastName", /lastName)
And that would give us something like:
{ "firstName": "John", "lastName": "Snow", "fullName": {"firstName": "John", "lastName": "Snow"} }
This function requires an even number of arguments and the record paths must represent simple field values.
Filter Functions
contains
Returns true
if a String value contains the provided substring, false
otherwise
RecordPath |
Return value |
|
John Doe |
|
<returns no results> |
|
John Doe |
matchesRegex
Evaluates a Regular Expression against the contents of a String value and returns true
if the Regular Expression
exactly matches the String value, false
otherwise.
This function requires 2 arguments: the String to run the regular expression against, and the regular expression to run.
RecordPath |
Return value |
|
John Doe |
|
<returns no results> |
|
John Doe |
startsWith
Returns true
if a String value starts with the provided substring, false
otherwise
RecordPath |
Return value |
|
John Doe |
|
<returns no results> |
|
<returns no results> |
|
John Doe |
endsWith
Returns true
if a String value ends with the provided substring, false
otherwise
RecordPath |
Return value |
|
John Doe |
|
<returns no results> |
|
<returns no results> |
|
John Doe |
not
Inverts the value of the function or expression that is passed into the not
function.
RecordPath |
Return value |
|
John Doe |
|
John Doe |
|
<returns no results> |
isEmpty
Returns true
if the provided value is either null or is an empty string.
RecordPath |
Return value |
|
John Doe |
|
John Doe |
|
<returns no results> |
|
<returns no results> |
isBlank
Returns true
if the provided value is either null or is an empty string or a string that consists
only of white space (spaces, tabs, carriage returns, and new-line characters).
RecordPath |
Return value |
|
John Doe |
|
John Doe |
|
John Doe |
|
<returns no results> |