You can change the way that a string appears by using a mutate string processesor. For example, you can use the `uppercase_string` processor to convert a string to uppercase, and you can use the `lowercase_string` processor to convert a string to lowercase. The following is a list of processors that allow you to mutate a string:
* [substitute_string](#substitute_string)
* [split_string](#split_string)
* [uppercase_string](#uppercase_string)
* [lowercase_string](#lowercase_string)
* [trim_string](#trim_string)
## substitute_string
The `substitute_string` processor matches a key's value against a regular expression (regex) and replaces all returned matches with a replacement string.
### Configuration
You can configure the `substitute_string` processor with the following options.
Option | Required | Description
:--- | :--- | :---
`entries` | Yes | A list of entries to add to an event. |
`source` | Yes | The key to be modified. |
`from` | Yes | The regex string to be replaced. Special regex characters such as `[` and `]` must be escaped using `\\` when using double quotes and `\` when using single quotes. For more information, see [Class Pattern](https://docs.oracle.com/en/java/javase/17/docs/api/java.base/java/util/regex/Pattern.html) in the Java documentation. |
`to` | Yes | The string that replaces each match of `from`. |
### Usage
To get started, create the following `pipeline.yaml` file:
```yaml
pipeline:
source:
file:
path: "/full/path/to/logs_json.log"
record_type: "event"
format: "json"
processor:
- substitute_string:
entries:
- source: "message"
from: ":"
to: "-"
sink:
- stdout:
```
{% include copy.html %}
Next, create a log file named `logs_json.log`. After that, replace the `path` of the file source in your `pipeline.yaml` file with your file path. For more detailed information, see [Configuring Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/#2-configuring-data-prepper).
Before you run Data Prepper, the source appears in the following format:
```json
{"message": "ab:cd:ab:cd"}
```
After you run Data Prepper, the source is converted to the following format:
```json
{"message": "ab-cd-ab-cd"}
```
`from` defines which string is replaced, and `to` defines the string that replaces the `from` string. In the preceding example, string `ab:cd:ab:cd` becomes `ab-cd-ab-cd`. If the `from` regex string does not return a match, the key is returned without any changes.
## split_string
The `split_string` processor splits a field into an array using a delimiter character.
### Configuration
You can configure the `split_string` processor with the following options.
Option | Required | Description
:--- | :--- | :---
`entries` | Yes | A list of entries to add to an event. |
`source` | Yes | The key to be split. |
`delimiter` | No | The separator character responsible for the split. Cannot be defined at the same time as `delimiter_regex`. At least `delimiter` or `delimiter_regex` must be defined. |
`delimiter_regex` | No | A regex string responsible for the split. Cannot be defined at the same time as `delimiter`. Either `delimiter` or `delimiter_regex` must be defined. |
### Usage
To get started, create the following `pipeline.yaml` file:
```yaml
pipeline:
source:
file:
path: "/full/path/to/logs_json.log"
record_type: "event"
format: "json"
processor:
- split_string:
entries:
- source: "message"
delimiter: ","
sink:
- stdout:
```
{% include copy.html %}
Next, create a log file named `logs_json.log`. After that, replace the `path` in the file source of your `pipeline.yaml` file with your file path. For more detailed information, see [Configuring Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/#2-configuring-data-prepper).
Before you run Data Prepper, the source appears in the following format:
```json
{"message": "hello,world"}
```
After you run Data Prepper, the source is converted to the following format:
```json
{"message":["hello","world"]}
```
## uppercase_string
The `uppercase_string` processor converts the value (a string) of a key from its current case to uppercase.
### Configuration
You can configure the `uppercase_string` processor with the following options.
Option | Required | Description
:--- | :--- | :---
`with_keys` | Yes | A list of keys to convert to uppercase. |
### Usage
To get started, create the following `pipeline.yaml` file:
```yaml
pipeline:
source:
file:
path: "/full/path/to/logs_json.log"
record_type: "event"
format: "json"
processor:
- uppercase_string:
with_keys:
- "uppercaseField"
sink:
- stdout:
```
{% include copy.html %}
Next, create a log file named `logs_json.log`. After that, replace the `path` in the file source of your `pipeline.yaml` file with the correct file path. For more detailed information, see [Configuring Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/#2-configuring-data-prepper).
Before you run Data Prepper, the source appears in the following format:
```json
{"uppercaseField": "hello"}
```
After you run Data Prepper, the source is converted to the following format:
```json
{"uppercaseField": "HELLO"}
```
## lowercase_string
The `lowercase string` processor converts a string to lowercase.
### Configuration
You can configure the `lowercase string` processor with the following options.
Option | Required | Description
:--- | :--- | :---
`with_keys` | Yes | A list of keys to convert to lowercase. |
### Usage
To get started, create the following `pipeline.yaml` file:
```yaml
pipeline:
source:
file:
path: "/full/path/to/logs_json.log"
record_type: "event"
format: "json"
processor:
- lowercase_string:
with_keys:
- "lowercaseField"
sink:
- stdout:
```
{% include copy.html %}
Next, create a log file named `logs_json.log`. After that, replace the `path` in the file source of your `pipeline.yaml` file with the correct file path. For more detailed information, see [Configuring Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/#2-configuring-data-prepper).
Before you run Data Prepper, the source appears in the following format:
```json
{"lowercaseField": "TESTmeSSage"}
```
After you run Data Prepper, the source is converted to the following format:
```json
{"lowercaseField": "testmessage"}
```
## trim_string
The `trim_string` processor removes whitespace from the beginning and end of a key.
### Configuration
You can configure the `trim_string` processor with the following options.
Option | Required | Description
:--- | :--- | :---
`with_keys` | Yes | A list of keys from which to trim the whitespace. |
### Usage
To get started, create the following `pipeline.yaml` file:
```yaml
pipeline:
source:
file:
path: "/full/path/to/logs_json.log"
record_type: "event"
format: "json"
processor:
- trim_string:
with_keys:
- "trimField"
sink:
- stdout:
```
{% include copy.html %}
Next, create a log file named `logs_json.log`. After that, replace the `path` in the file source of your `pipeline.yaml` file with the correct file path. For more detailed information, see [Configuring Data Prepper]({{site.url}}{{site.baseurl}}/data-prepper/getting-started/#2-configuring-data-prepper).
Before you run Data Prepper, the source appears in the following format:
```json
{"trimField": " Space Ship "}
```
After you run Data Prepper, the source is converted to the following format:
