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The AWS Filter Enriches logs with AWS Metadata. Currently the plugin adds the EC2 instance ID and availability zone to log records. To use this plugin, you must be running in EC2 and have the instance metadata service enabled.
The plugin supports the following configuration parameters:
Note: If you run Fluent Bit in a container, you may have to use instance metadata v1. The plugin behaves the same regardless of which version is used.
Select or exclude records per patterns
The Grep Filter plugin allows you to match or exclude specific records based on regular expression patterns for values or nested values.
The plugin supports the following configuration parameters:
This plugin enables the Record Accessor feature to specify the KEY. Using the record accessor is suggested if you want to match values against nested values.
In order to start filtering records, you can run the filter from the command line or through the configuration file. The following example assumes that you have a file called lines.txt with the following content:
Note: using the command line mode need special attention to quote the regular expressions properly. It's suggested to use a configuration file.
The following command will load the tail plugin and read the content of lines.txt file. Then the grep filter will apply a regular expression rule over the log field (created by tail plugin) and only pass the records which field value starts with aa:
The filter allows to use multiple rules which are applied in order, you can have many Regex and Exclude entries as required.
If you want to match or exclude records based on nested values, you can use a Record Accessor format as the KEY name. Consider the following record example:
if you want to exclude records that match given nested field (for example kubernetes.labels.app), you can use the following rule:
The Parser Filter plugin allows for parsing fields in event records.
The plugin supports the following configuration parameters:
This is an example of parsing a record {"data":"100 0.5 true This is example"}.
The plugin needs a parser file which defines how to parse each field.
The path of the parser file should be written in configuration file under the [SERVICE] section.
The output is
You can see the records {"data":"100 0.5 true This is example"} are parsed.
By default, the parser plugin only keeps the parsed fields in its output.
If you enable Reserve_Data, all other fields are preserved:
This will produce the output:
If you enable Reserved_Data and Preserve_Key, the original key field will be preserved as well:
This will produce the following output:
Made for testing: make sure that your records contain the expected key and values
The expect filter plugin allows you to validate that records match certain criteria in their structure, like validating that a key exists or it has a specific value.
The following page just describes the configuration properties available, for a detailed explanation of its usage and use cases, please refer the following page:
The plugin supports the following configuration parameters:
As mentioned on top, refer to the following page for specific details of usage of this filter:
Fluent Bit Kubernetes Filter allows to enrich your log files with Kubernetes metadata.
When Fluent Bit is deployed in Kubernetes as a DaemonSet and configured to read the log files from the containers (using tail or systemd input plugins), this filter aims to perform the following operations:
Analyze the Tag and extract the following metadata:
Pod Name
Namespace
Container Name
Container ID
Query Kubernetes API Server to obtain extra metadata for the POD in question:
Pod ID
Labels
Annotations
The data is cached locally in memory and appended to each record.
The plugin supports the following configuration parameters:
Kubernetes Filter aims to provide several ways to process the data contained in the log key. The following explanation of the workflow assumes that your original Docker parser defined in parsers.conf is as follows:
Since Fluent Bit v1.2 we are not suggesting the use of decoders (Decode_Field_As) if you are using Elasticsearch database in the output to avoid data type conflicts.
To perform processing of the log key, it's mandatory to enable the Merge_Log configuration property in this filter, then the following processing order will be done:
If a Pod suggest a parser, the filter will use that parser to process the content of log.
If the option Merge_Parser was set and the Pod did not suggest a parser, process the log content using the suggested parser in the configuration.
If no Pod was suggested and no Merge_Parser is set, try to handle the content as JSON.
If log value processing fails, the value is untouched. The order above is not chained, meaning it's exclusive and the filter will try only one of the options above, not all of them.
A flexible feature of Fluent Bit Kubernetes filter is that allow Kubernetes Pods to suggest certain behaviors for the log processor pipeline when processing the records. At the moment it support:
Suggest a pre-defined parser
Request to exclude logs
The following annotations are available:
The following Pod definition runs a Pod that emits Apache logs to the standard output, in the Annotations it suggest that the data should be processed using the pre-defined parser called apache:
There are certain situations where the user would like to request that the log processor simply skip the logs from the Pod in question:
Note that the annotation value is boolean which can take a true or false and must be quoted.
Tail support Tags expansion, which means that if a tag have a star character (*), it will replace the value with the absolute path of the monitored file, so if you file name and path is:
then the Tag for every record of that file becomes:
note that slashes are replaced with dots.
Kubernetes Filter do not care from where the logs comes from, but it cares about the absolute name of the monitored file, because that information contains the pod name and namespace name that are used to retrieve associated metadata to the running Pod from the Kubernetes Master/API Server.
If you have large pod specifications (can be caused by large numbers of environment variables, etc.), be sure to increase the
Buffer_Sizeparameter of the kubernetes filter. If object sizes exceed this buffer, some metadata will fail to be injected to the logs.
If the configuration property Kube_Tag_Prefix was configured (available on Fluent Bit >= 1.1.x), it will use that value to remove the prefix that was appended to the Tag in the previous Input section. Note that the configuration property defaults to _kube._var.logs.containers. , so the previous Tag content will be transformed from:
to:
the transformation above do not modify the original Tag, just creates a new representation for the filter to perform metadata lookup.
that new value is used by the filter to lookup the pod name and namespace, for that purpose it uses an internal Regular expression:
Under certain and not common conditions, a user would want to alter that hard-coded regular expression, for that purpose the option Regex_Parser can be used (documented on top).
So at this point the filter is able to gather the values of pod_name and namespace, with that information it will check in the local cache (internal hash table) if some metadata for that key pair exists, if so, it will enrich the record with the metadata value, otherwise it will connect to the Kubernetes Master/API Server and retrieve that information.
Kubernetes Filter depends on either or input plugins to process and enrich records with Kubernetes metadata. Here we will explain the workflow of Tail and how it configuration is correlated with Kubernetes filter. Consider the following configuration example (just for demo purposes, not production):
In the input section, the plugin will monitor all files ending in .log in path /var/log/containers/. For every file it will read every line and apply the docker parser. Then the records are emitted to the next step with an expanded tag.
When runs, it will try to match all records that starts with kube. (note the ending dot), so records from the file mentioned above will hit the matching rule and the filter will try to enrich the records
If you want to know more details, check the source code of that definition .
You can see on web site how this operation is performed, check the following demo link:
Key
Description
Default
imds_version
Specify which version of the instance metadata service to use. Valid values are 'v1' or 'v2'.
v2
az
The availability zone; for example, "us-east-1a".
true
ec2_instance_id
The EC2 instance ID.
true
ec2_instance_type
The EC2 instance type.
false
private_ip
The EC2 instance private ip.
false
ami_id
The EC2 instance image id.
false
account_id
The account ID for current EC2 instance.
false
hostname
The hostname for current EC2 instance.
false
vpc_id
The VPC ID for current EC2 instance.
false
Key
Value Format
Description
Regex
KEY REGEX
Keep records in which the content of KEY matches the regular expression.
Exclude
KEY REGEX
Exclude records in which the content of KEY matches the regular expression.
Annotation | Description | Default |
fluentbit.io/parser[_stream][-container] | Suggest a pre-defined parser. The parser must be registered already by Fluent Bit. This option will only be processed if Fluent Bit configuration (Kubernetes Filter) have enabled the option K8S-Logging.Parser. If present, the stream (stdout or stderr) will restrict that specific stream. If present, the container can override a specific container in a Pod. |
fluentbit.io/exclude[_stream][-container] | Request to Fluent Bit to exclude or not the logs generated by the Pod. This option will only be processed if Fluent Bit configuration (Kubernetes Filter) have enabled the option K8S-Logging.Exclude. | False |
Property | Description |
key_exists | Check if a key with a given name exists in the record. |
key_not_exists | Check if a key does not exist in the record. |
key_val_is_null | check that the value of the key is NULL. |
key_val_is_not_null | check that the value of the key is NOT NULL. |
key_val_eq | check that the value of the key equals the given value in the configuration. |
action | action to take when a rule does not match. The available options are |
Key | Description | Default |
Key_Name | Specify field name in record to parse. |
Parser | Specify the parser name to interpret the field. Multiple Parser entries are allowed (one per line). |
Preserve_Key | Keep original | False |
Reserve_Data | Keep all other original fields in the parsed result. If false, all other original fields will be removed. | False |
Unescape_Key | If the key is a escaped string (e.g: stringify JSON), unescape the string before to apply the parser. | False |
Key | Description | Default |
Buffer_Size | 32k |
Kube_URL | API Server end-point |
Kube_CA_File | CA certificate file | /var/run/secrets/kubernetes.io/serviceaccount/ca.crt |
Kube_CA_Path | Absolute path to scan for certificate files |
Kube_Token_File | Token file | /var/run/secrets/kubernetes.io/serviceaccount/token |
Kube_Tag_Prefix | When the source records comes from Tail input plugin, this option allows to specify what's the prefix used in Tail configuration. | kube.var.log.containers. |
Merge_Log | When enabled, it checks if the | Off |
Merge_Log_Key | When |
Merge_Log_Trim | When | On |
Merge_Parser | Optional parser name to specify how to parse the data contained in the log key. Recommended use is for developers or testing only. |
Keep_Log | When | On |
tls.debug | Debug level between 0 (nothing) and 4 (every detail). | -1 |
tls.verify | When enabled, turns on certificate validation when connecting to the Kubernetes API server. | On |
Use_Journal | When enabled, the filter reads logs coming in Journald format. | Off |
Regex_Parser |
K8S-Logging.Parser | Allow Kubernetes Pods to suggest a pre-defined Parser (read more about it in Kubernetes Annotations section) | Off |
K8S-Logging.Exclude | Allow Kubernetes Pods to exclude their logs from the log processor (read more about it in Kubernetes Annotations section). | Off |
Labels | Include Kubernetes resource labels in the extra metadata. | On |
Annotations | Include Kubernetes resource annotations in the extra metadata. | On |
Kube_meta_preload_cache_dir | If set, Kubernetes meta-data can be cached/pre-loaded from files in JSON format in this directory, named as namespace-pod.meta |
Dummy_Meta | If set, use dummy-meta data (for test/dev purposes) | Off |
DNS_Retries | DNS lookup retries N times until the network start working | 6 |
DNS_Wait_Time | DNS lookup interval between network status checks | 30 |
The Nest Filter plugin allows you to operate on or with nested data. Its modes of operation are
nest - Take a set of records and place them in a map
lift - Take a map by key and lift its records up
As an example using JSON notation, to nest keys matching the Wildcard value Key* under a new key NestKey the transformation becomes,
Example (input)
Example (output)
As an example using JSON notation, to lift keys nested under the Nested_under value NestKey* the transformation becomes,
Example (input)
Example (output)
The plugin supports the following configuration parameters:
In order to start filtering records, you can run the filter from the command line or through the configuration file. The following invokes the Memory Usage Input Plugin, which outputs the following (example),
Note: Using the command line mode requires quotes parse the wildcard properly. The use of a configuration file is recommended.
The following command will load the mem plugin. Then the nest filter will match the wildcard rule to the keys and nest the keys matching Mem.* under the new key NEST.
The output of both the command line and configuration invocations should be identical and result in the following output.
This example nests all Mem.* and Swap,* items under the Stats key and then reverses these actions with a lift operation. The output appears unchanged.
This example takes the keys starting with Mem.* and nests them under LAYER1, which itself is then nested under LAYER2, which is nested under LAYER3.
This example starts with the 3-level deep nesting of Example 2 and applies the lift filter three times to reverse the operations. The end result is that all records are at the top level, without nesting, again. One prefix is added for each level that is lifted.
The Modify Filter plugin allows you to change records using rules and conditions.
As an example using JSON notation to,
Rename Key2 to RenamedKey
Add a key OtherKey with value Value3 if OtherKey does not yet exist
Example (input)
Example (output)
The plugin supports the following rules:
Rules are case insensitive, parameters are not
Any number of rules can be set in a filter instance.
Rules are applied in the order they appear, with each rule operating on the result of the previous rule.
The plugin supports the following conditions:
Conditions are case insensitive, parameters are not
Any number of conditions can be set.
Conditions apply to the whole filter instance and all its rules. Not to individual rules.
All conditions have to be true for the rules to be applied.
In order to start filtering records, you can run the filter from the command line or through the configuration file. The following invokes the Memory Usage Input Plugin, which outputs the following (example),
Note: Using the command line mode requires quotes parse the wildcard properly. The use of a configuration file is recommended.
The output of both the command line and configuration invocations should be identical and result in the following output.
The Record Modifier Filter plugin allows to append fields or to exclude specific fields.
The plugin supports the following configuration parameters: Remove_key and Whitelist_key are exclusive.
In order to start filtering records, you can run the filter from the command line or through the configuration file.
This is a sample in_mem record to filter.
The following configuration file is to append product name and hostname (via environment variable) to record.
You can also run the filter from command line.
The output will be
The following configuration file is to remove 'Swap.*' fields.
You can also run the filter from command line.
The output will be
The following configuration file is to remain 'Mem.*' fields.
You can also run the filter from command line.
The output will be
Lua Filter allows you to modify the incoming records using custom Lua Scripts.
Due to the necessity to have a flexible filtering mechanism, now is possible to extend Fluent Bit capabilities writing simple filters using Lua programming language. A Lua based filter takes two steps:
Configure the Filter in the main configuration
Prepare a Lua script that will be used by the Filter
The plugin supports the following configuration parameters:
In order to test the filter, you can run the plugin from the command line or through the configuration file. The following examples uses the dummy input plugin for data ingestion, invoke Lua filter using the test.lua script and calls the cb_print() function which only print the same information to the standard output:
From the command line you can use the following options:
In your main configuration file append the following Input, Filter & Output sections:
The life cycle of a filter have the following steps:
Upon Tag matching by filter_lua, it may process or bypass the record.
If filter_lua accepts the record, it will invoke the function defined in the call property which basically is the name of a function defined in the Lua script.
Invoke Lua function passing each record in JSON format.
Upon return, validate return value and take some action (described above)
The Lua script can have one or multiple callbacks that can be used by filter_lua, it prototype is as follows:
Each callback must return three values:
For functional examples of this interface, please refer to the code samples provided in the source code of the project located here:
https://github.com/fluent/fluent-bit/tree/master/scripts
In Lua, Fluent Bit treats number as double. It means an integer field (e.g. IDs, log levels) will be converted double. To avoid type conversion, Type_int_key property is available.
Fluent Bit supports protected mode to prevent crash when executes invalid Lua script. See also Error Handling in Application Code.
Powerful and flexible routing
Tags are what makes routing possible. Tags are set in the configuration of the Input definitions where the records are generated, but there are certain scenarios where might be useful to modify the Tag in the pipeline so we can perform more advanced and flexible routing.
The rewrite_tag filter, allows to re-emit a record under a new Tag. Once a record has been re-emitted, the original record can be preserved or discarded.
The way it works is defining rules that matches specific record key content against a regular expression, if a match exists, a new record with the defined Tag will be emitted and go through the beginning of the pipeline. Multiple rules can be specified and they are processed in order until one of them matches.
The new Tag to define can be composed by:
Alphabet characters & Numbers
Original Tag string or part of it
Regular Expressions groups capture
Any key or sub-key of the processed record
Environment variables
The rewrite_tag filter supports the following configuration parameters:
A rule aims to define matching criteria and specify how to create a new Tag for a record. You can define one or multiple rules in the same configuration section. The rules have the following format:
The key represents the name of the record key that holds the value that we want to use to match our regular expression. A key name is specified and prefixed with a $. Consider the following structured record (formatted for readability):
If we wanted to match against the value of the key name we must use $name. The key selector is flexible enough to allow to match nested levels of sub-maps from the structure. If we wanted to check the value of the nested key s2 we can do it specifying $ss['s1']['s2'], for short:
$name = "abc-123"
$ss['s1']['s2'] = "flb"
Note that a key must point a value that contains a string, it's not valid for numbers, booleans, maps or arrays.
Using a simple regular expression we can specify a matching pattern to use against the value of the key specified above, also we can take advantage of group capturing to create custom placeholder values.
If we wanted to match any record that it $name contains a value of the format string-number like the example provided above, we might use:
Note that in our example we are using parentheses, this teams that we are specifying groups of data. If the pattern matches the value a placeholder will be created that can be consumed by the NEW_TAG section.
If $name equals abc-123 , then the following placeholders will be created:
$0 = "abc-123"
$1 = "abc"
$2 = "123"
If the Regular expression do not matches an incoming record, the rule will be skipped and the next rule (if any) will be processed.
If a regular expression has matched the value of the defined key in the rule, we are ready to compose a new Tag for that specific record. The tag is a concatenated string that can contain any of the following characters: a-z,A-Z, 0-9 and .-,.
A Tag can take any string value from the matching record, the original tag it self, environment variable or general placeholder.
Consider the following incoming data on the rule:
Tag = aa.bb.cc
Record = {"name": "abc-123", "ss": {"s1": {"s2": "flb"}}}
Environment variable $HOSTNAME = fluent
With such information we could create a very custom Tag for our record like the following:
the expected Tag to generated will be:
We make use of placeholders, record content and environment variables.
If a rule matches the criteria the filter will emit a copy of the record with the new defined Tag. The property keep takes a boolean value to define if the original record with the old Tag must be preserved and continue in the pipeline or just be discarded.
You can use true or false to decide the expected behavior. There is no default value and this is a mandatory field in the rule.
The following configuration example will emit a dummy (hand-crafted) record, the filter will rewrite the tag, discard the old record and print the new record to the standard output interface:
The original tag test_tag will be rewritten as from.test_tag.new.fluent.bit.out:
As described in the Monitoring section, every component of the pipeline of Fluent Bit exposes metrics. The basic metrics exposed by this filter are drop_records and add_records, they summarize the total of dropped records from the incoming data chunk or the new records added.
Since rewrite_tag emit new records that goes through the beginning of the pipeline, it exposes an additional metric called emit_records that summarize the total number of emitted records.
Using the configuration provided above, if we query the metrics exposed in the HTTP interface we will see the following:
Command:
Metrics output:
The dummy input generated two records, the filter dropped two from the chunks and emitted two new ones under a different Tag.
The records generated are handled by the internal Emitter, so the new records are summarized in the Emitter metrics, take a look at the entry called emitter_for_rewrite_tag.0.
The Emitter is an internal Fluent Bit plugin that allows other components of the pipeline to emit custom records. On this case rewrite_tag creates an Emitter instance to use it exclusively to emit records, on that way we can have a granular control of who is emitting what.
The Emitter name in the metrics can be changed setting up the Emitter_Name configuration property described above.
The Throttle Filter plugin sets the average Rate of messages per Interval, based on leaky bucket and sliding window algorithm. In case of overflood, it will leak within certain rate.
The plugin supports the following configuration parameters:
Lets imagine we have configured:
we received 1 message first second, 3 messages 2nd, and 5 3rd. As you can see, disregard that Window is actually 5, we use "slow" start to prevent overflooding during the startup.
But as soon as we reached Window size * Interval, we will have true sliding window with aggregation over complete window.
When we have average over window is more than Rate, we will start dropping messages, so that
will become:
As you can see, last pane of the window was overwritten and 1 message was dropped.
You might noticed possibility to configure Interval of the Window shift. It is counter intuitive, but there is a difference between two examples above:
and
Even though both examples will allow maximum Rate of 60 messages per minute, first example may get all 60 messages within first second, and will drop all the rest for the entire minute:
While the second example will not allow more than 1 message per second every second, making output rate more smooth:
It may drop some data if the rate is ragged. I would recommend to use bigger interval and rate for streams of rare but important events, while keep Window bigger and Interval small for constantly intensive inputs.
Note: It's suggested to use a configuration file.
The following command will load the tail plugin and read the content of lines.txt file. Then the throttle filter will apply a rate limit and only pass the records which are read below the certain rate:
The example above will pass 1000 messages per second in average over 300 seconds.
The stdout output plugin allows to print to the standard output the data received through the input plugin. Their usage is very simple as follows:
We have specified to gather CPU usage metrics and print them out to the standard output in a human readable way:
No more, no less, it just works.
Tensorflow Filter allows running Machine Learning inference tasks on the records of data coming from input plugins or stream processor. This filter uses Tensorflow Lite as the inference engine, and requires Tensorflow Lite shared library to be present during build and at runtime.
Tensorflow Lite is a lightweight open-source deep learning framework that is used for mobile and IoT applications. Tensorflow Lite only handles inference (not training), therefore, it loads pre-trained models (.tflite files) that are converted into Tensorflow Lite format (FlatBuffer). You can read more on converting Tensorflow models here
The plugin supports the following configuration parameters:
Clone Tensorflow repository, install bazel package manager, and run the following command in order to create the shared library:
The script creates the shared library bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so. You need to copy the library to a location (such as /usr/lib) that can be used by Fluent Bit.
Tensorflow filter plugin is disabled by default. You need to build Fluent Bit with Tensorflow plugin enabled. In addition, it requires access to Tensorflow Lite header files to compile. Therefore, you also need to pass the address of the Tensorflow source code on your machine to the build script:
If Tensorflow plugin initializes correctly, it reports successful creation of the interpreter, and prints a summary of model's input/output types and dimensions.
Currently supports single-input models
Uses Tensorflow 2.3 header files
Set the buffer size for HTTP client when reading responses from Kubernetes API server. The value must be according to the specification. A value of 0 results in no limit, and the buffer will expand as-needed. Note that if pod specifications exceed the buffer limit, the API response will be discarded when retrieving metadata, and some kubernetes metadata will fail to be injected to the logs.
Set an alternative Parser to process record Tag and extract pod_name, namespace_name, container_name and docker_id. The parser must be registered in a (refer to parser filter-kube-test as an example).
Key
Description
Record
Append fields. This parameter needs key and value pair.
Remove_key
If the key is matched, that field is removed.
Whitelist_key
If the key is not matched, that field is removed.
Key
Description
script
Path to the Lua script that will be used.
call
Lua function name that will be triggered to do filtering. It's assumed that the function is declared inside the Script defined above.
type_int_key
If these keys are matched, the fields are converted to integer. If more than one key, delimit by space. Note that starting from Fluent Bit v1.6 integer data types are preserved and not converted to double as in previous versions.
protected_mode
If enabled, Lua script will be executed in protected mode. It prevents to crash when invalid Lua script is executed. Default is true.
time_as_table
By default when the Lua script is invoked, the record timestamp is passed as a Floating number which might lead to loss precision when the data is converted back. If you desire timestamp precision enabling this option will pass the timestamp as a Lua table with keys sec for seconds since epoch and nsec for nanoseconds.
name
description
tag
Name of the tag associated with the incoming record.
timestamp
Unix timestamp with nanoseconds associated with the incoming record. The original format is a double (seconds.nanoseconds)
record
Lua table with the record content
name
data type
description
code
integer
The code return value represents the result and further action that may follows. If code equals -1, means that filter_lua must drop the record. If code equals 0 the record will not be modified, otherwise if code equals 1, means the original timestamp and record have been modified so it must be replaced by the returned values from timestamp (second return value) and record (third return value). If code equals 2, means the original timestamp is not modified and the record has been modified so it must be replaced by the returned values from record (third return value). The code 2 is supported from v1.4.3.
timestamp
double
If code equals 1, the original record timestamp will be replaced with this new value.
record
table
if code equals 1, the original record information will be replaced with this new value. Note that the format of this value must be a valid Lua table.
Key
Description
Rule
Defines the matching criteria and the format of the Tag for the matching record. The Rule format have four components: KEY REGEX NEW_TAG KEEP. For more specific details of the Rule format and it composition read the next section.
Emitter_Name
When the filter emits a record under the new Tag, there is an internal emitter plugin that takes care of the job. Since this emitter expose metrics as any other component of the pipeline, you can use this property to configure an optional name for it.
Emitter_Storage.type
Define a buffering mechanism for the new records created. Note these records are part of the emitter plugin. This option support the values memory (default) or filesystem. If the destination for the new records generated might face backpressure due to latency or slow network, we strongly recommend enabling the filesystem mode.
Emitter_Mem_Buf_Limit
Set a limit on the amount of memory the tag rewrite emitter can consume if the outputs provide backpressure. The default for this limit is 10M. The pipeline will pause once the buffer exceeds the value of this setting. For example, if the value is set to 10M then the pipeline will pause if the buffer exceeds 10M. The pipeline will remain paused until the output drains the buffer below the 10M limit.
Key
Value Format
Description
Rate
Integer
Amount of messages for the time.
Window
Integer
Amount of intervals to calculate average over. Default 5.
Interval
String
Time interval, expressed in "sleep" format. e.g 3s, 1.5m, 0.5h etc
Print_Status
Bool
Whether to print status messages with current rate and the limits to information logs
Key
Description
default
Format
Specify the data format to be printed. Supported formats are msgpack json, json_lines and json_stream.
msgpack
json_date_key
Specify the name of the date field in output
date
json_date_format
Specify the format of the date. Supported formats are double, iso8601 (eg: 2018-05-30T09:39:52.000681Z) and epoch.
double
Key
Value Format
Operation
Description
Operation
ENUM [nest or lift]
Select the operation nest or lift
Wildcard
FIELD WILDCARD
nest
Nest records which field matches the wildcard
Nest_under
FIELD STRING
nest
Nest records matching the Wildcard under this key
Nested_under
FIELD STRING
lift
Lift records nested under the Nested_under key
Add_prefix
FIELD STRING
ANY
Prefix affected keys with this string
Remove_prefix
FIELD STRING
ANY
Remove prefix from affected keys if it matches this string
Key
Description
Default
input_field
Specify the name of the field in the record to apply inference on.
model_file
Path to the model file (.tflite) to be loaded by Tensorflow Lite.
include_input_fields
Include all input filed in filter's output
True
normalization_value
Divide input values to normalization_value
Operation
Parameter 1
Parameter 2
Description
Set
STRING:KEY
STRING:VALUE
Add a key/value pair with key KEY and value VALUE. If KEY already exists, this field is overwritten
Add
STRING:KEY
STRING:VALUE
Add a key/value pair with key KEY and value VALUE if KEY does not exist
Remove
STRING:KEY
NONE
Remove a key/value pair with key KEY if it exists
Remove_wildcard
WILDCARD:KEY
NONE
Remove all key/value pairs with key matching wildcard KEY
Remove_regex
REGEXP:KEY
NONE
Remove all key/value pairs with key matching regexp KEY
Rename
STRING:KEY
STRING:RENAMED_KEY
Rename a key/value pair with key KEY to RENAMED_KEY if KEY exists AND RENAMED_KEY does not exist
Hard_rename
STRING:KEY
STRING:RENAMED_KEY
Rename a key/value pair with key KEY to RENAMED_KEY if KEY exists. If RENAMED_KEY already exists, this field is overwritten
Copy
STRING:KEY
STRING:COPIED_KEY
Copy a key/value pair with key KEY to COPIED_KEY if KEY exists AND COPIED_KEY does not exist
Hard_copy
STRING:KEY
STRING:COPIED_KEY
Copy a key/value pair with key KEY to COPIED_KEY if KEY exists. If COPIED_KEY already exists, this field is overwritten
Condition
Parameter
Parameter 2
Description
Key_exists
STRING:KEY
NONE
Is true if KEY exists
Key_does_not_exist
STRING:KEY
STRING:VALUE
Is true if KEY does not exist
A_key_matches
REGEXP:KEY
NONE
Is true if a key matches regex KEY
No_key_matches
REGEXP:KEY
NONE
Is true if no key matches regex KEY
Key_value_equals
STRING:KEY
STRING:VALUE
Is true if KEY exists and its value is VALUE
Key_value_does_not_equal
STRING:KEY
STRING:VALUE
Is true if KEY exists and its value is not VALUE
Key_value_matches
STRING:KEY
REGEXP:VALUE
Is true if key KEY exists and its value matches VALUE
Key_value_does_not_match
STRING:KEY
REGEXP:VALUE
Is true if key KEY exists and its value does not match VALUE
Matching_keys_have_matching_values
REGEXP:KEY
REGEXP:VALUE
Is true if all keys matching KEY have values that match VALUE
Matching_keys_do_not_have_matching_values
REGEXP:KEY
REGEXP:VALUE
Is true if all keys matching KEY have values that do not match VALUE