| Name | logprep JSON |
| Version |
17.0.3
JSON |
| download |
| home_page | None |
| Summary | Logprep allows to collect, process and forward log messages from various data sources. |
| upload_time | 2025-10-28 12:40:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | GNU LESSER GENERAL PUBLIC LICENSE
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That's all there is to it!
|
| keywords |
kafka
etl
sre
preprocessing
opensearch
soar
logdata
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<h1 align="center">Logprep</h1>
<h3 align="center">
[](http://logprep.readthedocs.io/?badge=latest)
<a href="https://codecov.io/github/fkie-cad/Logprep" target="_blank">
<img src="https://img.shields.io/codecov/c/github/fkie-cad/Logprep?color=%2334D058" alt="Coverage">
</a>
</h3>
## Introduction
Logprep allows to collect, process and forward log messages from various data sources.
Log messages are being read and written by so-called connectors.
Currently, connectors for Kafka, Opensearch, S3, HTTP and JSON(L) files exist.
The log messages are processed in serial by a pipeline of processors,
where each processor modifies an event that is being passed through.
The main idea is that each processor performs a simple task that is easy to carry out.
Once the log message is passed through all processors in the pipeline the resulting
message is sent to a configured output connector.
Logprep is primarily designed to process log messages. Generally, Logprep can handle JSON messages,
allowing further applications besides log handling.
- [About Logprep](https://github.com/fkie-cad/Logprep/blob/main/README.md#about-logprep)
- [Installation](https://logprep.readthedocs.io/en/latest/installation.html)
- [Deployment Examples](https://logprep.readthedocs.io/en/latest/examples/index.html)
- [Event Generation](https://logprep.readthedocs.io/en/latest/user_manual/execution.html#event-generation)
- [Documentation](https://logprep.readthedocs.io/en/latest)
- [Container signatures](https://github.com/fkie-cad/Logprep/blob/main/README.md#container-signatures)
- [Container SBOM](https://github.com/fkie-cad/Logprep/blob/main/README.md#container-sbom)
- [Contributing](https://github.com/fkie-cad/Logprep/blob/main/CONTRIBUTING.md)
- [License](https://github.com/fkie-cad/Logprep/blob/main/LICENSE)
- [Changelog](https://github.com/fkie-cad/Logprep/blob/main/CHANGELOG.md)
## About Logprep
### Pipelines
Logprep processes incoming log messages with a configured pipeline that can be spawned
multiple times via multiprocessing.
The following chart shows a basic setup that represents this behaviour.
The pipeline consists of three processors: the `Dissector`, `Geo-IP Enricher` and the
`Dropper`.
Each pipeline runs concurrently and takes one event from it's `Input Connector`.
Once the log messages is fully processed the result will be forwarded to the `Output Connector`,
after which the pipeline will take the next message, repeating the processing cycle.
```mermaid
flowchart LR
A1[Input\nConnector] --> B
A2[Input\nConnector] --> C
A3[Input\nConnector] --> D
subgraph Pipeline 1
B[Dissector] --> E[Geo-IP Enricher]
E --> F[Dropper]
end
subgraph Pipeline 2
C[Dissector] --> G[Geo-IP Enricher]
G --> H[Dropper]
end
subgraph Pipeline n
D[Dissector] --> I[Geo-IP Enricher]
I --> J[Dropper]
end
F --> K1[Output\nConnector]
H --> K2[Output\nConnector]
J --> K3[Output\nConnector]
```
### Processors
Every processor has one simple task to fulfill.
For example, the `Dissector` can split up long message fields into multiple subfields
to facilitate structural normalization.
The `Geo-IP Enricher`, for example, takes an ip-address and adds the geolocation of it to the
log message, based on a configured geo-ip database.
Or the `Dropper` deletes fields from the log message.
As detailed overview of all processors can be found in the
[processor documentation](https://logprep.readthedocs.io/en/latest/configuration/processor.html).
To influence the behaviour of those processors, each can be configured with a set of rules.
These rules define two things.
Firstly, they specify when the processor should process a log message
and secondly they specify how to process the message.
For example which fields should be deleted or to which IP-address the geolocation should be
retrieved.
### Connectors
Connectors are responsible for reading the input and writing the result to a desired output.
The main connectors that are currently used and implemented are a kafka-input-connector and a
kafka-output-connector allowing to receive messages from a kafka-topic and write messages into a
kafka-topic. Addionally, you can use the Opensearch or Opensearch output connectors to ship the
messages directly to Opensearch or Opensearch after processing.
The details regarding the connectors can be found in the
[input connector documentation](https://logprep.readthedocs.io/en/latest/configuration/input.html)
and
[output connector documentation](https://logprep.readthedocs.io/en/latest/configuration/output.html).
### Configuration
To run Logprep, certain configurations have to be provided. Because Logprep is designed to run in a
containerized environment like Kubernetes, these configurations can be provided via the filesystem or
http. By providing the configuration via http, it is possible to control the configuration change via
a flexible http api. This enables Logprep to quickly adapt to changes in your environment.
First, a general configuration is given that describes the pipeline and the connectors,
and lastly, the processors need rules in order to process messages correctly.
The following yaml configuration shows an example configuration for the pipeline shown
in the graph above:
```yaml
process_count: 3
timeout: 0.1
pipeline:
- dissector:
type: dissector
rules:
- https://your-api/dissector/
- rules/01_dissector/rules/
- geoip_enricher:
type: geoip_enricher
rules:
- https://your-api/geoip/
- rules/02_geoip_enricher/rules/
tree_config: artifacts/tree_config.json
db_path: artifacts/GeoDB.mmdb
- dropper:
type: dropper
rules:
- rules/03_dropper/rules/
input:
mykafka:
type: confluentkafka_input
bootstrapservers: [127.0.0.1:9092]
topic: consumer
group: cgroup
auto_commit: true
session_timeout: 6000
offset_reset_policy: smallest
output:
opensearch:
type: opensearch_output
hosts:
- 127.0.0.1:9200
default_index: default_index
error_index: error_index
message_backlog_size: 10000
timeout: 10000
max_retries:
user: the username
secret: the passord
cert: /path/to/cert.crt
```
The following yaml represents a dropper rule which according to the previous configuration
should be in the `rules/03_dropper/rules/` directory.
```yaml
filter: "message"
drop:
- message
description: "Drops the message field"
```
The condition of this rule would check if the field `message` exists in the log.
If it does exist then the dropper would delete this field from the log message.
Details about the rule language and how to write rules for the processors can be found in the
[rule configuration documentation](https://logprep.readthedocs.io/en/latest/configuration/rules.html).
## Documentation
The documentation for Logprep is online at https://logprep.readthedocs.io/en/latest/ or it can
be built locally via:
```
sudo apt install pandoc
pip install -e .[doc]
cd ./doc/
make html
```
A HTML documentation can be then found in `doc/_build/html/index.html`.
## Container signatures
From release 15 on, Logprep containers are signed using the
[cosign](https://github.com/sigstore/cosign) tool.
To verify the container, you can copy the following public key into a file
`logprep.pub`:
```
-----BEGIN PUBLIC KEY-----
MFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEgkQXDi/N4TDFE2Ao0pulOFfbGm5g
kVtARE+LJfSFI25BanOG9jaxxRGVt+Sa1KtQbMcy7Glxu0s7XgD9VFGjTA==
-----END PUBLIC KEY-----
```
And use it to verify the signature:
```
cosign verify --key logprep.pub ghcr.io/fkie-cad/logprep:py3.11-latest
```
The output should look like:
```
Verification for ghcr.io/fkie-cad/logprep:py3.11-latest --
The following checks were performed on each of these signatures:
- The cosign claims were validated
- Existence of the claims in the transparency log was verified offline
- The signatures were verified against the specified public key
[{"critical":{"identity":{"docker-reference":"ghcr.io/fkie-cad/logprep"}, ...
```
## Container SBOM
From release 15 on, Logprep container images are shipped with a generated sbom.
To verify the attestation and extract the SBOM use
[cosign](https://github.com/sigstore/cosign) with:
```
cosign verify-attestation --key logprep.pub ghcr.io/fkie-cad/logprep:py3.11-latest | jq '.payload | @base64d | fromjson | .predicate | .Data | fromjson' > sbom.json
```
The output should look like:
```
Verification for ghcr.io/fkie-cad/logprep:py3.11-latest --
The following checks were performed on each of these signatures:
- The cosign claims were validated
- Existence of the claims in the transparency log was verified offline
- The signatures were verified against the specified public key
```
Finally, you can view the extracted sbom with:
```
cat sbom.json | jq
```
Raw data
{
"_id": null,
"home_page": null,
"name": "logprep",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": null,
"keywords": "kafka, etl, sre, preprocessing, opensearch, soar, logdata",
"author": null,
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/e1/33/38bad0a7d00aa2e9bc516e23a59546e8cdb203a87ca756d60beadbe417a0/logprep-17.0.3.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">Logprep</h1>\n<h3 align=\"center\">\n\n\n\n[](http://logprep.readthedocs.io/?badge=latest)\n\n<a href=\"https://codecov.io/github/fkie-cad/Logprep\" target=\"_blank\">\n <img src=\"https://img.shields.io/codecov/c/github/fkie-cad/Logprep?color=%2334D058\" alt=\"Coverage\">\n</a>\n\n</h3>\n\n## Introduction\n\nLogprep allows to collect, process and forward log messages from various data sources.\nLog messages are being read and written by so-called connectors.\nCurrently, connectors for Kafka, Opensearch, S3, HTTP and JSON(L) files exist.\n\nThe log messages are processed in serial by a pipeline of processors,\nwhere each processor modifies an event that is being passed through.\nThe main idea is that each processor performs a simple task that is easy to carry out.\nOnce the log message is passed through all processors in the pipeline the resulting\nmessage is sent to a configured output connector.\n\nLogprep is primarily designed to process log messages. Generally, Logprep can handle JSON messages,\nallowing further applications besides log handling.\n\n- [About Logprep](https://github.com/fkie-cad/Logprep/blob/main/README.md#about-logprep)\n- [Installation](https://logprep.readthedocs.io/en/latest/installation.html)\n- [Deployment Examples](https://logprep.readthedocs.io/en/latest/examples/index.html)\n- [Event Generation](https://logprep.readthedocs.io/en/latest/user_manual/execution.html#event-generation)\n- [Documentation](https://logprep.readthedocs.io/en/latest)\n- [Container signatures](https://github.com/fkie-cad/Logprep/blob/main/README.md#container-signatures)\n- [Container SBOM](https://github.com/fkie-cad/Logprep/blob/main/README.md#container-sbom)\n- [Contributing](https://github.com/fkie-cad/Logprep/blob/main/CONTRIBUTING.md)\n- [License](https://github.com/fkie-cad/Logprep/blob/main/LICENSE)\n- [Changelog](https://github.com/fkie-cad/Logprep/blob/main/CHANGELOG.md)\n\n## About Logprep\n\n### Pipelines\n\nLogprep processes incoming log messages with a configured pipeline that can be spawned\nmultiple times via multiprocessing.\nThe following chart shows a basic setup that represents this behaviour.\nThe pipeline consists of three processors: the `Dissector`, `Geo-IP Enricher` and the\n`Dropper`.\nEach pipeline runs concurrently and takes one event from it's `Input Connector`.\nOnce the log messages is fully processed the result will be forwarded to the `Output Connector`,\nafter which the pipeline will take the next message, repeating the processing cycle.\n\n```mermaid\nflowchart LR\nA1[Input\\nConnector] --> B\nA2[Input\\nConnector] --> C\nA3[Input\\nConnector] --> D\nsubgraph Pipeline 1\nB[Dissector] --> E[Geo-IP Enricher]\nE --> F[Dropper]\nend\nsubgraph Pipeline 2\nC[Dissector] --> G[Geo-IP Enricher]\nG --> H[Dropper]\nend\nsubgraph Pipeline n\nD[Dissector] --> I[Geo-IP Enricher]\nI --> J[Dropper]\nend\nF --> K1[Output\\nConnector]\nH --> K2[Output\\nConnector]\nJ --> K3[Output\\nConnector]\n```\n\n### Processors\n\nEvery processor has one simple task to fulfill.\nFor example, the `Dissector` can split up long message fields into multiple subfields\nto facilitate structural normalization.\nThe `Geo-IP Enricher`, for example, takes an ip-address and adds the geolocation of it to the\nlog message, based on a configured geo-ip database.\nOr the `Dropper` deletes fields from the log message.\n\nAs detailed overview of all processors can be found in the\n[processor documentation](https://logprep.readthedocs.io/en/latest/configuration/processor.html).\n\nTo influence the behaviour of those processors, each can be configured with a set of rules.\nThese rules define two things.\nFirstly, they specify when the processor should process a log message\nand secondly they specify how to process the message.\nFor example which fields should be deleted or to which IP-address the geolocation should be\nretrieved.\n\n\n### Connectors\n\nConnectors are responsible for reading the input and writing the result to a desired output.\nThe main connectors that are currently used and implemented are a kafka-input-connector and a\nkafka-output-connector allowing to receive messages from a kafka-topic and write messages into a\nkafka-topic. Addionally, you can use the Opensearch or Opensearch output connectors to ship the\nmessages directly to Opensearch or Opensearch after processing.\n\nThe details regarding the connectors can be found in the\n[input connector documentation](https://logprep.readthedocs.io/en/latest/configuration/input.html)\nand\n[output connector documentation](https://logprep.readthedocs.io/en/latest/configuration/output.html).\n\n### Configuration\n\nTo run Logprep, certain configurations have to be provided. Because Logprep is designed to run in a\ncontainerized environment like Kubernetes, these configurations can be provided via the filesystem or\nhttp. By providing the configuration via http, it is possible to control the configuration change via\na flexible http api. This enables Logprep to quickly adapt to changes in your environment.\n\nFirst, a general configuration is given that describes the pipeline and the connectors,\nand lastly, the processors need rules in order to process messages correctly.\n\nThe following yaml configuration shows an example configuration for the pipeline shown\nin the graph above:\n\n```yaml\nprocess_count: 3\ntimeout: 0.1\n\npipeline:\n - dissector:\n type: dissector\n rules:\n - https://your-api/dissector/\n - rules/01_dissector/rules/\n - geoip_enricher:\n type: geoip_enricher\n rules:\n - https://your-api/geoip/\n - rules/02_geoip_enricher/rules/\n tree_config: artifacts/tree_config.json\n db_path: artifacts/GeoDB.mmdb\n - dropper:\n type: dropper\n rules:\n - rules/03_dropper/rules/\n\ninput:\n mykafka:\n type: confluentkafka_input\n bootstrapservers: [127.0.0.1:9092]\n topic: consumer\n group: cgroup\n auto_commit: true\n session_timeout: 6000\n offset_reset_policy: smallest\noutput:\n opensearch:\n type: opensearch_output\n hosts:\n - 127.0.0.1:9200\n default_index: default_index\n error_index: error_index\n message_backlog_size: 10000\n timeout: 10000\n max_retries:\n user: the username\n secret: the passord\n cert: /path/to/cert.crt\n```\n\nThe following yaml represents a dropper rule which according to the previous configuration\nshould be in the `rules/03_dropper/rules/` directory.\n\n```yaml\nfilter: \"message\"\ndrop:\n - message\ndescription: \"Drops the message field\"\n```\n\nThe condition of this rule would check if the field `message` exists in the log.\nIf it does exist then the dropper would delete this field from the log message.\n\nDetails about the rule language and how to write rules for the processors can be found in the\n[rule configuration documentation](https://logprep.readthedocs.io/en/latest/configuration/rules.html).\n\n## Documentation\n\nThe documentation for Logprep is online at https://logprep.readthedocs.io/en/latest/ or it can\nbe built locally via:\n\n```\nsudo apt install pandoc\npip install -e .[doc]\ncd ./doc/\nmake html\n```\n\nA HTML documentation can be then found in `doc/_build/html/index.html`.\n\n## Container signatures\n\nFrom release 15 on, Logprep containers are signed using the\n[cosign](https://github.com/sigstore/cosign) tool.\nTo verify the container, you can copy the following public key into a file\n`logprep.pub`:\n\n```\n-----BEGIN PUBLIC KEY-----\nMFkwEwYHKoZIzj0CAQYIKoZIzj0DAQcDQgAEgkQXDi/N4TDFE2Ao0pulOFfbGm5g\nkVtARE+LJfSFI25BanOG9jaxxRGVt+Sa1KtQbMcy7Glxu0s7XgD9VFGjTA==\n-----END PUBLIC KEY-----\n```\n\nAnd use it to verify the signature:\n\n```\ncosign verify --key logprep.pub ghcr.io/fkie-cad/logprep:py3.11-latest\n```\n\nThe output should look like:\n\n```\nVerification for ghcr.io/fkie-cad/logprep:py3.11-latest --\nThe following checks were performed on each of these signatures:\n - The cosign claims were validated\n - Existence of the claims in the transparency log was verified offline\n - The signatures were verified against the specified public key\n\n[{\"critical\":{\"identity\":{\"docker-reference\":\"ghcr.io/fkie-cad/logprep\"}, ...\n```\n\n## Container SBOM\n\nFrom release 15 on, Logprep container images are shipped with a generated sbom.\nTo verify the attestation and extract the SBOM use\n[cosign](https://github.com/sigstore/cosign) with:\n\n```\ncosign verify-attestation --key logprep.pub ghcr.io/fkie-cad/logprep:py3.11-latest | jq '.payload | @base64d | fromjson | .predicate | .Data | fromjson' > sbom.json\n```\n\nThe output should look like:\n\n```\nVerification for ghcr.io/fkie-cad/logprep:py3.11-latest --\nThe following checks were performed on each of these signatures:\n - The cosign claims were validated\n - Existence of the claims in the transparency log was verified offline\n - The signatures were verified against the specified public key\n```\n\nFinally, you can view the extracted sbom with:\n\n```\ncat sbom.json | jq\n```\n",
"bugtrack_url": null,
"license": "GNU LESSER GENERAL PUBLIC LICENSE\n Version 2.1, February 1999\n \n Copyright (C) 1991, 1999 Free Software Foundation, Inc.\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n \n [This is the first released version of the Lesser GPL. It also counts\n as the successor of the GNU Library Public License, version 2, hence\n the version number 2.1.]\n \n Preamble\n \n The licenses for most software are designed to take away your\n freedom to share and change it. By contrast, the GNU General Public\n Licenses are intended to guarantee your freedom to share and change\n free software--to make sure the software is free for all its users.\n \n This license, the Lesser General Public License, applies to some\n specially designated software packages--typically libraries--of the\n Free Software Foundation and other authors who decide to use it. You\n can use it too, but we suggest you first think carefully about whether\n this license or the ordinary General Public License is the better\n strategy to use in any particular case, based on the explanations below.\n \n When we speak of free software, we are referring to freedom of use,\n not price. Our General Public Licenses are designed to make sure that\n you have the freedom to distribute copies of free software (and charge\n for this service if you wish); that you receive source code or can get\n it if you want it; that you can change the software and use pieces of\n it in new free programs; and that you are informed that you can do\n these things.\n \n To protect your rights, we need to make restrictions that forbid\n distributors to deny you these rights or to ask you to surrender these\n rights. These restrictions translate to certain responsibilities for\n you if you distribute copies of the library or if you modify it.\n \n For example, if you distribute copies of the library, whether gratis\n or for a fee, you must give the recipients all the rights that we gave\n you. You must make sure that they, too, receive or can get the source\n code. If you link other code with the library, you must provide\n complete object files to the recipients, so that they can relink them\n with the library after making changes to the library and recompiling\n it. And you must show them these terms so they know their rights.\n \n We protect your rights with a two-step method: (1) we copyright the\n library, and (2) we offer you this license, which gives you legal\n permission to copy, distribute and/or modify the library.\n \n To protect each distributor, we want to make it very clear that\n there is no warranty for the free library. 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