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rubygem-logstash-patterns-core-4.0.2-1.lbn25.noarch

This gem is a Logstash plugin required to be installed on top of the Logstash core pipeline using $LS_HOME/bin/logstash-plugin install gemname. This gem is not a stand-alone program.

winlogbeat-7.3.0-1.git6f0ec01.lbn25.x86_64

You know, for windows event logs Winlogbeat is an open-source log collector that ships Windows Event Logs to Elasticsearch or Logstash. It installs as a Windows service on all versions since Windows XP.

auditbeat-7.4.2-1.git1507515.lbn25.x86_64

Collect your Linux audit framework data and monitor the integrity of your files. Auditbeat ships these events in real time to the rest of the Elastic Stack for further analysis. To learn more about Auditbeat, check out https://www.elastic.co/products/beats/auditbeat.

heartbeat-7.4.2-1.git1507515.lbn25.x86_64

Heartbeat is a lightweight daemon that you install on a remote server to periodically check the status of your services and determine whether they are available. Unlike Metricbeat, which only tells you if your servers are up or down, Heartbeat tells you whether your services are reachable. Heartbeat is useful when you need to verify that you’re meeting your service level agreements for service uptime. It’s also useful for other scenarios, such as security use cases, when you need to verify that no one from the outside can access services on your private enterprise server. You can configure Heartbeat to ping all DNS-resolvable IP addresses for a specified hostname. That way, you can check all services that are load-balanced to see if they are available. When you configure Heartbeat, you specify monitors that identify the hostnames that you want to check. Each monitor runs based on the schedule that you specify. For example, you can configure one monitor to run every 10 minutes, and a different monitor to run between the hours of 9:00 and 17:00. Heartbeat currently supports monitors for checking hosts via: ICMP (v4 and v6) Echo Requests. Use the icmp monitor when you simply want to check whether a service is available. This monitor requires root access. TCP. Use the tcp monitor to connect via TCP. You can optionally configure this monitor to verify the endpoint by sending and/or receiving a custom payload. HTTP. Use the http monitor to connect via HTTP. You can optionally configure this monitor to verify that the service returns the expected response, such as a specific status code, response header, or content. The tcp and http monitors both support SSL/TLS and some proxy settings.

packetbeat-7.4.2-1.git1507515.lbn25.x86_64

Packetbeat is an open source network packet analyzer that ships the data to Elasticsearch. Think of it like a distributed real-time Wireshark with a lot more analytics features. The Packetbeat shippers sniff the traffic between your application processes, parse on the fly protocols like HTTP, MySQL, PostgreSQL, Redis or Thrift and correlate the messages into transactions. For each transaction, the shipper inserts a JSON document into Elasticsearch, where it is stored and indexed. You can then use Kibana to view key metrics and do ad-hoc queries against the data. To learn more about Packetbeat, check out https://www.elastic.co/products/beats/packetbeat.

python3-djangorestframework-3.15.2-1.lbn36.noarch

[Django REST framework][docs][![build-status-image]][travis] [![coverage- status-image]][codecov] [![pypi-version]][pypi]**Awesome web-browsable Web APIs.**Full documentation for the project is available at [ FundingREST framework is a *collaboratively funded project*. If you use REST framework commercially we strongly encourage you to invest in its continued development by [signing up for a...

python3-jq-1.8.0-1.lbn36.x86_64

jq.py: a lightweight and flexible JSON processor This project contains Python bindings for jq < 1.7.1.Installation Wheels are built for various Python versions and architectures on Linux and Mac OS X. On these platforms, you should be able to install jq with a normal pip install:.. code-block:: sh pip install jqIf a wheel is not available, the source for jq 1.7.1 is built. This requires:*...

python3-json-stream-2.3.2-1.lbn36.noarch

json-stream Simple streaming JSON parser and encoder. When reading JSON data, json-stream can decode JSON data in a streaming manner, providing a pythonic dict/list-like interface, or a visitor-based interfeace. Can stream from files, URLs or iterators. When writing JSON data, json-stream can stream JSON objects as you generate them. These techniques allow you to reduce memory consumption and latency. Reading json-stream is a JSON parser just like the standard library's json.load(). It will read a JSON document and convert it into native python types. import json_stream data = json_stream.load(f) Features: stream all JSON data types (objects, lists and simple types) stream nested data simple pythonic list-like/dict-like interface stream truncated or malformed JSON data (up to the first error) native code parsing speedups for most common platforms pure python fallback if native extensions not available Unlike json.load(), json-stream can stream JSON data from any file-like or it

python3-json-stream-rs-tokenizer-0.4.26-1.lbn36.x86_64

> **NOTE:** json-stream-rs-tokenizer is now automatically used by > json- stream, so unless you find a bug, you can ignore this package's > existence! json- stream-rs-tokenizer[![CI build badge]( [![CI test badge]( [![PyPI package and version badge](

python3-json-stream-to-standard-types-0.1.4-1.lbn36.noarch

⚠ NOTE: Starting at json-stream 2.2.0,to_standard_types is part of json-stream itself, so there is no longer a need for this packgage. json-stream-to-standard-types Utility function to convert json-stream objects to normal Python dicts/lists. Parallel PR: https:/github.com/daggaz/json-stream/pull/17 Installation pip install json-stream-to-standard-types Usage To convert a json-stream dict-like or list-like object and all its descendants to a standard list and dict, simply apply the library's to_standard_types function: import json_stream from json_stream_to_standard_types import to_standard_types data = json_stream.load(f) results = data["results"] print(results) # prints <TransientStreamingJSONList: TRANSIENT, STREAMING> converted = to_standard_types(results) print(converted) # prints [1, 2, 3] License Do whatever you want with it license or whatever it was called