mirror of
https://github.com/nicolargo/glances.git
synced 2024-12-22 08:41:32 +03:00
51 lines
1.4 KiB
ReStructuredText
51 lines
1.4 KiB
ReStructuredText
.. _kafka:
|
|
|
|
Kafka
|
|
=====
|
|
|
|
You can export statistics to a ``Kafka`` server.
|
|
The connection should be defined in the Glances configuration file as
|
|
following:
|
|
|
|
.. code-block:: ini
|
|
|
|
[kafka]
|
|
host=localhost
|
|
port=9092
|
|
topic=glances
|
|
#compression=gzip
|
|
# Tags will be added for all events
|
|
#tags=foo:bar,spam:eggs
|
|
# You can also use dynamic values
|
|
#tags=hostname:`hostname -f`
|
|
|
|
Note: you can enable the compression but it consume CPU on your host.
|
|
|
|
and run Glances with:
|
|
|
|
.. code-block:: console
|
|
|
|
$ glances --export kafka
|
|
|
|
Stats are sent in native ``JSON`` format to the topic:
|
|
|
|
- ``key``: plugin name
|
|
- ``value``: JSON dict
|
|
|
|
Example of record for the memory plugin:
|
|
|
|
.. code-block:: ini
|
|
|
|
ConsumerRecord(topic=u'glances', partition=0, offset=1305, timestamp=1490460592248, timestamp_type=0, key='mem', value=u'{"available": 2094710784, "used": 5777428480, "cached": 2513543168, "mem_careful": 50.0, "percent": 73.4, "free": 2094710784, "mem_critical": 90.0, "inactive": 2361626624, "shared": 475504640, "history_size": 28800.0, "mem_warning": 70.0, "total": 7872139264, "active": 4834361344, "buffers": 160112640}', checksum=214895201, serialized_key_size=3, serialized_value_size=303)
|
|
|
|
Python code example to consume Kafka Glances plugin:
|
|
|
|
.. code-block:: python
|
|
|
|
from kafka import KafkaConsumer
|
|
import json
|
|
|
|
consumer = KafkaConsumer('glances', value_deserializer=json.loads)
|
|
for s in consumer:
|
|
print(s)
|