The difference between collectd and Splunk's HTTP Event Collector (HEC) lies in their functionality, purpose, and the way they integrate with Splunk for data collection. Here's a detailed comparison:
collectd:
collectd is a daemon that collects system performance metrics periodically. It is typically used to monitor system resources such as CPU usage, memory usage, network bandwidth, and disk performance.HTTP Event Collector (HEC):
collectd:
collectd collects system and application performance metrics at regular intervals and can be configured to send this data to Splunk, among other destinations.collectd can also be extended to collect custom metrics.HTTP Event Collector (HEC):
collectd:
collectd needs to be installed on each system or server where performance metrics need to be collected. It operates as an agent on these systems.collectd data can be forwarded using the Splunk Universal Forwarder or directly to HEC.HTTP Event Collector (HEC):
collectd:
collectd typically sends data in its own binary format or can be configured to send data in JSON, which Splunk can ingest and index as metrics.collectd is typically structured around performance metrics like CPU load, memory usage, etc.HTTP Event Collector (HEC):
collectd:
collectd is already in use or where detailed system performance data is needed in Splunk.HTTP Event Collector (HEC):
collectd:
collectd agents across various systems and the infrastructure set up to handle the data.collectd instances across all monitored systems.HTTP Event Collector (HEC):
collectd: A specialized tool for collecting system and application performance metrics, primarily used in infrastructure monitoring. It requires an agent on each system and is configured to send performance data to Splunk or other destinations.
HTTP Event Collector (HEC): A feature in Splunk for ingesting a wide variety of data directly over HTTP(S) from applications, services, or devices. It is flexible, scalable, and ideal for modern, distributed, or cloud-based environments where direct HTTP(S) communication is preferred.
Both tools serve different purposes, and your choice between them depends on the specific requirements of your monitoring and data ingestion strategy.
docker-compose exec collectd bash
nano /etc/collectd/collectd.conf
Hostname "dd15a3fc994c"
FQDNLookup false
Interval 10
Timeout 2
ReadThreads 5
WriteThreads 5
#Sree enabled these
LoadPlugin cpu
LoadPlugin interface
LoadPlugin load
LoadPlugin memory
TypesDB "/usr/share/collectd/types.db"
Include "/etc/collectd/collectd.d/*.conf"
<Plugin write_http>
<Node "node1">
URL "https://splunk:8088/services/collector/raw?channel=4609eb39-f258-435c-9a75-cfc2ea1303d4"
Header "Authorization: Splunk edc2b152-2e32-41db-ad62-72f3a9ae7c5b"
Format "JSON"
VerifyPeer false
VerifyHost false
Metrics true
StoreRates true
</Node>
</Plugin>
# SEE ALREADY INCLUDING THIS
LoadPlugin write_http
<Plugin write_http>
<Node "node-http-1">
URL "http://splunk:8088/services/collector/raw?channel={{SPLUNK_TOKEN}}"
Header "Authorization: Splunk {{SPLUNK_TOKEN}}"
Format "JSON"
Metrics true
StoreRates true
</Node>
</Plugin>
service collectd restart
service collectd statusNew Metrics India Collectd_httpd
| msearch index="collectd_index"
| mcatalog values(metric_name) WHERE index="collectd_index"
| mstats avg(_value) WHERE index="collectd_index" metric_name=cpu.idle.valu
| mstats avg(_value) WHERE index="collectd_index" metric_name=cpu.idle.value span=5s
| mstats avg(_value) where index="collectd_index" metric_name=cpu.idle.value span=10m
| mstats avg(_value) where index="collectd_index" metric_name="cpu.*" span=10m prestats=true | stats avg(_value) by metric_name
| mstats avg(_value) WHERE index="collectd_index" metric_name=memory.free.value span=1d
msearch index="collectd_metrics" =>>> search msearch index="collectd_metrics"
(DEPRECATED) | msearch index="collectd_metrics"
| mpreview index="collectd_metrics"
| mpreview index="collectd_index"
| mstats avg(_value) WHERE index="collectd_index" metric_name=cpu.idle.value span=5s
find / -type f -exec grep -l abcd {} \;
Explanation:
- Metrics are a type of data in Splunk that is specifically optimized for high-volume, time-series data, such as performance data from servers, applications, and networks. Unlike event data, which is typically unstructured text, metrics data is structured, meaning it has defined fields like metric name, value, and timestamp.
- Metrics are stored in metric indexes (metrics type index), which are optimized for storage and retrieval of time-series data, allowing for faster search and analysis.
cpu.usage, memory.used).host, region, service.mstats Commandmstats command is used to search metric data. It is similar to the stats command but optimized for metrics.| mstats avg(cpu.usage) WHERE index=metrics_index GROUPBY hostmcatalog Commandmcatalog command is used to explore the available metrics, such as listing metric names, dimensions, and available indexes.| mcatalog values(metric_name) WHERE index=metrics_indexmsearch Commandmsearch command is another way to search metrics, allowing you to perform advanced searches across multiple metric indexes.| msearch index=metrics_index metric_name="cpu.usage" | stats avg(_value) by hostcpu.usage metric and calculates the average value by host.timechart with Metricstimechart command can also be used with metrics to create time-series visualizations.| mstats avg(cpu.usage) WHERE index=metrics_index BY host | timechart avg(cpu.usage) by hostmetasearch Commandmetasearch command is used for searching metadata about metrics. It’s useful for quickly finding out what kind of metrics data is available without diving into the raw data.| metasearch index=metrics_index | stats count by metric_namemcollect Commandmcollect command is used to collect or store metrics data into a metrics index.| stats avg(cpu.usage) as avg_cpu by host | mcollect index=metrics_indexmstats queries with alerts to monitor critical metrics like CPU usage, memory usage, and application performance.By understanding and using these SPL commands, you'll be able to effectively work with metrics data in Splunk, enabling high-performance monitoring and analysis of time-series data.