(Senior DevOps Manager & Principal Architect)
Rajesh Kumar — an award-winning academician and consultant trainer, with 15+ years’ experience in diverse skill management, who has more than a decade of experience in training large and diverse groups across multiple industry sectors.
The agent - It gathers system metrics, integrates with key software we use, and provides a standard interface to which our applications can send custom metrics.
Integrations- Datadog has prebuilt integrations to pull data from almost every important service we use.
Events - Through the integrations datadog generates a consolidated event stream that we can filter and earch as needed.
Dashboards - Datadog lets us build dashboards that combine metrics from many different sources. We can combine and transform metrics to make them more useful. It also provides an powerful interface for interactive exploration of metrics.
Alerting - Datadog has nice stream processing capabilities for generating alerts, and it can surface them in services we use like pagerduty and slack.
The agent - We don't get nearly enough insight from cloudwatch alone, we need an on-instance tool to gather system and app metrics.
Integrations - There are lots of services with operational signficiance, but many of them don't provide a good way to access their data.
Events - We would spend dramatically longer investigating problems if we had to look at eash source of events in isolation. Many of our event sources don't even provide a way for us to view past events or to query them.
Dashboards - Per-service and per-instance dashboards are important for investigating problems quickly. The consolidation of data from multiple sources is again a key feature.
Alerting - We need to do anaylze trends in our metrics and alert on them.
The agent - The agent is deployable via a chef cookbook datadog wrote for us. It requires minimal configuration. It knows which system and application metrics are worth gathering.
Integrations - Integrating with all the data sources is literally a few clicks.
EventsThe interface makes searching and filtering events straightforward.
Dashboards - There are prebuilt dashbaords for lots of things we care about. Snazzy features like autocomplete and templating make building our own dashboards easy.
Alerting - The guided steps and previewed outputs make creating alerts simple.
Here I described a system of collectd, custom code to pull metrics from cloudwatch, custom code to pull or receive events from various sources (airbrake, cloudtrail, chef, pagerduty, jenkins, etc) influxdb, and grafana.
backend is built using a number of open and closed source technologies including D3, Apache Cassandra, Kafka, PostgreSQL, etc.
Datadog uses a Go based agent, rewritten from scratch since its major version 6.0.0 released on February 28, 2018. It was formerly Python based.
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The Agent is lightweight software installed on your hosts. It reports metrics and events from your host to Datadog via….
# With additional setup, the Agent can report live processes, logs, and traces.
In v6, DogStatsD is a Golang implementation of Etsy’s StatsD metric aggregation daemon. It is used to receive and roll up arbitrary metrics over UDP or Unix socket, thus allowing custom code to be instrumented without adding latency
The easiest way to get your custom application metrics into Datadog is to send them to DogStatsD, a metrics aggregation service bundled with the Datadog Agent. DogStatsD implements the StatsD protocol and adds a few Datadog-specific extensions:
Any compliant StatsD client works with DogStatsD and the Agent, but you won’t be able to use the Datadog-specific extensions.
DogStatsD is enabled by default over UDP port 8125 for Agent v6+. By default, DogStatsD listens on UDP port 8125. You can also configure DogStatsD to use a Unix domain socket. To enable a custom Agent DogStatsD server UDP port:
The collector gathers all standard metrics every 15 seconds. Agent v6 embeds a Python 2.7 interpreter to run integrations and custom checks.
The Agent forwarder send metrics over HTTPS to Datadog. Buffering prevents network splits from affecting metric reporting.
Metrics are buffered in memory until a limit in size or number of outstanding send requests are reached.
Afterwards, the oldest metrics are discarded to keep the forwarder’s memory footprint manageable. Logs are sent over an SSL-encrypted TCP connection to Datadog.
Agent v5 is composed of four major components, each written in Python running as a separate process:
Agent v6 and v7 are composed of a main process responsible for collecting infrastructure metrics, logs, and receiving DogStatsD metrics. The main components to this process are:
Two optional processes are spawned by the Agent if enabled in the datadog.yaml configuration file:
You can configure the port on which the GUI runs in the datadog.yaml file. To disable the GUI, set the port’s value to -1. For Windows and macOS, the GUI is enabled by default and runs on port 5002. For Linux, the GUI is disabled by default.
When the Agent is running, use the datadog-agent launch-gui command to open the GUI in your default web browser.
With Agent v6+, the command line interface is based on subcommands. To run a subcommand, first invoke the Agent binary:
Agent Configuration Files
Datadog Agent: datadog.yaml
Agent Configuration Files
Agent configuration directory
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3. Datadog Infrastructure Monitoring Part -2