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DDG fundamentals > Getting Started > Flashcards

Getting Started Flashcards

1
Q

What does the host map allow you to do? (4)

A

Quickly visualize your environment
Identify outliers
Detect usage patterns
Optimize resources

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2
Q

Why is tagging important for infra monitoring

A

All machines show up in the infrastructure list.
You can see the tags applied to each machine.
Tagging allows you to indicate which machines have a particular purpose.
Datadog attempts to automatically categorize your servers. If a new machine is tagged, you can immediately see the stats for that machine based on what was previously set up for that tag. Read more on tagging.

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3
Q

APM

A

Provides you with deep insight into your application’s performance:
- from automatically generated dashboards for monitoring key metrics, like request volume and latency,
- to detailed traces of individual requests

side by side with your logs and infrastructure monitoring.
When a request is made to an application, Datadog can see the traces across a distributed system, and show you systematic data about precisely what is happening to this request.

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4
Q

Events Explorer

A

The Event Explorer displays the most recent events generated by your infrastructure and services.

You can also submit your own custom events using the Datadog API, custom Agent checks, DogStatsD, or the Events email API.

In the Event Explorer, filter your events by facets or search queries. Group or filter events by attribute and graphically represent them with event analytics.

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5
Q

Events from Events Explorer can include (4)

A
  • Code deployments
  • Service health changes
  • Configuration changes
  • Monitoring alerts

The Event Explorer automatically gathers events collected by the Agent and installed integrations.

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6
Q

NPM

A
  • gives you visibility into your network traffic across any tagged object in Datadog: from containers to hosts, services, and availability zones.
  • Group by anything—from datacenters to teams to individual containers.
  • Use tags to filter traffic by source and destination. The filters then aggregate into flows, each showing traffic between one source and one destination, through a customizable network page and network map.
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7
Q

What does each NPM flow contain, and what doe they report?

A

network metrics such as throughput, bandwidth, retransmit count, and source/destination information down to the IP, port, and PID levels.

It then reports key metrics such as traffic volume and TCP retransmits.

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8
Q

RUM

A

allows you to visualize and analyze real-time user activities and experiences.

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9
Q

Session Replay

A

Allows you to capture and view the web browsing sessions of your users to better understand their behavior.

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10
Q

RUM explorer allows you to

A

not only visualize load times, frontend errors, and page dependencies, but also correlate business and application metrics to troubleshoot issues with application, infrastructure, and business metrics in one dashboard.

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11
Q

Serverless

A
  • Lets you write event-driven code and upload it to a cloud provider, which manages all of the underlying compute resources.
  • brings together metrics, traces, and logs from your AWS Lambda functions running serverless applications into one view, so that you can optimize performance by filtering to functions that are generating errors, high latency, or cold starts.
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12
Q

Cloud SIEM

A
  • automatically detects threats to your application or infrastructure.
  • ex: For example, a targeted attack, an IP communicating with your systems matching a threat intel list, or an insecure configuration.
  • These threats are surfaced in Datadog as Security Signals and can be correlated and triaged in the Security Explorer
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13
Q

Synthetics

A
  • allow you to create and run API and browser tests that proactively simulate user transactions on your applications and monitor all internal and external network endpoints across your system’s layers.
  • You can detect errors, identify regressions, and automate rollbacks to prevent issues from surfacing in production.
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14
Q

About the agent

A
  • it’s software that runs on your hosts.
  • It collects events and metrics from hosts and sends them to Datadog, where you can analyze your monitoring and performance data.
  • It can run on your local hosts (Windows, MacOS), containerized environments (Docker, Kubernetes), and in on-premises data centers.
  • You can install and configure it using configuration management tools (Chef, Puppet, Ansible).
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15
Q

How much & how often does the agent collect system level metrics

A

The Agent is able to collect 75 to 100 system level metrics every 15 to 20 seconds.

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16
Q

What can the agent collect?

A

System level metrics
+
With additional configuration, the Agent can send live data, logs, and traces from running processes to the Datadog Platform.

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17
Q

Where can I find info on the agent

A

The Datadog Agent is open source and its source code is available on GitHub at DataDog/datadog-agent.

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18
Q

Agent overhead

A

The amount of space and resources the Agent takes up depends on the configuration and what data the Agent is configured to send.
- At the onset, you can expect around 0.08% CPU used on average with a disk space of roughly 830MB to 880MB.

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19
Q

What agent metrics are sent by the agent about himself?

A

The following Agent metrics are information the Agent sends to Datadog about itself
- datadog.agent.python.version (Shows 1 if the Agent is reporting to Datadog. The metric is tagged with the python_version.)
- datadog.agent.running (Shows 1 if the Agent is reporting to Datadog)
datadog.agent.started (reports 1 when the Agent starts (available in v6.12+).)

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20
Q

Why are agent metrics useful?

A

Help you determine things like:
- what hosts or containers have running Agents
- when an Agent starts
- what version of Python it’s running.

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21
Q

Differences between installing the agent on a host or in a containerized environment

A
  • On a host:
    1. Agent is configured using a YAML file
    2. integrations are identified through the Agent configuration file
  • Container:
    1. Agent configuration options for a container’s Agent are passed in with environment variables, for example:
    » DD_API_KEY for the Datadog API key
    » DD_SITE for the Datadog site
    2. integrations are automatically identified through Datadog’s Autodiscovery feature
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22
Q

Why it it recommended I install the Agent? (2)

A
  • Agent needs to be installed to send data from any one of the many Agent based Integrations.
  • the Agent is the recommended method to forward your data to the Datadog Platform.
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23
Q

How many enabled checks does the Agent have?

A

Agent has several checks enabled which collect over 50 default metrics to provide greater insight on system level data.

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24
Q

What are the prerequisites required to install the agent?

A
  • Create a Datadog account.
  • Have your Datadog API key on hand.
  • Have the Datadog UI open.
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25
Q

How do you install the agent

A

To install the Datadog Agent on a host, use the one-line install command from that page, updated with your Datadog API key.

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26
Q

What message displays in your Events Explorer if your Agent successfully installs?

A

Datadog agent (v. 7.XX.X) started on <Hostname></Hostname>

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27
Q

What service checks is the agent set up to deliver?

A

datadog.agent.up: Returns OK if the Agent connects to Datadog.

datadog.agent.check_status: Returns CRITICAL if an Agent check is unable to send metrics to Datadog, otherwise returns OK.

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28
Q

What are the agent service checks useful for?

A

These checks (datadog.agent.up: & datadog.agent.check_status: ) can be used in the Datadog Platform to visualize the Agent status through monitors and dashboards at a quick glance

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29
Q

Which metrics shows that the agent is running?

A

datadog.agent.started
or
datadog.agent.running

30
Q

What are tags and why are they used?

A
  • Tags add an additional layer of metadata to your metrics and events.
  • They allow you to scope and compare your data in Datadog visualizations.
  • When data is sent to Datadog from multiple hosts, tagging this information allows you to scope down to the data you are most interested in visualizing.
31
Q

How to configure tags in the agent’s configuration file

A
  1. Locate your Agent’s main configuration file. For Ubuntu, the file locations is /etc/datadog-agent/datadog.yaml.
  2. In the datadog.yaml file, locate the tags parameter.
    Host level tags can be set in the datadog.yaml configuration to apply tags on all metrics, traces and logs forwarded from this host.
  3. Add your tags
  4. Restart the Agent by running the Agent’s restart command.
  5. Go back to Metrics summary, you should be able to see your tags now
  6. Go to Events > Explorer and find the custom tags displayed with the latest Agent Event.
32
Q

How to configure log, trace & processes collection

A

The collection of logs, traces, and processes data can be enabled through the Agent configuration file.
These are not features that are enabled by default.
For example, in the configuration file, for the logs_enabled parameter, it is set to false.

33
Q

What other features can be configured through the agent configuration file?

A
  • Enabling OTLP Trace Ingestion
  • Customizing log collection to filter or scrub sensitive data
  • Configuring custom data through DogStatsD
34
Q

Is the Agent required to forward data to the Datadog Platform?

A

No, for example, you can send Logs and Metrics through the Datadog API.

35
Q

What is Datadog’s Autodiscovery feature?

A
  1. It automatically identify the services running on a specific container and gather data from those services.
    (very useful as the dynamic nature of containerized systems makes them difficult to manually monitor)
  2. lets you define configuration templates for Agent checks and specify which containers each checks should apply to.

3.

36
Q

What does Autodiscovery do when a new container starts running?

A

the Datadog Agent identifies which services are running on this new container, looks for the corresponding monitoring configuration, and starts to collect metrics.

37
Q

What should you do if Autodiscovery isn’t working?

A

verify the detected features by running agent status.

38
Q

What is the Datadog Operator? (4)

A
  • It’s an open source Kubernetes Operator that enables you to deploy and configure the Datadog Agent in a Kubernetes environment.
  • It allows you to use a single Custom Resource Definition (CRD) to deploy the node-based Agent, Cluster Agent, and Cluster Checks Runner.
  • It reports deployment status, health, and errors in the Operator’s CRD status.
  • Because the Operator uses higher-level configuration options, it limits the risk of misconfiguration.
39
Q

Once you have deployed the Agent, the Datadog Operator provides the following benefits: (5)

A
  • Validation for your Agent configurations
  • Keeping all Agents up to date with your configuration
  • Orchestration for creating and updating Agent resources
  • Reporting of Agent configuration status in the Operator’s CRD status
  • Optionally, use of an advanced DaemonSet deployment by using Datadog’s ExtendedDaemonSet.
40
Q

Why use the Datadog Operator instead of a Helm chart or DaemonSet? (4)

A
  • The Operator has built-in defaults based on Datadog best practices.
  • Operator configuration is more flexible for future enhancements.
  • As a Kubernetes Operator, the Datadog Operator is treated as a first-class resource by the Kubernetes API.
  • Unlike the Helm chart, the Operator is included in the Kubernetes reconciliation loop.
41
Q

Prerequisites for using the DDG Operator? (3)

A
  • Kubernetes v1.14.X+
  • Helm for deploying the Datadog Operator
  • The Kubernetes command-line tool, kubectl, for installing the Datadog Agent
42
Q

What is Unified Service Tagging & why is it useful (3)?

A

Unified service tagging ties Datadog telemetry together through using three reserved tags: env, service, and version.

With these three tags, you can:
- Identify deployment impact with trace and container metrics filtered by version
- Navigate seamlessly across traces, metrics, and logs with consistent tags
- View service data based on environment or version in a unified fashion

43
Q

What is Serverless?

A

It’s a model where developers build and run applications and services using a cloud provider, rather than managing infrastructure themselves.

44
Q

What does Datadog Serverless allow you to do?

A

It collects metrics, logs, and traces from your serverless infrastructure, enabling you to monitor your application’s health and performance

45
Q

What is an integration used for?

A

At Datadog, you can use integrations to bring together all of the metrics and logs from your infrastructure and gain insight into the unified system as a whole—> you can see pieces individually and also how individual pieces are impacting the whole.

46
Q

What are the 3 main types of integration provided by Datadog?

A
  1. Agent-based: installed with the Datadog Agent
  2. Authentication (crawler) based: are set up in Datadog where you provide credentials for obtaining metrics with the API.
  3. Library integrations: use the Datadog API to allow you to monitor applications based on the language they are written in, like Node.js or Python.
47
Q

What python class method do agent-based integrations use, and why?

A

use a Python class method called check to define the metrics to collect.

48
Q

Common examples of authentication/crawler based integrations

A

some examples include Slack, AWS, Azure, and PagerDuty.

49
Q

How can you send metrics to DDG from your in house system?

A

Using custom checks

50
Q

Difference between integrations core and integrations extra?

A

Integrations core = integrations officially supported by Datadog included in the DD agent package. To use those integrations, download the Datadog Agent

Integrations extra = community-based integrations

51
Q

How do I know if an integration can be auto-detected?

A

auto-detected integrations are displayed in the Integrations search

52
Q

How can you integrate an Amazon Web Services(AWS) account with Datadog?

A

Using Datadog’s CloudFormation template

53
Q

How can you send AWS service logs to DDG?

A

There are two ways:

  1. Kinesis Firehose destination: Use the Datadog destination in your Kinesis Firehose delivery stream to forward logs to Datadog. Recommended when sending logs from CloudWatch in a very high volume.
  2. Forwarder Lambda function: Deploy the Datadog Forwarder Lambda function, which subscribes to S3 buckets or your CloudWatch log groups and forwards logs to Datadog.
54
Q

When should you send AWS service logs to DDG using a forwarder lambda function? (2)

A
  • This is the required approach to send traces, enhanced metrics, or custom metrics from Lambda functions asynchronously through logs.
  • Also recommended for sending logs from S3 or other resources that cannot directly stream data to Kinesis.
55
Q

How can you find your AWS service logs in log explorer

A

Once logs have been enabled, find them using either the source or service facets from the facet panel, such as this example from S3

56
Q

How do you enable log collection?

A
  • install the agent (if not already done)
  • set logs_enabled to true in your datadog.yaml file.
  • restart the agent
  • Follow the integration activation steps or the custom files log collection steps on the Datadog site.
57
Q

Which version of the agent is required for log collection?

A

Log collection requires Datadog Agent v6+.

58
Q

What are the two ways you can install the agent on Docker?

A
  1. on your host, where the Agent is external to the Docker environment
  2. deploying a containerized version of the Agent in your Docker environment.
59
Q

How can you configure log collection in k8s?

A
  • requires that the Datadog Agent run in your Kubernetes cluster
  • log collection can be configured using a DaemonSet spec, Helm chart, or with the Datadog Operator.
60
Q

How are AWS service logs generally stored and how do you send them to DDG?

A
  • usually stored in S3 buckets or CloudWatch Log groups
  • You can subscribe to these logs and forward them to an Amazon Kinesis stream to then forward them to one or multiple destinations.
61
Q

How does DDG collect logs from clients?

A

Through SDKs or libraries.
For example, use the datadog-logs SDK to send logs to Datadog from JavaScript clients.

62
Q

How do you send logs from rsyslog, Fluentd, or Logstash to DDG?

A

Datadog offers plugins and log forwarding options.

63
Q

Once alogging source is configured, where should you go in DDG to see your logs?

A

The Log Explorer

64
Q

Is APM enabled by default?

A

For the latest versions of Agent v6 and v7, APM is enabled by default. You can see this in the Agent datadog.yaml configuration file:

65
Q

What does Unified Service Tagging do?

A

Ties Datadog telemetry together through the use of three standard tags: env, service, and version

It allows you to:
- Identify deployment impact with trace and container metrics filtered by version
- Navigate seamlessly across traces, metrics, and logs with consistent tags
- View service data based on environment or version in a unified fashion

66
Q

What are the ways tags can be configured in DDG?

A
  • In the Datadog Agent configuration file or each individual integration configuration file
  • Through the Datadog UI (host map, infra list, monitors, distribution metrics, integrations)
  • With the Datadog API
  • With the DogStatsD
67
Q

What’s the DDG API?

A
  • It allows you to get data in and out of Datadog.
  • It uses resource-oriented URLs and status codes to indicate the success or failure of requests, then returns JSON from all requests.
68
Q

Synthetics - definition

A

allow you to observe how your systems and applications are performing using simulated requests and actions from around the globe.

69
Q

What are the types of synthetics tests offered by DDG?

A
  • API tests
  • Multistep API tests
  • Browser tests.
70
Q

What do multi-step API tests enable you to do?

A

Run HTTP tests in sequence to monitor the uptime of key journeys at the API level.

71
Q

What are browser tests?

A

= Scenarios that Datadog executes on your web applications.
You can configure periodic intervals to run tests from multiple locations, devices, and browsers as well as execute them from your CI/CD pipelines.

72
Q

What do private locations allow you to do?

A

Monitor internal-facing applications or private URLs that aren’t accessible from the public internet.

DDG fundamentals (2 decks)

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