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Z: Cloud Computing > L7 - Cloud Monitoring 2/2 > Flashcards

L7 - Cloud Monitoring 2/2 Flashcards

1
Q

What is Logstash

A

Logstash = light-weight, open-source, server-side data processing pipeline that allows you to collect data from a variety of sources, transform it on the fly, and send it to your desired destination.

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2
Q

X-pack

A

X-Pack brought a number of deeply integrated enterprise capabilities to the Elastic Stack which included security, alterting, monitoring, graph analytics etc.

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3
Q

What is Beats?

A

(lightweight, single-purpose data shippers)

agents to collect data
filebeat for logs
metricbeat for metrics
heartbeat for health
elastic search provides integration for data injection and major systems

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4
Q

What is Prometheus for Metrics?

A

Prometheus is an open source monitoring system
https://prometheus.io
Initially built by soundcloud.com now a Cloud Native Foundation project

Features
Metric collection in form of time series
Storage by a time series database
Query language for accessing the time series
Alerting
Visualization

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5
Q

Cloud Native Computing Foundation

A
  • pushes for a sustainable ecosystem for Cloud Native Computing
  • hosts several fast-growing open source projects including Kubernetes, Prometheus and Envoy
  • runs CloudNativeCon
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6
Q

Prometheus Architecture

A
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7
Q

Prometheus Scraping

A
  • metrics retrieved through /metrics endpoint
    metric types:
  • counter: cumulative metric montonically increasing
  • gauge: numerical value arbitrarily gone up and down
  • histogram: counts for buckets, total sum, # events
  • metric name and labels define a time series
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8
Q

Prometheus Exporters

A

exporters allow to provide metrics for services that cannot be instrumented

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9
Q

Prometheus Alerting

A
  • separated in prometheus server and alertmanager
  • rules determine an alert
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10
Q

What is Prometheus Alertmanager?

A
  • manages alerts, including silencing, inhibition, grouping and sending out notifications
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11
Q

What is Prometheus Scalability?

A
  • hierarchical federation of servers
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12
Q

How does visualization with Grafana work?

A
  • open-source
  • prometheus can be used as a data source (connect through ip-address and port; create your own dashboard)
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13
Q

What do Borgmon instances do?

A
  • receives a list of target services e.g. from a discovery service
  • periodically collects the service monitoring interface:
    (the collection is distributed over the period;
    it decodes the results and stores them in memory as a time series)
  • metrics are counters and gauges
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14
Q

What are gauges?

A

instantaneous measurements e.g. CPU utilization

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15
Q

How does time series storage work?

A
  • each metric is stored in a time series
  • entries are (timestamp, value) pairs
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16
Q

What is in-memory buffering for Google Borgmon?

A
  • local buffer is designed to hold the time series for a certain time horizon
  • oldest entries are deleted once the horizon is reached
17
Q

How to compute the ratio of error to requests?

A
  1. Aggregate rates of response codes across instances
  2. Compute error rate for entire cluster
  3. Compute ratio of error to requests
18
Q

Alerting rules in Borgmon

A
  • specify a condition for alerting
  • minimum duration of alerting situation (alerts have pending and fire state)
19
Q

Borgmon summary concerning monitoring and usage

A

monitoring system:
- provides measurements of metrics
- stores measurements as time series
- rules for aggregation
- hierarchical design for scalability

usage:
- alerting
- dashboard

20
Q

What is Amazon CloudWatch?

A
  • monitoring and management system
  • collects:
  • metrics and logs
21
Q

What are some preselected metrics in Amazon CloudWatch?

A
  • CPU utilization
  • read/write latency
  • request counts and latency for LB
22
Q

How can you access the management console for Amazon CloudWatch?

A
  • CLI
  • Web service API
  • Libraries for Java etc.
23
Q

What is Google Dapper?

A

For distributed tracing.

24
Q

What is tracing?

A
  • capture the interaction of different services
  • capture the individual events e.g. submit a request, receive the request, start processing, submit answer, receive answer
  • associate events with a given request to be able to analyze the execution of this request
25
Q

What are the pros of Google Dapper?

A
  • continuous and ubiquitous tracing
  • low-overhead
  • application transparency
  • scalability
  • can collect payload data if added by application developer
  • can be used to enforce security policies (authentication and encryption)
  • allows for runtime verification that provides greater assurance than source code audits
26
Q

What is a Dapper trace tree?

A
  • nodes are called spans: lifetime of a request
  • edges indicate temporal relationship
27
Q

What is there to know about spans?

A
  • represented as remote procedure call (RPC)
  • attributes:
  • span id: identifies a span
  • parent id: span id of triggering span
  • trace id: identifies triggering request
28
Q

What are the advantages of Dapper?

A
  • Dapper does not collect any payload data
  • Dapper can be used to enforce security policies
  • Such runtime verification provides greater assurance than source code
    audits
  • Continuous and ubiquitous tracing
  • Low-overhead
  • Application transparency
  • Scalability
29
Q

Why is there overhead in Dapper?

A
  • trace generation and collection
  • amount of resources to store and analyze trace data
30
Q

How to reduce overhead in Dapper?

A
  • coalesce events: multiple trace events are coalesced to a log file write operation
  • asynchronous writes: writes are asynchronous to the traced application
  • adaptive sampling at the application (only a certain rate of requests per second are captured)
31
Q

What can Dapper Depot API be used for?

A
  • access to traces via trace id
32
Q

What is an open-source alternative to Dapper?

A

Open Telemetry

Z: Cloud Computing (26 decks)

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