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OPENSOURCE.COM
The Open Source Guide to
DevOps Monitoring Tools
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ABOUT THE AUTHOR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
DAN BARKER
DAN SPENT 12 YEARS in the military as a fighter jet mechanic
before transitioning to a career in
technology as a Software Engineer. He's now the Chief Architect at the National
Association of Insurance Commissioners (NAIC).
He's leading technical and cultural transformations
for the NAIC, a nonprofit organization focused on
consumer protection in the insurance industry.
He's an active participant in the CNCFs Serverless
Working Group and CloudEvents project. Dan is
also an organizer of the DevOps KC Meetup and
the DevOpsDays KC conference.
CONTACT DAN
Website: http://danbarker.codes
Email: dan@danbarker.codes
Twitter: https://twitter.com/@barkerd427
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INTRODUCTION
A tale of two views 6
CHAPTERS
4 open source monitoring tools 8
3 open source log aggregation tools 12
5 alerting and visualization tools 15
3 open source distributed tracing tools 20
GET INVOLVED | ADDITIONAL RESOURCES
Get involved | Additional Resources 22
Write for Us | Keep in Touch 23
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A TALE OF TWO VIEWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A tale of two views
ONCE UPON A TIME, I was trouble-
shooting some
vexing problems in an application that needed to be scaled
You might think the second situation happened a long time
after the first and we had improved over time. Or maybe you
suspect that when I changed jobs, my new company had
several orders of magnitude with only a couple of weeks to better tooling. In reality, the second situation happened be-
re-architect it. We had no log aggregation, no metrics ag- fore the first. I moved from a company with fairly advanced
gregation, no distributed tracing, and no visualization. Most observability tools to one with no observability tools. It was
of our work had to be done on the actual production nodes strikingly disturbing as the developer to have an application
using tools like strace and grepping through logs. These in production and know nothing about it. I learned a lot about
are great tools, but they don’t make it easy to analyze a dis- the importance of system observability and the related tools
tributed system across doz- as I began rebuilding that
ens of hosts. We got the job infrastructure. Also, Mike Ju-
done, but it was painful and lian’s Practical Monitoring [1]
involved a lot more guessing is a must-read for those who
and risk than I’d prefer. want to know more about
At a different job, I was help- their systems.
ing to troubleshoot an app in
production that was suffering Observability principles
from an out of memory (OOM) So, what are observability
issue. The problem was in- tools? Actually, what is
consistent, as it didn’t seem observability?
to correlate with running time, Observability isn’t just a
load, time of day, or any other marketing term; it’s a com-
aspect that would provide some predictability. This was obvi- ponent of control theory [2]. If you want to get a quick primer,
ously going to be a difficult problem to diagnose on a system this video [3] might be helpful. Basically, observability means
that spanned hundreds of hosts with many applications calling that you can estimate a particular state of a system based
it. Luckily, we had log aggregation, distributed tracing, metrics on an output. More generally, a system’s state should be de-
aggregation, and a plethora of visualizations. We looked at terministic from its outputs. Controllability, the mathematical
our memory graph and saw a distinct spike in memory usage, dual of observability [4], of a system requires that a system
so we used that spike to alert us so we could diagnose the state be determined by the inputs to the system.
issue in real time when it occurred. This is a fairly simple concept, but it’s very challenging to
When we received an alert, we went to our log aggrega- put into practice. In a sufficiently complex system, it may be
tion system to correlate the logs to the memory spike. We nearly impossible to implement full observability. However,
found the OOM error and the related calls around it. We now you should strive to get the right outputs that allow you to de-
understood what application was calling the service that re- termine the system’s state, especially when you encounter
sulted in the spike and used that information to find the exact a failure mode.
transaction that caused the issue. We determined that some-
one had stored a huge file in a database that our service Observability tool types
was now trying to load, but the service was running out of Over the next few chapters we’ll dig into different types of
memory before it could fully load and process the record. We observability tools. For each type, we’ll cover what they’re
should have been defending against this in the first place, used for, what specific tools are available, some use cas-
but we were happy to find it so quickly and fix it with very little es, and any unique characteristics that may come up during
effort. Once we understood the error, we discovered a lot of your search for a new tool. These are presented in the order
records had large files like this, and we didn’t need that part you should implement them. Metrics aggregation is first, as
of the record to function properly. it’s often easy to instrument an application built with any
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modern language. Second is logging because it will require If your tool doesn’t have it yet, you may want to look else-
more application modifications but provides tremendous where. Tools that haven't implemented this specification or
value. Third is alerting and visualizations, which require the don't have it on their roadmap likely have other deficiencies
first two types for full functionality. And last is distributed trac- in adopting open, modern standards and code.
ing, as it may not be necessary in a simple monolith and is
much harder to implement fully. Open source
There are a lot of good tools in this space that aren’t open
Metrics aggregation source but may be the right fit for your company. If you
This type of tool generally consists of time-series data. pick one of those tools, make sure its documentation and
Time-series data is time-ordered data, and it is normally col- accessory tooling are open source. Open source observ-
lected with an internally consistent interval. This consistency ability tools can provide valuable insights into how your
allows for some advanced calculations to be applied to the other observability tools are functioning (or maybe not
series and provides for predictive analytics using simple functioning). They also offer all the other benefits of any
regressions or more advanced algorithms. open source project which you can read more about on
opensource.com [5].
Log aggregation
These tools deal with data types that are related more to Open standards
events than to a series of consistent data points. This output Regardless of whether or not a tool is open source, it should
is often emitted as a system enters some undesired state. always use open standards when possible. We’ve already
Some systems output a lot of logs that don’t fit this condi- discussed one of these, OpenAPI, but there are many more.
tion. We’ll cover more of the do’s and don’ts of logging in a We’ll discuss these standards in the appropriate sections to
future chapter. ensure you know they exist and where they’re used.
Alerting/visualizations Wide dissemination
This may not appear to fit with the other types listed, as it’s Part of observability and openness is allowing everyone to
really subsequent to the others, but it provides a consum- view data. The tools you pick should be open by default.
able output for the other types and can produce its own You may want to restrict some areas, but you’ll want to de-
outputs. These types of tools generally make the system fault to open and limit access only if it’s absolutely required.
more understandable to humans. They also help create a You never know who in your company might want to solve
more interactive system through both proactive and reac- your problem or who you’ll need to bring in to help solve
tive notifications about negative system states. a problem. The last thing you’ll want are access barriers
when troubleshooting your income source.
Distributed tracing
Much like tracing within a single application, distributed trac- Federated model (preferred)
ing allows you to follow a single transaction through an entire This is similar to defaulting to open, but it allows everyone
system. This allows you to home in on specific transactions to provide input and control their own areas more locally.
that might be experiencing problems. Due to performance Many legacy systems are architected in a way that requires
concerns, a sampling algorithm is often applied. all data to flow through a central system regardless of need.
This also centralizes control around that data. A federated
Common DevOps features system allows for local aggregation, processing, and control
There are several aspects you should look for in any type of while allowing a central organization to collect the same data
observability tool. We’ll cover these generally now and will or summarized data. The central system likely only wants
bring them back up in later chapters. a subset of the data stored at the local level. This model
increases agility, flexibility, and usability.
OpenAPI In the following chapters, we’ll be exploring each of the
This specification was previously called Swagger but re- observability tool types in more detail. We’ll also help you
named when it was adopted by the OpenAPI Initiative choose the right tool for your use case.
within the Linux Foundation. The OpenAPI Specification is
a language-agnostic tool that can automatically generate Links
documentation of methods, parameters, and models. This [1]
https://www.practicalmonitoring.com/
is commonly used to generate RESTful interfaces in HTTP, [2]
https://en.wikipedia.org/wiki/Control_theory
but it is also protocol-agnostic. A user can create a client in [3]
https://www.youtube.com/watch?v=iRZmJBcg1ZA
almost any language if one doesn't already exist. Every tool [4]
https://en.m.wikipedia.org/wiki/Duality_(mathematics)
should have this type of API (or should be getting it soon). [5]
https://opensource.com/
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4 open source
monitoring tools
ISN’T MONITORING JUST MONITORING? Doesn’t
it include logging, vi-
sualization, and time-series data? The terminology around
Quantiles
Quantiles aren’t a type of metric, but they’re germane to
the next two sections, histograms and summaries. Let’s
monitoring has caused a lot of confusion over the years clarify our understanding of quantiles with an example. A
and has led to some poor tools that tout the ability to do ev- percentile is a type of quantile. Percentiles are something
erything in one format. Observability proponents recognize we see regularly, and they should help us understand the
there are many levels for observing a system. Metrics ag- general concept more easily. A percentile has 100 “buck-
gregation is primarily time-series data, and that’s what we’ll ets” of values. We often see them related to testing or per-
discuss in this chapter. formance and generally stated as someone scoring within
the 85th percentile or some other value. This means the
Features of time-series data person scoring within that percentile had a real value that
fell within the bucket between the 85th and 86th percen-
Counters tile. This person also scored in the top 15% of all students.
A counter is a metric that represents a numeric value that We don’t know the scores in the bucket based off this met-
will only increase. (In other words, a counter should never ric, but that can be derived based on the sum of all scores
decrease.) Counters accumulate values and present the in the bucket divided by the count of those scores. Quan-
current total when requested. These are commonly used tiles allow us to understand our data better than using a
for things like the total number of web requests, number of mean or some other statistical function that doesn’t take
errors, number of visitors, into account outliers and
etc. This is analogous to the uneven distributions.
person with a counter device
standing at the entrance to an Histograms
event counting all the people A histogram is a little more
entering. There is generally complicated than a counter
no option to decrement the or a gauge. It is a sample of
counter without resetting it. observations. It consists of
a counter, which counts all
Gauges the observations, and what
A gauge is similar to a count- is essentially a gauge that
er in that it represents a sin- sums the values of the ob-
gle numeric value, but it can servations. It uses “buckets”
also decrease. It is essential- or groupings to segment the
ly a representation of some value at a point in time. A ther- values in order to bound the datasets in a productive way.
mometer is a good example of a gauge. It moves up and This is commonly seen with quantiles related to request
down with the temperature and offers a point-in-time read- service-level agreements (SLAs). Let’s say we want to en-
ing. Other uses include CPU usage, memory usage, network sure 95% of our requests are below 500ms. We could use
usage, and number of threads. a bucket with an upper bound of 0.5s to collect all values
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that fall under 500ms. We would then be able to determine collecting the data and updating that representation upon
how many of the total requests have fallen into that bucket. each request (or however the client is configured). This data
We can also determine how far we are from our SLA, but is collected and saved in a highly efficient storage engine on
this can be difficult to do (as is explained more in the Pro- local disk. The storage system uses an append-only file per
metheus documentation [1]). metric. This storage isn’t lossy, which means the fidelity of
Histograms are aggregate metrics that are accumulated data from a year ago is as high as the data you are collecting
from multiple instances into a central server. This provides today. However, you may not want to keep that much data
an opportunity to understand the system as a whole rather locally. Fortunately, there is an option for remote storage for
than on a node by node basis. long-term retention and analysis.
Prometheus includes an advanced expression language
Summaries for selecting and presenting data called PromQL. This data
Summaries are similar to histograms in that they are a sam- can be displayed graphically, tabularly, or used by external
ple of observations, but the aggregation occurs on the serv- systems through a REST API. The expression language
er side. Also, the estimate of the quantile is more accurate allows a user to create regressions, analyze real-time data,
than in a histogram. A summary also uses a sliding time or trend historical data. Labels are also a great tool for fil-
window, so it serves a slightly different case than a histo- tering and querying data. Labels can be associated with
gram but is generally used for the same types of metrics. each metric name.
I normally use a histogram unless I need a very accurate Prometheus also offers a federation model, which encour-
measure of the quantile. ages more localized control by allowing teams to have their
own Prometheis while central teams [8] can also have their
Push/pull own. The central systems could scrape the same endpoints
No chapter can be written about metrics aggregation tools as the local Prometheis, but they can also scrape the lo-
without addressing the push vs. pull debate. What is it? The cal Proemetheis to get the aggregated data that the local
debate centers around whether it is better for your metrics instances are collecting. This reduces overhead on the end-
aggregation system to have data pushed to it or to have points. This federation model also allows local instances to
your metrics aggregation system reach out and gather the collect data from each other.
data by scraping an endpoint. Multiple articles discuss this Prometheus comes with AlertManager to handle alerts.
(like this one [2] and this one [3]). My perspective is that This system allows for aggregation of alerts as well as
it mostly doesn’t matter. Additional research is left to the more complex flows to limit when an alert is sent. Let’s
reader’s discretion. say 10 nodes suddenly go down at the same time a switch
goes down. You probably don’t need to send an alert
Tool options about the 10 nodes, as everyone who receives them will
There are many tools available, both open source and com- likely be unable to do anything until the switch is fixed.
mercial. We will focus on open source tools, but some of With the AlertManager, it’s possible to send an alert only
these have an open core model with a paid component. to the networking team for the switch and include addition-
Some of these tools feature additional components of al information about other systems that might be affected.
observability—principally alerting and visualizations. These It’s also possible to send an email (rather than a page) to
will be covered in this section as additional features and the systems team so they know those nodes are down and
won’t be covered in subsequent chapters. they don’t need to respond unless the systems don’t come
up after the switch is repaired. If that occurs, then Alert-
Prometheus Manager will reactivate those alerts that were suppressed
This is the most well-recognized time-series monitoring by the switch alert.
solution for cloud-native applications. It is hosted within the
Cloud Native Computing Foundation (CNCF), but it was Graphite
created by Matt Proud and Julius Volz and sponsored by Graphite [9] has been around for a long time, and the recent
SoundCloud, with external contributors coming in early to book The Art of Monitoring [10] covers Graphite in detail.
help develop it. Brian Brazil of Robust Perception [4] has Graphite has become ubiquitous in the industry, with many
built a business of helping companies adopt Prometheus. large companies using it at scale.
He also has an excellent blog [5] on his website. The Pro- Graphite is a push-based system that receives data
metheus documentation [6] is extensive and provides a lot from applications by having the application push the data
of detail for understanding and using the tool. into Graphite’s Carbon component. Carbon stores this
Prometheus [7] is a pull-based system that uses local con- data in the Whisper database, and that database and Car-
figuration to describe the endpoints to collect from and the bon are read by the Graphite web component that allows
interval desired for collection. Each endpoint has a client a user to graph their data in a browser or pull it through
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an API. A really cool feature is the ability to export these single host, while the commercial version is inherently
graphs as images or data files to easily embed them in distributed. This is true of the other central components
other applications. as well. In the open source version, everything runs on a
Whisper is a fixed-size database that provides fast, reliable single host. No data or configuration is stored on external
storage of numeric data over time. It is a lossy database, systems, so it is fairly easy to manage, but it isn’t as ro-
which means the resolution of your metrics will degrade over bust as the commercial version.
time. It will provide high-fidelity metrics for the most recent InfluxDB includes a SQL-like language called InfluxQL for
collections and gradually reduce that fidelity over time. querying data from the databases. The primary means for
Graphite also uses dot-separated naming, which implies querying data is the HTTP API. The query language doesn’t
dimensionality. This dimensionality allows for some creative have as many built-in helper functions as Prometheus, but
aggregation of metrics and relationships between metrics. those familiar with SQL will likely feel more comfortable with
This enables aggregation of services across different ver- the language.
sions or data centers and (getting more specific) a single The TICK stack also includes an alerting system. This sys-
version running in one data center in a specific Kubernetes tem can do some mild aggregation but doesn’t have the full
cluster. Granular-level comparisons can also be made to de- capabilities of Prometheus’ AlertManager. It does offer many
termine if a particular cluster is underperforming. integrations, though. Also, to reduce load on InfluxDB, con-
Another interesting feature of Graphite is the ability to store tinuous queries can be scheduled to store results of queries
arbitrary events that should be related to time-series metrics. that Kapacitor will pick up for alerting.
In particular, application or infrastructure deployments can
be added and tracked within Graphite. This allows the op- OpenTSDB
erator or developer troubleshooting an issue to have more OpenTSDB [15] is an open source time-series database,
context about what has happened in the environment related as its name implies. It’s unique in this collection of tools
to the anomalous behavior being investigated. in that it stores its metrics in Hadoop. This means it is
Graphite also has a substantial list of functions [11] that inherently scalable. If you already have a Hadoop cluster,
can be applied to metrics series. However, it lacks a powerful this might be a good option for metrics you want to store
query language, which some other tools include. It also lacks over the long term. If you don’t have a Hadoop cluster, the
any alerting functionality or built-in alerting system. operational overhead might be too large of a burden for
you to bear. However, OpenTSDB now supports Google’s
InfluxDB Bigtable as a backend, which is a cloud service you don’t
InfluxDB [12] is a relatively new entrant, newer than Pro- have to operate.
metheus. It uses an open core model, which means scal- OpenTSDB shares a lot of features with the other systems.
ing and clustering cost extra. InfluxDB is part of the larger It uses a key-value pairing system it calls tags for identifying
TICK stack [13] (of Telegraf, InfluxDB, Chronograf, and metrics and adding dimensionality. It has a query language,
Kapacitor), so we will include all those components’ fea- but it is more limited than Prometheus’ PromQL. It does,
tures in this analysis. however, have several built-in functions that help with learn-
InfluxDB uses a key-value pair system called tags to ing and usage. The API is the main entry point for querying,
add dimensionality to metrics, similar to Prometheus and similar to InfluxDB. This system also stores all data forever,
Graphite. The results are similar to what we discussed unless there’s a time-to-live set in HBase, so you don't have
previously for the other systems. The metric data can be to worry about fidelity degradation.
of type float64, int64, bool, and string with nanosec- OpenTSDB doesn’t offer an alerting capability, which
ond resolution. This is a broader range than most other will make it harder to integrate with your incident response
tools in this space. In fact, the TICK stack is more of an process. This type of system might be great for long-term
event-aggregation platform than a native time-series met- Prometheus data storage and for performing more historical
rics-aggregation system. analytics to reveal systemic issues, rather than as a tool to
InfluxDB uses a system similar to a log-structured merge quickly identify and respond to acute concerns.
tree for storage. It is called a time-structured merge tree in
this context. It uses a write-ahead log and a collection of OpenMetrics standard
read-only data files, which are similar to Sorted Strings Ta- OpenMetrics [16] is a working group seeking to establish
bles but have series data rather than pure log data. These a standard exposition format for metrics data. It is influ-
files are sharded per block of time. To learn more, check out enced by Prometheus. If this initiative is successful, we’ll
this great resource on the InfluxData website [14]. have an industry-wide abstraction that would allow us to
The architecture of the TICK stack is different depend- switch between tools and providers with ease. Leading
ing on if it’s the open source or commercial version. The companies like Datadog [17] have already started offering
open source InfluxDB system is self-contained within a tools that can consume the Prometheus exposition format,
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which will be easy to convert to the OpenMetrics standard [4] https://www.robustperception.io/
once it’s released. [5] https://www.robustperception.io/blog
It’s also important to note that the contributors to this [6] https://prometheus.io/docs/
project include Google and InfluxData (among others). [7] https://prometheus.io/
This likely means InfluxDB will eventually adopt the [8] https://prometheus.io/docs/introduction/faq/#what-is-the-
OpenMetrics standard. This may also mean that one of plural-of-prometheus
the three largest cloud providers will adopt it, if Google’s [9] https://graphiteapp.org/
involvement is an indicator. Of course, the exposition for- [10] https://artofmonitoring.com/
mat is already being used in the Google-created Kuber- [11] http://graphite.readthedocs.io/en/latest/functions.html
netes project [18]. SolarWinds, Robust Perceptions, and [12] https://www.influxdata.com/
SpaceNet are also involved. [13] https://www.thoughtworks.com/radar/platforms/tick-stack
[14] https://docs.influxdata.com/influxdb/v1.5/concepts/
Links storage_engine/
[1] https://prometheus.io/docs/practices/histograms/ [15] http://opentsdb.net/
[2] https://thenewstack.io/exploring-prometheus-use-cases- [16] https://github.com/RichiH/OpenMetrics
brian-brazil/ [17] https://www.datadoghq.com/blog/monitor-prometheus-
[3] https://prometheus.io/blog/2016/07/23/pull-does-not-scale- metrics/
or-does-it/ [18] https://opensource.com/resources/what-is-kubernetes
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3 open source
log aggregation tools
HOW IS METRICS AGGREGATION different from
log aggregation? Can’t
logs include metrics? Can’t log aggregation systems do the
condition—because it is not a normal operating condition,
it might be valuable during troubleshooting.
A handful of rules for logging:
same things as metrics aggregation systems? These are • DO include a timestamp
questions I see a lot. I’ve also seen vendors pitching their • DO format in JSON
log aggregation system as the solution to all observability • DON’T log insignificant events
problems. Log aggregation is a valuable tool, but it isn’t • DO log all application errors
normally a good tool for time-series data. • MAYBE log warnings
A couple of valuable features in a time-series metrics ag- • DO turn on logging
gregation system are the regular interval and the storage • DO write messages in a human-readable form
system customized specifically for time-series data. The • DON’T log informational data in production
regular interval allows a user to derive real mathematical • DON’T log anything a human can’t read or react to
results consistently. If a log aggregation system is collect-
ing metrics in a regular interval, it can potentially work the Cloud costs
same way. However, the storage system isn’t optimized When investigating log aggregation tools, the cloud might
for the types of queries that are typical in a metrics ag- seem like an attractive option. However, it can come with
gregation system. These queries will take more resources significant costs. Logs represent a lot of data when aggre-
and time to process using gated across hundreds or
storage systems found in thousands of hosts and ap-
log aggregation tools. plications. The ingestion,
So, we know a log ag- storage, and retrieval of that
gregation system is likely data are expensive in cloud-
not suitable for time-series based systems.
data, but what is it good for? As a point of reference
A log aggregation system is from a real system, a col-
a great place for collecting lection of around 500 nodes
event data. These are irreg- with a few hundred apps re-
ular activities that are signif- sults in 200GB of log data
icant. An example might be per day. There’s probably
access logs for a web ser- room for improvement in that
vice. These are significant system, but even reducing it
because we want to know what is accessing our systems by half will cost nearly $10,000 per month in many SaaS
and when. Another example would be an application error offerings. This often includes retention of only 30 days,
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which isn’t very long if you want to look at trending data year- capability of a security information and event management
over-year. (SIEM) system [12].
This isn’t to discourage the use of these systems, as they The ELK stack also offers great visualization tools
can be very valuable—especially for smaller organizations. through Kibana, but it lacks an alerting function. Elastic
The purpose is to point out that there could be significant provides alerting functionality within the paid X-Pack add-
costs, and it can be discouraging when they are realized. on, but there is nothing built in for the open source sys-
The rest of this chapter will focus on open source and tem. Yelp has created a solution to this problem, called
commercial solutions that are self-hosted. ElastAlert [13], and there are probably others. This addi-
tional piece of software is fairly robust, but it increases the
Tool options complexity of an already complex system.
ELK Graylog
ELK [1], short for Elasticsearch, Logstash, and Kibana, is the Graylog [14] has recently risen in popularity, but it got its
most popular open source log aggregation tool on the market. start when Lennart Koopmann created it back in 2010. A
It’s used by Netflix, Facebook, Microsoft, LinkedIn, and Cisco. company was born with the same name two years later.
The three components are all developed and maintained by Despite its increasing use, it still lags far behind the ELK
Elastic [2]. Elasticsearch [3] is essentially a NoSQL, Lucene stack. This also means it has fewer community-developed
search engine implementation. Logstash [4] is a log pipeline features, but it can use the same Beats that the ELK stack
system that can ingest data, transform it, and load it into a uses. Graylog has gained praise in the Go community with
store like Elasticsearch. Kibana [5] is a visualization layer on the introduction of the Graylog Collector Sidecar written
top of Elasticsearch. in Go [15].
A few years ago, Beats were introduced. Beats are data Graylog uses Elasticsearch, MongoDB [16], and the Gray-
collectors. They simplify the process of shipping data to Log- log Server under the hood. This makes it as complex to run
stash. Instead of needing to understand the proper syntax as the ELK stack and maybe a little more. However, Graylog
of each type of log, a user can install a Beat that will export comes with alerting built into the open source version, as
NGINX logs or Envoy proxy logs properly so they can be well as several other notable features like streaming, mes-
used effectively within Elasticsearch. sage rewriting, and geolocation.
When installing a production-level ELK stack, a few The streaming feature allows for data to be routed to spe-
other pieces might be included, like Kafka [6], Redis [7], cific Streams in real time while they are being processed.
and NGINX [8]. Also, it is common to replace Logstash With this feature, a user can see all database errors in a
with Fluentd, which we’ll discuss later. This system can be single Stream and web server errors in a different Stream.
complex to operate, which in its early days led to a lot of Alerts can even be based on these Streams as new items
problems and complaints. These have largely been fixed, are added or when a threshold is exceeded. Latency is prob-
but it’s still a complex system, so you might not want to try ably one of the biggest issues with log aggregation systems,
it if you’re a smaller operation. and Streams eliminate that issue in Graylog. As soon as the
That said, there are services available so you don’t have log comes in, it can be routed to other systems through a
to worry about that. Logz.io [9] will run it for you, but its list Stream without being processed fully.
pricing is a little steep if you have a lot of data. Of course, The message rewriting feature uses the open source rules
you’re probably smaller and may not have a lot of data. If you engine Drools [17]. This allows all incoming messages to be
can’t afford Logz.io, you could look at something like AWS evaluated against a user-defined rules file enabling a mes-
Elasticsearch Service (ES) [10]. ES is a service Amazon sage to be dropped (called Blacklisting); a field to be added
Web Services (AWS) offers that makes it very easy to get or removed; or the message to be modified.
Elasticsearch working quickly. It also has tooling to get all The coolest feature might be Graylog’s geolocation capa-
AWS logs into ES using Lambda and S3. This is a much bility, which supports plotting IP addresses on a map. This is
cheaper option, but there is some management required and a fairly common feature and is available in Kibana as well,
there are a few limitations. but it adds a lot of value—especially if you want to use this as
Elastic, the parent company of the stack, offers [11] a your SIEM system. The geolocation functionality is provided
more robust product that uses the open core model, which in the open source version of the system.
provides additional options around analytics tools, security Graylog, the company, charges for support on the open
tools, and reporting. It can also be hosted on Google Cloud source version if you want it. It also offers an open core
Platform or AWS. This might be the best option, as this model for its Enterprise version that offers archiving, audit
combination of tools and hosting platforms offers a cheaper logging, and additional support. There aren’t many other
solution than most SaaS options and still provides a lot of options for support or hosting, so you’ll likely be on your
value. This system could effectively replace or give you the own if you don’t use Graylog (the company).
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Fluentd Links
Fluentd [18] was developed at Treasure Data [19], and the [1] https://www.elastic.co/webinars/introduction-elk-stack
CNCF [20] has adopted it as an Incubating project. It was [2] https://www.elastic.co/
written in C and Ruby and is recommended by AWS [21] [3] https://www.elastic.co/products/elasticsearch
and Google Cloud [22]. Fluentd has become a common [4] https://www.elastic.co/products/logstash
replacement for Logstash in many installations. It acts [5] https://www.elastic.co/products/kibana
as a local aggregator to collect all node logs and send [6] http://kafka.apache.org/
them off to central storage systems. It is not a log ag- [7] https://redis.io/
gregation system. [8] https://www.nginx.com/
It uses a robust plugin system to provide quick and [9] https://logz.io/
easy integrations with different data sources and data [10] https://aws.amazon.com/elasticsearch-service/
outputs. Since there are over 500 plugins available, most [11] https://www.elastic.co/cloud
of your use cases should be covered. If they aren’t, this [12] https://en.wikipedia.org/wiki/Security_information_and_
sounds like an opportunity to contribute back to the open event_management
source community. [13] https://github.com/Yelp/elastalert
Fluentd is a common choice in Kubernetes environ- [14] https://www.graylog.org/
ments due to its low memory requirements (just tens of [15] https://opensource.com/tags/go
megabytes) and its high throughput. In an environment [16] https://www.mongodb.com/
like Kubernetes [23], where each pod has a Fluentd side- [17] https://www.drools.org/
car, memory consumption will increase linearly with each [18] https://www.fluentd.org/
new pod created. Using Fluentd will drastically reduce [19] https://www.treasuredata.com/
your system utilization. This is becoming a common prob- [20] https://www.cncf.io/
lem with tools developed in Java that are intended to run [21] https://aws.amazon.com/blogs/aws/all-your-data-fluentd/
one per node where the memory overhead hasn’t been a [22] https://cloud.google.com/logging/docs/agent/
major issue. [23] https://opensource.com/resources/what-is-kubernetes
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5 alerting and
visualization tools
PERHAPS IT’S CLEAR BY THE NAME what alerting
and visualization tools are used
for, but it might not be clear why they are observability tools
from the alerting system. The operator will only respond
to a real incident when he or she is experiencing the prob-
lem, emailed by a customer, or called by the boss. In this
or why they’re separated here. Some systems include the case, alerts have lost their meaning and usefulness.
visualization component in their main product, so why sep- Alerts are not a constant stream of information or a
arate it here? Observability comes from control theory and status update. They are meant to convey a problem from
describes our ability to understand a system based on its which the system can’t automatically recover, and they
inputs and outputs. This chapter focuses on the output com- are sent only to the individual most likely to be able to
ponent of observability. recover the system. Everything that falls outside this defi-
Alerting and visualization systems are focused on under- nition isn’t an alert and is only hurting your employees and
standing the outputs of other systems. This is why they’re company culture.
grouped together. Visualization and alerting tools could be Everyone has a different set of alert types, so I’ll not
described as tools that provide structured representations cover things like priority levels (P1-P5) or models that use
of system outputs. Alerts are basically a synthesized un- words like Informational, Warning, and Critical. Instead,
derstanding of negative system outputs, and visualizations I’ll describe the generic categories emergent in complex
are disambiguated structured representations focused on systems’ incident response.
facilitating user comprehension. You might have noticed I mentioned an “Informational”
As already mentioned, some systems come with these alert type right after I wrote that alerts shouldn’t be in-
tools built in, and those have been covered in other sections formational. Well, not everyone agrees, but also I don’t
with those tools. consider something an alert if it isn’t sent to anyone. It
is a data point that many systems refer to as an alert. It
Common types of alerts and visualizations represents some event that should be known but not re-
sponded to. It is generally part of the visualization system
Alerts of the alerting tool and not an event that triggers actual
Let’s first cover what alerts are not. Alerts should not be sent notifications. Mike Julian covers this and other aspects of
if the human responder can’t do anything about the problem. alerting in his book Practical Monitoring [1]. It’s a must
This includes alerts that go to multiple individuals with only read for work in this area.
a few who can respond or situations where every anoma- Non-informational alerts consist of types that can be
ly in the system triggers an responded to or require ac-
alert. This leads to alert fa- tion. I group these into two
tigue and receivers ignoring categories: internal outage
all alerts within a specific and external outage. (Most
medium until the system es- companies have more lev-
calates to a medium that isn’t els than this for prioritizing
already saturated. their response efforts.) De-
For example, if an oper- graded system performance
ator is getting hundreds of is considered an outage in
emails a day from the alert- this model, as it’s usually
ing system, that operator is unknown how bad the im-
going to ignore all emails pact is to each user.
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Internal outages are lower priority than external outages, but Another feature of a line chart is that you can often stack
they still need to be responded to quickly. They often include them to show relationships. For example, you might want
internal systems that company employees use or components to look at requests on each server individually, but also in
of applications that are visible only to company employees. aggregate. This allows you to understand both the overall
External outages consist of any system outage that system as well as each instance in the same graph.
would immediately impact a customer. These don’t include a
system outage that prevents releasing updates to the sys-
tem. They do include customer-facing application failures,
database outages, and networking partitions that hurt avail-
ability or consistency if either can impact a user. They also
include outages of tools that may not have direct impact on
users, as the application continues to run, but this trans-
parent dependency impacts performance. This is common
when the system uses some external service or data source
that isn’t necessary for full functionality but may cause de-
lays as the application performs retries or handles errors Image source: Grafana (© Grafana Labs)
from this external dependency.
Heatmaps
Visualizations Another common visualization is the heatmap. It is useful
There are a lot of visualization types, and I won’t cover when looking at histograms. This type of visualization is sim-
them all here. It’s a fascinating area of research. On the ilar to a bar chart but can show gradients within the bars
data analytics side of my career, this is a constant struggle representing the different percentiles of the overall metric.
of learning and applying that knowledge. We need to pro- For example, maybe you’re looking at request latencies, and
vide simple representations of complex system outputs for you want to quickly understand the overall trend as well as
the widest dissemination of information. Google Charts [2] the distribution of all requests. A heatmap is great for this,
and Tableau [3] have a wide selection of visualization types. and it can use color to disambiguate the quantity of each
We’ll cover the most common visualizations and some in- section with a quick glance. The heatmap below shows the
novative solutions for quickly understanding systems. higher concentration around the centerline of the graph with
an easy-to-understand visualization of the distribution verti-
Line chart cally for each time bucket. We might want to review a couple
The line chart is probably the most common and ubiqui- of points in time where the distribution gets wide while the
tous visualization available. It also does a pretty good job others are fairly tight like at 14:00. This distribution might be
of producing an understanding of a system over time. A line a negative performance indicator.
chart in a metrics system would have a line for each unique
metric or some aggregation of metrics. This can get confus-
ing when there are a lot of metrics in the same dashboard
(as evidenced below), but most systems can select specific
metrics to view rather than having all of them visible. Also,
anomalous behavior is easy to spot if it’s significant enough
to escape the noise of normal operations. Below we can
see purple, yellow, and light blue lines that might indicate
anomalous behavior.
Image source: Grafana.org (© Grafana Labs)
Gauges
The last common visualization I’ll cover is used to under-
stand a single metric quickly. Gauges can be used to repre-
sent a single metric, like your speedometer represents your
speed or your gas gauge represents the amount of gas in
your car. Similar to the gas gauge, most monitoring gaug-
es clearly indicate what is good and what isn’t. Often (as is
shown below), good is represented by green, getting worse
by orange, and “everything is breaking” by red. The middle
Image source: Stackoverflow.com (Creative Commons BY SA 3.0) row below shows traditional gauges.
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able to contribute new and innovative features to make these
systems even better.
Alerting tools
Bosun
If you’ve ever done anything with computers and gotten
stuck, the help you received was probably thanks to a Stack
Image source: Grafana.org (© Grafana Labs)
Exchange system. Stack Exchange runs many different web-
This image shows more than just traditional gauges, sites around a crowdsourced question-and-answer model.
though. The other gauges are single stat representations Stack Overflow [5] is very popular with developers, and Su-
that are similar to the function of the classic gauge. They per User [6] is popular with operations. However, there are
all use the same color scheme for quickly indicating system now hundreds of sites ranging from parenting to sci-fi and
health with just a glance. Arguably, the bottom row is prob- philosophy to bicycles.
ably the best example of a gauge that allows you to glance Stack Exchange open sourced its alert management sys-
at a dashboard and know that everything is healthy (or not). tem, Bosun [7], around the same time Prometheus and its
This type of visualization is usually what I put on a top-level AlertManager [8] system were released. There were a lot of
dashboard. It offers a full, high-level understanding of system similarities in the two systems, and that’s a really good thing.
health in seconds. Like Prometheus, Bosun is written in Golang. Bosun’s scope
is more extensive than Prometheus’ as it can interact with
Flame graphs systems beyond metrics aggregation. It can also ingest data
A less common visualization is the flame graph. It’s not ide- from log and event aggregation systems. It supports Graph-
al for dashboarding or quickly observing high-level system ite, InfluxDB, OpenTSDB, and Elasticsearch.
concerns; it’s normally seen when trying to understand a Bosun’s architecture consists of a single server binary, a
specific application problem. Netflix’s Brendan Gregg intro- backend like OpenTSDB, Redis, and scollector agents. The
duced them in 2011 [4]. This visualization focuses on CPU scollector agents [9] automatically detect services on a host
and memory and the associated frames. The X-axis lists the and report metrics for those processes and other system re-
frames alphabetically, and the Y-axis shows stack depth. sources. This data is sent to a metrics backend. The Bosun
Each rectangle is a stack frame and includes the function server binary then queries the backends to determine if any
being called. The wider the rectangle, the more it appears in alerts need to be fired. Bosun can also be used by tools like
the stack. This method is invaluable when trying to diagnose Grafana [10] to query the underlying backends through one
system performance at the application level and I urge every- common interface. Redis is used to store state and metadata
one to give them a try. for Bosun.
A really neat feature of Bosun is that it lets you test your
alerts against historical data. This was something I missed in
Prometheus several years ago when I had data for an issue
I wanted alerts on, but no easy way to test my new alert to
make sure it would work. I had to create and insert dummy
data to test the alert. That was a very time-consuming pro-
cess, and this system alleviates that.
Bosun also has the usual features like showing simple
graphs and creating alerts. It has a powerful expression lan-
guage for writing alerting rules. However, it only has email
and HTTP notification configurations, which means connect-
ing to Slack and other tools requires a bit more customiza-
tion (which its documentation covers [11]). Similar to Pro-
metheus, Bosun can use templates for these notifications,
Image source: Wikimedia.org (Creative Commons BY SA 3.0) which means they can look as awesome as you want them
to. You can use all your HTML and CSS skills to create the
Tool options baddest email alert anyone has ever seen.
There are several commercial options for alerting, but this is
Opensource.com, so we’re not even gonna mention them! Cabot
We’ll cover systems that are being used at scale by real Cabot [12] was created by a company called Arachnys [13].
companies that you can use at no cost. Hopefully, you’ll be Many may not know who that is or what it does, but you
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have probably felt its impact without knowing it. It has built other systems. It supports Graphite, StatsD, InfluxDB, and
the leading cloud-based solution for fighting financial crimes. OpenTSDB as inputs, but it can also forward those metrics
That sounds pretty cool, right? At a previous company, I was to their respective platforms. This is an interesting concept,
involved in similar functions around “know your customer" but potentially risky as loads increase on a central service.
laws [14]. Many companies would see it as very bad press However, if the StatsAgg infrastructure is robust enough,
to be linked to a terrorist group funneling money through it can still produce alerts even when a backend storage
their systems. These solutions also help defend against platform has an outage.
less atrocious offenders like fraudsters who pose a risk to StatsAgg is written in Java and only consists of the main
the institution, even if less so. server and UI, which keeps complexity to a minimum. It can
So why did Arachnys create Cabot? Well, it is kind of send alerts based on regular expression matching and is fo-
a Christmas present to everyone, as it was a Christmas cused on alerting by service rather than host or instance. Its
project it built because its developers couldn’t wrap their goal was to fill a void in the open source observability stack,
heads around Nagios [15]. And really, who can blame and I think it does that quite well.
them? Cabot was written with Django and Bootstrap, so
it should be easy for most to contribute to the project. An- Visualization tools
other interesting factoid is that the name comes from the
creator’s dog. Grafana
The Cabot architecture is similar to Bosun in that it doesn’t Almost everyone knows about Grafana [21] and many have
collect any data. Instead, it accesses data through the APIs used it. I have been using it for years whenever I need a sim-
of the tools it is alerting for. Therefore, Cabot uses a pull ple dashboard. The tool I used before was deprecated, and
(rather than a push) model for alerting. It reaches out into Grafana made that okay when at first I was fairly distraught
each system’s API and retrieves the information it needs to when I saw the deprecation notice. Grafana was gifted to us
make a decision based on a specific check. Cabot stores the by Torkel Ödegaard. Oddly, Grafana is another project that
alerting data in a Postgres database and also has a cache was created around Christmas time and released in January
using Redis. 2014. It has come a long way in only a few years. It started
Cabot natively supports Graphite [16], but it also supports life as a Kibana dashboarding system, which Torkel forked
Jenkins [17], which is rare in this area. Arachnys [18] uses into what became Grafana.
Jenkins like a centralized cron, but I like this idea of treating Grafana’s sole focus is presenting monitoring data in a
build failures like outages. Obviously, a build failure isn’t as more usable and pleasing way. It can natively gather data
critical as a production outage, but it could still alert the team from Graphite, Elasticsearch, OpenTSDB, Prometheus, and
and escalate if the failure isn’t resolved. Who actually checks InfluxDB. There’s an Enterprise version that uses plugins
Jenkins every time an email comes in about a build failure? for more data sources, but there’s no reason those other
Yeah, me too! data source plugins couldn’t be created as open source, as
Another interesting feature is that Cabot can integrate the Grafana plugin ecosystem already offers many other
with Google Calendar for on-call rotations. Cabot calls data sources.
this feature Rota, which is a British term for a roster or What does Grafana do for me? It provides a central lo-
rotation. This makes a lot of sense, and I wish other sys- cation for understanding my system. It is web-based, so
tems would take this idea further. Cabot doesn’t support anyone can access the information, although it can be re-
anything more complex than primary and backup person- stricted using different authentication methods. Grafana
nel, but there is certainly room for additional features. can provide knowledge at a glance using many different
The docs say if you want something more advanced, you types of visualizations. However, it has started integrating
should look at a commercial option. alerting and other features that aren’t traditionally combined
with visualizations.
StatsAgg Now you can set alerts visually. That means you can look
StatsAgg [19]? How did that make the list? Well, it’s not at a graph, maybe even one showing where an alert should
every day you come across a publishing company that have triggered due to some degradation of the system, click
has created an alerting platform. I think that deserves on the graph where you want the alert to trigger, and then
recognition. Pearson [20] isn’t just a publishing company tell Grafana where to send the alert. That’s a pretty powerful
anymore, though. It has several web presences and a joint addition that won’t necessarily replace an alerting platform,
venture with O’Reilly Media. However, I still think of the but it can certainly help augment it by providing a different
company as the people who published my school books perspective on alerting criteria.
and tests. Grafana has also introduced more collaboration features.
StatsAgg isn’t just an alerting platform; it’s also a met- Users have been able to share dashboards for a long time,
rics aggregation platform. And it’s kind of like a proxy for meaning you don’t have to create your own dashboard for
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your Kubernetes [22] cluster because there are several al- Links
ready available—with some maintained by Kubernetes de- [1] https://www.practicalmonitoring.com/
velopers and others by Grafana developers. [2] https://developers.google.com/chart/interactive/docs/
The most significant addition around collaboration is anno- gallery
tations. Annotations allow a user to add context to part of a [3] https://libguides.libraries.claremont.edu/
graph. Then other users can use this context to understand c.php?g=474417&p=3286401
the system better. This is an invaluable tool when a team is [4] http://www.brendangregg.com/flamegraphs.html
in the middle of an incident and communication and common [5] https://stackoverflow.com/
understanding are critical. Having all the information right [6] https://superuser.com/
where you’re already looking makes it much more likely that [7] http://bosun.org/
knowledge will be shared across the team quickly. It’s also a [8] https://prometheus.io/docs/alerting/alertmanager/
nice feature to use during blameless postmortems when the [9] https://bosun.org/scollector/
team is trying to understand how the failure occurred and [10] https://grafana.com/
learn more about their system. [11] https://bosun.org/notifications
[12] https://cabotapp.com/
Vizceral [13] https://www.arachnys.com/
Netflix created Vizceral [23] to understand its traffic patterns [14] https://en.wikipedia.org/wiki/Know_your_customer
better when performing a traffic failover. Unlike Grafana, [15] https://www.nagios.org/
which is a more general tool, Vizceral serves a very specific [16] https://graphiteapp.org/
use-case. Netflix no longer uses this tool internally and says [17] https://jenkins.io/
it is no longer actively maintained, but it still updates the tool [18] https://www.arachnys.com/
periodically. I highlight it here primarily to point out an inter- [19] https://github.com/PearsonEducation/StatsAgg
esting visualization mechanism and how it can help solve a [20] https://www.pearson.com/us/
problem. It's worth running it in a demo environment just to [21] https://grafana.com/
better grasp the concepts and witness what's possible with [22] https://opensource.com/resources/what-is-kubernetes
these systems. [23] https://github.com/Netflix/vizceral
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3 OPEN SOURCE DISTRIBUTED TRACING TOOLS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
DISTRIBUTED TRACING
open source distributed
tracing tools
SYSTEMS enable
tracking a re-
quest through a software system that is distributed across
multiple applications, services, and databases as well as
intermediaries like proxies. This allows for a deeper under-
standing of what is happening within a software system.
These systems produce graphical representations that show
how much time the request took on each step and lists each
known step. Image by Dan Barker (Creative Commns BY SA 4.0)
A user reviewing this content can determine where the sys- This demo is using Istio’s built-in OpenTracing implemen-
tem is experiencing latencies or blockages. Instead of testing tation, so I can get tracing without even modifying my appli-
the system like a binary search tree when requests start failing, cation. It also uses Jaeger, which is OpenTracing-compatible.
operators and developers can see exactly where the issues So what is OpenTracing? Let’s find out.
begin. This can also reveal where performance changes might
be occurring from deployment to deployment. It’s always bet- OpenTracing API
ter to catch regressions automatically by alerting to the anom- OpenTracing [3] is a spec that grew out of Zipkin [4] to pro-
alous behavior rather than having your customers tell you. vide cross-platform compatibility. It offers a vendor-neutral
How does this tracing thing work? Well, each request gets API for adding tracing to applications and delivering that
a special ID that’s usually injected into the headers. This ID data into distributed tracing systems. A library written for
uniquely identifies that transaction. This transaction is nor- the OpenTracing spec can be used with any system that is
mally called a trace. The trace is the overall abstract idea OpenTracing compliant. Zipkin, Jaeger, and AppDash are
of the entire transaction. Each trace is made up of spans. examples of open source tools that have adopted the open
These spans are the actual work being performed, like a ser- standard, but even proprietary tools like Datadog and Insta-
vice call or a database request. Each span also has a unique na are adopting it. This is expected to continue as OpenTrac-
ID. Spans can create subsequent spans called child spans, ing reaches ubiquitous status.
and child spans can have multiple parents.
Once a transaction (or trace) has run its course, it can be OpenCensus
searched in a presentation layer. There are several tools in Okay, we have OpenTracing, but what is this OpenCensus [5]
this space that we’ll discuss later, but the picture below is thing that keeps popping up in my searches? Is it a compet-
of Jaeger [1] from my Istio walkthrough [2]. It shows multi- ing standard, something completely different, or something
ple spans of a single trace. The power of this is immediately complementary? That answer depends on who you ask. I
clear as you can better understand the transaction’s story at will do my best to explain the difference (as I understand it).
a glance. OpenCensus is a more holistic or all-inclusive approach.
OpenTracing is focused on establishing an open API and spec
and not on open implementations for each language and trac-
ing system. OpenCensus provides not only the specification
but also the language implementations and wire protocol. It
also goes beyond tracing by including additional metrics that
are normally outside the scope of distributed tracing systems.
OpenCensus allows viewing data on the host where the
application is running, but it also has a pluggable exporter
system for exporting data to central aggregators. The current
exporters produced by the OpenCensus team are Zipkin,
Prometheus, Jaeger, Stackdriver, Datadog, and SignalFx,
but anyone can create an exporter.
20 THE OPEN SOURCE GUIDE TO DEVOPS MONITORING TOOLS . CC BY-SA 4.0 . OPENSOURCE.COM
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 OPEN SOURCE DISTRIBUTED TRACING TOOLS
From my perspective, there’s a lot of overlap. One isn’t or ElasticSearch. The query service can access the data store
necessarily better than the other, but it’s important to know directly and provide that information to the web UI.
what each does and doesn’t do. OpenTracing is primarily a By default, a user won’t get all the traces from the Jaeger
spec with others doing the implementation and opinionation. clients. The system samples 0.1% (1 in 1,000) of traces that
OpenCensus provides a holistic approach for the local com- pass through each client. Keeping and transmitting all traces
ponent with more opinionation but still requires other sys- would be a bit overwhelming to most systems. However, this
tems for remote aggregation. can be increased or decreased by configuring the agents,
which the client consults with for its configuration. This sam-
Tool options pling isn’t completely random, though, and it’s getting better.
Jaeger uses probabilistic sampling, which tries to make an
Zipkin educated guess at whether a new trace should be sampled
Zipkin was one of the first systems of this kind. It was developed or not. Adaptive sampling is on its roadmap [14], which will
by Twitter based on the Google Dapper paper [6] about the in- improve the sampling algorithm by adding additional context
ternal system Google uses. Zipkin was written using Java, and for making decisions.
it can use Cassandra or ElasticSearch as scalable backends.
Most companies should be satisfied with one of those options. AppDash
The lowest supported Java version is Java 6. It also uses the AppDash [15] is a distributed tracing system written in Gol-
Thrift [7] binary communication protocol, which is popular in the ang, like Jaeger. It was created by Sourcegraph [16] based on
Twitter stack and is hosted as an Apache project. Google’s Dapper and Twitter’s Zipkin. Similar to Jaeger and
The system consists of reporters (clients), collectors, a Zipkin, AppDash supports the OpenTracing standard; this was
query service, and a web UI. Zipkin is meant to be safe in a later addition and requires a component that is different from
production by transmitting only a trace ID within the context the default component. This adds risk and complexity.
of a transaction to inform receivers that a trace is in pro- At a high level, AppDash’s architecture consists mostly of
cess. The data collected in each reporter is then transmitted three components: a client, a local collector, and a remote
asynchronously to the collectors. The collectors store these collector. There’s not a lot of documentation, so this descrip-
spans in the database, and the web UI presents this data to tion comes from testing the system and reviewing the code.
the end user in a consumable format. The delivery of data The client in AppDash gets added to your code. AppDash
to the collectors can occur in three different methods: HTTP, provides Python, Golang, and Ruby implementations, but
Kafka, and Scribe. OpenTracing libraries can be used with AppDash’s Open-
The Zipkin community [8] has also created Brave [9], a Tracing implementation. The client collects the spans and
Java client implementation compatible with Zipkin. It has no sends them to the local collector. The local collector then
dependencies, so it won’t drag your projects down or clutter sends the data to a centralized AppDash server running its
them with libraries that are incompatible with your corporate own local collector, which is the remote collector for all other
standards. There are many other implementations, and Zipkin nodes in the system.
is compatible with the OpenTracing standard, so these imple-
mentations should also work with other distributed tracing sys- Links
tems. The popular Spring framework has a component called [1] https://www.jaegertracing.io/
Spring Cloud Sleuth [10] that is compatible with Zipkin. [2] https://www.youtube.com/watch?v=T8BbeqZ0Rls
[3] http://opentracing.io/
Jaeger [4] https://zipkin.io/
Jaeger [11] is a newer project from Uber Technologies that [5] https://opencensus.io/
the CNCF [12] has since adopted as an Incubating project. [6] https://static.googleusercontent.com/media/research.
It is written in Golang, so you don’t have to worry about hav- google.com/en//archive/papers/dapper-2010-1.pdf
ing dependencies installed on the host or any overhead of [7] https://thrift.apache.org/
interpreters or language virtual machines. Similar to Zipkin, [8] https://zipkin.io/pages/community.html
Jaeger also supports Cassandra and ElasticSearch as scal- [9] https://github.com/openzipkin/brave
able storage backends. Jaeger is also fully compatible with [10] https://cloud.spring.io/spring-cloud-sleuth/
the OpenTracing standard. [11] https://www.jaegertracing.io/
Jaeger’s architecture is similar to Zipkin, with clients (report- [12] https://www.cncf.io/
ers), collectors, a query service, and a web UI, but it also has [13] https://en.wikipedia.org/wiki/Apache_Thrift
an agent on each host that locally aggregates the data. The [14] https://www.jaegertracing.io/docs/roadmap/#adaptive-
agent receives data over a UDP connection, which it batches sampling
and sends to a collector. The collector receives that data in the [15] https://github.com/sourcegraph/appdash
form of the Thrift [13] protocol and stores that data in Cassandra [16] https://about.sourcegraph.com/
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