Wednesday, December 23, 2015

Installing and configuring Raspbian Jessie on a Raspberry Pi B+

I blogged before about configuring a Raspberry Pi B+ with Raspbian Wheezy. Here are some notes I took today while going through the whole process again, but this time with the latest Raspbian version, Jessie, from 2015-11-21. Many steps are the same, but I will add instructions for configuring a wireless connection.

1) Bought micro SD card. Note: DO NOT get a regular SD card for the B+ because it will not fit in the SD card slot. You need a micro SD card.

2) Inserted the SD card via an SD USB adaptor in my MacBook Pro.

3) Went to the command line and ran df to see which volume the SD card was mounted as. In my case, it was /dev/disk2s1.

4) Unmounted the SD card from my Mac. I initially tried 'sudo umount /dev/disk2s1' but the system told me to use 'diskutil unmount', so the command that worked for me was:

$ diskutil unmount /dev/disk2s1

5) Downloaded from Unzipped it to obtain the image file 2015-11-21-raspbian-jessie.img

6) Used dd to copy the image from my Mac to the SD card. Thanks to an anonymous commenter on my previous blog post, I specified the target of the dd command as the raw device /dev/rdisk2. Note: DO NOT specify the target as /dev/disk2s1 or /dev/rdisk2s1. Either /dev/disk2 or /dev/rdisk2 will work, but copying to the raw device is faster. Here is the dd command I used:

$ sudo dd if=2015-11-21-raspbian-jessie.img of=/dev/rdisk2 bs=1m
3752+0 records in
3752+0 records out
3934257152 bytes transferred in 233.218961 secs (16869371 bytes/sec)

7) I unmounted the SD card from my Mac one more time:

$ diskutil unmount /dev/disk2s1

8) I inserted the SD card into my Raspberry Pi. I also inserted a USB WiFi adapter (I used the Wi-Pi 802.11n adapter). My Pi was also connected to a USB keyboard, to a USB mouse and to a monitor via HDMI. 

9) I powered up the Pi. It went through the Raspbian Jessie boot process uneventfully, and it brought up the X Windows GUI interface (which is the default in Jessie, as opposed to the console in Wheezy). At this point, I configured the Pi to boot back into console mode by going to Menu -> Preferences -> Raspberry Pi Configuration and changing the Boot option from "To Desktop" to "To CLI". While in the configuration dialog, I also changed the default password for user pi, and unchecked the autologin option.

10) I rebooted the Pi and this time it booted up in console mode and stopped at the login prompt. I logged in as user pi.

11) I spent the next 30 minutes googling around to find out how to make the wireless interface work. It's always been a chore for me to get wlan to work on a Pi, hence the following instructions (based on this really good blog post).

12) Edit /etc/network/interfaces:

(i)  change "auto l0" to "auto wlan0"
(ii) change "iface wlan0 inet manual" to "iface wlan0 inet dhcp"

13) Edit /etc/wpa_supplicant/wpa_supplicant.conf and add this at the end:


14) Rebooted the Pi and ran ifconfig. At this point I could see wlan0 configured properly with an IP address.

Hope these instructions work for you. Merry Christmas!

Monday, December 07, 2015

Protecting your site for free with Let's Encrypt SSL certificates and acmetool

The buzz level around Let's Encrypt has been more elevated lately, due to their opening up their service as a public beta. If you don't know what Let's Encrypt is, it's a Certificate Authority which provides SSL certificates free of charge. The twist is that they implement a protocol called ACME ("Automated Certificate Management Environment") for automating the management of domain-validation certificates, based on a simple JSON-over-HTTPS interface. Read more technical details about Let's Encrypt here.

The certificates from Let's Encrypt have a short life of 90 days, and this is done on purpose so that they encourage web site administrators to renew them programatically and automatically. In what follows, I'll walk you through how to obtain and install Let's Encrypt certificates for nginx on Ubuntu. I will use a tool called acmetool, and not the official Let's Encrypt client tools, because acmetool generates standalone SSL keys and certs and doesn't try to reconfigure a given web server automatically in order to use them (like the letsencrypt client tools do). I like this separation of concerns. Plus acmetool is written in Go, so you just deploy it as a binary and you're off to the races.

1) Configure nginx to serve your domain name

I will assume you want to protect with SSL certificates from Let's Encrypt. The very first step, which I assume you have already taken, is to configure nginx to serve on port 80. I also assume the document root is /var/www/mydomain.

2) Install acmetool

$ sudo apt-get install libcap-dev
$ git clone 
$ cd acme
$ make && sudo make install

3) Run "acmetool quickstart" to configure ACME

The ACME protocol requires a verification of your ownership of There are multiple ways to prove that ownership and the one I chose below was to let the ACME agent (in this case acmetool) to drop a file under the nginx document root. As part of the verification, the ACME agent will also generate a keypair under the covers, and sign a nonce sent from the ACME server with the private key, in order to prove possession of the keypair.
# acmetool quickstart

------------------------- Select ACME Server -----------------------
Please choose an ACME server from which to request certificates. Your principal choices are the Let's Encrypt Live Server, and the Let's Encrypt Staging Server.

You can use the Let's Encrypt Live Server to get real certificates.

The Let's Encrypt Staging Server does not issue publically trusted certificates. It is useful for development purposes, as it has far higher rate limits than the live server.

  1) Let's Encrypt Live Server - I want live certificates
  2) Let's Encrypt Staging Server - I want test certificates
  3) Enter an ACME server URL

I chose option 1 (Let's Encrypt Live Server).

----------------- Select Challenge Conveyance Method ---------------
acmetool needs to be able to convey challenge responses to the ACME server in order to prove its control of the domains for which you issue certificates. These authorizations expire rapidly, as do ACME-issued certificates (Let's Encrypt certificates have a 90 day lifetime), thus it is essential that the completion of these challenges is a) automated and b) functioning properly. There are several options by which challenges can be facilitated:

WEBROOT: The webroot option installs challenge files to a given directory. You must configure your web server so that the files will be available at <http://[HOST]/.well-known/acme-challenge/>. For example, if your webroot is "/var/www", specifying a webroot of "/var/www/.well-known/acme-challenge" is likely to work well. The directory will be created automatically if it does not already exist.

PROXY: The proxy option requires you to configure your web server to proxy requests for paths under /.well-known/acme-challenge/ to a special web server running on port 402, which will serve challenges appropriately.

REDIRECTOR: The redirector option runs a special web server daemon on port 80. This means that you cannot run your own web server on port 80. The redirector redirects all HTTP requests to the equivalent HTTPS URL, so this is useful if you want to enforce use of HTTPS. You will need to configure your web server to not listen on port 80, and you will need to configure your system to run "acmetool redirector" as a daemon. If your system uses systemd, an appropriate unit file can automatically be installed.

LISTEN: Directly listen on port 80 or 443, whichever is available, in order to complete challenges. This is useful only for development purposes.

  1) WEBROOT - Place challenges in a directory
  2) PROXY - I'll proxy challenge requests to an HTTP server
  3) REDIRECTOR - I want to use acmetool's redirect-to-HTTPS functionality
  4) LISTEN - Listen on port 80 or 443 (only useful for development purposes)

I chose option 1 (WEBROOT).

------------------------- Enter Webroot Path -----------------------
Please enter the path at which challenges should be stored.

If your webroot path is /var/www, you would enter /var/www/.well-known/acme-challenge here.
The directory will be created if it does not exist.

Webroot paths vary by OS; please consult your web server configuration.

I indicated /var/www/mydomain/.well-known/acme-challenge as the directory where the challenge will be stored.

------------------------- Quickstart Complete ----------------------
The quickstart process is complete.

Ensure your chosen challenge conveyance method is configured properly before attempting to request certificates. You can find more information about how to configure your system for each method in the acmetool documentation:

To request a certificate, run:

$ sudo acmetool want

If the certificate is successfully obtained, it will be placed in /var/lib/acme/live/{cert,chain,fullchain,privkey}.

Press Return to continue.

4) Obtain the Let's Encrypt SSL key and certificates for

As the quickstart output indicates above, we need to run:

# acmetool want

This should run with no errors and drop the following files in /var/lib/acme/live/ cert, chain, fullchain, privkey and url.

5) Configure nginx to use the Let's Encrypt SSL key and certificate chain

I found a good resource for specifying secure (as of Dec. 2015) SSL configurations for a variety of software, including nginx:

Here is the nginx configuration pertaining to SSL that I used, pointing to the SSL key and certificate chain retrieved by acmetool from Let's Encrypt:

        listen 443 ssl default_server;
        listen [::]:443 ssl default_server;

        ssl_certificate     /var/lib/acme/live/;
        ssl_certificate_key /var/lib/acme/live/;

        ssl_ciphers "EECDH+AESGCM:EDH+AESGCM:AES256+EECDH:AES256+EDH";
        ssl_protocols TLSv1 TLSv1.1 TLSv1.2;
        ssl_prefer_server_ciphers on;
        ssl_session_cache shared:SSL:10m;
        add_header Strict-Transport-Security "max-age=63072000; includeSubdomains; preload";
        add_header X-Frame-Options DENY;
        add_header X-Content-Type-Options nosniff;
        ssl_session_tickets off; # Requires nginx >= 1.5.9
        ssl_stapling on; # Requires nginx >= 1.3.7
        ssl_stapling_verify on; # Requires nginx => 1.3.7

At this point, if you hit over SSL, you should be able to inspect the SSL certificate and see that it's considered valid by your browser (I tested it in Chrome, Firefox and Safari). The Issuer Name has Organization Name "Let's Encrypt" and Common Name "Let's Encrypt Authority X1".

6) Configure cron job for SSL certificate renewal

Let's Encrypt certificates expire in 90 days after the issue date, so you need to renew them more often than you are used to with regular SSL certificates. I added this line to my crontab on the server that handles

# m h  dom mon dow   command
0 0 1 * * /usr/local/bin/acmetool reconcile --batch; service nginx restart

This runs the acmetool "reconcile" command in batch mode (with no input required from the user) at midnight on the 1st day of every month, then restarts nginx just in case the certificate has changed. If the Let's Encrypt SSL certificate is 30 days away from expiring, acmetool reconcile will renew it.

I think Let's Encrypt is a great service, and you should start using it if you're not already!

Friday, November 20, 2015

Initial experiences with the Prometheus monitoring system

I've been looking for a while for a monitoring system written in Go, self-contained and easy to deploy. I think I finally found what I was looking for in Prometheus, a monitoring system open-sourced by SoundCloud and started there by ex-Googlers who took their inspiration from Google's Borgmon system.

Prometheus is a pull system, where the monitoring server pulls data from its clients by hitting a special HTTP handler exposed by each client ("/metrics" by default) and retrieving a list of metrics from that handler. The output of /metrics is plain text, which makes it fairly easily parseable by humans as well, and also helps in troubleshooting.

Here's a subset of the OS-level metrics that are exposed by a client running the node_exporter Prometheus binary (and available when you hit http://client_ip_or_name:9100/metrics):

# HELP node_cpu Seconds the cpus spent in each mode.
# TYPE node_cpu counter
node_cpu{cpu="cpu0",mode="guest"} 0
node_cpu{cpu="cpu0",mode="idle"} 2803.93
node_cpu{cpu="cpu0",mode="iowait"} 31.38
node_cpu{cpu="cpu0",mode="irq"} 0
node_cpu{cpu="cpu0",mode="nice"} 2.26
node_cpu{cpu="cpu0",mode="softirq"} 0.23
node_cpu{cpu="cpu0",mode="steal"} 21.16
node_cpu{cpu="cpu0",mode="system"} 25.84
node_cpu{cpu="cpu0",mode="user"} 79.94
# HELP node_disk_io_now The number of I/Os currently in progress.
# TYPE node_disk_io_now gauge
node_disk_io_now{device="xvda"} 0
# HELP node_disk_io_time_ms Milliseconds spent doing I/Os.
# TYPE node_disk_io_time_ms counter
node_disk_io_time_ms{device="xvda"} 44608
# HELP node_disk_io_time_weighted The weighted # of milliseconds spent doing I/Os. See
# TYPE node_disk_io_time_weighted counter
node_disk_io_time_weighted{device="xvda"} 959264

There are many such "exporters" available for Prometheus, exposing metrics in the format expected by the Prometheus server from systems such as Apache, MySQL, PostgreSQL, HAProxy and many others (see a list here).

What drew me to Prometheus though was the fact that it allows for easy instrumentation of code by providing client libraries for many languages: Go, Java/Scala, Python, Ruby and others. 

One of the main advantages of Prometheus over alternative systems such as Graphite is the rich query language that it provides. You can associate labels (which are arbitrary key/value pairs) with any metrics, and you are then able to query the system by label. I'll show examples in this post. Here's a more in-depth comparison between Prometheus and Graphite.

Installation (on Ubuntu 14.04)

I put together an ansible role that is loosely based on Brian Brazil's demo_prometheus_ansible repo.

Check out my ansible-prometheus repo for this ansible role, which installs Prometheus, node_exporter and PromDash (a ruby-based dashboard builder). For people not familiar with ansible, most of the installation commands are in the install.yml task file. Here is the sequence of installation actions, in broad strokes.

For the Prometheus server:
  • download prometheus-0.16.1.linux-amd64.tar.gz from
  • extract tar.gz into /opt/prometheus/dist and link /opt/prometheus/prometheus-server to /opt/prometheus/dist/prometheus-0.16.1.linux-amd64
  • create Prometheus configuration file from ansible template and drop it in /etc/prometheus/prometheus.yml (more on the config file later)
  • create Prometheus default command-line options file from ansible template and drop it in /etc/default/prometheus
  • create Upstart script for Prometheus in /etc/init/prometheus.conf:
# Run prometheus

start on startup

chdir /opt/prometheus/prometheus-server

./prometheus -config.file /etc/prometheus/prometheus.yml
end script

For node_exporter:
  • download node_exporter-0.12.0rc1.linux-amd64.tar.gz from
  • extract tar.gz into /opt/prometheus/dist and move node_exporter binary to /opt/prometheus/bin/node_exporter
  • create Upstart script for Prometheus in /etc/init/prometheus_node_exporter.conf:
# Run prometheus node_exporter

start on startup

end script

For PromDash:
  • git clone from
  • follow instructions in the Prometheus tutorial from Digital Ocean (can't stop myself from repeating that D.O. publishes the best technical tutorials out there!)
Here is a minimal Prometheus configuration file (/etc/prometheus/prometheus.yml):

  scrape_interval: 30s
  evaluation_interval: 5s

  - job_name: 'prometheus'
      - targets:
  - job_name: 'node'
      - targets:

The configuration file format for Prometheus is well documented in the official docs. My example shows that the Prometheus server itself is monitored (or "scraped" in Prometheus parlance) on port 9090, and that OS metrics are also scraped from 5 clients which are running the node_exporter binary on port 9100, including the Prometheus server.

At this point, you can start Prometheus and node_exporter on your Prometheus server via Upstart:

# start prometheus
# start prometheus_node_exporter

Then you should be able to hit to see the metrics exposed by node_exporter, and more importantly to see the default Web console included in the Prometheus server. A demo page available from Robust Perception can be examined here.

Note that Prometheus also provides default Web consoles for node_exporter OS-level metrics. They are available at (the ansible-prometheus role installs nginx and redirects to the previous URL). The node consoles show CPU, Disk I/O and Memory graphs and also network traffic metrics for each client running node_exporter. 

Working with the MySQL exporter

I installed the mysqld_exporter binary on my Prometheus server box.

# cd /opt/prometheus/dist
# git clone
# cd mysqld_exporter
# make

Then I created a wrapper script I called

# cat

export DATA_SOURCE_NAME=“dbuser:dbpassword@tcp(dbserver:3306)/dbname”; ./mysqld_exporter

Two important notes here:

1) Note the somewhat awkward format for the DATA_SOURCE_NAME environment variable. I tried many other formats but only this one worked for me. The wrapper's script main purpose is to define this variable properly. With some of my other tries, I got this error message:

INFO[0089] Error scraping global state: Default addr for network 'dbserver:3306' unknown  file=mysqld_exporter.go line=697

You could also define this variable in ~/.bashrc but in that case it may clash with other  Prometheus exporters (the one for PostgreSQL for example) which also need to define this variable.

2) Note that the dbuser specified in the DATA_SOURCE_NAME variable needs to have either SUPER or REPLICATION CLIENT permissions to the MySQL server you need to monitor. I ran a SQL statement of this form:


I created an Upstart init script I called /etc/init/prometheus_mysqld_exporter.conf:

# cat /etc/init/prometheus_mysqld_exporter.conf
# Run prometheus mysqld exporter

start on startup

chdir /opt/prometheus/dist/mysqld_exporter

end script

I modified the Prometheus server configuration file (/etc/prometheus/prometheus.yml) and added a scrape job for the MySQL metrics:

  - job_name: 'mysql'
    honor_labels: true
      - targets:

I restarted the Prometheus server:

# stop prometheus
# start prometheus

Then I started up mysqld_exporter via Upstart:

# start prometheus_mysqld_exporter

If everything goes well, the metrics scraped from MySQL will be available at

Here are some of the available metrics:

# HELP mysql_global_status_innodb_data_reads Generic metric from SHOW GLOBAL STATUS.
# TYPE mysql_global_status_innodb_data_reads untyped
mysql_global_status_innodb_data_reads 12660
# HELP mysql_global_status_innodb_data_writes Generic metric from SHOW GLOBAL STATUS.
# TYPE mysql_global_status_innodb_data_writes untyped
mysql_global_status_innodb_data_writes 528790
# HELP mysql_global_status_innodb_data_written Generic metric from SHOW GLOBAL STATUS.
# TYPE mysql_global_status_innodb_data_written untyped
mysql_global_status_innodb_data_written 9.879318016e+09
# HELP mysql_global_status_innodb_dblwr_pages_written Generic metric from SHOW GLOBAL STATUS.
# TYPE mysql_global_status_innodb_dblwr_pages_written untyped
mysql_global_status_innodb_dblwr_pages_written 285184
# HELP mysql_global_status_innodb_row_ops_total Total number of MySQL InnoDB row operations.
# TYPE mysql_global_status_innodb_row_ops_total counter
mysql_global_status_innodb_row_ops_total{operation="deleted"} 14580
mysql_global_status_innodb_row_ops_total{operation="inserted"} 847656
mysql_global_status_innodb_row_ops_total{operation="read"} 8.1021419e+07
mysql_global_status_innodb_row_ops_total{operation="updated"} 35305

Most of the metrics exposed by mysqld_exporter are of type Counter, which means they always increase. A meaningful number to graph then is not their absolute value, but their rate of change. For example, for the mysql_global_status_innodb_row_ops_total metric, the rate of change of reads for the last 5 minutes (reads/sec) can be expressed as:


This is also an example of a Prometheus query which filters by a specific label (in this case {operation="read"})

A good way to get a feel for the metrics available to the Prometheus server is to go to the Web console and graphing tool available at You can copy and paste the ine above in the Expression edit box and click execute. You should see something like this graph in the Graph tab:

It's important to familiarize yourself with the 4 types of metrics handled by Prometheus: Counter, Gauge, Histogram and Summary. 

Working with the Postgres exporter

Although not an official Prometheus package, the Postgres exporter has worked just fine for me. 

I installed the postgres_exporter binary on my Prometheus server box.

# cd /opt/prometheus/dist
# git clone
# cd postgres_exporter
# make

Then I created a wrapper script I called

# cat

export DATA_SOURCE_NAME="postgres://dbuser:dbpassword@dbserver/dbname"; ./postgres_exporter

Note that the format for DATA_SOURCE_NAME is a bit different from the MySQL format.

I created an Upstart init script I called /etc/init/prometheus_postgres_exporter.conf:

# cat /etc/init/prometheus_postgres_exporter.conf
# Run prometheus postgres exporter

start on startup

chdir /opt/prometheus/dist/postgres_exporter

end script

I modified the Prometheus server configuration file (/etc/prometheus/prometheus.yml) and added a scrape job for the Postgres metrics:

  - job_name: 'postgres'
    honor_labels: true
      - targets:

I restarted the Prometheus server:

# stop prometheus
# start prometheus

Then I started up postgres_exporter via Upstart:

# start prometheus_postgres_exporter

If everything goes well, the metrics scraped from Postgres will be available at

Here are some of the available metrics:

# HELP pg_stat_database_tup_fetched Number of rows fetched by queries in this database
# TYPE pg_stat_database_tup_fetched counter
pg_stat_database_tup_fetched{datid="1",datname="template1"} 7.730469e+06
pg_stat_database_tup_fetched{datid="12998",datname="template0"} 0
pg_stat_database_tup_fetched{datid="13003",datname="postgres"} 7.74208e+06
pg_stat_database_tup_fetched{datid="16740",datname="mydb"} 2.18194538e+08
# HELP pg_stat_database_tup_inserted Number of rows inserted by queries in this database
# TYPE pg_stat_database_tup_inserted counter
pg_stat_database_tup_inserted{datid="1",datname="template1"} 0
pg_stat_database_tup_inserted{datid="12998",datname="template0"} 0
pg_stat_database_tup_inserted{datid="13003",datname="postgres"} 0
pg_stat_database_tup_inserted{datid="16740",datname="mydb"} 3.5467483e+07
# HELP pg_stat_database_tup_returned Number of rows returned by queries in this database
# TYPE pg_stat_database_tup_returned counter
pg_stat_database_tup_returned{datid="1",datname="template1"} 6.41976558e+08
pg_stat_database_tup_returned{datid="12998",datname="template0"} 0
pg_stat_database_tup_returned{datid="13003",datname="postgres"} 6.42022129e+08
pg_stat_database_tup_returned{datid="16740",datname="mydb"} 7.114057378094e+12
# HELP pg_stat_database_tup_updated Number of rows updated by queries in this database
# TYPE pg_stat_database_tup_updated counter
pg_stat_database_tup_updated{datid="1",datname="template1"} 1
pg_stat_database_tup_updated{datid="12998",datname="template0"} 0
pg_stat_database_tup_updated{datid="13003",datname="postgres"} 1
pg_stat_database_tup_updated{datid="16740",datname="mydb"} 4351

These metrics are also of type Counter, so to generate meaningful graphs for them, you need to plot their rates. For example, to see the rate of rows returned per second from the database called mydb, you would plot this expression:


The Prometheus expression evaluator available at is again your friend. BTW, if you start typing pg_ in the expression field, you'll see a drop-down filled automatically with all the available metrics starting with pg_. Handy!

Working with the AWS CloudWatch exporter

This is one of the officially supported Prometheus exporters, used for graphing and alerting on AWS CloudWatch metrics. I installed it on the Prometheus server box. It's a java app, so it needs a JDK installed, and also maven for building the app.

# cd /opt/prometheus/dist
# git clone
# apt-get install maven2 openjdk-7-jdk
# cd cloudwatch_exporter
# mvn package

The cloudwatch_exporter app needs AWS credentials in order to connect to CloudWatch and read the metrics. Here's what I did:
  1. created an AWS IAM user called cloudwatch_ro and downloaded its access key and secret key
  2. created an AWS IAM custom policy called CloudWatchReadOnlyAccess-201511181031, which includes the default CloudWatchReadOnlyAccess policy (the custom policy is not stricly necessary, and you can use the default one, but I preferred to use a custom one because I may need to further edits to the policy file)
  3. attached the CloudWatchReadOnlyAccess-201511181031 policy to the cloudwatch_ro user
  4. created a file called ~/.aws/credentials with the contents:

The cloudwatch_exporter app also needs a json file containing the CloudWatch metrics we want it to retrieve from AWS. Here is an example of ELB-related metrics I specified in a file called cloudwatch.json:

  "region": "us-west-2",
  "metrics": [
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "RequestCount",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "BackendConnectionErrors",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "HTTPCode_Backend_2XX",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "HTTPCode_Backend_4XX",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "HTTPCode_Backend_5XX",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "HTTPCode_ELB_4XX",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "HTTPCode_ELB_5XX",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "SurgeQueueLength",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Maximum", "Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "SpilloverCount",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Sum"]},
    {"aws_namespace": "AWS/ELB", "aws_metric_name": "Latency",
     "aws_dimensions": ["AvailabilityZone", "LoadBalancerName"],
     "aws_dimension_select": {"LoadBalancerName": [“LB1”, “LB2”]},
     "aws_statistics": ["Average"]},

Note that you need to look up the exact syntax for each metric name, dimensions and preferred statistics in the AWS CloudWatch documentation. For ELB metrics, the documentation is here. The CloudWatch name corresponds to the cloudwatch_exporter JSON parameter aws_metric_name, dimensions corresponds to aws_dimensions, and preferred statistics corresponds to aws_statistics.

I modified the Prometheus server configuration file (/etc/prometheus/prometheus.yml) and added a scrape job for the CloudWatch metrics:

  - job_name: 'cloudwatch'
    honor_labels: true
      - targets:

I restarted the Prometheus server:

# stop prometheus
# start prometheus

I created an Upstart init script I called /etc/init/prometheus_cloudwatch_exporter.conf:

# cat /etc/init/prometheus_cloudwatch_exporter.conf
# Run prometheus cloudwatch exporter

start on startup

chdir /opt/prometheus/dist/cloudwatch_exporter

   /usr/bin/java -jar target/cloudwatch_exporter-0.2-SNAPSHOT-jar-with-dependencies.jar 9106 cloudwatch.json
end script

Then I started up cloudwatch_exporter via Upstart:

# start prometheus_cloudwatch_exporter

If everything goes well, the metrics scraped from CloudWatch will be available at

Here are some of the available metrics:

# HELP aws_elb_request_count_sum CloudWatch metric AWS/ELB RequestCount Dimensions: [AvailabilityZone, LoadBalancerName] Statistic: Sum Unit: Count
# TYPE aws_elb_request_count_sum gauge
aws_elb_request_count_sum{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2a",} 1.0
aws_elb_request_count_sum{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2c",} 1.0
aws_elb_request_count_sum{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2c",} 2.0
aws_elb_request_count_sum{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2a",} 12.0
# HELP aws_elb_httpcode_backend_2_xx_sum CloudWatch metric AWS/ELB HTTPCode_Backend_2XX Dimensions: [AvailabilityZone, LoadBalancerName] Statistic: Sum Unit: Count
# TYPE aws_elb_httpcode_backend_2_xx_sum gauge
aws_elb_httpcode_backend_2_xx_sum{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2a",} 1.0
aws_elb_httpcode_backend_2_xx_sum{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2c",} 1.0
aws_elb_httpcode_backend_2_xx_sum{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2c",} 2.0
aws_elb_httpcode_backend_2_xx_sum{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2a",} 12.0
# HELP aws_elb_latency_average CloudWatch metric AWS/ELB Latency Dimensions: [AvailabilityZone, LoadBalancerName] Statistic: Average Unit: Seconds
# TYPE aws_elb_latency_average gauge
aws_elb_latency_average{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2a",} 0.5571935176849365
aws_elb_latency_average{job="aws_elb",load_balancer_name=“LB1”,availability_zone="us-west-2c",} 0.5089397430419922
aws_elb_latency_average{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2c",} 0.035556912422180176
aws_elb_latency_average{job="aws_elb",load_balancer_name=“LB2”,availability_zone="us-west-2a",} 0.0031794110933939614

Note that there are 3 labels available to query the metrics above: job, load_balancer_name and availability_zone. 

If we specify something like aws_elb_request_count_sum{job="aws_elb"} in the expression evaluator at, we'll see 4 graphs, one for each load_balancer_name/availability_zone combination. 

To see only graphs related to a specific load balancer, say LB1, we can specify an expression of the form:
In this case, we'll see 2 graphs for LB1, one for each availability zone.

In order to see the request count across all availability zones for a specific load balancer, we need to apply the sum function: sum(aws_elb_request_count_sum{job="aws_elb",load_balancer_name="LB1"}) by (load_balancer_name) 
In this case, we'll see one graph with the request count across the 2 availability zones pertaining to LB1.

If we want to graph all load balancers but only show one graph per balancer, summing all availability zones for each balancer, we would use an expression like this: sum(aws_elb_request_count_sum{job="aws_elb"}) by (load_balancer_name)
So in this case we'll see 2 graphs, one for LB1 and one for LB2, with each graph summing the request count across the availability zones for LB1 and LB2 respectively.

Note that in all the expressions above, since the job label has the value "aws_elb" common to all metrics, it can be dropped from the queries because it doesn't produce any useful filtering.

For other AWS CloudWatch metrics, consult the Amazon CloudWatch Namespaces, Dimensions and Metrics Reference.

Instrumenting Go code with Prometheus

For me, the most interesting feature of Prometheus is that allows for easy instrumentation of the code. Instead of pushing metrics a la statsd and Graphite, a web app needs to implement a /metrics handler and use the Prometheus client library code to publish app-level metrics to that handler. The Prometheus server will then hit /metrics on the client and pull/scrape the metrics.

More specifics for Go code instrumentation

1) Declare and register Prometheus metrics in your code

I have the following 2 variables defined in an init.go file in a common package that gets imported in all of the webapp code:

var PrometheusHTTPRequestCount = prometheus.NewCounterVec(
        Namespace: "myapp",
        Name:      "http_request_count",
        Help:      "The number of HTTP requests.",
    []string{"method", "type", "endpoint"},

var PrometheusHTTPRequestLatency = prometheus.NewSummaryVec(
        Namespace: "myapp",
        Name:      "http_request_latency",
        Help:      "The latency of HTTP requests.",
    []string{"method", "type", "endpoint"},

Note that the first metric is a CounterVec, which in the Prometheus client_golang library specifies a Counter metric that can also get labels associated with it. The labels in my case are "method", "type" and "endpoint". The purpose of this metric is to measure the HTTP request count. Since it's a Counter, it will increase monotonically, so for graphing purposes we'll need to plot its rate and not its absolute value.

The second metric is a SummaryVec, which in the client_golang library specifies a Summary metric with labels. I have the same labels are for the CounterVec metric. The purpose of this metric is to measure the HTTP request latency. Because it's a Summary, it will provide the absolute measurement, the count, as well as quantiles for the measurements.

These 2 variables then get registered in the init function:

func init() {
    // Register Prometheus metric trackers

2) Let Prometheus handle the /metrics endpoint

The GitHub README for client_golang shows the simplest way of doing this:

http.Handle("/metrics", prometheus.Handler())
http.ListenAndServe(":8080", nil)

However, most of the Go webapp code will rely on some sort of web framework, so YMMV. In our case, I had to insert the prometheus.Handler function as a variable pretty deep in our framework code in order to associate it with the /metrics endpoint.

3) Modify Prometheus metrics in your code

The final step in getting Prometheus to instrument your code is to modify the Prometheus metrics you registered by incrementing Counter variables and taking measurements for Summary variables in the appropriate places in your app. In my case, I increment PrometheusHTTPRequestCount in every HTTP handler in my webapp by calling its Inc() method. I also measure the HTTP latency, i.e. the time it took for the handler code to execute, and call the Observe() method on the PrometheusHTTPRequestLatency variable.

The values I associate with the "method""type" and "endpoint" labels come from the endpoint URL associated with each instrumented handler. As an example, for an HTTP GET request to a URL such as, "method" is the HTTP method used in the request ("GET"), "type" is "customers", and "endpoint" is "/customers/find".

Here is the code I use for modifying the Prometheus metrics (R is an object/struct which represents the HTTP request):

    // Modify Prometheus metrics
    pkg, endpoint := common.SplitUrlForMonitoring(R.URL.Path)
    method := R.Method
    PrometheusHTTPRequestCount.WithLabelValues(method, pkg, endpoint).Inc()
    PrometheusHTTPRequestLatency.WithLabelValues(method, pkg, endpoint).Observe(float64(elapsed) / float64(time.Millisecond))

4) Retrieving your metrics

Assuming your web app runs on port 8080, you'll need to modify the Prometheus server configuration file and add a scrape job for app-level metrics. I have something similar to this in /etc/prometheus/prometheus.xml:

- job_name: 'myapp-api'
      - targets:
          group: 'production'
      - targets:
          group: 'test'

Note an extra label called "group" defined in the configuration file. It has the values "production" and "test" respectively, and allows for the filtering of Prometheus measurements by the environment of the monitored nodes.

Whenever the Prometheus configuration file gets modified, you need to restart the Prometheus server:

# stop prometheus
# start prometheus

At this point, the metrics scraped from the webapp servers will be available at

Here are some of the available metrics:

# HELP myapp_http_request_count The number of HTTP requests.
# TYPE myapp_http_request_count counter
myapp_http_request_count{endpoint="/merchant/register",method="GET",type="admin"} 2928
# HELP myapp_http_request_latency The latency of HTTP requests.
# TYPE myapp_http_request_latency summary
myapp_http_request_latency{endpoint="/merchant/register",method="GET",type="admin",quantile="0.5"} 31.284808
myapp_http_request_latency{endpoint="/merchant/register",method="GET",type="admin",quantile="0.9"} 33.353354
myapp_http_request_latency{endpoint="/merchant/register",method="GET",type="admin",quantile="0.99"} 33.353354
myapp_http_request_latency_sum{endpoint="/merchant/register",method="GET",type="admin"} 93606.57930099976

myapp_http_request_latency_count{endpoint="/merchant/register",method="GET",type="admin"} 2928

Note that myapp_http_request_count and myapp_http_request_latency_count show the same value for the method/type/endpoint combination in this example. You could argue that myapp_http_request_count is redundant in this case. There could be instances where you want to increment a counter without taking a measurement for the summary, so it's still useful to have both. 

Also note that myapp_http_request_latency, being a summary, computes 3 different quantiles: 0.5, 0.9 and 0.99 (so 50%, 90% and 99% of the measurements respectively fall under the given numbers for the latencies).

5) Graphing your metrics with PromDash

The PromDash tool provides an easy way to create dashboards with a look and feel similar to Graphite. PromDash is available at

First you need to define a server by clicking on the Servers link up top, then entering a name ("prometheus") and the URL of the Prometheus server ("").

Then click on Dashboards up top, and create a new directory, which offers a way to group dashboards. You can call it something like "myapp". Now you can create a dashboard (you also need to select the directory it belongs to). Once you are in the Dashboard create/edit screen, you'll see one empty graph with the default title "Title". 

When you hover over the header of the graph, you'll see other buttons available. You want to click on the 2nd button from the left, called Datasources, then click Add Expression. Note that the server field is already pre-filled. If you start typing myapp in the expression field, you should see the metrics exposed by your application (for example myapp_http_request_count and myapp_http_request_latency).

To properly graph a Counter-type metric, you need to plot its rate. Use this expression to show the HTTP request/second rate measured in the last minute for all the production endpoints in my webapp:


(the job and group values correspond to what we specified in /etc/prometheus/prometheus.xml)

If you want to show the HTTP request/second rate for test endpoints of "admin" type, use this expression:


If you want to show the HTTP request/second rate for a specific production endpoint, use an expression similar to this:


Once you enter the expression you want, close the Datasources form (it will save everything). Also change the title by clicking on the button called "Graph and Axis Settings". In that form, you can also specify that you want the plot lines stacked as opposed to regular lines.

 For latency metrics, you don't need to look at the rate. Instead, you can look at a specific quantile. Let's say you want to plot the 99% quantile for latencies observed in all production endpoint, for write operations (corresponding to HTTP methods which are not GET). Then you would use an expression like this:


As for the HTTP request/second graphs, you can refine the latency queries by specifying a type, an endpoint or both:


I hope you have enough information at this point to go wild with dashboards! Remember, who has the most dashboards wins!

Wrapping up

I wanted to write this blog post so I don't forget all the stuff that was involved in setting up and using Prometheus. It's a lot, but it's also not that bad once you get a hang for it. In particular, the Prometheus server itself is remarkably easy to set up and maintain, a refreshing change from other monitoring systems I've used before.

One thing I haven't touched on is the alerting mechanism used in Prometheus. I haven't looked at that yet, since I'm still using a combination of Pingdom, monit and Jenkins for my alerting. I'll tackle Prometheus alerting in another blog post.

I really like Prometheus so far and I hope you'll give it a try!

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