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      How To Analyze Managed PostgreSQL Database Statistics Using the Elastic Stack on Ubuntu 18.04

      The author selected the Free and Open Source Fund to receive a donation as part of the Write for DOnations program.


      Database monitoring is the continuous process of systematically tracking various metrics that show how the database is performing. By observing the performance data, you can gain valuable insights and identify possible bottlenecks, as well as find additional ways of improving database performance. Such systems often implement alerting, which notifies administrators when things go wrong. Gathered statistics can be used to not only improve the configuration and workflow of the database, but also those of client applications.

      The benefit of using the Elastic Stack (ELK stack) for monitoring your managed database is its excellent support for searching and the ability to ingest new data very quickly. It does not excel at updating the data, but this trade off is acceptable for monitoring and logging purposes, where past data is almost never changed. Elasticsearch offers powerful means of querying the data, which you can use through Kibana to get a better understanding of how the database fares through different time periods. This will allow you to correlate database load with real-life events to gain insight into how the database is being used.

      In this tutorial, you’ll import database metrics, generated by the PostgreSQL statistics collector, into Elasticsearch via Logstash. This entails configuring Logstash to pull data from the database using the PostgreSQL JDBC connector to send it to Elasticsearch for indexing immediately afterward. The imported data can later be analyzed and visualized in Kibana. Then, if your database is brand new, you’ll use pgbench, a PostgreSQL benchmarking tool, to create more interesting visualizations. In the end, you’ll have an automated system pulling in PostgreSQL statistics for later analysis.


      Step 1 — Setting up Logstash and the PostgreSQL JDBC Driver

      In this section, you will install Logstash and download the PostgreSQL JDBC driver so that Logstash will be able to connect to your managed database.

      Start off by installing Logstash with the following command:

      • sudo apt install logstash -y

      Once Logstash is installed, enable the service to automatically start on boot:

      • sudo systemctl enable logstash

      Logstash is written in Java, so in order to connect to PostgreSQL it requires the PostgreSQL JDBC (Java Database Connectivity) library to be available on the system it is running on. Because of an internal limitation, Logstash will properly load the library only if it is found under the /usr/share/logstash/logstash-core/lib/jars directory, where it stores third-party libraries it uses.

      Head over to the download page of the JDBC library and copy the link to latest version. Then, download it using curl by running the following command:

      • sudo curl -o /usr/share/logstash/logstash-core/lib/jars/postgresql-jdbc.jar

      At the time of writing, the latest version of the library was 42.2.6, with Java 8 as the supported runtime version. Ensure you download the latest version; pairing it with the correct Java version that both JDBC and Logstash support.

      Logstash stores its configuration files under /etc/logstash/conf.d, and is itself stored under /usr/share/logstash/bin. Before you create a configuration that will pull statistics from your database, you’ll need to enable the JDBC plugin in Logstash by running the following command:

      • sudo /usr/share/logstash/bin/logstash-plugin install logstash-input-jdbc

      You’ve installed Logstash using apt and downloaded the PostgreSQL JDBC library so that Logstash can use it to connect to your managed database. In the next step, you will configure Logstash to pull statistical data from it.

      Step 2 — Configuring Logstash To Pull Statistics

      In this section, you will configure Logstash to pull metrics from your managed PostgreSQL database.

      You’ll configure Logstash to watch over three system databases in PostgreSQL, namely:

      • pg_stat_database: provides statistics about each database, including its name, number of connections, transactions, rollbacks, rows returned by querying the database, deadlocks, and so on. It has a stats_reset field, which specifies when the statistics were last reset.
      • pg_stat_user_tables: provides statistics about each table created by the user, such as the number of inserted, deleted, and updated rows.
      • pg_stat_user_indexes: collects data about all indexes in user-created tables, such as the number of times a particular index has been scanned.

      You’ll store the configuration for indexing PostgreSQL statistics in Elasticsearch in a file named postgresql.conf under the /etc/logstash/conf.d directory, where Logstash stores configuration files. When started as a service, it will automatically run them in the background.

      Create postgresql.conf using your favorite editor (for example, nano):

      • sudo nano /etc/logstash/conf.d/postgresql.conf

      Add the following lines:


      input {
              # pg_stat_database
              jdbc {
                      jdbc_driver_library => ""
                      jdbc_driver_class => "org.postgresql.Driver"
                      jdbc_connection_string => "jdbc:postgresql://host:port/defaultdb"
                      jdbc_user => "username"
                      jdbc_password => "password"
                      statement => "SELECT * FROM pg_stat_database"
                      schedule => "* * * * *"
                      type => "pg_stat_database"
              # pg_stat_user_tables
              jdbc {
                      jdbc_driver_library => ""
                      jdbc_driver_class => "org.postgresql.Driver"
                      jdbc_connection_string => "jdbc:postgresql://host:port/defaultdb"
                      jdbc_user => "username"
                      jdbc_password => "password"
                      statement => "SELECT * FROM pg_stat_user_tables"
                      schedule => "* * * * *"
                      type => "pg_stat_user_tables"
              # pg_stat_user_indexes
              jdbc {
                      jdbc_driver_library => ""
                      jdbc_driver_class => "org.postgresql.Driver"
                      jdbc_connection_string => "jdbc:postgresql://host:port/defaultdb"
                      jdbc_user => "username"
                      jdbc_password => "password"
                      statement => "SELECT * FROM pg_stat_user_indexes"
                      schedule => "* * * * *"
                      type => "pg_stat_user_indexes"
      output {
              elasticsearch {
                      hosts => "http://localhost:9200"
                      index => "%{type}"

      Remember to replace host with your host address, port with the port to which you can connect to your database, username with the database user username, and password with its password. All these values can be found in the Control Panel of your managed database.

      In this configuration, you define three JDBC inputs and one Elasticsearch output. The three inputs pull data from the pg_stat_database, pg_stat_user_tables, and pg_stat_user_indexes databases, respectively. They all set the jdbc_driver_library parameter to an empty string, because the PostgreSQL JDBC library is in a folder that Logstash automatically loads.

      Then, they set the jdbc_driver_class, whose value is specific to the JDBC library, and provide a jdbc_connection_string, which details how to connect to the database. The jdbc: part signifies that it is a JDBC connection, while postgres:// indicates that the target database is PostgreSQL. Next come the host and port of the database, and after the forward slash you also specify a database to connect to; this is because PostgreSQL requires you to be connected to a database to be able to issue any queries. Here, it is set to the default database that always exists and can not be deleted, aptly named defaultdb.

      Next, they set a username and password of the user through which the database will be accessed. The statement parameter contains a SQL query that should return the data you wish to process—in this configuration, it selects all rows from the appropriate database.

      The schedule parameter accepts a string in cron syntax that defines when Logstash should run this input; omitting it completely will make Logstash run it only once. Specifying * * * * *, as you have done so here, will tell Logstash to run it every minute. You can specify your own cron string if you want to collect data at different intervals.

      There is only one output, which accepts data from three inputs. They all send data to Elasticsearch, which is running locally and is reachable at http://localhost:9200. The index parameter defines to which Elasticsearch index it will send the data, and its value is passed in from the type field of the input.

      When you are done with editing, save and close the file.

      You’ve configured Logstash to gather data from various PostgreSQL statistical tables and send them to Elasticsearch for storage and indexing. Next, you’ll run Logstash to test the configuration.

      Step 3 — Testing the Logstash Configuration

      In this section, you will test the configuration by running Logstash to verify it will properly pull the data. Then, you will make this configuration run in the background by configuring it as a Logstash pipeline.

      Logstash supports running a specific configuration by passing its file path to the -f parameter. Run the following command to test your new configuration from the last step:

      • sudo /usr/share/logstash/bin/logstash -f /etc/logstash/conf.d/postgresql.conf

      It may take some time before it shows any output, which will look similar to this:


      Thread.exclusive is deprecated, use Thread::Mutex WARNING: Could not find logstash.yml which is typically located in $LS_HOME/config or /etc/logstash. You can specify the path using --path.settings. Continuing using the defaults Could not find log4j2 configuration at path /usr/share/logstash/config/ Using default config which logs errors to the console [WARN ] 2019-08-02 18:29:15.123 [LogStash::Runner] multilocal - Ignoring the 'pipelines.yml' file because modules or command line options are specified [INFO ] 2019-08-02 18:29:15.154 [LogStash::Runner] runner - Starting Logstash {"logstash.version"=>"7.3.0"} [INFO ] 2019-08-02 18:29:18.209 [Converge PipelineAction::Create<main>] Reflections - Reflections took 77 ms to scan 1 urls, producing 19 keys and 39 values [INFO ] 2019-08-02 18:29:20.195 [[main]-pipeline-manager] elasticsearch - Elasticsearch pool URLs updated {:changes=>{:removed=>[], :added=>[http://localhost:9200/]}} [WARN ] 2019-08-02 18:29:20.667 [[main]-pipeline-manager] elasticsearch - Restored connection to ES instance {:url=>"http://localhost:9200/"} [INFO ] 2019-08-02 18:29:21.221 [[main]-pipeline-manager] elasticsearch - ES Output version determined {:es_version=>7} [WARN ] 2019-08-02 18:29:21.230 [[main]-pipeline-manager] elasticsearch - Detected a 6.x and above cluster: the `type` event field won't be used to determine the document _type {:es_version=>7} [INFO ] 2019-08-02 18:29:21.274 [[main]-pipeline-manager] elasticsearch - New Elasticsearch output {:class=>"LogStash::Outputs::ElasticSearch", :hosts=>["http://localhost:9200"]} [INFO ] 2019-08-02 18:29:21.337 [[main]-pipeline-manager] elasticsearch - Elasticsearch pool URLs updated {:changes=>{:removed=>[], :added=>[http://localhost:9200/]}} [WARN ] 2019-08-02 18:29:21.369 [[main]-pipeline-manager] elasticsearch - Restored connection to ES instance {:url=>"http://localhost:9200/"} [INFO ] 2019-08-02 18:29:21.386 [[main]-pipeline-manager] elasticsearch - ES Output version determined {:es_version=>7} [WARN ] 2019-08-02 18:29:21.386 [[main]-pipeline-manager] elasticsearch - Detected a 6.x and above cluster: the `type` event field won't be used to determine the document _type {:es_version=>7} [INFO ] 2019-08-02 18:29:21.409 [[main]-pipeline-manager] elasticsearch - New Elasticsearch output {:class=>"LogStash::Outputs::ElasticSearch", :hosts=>["http://localhost:9200"]} [INFO ] 2019-08-02 18:29:21.430 [[main]-pipeline-manager] elasticsearch - Elasticsearch pool URLs updated {:changes=>{:removed=>[], :added=>[http://localhost:9200/]}} [WARN ] 2019-08-02 18:29:21.444 [[main]-pipeline-manager] elasticsearch - Restored connection to ES instance {:url=>"http://localhost:9200/"} [INFO ] 2019-08-02 18:29:21.465 [[main]-pipeline-manager] elasticsearch - ES Output version determined {:es_version=>7} [WARN ] 2019-08-02 18:29:21.466 [[main]-pipeline-manager] elasticsearch - Detected a 6.x and above cluster: the `type` event field won't be used to determine the document _type {:es_version=>7} [INFO ] 2019-08-02 18:29:21.468 [Ruby-0-Thread-7: :1] elasticsearch - Using default mapping template [INFO ] 2019-08-02 18:29:21.538 [Ruby-0-Thread-5: :1] elasticsearch - Using default mapping template [INFO ] 2019-08-02 18:29:21.545 [[main]-pipeline-manager] elasticsearch - New Elasticsearch output {:class=>"LogStash::Outputs::ElasticSearch", :hosts=>["http://localhost:9200"]} [INFO ] 2019-08-02 18:29:21.589 [Ruby-0-Thread-9: :1] elasticsearch - Using default mapping template [INFO ] 2019-08-02 18:29:21.696 [Ruby-0-Thread-5: :1] elasticsearch - Attempting to install template {:manage_template=>{"index_patterns"=>"logstash-*", "version"=>60001, "settings"=>{"index.refresh_interval"=>"5s", "number_of_shards"=>1}, "mappings"=>{"dynamic_templates"=>[{"message_field"=>{"path_match"=>"message", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false}}}, {"string_fields"=>{"match"=>"*", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false, "fields"=>{"keyword"=>{"type"=>"keyword", "ignore_above"=>256}}}}}], "properties"=>{"@timestamp"=>{"type"=>"date"}, "@version"=>{"type"=>"keyword"}, "geoip"=>{"dynamic"=>true, "properties"=>{"ip"=>{"type"=>"ip"}, "location"=>{"type"=>"geo_point"}, "latitude"=>{"type"=>"half_float"}, "longitude"=>{"type"=>"half_float"}}}}}}} [INFO ] 2019-08-02 18:29:21.769 [Ruby-0-Thread-7: :1] elasticsearch - Attempting to install template {:manage_template=>{"index_patterns"=>"logstash-*", "version"=>60001, "settings"=>{"index.refresh_interval"=>"5s", "number_of_shards"=>1}, "mappings"=>{"dynamic_templates"=>[{"message_field"=>{"path_match"=>"message", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false}}}, {"string_fields"=>{"match"=>"*", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false, "fields"=>{"keyword"=>{"type"=>"keyword", "ignore_above"=>256}}}}}], "properties"=>{"@timestamp"=>{"type"=>"date"}, "@version"=>{"type"=>"keyword"}, "geoip"=>{"dynamic"=>true, "properties"=>{"ip"=>{"type"=>"ip"}, "location"=>{"type"=>"geo_point"}, "latitude"=>{"type"=>"half_float"}, "longitude"=>{"type"=>"half_float"}}}}}}} [INFO ] 2019-08-02 18:29:21.771 [Ruby-0-Thread-9: :1] elasticsearch - Attempting to install template {:manage_template=>{"index_patterns"=>"logstash-*", "version"=>60001, "settings"=>{"index.refresh_interval"=>"5s", "number_of_shards"=>1}, "mappings"=>{"dynamic_templates"=>[{"message_field"=>{"path_match"=>"message", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false}}}, {"string_fields"=>{"match"=>"*", "match_mapping_type"=>"string", "mapping"=>{"type"=>"text", "norms"=>false, "fields"=>{"keyword"=>{"type"=>"keyword", "ignore_above"=>256}}}}}], "properties"=>{"@timestamp"=>{"type"=>"date"}, "@version"=>{"type"=>"keyword"}, "geoip"=>{"dynamic"=>true, "properties"=>{"ip"=>{"type"=>"ip"}, "location"=>{"type"=>"geo_point"}, "latitude"=>{"type"=>"half_float"}, "longitude"=>{"type"=>"half_float"}}}}}}} [WARN ] 2019-08-02 18:29:21.871 [[main]-pipeline-manager] LazyDelegatingGauge - A gauge metric of an unknown type (org.jruby.specialized.RubyArrayOneObject) has been create for key: cluster_uuids. This may result in invalid serialization. It is recommended to log an issue to the responsible developer/development team. [INFO ] 2019-08-02 18:29:21.878 [[main]-pipeline-manager] javapipeline - Starting pipeline {:pipeline_id=>"main", "pipeline.workers"=>1, "pipeline.batch.size"=>125, "pipeline.batch.delay"=>50, "pipeline.max_inflight"=>125, :thread=>"#<Thread:0x470bf1ca run>"} [INFO ] 2019-08-02 18:29:22.351 [[main]-pipeline-manager] javapipeline - Pipeline started {""=>"main"} [INFO ] 2019-08-02 18:29:22.721 [Ruby-0-Thread-1: /usr/share/logstash/lib/bootstrap/environment.rb:6] agent - Pipelines running {:count=>1, :running_pipelines=>[:main], :non_running_pipelines=>[]} [INFO ] 2019-08-02 18:29:23.798 [Api Webserver] agent - Successfully started Logstash API endpoint {:port=>9600} /usr/share/logstash/vendor/bundle/jruby/2.5.0/gems/rufus-scheduler-3.0.9/lib/rufus/scheduler/cronline.rb:77: warning: constant ::Fixnum is deprecated /usr/share/logstash/vendor/bundle/jruby/2.5.0/gems/rufus-scheduler-3.0.9/lib/rufus/scheduler/cronline.rb:77: warning: constant ::Fixnum is deprecated /usr/share/logstash/vendor/bundle/jruby/2.5.0/gems/rufus-scheduler-3.0.9/lib/rufus/scheduler/cronline.rb:77: warning: constant ::Fixnum is deprecated [INFO ] 2019-08-02 18:30:02.333 [Ruby-0-Thread-22: /usr/share/logstash/vendor/bundle/jruby/2.5.0/gems/rufus-scheduler-3.0.9/lib/rufus/scheduler/jobs.rb:284] jdbc - (0.042932s) SELECT * FROM pg_stat_user_indexes [INFO ] 2019-08-02 18:30:02.340 [Ruby-0-Thread-23: /usr/share/logstash/vendor/bundle/jruby/2.5.0/gems/rufus-scheduler-3.0.9/lib/rufus/scheduler/jobs.rb:331] jdbc - (0.043178s) SELECT * FROM pg_stat_user_tables [INFO ] 2019-08-02 18:30:02.340 [Ruby-0-Thread-24: :1] jdbc - (0.036469s) SELECT * FROM pg_stat_database ...

      If Logstash does not show any errors and logs that it has successfully SELECTed rows from the three databases, your database metrics will be shipped to Elasticsearch. If you get an error, double check all the values in the configuration file to ensure that the machine you’re running Logstash on can connect to the managed database.

      Logstash will continue importing the data at specified times. You can safely stop it by pressing CTRL+C.

      As previously mentioned, when started as a service, Logstash automatically runs all configuration files it finds under /etc/logstash/conf.d in the background. Run the following command to start it as a service:

      • sudo systemctl start logstash

      In this step, you ran Logstash to check if it can connect to your database and gather data. Next, you’ll visualize and explore some of the statistical data in Kibana.

      Step 4 — Exploring Imported Data in Kibana

      In this section, you’ll see how you can explore the statistical data describing your database’s performance in Kibana.

      In your browser, navigate to the Kibana installation you set up as a prerequisite. You’ll see the default welcome page.

      Kibana - Default Welcome Page

      To interact with Elasticsearch indexes in Kibana, you’ll need to create an index pattern. Index patterns specify on which indexes Kibana should operate. To create one, press on the last icon (wrench) from the left-hand vertical sidebar to open the Management page. Then, from the left menu, press on Index Patterns under Kibana. You’ll see a dialog box for creating an index pattern.

      Kibana - Add Index Pattern

      Listed are the three indexes where Logstash has been sending statistics. Type in pg_stat_database in the Index Pattern input box and then press Next step. You’ll be asked to select a field that stores time, so you’ll be able to later narrow your data by a time range. From the dropdown, select @timestamp.

      Kibana - Index Pattern Timestamp Field

      Press on Create index pattern to finish creating the index pattern. You’ll now be able to explore it using Kibana. To create a visualization, press on the second icon in the sidebar, and then on Create new visualization. Select the Line visualization when the form pops up, and then choose the index pattern you have just created (pg_stat_database). You’ll see an empty visualization.

      Kibana - Empty Visualisation

      On the central part of the screen is the resulting plot—the left-side panel governs its generation from which you can set the data for X and Y axis. In the upper right-hand side of the screen is the date range picker. Unless you specifically choose another range when configuring the data, that range will be shown on the plot.

      You’ll now visualize the average number of data tuples INSERTed on minutes in the given interval. Press on Y-Axis under Metrics in the panel on the left to unfold it. Select Average as the Aggregation and select tup_inserted as the Field. This will populate the Y axis of the plot with the average values.

      Next, press on X-Axis under Buckets. For the Aggregation, choose Date Histogram. @timestamp should be automatically selected as the Field. Then, press on the blue play button on the top of the panel to generate your graph. If your database is brand new and not used, you won’t see anything yet. In all cases, however, you will see an accurate portrayal of database usage.

      Kibana supports many other visualization forms—you can explore other forms in the Kibana documentation. You can also add the two remaining indexes, mentioned in Step 2, into Kibana to be able to visualize them as well.

      In this step, you have learned how to visualize some of the PostgreSQL statistical data, using Kibana.

      Step 5 — (Optional) Benchmarking Using pgbench

      If you haven’t yet worked in your database outside of this tutorial, you can complete this step to create more interesting visualizations by using pgbench to benchmark your database. pgbench will run the same SQL commands over and over, simulating real-world database use by an actual client.

      You’ll first need to install pgbench by running the following command:

      • sudo apt install postgresql-contrib -y

      Because pgbench will insert and update test data, you’ll need to create a separate database for it. To do so, head over to the Users & Databases tab in the Control Panel of your managed database, and scroll down to the Databases section. Type in pgbench as the name of the new database, and then press on Save. You’ll pass this name, as well as the host, port, and username information to pgbench.

      Accessing Databases section in DO control panel

      Before actually running pgbench, you’ll need to run it with the -i flag to initialize its database:

      • pgbench -h host -p port -U username -i pgbench

      You’ll need to replace host with your host address, port with the port to which you can connect to your database, and username with the database user username. You can find all these values in the Control Panel of your managed database.

      Notice that pgbench does not have a password argument; instead, you’ll be asked for it every time you run it.

      The output will look like the following:


      NOTICE: table "pgbench_history" does not exist, skipping NOTICE: table "pgbench_tellers" does not exist, skipping NOTICE: table "pgbench_accounts" does not exist, skipping NOTICE: table "pgbench_branches" does not exist, skipping creating tables... 100000 of 100000 tuples (100%) done (elapsed 0.16 s, remaining 0.00 s) vacuum... set primary keys... done.

      pgbench created four tables, which it will use for benchmarking, and populated them with some example rows. You’ll now be able to run benchmarks.

      The two most important arguments that limit for how long the benchmark will run are -t, which specifies the number of transactions to complete, and -T, which defines for how many seconds the benchmark should run. These two options are mutually exclusive. At the end of each benchmark, you’ll receive statistics, such as the number of transactions per second (tps).

      Now, start a benchmark that will last for 30 seconds by running the following command:

      • pgbench -h host -p port -U username pgbench -T 30

      The output will look like:


      starting vacuum...end. transaction type: <builtin: TPC-B (sort of)> scaling factor: 1 query mode: simple number of clients: 1 number of threads: 1 duration: 30 s number of transactions actually processed: 7602 latency average = 3.947 ms tps = 253.382298 (including connections establishing) tps = 253.535257 (excluding connections establishing)

      In this output, you see the general info about the benchmark, such as the total number of transactions executed. The effect of these benchmarks is that the statistics Logstash ships to Elasticsearch will reflect that number, which will in turn make visualizations in Kibana more interesting and closer to real-world graphs. You can run the preceding command a few more times, and possibly alter the duration.

      When you are done, head over to Kibana and press on Refresh in the upper right corner. You’ll now see a different line than before, which shows the number of INSERTs. Feel free to change the time range of the data shown by changing the values in the picker positioned above the refresh button. Here is how the graph may look after multiple benchmarks of varying duration:

      Kibana - Visualization After Benchmarks

      You’ve used pgbench to benchmark your database, and evaluated the resulting graphs in Kibana.


      You now have the Elastic stack installed on your server and configured to pull statistics data from your managed PostgreSQL database on a regular basis. You can analyze and visualize the data using Kibana, or some other suitable software, which will help you gather valuable insights and real-world correlations into how your database is performing.

      For more information about what you can do with your PostgreSQL Managed Database, visit the product docs.

      Source link

      How To Install Linux, Apache, MariaDB, PHP (LAMP) stack on Debian 10


      A “LAMP” stack is a group of open-source software that is typically installed together to enable a server to host dynamic websites and web apps. This term is actually an acronym which represents the Linux operating system, with the Apache web server. The site data is stored in a MariaDB database, and dynamic content is processed by PHP.

      Although this software stack typically includes MySQL as the database management system, some Linux distributions — including Debian — use MariaDB as a drop-in replacement for MySQL.

      In this guide, we will install a LAMP stack on a Debian 10 server, using MariaDB as the database management system.


      In order to complete this tutorial, you will need to have a Debian 10 server with a non-root sudo-enabled user account and a basic firewall. This can be configured using our initial server setup guide for Debian 10.

      Step 1 — Installing Apache and Updating the Firewall

      The Apache web server is among the most popular web servers in the world. It’s well-documented and has been in wide use for much of the history of the web, which makes it a great default choice for hosting a website.

      Install Apache using Debian’s package manager, APT:

      • sudo apt update
      • sudo apt install apache2

      Since this is a sudo command, these operations are executed with root privileges. It will ask you for your regular user’s password to verify your intentions.

      Once you’ve entered your password, apt will tell you which packages it plans to install and how much extra disk space they’ll take up. Press Y and hit ENTER to continue, and the installation will proceed.

      Next, assuming that you have followed the initial server setup instructions by installing and enabling the UFW firewall, make sure that your firewall allows HTTP and HTTPS traffic.

      When installed on Debian 10, UFW comes loaded with app profiles which you can use to tweak your firewall settings. View the full list of application profiles by running:

      The WWW profiles are used to manage ports used by web servers:


      Available applications: . . . WWW WWW Cache WWW Full WWW Secure . . .

      If you inspect the WWW Full profile, it shows that it enables traffic to ports 80 and 443:

      • sudo ufw app info "WWW Full"


      Profile: WWW Full Title: Web Server (HTTP,HTTPS) Description: Web Server (HTTP,HTTPS) Ports: 80,443/tcp

      Allow incoming HTTP and HTTPS traffic for this profile:

      • sudo ufw allow in "WWW Full"

      You can do a spot check right away to verify that everything went as planned by visiting your server's public IP address in your web browser:


      You will see the default Debian 10 Apache web page, which is there for informational and testing purposes. It should look something like this:

      Debian 10 Apache default

      If you see this page, then your web server is now correctly installed and accessible through your firewall.

      If you do not know what your server's public IP address is, there are a number of ways you can find it. Usually, this is the address you use to connect to your server through SSH.

      There are a few different ways to do this from the command line. First, you could use the iproute2 tools to get your IP address by typing this:

      • ip addr show eth0 | grep inet | awk '{ print $2; }' | sed 's//.*$//'

      This will give you two or three lines back. They are all correct addresses, but your computer may only be able to use one of them, so feel free to try each one.

      An alternative method is to use the curl utility to contact an outside party to tell you how it sees your server. This is done by asking a specific server what your IP address is:

      • sudo apt install curl
      • curl

      Regardless of the method you use to get your IP address, type it into your web browser's address bar to view the default Apache page.

      Step 2 — Installing MariaDB

      Now that you have a web server up and running, you need to install the database system to be able to store and manage data for your site.

      In Debian 10, the metapackage mysql-server, which was traditionally used to install the MySQL server, was replaced by default-mysql-server. This metapackage references MariaDB, a community fork of the original MySQL server by Oracle, and it's currently the default MySQL-compatible database server available on debian-based package manager repositories.

      For longer term compatibility, however, it’s recommended that instead of using the metapackage you install MariaDB using the program’s actual package, mariadb-server.

      To install this software, run:

      • sudo apt install mariadb-server

      When the installation is finished, it's recommended that you run a security script that comes pre-installed with MariaDB. This script will remove some insecure default settings and lock down access to your database system. Start the interactive script by running:

      • sudo mysql_secure_installation

      This script will take you through a series of prompts where you can make some changes to your MariaDB setup. The first prompt will ask you to enter the current database root password. This is not to be confused with the system root. The database root user is an administrative user with full privileges over the database system. Because you just installed MariaDB and haven’t made any configuration changes yet, this password will be blank, so just press ENTER at the prompt.

      The next prompt asks you whether you'd like to set up a database root password. Because MariaDB uses a special authentication method for the root user that is typically safer than using a password, you don't need to set this now. Type N and then press ENTER.

      From there, you can press Y and then ENTER to accept the defaults for all the subsequent questions. This will remove anonymous users and the test database, disable remote root login, and load these new rules so that MariaDB immediately respects the changes you have made.
      When you're finished, log in to the MariaDB console by typing:

      This will connect to the MariaDB server as the administrative database user root, which is inferred by the use of sudo when running this command. You should see output like this:


      Welcome to the MariaDB monitor. Commands end with ; or g. Your MariaDB connection id is 74 Server version: 10.3.15-MariaDB-1 Debian 10 Copyright (c) 2000, 2018, Oracle, MariaDB Corporation Ab and others. Type 'help;' or 'h' for help. Type 'c' to clear the current input statement. MariaDB [(none)]>

      Notice that you didn't need to provide a password to connect as the root user. That works because the default authentication method for the administrative MariaDB user is unix_socket instead of password. Even though this might look like a security concern at first, it makes the database server more secure because the only users allowed to log in as the root MariaDB user are the system users with sudo privileges connecting from the console or through an application running with the same privileges. In practical terms, that means you won't be able to use the administrative database root user to connect from your PHP application.

      For increased security, it's best to have dedicated user accounts with less expansive privileges set up for every database, especially if you plan on having multiple databases hosted on your server. To demonstrate such a setup, we'll create a database named example_database and a user named example_user, but you can replace these names with different values.
      To create a new database, run the following command from your MariaDB console:

      • CREATE DATABASE example_database;

      Now you can create a new user and grant them full privileges on the custom database you've just created. The following command defines this user's password as password, but you should replace this value with a secure password of your own choosing.

      • GRANT ALL ON example_database.* TO 'example_user'@'localhost' IDENTIFIED BY 'password' WITH GRANT OPTION;

      This will give the example_user user full privileges over the example_database database, while preventing this user from creating or modifying other databases on your server.

      Flush the privileges to ensure that they are saved and available in the current session:

      Following this, exit the MariaDB shell:

      You can test if the new user has the proper permissions by logging in to the MariaDB console again, this time using the custom user credentials:

      • mariadb -u example_user -p

      Note the -p flag in this command, which will prompt you for the password used when creating the example_user user. After logging in to the MariaDB console, confirm that you have access to the example_database database:

      This will give you the following output:


      +--------------------+ | Database | +--------------------+ | example_database | | information_schema | +--------------------+ 2 rows in set (0.000 sec)

      To exit the MariaDB shell, type:

      At this point, your database system is set up and you can move on to installing PHP, the final component of the LAMP stack.

      Step 3 — Installing PHP

      PHP is the component of your setup that will process code to display dynamic content. It can run scripts, connect to your MariaDB databases to get information, and hand the processed content over to your web server to display.

      Once again, leverage the apt system to install PHP. In addition, include some helper packages which will ensure that PHP code can run under the Apache server and talk to your MariaDB database:

      • sudo apt install php libapache2-mod-php php-mysql

      This should install PHP without any problems. We'll test this in a moment.

      In most cases, you will want to modify the way that Apache serves files. Currently, if a user requests a directory from the server, Apache will first look for a file called index.html. We want to tell the web server to prefer PHP files over others, so make Apache look for an index.php file first.

      To do this, type the following command to open the dir.conf file in a text editor with root privileges:

      • sudo nano /etc/apache2/mods-enabled/dir.conf

      It will look like this:


      <IfModule mod_dir.c>
          DirectoryIndex index.html index.cgi index.php index.xhtml index.htm

      Move the PHP index file (highlighted above) to the first position after the DirectoryIndex specification, like this:


      <IfModule mod_dir.c>
          DirectoryIndex index.php index.html index.cgi index.xhtml index.htm

      When you are finished, save and close the file. If you're using nano, you can do that by pressing CTRL+X, then Y and ENTER to confirm.

      Now reload Apache's configuration with:

      • sudo systemctl reload apache2

      You can check on the status of the apache2 service with systemctl status:

      • sudo systemctl status apache2

      Sample Output

      ● apache2.service - The Apache HTTP Server Loaded: loaded (/lib/systemd/system/apache2.service; enabled; vendor preset: enabled) Active: active (running) since Mon 2019-07-08 12:58:31 UTC; 8s ago Docs: Process: 11948 ExecStart=/usr/sbin/apachectl start (code=exited, status=0/SUCCESS) Main PID: 11954 (apache2) Tasks: 6 (limit: 4719) Memory: 11.5M CGroup: /system.slice/apache2.service ├─11954 /usr/sbin/apache2 -k start ├─11955 /usr/sbin/apache2 -k start ├─11956 /usr/sbin/apache2 -k start ├─11957 /usr/sbin/apache2 -k start ├─11958 /usr/sbin/apache2 -k start └─11959 /usr/sbin/apache2 -k start

      At this point, your LAMP stack is fully operational, but before you can test your setup with a PHP script it's best to set up a proper Apache Virtual Host to hold your website's files and folders. We'll do that in the next step.

      Step 4 — Creating a Virtual Host for your Website

      By default, Apache serves its content from a directory located at /var/www/html, using the configuration contained in /etc/apache2/sites-available/000-default.conf. Instead of modifying the default website configuration file, we are going to create a new virtual host for testing your PHP environment. Virtual hosts enable us to keep multiple websites hosted on a single Apache server.

      Following that, you'll create a directory structure within /var/www for an example website named your_domain.

      Create the root web directory for your_domain as follows:

      • sudo mkdir /var/www/your_domain

      Next, assign ownership of the directory with the $USER environment variable, which should reference your current system user:

      • sudo chown -R $USER:$USER /var/www/your_domain

      Then, open a new configuration file in Apache's sites-available directory using your preferred command-line editor. Here, we'll use nano:

      • sudo nano /etc/apache2/sites-available/your_domain.conf

      This will create a new blank file. Paste in the following bare-bones configuration:


      <VirtualHost *:80>
          ServerName your_domain
          ServerAlias www.your_domain 
          ServerAdmin webmaster@localhost
          DocumentRoot /var/www/your_domain
          ErrorLog ${APACHE_LOG_DIR}/error.log
          CustomLog ${APACHE_LOG_DIR}/access.log combined

      With this VirtualHost configuration, we're telling Apache to serve your_domain using /var/www/your_domain as the web root directory. If you'd like to test Apache without a domain name, you can remove or comment out the options ServerName and ServerAlias by adding a # character in the beginning of each option's lines.

      You can now use a2ensite to enable this virtual host:

      • sudo a2ensite your_domain

      You might want to disable the default website that comes installed with Apache. This is required if you're not using a custom domain name, because in this case Apache's default configuration would overwrite your Virtual Host. To disable Apache's default website, type:

      • sudo a2dissite 000-default

      To make sure your configuration file doesn't contain syntax errors, you can run:

      • sudo apache2ctl configtest

      Finally, reload Apache so these changes take effect:

      • sudo systemctl reload apache2

      Your new website is now active, but the web root /var/www/your_domain is still empty. In the next step, we'll create a PHP script to test the new setup and confirm that PHP is correctly installed and configured on your server.

      Step 5 — Testing PHP Processing on your Web Server

      Now that you have a custom location to host your website's files and folders, we'll create a simple PHP test script to confirm that Apache is able to handle and process requests for PHP files.

      Create a new file named info.php inside your custom web root folder:

      • nano /var/www/your_domain/info.php

      This will open a blank file. Add the following text, which is valid PHP code, inside the file:



      When you are finished, save and close the file.

      Now you can test whether your web server is able to correctly display content generated by this PHP script. To try this out, visit this page in your web browser. You'll need your server's public IP address again.

      The address you will want to visit is:


      You should see a page similar to this:

      Debian 10 default PHP info

      This page provides some basic information about your server from the perspective of PHP. It is useful for debugging and to ensure that your settings are being applied correctly.

      If you can see this page in your browser, then your PHP installation is working as expected.

      After checking the relevant information about your PHP server through that page, it's best to remove the file you created as it contains sensitive information about your PHP environment and your Debian server. You can use rm to do so:

      • sudo rm /var/www/your_domain/info.php

      You can always recreate this page if you need to access the information again later.

      Step 6 — Testing Database Connection from PHP (Optional)

      If you want to test whether PHP is able to connect to MariaDB and execute database queries, you can create a test table with dummy data and query for its contents from a PHP script.

      First, connect to the MariaDB console with the database user you created in Step 2 of this guide:

      • mariadb -u example_user -p

      Create a table named todo_list. From the MariaDB console, run the following statement:

      • CREATE TABLE example_database.todo_list (
      • item_id INT AUTO_INCREMENT,
      • content VARCHAR(255),
      • PRIMARY KEY(item_id)
      • );

      Now, insert a few rows of content in the test table. You might want to repeat the next command a few times, using different values:

      • INSERT INTO example_database.todo_list (content) VALUES ("My first important item");

      To confirm that the data was successfully saved to your table, run:

      • SELECT * FROM example_database.todo_list;

      You will see the following output:


      +---------+--------------------------+ | item_id | content | +---------+--------------------------+ | 1 | My first important item | | 2 | My second important item | | 3 | My third important item | | 4 | and this one more thing | +---------+--------------------------+ 4 rows in set (0.000 sec)

      After confirming that you have valid data in your test table, you can exit the MariaDB console:

      Now you can create the PHP script that will connect to MariaDB and query for your content. Create a new PHP file in your custom web root directory using your preferred editor. We'll use nano for that:

      • nano /var/www/your_domain/todo_list.php

      The following PHP script connects to the MariaDB database and queries for the content of the todo_list table, exhibiting the results in a list. If there's a problem with the database connection, it will throw an exception.
      Copy this content into your todo_list.php script:


      $user = "example_user";
      $password = "password";
      $database = "example_database";
      $table = "todo_list";
      try {
        $db = new PDO("mysql:host=localhost;dbname=$database", $user, $password);
        echo "<h2>TODO</h2><ol>"; 
        foreach($db->query("SELECT content FROM $table") as $row) {
          echo "<li>" . $row['content'] . "</li>";
        echo "</ol>";
      } catch (PDOException $e) {
          print "Error!: " . $e->getMessage() . "<br/>";

      Save and close the file when you're done editing.

      You can now access this page in your web browser by visiting the domain name or public IP address for your website, followed by /todo_list.php:


      You should see a page like this, showing the content you've inserted in your test table:

      Example PHP todo list

      That means your PHP environment is ready to connect and interact with your MariaDB server.


      In this guide, we've built a flexible foundation for serving PHP websites and applications to your visitors, using Apache as web server and MariaDB as database system.

      To further improve your current setup, you can install an OpenSSL certificate for your website using Let's Encrypt.

      Source link

      How to Set Up a Prometheus, Grafana and Alertmanager Monitoring Stack on DigitalOcean Kubernetes


      Along with tracing and logging, monitoring and alerting are essential components of a Kubernetes observability stack. Setting up monitoring for your DigitalOcean Kubernetes cluster allows you to track your resource usage and analyze and debug application errors.

      A monitoring system usually consists of a time-series database that houses metric data and a visualization layer. In addition, an alerting layer creates and manages alerts, handing them off to integrations and external services as necessary. Finally, one or more components generate or expose the metric data that will be stored, visualized, and processed for alerts by the stack.

      One popular monitoring solution is the open-source Prometheus, Grafana, and Alertmanager stack, deployed alongside kube-state-metrics and node_exporter to expose cluster-level Kubernetes object metrics as well as machine-level metrics like CPU and memory usage.

      Rolling out this monitoring stack on a Kubernetes cluster requires configuring individual components, manifests, Prometheus metrics, and Grafana dashboards, which can take some time. The DigitalOcean Kubernetes Cluster Monitoring Quickstart, released by the DigitalOcean Community Developer Education team, contains fully defined manifests for a Prometheus-Grafana-Alertmanager cluster monitoring stack, as well as a set of preconfigured alerts and Grafana dashboards. It can help you get up and running quickly, and forms a solid foundation from which to build your observability stack.

      In this tutorial, we’ll deploy this preconfigured stack on DigitalOcean Kubernetes, access the Prometheus, Grafana, and Alertmanager interfaces, and describe how to customize it.


      Before you begin, you’ll need a DigitalOcean Kubernetes cluster available to you, and the following tools installed in your local development environment:

      • The kubectl command-line interface installed on your local machine and configured to connect to your cluster. You can read more about installing and configuring kubectl in its official documentation.
      • The git version control system installed on your local machine. To learn how to install git on Ubuntu 18.04, consult How To Install Git on Ubuntu 18.04.
      • The Coreutils base64 tool installed on your local machine. If you’re using a Linux machine, this will most likely already be installed. If you’re using OS X, you can use openssl base64, which comes installed by default.

      Note: The Cluster Monitoring Quickstart has only been tested on DigitalOcean Kubernetes clusters. To use the Quickstart with other Kubernetes clusters, some modification to the manifest files may be necessary.

      Step 1 — Cloning the GitHub Repository and Configuring Environment Variables

      To start, clone the DigitalOcean Kubernetes Cluster Monitoring GitHub repository onto your local machine using git:

      • git clone

      Then, navigate into the repo:

      You should see the following directory structure:


      LICENSE changes.txt manifest

      The manifest directory contains Kubernetes manifests for all of the monitoring stack components, including Service Accounts, Deployments, StatefulSets, ConfigMaps, etc. To learn more about these manifest files and how to configure them, skip ahead to Configuring the Monitoring Stack.

      If you just want to get things up and running, begin by setting the APP_INSTANCE_NAME and NAMESPACE environment variables, which will be used to configure a unique name for the stack's components and configure the Namespace into which the stack will be deployed:

      • export APP_INSTANCE_NAME=sammy-cluster-monitoring
      • export NAMESPACE=default

      In this tutorial, we set APP_INSTANCE_NAME to sammy-cluster-monitoring, which will prepend all of the monitoring stack Kubernetes object names. You should substitute in a unique descriptive prefix for your monitoring stack. We also set the Namespace to default. If you’d like to deploy the monitoring stack to a Namespace other than default, ensure that you first create it in your cluster:

      • kubectl create namespace "$NAMESPACE"

      You should see the following output:


      namespace/sammy created

      In this case, the NAMESPACE environment variable was set to sammy. Throughout the rest of the tutorial we'll assume that NAMESPACE has been set to default.

      Now, use the base64 command to base64-encode a secure Grafana password. Be sure to substitute a password of your choosing for your_grafana_password:

      • export GRAFANA_GENERATED_PASSWORD="$(echo -n 'your_grafana_password' | base64)"

      If you're using macOS, you can substitute the openssl base64 command which comes installed by default.

      At this point, you've grabbed the stack's Kubernetes manifests and configured the required environment variables, so you're now ready to substitute the configured variables into the Kubernetes manifest files and create the stack in your Kubernetes cluster.

      Step 2 — Creating the Monitoring Stack

      The DigitalOcean Kubernetes Monitoring Quickstart repo contains manifests for the following monitoring, scraping, and visualization components:

      • Prometheus is a time series database and monitoring tool that works by polling metrics endpoints and scraping and processing the data exposed by these endpoints. It allows you to query this data using PromQL, a time series data query language. Prometheus will be deployed into the cluster as a StatefulSet with 2 replicas that uses Persistent Volumes with DigitalOcean Block Storage. In addition, a preconfigured set of Prometheus Alerts, Rules, and Jobs will be stored as a ConfigMap. To learn more about these, skip ahead to the Prometheus section of Configuring the Monitoring Stack.
      • Alertmanager, usually deployed alongside Prometheus, forms the alerting layer of the stack, handling alerts generated by Prometheus and deduplicating, grouping, and routing them to integrations like email or PagerDuty. Alertmanager will be installed as a StatefulSet with 2 replicas. To learn more about Alertmanager, consult Alerting from the Prometheus docs.
      • Grafana is a data visualization and analytics tool that allows you to build dashboards and graphs for your metrics data. Grafana will be installed as a StatefulSet with one replica. In addition, a preconfigured set of Dashboards generated by kubernetes-mixin will be stored as a ConfigMap.
      • kube-state-metrics is an add-on agent that listens to the Kubernetes API server and generates metrics about the state of Kubernetes objects like Deployments and Pods. These metrics are served as plaintext on HTTP endpoints and consumed by Prometheus. kube-state-metrics will be installed as an auto-scalable Deployment with one replica.
      • node-exporter, a Prometheus exporter that runs on cluster nodes and provides OS and hardware metrics like CPU and memory usage to Prometheus. These metrics are also served as plaintext on HTTP endpoints and consumed by Prometheus. node-exporter will be installed as a DaemonSet.

      By default, along with scraping metrics generated by node-exporter, kube-state-metrics, and the other components listed above, Prometheus will be configured to scrape metrics from the following components:

      • kube-apiserver, the Kubernetes API server.
      • kubelet, the primary node agent that interacts with kube-apiserver to manage Pods and containers on a node.
      • cAdvisor, a node agent that discovers running containers and collects their CPU, memory, filesystem, and network usage metrics.

      To learn more about configuring these components and Prometheus scraping jobs, skip ahead to Configuring the Monitoring Stack. We'll now substitute the environment variables defined in the previous step into the repo's manifest files, and concatenate the individual manifests into a single master file.

      Begin by using awk and envsubst to fill in the APP_INSTANCE_NAME, NAMESPACE, and GRAFANA_GENERATED_PASSWORD variables in the repo's manifest files. After substituting in the variable values, the files will be combined and saved into a master manifest file called sammy-cluster-monitoring_manifest.yaml.

      • awk 'FNR==1 {print "---"}{print}' manifest/*
      • > "${APP_INSTANCE_NAME}_manifest.yaml"

      You should consider storing this file in version control so that you can track changes to the monitoring stack and roll back to previous versions. If you do this, be sure to scrub the admin-password variable from the file so that you don't check your Grafana password into version control.

      Now that you've generated the master manifest file, use kubectl apply -f to apply the manifest and create the stack in the Namespace you configured:

      • kubectl apply -f "${APP_INSTANCE_NAME}_manifest.yaml" --namespace "${NAMESPACE}"

      You should see output similar to the following:


      serviceaccount/alertmanager created configmap/sammy-cluster-monitoring-alertmanager-config created service/sammy-cluster-monitoring-alertmanager-operated created service/sammy-cluster-monitoring-alertmanager created . . . created configmap/sammy-cluster-monitoring-prometheus-config created service/sammy-cluster-monitoring-prometheus created statefulset.apps/sammy-cluster-monitoring-prometheus created

      You can track the stack’s deployment progress using kubectl get all. Once all of the stack components are RUNNING, you can access the preconfigured Grafana dashboards through the Grafana web interface.

      Step 3 — Accessing Grafana and Exploring Metrics Data

      The Grafana Service manifest exposes Grafana as a ClusterIP Service, which means that it's only accessible via a cluster-internal IP address. To access Grafana outside of your Kubernetes cluster, you can either use kubectl patch to update the Service in-place to a public-facing type like NodePort or LoadBalancer, or kubectl port-forward to forward a local port to a Grafana Pod port. In this tutorial we'll forward ports, so you can skip ahead to Forwarding a Local Port to Access the Grafana Service. The following section on exposing Grafana externally is included for reference purposes.

      Exposing the Grafana Service using a Load Balancer (optional)

      If you'd like to create a DigitalOcean Load Balancer for Grafana with an external public IP, use kubectl patch to update the existing Grafana Service in-place to the LoadBalancer Service type:

      • kubectl patch svc "$APP_INSTANCE_NAME-grafana"
      • --namespace "$NAMESPACE"
      • -p '{"spec": {"type": "LoadBalancer"}}'

      The kubectl patch command allows you to update Kubernetes objects in-place to make changes without having to re-deploy the objects. You can also modify the master manifest file directly, adding a type: LoadBalancer parameter to the Grafana Service spec. To learn more about kubectl patch and Kubernetes Service types, you can consult the Update API Objects in Place Using kubectl patch and Services resources in the official Kubernetes docs.

      After running the above command, you should see the following:


      service/sammy-cluster-monitoring-grafana patched

      It may take several minutes to create the Load Balancer and assign it a public IP. You can track its progress using the following command with the -w flag to watch for changes:

      • kubectl get service "$APP_INSTANCE_NAME-grafana" -w

      Once the DigitalOcean Load Balancer has been created and assigned an external IP address, you can fetch its external IP using the following commands:

      • SERVICE_IP=$(kubectl get svc $APP_INSTANCE_NAME-grafana
      • --namespace $NAMESPACE
      • --output jsonpath='{.status.loadBalancer.ingress[0].ip}')
      • echo "http://${SERVICE_IP}/"

      You can now access the Grafana UI by navigating to http://SERVICE_IP/.

      Forwarding a Local Port to Access the Grafana Service

      If you don't want to expose the Grafana Service externally, you can also forward local port 3000 into the cluster directly to a Grafana Pod using kubectl port-forward.

      • kubectl port-forward --namespace ${NAMESPACE} ${APP_INSTANCE_NAME}-grafana-0 3000

      You should see the following output:


      Forwarding from -> 3000 Forwarding from [::1]:3000 -> 3000

      This will forward local port 3000 to containerPort 3000 of the Grafana Pod sammy-cluster-monitoring-grafana-0. To learn more about forwarding ports into a Kubernetes cluster, consult Use Port Forwarding to Access Applications in a Cluster.

      Visit http://localhost:3000 in your web browser. You should see the following Grafana login page:

      Grafana Login Page

      To log in, use the default username admin (if you haven't modified the admin-user parameter), and the password you configured in Step 1.

      You'll be brought to the following Home Dashboard:

      Grafana Home Page

      In the left-hand navigation bar, select the Dashboards button, then click on Manage:

      Grafana Dashboard Tab

      You'll be brought to the following dashboard management interface, which lists the dashboards configured in the dashboards-configmap.yaml manifest:

      Grafana Dashboard List

      These dashboards are generated by kubernetes-mixin, an open-source project that allows you to create a standardized set of cluster monitoring Grafana dashboards and Prometheus alerts. To learn more, consult the kubernetes-mixin GitHub repo.

      Click in to the Kubernetes / Nodes dashboard, which visualizes CPU, memory, disk, and network usage for a given node:

      Grafana Nodes Dashboard

      Describing how to use these dashboards is outside of this tutorial’s scope, but you can consult the following resources to learn more:

      In the next step, we'll follow a similar process to connect to and explore the Prometheus monitoring system.

      Step 4 — Accessing Prometheus and Alertmanager

      To connect to the Prometheus Pods, we can use kubectl port-forward to forward a local port. If you’re done exploring Grafana, you can close the port-forward tunnel by hitting CTRL-C. Alternatively, you can open a new shell and create a new port-forward connection.

      Begin by listing running Pods in the default namespace:

      • kubectl get pod -n default

      You should see the following Pods:


      sammy-cluster-monitoring-alertmanager-0 1/1 Running 0 17m sammy-cluster-monitoring-alertmanager-1 1/1 Running 0 15m sammy-cluster-monitoring-grafana-0 1/1 Running 0 16m sammy-cluster-monitoring-kube-state-metrics-d68bb884-gmgxt 2/2 Running 0 16m sammy-cluster-monitoring-node-exporter-7hvb7 1/1 Running 0 16m sammy-cluster-monitoring-node-exporter-c2rvj 1/1 Running 0 16m sammy-cluster-monitoring-node-exporter-w8j74 1/1 Running 0 16m sammy-cluster-monitoring-prometheus-0 1/1 Running 0 16m sammy-cluster-monitoring-prometheus-1 1/1 Running 0 16m

      We are going to forward local port 9090 to port 9090 of the sammy-cluster-monitoring-prometheus-0 Pod:

      • kubectl port-forward --namespace ${NAMESPACE} sammy-cluster-monitoring-prometheus-0 9090

      You should see the following output:


      Forwarding from -> 9090 Forwarding from [::1]:9090 -> 9090

      This indicates that local port 9090 is being forwarded successfully to the Prometheus Pod.

      Visit http://localhost:9090 in your web browser. You should see the following Prometheus Graph page:

      Prometheus Graph Page

      From here you can use PromQL, the Prometheus query language, to select and aggregate time series metrics stored in its database. To learn more about PromQL, consult Querying Prometheus from the official Prometheus docs.

      In the Expression field, type kubelet_node_name and hit Execute. You should see a list of time series with the metric kubelet_node_name that reports the Nodes in your Kubernetes cluster. You can see which node generated the metric and which job scraped the metric in the metric labels:

      Prometheus Query Results

      Finally, in the top navigation bar, click on Status and then Targets to see the list of targets Prometheus has been configured to scrape. You should see a list of targets corresponding to the list of monitoring endpoints described at the beginning of Step 2.

      To learn more about Prometheus and how to query your cluster metrics, consult the official Prometheus docs.

      To connect to Alertmanager, which manages Alerts generated by Prometheus, we'll follow a similar process to what we used to connect to Prometheus. . In general, you can explore Alertmanager Alerts by clicking into Alerts in the Prometheus top navigation bar.

      To connect to the Alertmanager Pods, we will once again use kubectl port-forward to forward a local port. If you’re done exploring Prometheus, you can close the port-forward tunnel by hitting CTRL-Cor open a new shell to create a new connection. .

      We are going to forward local port 9093 to port 9093 of the sammy-cluster-monitoring-alertmanager-0 Pod:

      • kubectl port-forward --namespace ${NAMESPACE} sammy-cluster-monitoring-alertmanager-0 9093

      You should see the following output:


      Forwarding from -> 9093 Forwarding from [::1]:9093 -> 9093

      This indicates that local port 9093 is being forwarded successfully to an Alertmanager Pod.

      Visit http://localhost:9093 in your web browser. You should see the following Alertmanager Alerts page:

      Alertmanager Alerts Page

      From here, you can explore firing alerts and optionally silencing them. To learn more about Alertmanager, consult the official Alertmanager documentation.

      In the next step, you'll learn how to optionally configure and scale some of the monitoring stack components.

      Step 6 — Configuring the Monitoring Stack (optional)

      The manifests included in the DigitalOcean Kubernetes Cluster Monitoring Quickstart repository can be modified to use different container images, different numbers of Pod replicas, different ports, and customized configuration files.

      In this step, we'll provide a high-level overview of each manifest’s purpose, and then demonstrate how to scale Prometheus up to 3 replicas by modifying the master manifest file.

      To begin, navigate into the manifests subdirectory in the repo, and list the directory’s contents:


      alertmanager-0serviceaccount.yaml alertmanager-configmap.yaml alertmanager-operated-service.yaml alertmanager-service.yaml . . . node-exporter-ds.yaml prometheus-0serviceaccount.yaml prometheus-configmap.yaml prometheus-service.yaml prometheus-statefulset.yaml

      Here you'll find manifests for the different monitoring stack components. To learn more about specific parameters in the manifests, click into the links and consult the comments included throughout the YAML files:






      • prometheus-0serviceaccount.yaml: The Prometheus Service Account, ClusterRole and ClusterRoleBinding.
      • prometheus-configmap.yaml: A ConfigMap that contains three configuration files:

        • alerts.yaml: Contains a preconfigured set of alerts generated by kubernetes-mixin (which was also used to generate the Grafana dashboards). To learn more about configuring alerting rules, consult Alerting Rules from the Prometheus docs.
        • prometheus.yaml: Prometheus's main configuration file. Prometheus has been preconfigured to scrape all the components listed at the beginning of Step 2. Configuring Prometheus goes beyond the scope of this article, but to learn more, you can consult Configuration from the official Prometheus docs.
        • rules.yaml: A set of Prometheus recording rules that enable Prometheus to compute frequently needed or computationally expensive expressions, and save their results as a new set of time series. These are also generated by kubernetes-mixin, and configuring them goes beyond the scope of this article. To learn more, you can consult Recording Rules from the official Prometheus documentation.
      • prometheus-service.yaml: The Service that exposes the Prometheus StatefulSet.

      • prometheus-statefulset.yaml: The Prometheus StatefulSet, configured with 2 replicas. This parameter can be scaled depending on your needs.

      Example: Scaling Prometheus

      To demonstrate how to modify the monitoring stack, we'll scale the number of Prometheus replicas from 2 to 3.

      Open the sammy-cluster-monitoring_manifest.yaml master manifest file using your editor of choice:

      • nano sammy-cluster-monitoring_manifest.yaml

      Scroll down to the Prometheus StatefulSet section of the manifest:


      . . . apiVersion: apps/v1beta2 kind: StatefulSet metadata: name: sammy-cluster-monitoring-prometheus labels: &Labels k8s-app: prometheus sammy-cluster-monitoring prometheus spec: serviceName: "sammy-cluster-monitoring-prometheus" replicas: 2 podManagementPolicy: "Parallel" updateStrategy: type: "RollingUpdate" selector: matchLabels: *Labels template: metadata: labels: *Labels spec: . . .

      Change the number of replicas from 2 to 3:


      . . . apiVersion: apps/v1beta2 kind: StatefulSet metadata: name: sammy-cluster-monitoring-prometheus labels: &Labels k8s-app: prometheus sammy-cluster-monitoring prometheus spec: serviceName: "sammy-cluster-monitoring-prometheus" replicas: 3 podManagementPolicy: "Parallel" updateStrategy: type: "RollingUpdate" selector: matchLabels: *Labels template: metadata: labels: *Labels spec: . . .

      When you're done, save and close the file.

      Apply the changes using kubectl apply -f:

      • kubectl apply -f sammy-cluster-monitoring_manifest.yaml --namespace default

      You can track progress using kubectl get pods. Using this same technique, you can update many of the Kubernetes parameters and much of the configuration for this observability stack.


      In this tutorial, you installed a Prometheus, Grafana, and Alertmanager monitoring stack into your DigitalOcean Kubernetes cluster with a standard set of dashboards, Prometheus rules, and alerts.

      You may also choose to deploy this monitoring stack using the Helm Kubernetes package manager. To learn more, consult How to Set Up DigitalOcean Kubernetes Cluster Monitoring with Helm and Prometheus. One additional way to get this stack up and running is to use the DigitalOcean Marketplace Kubernetes Monitoring Stack solution, currently in beta.

      The DigitalOcean Kubernetes Cluster Monitoring Quickstart repository is heavily based on and modified from Google Cloud Platform’s click-to-deploy Prometheus solution. A full manifest of modifications and changes from the original repository can be found in the Quickstart repo’s changes.txt file.

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