PostgreSQL metrics monitor/dashboard
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
docker Docker: explicitly use "pip2" instead of "pip" Jan 24, 2019
grafana_dashboards Dashboards: PG 'single query stats' minor adjustment Feb 7, 2019
openshift Openshift template minor correction Aug 29, 2018
pgwatch2 Add config store v1.5.1 metric name check migration SQL Feb 12, 2019
screenshots Screenshot update Nov 5, 2018
webpy Web UI: ignoring DB name on continuous-discovery Feb 11, 2019 CHANGELOG - mention config store SQL migrations in v1.5.1 notes Feb 12, 2019
Dockerfile Docker: explicitly use "pip2" instead of "pip" Jan 24, 2019 Releasing 1.5.1 Feb 11, 2019
LICENSE Initial commit Dec 29, 2016 Readme - link to the demo site added Feb 7, 2019 Integrated Go build May 18, 2017 Web UI SSL support Apr 13, 2017 Docker: non-root image has no "TESTDB" by default. Jan 24, 2019 PG as backend: support monitoring of "funny" DB names Feb 11, 2019 Minor adjustments to Docker setup Jan 22, 2019
pg_hba.conf new metrics (locks, blocking_locks) + config DB owned and accessed by… Jan 4, 2017
postgresql.conf Minor adjustments to Docker setup Jan 22, 2019
supervisord-nonroot.conf Reversed the single non-root Docker approach for 2 separate images Jan 23, 2018
supervisord-postgres.conf Postgres-as-backend: use PG11 and PGDG official repo Jan 10, 2019
supervisord.conf Docker: a try on resolving a launch issue on a specific platform Aug 2, 2018 README improvements on backup/restore and volumes Jan 22, 2018


Flexible self-contained PostgreSQL metrics monitoring/dashboarding solution


Note: has a self-signed certificate as configured out the box in Docker, so you need to click "Allow / Add exception / Trust" or similar


Software is packaged as Docker (for custom setup see the last paragraph below, for a Docker quickstart see here) for getting started quickly.

# fetch and run the latest Docker image, exposing Grafana on port 3000 and administrative web UI on 8080
docker run -d -p 3000:3000 -p 8080:8080 --name pw2 cybertec/pgwatch2

After some minutes you could open the "db-overview" dashboard and start looking at metrics. For defining your own dashboards you need to log in as admin (admin/pgwatch2admin). NB! If you don't want to add the "test" database (the pgwatch2 configuration db) for monitoring set the NOTESTDB=1 env parameter when launching the image.

For production setups without a container management framework also "--restart unless-stopped" (or custom startup scripts) is highly recommended. Also usage of volumes is then recommended to enable easier updating to newer pgwatch2 Docker images without going through the backup/restore procedure described towards the end of README. For maximum flexibility, security and update simplicity though, best would to do a custom setup - see paragraph "Installing without Docker" towards the end of README for that.

for v in pg influx grafana pw2 ; do docker volume create $v ; done
# with InfluxDB for metrics storage
docker run -d --name pw2 -v pg:/var/lib/postgresql -v influx:/var/lib/influxdb -v grafana:/var/lib/grafana -v pw2:/pgwatch2/persistent-config -p 8080:8080 -p 3000:3000 cybertec/pgwatch2
# with Postgres for metrics storage
docker run -d --name pw2 -v pg:/var/lib/postgresql -v grafana:/var/lib/grafana -v pw2:/pgwatch2/persistent-config -p 8080:8080 -p 3000:3000 cybertec/pgwatch2-postgres

For more advanced usecases (production setup backups) or for easier problemsolving you can decide to expose all services

# run with all ports exposed
docker run -d --restart unless-stopped -p 3000:3000 -p 5432:5432 -p 8086:8086 -p 8080:8080 -p 8081:8081 -p 8088:8088 --name pw2 cybertec/pgwatch2

NB! For production usage make sure you also specify listening IPs explicitly (-p IP:host_port:container_port), by default Docker uses (all network devices).

For custom options, more security, or specific component versions one could easily build the image themselves, just Docker needed:

docker build .

For a complete list of all supported Docker environment variables see


  • Easy extensibility by defining metrics in pure SQL (thus they could also be from business domain)
  • Non-invasive setup, no extensions nor superuser rights required for the base functionality
  • Global or DB level configuration of metrics/intervals
  • Central config DB based operation or local config file based for better automation (Ansible, etc) or ad-hoc/test mode for monitoring a single DB. See below for details
  • Intuitive metrics presentation using the Grafana dashboarding engine. Set of pre-defined dashboards provided
  • Optional alerting (Email, Slack, PagerDuty) provided by Grafana
  • PgBouncer and AWS RDS graphing/alerting supported in addition to PostgreSQL
  • Possible to monitoring all DBs found in a cluster automatically (with regex pattern matching)
  • Kubernetes/OpenShift ready with a "non-root" image and a deployment template
  • Multiple metric storage options - PostgreSQL, InfluxDB, Graphite, JSON file

Project background

For more background on the project motivations and design goals see the original series of blogposts announcing the project:

Limitations / Performance expectations

  • Min 1GB RAM required for Docker setup. Just the gatherer needs <50MB if metric strore is up, otherwise metrics are cached in RAM up to a limit of 10k data points.
  • 1 GB of disk space should be enough for monitoring 1 host for 1 month (1 month is the default metrics retention policy for Influx running in Docker, configurable). Depending on the amount of schema objects - tables, indexes, stored procedures and especially on number of unique SQL-s, it could be also more. With Postgres as metric store multiply it with 3-4x. There's also a "test data generation" mode in the collector to exactly determine disk footprint - see PW2_TESTDATA_DAYS and PW2_TESTDATA_MULTIPLIER params for that (requires also "ad-hoc" mode params).
  • A low-spec (1 vCPU, 2 GB RAM) cloud machine can easily monitor 100 DBs in "exhaustive" settings (i.e. almost all metrics are monitored in 1-2min intervals) without breaking a sweat (<20% load). When a single node where the metrics collector daemon is running is becoming a bottleneck, one can also do "sharding" i.e. limit the amount of monitored databases for that node based on the Group label(s) (--group), which is just a string for logical grouping.
  • A single InfluxDB node should handle thousands of requests per second but if this is not enough having a secondary/mirrored InfluxDB is also possible. If more than two needed (e.g. feeding many many Grafana instances or some custom exporting) one should look at Influx Enterprise (on-prem or cloud) or Graphite (which is also supported as metrics storage backend). For PostgreSQL metrics storage one could use streaming replicas for read scaling or for example Citus for write scaling.
  • When high metrics write latency is problematic (e.g. using a DBaaS across the atlantic) then increasing the default maximum batching delay of 250ms(--batching-delay-ms / PW2_BATCHING_MAX_DELAY_MS) could give good results.

Security/safety aspects

Settings can be configured for most components, but by default the Docker image doesn't focus on security though but rather on being quickly usable for ad-hoc performance troubleshooting.

  • No noticable impact for the monitored DB is expected with the default settings. For some metrics though can happen that the metric reading query (notably "stat_statements") takes some milliseconds, which might be more than an average application query. At any time only 2 metric fetching queries are running in parallel on the monitored DBs, with 5s per default "statement timeout", except for the "bloat" metrics where it is 15min.
  • Starting from v1.3.0 there's a non-root Docker version available (suitable for OpenShift)
  • The administrative Web UI doesn't have by default any security. Configurable via env. variables.
  • Viewing Grafana dashboards by default doesn't require login. Editing needs a password. Configurable via env. variables.
  • InfluxDB has no authentication in Docker setup, so one should just not expose the ports when having concerns.
  • Dashboards based on "pg_stat_statements" (Stat Statement Overview / Top) expose actual queries. They are mostly stripped of details though, but if no risks can be taken the dashboards (or at least according panels) should be deleted. As an alternative "pg_stat_statements_calls" can be used, which only records total runtimes and call counts.
  • Safe certificate connections to Postgres are supported as of v1.5.0
  • Encrypting/decrypting passwords stored in the config DB or in YAML config files possible from v1.5.0. An encryption passphrase/file needs to be specified then via PW2_AES_GCM_KEYPHRASE / PW2_AES_GCM_KEYPHRASE_FILE. By default passwords are stored in plaintext.


Alerting is very conveniently (point-and-click style) provided by Grafana - see here for documentation. All most popular notification services are supported. A hint - currently you can set alerts only on Graph panels and there must be no variables used in the query so you cannot use the pre-created pgwatch2 graphs.

If more complex scenarios/check conditions are required TICK stack and Kapacitor can be easily integrated - see here for more details.


  • pgwatch2 metrics gathering daemon / collector written in Go
  • Configuration store saying which databases and metrics to gather (3 options):
    • A PostgreSQL database
    • YAML config files + SQL metrics files
    • A temporary "ad-hoc" config i.e. just a single connect string (JDBC or Libpq type) for "throwaway" usage
  • Metrics storage DB (4 options)
    • InfluxDB Time Series Database for storing metrics.
    • PostgreSQL - world's most advanced Open Source RDBMS (based on JSONB, 9.4+ required). See "To use an existing Postgres DB for storing metrics" section below for setup details.
    • Graphite (no custom_tags and request batching support)
    • JSON files (for testing / special use cases)
  • Grafana for dashboarding (point-and-click, a set of predefined dashboards is provided)
  • An optional simple Web UI for administering the monitored DBs and metrics and for showing some custom metric overviews, if using PostgreSQL for storing config

NB! All component can be also used separately, thus you can decide to make use of an already existing installation of Postgres, Grafana or InfluxDB and run the pgwatch2 image for example only with the metrics gatherer and the configuration Web UI. These external installations must be accessible from within the Docker though. For info on installation without Docker at all see end of README.

Component diagram for default Docker setup

Component diagram

To use an existing Postgres DB for storing the monitoring config

Create a new pgwatch2 DB, preferrably also an accroding role who owns it. Then roll out the schema (pgwatch2/sql/config_store/config_store.sql) and set the following parameters when running the image: PW2_PGHOST, PW2_PGPORT, PW2_PGDATABASE, PW2_PGUSER, PW2_PGPASSWORD, PW2_PGSSL (optional).

To use an existing Grafana installation

Load the pgwatch2 dashboards from grafana_dashboard folder if needed (one can totally define their own) and set the following paramater: PW2_GRAFANA_BASEURL. This parameter only provides correct links to Grafana dashboards from the Web UI. Grafana is the most loosely coupled component for pgwatch2 and basically doesn't have to be used at all. One can make use of the gathered metrics directly over the Influx (or Graphite) API-s.

To use an existing InfluxDB installation

Set the following env variables: PW2_IHOST, PW2_IPORT, PW2_IDATABASE, PW2_IUSER, PW2_IPASSWORD, PW2_ISSL (optional)

To use an existing Graphite installation

One can also store the metrics in Graphite instead of InfluxDB (no predefined pgwatch2 dashboards for Graphite though). Following parameters needs to be set then: PW2_DATASTORE=graphite, PW2_GRAPHITEHOST, PW2_GRAPHITEPORT

To use an existing Postgres DB for storing metrics

  1. Roll out the metrics storage schema according to instructions from here
  2. Following parameters needs to be set for the gatherer:
  • --datastore=postgres or PW2_DATASTORE=postgres
  • --pg-metric-store-conn-str="postgresql://user:pwd@host:port/db" or PW2_PG_METRIC_STORE_CONN_STR="..."
  • optionally also adjust the --pg-retention-days parameter. By default 30 days (at least) of metrics are kept
  1. If using the Web UI also set the first two parameters (--datastore and --pg-metric-store-conn-str) there, if wanting to clean up data via the UI.

NB! The schema rollout script activates "asynchronous commiting" feature for the metrics storing user role by default! If this is not wanted (no metrics can be lost in case of a crash), then re-enstate normal (synchronous) commits with: ALTER ROLE pgwatch2 IN DATABASE $MY_METRICS_DB SET synchronous_commit TO on

Usage (Docker based, for file or ad-hoc based see further below)

  • by default the "pgwatch2" configuration database running inside Docker is being monitored so that you can immediately see some graphs, but you should add new databases by opening the "admin interface" at or logging into the Postgres config DB and inserting into "pgwatch2.monitored_db" table (db - pgwatch2 , default user/pw - pgwatch2/pgwatch2admin). Note that it can take up to 2min before you see any metrics for newly inserted databases.
  • one can create new Grafana dashboards (and change settings, create users, alerts, ...) after logging in as "admin" (admin/pgwatch2admin)
  • metrics (and their intervals) that are to be gathered can be customized for every database by using a preset config like "minimal", "basic" or "exhaustive" (monitored_db.preset_config table) or a custom JSON config.
  • to add a new metrics yourself (simple SQL queries returing point-in-time values) head to The queries should always include a "epoch_ns" column and "tag_" prefix can be used for columns that should be tags (thus indexed) in InfluxDB.
  • a list of available metrics together with some instructions is also visible from the "Documentation" dashboard
  • some predefine metrics (cpu_load, stat_statements) require installing helper functions (look into "pgwatch2/sql" folder) on monitored DBs
  • for effective graphing you want to familiarize yourself with basic InfluxQL and the non_negative_derivative() function which is very handy as Postgres statistics are mostly evergrowing counters. Documentation here.
  • for troubleshooting, logs for the components are visible under[pgwatch2|postgres|webui|influxdb|grafana] or by logging into the docker container under /var/logs/supervisor/

Steps to configure your database for monitoring

  • As a base requirement you'll need a login user (non-superuser suggested) for connecting to your server and fetching metrics queries. NB! Though theoretically you can use any username you like, but if not using "pgwatch2" you need to adjust the "helper" creation SQL scripts accordingly as in those by default only the "pgwatch2" will be granted execute privileges.
-- NB! For very important databases it might make sense to ensure that the user
-- account used for monitoring can only open a limited number of connections (there are according checks in code also though)
  • Define the helper function to enable the monitoring of sessions counts, types and durations by the pgwatch2 login defined above. If using a superuser login (not recommended) you can skip this step, just ensure that you check the Is superuser? checkbox (or "is_superuser: true" in YAML mode) when configuring databases.
psql -h -U superuser -f pgwatch2/sql/metric_fetching_helpers/stat_activity_wrapper.sql mydb
  • Additionally for extra insights ("Stat statements" dashboard and CPU load) it's also recommended to install the pg_stat_statement contrib extension (Postgres 9.2+ needed to be useful for pgwatch2) and the PL/Python language. The latter one though is usually disabled by DB-as-a-service providers for security reasons. For maximum pg_stat_statement benefit ("Top queries by IO time" dashboard), one should also then enable the track_io_timing setting.
# add pg_stat_statements to your postgresql.conf and restart the server
shared_preload_libraries = 'pg_stat_statements'

After restarting the server install the extensions as superuser

CREATE EXTENSION pg_stat_statements;

Now also install the wrapper functions (under superuser role) for enabling "Stat statement" and CPU load info fetching for non-superusers

psql -h -U superuser -f pgwatch2/sql/metric_fetching_helpers/stat_statements_wrapper.sql mydb
psql -h -U superuser -f pgwatch2/sql/metric_fetching_helpers/cpu_load_plpythonu.sql mydb

For more detailed statistics (OS monitoring, table bloat, WAL size, etc) it is recommended to install also all other helpers found from the pgwatch2/sql/metric_fetching_helpers folder (or pgwatch2/metrics/00_helpers for YAML based setup).

NB! When rolling out helpers make sure the search_path is set correctly (same as monitoring role's) as metrics using the helpers, assume that monitoring role's search_path includes everything needed i.e. they don't qualify any schemas.

Running without helper / wrapper functions

Helpers/wrappers are not needed actually, they just provide a bit more information. For unprivileged users (developers) with no means to install any wrappers as superuser it's also possible to benefit from pgwatch2 - for such use cases e.g. the "unprivileged" preset metrics profile and the according "DB overview Unprivileged / Developer" dashboard is a good starting point as it only assumes existance of pg_stat_statements which is available at all cloud providers.

Screenshot of the "DB overview" dashboard

"DB overview" dashboard

More screenshots here

Technical details

  • Dynamic management of monitored databases, metrics and their intervals - no need to restart/redeploy
  • Safety
    • Up to 2 concurrent queries per monitored database (thus more per cluster) are allowed
    • Configurable statement timeouts per DB
    • SSL connections support for safe over-the-internet monitoring (use "-e PW2_WEBSSL=1 -e PW2_GRAFANASSL=1" when launching Docker)
    • Optional authentication for the Web UI and Grafana (by default freely accessible)
  • Backup script ( provided for taking snapshots of the whole Docker setup. To make it easier (run outside the container) one should to expose ports 5432 (Postgres) and 8088 (InfluxDB backup protocol) at least for the loopback address.

Ports exposed by the Docker image:

  • 5432 - Postgres configuration (or metrics storage) DB
  • 8080 - Management Web UI (monitored hosts, metrics, metrics configurations)
  • 8081 - Gatherer healthcheck / statistics on number of gathered metrics (JSON).
  • 3000 - Grafana dashboarding
  • 8086 - InfluxDB API (when using the InfluxDB version)
  • 8088 - InfluxDB Backup port (when using the InfluxDB version)

The Admin Web UI

In the centrally managed (config DB based) mode, for easy configuration changes (adding databases to monitoring, adding metrics) there is a small Python Web application bundled (exposed on Docker port 8080), making use of the CherryPy Web-framework. For mass changes one could technically also log into the configuration database and change the tables in the “pgwatch2” schema directly. Besides managing the metrics gathering configurations, the two other useful features for the Web UI would be the possibility to look at the logs of the single components (when using Docker) and at the “Stat Statements Overview” page, which will e.g. enable finding out the query with the slowest average runtime for a time period.

By default the Web UI is not secured. If some security is needed then the following env. variables can be used to enforce password protection - PW2_WEBNOANONYMOUS, PW2_WEBUSER, PW2_WEBPASSWORD.

By default also the Docker component logs (Postgres, Influx, Grafana, Go daemon, Web UI itself) are exposed via the "/logs" endpoint. If this is not wanted set the PW2_WEBNOCOMPONENTLOGS env. variable.

Different Web UI "DB types" explained

  • postgres - connect data to a single to-be-monitored DB needs to be specified. If "DB name" field is left empty, then as a one time operation, all non-template DB names are fetched, prefixed with "Unique name" field value and added to monitoring (if not already monitored). Internally monitoring always happens "per DB" not "per cluster".
  • pg-continuous-discovery - connect data to a Postgres cluster (w/o a DB name) needs to be specified and then the metrics daemon will periodically scan the cluster (connecting to the "template1" database, which is expected to exist) and add any found and not yet monitored DBs to monitoring. In this mode it's also possible to specify regular expressions to include/exclude some database names.
  • pgbouncer - use to track metrics from PgBouncer's "SHOW STATS" command. In place of the Postgres "DB name" the name of a PgBouncer "pool" to be monitored must be inserted.

Adding metrics

Metric definitions – metrics are named SQL queries that can return pretty much everything you find useful and which can have different query text versions for different target PostgreSQL versions. Correct version of the metric definition will be chosen automatically by regularly connecting to the target database and checking the version. For defining metrics definitions you should adhere to a couple of basic concepts though:

  • Every metric query should have an “epoch_ns” (nanoseconds since epoch, default InfluxDB timestamp precision) column to record the metrics reading time. If the column is not there, things will still work though as gathering server’s timestamp will be used, you’ll just lose some milliseconds (assuming intra-datacenter monitoring) of precision.
  • Queries can only return text, integer, boolean or floating point (a.k.a. double precision) data.
  • Columns can be optionally “tagged” by prefixing them with “tag_”. By doing this, the column data will be indexed by the InfluxDB / Postgres giving following advantages:
    • Sophisticated auto-discovery support for indexed keys/values, when building charts with Grafana.
    • Faster queries for queries on those columns.
    • Less disk space used for repeating values in InfluxDB. Thus when you’re for example returning some longish and repetitive status strings (possible with Singlestat or Table panels) that you’ll be looking up by some ID column, it might still make sense to prefix the column with “tag_” to reduce disks space.
  • Fixed per host "custom tags" are also supported - these can contain any key-value data important to user and are added to all captured data rows

File based operation

From v1.4.0 one can also deploy pgwatch2 gatherer daemons decentrally, based on YAML config files - for both metric definitions and "hosts to be monitored" definitions. In that case there is no need for the central Postgres "config DB". See "pgwatch2/config/instances.yaml" for sample config file and "pgwatch2/metrics" folder for metrics (and preset metric configuration) definitions. Relevant Gatherer env. vars / flags: PW2_CONFIG / --config, PW2_METRICS_FOLDER / --metrics-folder.

Ad-hoc operation

From v1.4.0 it's also possible to run the gatherer daemon in ad-hoc / test mode, by giving a single standard connection string as input, and optionally also specifying the metrics to monitor (preset config name or a custom JSON string). In that case there is no need for the central Postgres "config DB" nor the YAML file specifying which hosts to monitor. NB! When using that mode with the default Docker image, the built-in metric definitions can't be changed via the Web UI. Relevant Gatherer env. vars / flags: --adhoc-conn-str, --adhoc-config, --adhoc-name, --metrics-folder / PW2_ADHOC_CONN_STR, PW2_ADHOC_CONFIG, PW2_ADHOC_NAME, PW2_METRICS_FOLDER, PW2_ADHOC_CREATE_HELPERS.

# launching in ad-hoc / test mode
docker run --rm -p 3000:3000 -p 8080:8080 -e PW2_ADHOC_CONN_STR="postgresql://user:pwd@mydb:5432/mydb1" \
    -e PW2_ADHOC_CONFIG=unprivileged --name pw2 cybertec/pgwatch2

# launching in ad-hoc / test mode, creating metrics helpers automatically (requires superuser)
docker run --rm -p 3000:3000 -p 8080:8080 -e PW2_ADHOC_CONN_STR="postgresql://user:pwd@mydb:5432/mydb1" \
    -e PW2_ADHOC_CONFIG=exhaustive -e PW2_ADHOC_CREATE_HELPERS=true --name pw2 cybertec/pgwatch2

NB! Using PW2_ADHOC_CREATE_HELPERS (tries to create all of the metrics fetching helpers automatically on the monitored DB) assumes superuser privileges and does not clean up the helpers on exitings so for a permanent setting one could change the user to an unprivileged 'pgwatch2' user.

Updating to a newer Docker version

pgwatch2 code part doesn't need too much maintenance itself (most issues seem to be related to dashboards that users can actually change themselves) but the main components that pgwatch2 relies on (Grafana, InfluxDB) are pretty active and get useful features and fixes quite regularly, thus we'll also try to push new 'latest' images, so it would make sense to check for updates time to time on Docker Hub. NB! You could also choose to build your own image any time and the build scripts will download the latest components for you.

If possible (e.g. previously gathered metrics are not important and there are no user added dashboard/graphs) then the easiest way to get the latest Docker image would be just to stop the old one and doing 'docker pull/run' again as described in beginning of the README.

If using a custom setup, switching out single components should be quite easy, just follow the component provider's
instructions. Migrating data from the current Docker container to a newer version of the pgwatch2 Docker image on the other hand needs quite some steps currently. See the script here for more details. To make updates a bit easier, the preferred way should be though to think about it previously and use Docker volumes accordingly - see the Dockerfile for details. On some rare occasions updating to newer pgwatch2 Web UI or gahterer daemon might additionally still require rollout of some manual config DB schema migrations scripts from the "migrations" subfolder - error messages will include "missing columns" or "wrong datatype" then. SQL "patches" might be provided also for important metric updates, but for dashboard changes there will be none - users need to import them from JSON directly!

Basically there are two options – first, go into the Docker container (e.g. docker exec -it pw2 /bin/bash) and just update the component yourself – i.e. download the latest Grafana .deb package and install it with “dpkg -i …”. This is actually the simplest way. The other way would be to fetch the latest pgwatch2 image, which already has the latest version of components, using “docker pull” and then restore the data (InfluxDB + Postgres) from a backup of old setup. For restoring one needs to go inside the Docker container again but by following the steps described in it shouldn't be a real problem.

A tip: to make the restore process easier it would already make sense to mount the host folder with the backups in it on the new container with “-v ~/pgwatch2_backups:/pgwatch2_backups:rw,z” when starting the Docker image. Otherwise one needs to set up SSH or use something like S3 for example. Also note that ports 5432 and 8088 need to be exposed to take backups outside of Docker.

Installing without Docker

All examples assuming Ubuntu.

  1. Install Postgres and create DB-s/roles for pgwatch2/Grafana

    sudo apt install postgresql

    Default port: 5432

    1.1. Create an User and a DB to hold Grafana config

    psql -c "create user pgwatch2_grafana password 'xyz'"
    psql -c "create database pgwatch2_grafana owner pgwatch2_grafana"

    1.2. Create an User and a DB to hold pgwatch2 config

    psql -c "create user pgwatch2 password 'xyz'"
    psql -c "create database pgwatch2 owner pgwatch2"

    1.3 Roll out the pgwatch2 schema (will holds connection strings of DB-s to be monitored + metric definitions)

    psql -f pgwatch2/sql/config_store/config_store.sql pgwatch2
    psql -f pgwatch2/sql/config_store/metric_definitions.sql pgwatch2
  2. Install InfluxDB (for Postgres as metrics storage DB see instructions here)

    INFLUX_LATEST=$(curl -so- | grep -Eo '"v[0-9\.]+"' | grep -Eo '[0-9\.]+' | sort -nr | head -1)
    sudo dpkg -i influxdb_${INFLUX_LATEST}_amd64.deb

    Take a look/edit the Influx config at /etc/influxdb/influxdb.conf and it's recommend to create also a separate limited login user e.g. "pgwatch2" to be used by the metrics gathering daemon to store metrics. See here on configuring InfluxDB and here for creating new users.

    Default port for the API: 8086

  3. Install Grafana

    GRAFANA_LATEST=$(curl -so- | grep -Eo '"v[0-9\.]+"' | grep -Eo '[0-9\.]+' | sort -nr | head -1)
    sudo dpkg -i grafana_${GRAFANA_LATEST}_amd64.deb

    Default port: 3000

    2.1. Configure Grafana config to use our pgwatch2_grafana DB

    Place something like below in the "[database]" section of /etc/grafana/grafana.ini

    type = postgres
    host = my-postgres-db:5432
    name = pgwatch2_grafana
    user = pgwatch2_grafana
    password = xyz

    Taking a look at [server], [security] and [auth*] sections is also recommended.

    2.2. Set up the Influx datasource as default

    Use the Grafana UI (Admin -> Data sources) or adjust and execute the "pgwatch2/bootstrap/grafana_datasource.sql"

    2.3. Add pgwatch2 predefined dashboards to Grafana

    This could be done by importing the JSON-s from the "grafana_dashboards" folder manually (Import Dashboard from the Grafana top menu) or via the Docker bootstrap script (pgwatch2/bootstrap/ Script needs some adjustment for connect data and file paths though and also the "grafana_datasource.sql" part should be commented out if already executed in the previous step.

  4. Install Python 3 and start the Web UI

    NB! The Web UI is not strictly required but makes life a lot easier. Technically it would be fine also to manage connection strings of the monitored DB-s directly in the "pgwatch2.monitored_db" table and add/adjust metrics in the "pgwatch2.metric" table, and "preset configs" in the "pgwatch2.preset_config" table.

    # first we need Python 3 and "pip" - the Python package manager
    sudo apt install python3 python3-pip
    sudo pip3 install -U -r webpy/requirements.txt 

    4.0. Optional step: for use cases where exposing component (Grafana, Postgres, Influx, gatherer daemon, Web UI itself) logs via the Web UI could be benficial, one should also change the log file paths hardcoded in the SERVICES variable of the source file. Defaults are set to work with the Docker image.

    4.1. Start the Web UI

    cd webpy

    Default port for the Web UI: 8080

    4.2. Configure DB-s to monitor from "/dbs" page

    NB! To get most out of your metrics some wrappers/extensions are required on the DB-s under monitoring. See section Steps to configure your database for monitoring on setup information.

  5. Install Go and compile the gatherer

    Check for the latest Go version from

    # install Go (latest version preferably, but at least 1.10)
    tar -C /usr/local -xzf go1.11.linux-amd64.tar.gz
    export PATH=$PATH:/usr/local/go/bin
    # compile the gatherer daemon
    cd pgwatch2
    # an executable named "pgwatch2" should be generated ...

    5.1. Run the gatherer

    ./pgwatch2 --host=my-postgres-db --user=pgwatch2 --password=xyz  \
        --ihost=my-influx-db --iuser=pgwatch2 --ipassword=xyz
    # for all starting options run "./pgwatch2 --help"

    Congrats! Now the metrics should start flowing in and after some minutes one should already see some graphs in Grafana.

  6. Install and configure SystemD init scripts for the Gatherer and the Web UI here and here or make sure to hatch up some "init scripts" so that the pgwatch2 daemon and the Web UI would be started automatically when the system reboots. For externally packaged components (Grafana, Influx, Postgres) it should be the case already.