pg_statviz

pg_statviz

pg_statviz is a minimalist extension and utility pair for time series analysis and visualization of PostgreSQL internal statistics.

Created for capturing PostgreSQL’s cumulative and dynamic statistics, pg_statviz enables deeper time series analysis than the standard PostgreSQL statistics views. The included utility generates visualizations for selected time ranges from the stored statistic snapshots, helping users track PostgreSQL performance over time and potentially aiding in performance tuning and troubleshooting.

Optionally, an AI analysis mode can produce per-module HTML reports with chart commentary from a cloud LLM (Claude or Gemini) or a local model via Ollama.

Design Philosophy

Designed with the K.I.S.S. and UNIX philosophies in mind, pg_statviz aims to be a modular, minimal and unobtrusive tool that does only what it’s meant for: create snapshots of PostgreSQL statistics for visualization and analysis. To this end, a utility is provided for retrieving and creating simple visualizations with the stored snapshots, by using pandas and Matplotlib.

Installing the extension

Debian (12+) / Ubuntu (22.04+)

  1. Configure the PostgreSQL Deb repository for your Linux distribution, as explained here for Debian and here for Ubuntu.

  2. Use apt to install the extension for your PostgreSQL version:

     sudo apt install postgresql-<pg_version>-statviz

Red Hat Enterprise Linux (v8.0+) / Fedora (37+)

  1. Configure the PostgreSQL Yum repository for your Linux distribution, as explained here.

  2. Use dnf or yum to install the extension for your PostgreSQL version:

     sudo dnf install pg_statviz_extension-<pg_version>
 OR
 sudo yum install pg_statviz_extension-<pg_version>

PGXN (PostgreSQL Extension Network)

The extension is available on PGXN.

To install from PGXN, either download the zip file and install manually or use the PGXN Client to install:

pgxn install pg_statviz

Manual installation

To install manually, clone this repository locally:

git clone https://github.com/vyruss/pg_statviz.git

This will install the extension in the appropriate location for your system ($SHAREDIR/extension):

cd pg_statviz
sudo make install

Enabling the extension

The extension can now be enabled inside the appropriate database like this, e.g. from psql:

\c mydatabase
CREATE EXTENSION pg_statviz;

This will create the needed tables and functions under schema pgstatviz (note the lack of underscore in the schema name).

Installing the utility

The visualization utility can be installed from PyPi:

pip install pg_statviz

The utility is also available in the Debian and Ubuntu PostgreSQL Deb Repositories, and can be installed using apt:

sudo apt install pg-statviz

Finally, it can be found in the PostgreSQL Yum Repository and can be installed using dnf or yum:

sudo dnf install pg_statviz
OR
sudo yum install pg_statviz

Requirements

Python 3.11+ is required for the visualization utility. Any recent PostgreSQL version up to and including 18 is supported.

Usage

The extension can be used by superusers or any user that has pg_monitor role privileges. To take a snapshot, e.g. from psql:

SELECT pgstatviz.snapshot();

NOTICE:  created pg_statviz snapshot
           snapshot
-------------------------------
 2026-01-01 11:04:58.055453+00
(1 row)

Older snapshots and their associated data can be removed using any time expression. For example, to remove data more than 90 days old:

DELETE FROM pgstatviz.snapshots
WHERE snapshot_tstamp < CURRENT_DATE - 90;

Or all snapshots can be removed like this:

SELECT pgstatviz.delete_snapshots();

NOTICE:  truncating table "snapshots"
NOTICE:  truncate cascades to table "buf"
NOTICE:  truncate cascades to table "conf"
NOTICE:  truncate cascades to table "conn"
NOTICE:  truncate cascades to table "db"
NOTICE:  truncate cascades to table "io"
NOTICE:  truncate cascades to table "lock"
NOTICE:  truncate cascades to table "repl"
NOTICE:  truncate cascades to table "slru"
NOTICE:  truncate cascades to table "wait"
NOTICE:  truncate cascades to table "wal"
 delete_snapshots
------------------

(1 row)

The pg_monitor role can be assigned to any user:

GRANT pg_monitor TO myuser;

Scheduling

Periodic snapshots can be set up with any job scheduler. For example with cron:

crontab -e -u postgres

Inside the postgres user’s crontab, add this line to take a snapshot every 15 minutes:

*/15 * * * * psql -c -d mydatabase "SELECT pgstatviz.snapshot()" >/dev/null 2>&1

Visualization

Potentially very large numbers of data points can be visualized with the aid of pandas resampling, displaying the mean value over 100 plot points as a default.

The visualization utility can be called like a PostgreSQL command line tool:

pg_statviz --help

usage: pg_statviz [--help] [--version] [-d DBNAME] [-h HOSTNAME] [-p PORT]
                  [-U USERNAME] [-W] [-D FROM TO] [-O OUTPUTDIR]
                  {analyze,buf,cache,checkp,checksum,conn,io,lock,repl,slru,tuple,wait,wal,xact} ...

run all analysis modules

positional arguments:
  {analyze,buf,cache,checkp,checksum,conf,conn,io,lock,repl,slru,tuple,wait,wal,xact}
    analyze             run all analysis modules
    buf                 run buffers written analysis module
    cache               run cache hit ratio analysis module
    checkp              run checkpoint analysis module
    checksum            run checksum failure analysis module
    conf                run configuration changes analysis module
    conn                run connection count analysis module
    io                  run I/O analysis module
    lock                run locks analysis module
    repl                run replication analysis module
    slru                run SLRU analysis module
    tuple               run tuple count analysis module
    wait                run wait events analysis module
    wal                 run WAL generation analysis module
    xact                run transaction count analysis module

options:
  --help
  --version             show program's version number and exit
  -d DBNAME, --dbname DBNAME
                        database name to analyze (default: 'myuser')
  -h HOSTNAME, --host HOSTNAME
                        database server host or socket directory (default: '/var/run/postgresql')
  -p PORT, --port PORT  database server port (default: '5432')
  -U USERNAME, --username USERNAME
                        database user name (default: 'myuser')
  -W, --password        force password prompt (should happen automatically) (default: False)
  -D FROM TO, --daterange FROM TO
                        date range to be analyzed in ISO 8601 format e.g. 2026-01-01T00:00
                        2026-01-01T23:59 (default: [])
  -O OUTPUTDIR, --outputdir OUTPUTDIR
                        output directory (default: -)

Specific module usage

pg_statviz conn --help

usage: pg_statviz conn [-h] [-d DBNAME] [--host HOSTNAME] [-p PORT] [-U USERNAME] [-W]
                       [-D FROM TO] [-O OUTPUTDIR] [-u [USERS ...]]

run connection count analysis module

options:
  -h, --help            show this help message and exit
  -d DBNAME, --dbname DBNAME
                        database name to analyze (default: 'myuser')
  --host HOSTNAME       database server host or socket directory (default: '/var/run/postgresql')
  -p PORT, --port PORT  database server port (default: '5432')
  -U USERNAME, --username USERNAME
                        database user name (default: 'myuser')
  -W, --password        force password prompt (should happen automatically) (default: False)
  -D FROM TO, --daterange FROM TO
                        date range to be analyzed in ISO 8601 format e.g. 2026-01-01T00:00
                        2026-01-01T23:59 (default: [])
  -O OUTPUTDIR, --outputdir OUTPUTDIR
                        output directory (default: -)
  -u [USERS ...], --users [USERS ...]
                        user name(s) to plot in analysis (default: [])

Example:

pg_statviz buf --host localhost -d postgres -U postgres -D 2026-01-01T00:00 2026-01-01T23:59

Produces:

buf output sample

buf output sample (rate)

conf output sample

AI Analysis (optional)

pg_statviz can optionally generate AI-powered analysis of each chart, producing per-module HTML reports with embedded chart images and LLM commentary. The AI acts as a Senior PostgreSQL DBA, reviewing each chart and providing a [HEALTHY], [WARNING], or [CRITICAL] verdict with a brief interpretation, and a concrete remediation step for any [WARNING] or [CRITICAL] finding.

Enabling AI analysis

Add --ai (or -A) to any command:

pg_statviz analyze -d mydb --ai

This uses Claude by default. Three providers are available:

Provider Flag Requires
Claude (Anthropic) --ai claude or --ai ANTHROPIC_API_KEY
Gemini (Google AI Studio) --ai gemini GOOGLE_API_KEY
Local (Ollama) --ai local Ollama running with gemma4:e4b

Installing AI dependencies

The AI libraries are not required for normal operation. Install them only if you want to use --ai:

pip install pg_statviz[ai]

For the local provider, install and start Ollama, then pull the model:

ollama pull gemma4:e4b

Output

When --ai is enabled, each module produces an HTML report alongside the chart PNGs (e.g. pg_statviz_localhost_5432_buf.html). The report embeds the chart images and renders the AI analysis as styled HTML.

When the analyze subcommand is invoked with --ai, an additional top-level pg_statviz_<host>_<port>_index.html report is generated. It synthesises the per-module verdicts into a single cross-cutting summary, correlating patterns across charts and surfacing the single most important next action.

AI report sample

How the analysis is grounded

The AI doesn’t just look at the chart data in isolation. Each prompt also includes:

  • The captured PostgreSQL version and role (primary/standby) and the hostname, so suggestions can be tailored to your actual server.
  • The relevant pg_settings snapshot for the chart in question (e.g. shared_buffers and bgwriter_* for buffer charts, checkpoint_* and max_wal_size for checkpoint charts), so the advice is grounded in your configuration rather than generic folklore.
  • Per-chart deterministic rule findings computed from the actual numeric data before the LLM call (for example: cache hit ratio below 95%, session age over one hour, any non-zero checksum failure). These are injected into the prompt and a severity floor ensures the LLM’s final verdict can never be downgraded below the worst rule finding.

User-supplied data (config values, role names, slot names, etc.) is wrapped in <user_data>...</user_data> envelopes and the system prompt instructs the model never to treat that content as instructions.

Schema

The pg_statviz extension stores its data in the following tables:

Table Description
pgstatviz.snapshots Timestamped snapshots
pgstatviz.buf Buffer, checkpointer and background writer data
pgstatviz.conf PostgreSQL server configuration data
pgstatviz.conn Connection data
pgstatviz.db PostgreSQL server and database statistics
pgstatviz.io I/O stats data
pgstatviz.lock Locks data
pgstatviz.repl Replication stats data
pgstatviz.slru SLRU cache stats data
pgstatviz.wait Wait events data
pgstatviz.wal WAL generation data

Export data

To dump the captured data, e.g. for analysis on a different machine, run:

pg_dump -d <dbname> -a -O -t pgstatviz.* > pg_statviz_data.dump

Load it like this on the target database (which should have pg_statviz installed) :

psql -d <other_dbname> -f pg_statviz_data.dump

Alternatively, pg_statviz internal tables can also be exported to a tab separated values (TSV) file for use by other tools:

psql -d <dbname> -c "COPY pgstatviz.conn TO STDOUT CSV HEADER DELIMITER E'\t'" > conn.tsv

These can then be loaded into another database like this, provided the tables exist (installing the extension will create them):

psql -d <other_dbname> -c "COPY pgstatviz.conn FROM STDIN CSV HEADER DELIMITER E'\t'" < conn.tsv