pg_gather aka pgGather

Scan and collect the minimal amount of data needed to identify potential problems in your PostgreSQL database, and then generate an analysis report using that data. This project provides two SQL scripts for users:

• gather.sql: Gathers performance and configuration data from PostgreSQL databases.
• gather_report.sql: Analyzes the collected data and generates detailed HTML reports.

Everything is SQL-only, leveraging the built-in features of psql, the command-line utility of PostgreSQL

Supported PostgreSQL Versions : 10, 11, 12, 13, 14, 15 & 16
Older versions : For PostgeSQL 9.6 and older, Please refer the documentation page

Highlights

1. Secure by Open : Simple, Transperent, Fully auditable code.
   To ensure full transparency of what is collected, transmitted, and analyzed, we use an SQL-only data collection script and avoid programs with any control structures, thus improving the readability and auditability of the data collection. This is one reason for separating data collection and analysis.
2. No Executables : No executables need to be deployed on the database host
   Using executables in secured environments poses unacceptable risks in many highly secure environments. pg_gather requires only the standard PostgreSQL command line utility, psql, and no other libraries or executables.
3. Authentication agnostic Any authentication mechanism supported by PostgreSQL works for data gathering in pg_gather, because it uses the standard psql command-line utility.
4. Any Operating System
   Linux (32/64-bit), Sun Solaris, Apple macOS, and Microsoft Windows: pg_gather works wherever psql is available, ensuring maximum portability. (Windows users, please see the Notes section below)
5. Architecture agnostic x86-64 bit, ARM, Sparc, Power, and other architectures. It works anywhere psql is available.
6. Auditable and optionally maskable data :
   pg_gather collects data in Tab Separated Values (TSV) format, making it easy to review and audit the information before sharing it for analysis. Additional masking or trimming is also possible with simple steps.
7. Any cloud/container/k8s :
   Works with AWS RDS, Azure, Google Cloud SQL, on-premises databases, and more.
   (Please see Heroku, AWS Aurora, Docker and K8s specific notes in the Notes section below)
8. Zero failure design :
   pg_gather can generate a report from available information even if data collection is partial or fails due to permission issues, unavailable tables/views, or other reasons.
9. Low overhead for data collection :
   By design, data collection is separate from data analysis. This allows the collected data to be analyzed on an independent system, so that analysis queries do not adversely impact critical systems. In most cases, the overhead of data collection is negligible.
10. Small, single file data dump :
    To generate the smallest possible file, which can be further compressed by gzip for the easy transmission and storage, pg_gather avoids redundancy in the collected data as much as possible.

How to Use

1. Data Gathering.

To gather configuration and performance information, run the gather.sql script against the database using psql:

psql <connection_parameters_if_any> -X -f gather.sql > out.tsv

OR ALTERNATIVELY pipe to a compression utilty to get a compressed output as follows:

psql <connection_parameters_if_any> -X -f gather.sql | gzip > out.tsv.gz

This script may take over 20 seconds to run because it contains sleeps/delays. We recommend running the script as a privileged user (such as superuser or rds_superuser) or as an account with the pg_monitor privilege. The output file contains performance and configuration data for analysis.

Notes:

1. Heroku and similar DaaS hostings impose very high restrictions on collecting performance data. Queries on views like pg_statistics may produce errors during data collection, but these errors can be ignored.
2. MS Windows users!, Client tools like pgAdmin include psql, which can be used to run pg_gather against local or remote databases. For example:

  "C:\Program Files\pgAdmin 4\v4\runtime\psql.exe" -h pghost -U postgres -f gather.sql > out.tsv

3. AWS Aurora offers a "PostgreSQL-compatible" database. However, it is not a true PostgreSQL database, even though it looks like one. Therefore, you should do the following to the gather.sql script to replace any unapplicable lines with "NULL".

  sed -i -e 's/^CASE WHEN pg_is_in_recovery().*/NULL/' gather.sql

4. Docker containers of PostgreSQL may not include the curl or wget`` utilities necessary to download gather.sql. Therefore, it is recommended to pipe the contents of the SQL file to psql` instead.

  cat gather.sql | docker exec -i <container> psql -X -f - > out.tsv

5. Kubernetes environments also have similar restrictions as those mentioned for Docker. Therefore, a similar approach is suggested.

  cat gather.sql | kubectl exec -i <PGpod> -- psql -X -f - > out.tsv

Gathering data continuosly

There could be requirements for collecting data continuously and repatedly. pg_gather has a special lightweight mode for continuous data gathering, which is automatically enabled when it connects to the "template1" database. Please refer to detailed documentation specific to continuous and repated data collection

2. Data Analysis

2.1 Importing collected data

The collected data can be imported to a PostgreSQL Instance. This creates required schema objects in the public schema of the database. CAUTION : Avoid importing the data into critical environments/databases. A temporary PostgreSQL instance is preferable.

 psql -f gather_schema.sql -f out.tsv

Deprecated usage of sed : sed -e '/^Pager/d; /^Tuples/d; /^Output/d; /^SELECT pg_sleep/d; /^PREPARE/d; /^\s*$/d' out.tsv | psql -f gather_schema.sql -

2.2 Generating Report

An analysis report in HTML format can be generated from the imported data as follows.

psql -X -f gather_report.sql > GatherReport.html

You may use your favourite web browser to read the report.

NOTE: PostgreSQL version 13 or above is required to generate the analysis report.

ANNEXTURE 1 : Using PostgreSQL container and wrapper script

The steps for data analysis mentioned above seem simple (single command), but they require a PostgreSQL instance to import the data into. An alternative is to use the generate_report.sh script, which can spin up a PostgreSQL Docker container and automate the entire process. To use this script, you must place it in a directory containing the gather_schema.sql and gather_report.sql files.

The script will spin up a Docker container, import the output of gather.sql (out.tsv) and then it generates an HTML report. This script expects at least a single argument: path to the out.tsv.

There are two more additional positional arguments:

• Desired report name with path.
• A flag to specify whether to keep the docker container. This flag allows to usage of the container and data for further analysis.

Example 1: Import data and generate an HTML file

$ ./generate_report.sh /tmp/out.tsv
...
Container 61fbc6d15c626b484bdf70352e94bbdb821971de1e00c6de774ca5cd460e8db3 deleted
Finished generating report in /tmp/out.txt.html

Example 2: Import data, keep the container intact and generate the report in the specified location

$ ./generate_report.sh /tmp/out.tsv /tmp/custom-name.html y
...
Container df7b228a5a6a49586e5424e5fe7a2065d8be78e0ae3aa5cddd8658ee27f4790c left around
Finished generating report in /tmp/custom-name.html

Advanced configurations

Timezone

By default, the pg_gather report uses the same timezone of the server from which the data is collected, because it considers the log_timezone parameter for generating the report. This default timezone setting helps to compare the PostgreSQL log entries with the pg_gather report. However, this may not be the right timezone for few users, especially when cloud hostings are used. The pg_gather allows the user to have a custom timezone by setting the environment variable PG_GATHER_TIMEZONE to override the default. For example,

export PG_GATHER_TIMEZONE='UTC'

Please use the timezone name or abbreviation available from pg_timezone_names

Demo

Data collection

Simple Report Generation (1min):

Report generation using postgresql docker container made easy (3min):