pgworkload 0.1.3

Workload framework for the PostgreSQL protocol

pgworkload - workload utility for the PostgreSQL protocol

Overview

The goal of pgworkload is to ease the creation of workload scripts by providing a utility with the most common functionality already implemented.

pgworkload is run in conjunction with a user supplied Python class. This class defines the workload transactions and flow.

The user has complete control of what statements the transactions actually execute, and what transactions are executed in which order.

pgworkload can seed a database with random generated data, whose definition is supplied in a YAML file.

A .sql file can be supplied to create the schema and run any special queries, eg. Zone Configuration changes.

Example

Class Bank in file workloads/bank.py is an example of one such user-created workload. The class defines 3 simple transactions that have to be executed by pgworkload. Have a look at the bank.py, bank.yaml and bank.sql in the workload folder in this project.

Head to file workload/bank.sql to see what the database schema look like. We have 2 tables:

• the transactions table, where we record the bank payment transactions.
• the ref_data table.

Take a close look at this last table: each column represent a different type, which brings us to the next file.

File bank.yaml is the data generation definition file. For each column of table ref_data, we deterministically generate random data. This file is meant as a guide to show what type of data can be generated, and what args are required.

File bank.py defines the workload. The workload is defined as a class object. The class defines 3 methods: init(), run() and the constructor, __init__(). All other methods are part of the application logic of the workload. Read the comments along the code for more information.

Let's run the sample Bank workload.

Step 0 - Create Python env

# upgrade pip - must have pip version 20.3+ 
pip3 install --upgrade pip

# we're now inside our virtual env
pip3 install pgworkload

# download the bank workload files
mkdir workloads
# the workload class
wget -P workloads https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.py
# the schema file
wget -P workloads https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.sql
# the data generation definition file
wget -P workloads https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.yaml
# the arguments to pass at runtime, optional
wget -P workloads https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.args.yaml

Step 1 - Init the workload

We assume that your CockroachDB cluster or PostgreSQL server is up and running.

Init the Bank workload.

# CockroachDB
pgworkload init -w workloads/bank.py -c 8 --url 'postgres://localhost:26257/postgres?sslmode=disable'

# PostgreSQL
pgworkload init --w workloads/bank.py -c 8 --url 'postgres://localhost:5432/postgres?sslmode=disable'

You should see something like below

2022-01-28 17:21:47,335 [INFO] (MainProcess 29422) URL: 'postgres://root@localhost:[5432|26257]/defaultdb?sslmode=disable&application_name=Bank'
2022-01-28 17:21:47,480 [INFO] (MainProcess 29422) Database 'bank' created.
2022-01-28 17:21:47,769 [INFO] (MainProcess 29422) Created workload schema
2022-01-28 17:21:47,789 [INFO] (MainProcess 29422) Generating dataset for table 'ref_data'
2022-01-28 17:22:07,088 [INFO] (MainProcess 29422) Importing data for table 'ref_data'
2022-01-28 17:22:21,063 [INFO] (MainProcess 29422) Init completed. Please update your database connection url to 'postgres://root@localhost:26257/bank?sslmode=disable&application_name=Bank'

pgworkload has read file bank.sql and has created the database and its schema. It has then read file bank.yaml and has generated the CSV files for the table ref_data. Finally, it imports the CSV files into database bank.

Step 2 - Run the workload

Run the workload using 8 connections for 120 seconds or 100k cycles, whichever comes first.

# CockroachDB
pgworkload run -w workloads/bank.py -c 8 --url 'postgres://root@localhost:26257/bank?sslmode=disable&application_name=Bank' -d 120 -i 100000

# PostgreSQL
pgworkload run -w workloads/bank.py -c 8 --url 'postgres://root@localhost:5432/bank?sslmode=disable&application_name=Bank' -d 120 -i 100000

pgworkload uses exclusively the excellent Psycopg 3 to connect. No other ORMs or drivers/libraries are used. Psycopg has a very simple, neat way to create connections and execute statements and transactions.

pgworkload will output something like below

2022-01-28 17:22:43,893 [INFO] (MainProcess 29511) URL: 'postgres://root@localhost:26257/bank?sslmode=disable&application_name=Bank'
id               elapsed    tot_ops    tot_ops/s    period_ops    period_ops/s    mean(ms)    p50(ms)    p90(ms)    p95(ms)    p99(ms)    pMax(ms)
-------------  ---------  ---------  -----------  ------------  --------------  ----------  ---------  ---------  ---------  ---------  ----------
__cycle__             10       1342       133.72          1342           134.2       54.9       35.76     165.94     192.89     245.42      333.6
read                  10       1215       121.03          1215           121.5       41.11      19.58     113.21     146.79     208.86      291.02
txn1_new              10        130        12.95           130            13         48.29      53.81      74.7       90.84      95.66      108.37
txn2_verify           10        129        12.85           129            12.9       70.9       73.73      94.3       99.69     137.99      164.96
txn3_finalize         10        127        12.65           127            12.7       67.21      72.48      93.64     105.97     129.57      166 

[...]

2022-01-28 17:24:44,765 [INFO] (MainProcess 29511) Requested iteration/duration limit reached. Printing final stats
id               elapsed    tot_ops    tot_ops/s    period_ops    period_ops/s    mean(ms)    p50(ms)    p90(ms)    p95(ms)    p99(ms)    pMax(ms)
-------------  ---------  ---------  -----------  ------------  --------------  ----------  ---------  ---------  ---------  ---------  ----------
__cycle__            121      14519       120.12            66             6.6       94.08      96.68     203.74     216.83     242.24      262.69
read                 121      13050       107.96            54             5.4       70.6       62.7      127.88     151.29     203.52      203.62
txn1_new             121       1469        12.15             7             0.7       51.08      51.07      71.71      73.66      75.23       75.62
txn2_verify          121       1469        12.15            11             1.1       70.52      76.92     102.31     102.32     102.33      102.33
txn3_finalize        121       1469        12.15            12             1.2       81.19      98.97     103.88     103.97     103.98      103.98 

There are many built-in options. Check them out with

pgworkload -h

Concurrency - processes and threads

pgworkload uses both the multiprocessing and threading library to achieve high concurrency, that is, opening multiple connections to the DBMS.

There are 2 parameters that can be used to configure how many processes you want to create, and for each process, how many threads:

• --procs, or -x, to configure the count of processes (defaults to the CPU count)
• --concurrency, or -c, to configure the total number of executing workloads to run (also referred to as executing threads)

pgworkload will spread the load across the processes, so that each process has an even amount of threads.

Example: if we set --procs 4 and --concurrency 10, pgworkload will create as follows:

• Process-1: MainThread + 2 extra threads. Total = 3
• Process-2: MainThread + 2 extra threads. Total = 3
• Process-3: MainThread + 1 extra thread. Total = 2
• Process-3: MainThread + 1 extra thread. Total = 2

Total workloads = 10

This allows you to fine tune the count of Python processes and threads to fit your system.

Built-in Workloads

pgworkload has the following workload already built-in and can be called without the need to pass a class file

Querybench

Querybench runs a list of SQL Statements sequentially and iteratively. It assumes the schema and data have been created and loaded.

SQL statements file mystmts.sql

-- Query 1
select 1;
select 
  version();
-- select now();

-- Query 2
SELECT * FROM my_table 
WHERE id = 1234;

Run Querybench like this:

pgworkload run --workload querybench --args mystmts.sql --url <conn-string>

Hovr

Coming soon

Acknowledgments

Some methods and classes have been taken and modified from, or inspired by, https://github.com/cockroachdb/movr