pgworkload has matured and became dbworkload!

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 and can be extracted from a DDL SQL file.

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 2 methods: 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 - env setup

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

pip3 install pgworkload

mkdir workloads
cd workloads

# the workload class
wget https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.py

# the DDL file
wget https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.sql

# the data generation definition file
wget https://raw.githubusercontent.com/fabiog1901/pgworkload/main/workloads/bank.yaml

Step 1 - init the workload

Make sure your CockroachDB cluster or PostgreSQL server is up and running.

Connect to the SQL prompt and execute the DDL statements in the bank.sql file. In CockroachDB, you can simply run

sql> \i bank.sql

Next, generate some CSV data to seed the database:

pgworkload util csv -i bank.yaml -x 1

The CSV files will be located inside a bank directory.

$ ls -lh bank
total 1032
-rw-r--r--  1 fabio  staff   513K Apr  9 13:01 ref_data.0_0_0.csv

$ head -n2 bank/ref_data.0_0_0.csv 
0       simplefaker     b66ab5dc-1fcc-4ac8-8ad0-70bbbb463f00    alpha   16381   {124216.6,416559.9,355271.42,443666.45,689859.03,461510.94,31766.46,727918.45,361202.5,561364.1}        12421672576.9632        2022-10-18 04:57:37.613512      2022-10-18     13:36:48 1001010011      \xe38a2e10b400a8e77eda  {ID-cUJeNcMZ,ID-mWxhyiqN,ID-0FnlVOO5}   0       "{""k"":""cUJNcMZ""}"
1       simplefaker     f2ebb78a-5af3-4755-8c22-2ad06aa3b26c    bravo   39080           35527177861.6551        2022-12-25 09:12:04.771673      2022-12-25      13:05:42        0110111101      \x5a2efedf253aa3fbeea8  {ID-gQkRkMxIkSjihWcWTcr,ID-o7iDzl9AMJoFfduo6Hz,ID-5BS3MlZgOjxFZRBgBmf}  0       "{""k"":""5Di0UHLWMEuR7""}"

Now you can import the CSV file. In CockroachDB, my favorite method is to use a webserver to serve the CSV file. Open a new terminal then start a simple python server

cd workloads
cd bank
python3 -m http.server 3000

If you open your browser at http://localhost:3000 you should see file ref_data.0_0_0.csv being served.

At the SQL prompt, import the file

sql> IMPORT INTO ref_data CSV DATA ('http://localhost:3000/ref_data.0_0_0.csv') WITH delimiter = e'\t'; 

In PostgreSQL Server, at the SQL prompt, just use COPY

bank=# COPY ref_data FROM '/Users/fabio/workloads/bank/ref_data.0_0_0.csv' WITH CSV DELIMITER AS e'\t';
COPY 100
Time: 2.713 ms

Step 2 - Run the workload

Run the workload using 4 connections for 120 seconds or 100k cycles, whichever comes first.

# CockroachDB
pgworkload run -w bank.py -c 4 --url 'postgres://root@localhost:26257/bank?sslmode=disable&application_name=Bank' -d 120 -i 100000

# PostgreSQL
pgworkload run -w bank.py -c 4 --url 'postgres://fabio:postgres@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 rolling statistics about throughput and latency for each transaction in your workload class

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       3206       320.13          3206           320.6       11.4        0.49      23.31      23.97      25.13       54.63
read                  10       1614       161.15          1614           161.4        0.19       0.17       0.28       0.31       0.4         1.82
txn1_new              10       1596       159.34          1596           159.6        0.34       0.29       0.44       0.49       0.67       21.13
txn2_verify           10       1594       159.13          1594           159.4       11.15      10.88      11.93      12.5       13.23       43.28
txn3_finalize         10       1592       158.92          1592           159.2       11.23      10.94      12.02      12.68      13.37       39.77 

[...]

2024-05-17 15:28:11,589 [INFO] (MainProcess MainThread) run:194: 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      40639       336.43           251            25.1       11.55       0.44      24.1       24.9       25.99       26.38
read                 121      20427       169.1            128            12.8        0.22       0.19       0.33       0.35       0.41        0.44
txn1_new             121      20212       167.32           120            12          0.38       0.34       0.5        0.58       0.76        0.79
txn2_verify          121      20212       167.32           121            12.1       11.38      11.17      12.65      12.93      13.38       13.39
txn3_finalize        121      20212       167.32           123            12.3       11.54      11.41      12.73      13.03      13.55       13.97

You can always use pgAdmin for PostgreSQL Server or the DB Console for CockroachDB to view your workload, too.

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

pgworkload --help

How it works

It’s helpful to understand first what pgworkload does:

• At runtime, pgworkload first imports the class you pass, bank.py.
• It spawns n threads for concurrent execution (see next section on Concurrency).
• By default, it sets the connection to autocommit mode.
• psycopg v3 will PREPARE statements automatically after 5 executions.
• Each thread creates a database connection - no need for a connection pool.
• In a loop, each pgworkload thread will:
  • execute function run() which returns a list of functions.
  • execute each function in the list sequentially. Each function, typically, executes a SQL statement/transaction.
• Execution stats are funneled back to the MainThread, which aggregates and prints them to stdout.
• If the connection drops, it will recreate it. You can also program how long you want the connection to last.
• pgworkload stops once a limit has been reached (iteration/duration), or you Ctrl+C.

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/-x, to configure the count of processes (defaults to the CPU count)
• --concurrency/-c, to configure the total number of connections (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 + 1 extra threads. Total = 2
• Process-2: MainThread + 1 extra threads. Total = 2
• Process-3: MainThread + 2 extra thread. Total = 3
• Process-4: MainThread + 2 extra thread. Total = 3

Total executing threads/connections = 10

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

Furthermore, each executing thread receives a unique ID (an integer). The ID is passed to the workload class with function setup(), along with the total count of threads, i.e. the value passed to -c/--concurrency. You can leverage the ID and the thread count in various ways, for example, to have each thread process a subset of a dataset.

Generating CSV files

• You can seed a database quickly by letting pgworkload generate pseudo-random data and import it.
• pgworkload takes the DDL as an input and creates an intermediate YAML file, with the definition of what data you want to create (a string, a number, a date, a bool..) based on the column data type.
• You then refine the YAML file to suit your needs, for example, the size of the string, a range for a date, the precision for a decimal, a choice among a discrete list of values..
• You can also specify what is the percentage of NULL for any column, or how many elements in an ARRAY type.
• You then specify the total row count, how many rows per file, and in what order, if any, to sort by.
• Then pgworkload will generate the data into CSV or TSV files, compress them if so requested.
• You can then optionally merge-sort the files using command merge.

Write up blog: Generate multiple large sorted csv files with pseudo-random data

Find out more on the yaml, csv and merge commands by running

pgworkload util --help

Consult file workloads/bank.yaml for a list of all available generators and options.

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 --builtin-workload Querybench --args mystmts.sql --url <conn-string>

Acknowledgments

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