path: root/tools/scripts/firehose.py

#! /usr/bin/env awx-python

#
# !!! READ BEFORE POINTING THIS AT YOUR FOOT !!!
#
# This script attempts to connect to an AWX database and insert (by default)
# a billion main_jobevent rows as screamingly fast as possible.
#
# tl;dr for best results, feed it high IOPS.
#
# this script exists *solely* for the purpose of generating *test* data very
# quickly; do *not* point this at a production installation or you *will* be
# very unhappy
#
# Before running this script, you should give postgres *GOBS* of memory
# and disk so it can create indexes and constraints as quickly as possible.
# In fact, it's probably not smart to attempt this on anything less than 8 core,
# 32GB of RAM, and tens of thousands of IOPS.
#
# Also, a billion events is a *lot* of data; make sure you've
# provisioned *at least* 750GB of disk space
#
# if you want this script to complete in a few hours, a good starting point
# is something like m5.4xlarge w/ 1TB provisioned IOPS SSD (io1)
#

import math
import argparse
import datetime
import itertools
import json
import multiprocessing
import pkg_resources
import random
import subprocess
import sys
from io import StringIO
from time import time
from uuid import uuid4

import psycopg2

from django import setup as setup_django
from django.db import connection
from django.db.models.sql import InsertQuery
from django.utils.timezone import now

db = json.loads(subprocess.check_output(['awx-manage', 'print_settings', 'DATABASES', '--format', 'json']))
name = db['DATABASES']['default']['NAME']
user = db['DATABASES']['default']['USER']
pw = db['DATABASES']['default']['PASSWORD']
host = db['DATABASES']['default']['HOST']

dsn = f'dbname={name} user={user} password={pw} host={host}'

u = str(uuid4())

STATUS_OPTIONS = ('successful', 'failed', 'error', 'canceled')

EVENT_OPTIONS = ('runner_on_ok', 'runner_on_failed', 'runner_on_changed', 'runner_on_skipped', 'runner_on_unreachable')

MODULE_OPTIONS = ('yup', 'stonchronize', 'templotz', 'deboog')


class YieldedRows(StringIO):
    def __init__(self, job_id, rows, created_stamp, modified_stamp, *args, **kwargs):
        self.rows = rows
        self.rowlist = []
        for (event, module) in itertools.product(EVENT_OPTIONS, MODULE_OPTIONS):
            event_data_json = {"task_action": module, "name": "Do a {} thing".format(module), "task": "Do a {} thing".format(module)}
            row = (
                "\t".join(
                    [
                        f"{created_stamp}",
                        f"{modified_stamp}",
                        f"{created_stamp}",
                        event,
                        json.dumps(event_data_json),
                        str(event in ('runner_on_failed', 'runner_on_unreachable')),
                        str(event == 'runner_on_changed'),
                        "localhost",
                        "Example Play",
                        "Hello World",
                        "",
                        "0",
                        "1",
                        job_id,
                        u,
                        "",
                        "1",
                        "hello_world.yml",
                        "0",
                        "X",
                        "1",
                    ]
                )
                + '\n'
            )
            self.rowlist.append(row)

    def read(self, x):
        if self.rows <= 0:
            self.close()
            return ''
        elif self.rows >= 1 and self.rows < 1000:
            event_rows = self.rowlist[random.randrange(len(self.rowlist))] * self.rows
            self.rows -= self.rows
            return event_rows
        self.rows -= 1000
        return self.rowlist[random.randrange(len(self.rowlist))] * 1000


def firehose(job, count, created_stamp, modified_stamp):
    conn = psycopg2.connect(dsn)
    f = YieldedRows(job, count, created_stamp, modified_stamp)
    with conn.cursor() as cursor:
        cursor.copy_expert(
            (
                'COPY '
                'main_jobevent('
                'created, modified, job_created, event, event_data, failed, changed, '
                'host_name, play, role, task, counter, host_id, job_id, uuid, '
                'parent_uuid, end_line, playbook, start_line, stdout, verbosity'
                ') '
                'FROM STDIN'
            ),
            f,
            size=1024 * 1000,
        )
    conn.commit()
    conn.close()


def cleanup(sql):
    print(sql)
    conn = psycopg2.connect(dsn)
    with conn.cursor() as cursor:
        cursor.execute(sql)
    conn.commit()
    conn.close()


def generate_jobs(jobs, batch_size, time_delta):
    print(f'inserting {jobs} job(s)')
    sys.path.insert(0, pkg_resources.get_distribution('awx').module_path)
    from awx import prepare_env

    prepare_env()
    setup_django()

    from awx.main.models import UnifiedJob, Job, JobTemplate

    fields = list(set(Job._meta.fields) - set(UnifiedJob._meta.fields))
    job_field_names = set([f.attname for f in fields])
    # extra unified job field names from base class
    for field_name in ('name', 'created_by_id', 'modified_by_id'):
        job_field_names.add(field_name)
    jt_count = JobTemplate.objects.count()

    def make_batch(N, jt_pos=0):
        jt = None
        while not jt:
            try:
                jt = JobTemplate.objects.all()[jt_pos % jt_count]
            except IndexError as e:
                # seems to happen every now and then due to some race condition
                print('Warning: IndexError on {} JT, error: {}'.format(jt_pos % jt_count, e))
            jt_pos += 1
        jt_defaults = dict(
            (f.attname, getattr(jt, f.attname))
            for f in JobTemplate._meta.get_fields()
            if f.concrete and f.attname in job_field_names and getattr(jt, f.attname)
        )
        jt_defaults['job_template_id'] = jt.pk
        jt_defaults['unified_job_template_id'] = jt.pk  # populated by save method

        jobs = [
            Job(
                status=STATUS_OPTIONS[i % len(STATUS_OPTIONS)],
                started=now() - time_delta,
                created=now() - time_delta,
                modified=now() - time_delta,
                finished=now() - time_delta,
                elapsed=0.0,
                **jt_defaults,
            )
            for i in range(N)
        ]
        ujs = UnifiedJob.objects.bulk_create(jobs)
        for uj in ujs:
            uj.unifiedjob_ptr_id = uj.id  # hack around the polymorphic id field not being picked up
        query = InsertQuery(Job)
        query.insert_values(fields, ujs)
        with connection.cursor() as cursor:
            query, params = query.sql_with_params()[0]
            cursor.execute(query, params)
        return ujs[-1], jt_pos, [uj.pk for uj in ujs]

    i = 1
    jt_pos = 0
    created_job_ids = []
    s = time()

    from awx.main.models import JobEvent
    from awx.main.utils.common import create_partition

    start_partition = (now() - time_delta).replace(minute=0, second=0, microsecond=0)
    create_partition(JobEvent._meta.db_table, start_partition)

    while jobs > 0:
        s_loop = time()
        print('running batch {}, runtime {}'.format(i, time() - s))
        created, jt_pos, ujs_pk = make_batch(min(jobs, batch_size), jt_pos)
        print('took {}'.format(time() - s_loop))
        i += 1
        jobs -= batch_size
        created_job_ids += ujs_pk
    print('Created Job IDS: {}'.format(created_job_ids))
    # return created
    return created_job_ids


def generate_events(events, job, time_delta):
    conn = psycopg2.connect(dsn)
    cursor = conn.cursor()

    created_time = datetime.datetime.today() - time_delta - datetime.timedelta(seconds=5)
    modified_time = datetime.datetime.today() - time_delta
    created_stamp = created_time.strftime("%Y-%m-%d %H:%M:%S")
    modified_stamp = modified_time.strftime("%Y-%m-%d %H:%M:%S")

    print(f'attaching {events} events to job {job}')
    cores = multiprocessing.cpu_count()
    workers = []

    num_procs = min(cores, events)
    num_events = math.ceil(events / num_procs)
    if num_events <= 1:
        num_events = events

    for i in range(num_procs):
        p = multiprocessing.Process(target=firehose, args=(job, num_events, created_stamp, modified_stamp))
        p.daemon = True
        workers.append(p)

    for w in workers:
        w.start()

    for w in workers:
        w.join()

    workers = []

    print('generating unique start/end line counts')
    cursor.execute('CREATE SEQUENCE IF NOT EXISTS firehose_seq;')
    cursor.execute('CREATE SEQUENCE IF NOT EXISTS firehose_line_seq MINVALUE 0;')
    cursor.execute('ALTER SEQUENCE firehose_seq RESTART WITH 1;')
    cursor.execute('ALTER SEQUENCE firehose_line_seq RESTART WITH 0;')
    cursor.execute("SELECT nextval('firehose_line_seq')")
    conn.commit()

    cursor.execute(
        "UPDATE main_jobevent SET "
        "counter=nextval('firehose_seq')::integer,"
        "start_line=nextval('firehose_line_seq')::integer,"
        "end_line=currval('firehose_line_seq')::integer + 2 "
        f"WHERE job_id={job}"
    )
    conn.commit()
    conn.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--jobs-per-hour', type=int, help='Number of jobs to create.', default=1000)  # 1M by default
    parser.add_argument('--events-per-job', type=int, help='Number of events to create.', default=1345)  # 1B by default
    parser.add_argument('--batch-size', type=int, help='Number of jobs to create in a single batch.', default=100)
    parser.add_argument('--days-delta', type=int, help='Number of days old to create the events. Defaults to 31.', default=31)
    params = parser.parse_args()
    jobs = params.jobs_per_hour
    events = params.events_per_job
    days_delta = params.days_delta
    batch_size = params.batch_size
    try:
        conn = psycopg2.connect(dsn)
        cursor = conn.cursor()

        # Drop all the indexes before generating jobs
        print('removing indexes and constraints')
        # get all the indexes for main_jobevent
        # disable WAL to drastically increase write speed
        # we're not doing replication, and the goal of this script is to just
        # insert data as quickly as possible without concern for the risk of
        # data loss on crash
        # see: https://www.compose.com/articles/faster-performance-with-unlogged-tables-in-postgresql/

        cursor.execute('ALTER TABLE main_jobevent SET UNLOGGED')
        cursor.execute("SELECT indexname, indexdef FROM pg_indexes WHERE tablename='main_jobevent' AND indexname != 'main_jobevent_pkey1';")
        indexes = cursor.fetchall()

        cursor.execute(
            "SELECT conname, contype, pg_catalog.pg_get_constraintdef(r.oid, true) as condef FROM pg_catalog.pg_constraint r WHERE r.conrelid = 'main_jobevent'::regclass AND conname != 'main_jobevent_pkey1';"
        )  # noqa
        constraints = cursor.fetchall()

        # drop all indexes for speed
        for indexname, indexdef in indexes:
            if indexname == 'main_jobevent_pkey_new':  # Dropped by the constraint
                continue
            cursor.execute(f'DROP INDEX IF EXISTS {indexname}')
            print(f'DROP INDEX IF EXISTS {indexname}')
        for conname, contype, condef in constraints:
            cursor.execute(f'ALTER TABLE main_jobevent DROP CONSTRAINT IF EXISTS {conname}')
            print(f'ALTER TABLE main_jobevent DROP CONSTRAINT IF EXISTS {conname}')
        conn.commit()

        for i_day in range(days_delta, 0, -1):
            for j_hour in range(24):
                time_delta = datetime.timedelta(days=i_day, hours=j_hour, seconds=0)
                created_job_ids = generate_jobs(jobs, batch_size=batch_size, time_delta=time_delta)
                if events > 0:
                    for k_id in created_job_ids:
                        generate_events(events, str(k_id), time_delta)
                print(datetime.datetime.utcnow().isoformat())
        conn.close()

    finally:
        # restore all indexes
        print(datetime.datetime.utcnow().isoformat())
        print('restoring indexes and constraints (this may take awhile)')

        workers = []
        for indexname, indexdef in indexes:
            if indexname == 'main_jobevent_pkey_new':  # Created by the constraint
                continue
            p = multiprocessing.Process(target=cleanup, args=(indexdef,))
            p.daemon = True
            workers.append(p)

        for w in workers:
            w.start()

        for w in workers:
            w.join()

        for conname, contype, condef in constraints:
            if contype == 'c':
                # if there are any check constraints, don't add them back
                # (historically, these are > 0 checks, which are basically
                # worthless, because Ansible doesn't emit counters, line
                # numbers, verbosity, etc... < 0)
                continue

            sql = f'ALTER TABLE main_jobevent ADD CONSTRAINT {conname} {condef}'
            cleanup(sql)

        print(datetime.datetime.utcnow().isoformat())