pvc/api-daemon/pvcapid/benchmark.py

#!/usr/bin/env python3

# benchmark.py - PVC API Benchmark functions
# Part of the Parallel Virtual Cluster (PVC) system
#
#    Copyright (C) 2018-2022 Joshua M. Boniface <joshua@boniface.me>
#
#    This program is free software: you can redistribute it and/or modify
#    it under the terms of the GNU General Public License as published by
#    the Free Software Foundation, version 3.
#
#    This program is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU General Public License for more details.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <https://www.gnu.org/licenses/>.
#
###############################################################################

import psycopg2
import psycopg2.extras

from json import loads, dumps

from pvcapid.Daemon import config

from daemon_lib.zkhandler import ZKHandler

import daemon_lib.common as pvc_common
import daemon_lib.ceph as pvc_ceph


#
# Exceptions (used by Celery tasks)
#
class BenchmarkError(Exception):
    """
    An exception that results from the Benchmark job.
    """

    def __init__(
        self, message, job_name=None, db_conn=None, db_cur=None, zkhandler=None
    ):
        self.message = message
        if job_name is not None:
            # Clean up our dangling result
            query = "DELETE FROM storage_benchmarks WHERE job = %s;"
            args = (job_name,)
            db_cur.execute(query, args)
            db_conn.commit()
            # Close the database connections cleanly
            close_database(db_conn, db_cur)
            zkhandler.disconnect()

    def __str__(self):
        return str(self.message)


#
# Common functions
#


# Database connections
def open_database(config):
    conn = psycopg2.connect(
        host=config["database_host"],
        port=config["database_port"],
        dbname=config["database_name"],
        user=config["database_user"],
        password=config["database_password"],
    )
    cur = conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
    return conn, cur


def close_database(conn, cur, failed=False):
    if not failed:
        conn.commit()
    cur.close()
    conn.close()


def list_benchmarks(job=None):
    if job is not None:
        query = "SELECT * FROM {} WHERE job = %s;".format("storage_benchmarks")
        args = (job,)
    else:
        query = "SELECT * FROM {} ORDER BY id DESC;".format("storage_benchmarks")
        args = ()

    conn, cur = open_database(config)
    cur.execute(query, args)
    orig_data = cur.fetchall()
    data = list()
    for benchmark in orig_data:
        benchmark_data = dict()
        benchmark_data["id"] = benchmark["id"]
        benchmark_data["job"] = benchmark["job"]
        benchmark_data["test_format"] = benchmark["test_format"]
        if benchmark["result"] == "Running":
            benchmark_data["benchmark_result"] = "Running"
        else:
            try:
                benchmark_data["benchmark_result"] = loads(benchmark["result"])
            except Exception:
                benchmark_data["benchmark_result"] = {}
        # Append the new data to our actual output structure
        data.append(benchmark_data)
    close_database(conn, cur)
    if data:
        return data, 200
    else:
        return {"message": "No benchmark found."}, 404


def run_benchmark(self, pool):
    # Runtime imports
    import time
    from datetime import datetime

    # Define the current test format
    TEST_FORMAT = 1

    time.sleep(2)

    # Phase 0 - connect to databases
    try:
        db_conn, db_cur = open_database(config)
    except Exception:
        print("FATAL - failed to connect to Postgres")
        raise Exception

    try:
        zkhandler = ZKHandler(config)
        zkhandler.connect()
    except Exception:
        print("FATAL - failed to connect to Zookeeper")
        raise Exception

    cur_time = datetime.now().isoformat(timespec="seconds")
    cur_primary = zkhandler.read("base.config.primary_node")
    job_name = "{}_{}".format(cur_time, cur_primary)

    print("Starting storage benchmark '{}' on pool '{}'".format(job_name, pool))

    print("Storing running status for job '{}' in database".format(job_name))
    try:
        query = "INSERT INTO storage_benchmarks (job, test_format, result) VALUES (%s, %s, %s);"
        args = (
            job_name,
            TEST_FORMAT,
            "Running",
        )
        db_cur.execute(query, args)
        db_conn.commit()
    except Exception as e:
        raise BenchmarkError(
            "Failed to store running status: {}".format(e),
            job_name=job_name,
            db_conn=db_conn,
            db_cur=db_cur,
            zkhandler=zkhandler,
        )

    # Phase 1 - volume preparation
    self.update_state(
        state="RUNNING",
        meta={"current": 1, "total": 3, "status": "Creating benchmark volume"},
    )
    time.sleep(1)

    volume = "pvcbenchmark"

    # Create the RBD volume
    retcode, retmsg = pvc_ceph.add_volume(zkhandler, pool, volume, "8G")
    if not retcode:
        raise BenchmarkError(
            'Failed to create volume "{}": {}'.format(volume, retmsg),
            job_name=job_name,
            db_conn=db_conn,
            db_cur=db_cur,
            zkhandler=zkhandler,
        )
    else:
        print(retmsg)

    # Phase 2 - benchmark run
    self.update_state(
        state="RUNNING",
        meta={"current": 2, "total": 3, "status": "Running fio benchmarks on volume"},
    )
    time.sleep(1)

    # We run a total of 8 tests, to give a generalized idea of performance on the cluster:
    #   1. A sequential read test of 8GB with a 4M block size
    #   2. A sequential write test of 8GB with a 4M block size
    #   3. A random read test of 8GB with a 4M block size
    #   4. A random write test of 8GB with a 4M block size
    #   5. A random read test of 8GB with a 256k block size
    #   6. A random write test of 8GB with a 256k block size
    #   7. A random read test of 8GB with a 4k block size
    #   8. A random write test of 8GB with a 4k block size
    # Taken together, these 8 results should give a very good indication of the overall storage performance
    # for a variety of workloads.
    test_matrix = {
        "seq_read": {"direction": "read", "iodepth": "64", "bs": "4M", "rw": "read"},
        "seq_write": {"direction": "write", "iodepth": "64", "bs": "4M", "rw": "write"},
        "rand_read_4M": {
            "direction": "read",
            "iodepth": "64",
            "bs": "4M",
            "rw": "randread",
        },
        "rand_write_4M": {
            "direction": "write",
            "iodepth": "64",
            "bs": "4M",
            "rw": "randwrite",
        },
        "rand_read_4K": {
            "direction": "read",
            "iodepth": "64",
            "bs": "4K",
            "rw": "randread",
        },
        "rand_write_4K": {
            "direction": "write",
            "iodepth": "64",
            "bs": "4K",
            "rw": "randwrite",
        },
        "rand_read_4K_lowdepth": {
            "direction": "read",
            "iodepth": "1",
            "bs": "4K",
            "rw": "randread",
        },
        "rand_write_4K_lowdepth": {
            "direction": "write",
            "iodepth": "1",
            "bs": "4K",
            "rw": "randwrite",
        },
    }

    results = dict()
    for test in test_matrix:
        print("Running test '{}'".format(test))
        fio_cmd = """
            fio \
                --name={test} \
                --ioengine=rbd \
                --pool={pool} \
                --rbdname={volume} \
                --output-format=json \
                --direct=1 \
                --randrepeat=1 \
                --numjobs=1 \
                --time_based \
                --runtime=75 \
                --group_reporting \
                --iodepth={iodepth} \
                --bs={bs} \
                --readwrite={rw}
        """.format(
            test=test,
            pool=pool,
            volume=volume,
            iodepth=test_matrix[test]["iodepth"],
            bs=test_matrix[test]["bs"],
            rw=test_matrix[test]["rw"],
        )

        print("Running fio job: {}".format(" ".join(fio_cmd.split())))
        retcode, stdout, stderr = pvc_common.run_os_command(fio_cmd)
        if retcode:
            raise BenchmarkError(
                "Failed to run fio test: {}".format(stderr),
                job_name=job_name,
                db_conn=db_conn,
                db_cur=db_cur,
                zkhandler=zkhandler,
            )

        results[test] = loads(stdout)

    # Phase 3 - cleanup
    self.update_state(
        state="RUNNING",
        meta={"current": 3, "total": 3, "status": "Cleaning up and storing results"},
    )
    time.sleep(1)

    # Remove the RBD volume
    retcode, retmsg = pvc_ceph.remove_volume(zkhandler, pool, volume)
    if not retcode:
        raise BenchmarkError(
            'Failed to remove volume "{}": {}'.format(volume, retmsg),
            job_name=job_name,
            db_conn=db_conn,
            db_cur=db_cur,
            zkhandler=zkhandler,
        )
    else:
        print(retmsg)

    print("Storing result of tests for job '{}' in database".format(job_name))
    try:
        query = "UPDATE storage_benchmarks SET result = %s WHERE job = %s;"
        args = (dumps(results), job_name)
        db_cur.execute(query, args)
        db_conn.commit()
    except Exception as e:
        raise BenchmarkError(
            "Failed to store test results: {}".format(e),
            job_name=job_name,
            db_conn=db_conn,
            db_cur=db_cur,
            zkhandler=zkhandler,
        )

    close_database(db_conn, db_cur)
    zkhandler.disconnect()
    del zkhandler

    return {
        "status": "Storage benchmark '{}' completed successfully.",
        "current": 3,
        "total": 3,
    }