# Copyright 2017-present Open Networking Foundation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# This tool collects CPU and Memory informations for each container in the VOLTHA stack

# NOTE
# Collecting the info for all containers in the same chart can be confusing,
# we may want to create subcharts for the different groups, eg: infra, ONOS, core, adapters

import csv
from sys import platform as sys_pf

if sys_pf == 'darwin':
    import matplotlib

    matplotlib.use("TkAgg")

import argparse
import requests
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime
import time

EXCLUDED_POD_NAMES = [
    "kube", "coredns", "kind", "grafana",
    "prometheus", "tiller", "control-plane",
    "calico", "nginx", "registry", "cattle", "canal", "metrics",
]

DATE_FORMATTER_FN = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')

KAFKA_TOPICS = [
    "openolt",
    "brcm_openomci_onu",
    "voltha",
    "adapters",
    "rwcore"
]

def main(address, out_folder, since):
    """
    Query Prometheus and generate .pdf files for CPU and Memory consumption for each POD
    :param address: string The address of the Prometheus instance to query
    :param out_folder: string The output folder (where to save the .pdf files)
    :param since: int When to start collection data (minutes in the past)
    :return: void
    """
    time_delta = int(since) * 60

    container_mem_query = "sum by(pod) (container_memory_working_set_bytes{namespace='default',container!='',container!='POD'})"

    container_cpu_query = "sum by(pod) (rate(container_cpu_usage_seconds_total{namespace='default',container!='',container!='POD'}[%sm]))" % since

    now = time.time()
    cpu_params = {
        "query": container_cpu_query,
        "start": now - time_delta,
        "end": now,
        "step": "30",
    }

    r = requests.get("http://%s/api/v1/query_range" % address, cpu_params)
    print("Downloading CPU info from: %s" % r.url)
    container_cpu = r.json()["data"]["result"]
    containers = remove_unwanted_containers(container_cpu)
    plot_cpu_consumption(containers,
                         output="%s/cpu.pdf" % out_folder)
    data_to_csv(containers, output="%s/cpu.csv" % out_folder,
                convert_values=lambda values: ["{:.2f}".format(v) for v in values])

    mem_params = {
        "query": container_mem_query,
        "start": now - time_delta,
        "end": now,
        "step": "30",
    }

    r = requests.get("http://%s/api/v1/query_range" % address, mem_params)
    print("Downloading Memory info from: %s" % r.url)
    container_mem = r.json()["data"]["result"]
    containers = remove_unwanted_containers(container_mem)
    plot_memory_consumption(containers, output="%s/memory.pdf" % out_folder)
    data_to_csv(containers, output="%s/memory.csv" % out_folder,
                convert_values=lambda values: ["{:.2f}".format(bytesto(v, "m")) for v in values])

    print("Downloading KAFKA stats")
    get_kafka_stats(address, out_folder)
    print("Downloading ETCD stats")
    get_etcd_stats(address, out_folder)


def data_to_csv(containers, output=None, convert_values=None):
    """
    Get a list of prometheus metrics and dumps them in a csv
    :param containers: Prometheus metrics
    :param output: Destination file
    :param convert_values: Function to convert the valus, take a list on numbers
    """
    csv_file = open(output, "w+")
    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)

    # we assume all the containers have the same timestamps
    # FIXME pods may have different timestamps depending on when the collection started
    # - find the longest list in containers
    # - add empty values at the beginning of the other list
    dates = [datetime.fromtimestamp(x[0]) for x in containers[0]["values"]]
    csv_writer.writerow([''] + dates)

    for c in containers:
        name = c["metric"]["pod"]
        data = c["values"]

        values = [float(x[1]) for x in data]

        if convert_values:
            values = convert_values(values)
        csv_writer.writerow([name] + values)


def plot_cpu_consumption(containers, output=None):
    plt.figure('cpu')
    fig, ax = plt.subplots()
    ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
    ax.xaxis_date()
    fig.autofmt_xdate()

    plt.title("CPU Usage per POD")
    plt.xlabel("Timestamp")
    plt.ylabel("CPU cores used")

    for c in containers:
        name = c["metric"]["pod"]
        data = c["values"]

        dates = [datetime.fromtimestamp(x[0]) for x in data]

        values = [float(x[1]) for x in data]

        plt.plot(dates, values, label=name, lw=2, color=get_line_color(name))
        # plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))

    plt.legend(loc='upper left', title="CPU Consumption", bbox_to_anchor=(1.05, 1))

    fig = plt.gcf()
    fig.set_size_inches(20, 11)

    plt.savefig(output, bbox_inches="tight")


def plot_memory_consumption(containers, output=None):
    plt.figure("memory")
    fig, ax = plt.subplots()
    ax.xaxis.set_major_formatter(DATE_FORMATTER_FN)
    ax.xaxis_date()
    fig.autofmt_xdate()
    plt.title("Memory Usage")
    plt.xlabel("Timestamp")
    plt.ylabel("MB")

    for c in containers:
        name = c["metric"]["pod"]
        data = c["values"]

        dates = [datetime.fromtimestamp(x[0]) for x in data]
        values = [bytesto(float(x[1]), "m") for x in data]

        # plt.plot(dates[1:], get_diff(values), label=name, lw=2, color=get_line_color(name))
        plt.plot(dates[1:], values[1:], label=name, lw=2, color=get_line_color(name))

    plt.legend(loc='upper left', title="Memory Usage", bbox_to_anchor=(1.05, 1))

    fig = plt.gcf()
    fig.set_size_inches(20, 11)

    plt.savefig(output, bbox_inches="tight")


def remove_unwanted_containers(cpus):
    res = []
    for c in cpus:

        if "pod" in c["metric"]:
            pod_name = c["metric"]["pod"]
            if any(x in pod_name for x in EXCLUDED_POD_NAMES):
                continue
            res.append(c)

    return res


def get_line_color(container_name):
    colors = {
        "bbsim0": "#884EA0",
        "bbsim1": "#9B59B6",
        "bbsim-sadis-server": "#D2B4DE",
        "onos-atomix-0": "#85C1E9",
        "onos-atomix-1": "#7FB3D5",
        "onos-atomix-2": "#3498DB",
        "onos-onos-classic-0": "#1A5276",
        "onos-onos-classic-1": "#1B4F72",
        "onos-onos-classic-2": "#154360",
        "etcd-0": "#7D6608",
        "etcd-1": "#9A7D0A",
        "etcd-2": "#B7950B",
        "open-olt-voltha-adapter-openolt": "#7E5109",
        "open-onu-voltha-adapter-openonu-0": "#6E2C00",
        "open-onu-voltha-adapter-openonu-1": "#873600",
        "open-onu-voltha-adapter-openonu-2": "#A04000",
        "open-onu-voltha-adapter-openonu-3": "#BA4A00",
        "open-onu-voltha-adapter-openonu-4": "#D35400",
        "open-onu-voltha-adapter-openonu-5": "#D35400",
        "open-onu-voltha-adapter-openonu-6": "#E59866",
        "open-onu-voltha-adapter-openonu-7": "#EDBB99",
        "kafka-0": "#4D5656",
        "kafka-1": "#5F6A6A",
        "kafka-2": "#717D7E",
        "kafka-zookeeper-0": "#839192",
        "kafka-zookeeper-1": "#95A5A6",
        "kafka-zookeeper-2": "#717D7E",
        "radius": "#82E0AA",
        "voltha-voltha-ofagent": "#641E16",
        "voltha-voltha-rw-core": "#7B241C",
    }

    if container_name in colors:
        return colors[container_name]
    elif "openolt" in container_name:
        return colors["open-olt-voltha-adapter-openolt"]
    elif "ofagent" in container_name:
        return colors["voltha-voltha-ofagent"]
    elif "rw-core" in container_name:
        return colors["voltha-voltha-rw-core"]
    elif "bbsim0" in container_name:
        return colors["bbsim0"]
    elif "bbsim1" in container_name:
        return colors["bbsim1"]
    elif "bbsim-sadis-server" in container_name:
        return colors["bbsim-sadis-server"]
    elif "radius" in container_name:
        return colors["radius"]
    else:
        return "black"


def get_diff(data):
    # get the delta between the current data and the previous point
    return [x - data[i - 1] for i, x in enumerate(data)][1:]


def bytesto(b, to, bsize=1024):
    """convert bytes to megabytes, etc.
       sample code:
           print('mb= ' + str(bytesto(314575262000000, 'm')))
       sample output:
           mb= 300002347.946
    """

    a = {'k': 1, 'm': 2, 'g': 3, 't': 4, 'p': 5, 'e': 6}
    r = float(b)
    for i in range(a[to]):
        r = r / bsize

    return r


def get_etcd_stats(address, out_folder):
    """
    :param address: The prometheus address
    :param out_folder: The folder in which store the output files
    """

    etcd_stats = {
        "size":"etcd_debugging_mvcc_db_total_size_in_bytes",
        "keys":"etcd_debugging_mvcc_keys_total"
    }

    etcd = {}

    time_delta = 80
    for  stat,query in etcd_stats.items():
        now = time.time()
        etcd_params = {
            "query": "%s{}" % query,
            "start": now - time_delta,
            "end": now,
            "step": "30",
        }
        r = requests.get("http://%s/api/v1/query_range" % address, etcd_params)

        i = r.json()["data"]["result"][0]
        etcd[stat] = i["values"][-1][1]

    csv_file = open("%s/etcd_stats.csv" % out_folder, "w+")
    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)

    for k,v in etcd.items():
        csv_writer.writerow([k, v])

def get_kafka_stats(address, out_folder):
    """
    :param address: The prometheus address
    :param out_folder: The folder in which store the output files
    """
    # get the last information for all topics, we only care about the last value so a short interval is fine
    now = time.time()
    time_delta = 80
    kafka_params = {
        "query": "kafka_topic_partition_current_offset{}",
        "start": now - time_delta,
        "end": now,
        "step": "30",
    }

    r = requests.get("http://%s/api/v1/query_range" % address, kafka_params)

    msg_per_topic = {}

    for t  in r.json()["data"]["result"]:
        # we only care about some topics
        topic_name = t["metric"]["topic"]

        if any(x in topic_name for x in KAFKA_TOPICS):
            # get only the value at the last timestamp
            msg_per_topic[t["metric"]["topic"]] = t["values"][-1][1]

    csv_file = open("%s/kafka_msg_per_topic.csv" % out_folder, "w+")
    csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)

    for k,v in msg_per_topic.items():
        csv_writer.writerow([k, v])

if __name__ == "__main__":
    parser = argparse.ArgumentParser(prog="sizing")
    parser.add_argument("-a", "--address", help="The address of the Prometheus instance we're targeting",
                        default="127.0.0.1:31301")
    parser.add_argument("-o", "--output", help="Where to output the generated files",
                        default="plots")
    parser.add_argument("-s", "--since", help="When to start sampling the data (in minutes before now)",
                        default=10)

    args = parser.parse_args()
    main(args.address, args.output, args.since)