apptuit 0.3.0

Apptuit Python Client

Python client for Apptuit.AI

Installation

pip install apptuit

Dependencies

Supported Python versions: 2.7.x, 3.4, 3.5, 3.6, 3.7

Requirements (installed automatically if you use pip): pandas, numpy, requests, pyformance

Usage

Contents

• Sending Data
  • Sending Data using ApptuitReporter
  • Sending Data using send() API
• Querying for Data

Sending data

There are two ways of sending the data to Apptuit. First is to use the ApptuitReporter class which provides high level abstraction for accumulating the data in various metrics such as counters, timers, gauge etc. and sending them to Apptuit. The second options is to use the send() method of the Apptuit client. We will show how to use both of the options below.

Sending the data using ApptuitReporter

You can use apptuit's pyformance reporter to report the data. ApptuitReporter. Pyformance is a Python implementation of Coda Hale's Yammer metrics. It provides high level abstractions for various metrics such as meter, counter, gauge etc. and seamlessly sends the data to the Apptuit service. For learning about the various metrics we refer you to the Pyformance documentation.

Getting started with Apptuit pyformance reporter

import socket
from pyformance import MetricsRegistry
from apptuit import timeseries
from apptuit.pyformance.apptuit_reporter import ApptuitReporter

class OrderService:
    def __init__(self, apptuit_token):
        self.registry = MetricsRegistry()
        self.init_reporter(apptuit_token, self.registry)

    def init_reporter(self, token, registry):
        hostname = socket.gethostname()
        global_tags = {"host": hostname, "env": "dev", "service": "order-service"}
        self.reporter = ApptuitReporter(registry=registry,
                                    reporting_interval=60, # data reported every 1 minute
                                    token=token,
                                    tags=global_tags)
        # reporter.start() will start reporting the data asynchronously based on the reporting_interval set.
        self.reporter.start()

    def handle_order(self, order):
        order_counter = self.registry.counter("order_count")
        # order handling related code
        order_counter.inc()

    def shutdown(self):
        # you can stop the reporter when you no longer wish to send data or when shutting down
        self.reporter.stop()

Few things worth pointing out in the above example:

• reporting_interval parameter of ApptuitReporter is the interval in seconds at which you wish to report your data.
• tags parameter of ApptuitReporter specifies the global tags for this reporter. Any metric reported by this reporter will have these set of tags added to them.
• In handle_order we create a new counter order_counter with the metric name order_count. The first time this method is called a new counter object will be created and registered with the registry. For subsequent calls, that counter will get reused since internally the registry will already have a counter with that name.

MetricsRegistry

MetricsRegistry is the container for all the metrics in your application. You can use it to register and create various kinds of metrics (meter, gauge, counter etc.). For example:

from pyformance import MetricsRegistry

registry = MetricsRegistry()
counter = registry.counter("order_count")
meter = registry.meter("order_requests_rate")
timer = registry.timer("order_requests_processing_time")

Now, let's take a look at the different types of metrics and how to use them.

Meter

A meter measures the the rate of events, such as requests per second. Meter maintains the mean rate, and 1-, 5-, 15- minute moving averages.

from pyformance import MetricsRegistry

registry = MetricsRegistry()
metric_name = "order_requests_rate"
requests_meter = registry.meter(metric_name)

def handle_request(request):
    requests_meter.mark()
    # handle request

Gauge

A gauge is an instantaneous measurement of a value. For example, number of pending jobs in a queue.

from queue import Queue
from pyformance import MetricsRegistry
from pyformance.meters.gauge import CallbackGauge

class QueueManager:

    def __init__(self, registry, name):
        self.q = Queue()
        jobs_metric = registry.add(name, CallbackGauge(self.get_queue_size))

    def get_queue_size(self):
        return self.q.size()

The reporter will call the get_queue_size function at its scheduled frequency and report the size of the queue.

Counter

A counter can be used to simply count some data. It provides two methods inc() to increment its value and dec() to decrement it.

from pyformance import MetricsRegistry

registry = MetricsRegistry()
jobs_counter = registry.counter('pending_jobs')

def add_job(self, job):
    jobs_counter.inc(1)
    self.q.put(job)

def take_job(self):
    jobs_counter.dec(1)
    self.q.get()

Timer

A timer aggregates timing durations and provides duration statistics, as well as throughput statistics.

from pyformance import MetricsRegistry

registry = MetricsRegistry()
timer = registry.timer("response_time")

def handle_request(request):
    with timer.time():
        return "OK"

The above example will use the timer to report the time taken to serve each request.

Histogram

A histogram measures the statistical distribution of values in a stream of data. It provides aggregate data such as the min, max, mean, sum, and count.

from pyformance import MetricsRegistry

registry = MetricsRegistry()

response_sizes = registry.histogram('response_size')

def handle_request(request):
    response = do_query(request) # process the query
    response_sizes.add(response.size())

Tags/Metadata

When creating the ApptuitReporter, you can provide a set of global tags which will be part of all the metrics reported by that reporter. However, in order to provide tags specific to each metric you need to provide them when registering the metric with the registry. For example:

from apptuit import timeseries
from pyformance import MetricsRegistry

registry = MetricsRegistry()
metric_name = "node_cpu"
tags = {"type": "idle", "host": "node-foo", "service": "order-service"}
metric = timeseries.encode_metric(metric_name, tags)
meter = registry.meter(metric)

Here we provided the metric specific tags by calling timeseries.encode_metric and providing the metric name and the tags as parameters. When registering the metric we provide this encoded name to the registry instead of the plain metric name.

To decode an encoded metric name use the decode_metric() function from timeseries module.

from apptuit import timeseries

encoded_metric = timeseries.encode_metric("node.cpu", {"type": "idle"})
metric_name, tags = timeseries.decode_metric(encoded_metric)

A good practise is to maintain a local cache of the created metrics and reuse them, rather than creating them every time:

import socket
import time
from apptuit import timeseries
from pyformance import MetricsRegistry

class OrderService:

    def __init__(self, apptuit_token):
        self.registry = MetricsRegistry()
        self.init_reporter(apptuit_token, self.registry)
        self.order_counters = {}

    def init_reporter(self, token, registry):
        hostname = socket.gethostname()
        global_tags = {"host": hostname, "env": "dev", "service": "order-service"}
        self.reporter = ApptuitReporter(registry=registry,
                                    reporting_interval=60, # data reported every 1 minute
                                    token=token,
                                    tags=global_tags)
        # reporter.start() will start reporting the data asynchronously based on the reporting_interval set.
        self.reporter.start()

    def get_order_counter(self, city_code):
        # We have counters for every city code
        if city_code not in self.order_counters:
            tags = {"city-code": city_code}
            metric = timeseries.encode_metric("order_count", tags=tags)
            self.order_counters[city_code] = self.registry.counter(metric)
        return self.order_counters[city_code]

    def handle_order(self, order):
        order_counter = self.get_order_counter(order.city_code)
        order_counter.inc()
        self.process_order(order)

    def shutdown(self):
        # you can stop the reporter when you no longer wish to send data or when shutting down
        self.reporter.stop()

    def process_order(self, order):
        time.sleep(5)

Here we have a method get_order_counter which takes the city_code as a parameter. There is a local cache of counters keyed by the encoded metric names. This avoids the unnecessary overhead of encoding the metric name and tags every time, if we already have created a counter for that city. It also ensures that we will report separate time-series for order-counts of different city codes.

Sending data using send() API

Apart from using the Pyformance reporter, you can also use the low level send() API from the apptuit client to directly send the data.

from apptuit import Apptuit, DataPoint
import time
import random
import socket

token = "mytoken"
client = Apptuit(token=token)
metrics = ["proc.cpu.percent", "node.memory.bytes", "network.send.bytes", "network.receive.bytes", "node.load.avg"]
tags = {"host": socket.gethostname()}
curtime = int(time.time())
dps = []
while True:
    curtime = int(time.time())
    for metric in metrics:
        dps.append(DataPoint(metric, tags, curtime, random.random()))
    if len(dps) == 100:
        client.send(dps)
        dps = []
    time.sleep(60)

Querying for data

from apptuit import Apptuit
import time
token = 'my_token' # replace with your Apptuit token
apptuit = Apptuit(token=token)
start_time = int(time.time()) - 3600 # let's query for data going back 1 hour from now
query_res = apptuit.query("fetch('proc.cpu.percent').downsample('1m', 'avg')", start=start_time)
# we can create a Pandas dataframe from the result object by calling to_df()
df = query_res[0].to_df()
# Another way of creating the DF is accessing by the metric name in the query
another_df = query_res['proc.cpu.percent'].to_df()