An Eventbus for Python

Sometimes we need publish-subscribe kind of communications between components running in the same process. It is even better if the components don't need to know each-other as that simplifies the design of complex and highly-concurrent components. 

We may design similar systems using Queues also. But then also, coupling between components are not completely eliminated and we end up spawning threads and writing lots of complex logic ourselves. Moreover the pattern gets repeated across many pieces and ultimately making it a nightmare to weave the components together in a simpler way. 

So, Here is a Python eventbus library, geeteventbus  which is addressing exactly these problems we are facing. 

The API related documentations are available  here .

geeteventbus is inspired by the Java library guava eventbus. But it is not exactly the similar to Guava eventbus. 

To install the library, issue the below command:

$ sudo pip install geeteventbus

This is a platform independent package and so may be used on any OS where Python can be run.

Below diagram explains the architecture of the event-bus.

There are three type of components here. The publishers post events to the event-bus. The event-bus takes care of delivering the events to appropriate subscribers. The subscribers registers themselves to the event-bus and they register themselves for certain topics. Each event is associated with a topic, data and an optional ordering field. The topic of the event decides which subscribers it will be delivered to by the event-bus.

The event-bus can be synchronous or asynchronous. 

• A synchronous event-bus delivers the events from the same threads the were posted from.

• An asynchronous event-bus delivers the events to the subscribers from different threads. Basically, here the events are delivered from some of the executor threads run by the event-bus internally.

• While creating the event-bus, we may declare the future subscribers to be thread-safe. In that case, the subscribers invocation won't be synchronized. 

• In some cases, we may want the events to be processed by the subscribers in the same order as they were posted. To enforce that, event objects are created with a special ordering field. E.g. event('atopic', 'somedata for this event', 'an-ordering-key'). All the events with the ordering key "an-ordering-key" will be processed in the same order they were posted to the event-bus.

• Multiple event-bus can be created in the same process if needed.

Basic working

1. We create an eventbus

   from geeteventbus.eventbus import eventbus
   eb = eventbus()

   This will create an eventbus with the defaults. The default eventbus will have below characteristics:

   1. the maximum queued event limit is set to 10000
   2. number of executor thread is 8
   3. the subscribers will be called asynchronously
   4. subscibers are treated as thread-safe and hence same subscribers may be invoked simultaneously on different threads

2. Create a subsclass of subscriber and override the process method. Create an object of this class and register it to the eventbus for receiving messages with certain topics:

   from geeteventbus.subscriber import subscriber
   from geeteventbus.eventbus import eventbus
   from geeteventbus.event import event
   
   class mysubscriber(subscriber):
       def process(self, eventobj):
           if not isinstance(eventobj, event):
               print('Invalid object type is passed.')
               return
           topic = eventobj.get_topic()
           data = eventobj.get_data()
           print('Processing event with TOPIC: %s, DATA: %s' % (topic, data))
   
   subscr = mysubscriber()
   eb.register_consumer(subscr, 'an_important_topic')

3. Post some events to the eventbus with the topic "an_important_topic".

   from geeteventbus.event import event
   
   eobj1 = ('an_important_topic', 'This is some data for the event 1')
   eobj2 = ('an_important_topic', 'This is some data for the event 2')
   eobj3 = ('an_important_topic', 'This is some data for the event 3')
   eobj3 = ('an_important_topic', 'This is some data for the event 4')
   
   eb.post(eobj1)
   eb.post(eobj2)
   eb.post(eobj3)
   eb.post(eobj4)

4. We may gracefully shutdown the eventbus before exiting the process

   eb.shutdown()

The complete example is below:

from time import sleep
from geeteventbus.subscriber import subscriber
from geeteventbus.eventbus import eventbus
from geeteventbus.event import event

class mysubscriber(subscriber):
    def process(self, eventobj):
        if not isinstance(eventobj, event):
            print('Invalid object type is passed.')
            return
        topic = eventobj.get_topic()
        data = eventobj.get_data()
        print('Processing event with TOPIC: %s, DATA: %s' % (topic, data))


eb = eventbus()
subscr = mysubscriber()
eb.register_consumer(subscr, 'an_important_topic')


eobj1 = event('an_important_topic', 'This is some data for the event 1')
eobj2 = event('an_important_topic', 'This is some data for the event 2')
eobj3 = event('an_important_topic', 'This is some data for the event 3')
eobj4 = event('an_important_topic', 'This is some data for the event 4')

eb.post(eobj1)
eb.post(eobj2)
eb.post(eobj3)
eb.post(eobj4)

eb.shutdown()
sleep(2)

A more detailed example is given below. A subscriber (counter_aggregator) aggregates the values for a set of counters. It registers itself to an eventbus for receiving events for the counters(topics). A set of producers update the values for the counters and post events describing the counter to the eventbus:

from threading import Lock, Thread
from time import sleep, time
from geeteventbus.eventbus import eventbus
from geeteventbus.event import event
from geeteventbus.subscriber import subscriber
from random import randint


class counter_aggregator(subscriber, Thread):
    '''
    Aggregator for a set of counters. Multiple threads updates the counts which
    are aggregated by this class and output the aggregated value periodically.
    '''
    def __init__(self, counter_names):
        Thread.__init__(self)
        self.counter_names = counter_names
        self.locks = {}
        self.counts = {}
        self.keep_running = True
        self.collect_times = {}
        for counter in counter_names:
            self.locks[counter] = Lock()
            self.counts[counter] = 0
            self.collect_times[counter] = time()

    def process(self, eobj):
        '''
        Process method calls with the event object eobj. eobj has the counter name as the topic
        and an int count as the value for the counter.
        '''
        counter_name = eobj.get_topic()
        if counter_name not in self.counter_names:
            return
        count = eobj.get_data()
        with self.locks[counter_name]:
            self.counts[counter_name] += count

    def stop(self):
        self.keep_running = False

    def __call__(self):
        '''
        Keep outputing the aggregated counts every 2 seconds
        '''
        while self.keep_running:
            sleep(2)
            for counter_name in self.counter_names:
                with self.locks[counter_name]:
                    print('Change for counter %s = %d, in last %f secs' % (counter_name,
                          self.counts[counter_name], time() - self.collect_times[counter_name]))
                    self.counts[counter_name] = 0
                    self.collect_times[counter_name] = time()
        print('Aggregator exited')


class count_producer:
    '''
    Producer for counters. Every 0.02 seconds post the "updated" value for a
    counter randomly
    '''
    def __init__(self, counters, ebus):
        self.counters = counters
        self.ebus = ebus
        self.keep_running = True
        self.num_counter = len(counters)

    def stop(self):
        self.keep_running = False

    def __call__(self):
        while self.keep_running:
            ev = event(self.counters[randint(0, self.num_counter - 1)], randint(1, 100))
            ebus.post(ev)
            sleep(0.02)
        print('producer exited')

if __name__ == '__main__':
    ebus = eventbus()
    counters = ['c1', 'c2', 'c3', 'c4']
    subcr = counter_aggregator(counters)
    producer = count_producer(counters, ebus)
    for counter in counters:
        ebus.register_consumer(subcr, counter)
    threads = []
    i = 30
    while i > 0:
        threads.append(Thread(target=producer))
        i -= 1

    aggregator_thread = Thread(target=subcr)
    aggregator_thread.start()
    for thrd in threads:
        thrd.start()
    sleep(20)
    producer.stop()
    subcr.stop()
    sleep(2)
    ebus.shutdown()

Using GDB to debug Java!

GDB is a very powerful debugger which is mainly used to debug C/C++ programs. But as many of the languages such as Java, Python, Perl, PHP, Ruby etc. are implemented in C/C++, so sometimes GDB is even useful in debugging some issues in programs implemented in these languages as well. Today I demonstrate one such use cases with Java programs.

Below is the Java program that calls a native API HelloWorld.  The HelloWorld internally calls printf to print some string to console and also prints some string to a file /tmp/testout.txt

//JniHelloWorld.java
import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.IOException;

public class JniHelloWorld implements Runnable{
	
	private native void HelloWorld();
	private Object lock1 = null;
	private Object lock2 = null;
	private FileOutputStream fos = null;

	public JniHelloWorld() {
		lock1 = new Object();
		lock2 = new Object();
		try {
			fos = new FileOutputStream(new File("/tmp/testout.txt"));
		} catch (FileNotFoundException e) {
			e.printStackTrace();
			System.exit(1);
		}
	}
	
	@Override
	public void run() {
		byte []outbytes = "This will go to /tmp/testout.txt file\n".getBytes();
        while (true) {
            try {
                Thread.sleep(200);
            } catch (InterruptedException e) {
            }
            synchronized (lock1) {
                HelloWorld();
            }
            synchronized (lock2) {
                try {
                    fos.write(outbytes);
                } catch (IOException e) {
                }
            }
        }
	}	
	
	public static void main(String[] args) {
        System.loadLibrary("test");
		JniHelloWorld jnh = new JniHelloWorld();
		int i = 0;
		while (i++ < 20){
			new Thread(jnh).start();
		}
	}
}

Let us compile the Java code. $ javac javac JniHelloWorld.java Generate the JNI header file: $ javah JniHelloWorld Create the C file test.c

#include <stdio.h>
#include "JniHelloWorld.h"

JNIEXPORT void JNICALL Java_JniHelloWorld_HelloWorld
  (JNIEnv *env, jobject o){
              printf("hello from printf\n");  
  }

Now let us create the shared library:
$ gcc -I . -I $JAVA_HOME/include -I $JAVA_HOME/include/linux -fPIC -c test.c
In my machine JAVA_HOME points to /home/geet/sws/jdk1.8.0_20.

Now create the shared library:
$ gcc -shared -o libtest.so test.o

Set LD_LIBRARY_PATH (eg. export LD_LIBRARY_PATH=.) and then run the example Java program.
$ java JniHelloWorld

The process keeps running continuously printing to console and /tmp/testout.txt. If you check the program there is no chance for any dead lock in the code.

We may check the threads of Java thread using the commands in the below example (I am not using Jstack or jconsole, JVisualVM etc. as I want to check even the threads created by the native library). Every Java thread is mapped to a native thread (a posix thread on a Linux machine).

$ jps
4512 JniHelloWorld
4602 Jps

$ ps -fT -p 4512

Output will be something like:
UID PID SPID PPID C STIME TTY TIME CMD
geet 4512 4512 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4513 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4514 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4515 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4516 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4517 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4518 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4519 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4520 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4521 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4522 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4523 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
geet 4512 4524 3068 0 08:56 pts/15 00:00:00 java JniHelloWorld
...
...

We may attach gdb to any of the thread to examine what it is doing
Eg. $ sudo gdb -p 4551

Now everything runs fine and no issues. Now let us spawn this Java program from another Java program.

// Spawner.java
import java.io.IOException;

public class Spawner {
	
	public static void main(String[] args) {
		ProcessBuilder pb =
				   new ProcessBuilder("java", "JniHelloWorld");
		Process p;
		try {
			p = pb.start();
			while (p.isAlive()){
				Thread.sleep(200);
			}
		} catch (IOException e) {
		} catch (InterruptedException e ){
		}
	}

}

$javac Spawner.java

Now run the Java process
$java Spawner

We may look at the modification time of /tmp/testout.txt to check if it is being updated continuously. After some time, we will see that the file is not being updated at all, but all the Java processes are running!

$ jps
5602 Spawner
6086 Jps
5622 JniHelloWorld


Definitely something wrong with JniHelloWorld process. Let us take its thread-dump

$jstack 5622

Now we examine the output of jstack.
Most of the threads are blocked,
"Thread-0" #10 prio=5 os_prio=0 tid=0x00007fde040e5000 nid=0x160b waiting for monitor entry [0x00007fdddae06000]
java.lang.Thread.State: BLOCKED (on object monitor)
at JniHelloWorld.run(JniHelloWorld.java:33)
- waiting to lock <0x000000076ce5b140> (a java.lang.Object)
at java.lang.Thread.run(Thread.java:745)


They are waiting for lock 0x000000076ce5b140 which corresponds to lock1.

Let us see who acquired the lock lock1 from output of jstack, I found it be the below thread
"Thread-6" #16 prio=5 os_prio=0 tid=0x00007fde040ef800 nid=0x1611 runnable [0x00007fddda800000]
java.lang.Thread.State: RUNNABLE
at JniHelloWorld.HelloWorld(Native Method)
at JniHelloWorld.run(JniHelloWorld.java:33)
- locked <0x000000076ce5b140> (a java.lang.Object)
at java.lang.Thread.run(Thread.java:745)

It has locked lock1 and stuck at native HelloWorld method, effectively blocking other threads waiting for that lock. It is a special kind of dead lock.

Now we need to check why this thread stuck at native method, Java debugger or tools won't help as we are in the native world ! The value of nid for this thread is 0x1611 (= 5649) which is the light weight process id for this thread. Let us attach gdb to this process and get its backtrace,

$sudo gdb -p 0x1611

The backtrace shows:
(gdb) bt
#0 0x00007fde09d7c3cd in write () at ../sysdeps/unix/syscall-template.S:81
#1 0x00007fde09d09a83 in _IO_new_file_write (f=0x7fde0a050400 <_io_2_1_stdout_>, data=0x7fde0a8b4000, n=4096)
at fileops.c:1261
#2 0x00007fde09d0af5c in new_do_write (to_do=4096,
data=0x7fde0a8b4000 "f\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\n"...,
fp=0x7fde0a050400 <_io_2_1_stdout_>) at fileops.c:538
#3 _IO_new_do_write (fp=0x7fde0a050400 <_io_2_1_stdout_>,
data=0x7fde0a8b4000 "f\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\nhello from printf\n"...,
to_do=4096) at fileops.c:511
#4 0x00007fde09d0a121 in _IO_new_file_xsputn (f=0x7fde0a050400 <_io_2_1_stdout_>, data=, n=17)
at fileops.c:1332
#5 0x00007fde09d00957 in _IO_puts (str=0x7fdddae086fd "hello from printf") at ioputs.c:41
#6 0x00007fdddae086f1 in Java_JniHelloWorld_HelloWorld (env=0x7fde040ef9e8, o=0x7fddda8005f0) at test.c:6
#7 0x00007fddf489f8e6 in ?? ()
#8 0x000000076ce5b128 in ?? ()
#9 0x00007fde0935f9ac in InterpreterRuntime::monitorexit(JavaThread*, BasicObjectLock*) ()
from /home/geet/sws/jdk1.8.0_20/jre/lib/amd64/server/libjvm.so
#10 0x00007fddf47a798d in ?? ()
#11 0x00007fddf47a798d in ?? ()
#12 0x000000076ce5b128 in ?? ()
#13 0x0000000000000003 in ?? ()
#14 0x000000076ce5b140 in ?? ()
#15 0x00007fddda800650 in ?? ()
#16 0x00007fddf2410668 in ?? ()
#17 0x00007fddda8006d0 in ?? ()
#18 0x00007fddf2410850 in ?? ()
#19 0x0000000000000000 in ?? ()


Clearly the back trace shows that the thread is stuck at write system call (results from call to printf). Why is it blocked? Because its output buffer is full and nobody is taking out or cleaning the data. When we directly run JniHelloWorld from a terminal, the terminal is continuously clearing and spitting out this data and hence then it doesn't block.

BTW, there are neat ways to consume or redirect output buffers of spwaned processes to avoid such problems. I have used this not-very-neat example mainly to show how we can use GDB to debug Java programs in such situations.

A ganglia plugin for collecting Stats from Storm topologies

Ganglia is a highly scalable monitor for distributed systems. It facilitates users to examine  different stats of the monitored machines in a single pane. It maintains historical stats and hence very good for checking how the CPU usage, memory usage, disk usage etc., JVM GC pauses are varying over time on the distributed machines.
Ganglia basically have three components.  They are Ganglia monitoring daemon(gmond), Ganglia meta daemon (gmetad) and Ganglia web front end.

Gmond monitors the changes in the host state, announces them, listen state changes from other nodes running gmond, and returns the XML description of the cluster state when queried. Gmond depends on plug-ins to collect host stats. The plug-ins may be written in C,C++,Python, Perl or PHP. In fact, we can implement real piece of code that collects the stats in any language with some tricks. Also, it won't be difficult to extend it support plug-ins  from other languages as well.


Gmetad polls the clusters it is monitoring and collects the reported metrics and write them to individual round robin databases.


Ganglia web front end provides  the metric Visualization UI.

There is a very good link to install and configure Ganglia on Ubuntu 14.04 here (from digital ocean).  Please follow the steps to setup Ganglia monitoring on your machines.  You may follow the similar steps to install on other Linux systems.

As you have installed and and configured Ganglia, now it is time to write and test a new Ganglia plug-in. From Ganglia 3.1 onwards,  Gmond has a modular interface that allows us to extend its capability through plug-ins. We can write modules or plug-ins to collect new kind of metrices. Gmond uses the Mod_Python module (modpython.so) to interface with all the Pythin plug-ins to collect the metrices published by them.

First we need to create an entry for modpython.so in gmond configuration file. Edit gmond.conf (by default it is installed at /etc/ganglia/gmond.conf) and add the below lines in the "modules" section:

  module {
    name = "python_module"
    path = "/usr/lib/ganglia/modpython.so"
    params = "/usr/lib/ganglia/python"
  }

So, the modules section in gmond.conf will look like somewhat like as shown below:

modules {
  module {
    name = "core_metrics"
  }
  module {
    name = "cpu_module"
    path = "/usr/lib/ganglia/modcpu.so"
  }
  module {
    name = "disk_module"
    path = "/usr/lib/ganglia/moddisk.so"
  }
  module {
    name = "load_module"
    path = "/usr/lib/ganglia/modload.so"
  }
  module {
    name = "mem_module"
    path = "/usr/lib/ganglia/modmem.so"
  }
  module {
    name = "net_module"
    path = "/usr/lib/ganglia/modnet.so"
  }
  module {
    name = "proc_module"
    path = "/usr/lib/ganglia/modproc.so"
  }
  module {
    name = "sys_module"
    path = "/usr/lib/ganglia/modsys.so"
  }
  module {
    name = "python_module"
    path = "/usr/lib/ganglia/modpython.so"
    params = "/usr/lib/ganglia/python"
  } 

} 


Also, create a directory /etc/ganglia/pyconfs to store the configuration files for the python modules. Let us use .pyconf as the extension of the Python modules.  Add the below line in /etc/ganglia/gmond.conf
include ('/etc/ganglia/pyconfs/*.pyconf')

We will add a configuration file for the sample plugin (/etc/ganglia/pyconfs/ganglia_plugin_sample.pyconf). Content of this file is pasted below:
modules {
    module {
        name = "ganglia_plugin_sample"
        language = "python"
        param firstparam {
            value = 100
        }
        param secondparam {
            value = 500
        }
    }
}

collection_group {
  collect_every = 20
  time_threshold = 90
  metric {
    name_match = ".*" 
    value_threshold = "1"
  }
}


Now let us add a very simple Ganglia python plug-in. The module should implement the two functions at least, they are metric_init and metric_cleanup.  metric_init generally initializes the values for different metrices that the plug-in will be publishing. 

Below is the script:

import sys
import os
import random

descriptors = list()
firstparam_max = 1000
secondparam_max = 1000

def callback_fun1(name):
    '''
    Returns a random number between 1 and firstparam_max
    '''
    random.seed()
    return random.randint(1, firstparam_max)

def callback_fun2(name):
    '''
    Returns a random number between 5 and secondparam_max
    '''
    random.seed()
    return random.randint(5,secondparam_max)

def metric_init(params):
    global descriptors, firstparam_max, secondparam_max
    if 'firstparam' in params:
        firstparam_max = int(params['firstparam'])
        d = {'name': 'firstparam',
                'call_back': callback_fun1,
                'time_max': 90,
                'value_type': 'uint',
                'units': 'Count',
                'slope': 'both',
                'format': '%u',
                'description': 'Sample metric',
                'groups': 'Sample'}
        descriptors.append(d)
    
    if 'secondparam' in params:
        secondparam_max = int(params['secondparam'])
        d = {'name': 'secondparam',
                'call_back': callback_fun2,
                'time_max': 90,
                'value_type': 'uint',
                'units': 'Count',
                'slope': 'both',
                'format': '%u',
                'description': 'Sample metric',
                'groups': 'Sample'}
        descriptors.append(d)
    return descriptors

    def metric_cleanup():
        '''
        We don't need any cleanup :) :)
        '''
        pass


# This routine is for debugging purpose only and not used by gmond
# To debug the output, run as below:
# $ python ganglia_plugin_sample.py
if __name__ == '__main__':
    params = {'firstparam': 100, 'secondparam' : 500}
    metric_init(params)
    for d in descriptors:
        v = d['call_back'](d['name'])
        print '%s --> %u' % (d['name'], v)

Here, the plug-in is collecting two metrices (firstparam and secondparam). The value for firstparam is computed by function callback_fun1 and for secondparam is collected by callback_fun2. THe "slope" is set to "both" for both the metrices. This a hint for the gmetad to know that the value may increase or decrease.

We should copy this script to/usr/lib/ganglia/python/ganglia_plugin_sample.py.

After you copy the plugin-script and make the configuration changes, restart ganglia-monitor (gmond) service.

$sudo service ganglia-monitor restart

After some time you should see the "Sample metrics" group and the graphs for the metrices firstparam and secondparam under it.


I have created a Python module for collecting storm stats. The module is available here . Also for instructions please refer this readme.

I have pasted the module below for your easy reference:

import logging
import md5
import pickle
import re
import sys
from time import time
from thrift.transport import TTransport, TSocket
from thrift.protocol import TBinaryProtocol

clusterinfo = None
topology_found = True
descriptors = list()
topologies = []
serialfile_dir = '/tmp'
topology_summary_cols_map = {'status': 'Status', 'num_workers': 'Worker Count',
                             'num_executors': 'ExecutorCount', 'uptime_secs': 'Uptime',
                             'num_tasks': 'TaskCount'}

spout_stats = {'Executors': ['Count', '%u', 'uint'], 'Tasks': ['Count', '%u', 'uint'],
               'Emitted': ['Count', '%f', 'double'],
               'Transferred': ['Count/sec', '%f', 'double'],
               'CompleteLatency': ['ms', '%f', 'double'], 'Acked': ['Count/Sec', '%f', 'double'],
               'Failed': ['Count/sec', '%f', 'double']}

bolt_stats = {'Executors': ['Count', '%u', 'uint'], 'Tasks': ['Count', '%u', 'uint'],
              'Emitted': ['Count/sec', '%f', 'double'],
              'Executed': ['Count/sec', '%f', 'double'],
              'Transferred': ['Count/sec', '%f', 'double'],
              'ExecuteLatency': ['ms', '%f', 'double'],
              'ProcessLatency': ['ms', '%f', 'double'],
              'Acked': ['Count/sec', '%f', 'double'],
              'Failed': ['Count/sec', '%f', 'double']}

diff_cols = ['Acked', 'Failed', 'Executed', 'Transferred', 'Emitted']
overall = {'ExecutorCount': ['Count', '%u', 'uint'],
           'WorkerCount': ['Count', '%u', 'uint'],
           'TaskCount': ['Count', '%u', 'uint'],
           'UptimeSecs': ['Count', '%u', 'uint']}

toplogy_mods = {}
lastchecktime = 0
lastinfotime = 0
maxinterval = 6
all_topology_stats = {}
bolt_array = {}
spout_array = {}
nimbus_host = '127.0.0.1'
nimbus_port = 6627
logging.basicConfig(filename='/tmp/storm_topology.log', level=logging.INFO,
                    format='%(asctime)s  %(levelname)s line:%(lineno)d %(message)s', filemode='w')
logging_levels = {'INFO': logging.INFO, 'DEBUG': logging.DEBUG, 'WARNING': logging.WARNING,
                  'ERROR': logging.ERROR, 'CRITICAL': logging.CRITICAL}


def get_avg(arr):
    if len(arr) < 1:
        return 0
    return sum(arr) / len(arr)


def normalize_stats(stats, duration):
    for k in stats:
        statsk = stats[k]
        if 'Emitted' in statsk and duration > 0:
            if statsk['Emitted'] > 0:
                statsk['Emitted'] = statsk['Emitted'] / duration
        if 'Acked' in statsk and duration > 0:
            if statsk['Acked'] > 0:
                statsk['Acked'] = statsk['Acked'] / duration
        if 'Executed' in statsk and duration > 0:
            if statsk['Executed'] > 0:
                statsk['Executed'] = statsk['Executed'] / duration


def freshen_topology(topology):
    tmpsavestats = None
    inf = None
    savedlastchecktime = 0
    tmp = md5.new()
    tmp.update(topology)
    filename = '/tmp/save_stats_for_' + tmp.hexdigest() + '.pk'
    try:
        inf = open(filename, 'rb')
    except IOError as e:
        logging.warn(e)
    if inf is not None:
        try:
            tmpsavestats = pickle.load(inf)
            savedlastchecktime = pickle.load(inf)
        except EOFError as e:
            logging.warn(e.message())
        inf.close()
    if not all_topology_stats[topology]['topology_stats_got']:
        logging.warn('Info not got for topology ' + topology)
        return
    overallstats = all_topology_stats[topology]['overallstats']
    boltspoutstats = all_topology_stats[topology]['boltspoutstats']
    of = open(filename, 'wb')
    if of is not None:
        pickle.dump(boltspoutstats, of)
        pickle.dump(time(), of)
        of.close()
    if overallstats['UptimeSecs'] > (lastchecktime - savedlastchecktime):
        if tmpsavestats is not None:
            for bolt in bolt_array[topology]:
                if bolt in tmpsavestats and bolt in boltspoutstats:
                    stats_new = boltspoutstats[bolt]
                    stats_old = tmpsavestats[bolt]
                    for key in bolt_stats:
                        if key == 'ExecuteLatency' or key == 'ProcessLatency':
                            continue
                        if key not in stats_new:
                            continue
                        if key not in stats_old:
                            continue
                        if key in diff_cols:
                            stats_new[key] -= stats_old[key]
            for spout in spout_array[topology]:
                if spout in tmpsavestats and spout in boltspoutstats:
                    stats_new = boltspoutstats[spout]
                    stats_old = tmpsavestats[spout]
                    for key in spout_stats:
                        if key == 'CompleteLatency':
                            continue
                        if key not in stats_new:
                            continue
                        if key not in stats_old:
                            continue
                        if key in diff_cols:
                            stats_new[key] -= stats_old[key]
            normalize_stats(boltspoutstats, lastchecktime - savedlastchecktime)
        else:
            normalize_stats(boltspoutstats, overallstats['UptimeSecs'])
    else:
        normalize_stats(boltspoutstats, overallstats['UptimeSecs'])


def freshen():
    global lastchecktime
    if time() > (lastchecktime + maxinterval):
        lastchecktime = time()
        get_topology_stats_for(topologies)
        for topology in topologies:
            freshen_topology(topology)


def callback_boltspout(name):
    freshen()
    first_pos = name.find('_')
    last_pos = name.rfind('_')
    topology_mod = name[0:first_pos]
    bolt = name[first_pos + 1: last_pos]
    statname = name[last_pos + 1:]
    topology = toplogy_mods[topology_mod]
    if not all_topology_stats[topology]['topology_stats_got']:
        logging.debug('Returing 0 for ' + name)
        return 0
    logging.debug('Got stats for ' + name + " " +
                  str(all_topology_stats[topology]['boltspoutstats'][bolt][statname]))
    return all_topology_stats[topology]['boltspoutstats'][bolt][statname]


def callback_overall(name):
    freshen()
    topology_mod, name = name.split('_')
    topology = toplogy_mods[topology_mod]
    if not all_topology_stats[topology]['topology_stats_got']:
        logging.debug('Returing 0 for ' + name)
        return 0
    logging.debug(topology + ' ' + name + ' ' +
                  str(all_topology_stats[topology]['overallstats'][name]))
    return all_topology_stats[topology]['overallstats'][name]


def update_task_count(component_task_count, component_name, count):
    if component_name not in component_task_count:
        component_task_count[component_name] = 0
    component_task_count[component_name] += count


def update_exec_count(component_exec_count, component_name, count):
    if component_name not in component_exec_count:
        component_exec_count[component_name] = 0
    component_exec_count[component_name] += count


def update_whole_num_stat_special(stats, store, boltname, statname):
    if boltname not in store:
        store[boltname] = {}
    if statname not in store[boltname]:
        store[boltname][statname] = 0
    for k in stats:
        if k == '__metrics' or k == '__ack_init' or k == '__ack_ack' or k == '__system':
            continue
        store[boltname][statname] += stats[k]


def update_whole_num_stat(stats, store, boltname, statname):
    if boltname not in store:
        store[boltname] = {}
    if statname not in store[boltname]:
        store[boltname][statname] = 0
    for k in stats:
        store[boltname][statname] += stats[k]


def update_avg_stats(stats, store, boltname, statname):
    if boltname not in store:
        store[boltname] = {}
    if statname not in store[boltname]:
        store[boltname][statname] = []
    for k in stats:
        store[boltname][statname].append(stats[k])


def get_topology_stats_for(topologies):
    all_topology_stats.clear()
    for topology in topologies:
        all_topology_stats[topology] = get_topology_stats(topology)


def refresh_topology_stats():
    logging.debug('Refreshing topology stats')
    for t in topologies:
        all_topology_stats[t] = {'topology_stats_got': False}
    global clusterinfo
    try:
        transport = TSocket.TSocket(nimbus_host, nimbus_port)
        transport.setTimeout(1000)
        framedtrasp = TTransport.TFramedTransport(transport)
        protocol = TBinaryProtocol.TBinaryProtocol(framedtrasp)
        client = Nimbus.Client(protocol)
        framedtrasp.open()
        boltspoutstats = None
        component_task_count = None
        component_exec_count = None
        clusterinfo = client.getClusterInfo()
        for tsummary in clusterinfo.topologies:
            if tsummary.name not in topologies:
                continue
            toplogyname = tsummary.name
            overallstats = {}
            overallstats['ExecutorCount'] = tsummary.num_executors
            overallstats['TaskCount'] = tsummary.num_tasks
            overallstats['WorkerCount'] = tsummary.num_workers
            overallstats['UptimeSecs'] = tsummary.uptime_secs
            all_topology_stats[toplogyname]['overallstats'] = overallstats
            boltspoutstats = {}
            component_task_count = {}
            component_exec_count = {}
            all_topology_stats[toplogyname]['boltspoutstats'] = boltspoutstats
            all_topology_stats[toplogyname]['component_task_count'] = component_task_count
            all_topology_stats[toplogyname]['component_exec_count'] = component_exec_count
            tinfo = client.getTopologyInfo(tsummary.id)
            all_topology_stats[toplogyname]['topology_stats_got'] = True
            for exstat in tinfo.executors:
                stats = exstat.stats
                update_whole_num_stat_special(stats.emitted[":all-time"], boltspoutstats,
                                              exstat.component_id, 'Emitted')
                update_whole_num_stat_special(stats.transferred[":all-time"], boltspoutstats,
                                              exstat.component_id, 'Transferred')

                numtask = exstat.executor_info.task_end - exstat.executor_info.task_end + 1
                update_task_count(component_task_count, exstat.component_id, numtask)
                update_exec_count(component_exec_count, exstat.component_id, 1)
                if stats.specific.bolt is not None:
                    update_whole_num_stat(stats.specific.bolt.acked[":all-time"], boltspoutstats,
                                          exstat.component_id, 'Acked')
                    update_whole_num_stat(stats.specific.bolt.failed[":all-time"], boltspoutstats,
                                          exstat.component_id, 'Failed')
                    update_whole_num_stat(stats.specific.bolt.executed[":all-time"], boltspoutstats,
                                          exstat.component_id, 'Executed')
                    update_avg_stats(stats.specific.bolt.process_ms_avg["600"], boltspoutstats,
                                     exstat.component_id, 'process_ms_avg')
                    update_avg_stats(stats.specific.bolt.execute_ms_avg["600"], boltspoutstats,
                                     exstat.component_id, 'execute_ms_avg')
                if stats.specific.spout is not None:
                    update_whole_num_stat(stats.specific.spout.acked[":all-time"], boltspoutstats,
                                          exstat.component_id, 'Acked')
                    update_whole_num_stat(stats.specific.spout.failed[":all-time"], boltspoutstats,
                                          exstat.component_id, 'Failed')
                    update_avg_stats(stats.specific.spout.complete_ms_avg[":all-time"], boltspoutstats,
                                     exstat.component_id, 'complete_ms_avg')
            if '__acker' in boltspoutstats:
                del boltspoutstats['__acker']
            for key in boltspoutstats:
                if 'complete_ms_avg' in boltspoutstats[key]:
                    avg = get_avg(boltspoutstats[key]['complete_ms_avg'])
                    boltspoutstats[key]['CompleteLatency'] = avg
                    del boltspoutstats[key]['complete_ms_avg']
                if 'process_ms_avg' in boltspoutstats[key]:
                    avg = get_avg(boltspoutstats[key]['process_ms_avg'])
                    boltspoutstats[key]['ProcessLatency'] = avg
                    del boltspoutstats[key]['process_ms_avg']
                if 'execute_ms_avg' in boltspoutstats[key]:
                    avg = get_avg(boltspoutstats[key]['execute_ms_avg'])
                    boltspoutstats[key]['ExecuteLatency'] = avg
                    del boltspoutstats[key]['execute_ms_avg']

            for key in component_task_count:
                if key in boltspoutstats:
                    boltspoutstats[key]['Tasks'] = component_task_count[key]
            for key in component_exec_count:
                if key in boltspoutstats:
                    boltspoutstats[key]['Executors'] = component_exec_count[key]
        framedtrasp.close()

    except Exception as e:
        clusterinfo = None
        logging.warn(e)


def get_topology_stats(toplogyname):
    global lastinfotime
    if (lastinfotime + 4) < time():
        for t in all_topology_stats:
            all_topology_stats[t] = None
        lastinfotime = time()
        refresh_topology_stats()
    return all_topology_stats[toplogyname]


def metric_init_topology(params):
    global descriptors
    groupname = ''
    if 'topology' in params and len(params['topology']):
        groupname = params['topology']
    else:
        return
    topology = groupname
    topology_mod = re.sub("\s+", "", topology)
    topology_mod = re.sub("[_]+", "", topology_mod)
    toplogy_mods[topology_mod] = topology
    if 'spouts' in params:
        spout_array[topology] = re.split('[,]+', params['spouts'])
        for spout in spout_array[topology]:
            for statname in spout_stats:
                d = {'name': topology_mod + '_' + spout + '_' + statname, 'call_back': callback_boltspout,
                     'time_max': 90,
                     'value_type': spout_stats[statname][2],
                     'units': spout_stats[statname][0],
                     'slope': 'both',
                     'format': spout_stats[statname][1],
                     'description': '',
                     'groups': groupname}
                descriptors.append(d)

    if 'bolts' in params:
        bolt_array[topology] = re.split('[,]+', params['bolts'])
        for bolt in bolt_array[topology]:
            for statname in bolt_stats:
                d = {'name': topology_mod + '_' + bolt + '_' + statname, 'call_back': callback_boltspout,
                     'time_max': 90,
                     'value_type': bolt_stats[statname][2],
                     'units': bolt_stats[statname][0],
                     'slope': 'both',
                     'format': bolt_stats[statname][1],
                     'description': '',
                     'groups': groupname}
                descriptors.append(d)

    for key in overall:
        d = {'name': topology_mod + '_' + key, 'call_back': callback_overall,
             'time_max': 90,
             'value_type': overall[key][2],
             'units': overall[key][0],
             'slope': 'both',
             'format': overall[key][1],
             'description': '',
             'groups': groupname}
        descriptors.append(d)
    logging.info('Inited metric for ' + groupname)


def metric_init(params):
    global topologies, nimbus_host, nimbus_port
    if 'nimbus_host' in params:
        nimbus_host = params['nimbus_host']
    if 'nimbus_port' in params:
        nimbus_port = params['nimbus_port']
    if 'topologies' not in params:
        return
    if 'storm_thrift_gen' in params:
        sys.path.append(params['storm_thrift_gen'])
    else:
        sys.path.append('/usr/lib/ganglia')
    if 'loglevel' in params:
        loglevel = params['loglevel'].strip().upper()
        if loglevel in logging_levels:
            logging.getLogger().setLevel(logging_levels[loglevel])

    global Nimbus, ttypes
    from stormpy.storm import Nimbus, ttypes

    tss = re.split('[,]+', params['topologies'])
    topologies = tss
    alltops = {}
    for t in tss:
        alltops[t] = {'topology': t}
        alltops[t]['tlen'] = len(t)
        t_bolts = t + '_bolts'
        if t_bolts in params:
            alltops[t]['bolts'] = params[t_bolts]
        t_spouts = t + '_spouts'
        if t_spouts in params:
            alltops[t]['spouts'] = params[t_spouts]
    for t in alltops:
        logging.info('Initing metric for ' + t)
        metric_init_topology(alltops[t])
    return descriptors

if __name__ == '__main__':
    params = {'topologies': 'SampleTopology,AnotherTopology',
              'SampleTopology_spouts': 'SampleSpoutTwo', 'SampleTopology_bolts': 'boltc',
              'AnotherTopology_spouts': 'Spout', 'AnotherTopology_bolts': 'bolta,boltb,boltd',
              'loglevel': 'ERROR'}
    metric_init(params)
    for d in descriptors:
        v = d['call_back'](d['name'])
        formt = "%s " + d['format']
        print formt % (d['name'], v)

Splitting Elasticsearch Index

Elasticsearch doesn't provide facilities for splitting an index. The main reason may be because the Elasticsearch nodes may not be able to hold the intermediate data created for splitting an index. So, if we need split an index,  we need to do something like  (a) Create the two new indices (b) reindex the data from the original index to the new indices by adding the alternate documents to the two new indices created.

Problem with the above approach is that, most of the time we disable to storing the source documents in the index. For example we may index 5 petabytes of data in an index, but we may not like to store the documents in the index as it will result in a very large index. So, for re-indexing we need have all original documents somewhere. We cannot just get all the documents from the original index itself.

But sometimes we may want to split an existing index when the index grows very large. This may be due to performance issues, when an index is too big there is a performance hit.

So, I came up with the below approach which worked fine. Hopefully it will be useful for you as well.

Let us assume, we have an index "original-index"  and we may want to split it to "original-index-firsthalf" and "original-index-secondhalf". 

Basically we need to follow the below steps.

• Create an index  original-index-firsthalf with the same settings as that of original-index,  and put same mappings on the new index.
•  Stop adding new docs to original-index-firsthalf and original-index till the splitting is over.
• Flush original-index
• Shutdown Elasticsearch nodes
•  Copy (scp or something like that) the lucne indices in shards from original-index to original-index-firsthalf. We need to copy shard 0 directory index from source to shard 0 directory index of destination (Eg. original-index-firsthalf/0/index/* is copied to original-index-firsthalf/0/index/*.  Same needs to be repeated for all other shards (and for the replicas as well)
• Restart Elasticsearch cluster
•  Now original-index and original-index-firsthalf contain same documents indexed and will produce similar search results
• Let us assume there were two mappings mapping1 and mapping2 in the indices for two types type1 and type2. Let us assume there is a field mapping1.date1 and mapping2.date2 in the two mappings and they are of "date" types (We may chose to split on the basis of some other mapping field as well, just for this example I am chosing some date fields
• Let us assume docs in type1 includes values for mapping1.date1  in the range start_date and end_date and for simplicity let us assume docs in type2 also includes dates in the same range (from start_date to end_date).  Let us assume middle_date is the date which lies almost halfway from start_date and end_date.
•  Delete all the documents in type1 and type2 that matches the queries with "type1.date1 >=  middle_date"  and "type2.date2 >=  middle_date"  respectively from original-index-firsthalf.
• Delete all the documents in type1 and type2 that matches the queries with "type1.date1 <  middle_date"  and "type2.date2 < middle_date"  respectively from original-index.
• Optimize original-index and original-index-firsthalf.
• Now original-index-firsthalf and original-index contains almost half the documents from the original index, but they don’t share any documents
•  May be we create an alias for original-index as original-index-secondhalf or simply create original-index-secondhalf index and replace its data from original-index and then delete original-index.

This may be useful when we want to split big indices into smaller indices (with same number of shards) as we don’t have to re-index the all the documents again. I could have written a shell-script to demonstrate the operation, but don't have time today. But shortly will post a shell script for your benefits :):)