Nov 04

Internet of Things Research
Atbrox has been fortunate to get a 2nd EU research project (part of Horizon 2020 Framework). This project is within Internet of Things (IoT) together with European Universities, IoT and Car Industry. It has a project period from Q1 2015 – Q1 2018.

Cloud Computing Research
We’re also a participant (where Memkite is a case study) in another EU research project (part of 7th Framework) – Envisage – within Cloud Computing together with European Universities, Research Institutions and Industry partners. Envisage has a project period from Q4 2013 – Q4 2016.

More about Atbrox –

Best regards,
Amund Tveit

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Aug 26

Atbrox is one of 8 European partners in a research project on cloud computing. This is a great opportunity for us to learn how and help out in making cloud computing more efficient.

Underneath is a translation of the leading partner’s – Department of Computer Science (IFI), The Faculty of Mathematics and Natural Sciences, University of Oslo – description of the project:

The planet’s data storage and processing is about to move up in the clouds. Sharing and rental of computing resources across geographic boundaries creates new opportunities, especially for companies who can now access the computing power they couldn’t previously afford.

Professor Einar Broch Johnsen at IFI has received financial support from the European Commission to conduct a research project to make the transition to the cloud more attractive, especially for industry. The main advantage of cloud-driven computing is to use and pay for what you need. But how a business can predict and estimate the resources used in the design phase of a project is not nearly well enough developed, which can easily lead to bad miscalculations. This will ENVISAGE try to change. ENVISAGE project has eight partners in five countries and has as main objective to facilitate the development of virtualized services. By building parts of the legal basis of the service agreement between the customer and the provider into the system, the customer / business easier to fine-tune their consumption and thereby, i.e. save time and money. Potential users for ENVISAGEs technology are companies that develop software. The technology will giving them the opportunity to improve utilization of cloud resources. The benefits of this are obvious, and being at the forefront of this development project hopes to help businesses can improve profitability significantly. ENVISAGE will run until autumn 2016 and is funded through the EU 7th Framework.

Best regards,
Amund Tveit

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May 28

The purpose of Continuous Deployment is to increase Quality and Efficiency,
see e.g. The Software Revolution behind Linkedin’t Gushing Profits or read on

This posting presents an overview of Atbrox’ ongoing work on Automated Continuous Deployment. We develop in several languages depending on project or product, e.g. C/C++ (typically with SWIG combined with Python, or combined with Objective C), C# , Java (typically Hadoop/Mapreduce-related) and Objective-C (iOS). But most of our code is in Python (together with HTML/Javascript for frontends and APIs) and this posting will primarily show Python-centric continuous deployment with Jenkins (total flow) and also some more detail on the testing Tornado apps with Selenium.

Continuous Deployment of a Python-based Web Service / API

Many of the projects we develop involve creating a HTTP/REST or websocket API that generically said “does something with data” and has a corresponding UI in Javascript/HTML. The typical building stones of such a service is shown in the figure:

The flow is roughly as follows

  1. An Atbrox developer submits code into a git repo (e.g. or repo)
  2. Jenkins picks up the change (by notification from git or by polling)
  3. Tests are run, e.g.
    py.test -v --junitxml=result.xml --cov-report html --cov-report xml --cov .
    1. Traditional Python unit tests
    2. Tornado web app asynchronous tests –
    3. Selenium UI Tests (e.g. with PhantomJS or xvfb/pyvirtualdisplay)
    4. Various metrics, e.g. test coverage, lines of code (sloccount), code duplication (PMD) and static analysis (e.g. pylint or pychecker)
  4. If tests and metrics are ok:
    1. provision cloud virtual machines (currently AWS EC2) if needed with fabric and boto, e.g.
      fab service launch
    2. deploy to provisioned or existing machines with fabric and chef (solo), e.g.
      fab service deploy
  5. Fortunately Happy customer (and atbrox developer). Goto 1.

Example of selenium test of Tornado Web Apps with PhantomJS

Tornado is a python-based app server that supports Websocket and HTTP (it was originally developed by Bret Taylor while he was a FriendFeed). In addition to the python-based tornado apps you typically write a mix of javascript code and html templates for the frontend. The following example shows how to selenium tests for Tornado can be run:

Utility methods for starting a Tornado application and pick a port for it

import os
import tornado.ioloop
import tornado.httpserver
import multiprocessing

def create_process(port, queue, boot_function, application, name, 
                    instance_number, service, 
    p = processor.Process(target=boot_function, 
                          args=(queue, port, 
                               application, name,
                               instance_number, service))
    return p

def start_application_server(queue, port, application, name, 
                             instance_number, service):
    http_server = tornado.httpserver.HTTPServer(application)
    actual_port = port
    if port == 0: # special case, an available port is picked automatically
        # only pick first! (for now)
        assert len(http_server._sockets) > 0
        for s in http_server._sockets:
            actual_port = http_server._sockets[s].getsockname()[1]
    pid = os.getpid()
    ppid = os.getppid()
    print "INTERNAL: actual_port = ", actual_port
    info = {"name":name, "instance_number": instance_number, 
            "ppid": ppid, 

Example Tornado HTTP Application Class with an HTML form

class MainHandler(tornado.web.RequestHandler):
    def get(self):
        html = """
<head><title>form title</title></head>
<form name="input" action="http://localhost" method="post" id="formid">
Query: <input type="text" name="query" id="myquery">
<input type="submit" value="Submit" id="mybutton">

    def post(self):
        self.write("post returned")

Selenium unit test for the above Tornado class

class MainHandlerTest(unittest.TestCase):                                                                                        
    def setUp(self):                                                                                                             
        self.application = tornado.web.Application([                                                                             
            (r"/", MainHandler),                                                                                                 
        self.queue = multiprocessing.Queue()                                                                                                                                                                                                        
        self.server_process = create_process(0,self.queue,start_application_server,self.application,"mainapp", 123, "myservice") 
        self.driver = webdriver.PhantomJS('/usr/local/bin/phantomjs')                                                            
    def testFormSubmit(self):                                                                                                    
        data = self.queue.get()                                                                                                  
        URL = "http://localhost:%s" % (data['port'])                                                                             
        self.driver.get('http://localhost:%s' % (data['port']))                                                                  
        assert "form title" in self.driver.title                                                                                 
        element = self.driver.find_element_by_id("formid")      
        # since port is dynamically assigned it needs to be updated with the port in order to work                                                         
        self.driver.execute_script("document.getElementById('formid').action='http://localhost:%s'" % (data['port']))            
        # send click to form and receive result??                                                                                
        self.driver.find_element_by_id("myquery").send_keys("a random query")                                                    
        assert 'post returned' in self.driver.page_source                                                                        
    def tearDown(self):                                                                                                          
if __name__ == "__main__":                                                                                                       

The posting have given and overview of Atbrox’ (in-progress) Python-centric continuous deployment setup, with some more details how to do testing of Tornado web apps with Selenium. There are lots of inspirational and relatively recent articles and presentations about continuous deployment, in particular we recommend you to check out:

  1. Etsy’s slideshare about continuous deployment and delivery
  2. the Wired article about The Software Revolution Behind LinkedIn’s Gushing Profits
  3. Continuous Deployment at Quora

Please let us know if you have any comments or questions (comments to this blog post or mail to

Best regards,
The Atbrox Team

Side note: We’re proponents and bullish of Python and it is inspirational to observe the trend that several major Internet/Mobile startups/companies are using it for their backend development, e.g. Instagram, Path, Quora, Pinterest, Reddit, Disqus, Mozilla and Dropbox. The largest python-based backends probably serve more traffic than 99.9% of the world’s web and mobile sites, and that is usually sufficient capability for most projects.

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Feb 25

There are currently interesting developments of scalable (up to Petabytes), low-latency and affordable datawarehouse related solutions, e.g.

  1. AWS Redshift (cloud-based) [1]
  2. Cloudera’s Impala (open source) [2,3]
  3. Apache Thrill (open source) [4]

This posting shows how one of them – AWS Redshift – can be combined with Hadoop/Elastic mapreduce for processing of semi/unstructured data.

1. Processing of structured vs unstructured/semistructured data

A good gold mine has 8-10 grams of gold per ton of gold ore (i.e. 0.008-0.01%), the amount of structured (“gold”) vs unstructured data (“gold ore”) is not that dissimilar (probably between 0.01-10% in many cases)

What is common for the solutions above is that they are primarily targeted towards efficient processing of structured data – as opposed to un/semi-structured data. This posting gives a simple integration example of how Elastic Mapreduce/Hadoop can be used to preprocess data into structured data that can be easily imported into and analyzed with AWS Redshift.

In the general case – and not the simplistic json data used in this example – Mapreduce algorithms could be used to process any type of input un/semi-structured data (e.g. video, audio, images and text) and where fit produce structured data that can be imported into Redshift. See my O’Reilly Strata Presentation – Mapreduce Algorithms – for more examples/pointers about capabilities of Mapreduce [5].

2. Processing input data with Elastic Mapreduce/Hadoop and import results to Redshift

The input data used in this example is parts of the the bookmarking data set collected (crawled) by Arvind Naraynanan (CS Professor at University of Princeton) [6,7]. Since the main purpose of this is to show integration between Mapreduce and Redshift the example is rather simple:

  1. the mapper function processes individual json records and produces records that contains some basic stats about tag lengths used in bookmarks,
  2. the reducer just writes out the results as tab-separated files on AWS S3.
  3. Finally the Mapreduce output is imported into AWS Redshift where further query-based analytics can begin.

3. Example input JSON record

    "author": "linooliveira",
    "comments": "",
    "guidislink": false,
    "id": "",
    "link": "",
    "links": [
            "href": "",
            "rel": "alternate",
            "type": "text/html"
    "source": {},
    "tags": [
            "label": null,
            "scheme": "",
            "term": "trips"
            "label": null,
            "scheme": "",
            "term": "howto"
            "label": null,
            "scheme": "",
            "term": "tips"
            "label": null,
            "scheme": "",
            "term": "viagens"
    "title": "Flight Times, Flight Schedules, Best fares, Best rates, Hotel Rooms, Car Rental, Travel Guides, Trip Planning -",
    "title_detail": {
        "base": "",
        "language": null,
        "type": "text/plain",
        "value": "Flight Times, Flight Schedules, Best fares, Best rates, Hotel Rooms, Car Rental, Travel Guides, Trip Planning -"
    "updated": "Sun, 06 Sep 2009 11:36:20 +0000",
    "wfw_commentrss": ""

4. Example of output TSV record produced by Mapreduce

# fields: id, weekday, month, year, hour, minute, second, num_tags, sum_tag_len, avg_tag_len, num_tags_with_len0,num_tags_with_len1,.., num_tags_with_len9 Sun Sep 2009 11 36 20 4 21.0 5.25 0 0 0 0 1 2 0 1 0 0

5. Elastic Mapreduce/Hadoop code in Python

Probably one of the easiest ways to use Elastic Mapreduce is to write the mapreduce code in Python using Yelp’s (excellent) mrjob [8]. And there are of course plenty of reasons to choose Python as the programming language, see [9-14].

from mrjob.job import MRJob
from mrjob.protocol import RawProtocol
import json
import sys
import logging

class PreprocessDeliciousJsonMapreduce(MRJob):
    INPUT_PROTOCOL = RawProtocol # mrjob trick 1
    OUTPUT_PROTOCOL = RawProtocol # mrjob trick 2

    def calc_tag_stats(self, jvalue):
        tag_len_freqs = {}
        num_tags = len(jvalue["tags"])
        sum_tag_len = 0.0
        for taginfo in jvalue["tags"]:
            tag_len = len(taginfo["term"])
            if tag_len < 10: # only keep short tags
                sum_tag_len += tag_len
                tag_len_freqs[tag_len] = tag_len_freqs.get(tag_len, 0) + 1
        for j in range(10):
            if not tag_len_freqs.has_key(j):
                tag_len_freqs[j] = 0 # fill in the blanks
        avg_tag_len = sum_tag_len / num_tags
        return avg_tag_len, num_tags, sum_tag_len, tag_len_freqs

    def get_date_parts(self, jvalue):
        (weekday, day, month, year, timestamp) = jvalue["updated"].replace(",", "").split(" ")[:5]
        (hour, minute, second) = timestamp.split(':')[:3]
        return hour, minute, month, second, weekday, year

    def mapper(self, key, value):
            jvalue = json.loads(key)
            if jvalue.has_key("tags"):
                avg_tag_len, num_tags, sum_tag_len, tag_len_freqs = self.calc_tag_stats(jvalue)
                hour, minute, month, second, weekday, year = self.get_date_parts(jvalue)

                out_data = [weekday, month, year, hour,minute,second, num_tags, sum_tag_len, avg_tag_len]

                for tag_len in sorted(tag_len_freqs.keys()):

                str_out_data = [str(v) for v in out_data]

                self.increment_counter("mapper", "kept_entries", 1)

                yield jvalue["id"], "\t".join(str_out_data)
        except Exception, e:
            self.increment_counter("mapper", "skipped_entries", 1)

    def reducer(self, key, values):
        for value in values:
            yield key, value

    def steps(self):
        return [,

if __name__ == '__main__':

6. Running the Elastic Mapreduce job

Assuming you’ve uploaded the (or other) data set to s3, you can start the job like this (implicitly using mrjob)


# TODO(READER): set these variables first
export INPUT_S3=”s3://somes3pathhere”
export LOG_S3=”s3://another3pathhere”
export OUTPUT_S3=”s3://someothers3pathhere”

nohup python --ssh-tunnel-to-job-tracker --jobconf mapreduce.output.compress=true --ssh-tunnel-is-closed --ec2-instance-type=m1.small --no-output --enable-emr-debugging --ami-version=latest --s3-log-uri=${LOGS_S3} -o ${OUTPUT_S3} -r emr ${INPUT_S3} --num-ec2-instances=1 &

note: for larger data sets you probably want to use other instance types (e.g. c1.xlarge) and a higher number of instances.

7. Connecting, Creating Tables and Importing Mapreduce Data with AWS Redshift

There are several ways of creating and using a Redshift cluster, for this example I used the AWS Console [15], but for an automated approach using the Redshift API would be more approriate (e.g. with boto [16,17])

AWS Redshift Web Console

When you have created the cluster (and given access permissions to the machine you are accessing the Redshift cluster the from), you can access the Redshift cluster e.g. using a Postgresql Client – as below:

psql -d "[your-db-name]" -h "[your-redshift-cluster-host]" -p "[port-number]" -U "[user-name]"

and login with password and then you should be connected.

Creating table can e.g. be done with

CREATE TABLE deliciousdata (
       id varchar(255) not null distkey,
       weekday varchar(255),
       month varchar(255),
       year varchar(255),
       hour varchar(255),
       minute varchar(255),
       second varchar(255),
       num_tags varchar(255),
       sum_tag_len varchar(255),
       avg_tag_len varchar(255),
       num_tags_with_len0 varchar(255),
       num_tags_with_len1 varchar(255),
       num_tags_with_len2 varchar(255),
       num_tags_with_len3 varchar(255),
       num_tags_with_len4 varchar(255),
       num_tags_with_len5 varchar(255),
       num_tags_with_len6 varchar(255),
       num_tags_with_len7 varchar(255),
       num_tags_with_len8 varchar(255),
       num_tags_with_len9 varchar(255)

And data can be imported by substituting the values used the export statements earlier in the blog post (i.e. OUTPUT_S3, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY) in the copy-command below.

copy deliciousdata from 'OUTPUT_S3/part-00000' CREDENTIALS 'aws_access_key_id=AWS_ACCESS_KEY_ID;aws_secret_access_key=AWS_SECRET_ACCESS_KEY' delimiter '\t';

8. Analytics with AWS Redshift

If everything went well, you should now be able to do SQL-queries on the data you produced with mapreduce now stored in Redshift, e.g.

select count(*) from deliciousdata;

Since this posting is about integration I leave this part as an exercise to the reader..

9. Conclusion

This posting has given an example how Elastic Mapreduce/Hadoop can produce structured data that can be imported into AWS Redshift datawarehouse.

Redshift Pricing Example
But since Redshift is a cloud-based solution (i.e. with more transparent pricing than one typically find in enterprise software) you probably wonder what it costs? If you sign up for a 3 year reserved plan with 16TB of storage (hs1.8xlarge), the efficient annual price per Terabyte is $999[1], but what does this mean? Back in 2009 Joe Cunningham from VISA disclosed[18] that they had 42 Terabytes that covered 2 years of raw transaction logs. if one assumes that they would run this on Redshift on 3 hs1.8xlarge instances on a 3 year reserved plan (with 3*16 = 48 TB available storage), the efficient price would be 48*999 = 47.9K$ per year. Since most companies probably have less amounts of structured data than VISA this amount is perhaps an upper bound for most companies?

For examples other Data Warehouse prices check out this blog post (covers HANA, Exadata, Teradata and Greenplum)[19]

Best regards,
Amund Tveit

A. References

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Oct 01

My presentation held at O’Reilly Strata Conference in London, UK, October 1st 2012

Best regards,
Amund Tveit

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