PHP Continuous Data Processing

PHP & Continuous Data Processing
Michael Peacock, October, 2011

No. Not milk floats (anymore)
All Electric, Commercial Vehicles.
Photo courtesy of kenjonbro: http://www.flickr.com/photos/kenjonbro/4037649210/in/set-72157623026469013

About Michael Peacock
Senior/Lead Web Developer
Web Systems Developer
Telemetry Team – Smith Electric Vehicles US Corp
Author
PHP 5 Social Networking, PHP 5 E-Commerce Development, Drupal Social Networking (6 & 7), Selling online with Drupal e-Commerce, Building Websites with TYPO3
PHPNE Volunteer
Occasional technical speaker
PHP North-East, PHPNW 2010, SuperMondays, PHPNW 2011 Unconference, ConFoo 2012

Smith Electric Vehicles & Telemetry
Worlds largest manufacturer of Commercial, all-electric vehicles
Smith Link – on-board vehicle telematics system, capturing over 2500 data points each second on the vehicle and broadcasting them over mobile network
~400 telemetry enabled vehicles on the road
Worlds largest telemetry project outside of F1

System Architecture

Problem #1: We Can’t Loose Any Data
Data is required as part of a $32 million grant from the US Department of Energy
Thousands of pieces of information collected on a per second basis from a range of remote collection devices
Un-predictable amounts of data at any one time
More vehicles rolling off the production line with telemetry enabled
What about system downtime, upgrades, roll-outs and connectivity problems?

Message Queuing
Solution: We use a fast, reliable, scalable, secure, hosted message queue
If our systems are offline, data builds up in the external message queue
If we are processing at full capacity, surplus builds in in the message queue
If the vehicle loses GPRS signal, or message queue were to be inaccessible, vehicles have an internal buffer of up to 7 days

Secret Weapon #1: StormMQ
Based on AMQP, an open standard
Secure: All data is encrypted and sent over SSL
Reliable: Huge investment in server infrastructure
Hosted: Backed up with an SLA
Scalable: Capable of processing huge numbers of incoming messages, with capacity to store the messages when we perform maintenance on our systems

Problem #2: Processing data quickly
We utilise a dedicated server and number of dedicated applications to pull these messages and process them
This needs to happen quick enough for live data to be seen through the web interface
Data is rapidly converted into batch SQL files, which are imported to MySQL via “LOAD DATA INFILE”
Results in high number of inserts per second (20,000 – 80,000)
LOAD DATA INFILE isn’t enough on its own...

Secret Weapon #2: DBA
Sam Lambert – DBA Extraordinaire
Constantly tweaking the servers and configuration to get more and more performance
Pushing the capabilities of our SAN, tweaking configs where no DBA has gone before
www.samlambert.com
http://www.samlambert.com/2011/07/how-to-push-your-san-with-open-iscsi_13.html
http://www.samlambert.com/2011/07/diagnosing-and-fixing-mysql-io.html
sam.lambert@smithelectric.com

Sharding
Huge volumes of data being stored
We shard the data based on the truck it came from, each truck has its own database
Databases held on one of many database servers in our cluster each with ~100GB RAM

Live, Real Time Information
[live screen photo]

Real Time Status and Tracking

Live, Real Time Information: Problem
Original database design dictated:
All data-points were stored in the same table
Each type of data point required a separate query, sub-query or join to obtain
Workings of the remote device collecting the data, and the processing server, dictated:
GPS Co-ordinates can be up to 6 separate data points, including: Longitude; Latitude; Altitude; Speed; Number of Satellites used to get location; Direction

Real Time Information: Concurrent
Initial Solution from the original developers:
Pull as many pieces of real time information through asynchronously
Involved the use of Flash based “widgets” which called separate PHP scripts to query the data
Pages loaded relatively quickly
Data points took a little time to load
Not good enough

Real Time Information: Caching
High volumes of data, and varying levels of concurrent processing means query times are often not consistent
Memcachewas used when processing the data from the message queue, keeping a copy of the most recent of each data point for each truck
Live, Real-Time information accessed directly from memcache, bypassing the database

Caching: Registry/DI is Ideal
Sporadic use of memcache within the web application – ideal use case for a lazy loading registry or DI container
Give the registry or container details of memcache
Object only instantiated and connection made only when data is requested from memcache

Lazy Loading
public function getObject( $key )
{
if( in_array( $key, array_keys( $this->objects ) ) )
{
return $this->objects[$key];
}
elseif( in_array( $key, array_keys( $this->objectSetup ) ) )
{
if( ! is_null( $this->objectSetup[ $key ]['abstract'] ) )
{
require_once( FRAMEWORK_PATH . 'registry/aspects/' . $this->objectSetup[ $key ]['folder'] . '/' . $this->objectSetup[ $key ]['abstract'] .'.abstract.php' );
}
require_once( FRAMEWORK_PATH . 'registry/aspects/' . $this->objectSetup[ $key ]['folder'] . '/' . $this- >objectSetup[ $key ]['file'] . '.class.php' );
$o = new $this->objectSetup[ $key ]['class']( $this );
$this->storeObject( $o, $key );
return $o;
}
elseif( $key == 'memcache' )
{
// requesting memcache for the first time, instantiate, connect, store and return
$mc = new Memcache();
$mc->connect( MEMCACHE_SERVER, MEMCACHE_PORT );
$this->storeObject( $mc, 'memcache' );
return $mc;
}
}
Becomes the limit for the registry pattern, DI container more suitable

Real Time Information: Extrapolate and Assume
Our telemetry unit broadcasts each data point once per second
Data doesn’t change every second, e.g.
Battery state of charge may take several minutes to loose a percentage point
Fault flags only change to 1 when there is a fault
Make an assumption.
We compare the data to the last known value…if it’s the same we don’t insert, instead we assume it was the same
Unfortunately, this requires us to put additional checks and balances in place

Extrapolate and Assume: “Interlation”
Built a special library which:
Accepted a number of arrays, each representing a collection of data points for one variable on the truck
Used key indicators and time differences to work out if/when the truck was off, and extrapolation should stop
For each time data was recorded, pull down data for other variables for consistency

Interlace
* Add an array to the interlation
public function addArray( $name, $array )
* Get the time that we first receive data in one of our arrays
public function getFirst( $field )
* Get the time that we last received data in any of our arrays
public function getLast( $field )
* Generate the interlaced array
public function generate( $keyField, $valueField)
* Beak the interlaced array down into seperate days
public function dayBreak( $interlationArray)
* Generate an interlaced array and fill for all timestamps withinthe range of _first_ to _last_
public function generateAndFill( $keyField, $valueField)
* Populate the new combined array with key fields using the common field
public function populateKeysFromField( $field, $valueField=null )
http://www.michaelpeacock.co.uk/interlation-library

Real Time Information: Single Request
Currently, each piece of “live data” is loaded into a flash graph or widget, which updates every 30 seconds using an AJAX request
The move from MySQL to Memcache reduces database load, but large number of requests still add strain to web server
Moving to image and JavaScript widgets, which are updated from a single AJAX request

Lots of Data: Race Conditions
Sessions in PHP close at the end of the execution cycle
Unpredictable query times
Large number of concurrent requests per screen
Session Locking
Completely locks out a users session, as PHP hasn’t closed the session

Race Conditions: PHP & Sessions
session_write_close()
Added after each write to the $_SESSION array. Closes the current session.
(requires a call to session_start immediately before any further reads or writes)

Race Conditions: Use a ******* Template Engine
V1 of the system mixed PHP and HTML 
You can’t re-initialise your session once output has been sent
All new code uses a template engine, so session interaction has no bearing on output. When the template is processed and output, all database and session work has been completed long before.

Race Conditions: Use a Single Entry Point
Race conditions are further exacerbated by the PHP timeout values
Certain exports, actions and processes take longer than 30 seconds, so the default execution time is longer
Initially the project lacked a single entry point, and execution flow was muddled
Single Entry Point makes it easier to enforce a lower time out, which is overridden by intensive controllers or models

Intensive queries & Calculations
How far did this vehicle travel?
Motor RPM x Various vehicle specific constants
Calculated for every RPM value held during drive process
How much energy did the vehicle use
Battery Current x Battery Voltage x Time
For every current and voltage value combination held during the driving process
How well was the vehicle driven
Analysis of idle time
Harshness of accelerator and brake pedal usage
Inappropriate duration of AC / Heater on time?
What about for a customers fleet, or all of our vehicles sold?

Intensive Queries & Calculations

Intensive queries & Calculations
Involves a fair number of queries per vehicle
Calculations involve holding this data in memory
Processing required for every single record for that piece of data during that day
Takes a while!
Solution:
Calculate information overnight
Save it as a compiled report
Lookups and comparisons only need to look at the compiled / saved reports in the database

Reports
In addition to our calculated reports, we also need to export key bits of information to grant authorities
Initially our PHP based export scripts held one database connection per database (~400 databases)
Re-wrote to maintain only one connection per server, and switch the database used
Toggles to instruct the export to only apply for 1 of the servers at a time
Modulus magic to run multiple export scripts per server

Triggers and Events
Currently a work-in-progress R&D project, evaluating two options:
Golden hammer: Use PHP
Run PHP as a daemon
http://kevin.vanzonneveld.net/techblog/article/create_daemons_in_php/
Continually monitor for specific changes to memcache variables
Node.js
Light weight and fast
Give PHP another friend
Link into PHP based API to run triggers

The Future
More sharding
Based on time – keep the individual tables smaller
NoSQL?
Currently investigating NoSQL solutions as alternatives
Rationalisation
Do we need as much data as we collect?
Abstraction
We need to continually abstract concepts and ideas to make on-going maintenance and expansion easier; especially in terms of mapping code to database shards
More hardware
Expand our DB cluster, more RAM, R&D
Design
A much needed design refresh

Conclusions
Make the solution scalable from the start
Where data collection is critical, use a message queue, ideally hosted or “cloud based”
Hire a genius DBA to push your database engine
Make use of data caching systems to reduce strain on the database
Calculations and post-processing should be done during dead time and automated
Add more tools to your toolbox – PHP needs lots of friends in these situations
Watch out for Session race conditions: where they can’t be avoided, use session_write_close, a template engine and a single entry point
Reduce the number of continuous AJAX calls

Q & A
Michael Peacock
Web Systems Developer – Telemetry Team – Smith Electric Vehicles US Corp
michael.peacock@smithelectric.com
Senior / Lead Developer, Author & Entrepreneur
me@michaelpeacock.co.uk
www.michaelpeacock.co.uk
@michaelpeacock
http://joind.in/3808
http://www.slideshare.net/michaelpeacock
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PHP Continuous Data Processing

by Michael Peacock

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