AIS NMEA and Google Maps API

13 11 2012

Those who know me will know I like nothing better than to get well offshore away from any hope of network connectivity. It’s like stepping back 20 years to before the internet and its blissfully quite. The only problem is that 20 years ago it was too quiet. Crossing the English Channel in thick fog with no radar and a Decca unit that could only be relied on to give you a fix to within a mile some of the time, made you glad to be alive when you stood on solid ground again. Rocks and strong currents round the Alderney race were not nearly as frightening as the St Malo ferry looming out of the fog, horns blaring, as if there was anything a 12m yacht could do in reply. After a white knuckle trips I bought a 16nm Radar which turned the unseen steel monster of the Channel into a passage like a tortoise crossing a freeway reading an ipod. I don’t know which was better, trying to guess if the container ship making 25kn, 10nm away was going to pass in front or behind you, or placing your trust in the unseen ships crew who had spent the past 4 days rolling north through Biscay with no sleep.

Those going to sea today will not experience any of this excitement. They will probably have at least 3 active GPS receivers on board and which will be able to tell them when they are at the bow, stern and sitting on or standing in the heads, (W/C for landlubber).  The second bit of kit that they will probably have is an AIS receiver. All ships now carry AIS transmitters, as do some yachts whose owners. Vanity domains for boat owners. AIS transmits on marine 2 marine VHF channels 161.975 MHz and 162.025 MHz using variants of TDMA sharing. The data that is transmitted  is in a standard form NMEA0183 which is the same standard as used in many older marine systems. In the case of AIS, the payload is 8 bit text containing 6bit data in 168bit payload containing a checksum. The information that’s broadcast is mostly position  speed, course and identification of the sender, which although its intended mainly as a instant communication of intentions between large ships is also invaluable to any smaller craft in fog. Its like being on the bridge of all ships in VHF range at the same time and its relatively simple to calculate the closest point of approach (CPA) for all targets. Pre affordable radar, we used to guess CPA using our ears, and sometimes smell (you can smell a super tanker in a strong wind). With radar we used to try and guess the path of an approaching target from the screen. Easy on a stable platform, not so easy when your radome is doing the samba. Today we have speed and real course often to three significant figures.

I now live about 20km from the Sea north of Sydney harbor. VHF is line of sight so I would have thought it was not going to be possible to receive VHF from that distance taking into account buildings and trees. Normal marine whip aerials probably would not work, but a strip of 300 Ohm ribbon cable cut precisely to length and tuned to resonate with 1/4 wavelengths at 162 MHz is receiving and decoding signals from Newcastle to the north and Wollongong to the south, around 80km in each direction. Not bad for $5 worth of cable. The receiver is a cheap headless unit from ACR that sends the NMEA0183 signals down a USB/serial port. A simple Python scripts receives and decodes the NMEA0183 stream, converting (using ais.py from GPSD project) it into JSON containing current position,  speed, MMSI number and a host of other information. All very interesting, but not very visual. I could just use one of the many free apps to display the NMEA0183 information over TCP/UDP, but they are limited.

Google Maps v3 API allows Javascript to create an overlay of markers. So a few 100 lines of Javascript loads the json file into a browser every 15s and displays the results on an overlay on Goole Maps. Ships are red with a vector, the wake is green. Sydney is a good place to test this as nearly all the ferries in the harbor transmit AIS messages all the time. Its a busy place. Obviously using Google maps 100nm offshore isn’t going to work. The next step is to load the Python onto a Raspbery Pi board, plug in a USB Wifi dongle and create my own mobile wifi hotspot which an iPad loaded with marine charts can connect to, all for significantly less that 1 Amp. If your on 12V you care about juice. Having IP offshore does defeat the purpose of being there, I may have to turn it off from time to time to remind myself I am alive.

This interface was just an exercise to validate the NMEA to TCP over Wifi sever works. If you want to know when you ship will come in, visit http://www.marinetraffic.com/ais/, but don’t try and use it at sea.





HowTo: Quickly resolve what an Sling/OSGi bundle needs.

30 10 2012

Resolving dependencies for an OSGi bundle can be hard at times, especially if working with legacy code. The sure-fire way of finding all the dependencies is to spin the bundle up in an OSGi container, but that requires building the bundle and deploying it. Here is a quick way of doing it with maven, that may at first sound odd.

If your building your bundle with maven, you will be using the BND tool via the maven-bundle-plugin. This analyses all the byte code that is going into the bundle to work out what will cross over the class-loader boundary. BND via the maven-bundle-plugin has a default import rule of ‘*’. ie import everything. If you are trying to control which dependencies are embedded, which are ignored and which are imported, this can be a hinderance. Strange though it sounds, if you remove it life will be easier. BND will immediately report everything that it needs to import that can’t be imported. It will refuse to build which is a lot faster than generating a build that won’t deploy. The way BND reports is also useful. It tells you exactly what it can’t find and this gives you a list of packages to import, ignore or embed. Once you think you have your list of package imports down to a set that you expect to come from other bundles in your container, turn the ‘*’ import back on and away you go.

In maven that means editing the pom.xml eg:

...
 <plugin>
   <groupId>org.apache.felix</groupId>
   <artifactId>maven-bundle-plugin</artifactId>
   <version>2.3.6</version>
   <extensions>true</extensions>
   <configuration>
     <instructions>
       <Import-Package>
         <!-- add ignore packages before the * as required eg. !org.testng.annotations, -->
         * <!-- comment the * out to cause BND to report everything its not been told to import -->
       </Import-Package>
       <Private-Package>
         <!-- add packages that you want to appear as raw classes in the jar as private packages Note, they dont have to source code in the project, they can be anywhere on the classpath for the project, but be careful about resources eg org.apache.sling.commons.cache.infinispan.* -->
       </Private-Package>
       <DynamicImport-Package>sun.misc.*</DynamicImport-Package>
       true</Embed-Transitive>

           <!-- embed dependencies (by artifact ID, including transitives if Embed-Transitive is true) that you dont want exposed to OSGi -->
       </Embed-Dependency>
     </instructions>
   </configuration>
 </plugin>

The OSGi purists will tell us that it’s heresy to embed anything but sometimes with legacy systems it’s just too painful to deal with the classloader issues.

There is probably a better way of doing this, if so, do tell.


				




Sakai CLE ElasticSearch

11 10 2012

A long time ago, I wrote a search module for Sakai 2 as CLE was known then. It attempted to make every node in a CLE instance share the load of indexing and searching and make the search aspect of a CLE cluster scale elastically. To some extents it worked, but it had problems. The indexing queue was persisted in a DB table and it was based on a old version of Lucene that didn’t have anything as useful as commits. Consequently it could get its segments into a bit of mess at times. The world has moved on in the 5 years since I wrote that code, and two viable alternatives for supporting Search in Sakai CLE have emerged. Apache Solr and Elastic Search. Both can be run as remote servers or embedded. Both are solid reliable releases. It could be argued that Solr has more support for sophisticated index schema, and it’s probably true that Elastic Search is easier to deploy for elastic scaling and real time indexing as that’s its default behaviour.

For those wanting to try Sakai CLE with Apache Solr as the search server then look no further than the work that Adam Marshall has been doing at Oxford University. That allows you to spin up a Solr instance and connect your Sakai CLE instances to it. You will have to do some reasonably sophisticated master slave configuration to make it resilient to failures and don’t expect the indexing operations to be real-time. There are plenty of references to the work required to do that in this blog, and arguments why I currently prefer ElasticSearch over Solr.

Deployment and reliability

ElasticSearch comes out the box being real-time, elastic and cloud aware, with built-in AWS EC2 knowledge as well as rack awareness. Its been built to shard, partition and replicate indexes out of the box. The ElasticSearch client as I am finding out is simple to embed into most environments including OSGi and when embedded makes each app server node a part of elastic search cluster. Best of all, for the nervous by nature, is the resilience that comes from spinning up more than 3 instances in the same cluster. In fact, I have been finding it hard to damage elastic search indexes in tests. It’s perfectly possible to do all of this with Solr, but the deployer has to work a little harder adding some custom components to support a writeahead log and a Zookeeper instance to manage the cloud.

Metadata Indexing

Probably the best part of ElasticSearch is the client which is a fully multithreaded client following the same pattern Communicating Sequential Processes first described by Tony Hoare and one of the motivators for the Go language. This allows a client for submit suitably light weight indexing requests to the ElasticSearch cluster via an embedded client without needing to think about managing a queue or the latency of indexing. This nice little feature turns the 1000 lines of code I had to write for Sakai CLE  and OAE search into about 20. Initial tests show that indexing can be done within the request loop and because of the true real-time nature ElasticSearch with its write ahead log, results are available about 50ms after the transaction commits. To maintain that latency, I only index metadata via this route. Document indexing takes a different route.

Document Indexing

I found with the original Sakai 2 search and subsequent Solr based indexing of documents in Sakai OAE that indexing bodies was expensive. In some instances tokenizing office documents could place extreme strain on a JVM heap. For that reason when I did the indexing service in the Django version of OAE I did two things. I offloaded the document body indexing operations to separate processors driven by a queue of events, following the CSP pattern mentioned above, and I made the content store single instance. Where users collaborate, they often upload the same document. With a single instance content store, only a single instance of a document is stored and hence, tokenizing and information extraction is only performed once. This greatly reduces the cost of indexing. The store isn’t collision perfect but by performing a hash on the document body as its saved its possible to eliminate most if not all collisions. Certainly SHA1(ing) enough of the body eliminates all collisions.

So the document indexing processes use the index to locate documents that need to be indexed and then use the single instance content store to eliminate duplicate tokenizing. Using this approach in the Sparse Content Map content system which is already single instance has a dramatic impact on IO. Sakai CLE Content Hosting Service is not single instance at present but could be adjusted to be so once hashes are known. It would be nice to fix that aspect of CHS at some point.

Current state

I am still working on this code, and this post is part notes, part notification should I get distracted. My testbed is the Sparse Content Map content system only because it builds in 20s, starts in 5, has full integration test coverage and compliant webdav support thanks to Milton. There is currently nothing in the code base that prevents it using Spring or a Webapp container as opposed to OSGi, and the coupling is loose being event driven. The best part is the result should scale as far as ES can scale which is probably a lot larger than any CLE instance in production.





Fibonacci ring for Cassandra

10 10 2012
King Protea (Protea cynaroides)

King Protea (Protea cynaroides) (Photo credit: Wikipedia)

No this isn’t a greek tragedy or some software that I have written, but a thought about the way in which Apache Cassandra an other distributed systems perform problem space decomposition. Cassandra is a good example of a distributed system with problem space decomposition. Its problem space is keys. To be efficient it needs to distribute those keys evenly around its cluster. The key partitioning algorithm normally uses something that generates a flat even distribution. A Linear Congruential Generator  could be used if you are prepared to live with some banding in the problem space. If not and you are prepared to live with a bit more computational expense one of the hash functions like MD5 or SHAx. In fact the standard key distribution functions in Cassandra use something based on MD5, which to my naive mind must have some collisions.

In reading the Cassandra documentation and using it some years back I became concerned about how elastic Cassandra is. The decomposition of Cassandra’s key domain is often represented as a ring. That ring is constructed when the cluster is creates and elements are allocated via the key-> ring function, I think they are called partitioners. From reading the documentation, partitioning of this space if fixed and static. If more nodes need to be added to a Cassandra cluster then the partitioning scheme must be updated and data must be migrated from existing nodes in the cluster to their new home before the cluster can become full active again. I think I got that right. That means, although you can replace nodes, you can’t elastically scale without partitioning work. I am not absolutely clear if that means the re-partitioning work can be done on a live system, or not. I would hope it can.

That got me thinking. There are other systems that repartition effectively during operation. Algebraic Multigrids used to solve high Reynolds number Eulerian grids repartition to accelerate the solution phase. I wrote a parallel AMG solver to run on Cray T3Ds in 1995. It was fast, efficient with good conversion rates  but struggled to beat the Cray vectorised versions of the code base on reasonable sized clusters. There is another. A plant. A plant doesn’t shutdown when it adds petals to its flower or leaves to its stem it keeps running (so to speak, I havent seen a running flower since University). The plants domain space that its partitioning is sunlight. As it adds leaves doesn’t add leaves as a whole ring, but it adds them one by one to make the most use of the available sunlight without shading other spaces. It doesn’t require that the cells from one leaf or petal migrate to the new leaf. In essence a plant has achieved the trick of scaling elastically.

How does it do this ?

There is a biological explanation associated to levels of hormones in the stem which are triggered by light levels which could be considered to be as adaptive as the AMG solver is, driven by its solution. Stepping back a bit there is an observation often used in math classes. The number of spirals in many plants is observed to be adjacent numbers in the Fibonacci sequence, often 8, 13 and 21 but sometimes as high as 144 spirals. There is a delightful explanation of Pinecones, Pineapples, Protea and the Fibonacci sequence by Vi Hart, even if you think you have learnt everything, its fun to watch.

How is this relevant ?

I wonder if a Cassandra ring seeded with an initial space that allowed say 5 partitions, but as those partitions passed a threshold of say 30% (with an even distribution) another partition was added. That new partition would attract new keys without requiring migration of the existing keys ensuring that the original partitions never filled. If successful as new nodes were added in the same way as segments are added to a pineapple the Cassandra cluster could scale elastically, or more elastically than it appears to do currently. That really is just a thought, and I havent written a partitioner yet to see if it would work. I think the partitioner would be based on the the ratio of adjacent numbers in the Fibonacci sequence. ie, the Golden Angle





Node.js vs SilkJS

28 09 2012

synchronous ducks

Node.js, everyone on the planet has heard about. Every developer at least. SilkJS is relatively new and creates an interesting server to compare Node.js against because it shares so much of the same code base. Both are based on the Google V8 Javascript engine that convert JS into compiled code before executing. Node.js as we all know uses a single thread that uses a OS level event queue to process events. What is often overlooked is that Node.js uses a single thread, and therefore a single core of the host machine. SilkJS is a threaded server using pthreads where each thread processes the request leaving it upto the OS to manage interleaving between threads while waiting for IO to complete. Node.js is often refereed to as Async and SilkJS is Sync. The advantages to both approaches that are the source of many flame wars. There is a good summary of the differences and reasons for each approach on the SilkJS website. In essence SilkJS claims to have a less complex programming model that does not require the developer to constantly think of everything in terms of events and callbacks in order to coerce a single thread into doing useful work whilst IO is happening. Although this approach hands the interleaving of IO over to the OS letting it decide when each pthread should be run. OS developers will argue that thats what an OS should be doing and certainly to get the most out of modern multicore hardware there is almost no way of getting away from the need to run multiple processes or threads to use all cores. There is some evidence in the benchmarks (horror, benchmarks, that’s a red rag to a bull!) from Node.js, SilkJS, Tomcat7, Jetty8, Tornado etc that using multiple threads or processes is a requirement for making use of all cores. So what is that evidence ?

Well, first read why not to trust benchmarks http://webtide.intalio.com/2010/06/lies-damned-lies-and-benchmarks-2/ once you’ve read that lets assume that everyone creating a benchmark is trying to show their software off best.

The Node.js 0.8.0 gives a request/second benchmark for a 1K response at 3585.62 request/second. http://blog.nodejs.org/2012/06/25/node-v0-8-0/

Over at Vert.x there was an of Vert.x and Node.js showing Vert.x running at 300,00 requests/s. You do have to take it with a pinch of salt after you have read another post http://webtide.intalio.com/2012/05/truth-in-benchmarking/ with some detailed analysis that points out testing performance on the same box with no network and no latency is theoretically interesting, but probably not informative for the real world. What is more important is can the server stand up reliably forever with no downtime and perform normal server side processing.

So the SilkJS benchmarks in one of its more reasonable benchmarks claim it runs at around 22,000 request per second delivering 13K of file from disk with a very high levels of concurrency 20000. Again its hard to tell how true the benchmark is since many of those requests are pipelined (no socket open overhead), but one thing is clear. With a server capable of handling that level of concurrency some of the passionate arguments supporting async servers running one thread per core are lost. Either way works.

There is a second side to the SilkJS claims that bears some weight. With 200 server threads, what happens when one dies or needs to do something that is not IO bound? Something mildly non trivial that might use a tiny bit of CPU. With 1 server thread we know what happens, the server queues everything up while the on server thread does that computation. With 200, the OS manages the time spent working on the 1 thread. There is a simple answer, offload anything that does and processing to a threaded environment, but then you might as well use an async proxy front end to achieve the same.

There is a second part of the SilkJS argument that holds some weight. What happens when 1 of the SilkJS workers dies? Errors that kill processes happen for all sorts of reasons, some of them nothing to do with the code in the thread. With 199 threads the server continues to respond, with 0 it does not. At this point everyone who is enjoying the single-threaded simplicity of an async server will, I am sure, be telling me their process is so robust it will never die. That may well be true, but process sometimes dont always die, sometimes they get killed. The counter argument is, what happens when all 199 threads are busy running something. The threaded server dies.

To be balanced, life in an async server can be wonderfully simple. There is absolutely no risk of thread contention since there is only ever one thread, and it doesn’t matter how long a request might be pending for IO for as all IO is theoretically non blocking. It doesn’t mater how many requests there are provided there is enough memory to represent the queue. Synchronous servers can’t do long requests required by WebSockets and CometD. Well they can, but the thread pool soon gets exhausted. The ugly truth is that async servers also have something that gets exhausted  Memory. Every operation in the event queue consumes valuable memory, and with many garbage collected system, garbage collection is significant. Although it may not be apparent at light loads, at heavy loads even if CPU and IO are not saturated, async servers suffer from memory exhaustion and or garbage collection trying to avoid memory exhaustion, which, may appear as CPU exhaustion. So life is not so simple, thread contention is replaced by memory contention which is arguably harder to address.

So what is the best server architecture for modern web application?

An architecture that uses threads for requests that can be processed and delivered in ms, consuming no memory and delegating responsibility for interleaving IO to the OS, the resident expert at that task. Coupled with an architecture that recognises long IO intensive requests as such and delegates them to async part of the server, and above all, an architecture on which a simple and straightforward framework can be built to allow developers to get on with the task of delivering applications at webscale, rather than wondering how to achieve webscale with high load reliability. I don’t have an answer, other than it could be built with Jetty, but I know one thing, the golden bullets on each side of this particular flame war are only part of the solution.





Google CourseBuilder, a scalable course delivery platform ?

15 09 2012

This week I discovered Google CourseBuilder, the latest entry into the MOOC arena. It’s a Google App Engine application that Google Research used to host a MOOC to 155K students a few months ago. It follows a simular pedagogy to that used by other MOOC providers with high quality video lessons, that give the student the feeling they are working one on one with the lecturer. Google have open sourced the code under and Apache 2 license which gives us all an insight into the economies of scale that a MOOC represents. Unlike the traditional Virtual Learning Environment where the needs of staff are catered for in the user interface, Google CourseBuilder currently delegates all the functionality to spreadsheets, editing snippets of javascript and html. There is no reason why it could not be given an user interface, but when you consider what its is trying to do you realise that staff user interfaces for course creation are less important than the delivery of the course at scale. Consequently the application itself is tightly focused on delivering the course as quickly and as simply as possible to as many users as possible. Google App Engine makes this easy, even for meer mortals. Once you have accepted that nothing is really for free, and you do have to pay for bandwidth used and energy in at some point scaling this application upto 100K or even 1M users requires little or no effort on  your part. You also, at the moment, have to accept if you are going to reach that many students, you are going to have to ask for a little bit of help from someone to write some HTML, drive a spreadsheet and write a bit of Javascript as well as hit the “deploy” button on the App Engine SDK. I say, at the moment, because it isn’t going to be that hard to create an administrative UI, and thats what I have been doing for a few hours this week.

So the reality is, very few lecturers are going to create a course that will be delivered to 155K students, and if they succeed in going viral, the drop out rate is likely to be very high. The course Google ran issued 22K certificates, indicating a drop out rate of 85%. Its still an impressive number when many campuses are no where near that size however, most institutions would not survive with that level of drop out and all would be looking at ways of reducing it. Institutions invest more in their students and so need lower levels of drop out. As a result, their courses are smaller, they don’t have the economies of scale and can’t invest as much in the delivery of each individual course. All is not lost however, the opportunity that Googles CourseBuilder represents could be utilized if there was a small reduction in effort associated with course creation and course delivery.

The video attached to this blog post shows how that might be achieved. This is a modified version of Google CourseBuilder that allows a single Google App Engine to host more than one course. It could easily host a course catalogue from an small institution or medium size faculty. That course catalogue is uploaded via a spreadsheet. Individual courses containing units and lessons are also uploaded via seperate spreadsheets.

Students sign in using their Google ID, Google Apps for Education ID, or OpenID. They then register with the the courses they want to take. If you want to give it a try there is a App Engine Instance running at http://cbmultidemo.appspot.com/, bear in mind its a free instance so may become unavailable.

At the moment the administrative interface is very basic, but the intention is to build that up to allow courses to be created without needing to resort to technical resources. So far I have spent about 4h eliminating most of the code base editing and adding multi course capability. The code base is available as a fork of the Google CourseBuilder project and can be deployed by anyone with a Google ID. Since the original code was written in Python, using a modern variant of the GAE framework porting to Django would be trivial  with those who have concern about running on Google infrastructure. Obviously in doing so, you will have to work out how to do the scaling, see Instagram for pointers on that.





Jackrabbit, Oak, Sling, maybe even OAE

30 08 2012


Back in January 2010 the Jackrabbit team starting asking its community what did it want to see in the next version of
Jackrabbit. There were some themes in the responses. High(er) levels of write concurrency, millions of child nodes and cloud scale clustering with NoSQL backends. I voted for all of these.

I was reminded of this activity by the announcement of a Oak Hackathon, or rather an Oakathon that is being organised this September at the .adaptTo conference in Berlin. This Oakathon seems to be intended to get users upto speed on using Oak, which means that it might be ready for users to take a look. So I did.  The code checks out, builds and passes all its integration tests. No surprises there from the Jackrabbit team.

I am not going to pretend I understand the storage model being used or how it addresses the requirements that came out of Jackrabbit, but the Persistence implementation looks like it could be adapted to a sharded schema over many DB instances or even ontop of Cassandra. The storage model looks something like a git tree. It seems to solve the many child nodes issue that Sparse Map Content solved for OAE in a slightly different, but more efficient way by using a DAG structure with pointers to a child tree rather than a parent pointer. I won’t be able to tell if the concurrency issues that caused me to have to squeeze Sparse Map Content into the Sling repository API layers, without some testing, but the approach looks like it might. Certainly the aims of the Road map cover the needs of OAE and go beyond the scale and concurrency required.

Best of all, it already has a Sling Repository implemented, so it should be relatively easy to spin up Sling on Oak and run all the tests that caused OAE to move from Sling on Jackrabbit to Sling on a hybrid of SMC and Jackrabbit.