> Am 26.09.2018 um 19:22 schrieb Nicolas Cellier > <[email protected]>: > > Very nice proof that we can leverage efficient and up to date technologies !
That was part of the plan ;) > Thank you so much for sharing, that's the right way to make Pharo (and > Smalltalk) alive and kicking. How did the Pharo IDE help in such context? > (did you use debugging facility extensively?). > What tool is missing? At first pharo is always a bliss to work with. In such a project I try to make the whole application managable on different levels. The most important one is to have the whole application in one image. We have tests for the broker and for each micro service. But we also have tests that operate on the whole stack. Here the Q is being shortcut and handling is more synchronous than in the Q case. With this it is easier to get a stack in the debugger of the whole roundtrip. Using docker we can have the whole application started on a local laptop. This way all components are much closer to investigate. We can switch the frontend server with a local development entity. I started to do that for the pharo images but did not finish yet. The idea is to start the same stack as in the swarm but replace one image with an actual development image where you can use the debugger. On the swarm itself we configure each microservice to upload a fuel context dump to a central server on exception. From my development image I have a simple client to look for exceptions for a project and version number. Clicking on one it downloads the dump and opens a debugger locally. I can fix the bug and commit with iceberg. This goes well with our continuous deployment. When a commit is done, jenkins builds the whole product and deploys that automatically on the alpha swarm. This way from seeing an error the only thing to do is clicking on it solve the problem in the debugger and commit. Well, in most of the cases ;) What I’m working on and gave a quick preview on ESUg is to have a client for docker swarm. It is another way to close the cycle to use pharo to manage things in a swarm that is build from pharo images. I did a first prototype how to connect on a particular image in the swarm and start TelePharo on it so you can set breakpoints for certain things and have a debugger in your image from the live swarm. The last things we did is to add proper monitoring of metrics withing the service so you can spot problems that belong to any kind of resource shortage. In this case it would be especially useful to connect to such an image to do live investigations. Yes and object centric debugging/logging will help here Steven/guys ;) Or two say it in less words :) The two problems we had to solve is to remove complexity where possible and to have all the mentioned approaches to enable our team to tackle an occurring problem from different angles. If no one is blocked in work the project does not stagnate. It is not a guarantee to succeed but a requirement Hope this is the information you were asking for. Norbert > > Le mar. 25 sept. 2018 à 17:45, Sven Van Caekenberghe <[email protected] > <mailto:[email protected]>> a écrit : > > > > On 25 Sep 2018, at 14:39, Norbert Hartl <[email protected] > > <mailto:[email protected]>> wrote: > > > > > > > >> Am 25.09.2018 um 12:52 schrieb Sven Van Caekenberghe <[email protected] > >> <mailto:[email protected]>>: > >> > >> Wow. Very nice, well done. > >> > >> Any chance on some more technical details, as in what 'connected by a > >> message queue for the communication' exactly means ? How did you approach > >> micro services exactly ? > >> > > Sure :) > > Thanks, this is very interesting ! > > > The installation spawns multiple physical machines. All the machines are > > joined to a docker swarm. The installation is reified as either task or > > service from the view on the docker swarm. Meaning you instantiate an > > arbitrary amount of services and docker swarm distributes them among the > > physical machines. Usually you don’t take control which is running where > > but you can. At this point you have spread dozens of pharo images among > > multiple machines and each of them has an IP address. Furthermore in docker > > swarm you have a reification of a network meaning that every instance in a > > network can see all other instances on this network. Each service can be > > reached by its service name in that network. Docker swarm does all the > > iptables/firewall and DNS setup for you. > > Are you happy with docker swarm's availability/fail-over behaviour ? In other > words: does it work when one image/instance goes bad, does it detect and > restore the missing functionality ? > > > In order to have communication between those runtimes we use rabbitmq > > because you were so nice writing a driver for it ;) The rabbitmq does have > > a support for cluster setup, meaning each of the physical machines has a > > rabbitmq installation and they know each other. So it does not matter to > > which instance you send messages to and on which you register for receiving > > messages. So every pharo image connects to the service rabbitmq and opens a > > queue for interaction. > > Same question: does RabbitMQ's clustering work well under stress/problems ? > Syncing all queues between all machines sounds quite heavy (I never tried it, > but maybe it just works). > > > Each service like the car sharing opens a queue e.g. /queue/carSharing and > > listens on it. The broker images are stateful so they open queues like > > /queue/mobility-map-afdeg32 where afdeg32 is the container id of the > > instance (hostname in docker). In each request the queue name to reply is > > sent as a header. So we can make sure that the right image gets the message > > back. This way we can have sticky sessions keeping volatile data in memory > > for the lifecycle of a session. There is one worker image which opens a > > queue /queue/mobility-map where session independent requests can be > > processed. > > I think I understand ;-) > > > In order to ease development we are sharing code between the broker and the > > micro service. Each micro service has a -Common package where the classes > > are in that build the interface. The classes in here are a kind of data > > entity facades. They use NeoJSON to map to and from a stream. The class > > name is send with the message as a header so the remote side knows what to > > materialize. The handling is unified for the four cases > > > > - Request as inquiry to another micro service > > - Response returns values to a Request > > - Error is transferred like a Response but is then signalled on the > > receiving side > > - Notification connects the announcers on the broker and the micro service > > side. > > Yes, makes total sense. > > > Asynchronous calls we solved using Promises and Futures. Each async call to > > the Q becomes a promise (that blocks on #value) and is combined to a future > > value containing all promises with support to generate a delta of all > > resolved promises. This we need because you issue a search that takes > > longer and you want to display results as soon as they are resolved not > > after all haven been resolved. > > Which Promise/Future framework/library are you using in Pharo ? > > You did not go for single threaded worker images ? > > > And a lot more. This is a coarse grained overview over the architecture. > > I’m happy to answer further questions about this. > > > > Norbert > > > >>> On 25 Sep 2018, at 12:20, Norbert Hartl <[email protected] > >>> <mailto:[email protected]>> wrote: > >>> > >>> As presented on ESUG here is the brief description of one of our current > >>> projects. > >>> > >>> Mobility Map > >>> —————— > >>> > >>> Mobility Map is a broker for mobility services. It offers multi-modal > >>> routing search enabling users to find the best travel options between > >>> locations. Travel options include car sharing, bikes, trains, busses etc. > >>> Rented cars can be offered for ride sharing on booking time letting other > >>> people find it to participate in the ride. Single travel options are > >>> combined in travel plans that can be booked and managed in a very easy > >>> way. > >>> > >>> For this project main requirements were scalability to serve a large user > >>> base and flexibility to add more additional providers to the broker. The > >>> application has been realized using web technologies for the frontend and > >>> pharo for the backend. Using a microservice architecture combined with a > >>> broker it is easy to extend the platform with additional brokers. > >>> Deployment is done using docker swarm for distributing dozens of pharo > >>> images among multiple server machines connected by a message queue for > >>> the communication. Pharo supported that scenario very well enabling us > >>> the meet the requirements with less effort. > >>> > >>> Pharo turned out to be a perfect fit to develop the application in a > >>> agile way. Small development cycles with continuous integration and > >>> continuous delivery enables fast turnarounds for the customers to > >>> validate progress. > >>> > >>> This is a screenshot of the search page for multi-modal results: > >>> > >>> > >>> <Screen Shot 2018-09-21 at 16.54.30.png> > >> > >> > > > > > >
