Normally we recommend using thin-clients if you can. Though, in this case, using a thick-client makes your life easier. Thick clients can deploy Java code for you.
There are a few different ways to do it. The "easy" option is to just deploy the JAR files to the server nodes "manually." You could also consider peer class loading ( https://ignite.apache.org/docs/2.11.1/code-deployment/peer-class-loading), which is where the client automatically sends classes to the remote nodes. Or UriDeployment ( https://ignite.apache.org/docs/2.11.1/code-deployment/deploying-user-code), where Ignite copies the Jar files from a central location. GridGain's Control Center (not open source) is also able to deploy code. On Fri, 5 Jan 2024 at 14:04, Angelo Immediata <angelo...@gmail.com> wrote: > hello Gianluca and all > > Regarding to thin client, in my architecture I avoided to use thin > clients; I'm using thick clients; so if python is supported only in "thin > client" mode, I'd prefer to avoid it > > Regarding distributed computing, I didn't see it but it seems to be > interesting but something is missing me. Let's suppose I want to use djl > https://djl.ai/ and its timeseries support ( > https://djl.ai/extensions/timeseries/) I can use the distributed > computing; as far as I understood the distributed computing allows to me to > distribute computations across all my cluster nodes. Now I'm using thick > clients, this means my java application is remotely connected to the apache > ignite "master nodes"; in distributed computing I should execute the > computation on master nodes but if I use a custom dependency (e.g. djl) how > can these master remote nodes execute the computation if they don't have > the libraries? > Am I missing anything? > > Thank you > Angelo > > Il giorno ven 5 gen 2024 alle ore 14:24 Gianluca Bonetti < > gianluca.bone...@gmail.com> ha scritto: > >> Hello Jagat >> >> There are Ignite thin clients for a number of languages, including Python. >> For a full list of functionalities and comparison, please always refer to >> the official documentation. >> >> https://ignite.apache.org/docs/latest/thin-clients/getting-started-with-thin-clients >> >> All thin clients should perform around the same in tasks such as storing >> and retrieving data as they use the Apache Ignite binary protocol. >> As you know performance also varies case by case, because of different >> setups, configurations, and versions of software/frameworks/libraries >> being used, and of course the performance of the code that you will write >> yourself. >> >> For my specific use cases, Apache Ignite always performed extremely well. >> As I don't know anything about your project, there are far too many >> possible variables to be able to reduce to a yes/no answer. >> The advice is to run your own benchmarks on your infrastructure to get >> some meaningful figures for your specific project and infrastructure. >> >> Cheers >> Gianluca Bonetti >> >> On Fri, 5 Jan 2024 at 12:40, Jagat Singh <jagatsi...@gmail.com> wrote: >> >>> Hi Gianluna, >>> >>> Does the Python client miss any functionality or performance compared to >>> Java? >>> >>> Thanks >>> >>> On Fri, 5 Jan 2024 at 15:55, Gianluca Bonetti < >>> gianluca.bone...@gmail.com> wrote: >>> >>>> Hello Angelo >>>> >>>> It seems to be an interesting use case for Ignite. >>>> >>>> However, you should consider what Ignite is, and what is not. >>>> Essentially, Ignite is a distributed in-memory >>>> database/cache/grid/etc... >>>> It also has some distributed computing API capabilities. >>>> >>>> You can store data easily in Ignite, and consume data by your code >>>> written in Java. >>>> You can also use Python since there is a Python Ignite Client available >>>> if it makes your time series analysis easier. >>>> You can also use the Ignite Computing API to execute code on your >>>> cluster >>>> https://ignite.apache.org/docs/latest/distributed-computing/distributed-computing >>>> but in this case I think Python is not supported. >>>> >>>> Cheers >>>> Gianluca Bonetti >>>> >>>> On Fri, 5 Jan 2024 at 08:52, Angelo Immediata <angelo...@gmail.com> >>>> wrote: >>>> >>>>> I'm pretty new to Apache Ignite >>>>> >>>>> >>>>> I asked this also on stackoverflow ( >>>>> https://stackoverflow.com/questions/77667648/apache-ignite-time-series-forecasting) >>>>> but I received no answer >>>>> >>>>> I need to make some forecasting analysis >>>>> >>>>> Basically I can collect data in Ignite in real time. Ignite will store >>>>> data in its own caches >>>>> >>>>> Now I need to make some forecasting showing me the distribution of >>>>> data in the next X months/years by starting from observed and collected >>>>> data. >>>>> >>>>> As far as I know, this kind of forecasting can be realized by time >>>>> series forecasting. In Ignite I see no time series based algorithm. Am I >>>>> right? >>>>> >>>>> If I'm correct I may use or tensor flow or Deep Java Library. But in >>>>> this case what I don't understand is: where should I use these libraries? >>>>> Inside my thick client microservice or should I write an Ignite plugin in >>>>> order to use the scalability feature provided by Ignite? >>>>> >>>>> Thank you >>>>> >>>>> Angelo >>>>> >>>>
