To answer the OP question, maybe linear regression is sufficient for making predictions in your data.
Ignite isn't really designed for exploratory data analysis, so it really helps to understand the character of your data. Linear models are usually a good place to start. Does a regression line make sense if you plot one on one of your time value columns in a spreadsheet? If so, you may not need to deploy any additional libraries. https://ignite.apache.org/docs/latest/machine-learning/regression/linear-regression On Fri, Jan 5, 2024, 08:25 Stephen Darlington <sdarling...@apache.org> wrote: > 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 >>>>>> >>>>>
