How To Make A Serverside Executor
I have included code below as an example of my problem. The app is initialized with null values (999) and then every 20 seconds (instead of every hour) it updates the app components using current time/date information. Every time I reload the app it comes up with the null values and then updates the values after 20 seconds. I would prefer it to somehow used the latest data stored in the hidden div. I appreciate that the interval component runs on the client side, but is there a way to make it a server process? Am I trying to do something way different to what Dash is designed to do?
How to make a Serverside Executor
Thanks very much for your time and effort @nedned. making the layout a function seems very sensible. You are exactly right about this being a concurrency problem and your solution works nicely. The data is picklable and I modified your function to write to a file rather than using a global variable np.save('data1',data) and then read it back in the make_layout function data = np.load('data1.npy'). The concurrent.futures modules seems perfect, however I tried using a separate process instead of a separate thread replacing executor = ThreadPoolExecutor(max_workers=1) with executor = ProcessPoolExecutor(max_workers=1) or executor = ProcessPoolExecutor() but it seems like it never actually reruns the get_new_data_every() function. Am I missing a trick there?
If your computationally expensive function is doing web requests, then threading makes plenty of sense rather than multiprocesses, as your other thread will release the global interpreter lock while waiting for the web requests to come back.
KNIME Server is an enterprise-grade solution for Advanced Analyticsworkloads such as sharing workflows, and executing workflows. The Server isbased on the Tomcat application server, and uses a core of KNIME AnalyticsPlatform in order to execute workflows. The KNIME Server installer can installboth of these components that make up KNIME Server. This document aims to givea quick overview of the steps needed to perform the installation, and ashort description of the options that can be changed at install time. Afterinstallation, you can refer to the KNIME ServerAdministration Guide for detailed informationabout the Server architecture, configuration options and general administrationtasks. More details about the functionality of KNIME Server are listed in thefollowing paragraphs.
Line 1 specifies that during the interval between 8:30am and 7:30pm, this host will honor a distributed request when it is at least 60% idle. Line 2 specifies that during the interval between 7:30pm and 5:30am, this host will honor any distributed request, no matter how busy it is. Line 3 specifies that a distributed build request from a clearmake invoked by user bldmeister will always be honored.
A clearmake process is searching for hosts that are at least 50% idle (the default). A build server that would appear to qualify because it is 70% idle will not be used if its bldserver.control file includes an -idle 75 specification.
One may want to configure multiple connections to the same server in order to have one or more sets of consumers to be executed on a different thread pool. Additionally, the below config could be used to connect to different servers with the same consumer executor by simply omitting the consumer-executor configuration option or supplying the same value.
RabbitMQ allows an ExecutorService to be supplied for new connections. The service is used to execute consumers. A single connection is used for the entire application and it is configured to use the consumer named executor service. The executor can be configured through application configuration. See ExecutorConfiguration for the full list of options.
The model layer mimics the database tables and it consists of differentmodels for representing the tools and workflows. Each workflow consists ofmultiple job models and each job contains a result model. The view layer isbuilt using HTML, CSS and jQuery. There are different views for adding tools,creating workflows and displaying outputs. Each view communicates with adifferent controller. This makes BioFlow modular and enables the layers tofunction independently of each other.
One of the main drawbacks for using graphical workflow automation tools isthe difficulty in adding new tools to the software package. Researchersregularly experiment with various tools. BioFlow provides an easy andintuitive interface for adding command line tools and scripts to itsdatabase. Any user who wishes to use a new tool in a workflow has to firstadd it to the bioflow database.Figure 1 shows the interface that allows usersto add tools to BioFlow. Complex command lines, when converted to a workflowtool, eliminate the need to remember the command line. For each tool, a name,summary and the parameters have to be provided. Optional and workflowspecific parameters can be passed along during workflow execution. Whenadding a tool, its name, a short summary and the command line used forexecuting it should be specified. The tool should be installed on the serverand available in a directory in user's PATH settings. For example, thetool in Figure 1, accepts one input with the parameter "-b" and theexecutable name "samtools view". Parameters to generate an outputfile can also be specified or the output can be redirected from the standardoutput to a file using the redirection operator. So, in the later case, thegenerated command will be "samtools view -b INPUT_FILE >OUTPUT_FILE". The name of the input file is automatically passed alongby the workflow executor. The name of the output file can be either specifiedby the user or automatically generated.
BioFlow contains a workflow designer, which allows various tools to bechained together to create workflows. The designer is divided into 3 panes -the tools pane on the left, the designer pane in the center and the optionalparameters pane on the right.The tools pane lists all available tools withincollapsible panels grouped together based on category. Creating workflowsrequire users to choose the tools that are part of the workflow andinterconnect them to define the flow of data in the workflow. Users can dragand drop tools to the center panel to make them part of the workflow. Eachtool has input and output connections available. To create a pipeline, theoutput connection of one tool is connected to the input of another tool,which is the next stage in the pipeline. This creates an internal rule topass the output file from one tool as input to the other. Figure 2 shows asample workflow where the pipeline has 2 input files and 3 tools. 041b061a72