Our synchronization tool is responsible for maintaining the split data updated and flushing impressions, events and metrics to the split servers. To avoid this scenario, the Split.IO SDK for Python supports an alternative operation mode, which uses an external tool called Split-Synchronizer and a redis cache. Since processes cannot access each other's memory, using the standalone operation mode will result in several sets of synchronisation tasks (threads) doing the same job (at least one per http worker - possibly more, since workers are often restarted). When the application is run in a server that spawns multiple processes (workers) to handle HTTP requests, all of them need to access fetched feature flags and segments as well as queuing up impressions and events. Before hopping into the details, we will quickly review the multi-process mode setup differences. There are a few extra steps for setting up our SDK with Python in multi-process mode, described below. Now you can start asking the SDK to evaluate treatments for your customers. # without a ready client, which if configured properly, should become ready at some point. # Now the user can choose whether to abort the whole execution, or just keep going For more information, see Understanding API Keys.įrom splitio.exceptions import TimeoutExceptionįactory.block_until_ready(5) # wait up to 5 seconds The API key is available on your Organization Settings page, on the APIs tab.
#Pip install slack sdk code#
Use the code snippet below and plug in your API key. We recommend instantiating the SDK once as a singleton and reusing it throughout your application.
This method also accepts a maximum time (in seconds or fractions of it) to wait until the SDK is ready, or throw an exception in case it's not. block_until_ready() method in the factory object. Since version 8.0.0 This is done by calling the. To make sure the SDK is properly loaded before asking it for a treatment, block until the SDK is ready. In this case, the SDK doesn't fail, rather, it returns the control treatment. If the SDK is asked to evaluate which treatment to show to a customer for a specific feature flag while its in this intermediate state, it may not have the data necessary to run the evaluation. This process can take up to a few hundred milliseconds depending on the size of data.
#Pip install slack sdk update#
When the SDK is instantiated in in-memory mode, it kicks off background tasks to update an in-memory cache with small amounts of data fetched from Split servers. See below for syntax changes you must make if upgrading your SDK to the newest version. Starting in version 8.0.0, readiness has been migrated to a two part implementation.
Import the SDK into your project using pip Set up Split in your code base with two simple steps. (Note: Django projects are multi-process by default)
Jump to the setup process for the mode your application is built in: However, multiple processes in Python are unable to share memory space, so the setup and instantiation process is different for each mode. You can implement your project in a multi-threaded or multi-process mode, depending on what works best for you and your team. One of Python's great built-in features is the ability to parallelize your code to optimize the execution-performance of any module. Multi-thread and multi-process mode support The Python SDK supports Python 3 (3.6.15 or later). Go to our Python SDK GitHub repository to learn more.
This guide provides detailed information about our Python SDK.
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