The number of databases connected to Metabase.Major factors that impact your experience using Metabase include: Metabase scales well both vertically and horizontally, but it is only one component of your data warehouse, and the overall performance of your system will depend on the composition of your system and your usage patterns. Factors that impact Metabase performance and availability Each data system is different, so we can only discuss scaling strategies at a high level, but you should be able to translate these strategies to your particular environment and usage. This article provides high-level guidance and best practices on how to keep Metabase running smoothly in production as the numbers of users and data sources increase. It supports high availability via horizontal scaling, and it’s efficient out of the box: a single core machine with 4 Gbyte of RAM can scale Metabase to hundreds of users. Metabase is scalable, battle-hardened software used by tens of thousands of companies to deliver high quality, self-service analytics. Upgrade to the latest version of Metabase.Sync with your databases only when you need to.Increase the maximum number of connections to each database.Increase the maximum number of connections to the app db.Use a managed relational database to store your Metabase application data.Taking advantage of time-based horizontal scaling.For every 20 concurrent users, figure roughly need 1 CPU core and 1GB of RAM.Factors that impact Metabase performance and availability.This library is distributed under the MIT license. compile (), using = metabase ) Endpointsįor a full list of endpoints and methods, see Metabase API. create ( name = "Gmail Users", description = "Number of users with a email address.", table_id = 2, definition = Query ( table_id = 1, aggregations =, filters = ). from metabase import Metric, Query, Count, EndsWith, CaseOption metric = Metric. This can also be used to more easily create Metric objects. ![]() create ( database = 1, type = "query", query = from metabase import Dataset, Query, Count, GroupBy, TemporalOption dataset = Dataset. Metabase Query Language) from Python classes included in this package. ![]() You can also execute queries and get results back as a Pandas DataFrame. id, using = metabase, ) Querying & MBQL list (): # add all users to my_group PermissionMembership. create ( name = "My Group", using = metabase ) for user in User. Here's a slightly more advanced example: from metabase import User, PermissionGroup, PermissionMembership # create a new PermissionGroup my_group = PermissionGroup. send_invite () # Resend the user invite email for a given user. Some endpoints also support additional methods: from metabase import User user = User. delete() are available on allĮndpoints that support them in Metabase API. create ( using = metabase, first_name = "", last_name = "", email = "", password = "" ) delete () # create an object new_user = User. update ( is_superuser = True ) # delete an object user. is_active : print ( "User is active!" ) # update any available attribute user. get ( 1, using = metabase ) # attributes are automatically loaded and available in the instance if user. list ( using = metabase ) # get an object by ID user = User. from metabase import User # get all objects users = User. All changes are reflected in Metabase instantly. Instantiate an object from the Metabase API require the using parameter which expects an instance of Metabase suchĪs the one we just instantiated above. You can then interact with any of the supported endpoints through the classes included in this package. from metabase import Metabase metabase = Metabase ( host = "", user = "", password = "", ) Interacting with Endpoints Start by creating an instance of Metabase with your credentials. An unofficial Python library for the Metabase API.
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