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Hello, I was wondering how other people/organisations deal with making changes to the LookML code without affecting Looks and dashboards that end users might have created. We are slowly growing the presence of Looker within our organisation, and some people really love making their own insights and Looks which is great. However, when we change or restructure a view or explore, it becomes very hard to oversee the implications end users might have. We basically have two to three people developing the underlying Looker model, but are getting close to 20+ active end users. The developers commit to their personal dev branch and merge this with production, that’s the current dev cycle. How do you deal with making structural changes and assessing the impact on the current Looker setup with all the specific user needs? Hope to hear some useful insights from the community here! Thanks in advance and cheers!
Hello! Just found some behaviour in Looker that I cannot explain based on the generated SQL. We have an explore that counts unique users per 5 minutes. Because of mediocre housekeeping we have a view with the following two time dimension definitions: dimension: minute5 { type: date_minute5 sql: ${TABLE}.timestamp ;; } dimension_group: timestamp { type: time sql: ${TABLE}.timestamp ;; timeframes: [ minute5, time, raw ] } If I run a simple explore counting events per 5 minutes once using the minute5 single time dimension and once using the timestamp_minute5 dimension group I get slightly different results though the generated SQL appears to be identical. In the case of using the dimension_group the SQL also returns a row for the most recent timestamp where the results are null. This doesn’t happen when I use the single timestamp dimension. Single dimension Dimension group When executing the generated SQL in BigQuery the top null row d
Hi there, We are running into an issue that I’d love some feedback and suggestions on. We have a nice Looker model running for which my team is responsible on the data side of things. We make sure the data ends up in the correct database, but also maintain the Looker model. We have prepared a couple of dashboards for the end users, so that even people with no Looker experience can see the data. This setup worked fine when we were slowly releasing Looker into the organisation with a couple of end-users and the communication lines were short and easy. Due to the success of the tool, more and more people take an interest in the data. This on itself is awesome of course! However, this means we have to find another way to communicate some of the properties and quirks of the data and model to the end user. I am looking at a central place to communicate this to all users, a bit similar to a .README for the model. Any suggestions for this? Interested to hear how other people deal with this as
Hello, I have a dashboard with a global filter implemented (date_filter). I have several different visualisations listen to this filter which is awesome. However, some visualisations are currently incompatible with the date_filter I have setup, because they take a slightly different filter input value. I have tried manipulating the filter value for these visualisations within the listen field, e.g.: #dashboard filter definition: - name: time_range title: 'current period for review' type: date_filter default_value: 2 hours #usage within the dashboard visualisation: - name: some_arbitrary_graph title: foobar listen: time_range: 10 * product_sales.timestamp_time Looker starts throwing warnings that it doesn’t recognize the value submitted in the listen/time_range. Is this currently not possible or am I implementing this not in the correct manner? Thanks in advance!
Hi there, I am trying to implement a filter on a particular dashboard. As a default value I have now set the dashboard filter to something like this: filters: - name: filter_name_1 title: 'filter_title' type: field_filter model: looker_model explore: explore_1 field: explore_1.id default_value: "Please enter an ID." The user can get to this dashboard either by drilling down into an ID, for which the filter will be pre-filled: great. However, if the user gets to the dashboard without drilling, there will be no data because the default value does not exist. That’s great too. However, if the user removes the default value from the filter value box, the dashboard will return a full query, for which the data does not make sense. As such, I want to implement a condition on the dashboard filter that it cannot be empty. I have seen documentation that this can be implemented on view/explore level, but I cannot figure out how to do this only for this one particular das
Hi Looker team, I have added some url-links to a dimension, which work fine when displayed in table form. With a single dimension (string) and a measure (count) the drill down menu is also available in a visualisation (e.g. bar plot). If I add a second dimension (also a string), the barplot dimension values become a concatenation of the two strings from the two dimensions. This is also fine. However, the drop down menu is not available anymore. Is this not available yet, or am I missing something? Thanks in advance! Cheers, Barend
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