![]() ![]() I get the CREATE TABLE DDL SQL command of the table using the Redshift database client tool.įor example, if you are using SQL Workbench/J SQL programmer can right click on the table and then choose " Show source" context menu option. The size of the table in MB and the number of table rows (including rows marked as deleted waiting for a vacuum) are also visible in this system view for database tables. ![]() Īmazon Redshift database administrators and SQL developers can check the existing sort key and distribution key of a database table by querying the SVV_TABLE_INFO system view. (500310) Invalid operation: Too much content of 'dl_th_customer_events_incl_dim' are deleted during executing alter distkey command. Let’s see for example the structure of the events that Mixpanel has, which is a typical case of a service that tracks user events.An error occurred when executing the SQL command:ĪLTER TABLE "public"."dl_th_customer_events_incl_dim" ALTER SORTKEY ("event_date") But the overall structure of the points in the time series will be flat in most cases. Of course, more data can be added, like custom attributes with which we’d like to enrich the time series, to perform some deeper analysis. The simple triplet above, taken as a time series, contains enough information to help us understand the behavior of our user. If for example, we consider user events, the minimum required information that we need is the following The second characteristic of time series data is that its structure is usually quite simple. So, it should be easy to discard older data, something that is also related to the cost of running our infrastructure. Second, older data might not be that relevant in the future, or we should not use it in any case as it might skew our analytic results. First of all, we need somehow to guarantee that we will have predictable query times regardless of the point in time which we want to analyze our data. When we work with time series data, we are expecting to have an ever-growing table on our data warehouse.įrom a data warehouse maintenance perspective, this is important. Primarily, updating a row rarely happens. Time Series data are unique for a number of reasons. ![]() We will see what options we have to optimize our data model and what tools Redshift has in its arsenal for optimizing the data and achieve faster query times. In this post, we will see how we can work efficiently with time series data, using Redshift as a data warehouse and data that is coming from events triggered by the interaction of users with our product. Working with data ordered in time has some unique challenges that we should take into consideration when we design our data warehouse solution. The same is also true if we consider the interactions of a recipient to one of our MailChimp campaigns. The interaction of a user with our product is a sequence of events where time is important. Especially when we start working with user generated events. Time series data, a sequence of data points that are time ordered, often arise in analytics. ![]()
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